In the South Casanare Area, Llanos Basin

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GEOPHYSICAL AND GEOLOGICAL INTERPRETATION OF CRETACEOUS ROCKS (GACHETÁ AND BASAL GACHETÁ FORMATIONS) IN THE SOUTH CASANARE AREA, LLANOS BASIN

Carolina Quintana García

Advisor: Jean-Baptiste Tary, PhD. Co-Advisor: Ignacio Iregui MSc. (CEPSA)

Faculty of Science Department of Geosciences Universidad de los Andes

Bogotá D.C, Colombia November, 2017

Undergraduate Thesis Project

Universidad de los Andes

By ______Carolina Quintana Garcia

Advisor ______Jean-Baptiste Tary

Co-advisor ______Ignacio Iregui

Acknowledgements

First, I would like to thank my father for all his support and guidance throughout this project. He was the person that stood by me all this time and encouraged me to be a person capable of achieving all my goals with love and dedication. To my mom and my sister for their patience and support.

To CEPSA for the opportunity to do my internship, for allowing me to access the data, and for their permanent support and guidance during the course of this project.

I would also like to express my gratitude to Jean Baptiste Tary for his support and mentorship.

I would like to extend my thanks to those who offered me their help and support: Ignacio Iregui, Tomas Villamil, Ivan Becerra, Zenaida Marcano, Martin Morales, Daniel A. Bello, Oscar M. Moreno, Oscar M. Salazar, Andrés Roberto Mora, Maria del Pilar Stiffano and Carlfred Bautista.

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ABSTRACT

The limited understanding of the sandstones distribution in the Gachetá and Gachetá Basal Formations and the economic interest in the oil and gas companies was the motivation for developing this project. With this in mind the project was structured following a methodology proposed for the seismic analysis of the units of interest in an area located in Casanare, Colombia.

Through the integration of seismic and well data, it is possible to construct a model of the subsurface, which is analyzed to identify sandstones bodies, their stratigraphic location and possible structural deformation. This integrated interpretation is used to analyze elements of the petroleum system, such as the trap and the reservoir. The area of study is located on a basement high which, together with the regional dip of the basin have favored hydrocarbon migration.

Coherence/variance, root-mean square (RMS), and spectral decomposition seismic attributes helped to define the structural and stratigraphic features, as well as the distribution of sandstones. Two structural trends are identified, the first one with a predominantly N-S direction that controls the configuration of the geobodies identified. The second with a NE-SW direction that is parallel to the direction of the deformation front of the orogen. From the Paleozoic top to the Barcos-Cuervos top, two sedimentary cycles were distinguished in the time slice: a) Gachetá Basal Fm. and Gachetá Fm. b) Guadalupe Fm. and Barcos-Cuervo Fm.

The Gachetá Formation presents an approximate thickness of 300 ft., equivalent to 50 ms (TWT). The formation was divided into two intervals separated by a boundary established by an analysis developed in a previous study of stratigraphic sequences made by Cepsa. This division was made aiming to perform the supervised facies classification inside each interval.

The results evidence the existence of two sedimentary cycle within the area. The first (a) has an inner shelf dominance, and the second (b) is dominated by coastal plain environments. A detail sequence stratigraphy analysis in the interval of interest is recommended.

The wells corresponding to facies 1 have the greatest sandstones content. Considering this analysis, the distribution of the previously mentioned sandstones can be predicted. The sandstones of the Gachetá Formation are an exploratory target and it is recommended to study further these sandstones using quantitative seismic interpretation methodology.

RESUMEN

El escaso entendimiento de la distribución de las arenas de las Formaciones Gachetá y Gachetá Basal y el interés de las compañías petroleras en este objetivo de investigación fue la motivación para realizar este trabajo. Con esta necesidad en mente se estructura este proyecto siguiendo la metodología propuesta para en análisis sismo-estratigráfico de las unidades de interés en un área del departamento del Casanare.

Mediante la integración de datos sísmicos y datos de pozos se puede obtener un modelo del subsuelo, el cual es analizado para identificar cuerpos de arena, su ubicación estratigráfica y su posible deformación estructural. Con esta interpretación integrada se están analizando dos elementos del sistema petrolífero: trampa y roca almacén. El área de estudio se encuentra en un alto de basamento, y junto con el buzamiento regional de la cuenca han favorecido la migración de hidrocarburo

Los atributos sísmicos de coherencia/variancia, root-mean square (RMS) y descomposición espectral permitieron definir el control estructural y estratigráfico del área y la dependencia en este sector de la distribución de las arenas. Se identifican dos trenes estructurales, el primero y preponderante con dirección N-S controla la configuración de los geocuerpos identificados; el segundo de dirección NE-SW, paralelo a la dirección del frente de deformación del orógeno. En la zona correspondiente entre el tope del Paleozoico y el Tope de Barcos-Cuervos, se distinguieron dos a ciclos sedimentarios con asociaciones propias de un mismo ambiente visualizado en los times slice: a) Fm. Gachetá Basal y Fm. Gacheta y b) Fm. Guadalupe y Fm. Barcos-Cuervo

La Formación Gachetá en el área presenta un espesor aproximado de 300 pies correspondiente a 50 ms (TWT). La Formación fue dividida en dos intervalos separados por un límite de secuencia según el análisis realizado en un estudio previo de secuencias estratigráficas hecho por Cepsa. Esta división se hizo con el objetivo de realizar la clasificación supervisada de facies dentro de cada intervalo.

Los resultados soportan la existencia de dos ciclos sedimentarios. El primero (a) corresponde a un dominio de plataforma interna y el segundo (b) a una planicie costera. Se recomienda hacer un análisis detallado de las secuencias estratigráficas teniendo en cuenta este enunciado. Los pozos con las facies denominadas facies 1 son las que tienen mejor contenido de arenas. Con base en este análisis se puede hacer una predicción de la distribución de dichas arenas. Las areniscas de la Formación Gachetá constituyen en un objetivo exploratorio y se recomienda su estudio con la aplicación detallada de la metodología de interpretación cuantitativa conocida.

TABLE OF CONTENTS

ABSTRACT ...... IV RESUMEN ...... V 1. INTRODUCTION ...... 1

1.1. GENERAL OBJECTIVES...... 2

1.2. SPECIFIC OBJECTIVES ...... 2 2. LOCATION ...... 2 3. TECTONIC EVOLUTION ...... 3 4. STRUCTURAL FRAMEWORK ...... 5 5. STRATIGRAPHY ...... 7

5.1. BASAL GACHETÁ (UNE) FORMATION ...... 9

5.2. GACHETÁ FORMATION ...... 10 5.2.1. GACHETA SEQUENCE STRATIGRAPHY ...... 10 6. PETROLEUM GEOLOGY OF THE LLANOS BASIN ...... 12 6.1. SOURCE ...... 12 6.2. MIGRATION ...... 13 6.3. RESERVOIR ...... 13 6.4. SEAL ...... 13 6.5. TRAP ...... 14

6.6. OVERBURDEN ROCKS ...... 14 6.7. TIMING ...... 14 7. METHODOLOGY ...... 15 7.1. DATABASE ...... 16 7.1.1. SEISMIC AVAILABILTY ...... 17 7.1.2. ELECTRIC LOGS ...... 20 7.1.2.1.GAMMA RAY LOG ...... 20 7.1.2.2.VOLUME OF CLAY LOG ...... 21 7.1.2.3.EFFECTIVE POROSITY LOG ...... 23 7.1.3. CHECKSHOT SURVEY AND VERTICAL SEISMIC PROFILE ...... 24

7.2. PETREL PROJECT ...... 24

7.3. WELL-SEISMIC DATA CALIBRATION ...... 24 7.3.1. SONIC CALIBRATION ...... 25 7.3.2. REFLECTION COEFFICIENT ...... 26

7.3.3. WAVELET EXTRACTION ...... 27

7.4. SEISMIC ATTRIBUTES ...... 29 7.4.1. COHERENCE/VARIANCE ...... 30 7.4.2. ROOT-MEAN SQUARE AMPLITUDE ...... 30 7.4.3. SPECTRAL DECOMPOSITION ...... 31

7.5. STRUCTURAL INTERPRETATION ...... 32

7.6. STRATIGRAPHIC INTERPRETATION ...... 33 7.7. FACIES ...... 33 7.7.1. WAVEFORM CLASSIFICATION ...... 34 7.7.1.1 .UNSUPERVISED CLASSIFICATION ...... 34 7.7.1.2 SUPERVISED CLASSIFICATION ...... 35

7.8. VELOCITY MODEL FOR DEPTH CONVERSION ...... 36 8. PROCEDURES ...... 36

8.1. WELL SEISMIC CALIBRATION ...... 37

8.2. HORIZON INTERPRETATION ...... 40

8.3. SEISMIC ATTRIBUTES ...... 41 9. RESULTS ...... 50

9.1. STRUCTURAL INTERPRETATION ...... 50

9.2. STRATIGRAPHIC INTERPRETATION ...... 60 9.2.1. DEPOSITIONAL ENVIRONMENTS ...... 60 9.2.2. ANALYSIS OF THE GACHETÁ FORMATION ...... 72

9.3. DEPTH CONVERSION ...... 82 9.4. GEOFORMS ...... 85 10. DISCUSSION ...... 86 11. CONCLUSIONS ...... 91 12. BIBLIOGRAPHY ...... 93 APPENDIX A – 3-D MERGE SEISMIC REPROCESSING ...... 97 APPENDIX B – ALTERNATE METHODOLOGY FOR THE VELOCITY MODEL IN PETREL ...... 98 APPENDIX C - SEISMIC INVERSION ...... 103

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FIGURES Figure 1 Llanos basin Location. SM: Sierra de la Macaren, VA: Vaupés Arc. PM: Precambrian metamorphic rocks. GS: Guyana Shield. GFS: Guaicarámo Fault System. Created using ArcGis/ArcMap...... 3 Figure 2 Llanos Basin Boundaries and fault systems. Taken from Barrero et al., 2007 ..... 5 Figure 3 Structural domains in the Llanos Basin. Each domain corresponds to a different tectonic setting. The Casanare domain (red polygon) is an active alluvial plain, made predominantly of normal antithetic faults (up-to-the-basin faults). Taken from (ANH, 2012) ...... 6 Figure 4 Casanare domain mode. An extensive west dipping homocline. Modified from Bayona et al., 2008...... 7 Figure 5 Cartoon of the relative gravimetric high in the area of study. Redrawn (ANH, 2010a)...... 7 Figure 6 Llanos basin stratigraphic column. Modified from Moretti, Mondragon, et al., 2009 ...... 9 Figure 7 Sequence stratigraphy of Gachetá. Modified from CEPSA, 2013...... 12 Figure 8 Petroleum System Event Chart. Taken from Sarmiento, 2011...... 15 Figure 9 Methodology used in the development of this project...... 16 Figure 10 Vertical zonation of the seismic image showing either high (H) and low(L) frequencies (F), high (H) and low (L) amplitudes (A) and poor (P) and good (G) lateral continuity (LC). For example, package V (interval of interest) has high frequency content, as well as high amplitude content and poor lateral continuity...... 17 Figure 11 Top: Seismic data dominant frequency range in the interval of interest. Bottom: Location of the area from where the frequency was obtained. Given by CEPSA, 2017...... 19 Figure 12 Different ways of viewing a seismic cube. Taken from Bacon et al., 2003...... 20 Figure 13 Gamma ray response to differentiate lithologies, from Glover, 2011...... 21 Figure 14 Vclay log compared to GR log. The magenta area represents areas where the GR has high responses, hence the Vclay has high values representing an area with high content of clays and shales. The red rectangle is the opposite, low GR results in low Vclay ...... 23 Figure 15 Reflection Coefficient calculation. It is obtained from the Acoustic Impedance log (pink): the multiplication of the density log (blue) and velocity log (red). The peaks on the RC log represent the changes in impedance from different lithologies. Longer lines represent greater changes in Acoustic Impedance. Modified from Veeken, 2007 . 27 Figure 16 Synthetic seismogram procedure. Modified from Barna & Anikó, 2014 ...... 29 Figure 17 Categories of seismic attributes. Taken from Barnes, 2016...... 30 Figure 18 Spectral decomposition workflow proposed by Chopra & Marfurt (2007). Modified from CEPSA, 2013...... 32 viii

Figure 19 Supervised and unsupervised methods compare an observed waveform with a template waveform. The observed waveform matches the template waveform that is most similar. In this case the black waveform matches the first template, so class 1 is created. Taken from Barnes, 2016...... 35 Figure 20 Seismic Well tie. Sonic Calibration and synthetic generation are integrated in the image. Each column represents a different result...... 38 Figure 21 Extracted wavelet of the seismic data. Correlation between the synthetic trace and the reference seismic data showing concordance between peaks (red amplitudes) and troughs (blue amplitudes)...... 39 Figure 22 Synthetic trace generated that matches the seismic trace. Positive wiggles (red) in the synthetic are correlated to positive reflectors and vice versa. Zoomed in the interval of interest...... 39 Figure 23 Example of synthetic seismogram superposed on seismic section at a well location. Peaks in the synthetic trace match peaks in the seismic data, and troughs in the synthetic traces correlate to their corresponding throughs on the seismic data. This figure covers the section from package II to package V...... 40 Figure 24 Horizons interpreted by using different methods. A) Mirador Fm (seeded autotracking) B) Gachetá Fm (seeded autotracking) C) Paleozoic rocks ( 3-D autotracking D) Basement (3-D autotracking)...... 42 Figure 25 Cosine of phase enhances faults polygons and structural delineation. Top- Raw attribute. Bottom- Faults interpreted overlaid on the attribute...... 43 Figure 26 Variance/Coherence attribute (a) Inline 620 with no interpretation and (b) with the interpreted faults (c) time slice showing the faults ...... 44 Figure 27 Time slice at t=2300 ms through coherence volume showing the interpreted fault polygons...... 45 Figure 28 An inline of the original amplitudes portraying changes in amplitudes that represent faults (left). The faults and main horizons interpreted (right). Yellow-Mirador Fm, Green-Gachetá Fm and Purple- Paleozoic beds. Red represents high amplitudes and blue low amplitudes...... 45 Figure 29 a) Most Negative Curvature, b) Most Positive Curvature and c) blended a) and b) enhancing channels (purple arrows) and faults (yellow circles) ...... 46 Figure 30 Time Slices of the amplitude cube illustrating a sedimentary reconstruction in 8 ms TWT window. a) Time slice to Guadalupe Fm, b) time slice to Gachetá Fm and c) time slice to Une Fm. The Yellow line represents inline 620 shown in the coherence volume...... 46 Figure 31 Variance blended with RMS exhibiting the same sedimentary reconstruction in 12 ms TWT window, 36 ft or 10m there is a change from marine to non-marine environment. The three images represent Guadalupe Fm (Top left), Gachetá Fm (top right) and Basal Gachetá (Bottom) ...... 47

Figure 32 Time slices through a coherence volume corresponding to the Gachetá Fm. The cube was flattened to Mirador Fm. a) channels and faults are enhanced before interpretation b) fault lines (purple) interpreted from the variance 3D cube...... 48 Figure 33 Opacity of 50% in the variance cube to show the fault lines more clearly (left). The fault lines and fault polygons intercepted and applied to the horizon that results in a surface with their respective faults (right)...... 49 Figure 34 In order to do an adequate interpretation, faults need to be incorporated into the surface to differentiate the hanging wall and foot wall...... 49 Figure 35 Amplitude (left) and Sweetness (right) time slices to Gachetá Fm. Magenta arrow shows a channel who’s deposition was controlled by faults (abrupt limit)...... 50 Figure 36 Xline of faults affecting the interval of interest, from the Basement up to Mirador Fm. Each vertical line represents a fault...... 52 Figure 37 Inline of the amplitude seismic cube showing how the stratigraphic sequence has been affected by the second fault trend. Each vertical line or color represents a fault...... 53 Figure 38 Basement´s structural map showing three fault trends: N-S, NE-SW and NW- SE. The magenta arrow shows the gravimetric high (orange color) mention in Figure 5 that controls the sedimentation in the area, the contours enclosed the high. The circles, each with a different color represent wells in the area...... 54 Figure 39 Structural map showing the basement high, blue arrows are the NW-SE faults and green circle represent a domino effect...... 54 Figure 40 Second group of faults within the basement portraying a N-S trend...... 55 Figure 41 Third group of faults with a SE-NW trend, similar to the trend in the Gachetá Fm...... 55 Figure 42 Structural map of the Gachetá Formation. The magenta arrow shows the N-S fault trend that is the most important for this project, NW-SE and NE-SW trends are present. The contours have different direction, NE-SW, than the basement map due to Paleozoic sedimentation, which created a horizontal surface that was overlapped by Cretaceous sediments...... 56 Figure 43 Inverted structure due to tectonic load affecting the sedimentary section from the basement to Carbonera Fm in the northern part of the area. The inverted block is limited by the green fault (west) and the red fault (east). The inverted structure is delimited by the N-S fault trend mention in Figure 41...... 57 Figure 44 Inverted structure to the south of the area (purple line) affecting from the basement to Mirador Fm. This structure is limited to the east by the green fault and to the west by the red fault. Each vertical line represents a fault...... 58 Figure 45 Xline...... 59 Figure 46 Stratigraphic intervals used in this work ...... 61 Figure 47 Close up of the interval of interest flattened to Mirador (50 ms TWT) ...... 61

Figure 48 Amplitudes blended with RMS showing the Basal Gachetá Channel in a time slice with their respective inline and crossline. The red circle represents the main channel within Gachetá Formation...... 63 Figure 49 Sweetness attribute to showing intersection between an inline and a time slice. The grey arrows show the changes in color, and the brown is the onlap of the channel...... 63 Figure 50 a) Variance/Coherence, b) Root Mean Square and c) The coherence volume blended with RMS showing the deposition environment of Carbonera Formation. Depositional Environment: fluvial and estuarine channels (Bayona, et al., 2007)...... 64 Figure 51 a) Variance/Coherence, b) Root Mean Square and c) The coherence volume blended with RMS showing the deposition environment of Mirado Formationr. Depositional Environment: fluvial channels and mouth bar sands (Bayona, et al., 2007) ...... 65 Figure 52 The Barcos- Cuervos Fm. assemblage comprises a fluvial-coastal plain package (Bayona, et al,. 2007). a) Coherence seismic cube flatten to Mirador Fm., as well as b) RMS volume. Both attributes were blended as shown in c). d) Incised valley fill (IVF) examples, red arrows show the direction of sedimentation inside the IVF...... 66 Figure 53 The depositional environment of Guadalupe Fm. are lower shore face, estuarine channels, tidal to wave-influenced marine channels and bars, and fluvial with estuarine influence (Sarmiento, 2011) ...... 67 Figure 54 Gachetá Fm. corresponds to a flood plain or deltaic plain with marine influence (inner shelf). a) Variance, b) RMS and c) mixed image of the two input attributes...... 68 Figure 55 Strata slice through Basal Gachetá Fm., showing evidence of the depositional environment. a) Variance and b) root-mean square. c) Blended of a) and b). The depositional environment corresponds to fluvial channels from the base to estuarine channels or bay deposits and marine shelf deposits in the upper part of the unit. (Sarmiento, 2011) ...... 69 Figure 56 Time slice through Paleozoic sediment, in this area there is no metamorphism. The orange arrows represent the onlap over the basement. a) Variance/Coherence, b) Root Mean Square and c) The coherence volume blended with RMS ...... 70 Figure 57 Time slice showing the basement. a) Coherence seismic cube flatten to Mirador Fm., b) RMS volume. Both attributes were blended as shown in c). Yellow arrow portrays the domino effect due to extension...... 71 Figure 58 Subdivision within the Gachetá Formation. Each one represents a sequence that was used to make the facies maps...... 74 Figure 59 Zoomed into the subdivision within Gachetá. The Gamma Ray is shown as well as the 6 division of the sequence stratigraphy ...... 75 Figure 60 Interval of interest divided in three: Mirador-Guadalupe, Gachetá A and Gachetá B. Gamma Ray log on the back...... 75

Figure 61 Unsupervised facies classification for Gachetá A. There are lateral changes in the interval, thus is possible to obtain supervised classification ...... 76 Figure 62 Resultant wavelets from the waveform classification in Gachetá A ...... 77 Figure 63 Unsupervised facies classification for Gachetá A. There are lateral changes in the interval, thus is possible to obtain supervised classification ...... 78 Figure 64 Resultant wavelets from the waveform classification in Gachetá B...... 79 Figure 65 Image showing the lateral stratigraphic changes in Gachetá A associated to facies by colors...... 80 Figure 66 Image showing the lateral stratigraphic changes in Gachetá B associated to facies by colors...... 81 Figure 67 Average velocity map, Gachetá Fm. top...... 83 Figure 68 Depth Conversion map, the gradient of average velocity Gachetá Fm. top. .. 84 Figure 69 Average velocity map adjusted to fit the Gachetá ...... 84 Figure 70 Depth conversion adjusted to Gachetá Fm. top...... 85 Figure 71 Spectral decomposition image that represents the facies on Gachetá A. Frequencies used: 20Hz, 50 Hz and 80 Hz ...... 86 Figure 72 High- resolution spectral decomposition, showing similarities with the facies map extracted before to Gachetá B. The frequencies used were 20,30 and 50 Hz...... 87 Figure 73 Reconstruction of tectonic history in the area of study. A) inversion structure ...... 89 Figure 74 Cartoon of the depositional environment of Gachetá. Redrawn after Veeken, 2007 ...... 90

TABLES

Table 1 Average velocity calculation ...... 36 Table 2 Vclay and effective porosity cut-offs for defining the facies and N/G of each well ...... 79 Table 3 Average Velocity calculation for 9 wells...... 82

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1. INTRODUCTION

Seismic and well interpretation are fundamental techniques to understand subsurface geological characteristics and reservoir properties. When the data is properly integrated they offer a variety of inputs for identifying stratigraphic and/or structural targets for hydrocarbon exploration. These techniques are applied in the oil industry to identify potential areas of interest (AOI).

The aim of this work is to apply a methodology for the identification and configuration of stratigraphic and structural targets in an exploratory area. The blocks are located in the central Casanare area, Llanos Basin. This basin is at the eastern part of Colombia and it is one of the most prolific sedimentary basins in the country for oil and gas. The Llanos Basin is a sub-Andean foreland-type basin whose configuration is controlled by Miocene Andean tectonic events (Vasquez, 2014). In this area the successful traps are structural closures controlled by antithetic normal faults.

The main target is within Cretaceous rocks, the Gachetá Formation and the Basal Gachetá Formation, also known as Une Formation, which form the stratigraphic interval of interest. The Gachetá Fm is the result of marine transgressive sedimentation, and is widely known as a source rock for the basin. It is described in the literature as predominantly composed by lutites (CEPSA, 2013), but new studies have shown the development of sandstones in specific places of the basin (CEPSA, 2013). The Basal Gachetá Formation is compose d by sandstones that overlie Paleozoic rocks.

The methodology consists of: the creation of the project from the database, followed by well to seismic tie, log interpretation, structural and stratigraphic interpretation, 3-D seismic interpretation, facies classification, extraction of seismic attributes, time-depth conversion, acoustic impedance inversion, and geoform identification. The geological and geophysical (G&G) interpretation is made in a Petrel Platform (Petrel Version 2016) and Hampson and Russell. The information was provided by CEPSA and it consists of a

1 database composed by a 3-D seismic volume in Pre-Stack Time Migration (PSTM) and well data (logs, cores and images interpretation).

1.1. GENERAL OBJECTIVES

To apply seismic and well interpretation methodology and to integrate different petroleum geology disciplines for hydrocarbon exploration in an area in the Casanare Department.

1.2. SPECIFIC OBJECTIVES • To develop and become familiar with a methodology (Chapter7) for hydrocarbon exploration. • To interpret stratigraphic and structural configurations from the Paleozoic top to the top of the Gachetá Formation on seismic 3-D data blocks. • To present an additional contribution by interpreting additional geoforms in the interval of interest. • To generate different facies and structural maps to show the distribution of the sandstone bodies, along with the generation of seismic attributes and the integration of previous studies of CEPSA. • To identify new stratigraphic or structural targets of possible interest within the Gachetá Fm (reservoir) and the Basal Gachetá Fm.

2. LOCATION

The study area is located in the Casanare Province, most specifically in the Maní and Tauramena Municipalities. Casanare is one of the four provinces that make up the Llanos Orientales or Llanos basin. The northern limit is defined by the Casanare River which separates Casanare from the Arauca Department, to the south the Province is limited by the Meta River dividing Meta and Vichada provinces from Casanare, to the east0 Vichada Province and to the west Cundinamarca Province (Gobernación de Casanare, 2017) (Figure 1). 2

The Llanos basin is a major sedimentary basin and is the most prolific hydrocarbon basin located in the eastern part of Colombia (Figure 2), the basin is bordered to the north by the Colombia-Venezuela border, and to the south the basin extends to Serrania de la Macarena (SM), the Vaupés Arch (VA) and the Precambrian metamorphic (PM) rocks that outcrops to the south of the Guaviare River. To the east, the basin is bordered by the Guyana Shield (GS) and to the west by the frontal thrust system of the Eastern Cordillera (Guaicáramo Fault System, G.F.S) (Barrero, Pardo, Vargas, & Martínez, 2007)

G.F.S GS

PM SM VA

Figure 1 Llanos basin Location. SM: Sierra de la Macaren, VA: Vaupés Arc. PM: Precambrian metamorphic rocks. GS: Guyana Shield. GFS: Guaicarámo Fault System. Created using ArcGis/ArcMap.

3. TECTONIC EVOLUTION

The development of the structural style of the Llanos retroarc foreland basin is an outcome of a complex geological evolution. The Llanos Basin tectonic evolution began in the Paleozoic and evolved until the Meso-Cenozoic (Sarmiento, 2011). The tectonic events that took place during the Paleozoic in the Llanos basin are limited by ocean floor magnetic anomaly data (Cooper et al., 1995) . However, it is speculated that the basin started as a relatively shallow epicontinental sea in the Paleozoic (Sarmiento, 2011). During the Triassic-Early Cretaceous, the area of the Llanos Basin then corresponded to 3 an extensional rift basin related to a back arc setting probably related to the breakup of Pangea (Cooper et al., 1995; Sarmiento-Rojas, Van Wess, & Cloetingh, 2006).

The formation of the Andes mountain chain started in the Early Cretaceous as a consequence of the accretion of an oceanic plateau, forming the present Western Cordillera and starting a compressional regime in the basin (Une, Gachetá and Guadalupe Formations) (Moretti, Mondragon, Garzon, Bosio, & Daniel, 2009; Sarmiento-Rojas et al., 2006). At the end of the Maastrichtian, the uplift of the Central Cordillera took place and the first inversion in the area of present day Magdalena Valley occurred due to accretion from the west (Moretti, Mondragon, et al., 2009). At the same time, Llanos subsidence continued in the fore bulge (Bayona et al., 2008).

During the Late Cretaceous – Paleocene, deformation in Colombia was the result of the final accretion of the Western Cordillera (Cooper et al., 1995). This generated a regional foreland basin (Sarmiento, 2011), and marked a significant change in depositional environments from marine to continental. This compressive context migrated eastward and predominated in the Eastern Cordillera during the Eocene. A local inversion of Mesozoic extensional grabens occurred during the Late Eocene to Early Oligocene, and a more classical thrust regime developed resulting in the formation of the external foreland Llanos basin (Carbonera and Leon Formations) (Moretti, Mondragon, et al., 2009; Sarmiento, 2011).

From the Late Eocene to the Early Miocene, an inversion of the extensional basin resulted in the formation of the Eastern Cordillera (Cooper et al., 1995; Moretti, Mondragon, et al., 2009). Also, pre-existing extensional faults were inverted, and new compressional structures developed in the Llanos basin due to the compression of the eastern flank of the Eastern Cordillera (Guayabo Formation). Deformation and uplift are still occurring, generating tectonic events (Cooper et al., 1995).

Figure 2 Llanos Basin Boundaries and fault systems. Taken from Barrero et al., 2007

4. STRUCTURAL FRAMEWORK

The Llanos Basin has five structural domains: Arauca, Casanare, Meta, Vichada and Foothills (Figure 3). The most relevant to this work is the Casanare area. This domain is characterized by a Paleozoic sedimentary section overlapping the basement. The main structure is an extensive west dipping homocline (Figure 4) (Sarmiento, 2011) that comprises the entire stratigraphic sequence of the basin. There are two families of normal faults trending N-S and NE-SW that affect the entire sedimentary column.

Figure 3 Structural domains in the Llanos Basin. Each domain corresponds to a different tectonic setting. The Casanare domain (red polygon) is an active alluvial plain, made predominantly of normal antithetic faults (up-to-the-basin faults). Taken from (ANH, 2012)

The structural traps are formed by antithetic faults, or "up to the basin" faults, caused by extensional rifting (Dasilva, Gomez, Villa, Yoris, & Morales, 2014). The Casanare domain is divided into two areas: the western Casanare and Eastern Casanare. The structural style of the first is a structural inversion with strike slip faults. The second is characterized by normal faulting with stronger reactivation towards the western part (Sarmiento, 2011); this type of setting forms the largest productive structural traps of the Llanos Basin. The study area is in the Eastern part of the domain, it is dominated by normal faults that cross through the entire sedimentary section (Figure 3) and it is controlled by a plunge of the basement high that corresponds to a relative gravimetric high (Figure 5).

Figure 4 Casanare domain mode. An extensive west dipping homocline. Modified from Bayona et al., 2008.

Figure 5 Cartoon of the relative gravimetric high in the area of study. Redrawn (ANH, 2010a).

5. STRATIGRAPHY

The stratigraphy in the Llanos Basin consists of rocks from the Precambrian, the Paleozoic, Cretaceous, Palaeogene and Neogene. The basement in the Llanos foreland includes mostly Mesoproterozoic to Paleoproterozoic rocks (Delgado, Mora, & Reyes-Harker, 2012),Paleozoic sediments, predominately from marine environment, unconformably

7 overly the metamorphic basement (Precambrian rocks) in the Llanos Basin. Triassic-Lower Cretaceous sediments are absent in the Llanos (Moretti, Mora, et al., 2009). The Gachetá Basal Fm (Albian-Cenomanian), Gachetá Fm (Cenomanian-Coniacian) are the stratigraphic interval of interest and they will be described later. Guadalupe Formation (Campanian-Early Maastrichtian): The top of the Cretaceous is composed by Guadalupe Fm massive sandstones. These sandstones were deposited on the shallow marine shelf created due to a decrease in sea level (Cooper et al., 1995).

The Palaeogene and Neogene sequence started with a transgressive cycle in which Barco Sandstones and Los Cuervos Shales (Paleocene) were deposited. A major drop in relative sea level resulted in a Hiatus (Cooper et al., 1995), followed by a new transgression and a new deposition of the Mirador Fm massive quartz sandstones (Eocene) (Sarmiento, 2011). Overlapping the Mirador Fm is the Carbonera Formation (Oligocene-Lower Miocene) (Sarmiento, 2011), this Formation is divided into eight (8) members: C1-C3-C5- C7 (interbedded regressive sand units) and C2-C4-C6-C8 (transgressive shale units) (Campos & Mann, 2015).

Finally, the Leon Formation (Middle Miocene) represents the last sea level rise in the basin and is composed by a homogenous sequence of laminated shales (Sarmiento, 2001). This is followed by a heterogeneous sequence of sandstones from the Guayabo Formation (Miocene-Pliocene), deposited in continental environments (Sarmiento, 2011). An unconformity separates the Paleozoic from the Cretaceous sediments. The Cretaceous sequence is divided into three Formations: Basal Gachetá, Gachetá, and Guadalupe. The first two will be described in more details since they are the focus of this project.

5.1. BASAL GACHETÁ (UNE) FORMATION

Basal Gachetá (Une) Formation (Albian-Cenomanian): It is composed predominantly by clean to argillaceous quartzose sandstones, some of reservoir quality (CEPSA, 2013), interbedded with siltstones and shales (Figure 6).

Figure 6 Llanos basin stratigraphic column. Modified from Moretti, Mondragon, et al., 2009

The base of the Formation is characterized by an unconformity that overlies the Paleozoic sediments. The top of the Formation was deposited during a regional transgression caused by a gradual rise in sea level combined with continued subsidence (Cooper et al., 1995), which is one of the main flooding events during the Cretaceous (97 Ma) (CEPSA, 2013). Also, from the base to the top is possible to observe a transgression from continental to transitional environments (ANH, 2012).

5.2. GACHETÁ FORMATION

Gachetá Formation (Cenomanian-Coniacian): It consists of shales interbedded with argillaceous sandstones and clean sandstones (Sarmiento, 2011). Some other intervals are composed by shale, mudstones and silty sandy mudstones (CEPSA, 2013). These mudstones are oil prone and are a source rock for the basin (Cooper et al., 1995). Gachetá Fm sediments were deposited in an outer shelf to slope/clastic ramp setting as a combination of slope apron sediments and channelized sands (CEPSA, 2013).

Towards the base of the Formation, there is a maximum flooding surface, which corresponds to an oceanic anoxic event (OAE2) during a global sea level rise (CEPSA, 2013). And the top of the Formation is an erosive surface underlying the Guadalupe Formation.

5.2.1. GACHETA SEQUENCE STRATIGRAPHY

The sequence stratigraphic interpretation used in this work has been stablished by CEPSA. According to previous studies, this succession is composed of several smaller regressive- transgressive cycles, most of them are progradational sequence (sediment supply is greater than accommodation space) (Miall, 2010) but transgressive intervals are also present. These cycles show a decrease in thickness towards the top, as well as an increase in sand content (CEPSA, 2013).Overall Gachetá is divided in six main division labelled from

10 the base upwards as Gachetá 1 to Gachetá 6 (Figure 7). This description is based on the interpretation of one well (well) but can be apply to other wells (CEPSA, 2013). • Gachetá 1: Basal Transgressive Gachetá (overall upward increase in gamma ray logs to a maximum or turn around in trends). This sequence stratigraphic interval includes the Gachetá Basal Sand lithostratigraphic unit. • Gachetá 2: Relative High Stand and then Transgression to major flooding surface (higher than average gamma with a weak increase (progradation) followed by an increase (transgression)). • Gachetá 3: Pair of relatively thin High Stand and Transgressive cycles, can be counted as base of Gachetá 4 or split into two, but is usually distinctive. • Gachetá 4: High Stand, rapid progradation followed by Transgressive (sand-rich package with a sharp base, cleaner base of variable thickness, and an overall upward fining trend). The base of this unit is often sharp and overlain by a sand body. • Gachetá 5: High stand and Transgressive, rapid progradation followed by Transgression (sand-rich package with a sharp base and an overall upward fining trend). • Gachetá 6: High Stand and Transgressive, rapid progradation of a generally sand- rich interval with a serrate funnel motif where fully developed.

Figure 7 Sequence stratigraphy of Gachetá. Modified from CEPSA, 2013.

6. PETROLEUM GEOLOGY OF THE LLANOS BASIN

Colombia has a total of 450 petroleum fields, but according to the most recent daily production report only 2.8% of the fields located in the Llanos basin represent the 54% of the total production in the country during 2016 (El Tiempo, 2017). The four most productive heavy oil fields in the country are Campo Rubiales, Campo Castilla, Campo Chichimene, and Campo Quifa, that represent approximately 42% of the total production in Colombia with an average production of 373.970 Barrels of Petroleum Per day (BOPD) (El Tiempo, 2017). The petroleum system of this successful field is described below:

6.1. SOURCE

The Gachetá Formation (Upper Cretaceous) constitutes the main source rock of the Basin (Figure 8). It is composed of black mudstones and calcareous mudstones with kerogens type II and III and has an average Total Organic Content (TOC) that varies from 1% to 3%.

The initial potential of the Gachetá Formation is 10 milligrams of hydrocarbons per gram of rock (mg HC/g rock) with a Hydrocarbon Index (HI) of 300, but this potential decreases eastward (Moretti, Mora, et al., 2009). The mudstones of the Carbonera Formation in the foreland and Barcos/Cuervos formations in the Foothills, contain some intervals with good source rock characterization. The highest thermal maturity of the basin is associated with the Guaicaramo Fault to the SW of the basin (Sarmiento, 2011).

6.2. MIGRATION

The hydrocarbon source is the deepest part of the basin eastward; from a long-distance pathway in two migration pulses. The first pulse was in the Upper Eocene-Oligocene and the second generated after the Andean Orogeny in the Middle Miocene and is still occurring (García González, Mier Umaña, Cruz Guevara, & Vásquez, 2009).

6.3. RESERVOIR

There are numerous reservoirs in the basin. The most productive are the sandstones of the Mirador Formation (Eocene), followed by the sandstones of the Paleocene Barco Formation, the Santonian-Campanian Guadalupe Formation, and the Albian-Cenomanian Une Formation. These intervals have porosity values between 10-30% (ANH, 2010b). There are other potential reservoir rocks such as the C-7, C-5 and C-3 units of the Carbonera Formation. They have an average net reservoir thickness between 25 and 30 feet (Sarmiento, 2011).

6.4. SEAL

The claystones of the León Formation constitute the main seal rock in the basin. The shales from the Gachetá Formation are an effective seal for the Llanos basin. Another

13 important seal is the even intervals of the Carbonera Formation, especially C-8 (Barrero et al., 2007).

6.5. TRAP

There are five (5) structural domains and each of them has its own trap style (García González et al., 2009). • Llanos Foothills: Fault propagating folds, and fault bend folding (Miocene). • Casanare: This domain has both structural and stratigraphic traps, and a combination of both. o Structural: up-to-the basin (antithetic) faults that were reactivated in the Miocene, strike-slip faults due to Andean compression. o Stratigraphic: related to pinch outs, bars, channels and incised valley fill. • Arauca: Folds in wrench fault systems from the Oligocene. • Vichada: Normal faulting that took place from Miocene to Pliocene. • Meta: Folds, high angle reverse faults and stratigraphic traps.

6.6. OVERBURDEN ROCKS

The overburden rocks of the Llanos basin are constituted by all the Formations from the Carbonera Formation to Necesidad Formation (Sarmiento, 2011).

6.7. TIMING

Trap formation occurred during different periods in the Palaeogene-Neogene (Eocene, Mid-Miocene and Pleistocene), and the critical moment (generation-migration- accumulation) occurred from the Late Miocene to Early Pliocene.

The following chart summarizes all the information mentioned above:

Figure 8 Petroleum System Event Chart. Taken from Sarmiento, 2011.

7. METHODOLOGY

For the development of this project a seismic-well interpretation methodology was developed. It eased the process of interpreting the facies of the Gachetá and Basal Gachetá formations by integrating all the geological and geophysical data available. The methodology consisted of different stages (Figure 9), starting from a database collection to the facies interpretation for both formations. In the following section, each stage will be described.

Figure 9 Methodology used in the development of this project. 7.1. DATABASE

First, the database was created with information provided by CEPSA. It consists of: • 3-D Seismic Cube • 27 wells with its corresponding Gamma Ray (GR), Volume of Clay (VCLAY) and effective porosity (PHIE) logs; all of them were input into Petrel, but only 10 of them were used because of the methodology steps • Checkshot or Vertical Seismic Profile (VSP) • Well tops (boundary between geological units)

7.1.1. SEISMIC AVAILABILTY

The seismic data used to develop this project was a Pre-Stack Time Migration (PSTM) 3-D Cube (The Pre-Stack Data conditioning of the 3D Merge can be found on Exhibit A). The total area is around 1000 km2, and it is composed of a merge of different seismic cubes reprocessed by CEPSA in 2015. The seismic datum, a flat surface to which statics corrections are made (Yilmaz, 2001), is 500 m and the replacement velocity is 2000 m/s, these values are very important for creating the project and geo-referencing the wells. Since the area of study is located in the Llanos Basin the volume projected zone is Bogota, the Datum is Magna-Sirgas.

The quality of the 3-D seismic image helps understand different seismic responses of the physical rock properties. Figure 10 shows the divisions made based on their contents of frequencies, amplitudes, and lateral continuity of the reflectors. Finally, good quality information means a seismic image with character.

Figure 10 Vertical zonation of the seismic image showing either high (H) and low(L) frequencies (F), high (H) and low (L) amplitudes (A) and poor (P) and good (G) lateral continuity (LC). For example, package V (interval of interest) has high frequency content, as well as high amplitude content and poor lateral continuity.

Package I Shallowest section of the seismic information. Package II 300 ms (TWT), separated from the previous package by a yellow reflector corresponding to a horizon near to the top of the Fm. Leon: relatively high frequency content, very characteristic for being a section with a low amplitude content, but with good lateral continuity. Package III 250 ms approx. (TWT), located between 2000 and 2250 ms (time scale on the left of the figure): relative high frequencies, strong amplitudes (package with the highest amplitudes of the section), good lateral continuity. Package IV 200 ms approx. (TWT), Moderate to medium frequencies and moderate amplitudes of relative lateral continuity. Package V 150 ms approx. (TWT), relative high frequencies, high amplitudes with low lateral continuity. Packages VI and VII: low spectral content, the package VII is the typical basement image without reflections, appears as a bright area.

The dominant frequency range of the data in the interval of interest varies from 15 Hz to 80 Hz as shown in Figure 11. The bandwidth was obtained from the seismic data from CEPSA (2017) by using a plug-in Blueback Geophysics Toolbox in Petrel. This information will help to determine the spectrum for the Spectral Decomposition attribute.

Figure 11 Top: Seismic data dominant frequency range in the interval of interest. Bottom: Location of the area from where the frequency was obtained. Given by CEPSA, 2017.

There are different seismic profiles to visualize the image of the beds. As shown in Figure 12, the crosslines represent the lines perpendicular to the strike of the structure, generally the lines where the sources or shots are located. The in-lines are profiles parallel to the receivers line of the seismic design. Also, it is possible to view the time (horizontal) slices of a 3-D cube and arbitrary lines that do not need to have to be straight (Bacon, Simm, & Redshaw, 2003).

Figure 12 Different ways of viewing a seismic cube. Taken from Bacon et al., 2003.

7.1.2. ELECTRIC LOGS

7.1.2.1. GAMMA RAY LOG

The Gamma Ray (GR) log measures the total natural radioactivity emanated from a formation (Glover, 2011). This radiation originates from Potassium-40 and isotopes of the Uranium-Radium and Thorium series. The units for the GR are American Petroleum Institute (API) and they vary depending on the lithology of the area (Glover, 2011). In a sedimentary formation, it helps to differentiate the shales and clays, that have high GR values from sands, carbonates and anhydrites, which have low GR values (Glover, 2011) (Figure 13). The GR log has different applications, the most important are: • Extremely useful for discrimination of different lithologies (Figure 13). • Determination of shale content • Correlation of wells 20

• Lithology density

Figure 13 Gamma ray response to differentiate lithologies, from Glover, 2011. 7.1.2.2. VOLUME OF CLAY LOG

Volume of Clay (푉푐푙푎푦) is an important parameter for the reservoir quality analysis (Figure

14). 푉푐푙푎푦 evaluates the quantity of clay minerals present in the reservoir, their type, and their distribution. Effective water saturation and effective porosity can be calculated more accurately if the 푉푐푙푎푦 is determined (Causey, 1991).

There are different methods for calculating the volume of clay. The most relevant are the Gamma Ray Method and Gamma Ray-Bulk Density Method. It is widely known that some clay minerals tend to possess radioactive elements, therefore, it is easy to detect clay free sandstones (low GR values) from clay rich sandstones (high GR values) when the GR readings are made by the pretophysics (Causey, 1991).

The GR method is the most accurate method in determining volume of clay, “by assuming constant radioactivity, no additional radioactive elements, good borehole conditions and no variations in density, the GR response can be expressed as a linear function of clay”(Causey, 1991, p. 26); this relationship is called Gamma Ray Index, taken from Causey (1991):

퐺푅푙표𝑔 − 퐺푅푚𝑖푛 퐺푎푚푚푎 푅푎푦 퐼푛푑푒푥 (퐼퐺푅) = , (1) 퐺푅푚푎푥 − 퐺푅푚𝑖푛

where 퐺푅푙표𝑔 is the GR reading in the interval of interest, 퐺푅푚𝑖푛 is the minimum GR reading (clean zone) and 퐺푅푚푎푥 is the maximum GR reading (shale zone).

On the other hand, not all clays have radioactive elements. Thus, bulk density logs are used to measure the GR attenuation between a source and detector. This means that a formation with high bulk density results in low GR responses (clay rich sandstones) and formations with low bulk density result in high GR responses (clay free sandstones) (Causey, 1991). The measured densities can be combined with the IGR to determine the adequate volume of clay by the following equation, taken from Causey (1991):

휌푏 푉표푙푢푚푒 표푓 퐶푙푎푦 (푉푐푙푎푦) = 퐼퐺푅 ∗ (2) 휌푠ℎ

휌푏 is the bulk-density of the interval of interest and 휌푠ℎ is the bulk-density of the shale zone.

Figure 14 푉푐푙푎푦 log compared to GR log. The magenta area represents areas where the GR has high responses, hence the 푉푐푙푎푦 has high values representing an area with high content of clays and shales. The red rectangle is the opposite, low GR results in low 푉푐푙푎푦

7.1.2.3. EFFECTIVE POROSITY LOG

Effective porosity is the interconnected pore volume, or void space, in a rock that contributes to permeability in a reservoir. In formation evaluation, the effective porosity is the total porosity minus the clay-boundary water, taken from Causey (1991). This definition is based on shaly formations where the clay-boundary water is immobile.

∅푒 = ∅푇 − 푉푐푙푎푦∅푐푙푎푦 (3)

7.1.3. CHECKSHOT SURVEY AND VERTICAL SEISMIC PROFILE

Checkshot survey is similar to a Vertical Seismic Profile (VSP). The profile is a set of data of signals from sources recorded at the surface near a wellhead when drilling a well. This data is an accurate time-to-depth relationship at the wellbore (drilled hole) location (Schultz, 1998).

The difference between a VSP and Checkshot survey is that the measurements in the first are recorded at closely spaced depths and the checkshot is measured at widely spaced depths. In addition, the checkshot measurements are made on the first arrival, while the VSP records waveforms (Schultz, 1998).

7.2. PETREL PROJECT

After gathering the data, a new project was created by using Petrel E&P (Schlumberger platform for geological, geophysical, and petrophysical interpretation). The database was uploaded in the Petrel project. The first step was to assign a coordinate system to the seismic data, and the respective CDP inline and crossline relationship. Second different folders were created for each well with their log and VSP information, followed by a folder for the well tops that helped to correlate seismic data with geological formations. After uploading and organizing all the information, a seismic calibration was made with the 3- D seismic cube and the wells to start the structural and stratigraphic interpretation to reach the final aim of the thesis.

7.3. WELL-SEISMIC DATA CALIBRATION

Seismic calibration is an essential tool for correlating the well data in depth to seismic reflectors in time. This tool allows geophysicists/geologists to accurately interpret internal stratigraphy and geometry of the structure in the interval of interest. In order to 24 make an adequate calibration, a synthetic seismogram was used that generates a synthetic trace that will be compared to a seismic trace. A seismic trace is defined by the convolution model, taken from Veeken (2007)

푆(푡) = 푟(푡) ∗ 푤(푡) + 푛(푡). (4)

Where 푡 represents time and ∗ indicates convolution, and 푟(푡) is the reflection coefficient, 푤(푡) is the wavelet extracted from the seismic and 푛(푡) is the noise. The main inputs for the synthetic seismogram are (Veeken, 2007, p. 166) : • Sonic log • Density log • A checkshot survey or VSP • A seismic wavelet

Eleven wells had the complete data set for the well-seismic calibration. The synthetic seismogram generation was a three-step process: first, the sonic log of the 11 wells was calibrated to correctly match the synthetic trace to the seismic trace and to establish a time-depth relationship. Second, the reflection coefficient had to be determined, and third, a wavelet extraction from the seismic for the 11 wells was undertaken. In the area of study only eight of the eleven wells were included.

7.3.1. SONIC CALIBRATION

Sonic logs measure the travel time of P-waves or the transit time (DT) of the waves. The travel time varies with lithology, and effective porosity increases as the DT decreases (Veeken, 2007). Thus, it is important to calibrate the sonic log with the checkshot information to ensure that the time-depth relationship matches the seismic data in each well (Edgar & van der Baan, 2011).

7.3.2. REFLECTION COEFFICIENT

The reflection coefficient (RC) is the ratio of the amplitude of the displacement of a reflected wave to that of the incident wave (Sheriff, 2002, p. 290). In case of perpendicular incidence, the RC is given by the formula:

푍2 − 푍1 휌2푣2 − 휌1푣1 푟(푡) = 푅퐶 = = (5) 푍 + 푍 휌 푣 + 휌 푣 2 1 2 2 1 1

퐴푐표푢푠푡푖푐 푖푚푝푒푑푎푛푐푒 (푍) = 휌푉 (6)

Where

휌1= density of medium 1

휌2= density of medium 2

푣1= P-wave velocity of medium 1

푣2= P-wave velocity of medium 2

By looking at the formula, the travel time of a reflection can be determined by the density and seismic velocities of the layers where the seismic ray crosses. In seismic, the geophysicist works with small angles, thus the relationship mentioned above can be ignored. From the sonic log it is possible to obtain a sonic velocity that was used to determine the RC (Veeken, 2007):

1 푆표푛푖푐 푣푒푙표푐푖푡푦 = ∗ 304800 (7) 퐷푇 After finding the velocity of each layer, the results are multiplied by density to generate an acoustic impedance log (AI). Then, the RC is calculated by the contrast in seismic reflectors. If 푍2 > 푍1, the RC is positive and if 푍2 < 푍1 the RC is negative. If the reflection occurs within a layer of constant velocity, then the AI does not change, and the RC is zero. The resulting log of RC is shown in Figure 15. Subsequently, the corresponding values for

26 the RC are then convolved with a seismic wavelet to obtain the synthetic seismogram (Veeken, 2007).

7.3.3. WAVELET EXTRACTION

An accurate determination of the wavelet is essential to obtain a precise result from the synthetic seismogram. The shape of the wavelet influences the results of the interpreter, hence, for this project, 11 wavelets were extracted from 11 wells dispersed in the entire area (only eight of them were used in the delimited area). With the well analysis it is possible to examine the quality of the seismic data in different areas, to correlate subsurface geology to seismic data and to correlate the synthetic trace to the seismic trace.

Time (ms)

Figure 15 Reflection Coefficient calculation. It is obtained from the Acoustic Impedance log (pink): the multiplication of the density log (blue) and velocity log (red). The peaks on the RC log represent the changes in impedance from different lithologies. Longer lines represent greater changes in Acoustic Impedance. Modified from Veeken, 2007 .

A wavelet is a seismic pulse consisting of only a few cycles (Sheriff, 2002). The wavelet extraction is the process used to determine the shape of the wavelet (Sheriff, 2002). There are three different methods: • Deterministic • Analytical • Statistical

The statistical method was used for the correlation of the synthetic and seismic trace. It is used to estimate wavelets from seismic data only, without the need for well control (Edgar & van der Baan, 2011). In the statistical extraction method, the embedded wavelet can be approximated using the truncated autocorrelation of a seismic trace (Petrel E&P, 2016). The statistical method transforms all the input seismic traces into frequency domain, then they are transformed into phase specified from the average of the frequency spectra that results in a zero-phase wavelet (Petrel E&P, 2016). The final result from the sonic calibration, reflection coefficient determination and wavelet extraction is a synthetic seismogram, as shown in Figure 16.

Figure 16 Synthetic seismogram procedure. Modified from Barna & Anikó, 2014

7.4. SEISMIC ATTRIBUTES

A seismic attribute is defined as: “A measurement derived from seismic data, usually based on measurements of time, amplitude, frequency and/or attenuation”(Chopra & Marfurt, 2007, p. 453). In this project, seismic attributes are a fundamental part for both structural and stratigraphic interpretation, by using them, it was possible to detect faults, channels and different features associated with the area of interest. Once the interval of interest is determined by well-seismic data calibration, the attributes helped with the final objective of the project: to correlate seismic data to geological information. In this section, the most important attributes will be described to familiarize the reader with its meaning. Some attributes were used for structural and stratigraphic interpretation, while others were used post-interpretation (Figure 17).

Figure 17 Categories of seismic attributes. Taken from Barnes, 2016.

7.4.1. COHERENCE/VARIANCE

Coherence is a seismic attribute that measures similarities between traces(Chopra & Marfurt, 2007). To observe continuity and similarity in the traces it is necessary to use time slices (show reflector strike) along with inlines and Xlines (describe dips). If the characteristics of one trace changes with respect to the next, then a change in dip/strike will come up. The attribute will enhance the area which represents a change in color. Those changes represent faults or structural characteristics.

7.4.2. ROOT-MEAN SQUARE AMPLITUDE

Root-Mean Square (rms) is a procedure for finding a representative value of a set of data values (Petrel E&P, 2016). It is given by the following formula:

푁 1 푥 = √ ∑ 푥2. (8) 푟푚푠 푁 푛 푛=1

The 푥푟푚푠 of s seismic trace 푥푛 with N samples. The rms amplitude, which is the attribute used for stratigraphic features, is equal to the square root of the average trace energy.

7.4.3. SPECTRAL DECOMPOSITION

Spectral decomposition is the “decomposition of a temporal window of data into its Fourier magnitude and phase components” (Chopra & Marfurt, 2007, p. 454). It applies time-frequency analysis to seismic data to produce frequency maps or volumes (Barnes, 2016). Conventional spectral decomposition usually uses the Short Window Discrete Fourier Transform (SWDFT). There is a workflow for carrying out spectral decomposition, proposed by Chopra & Marfurt (2007) (Figure 18): 1. Select the zone of interest, typically a horizon within the area. Define a constant-thickness interval of data that lies a number of milliseconds above and below. 2. Extract and flatten data. 3. Apply a Discrete Fourier Transform to the interval of data, frequency by frequency. This results in different maps/volumes of frequency with a characteristic bandwidth. 4. Use the Red-Green-Blue (RGB) mixer for three frequencies (highest to lowest). 5. Try different opacities for each color, which will result in an image showing details of different lithologies.

INPUT

FREQUENCIES

RGB

OUTPUT

Figure 18 Spectral decomposition workflow proposed by Chopra & Marfurt (2007). Modified from CEPSA, 2013.

7.5. STRUCTURAL INTERPRETATION

In order to start the structural interpretation, it was necessary to make a bibliographic revision of the area. The first step towards structural interpretation was to extract attributes that helped to detect lateral changes. One of them is Coherence, as mentioned before it measures similarity between traces. Coherence is relevant for fault and channel detection.

The horizons were picked using seeded 2-D auto tracking (interpreted by picking seed points on an intersection) at locations with good continuity. On the contrary, manual interpretation (interpreting a horizon by clicking or drawing points on a seismic intersection) was used for areas with poor continuity and quality (Petrel E&P, 2016) .

7.6. STRATIGRAPHIC INTERPRETATION

There are two criteria for making a stratigraphic correlation that helps the interpreter to establish relationships between different areas. One is a lithostratigraphic correlation that matches bodies of rocks depending on their respective lithology (formations). Additionally, biostratigraphy or the use of fossils, helps to make a chronostratigraphic correlation that pairs rocks of the same age. It was possible to understand the dominant lithology and to determine the depositional environments of the area by using these correlations. For an adequate understanding of the stratigraphy, a stratigraphic column was created using the software Canvas.

7.7. FACIES

A facies is a body of rock associated to a specific lithology and morphological texture formed in a depositional environment, at a specific location and in a specific time as the depositional system evolves (Chopra & Marfurt, 2007). The main objective of this project is to analyze the facies distribution of the Gachetá and Basal Gachetá formations. It is important to gather all the inputs described in the previous sections in order to generate a facies map. This map was analyzed and compared/correlated with sedimentary environments to create an approximate geological model of the area.

First, to understand the sedimentary facies it is important to understand and become familiar with the respective depositional environments. This has already been mentioned in section 5, the stratigraphy of the area. Also, it is relevant to comprehend the response of those facies in electric logs (Gamma Ray and density) and to distinguish the depositional environments of the formations in all the wells. Those responses are called electro facies, which are “the set of log responses that characterized a sediment and permits the sediment to be distinguished from other” (Luthi, 2001, p. 260). To analyze and interpret the facies distribution of the area a method called waveform classification was used, as described below.

7.7.1. WAVEFORM CLASSIFICATION

Post-stack seismic attributes are computed by different methods such as pattern recognition. This method is used to classify stratigraphic features in seismic data according to characteristic patterns in the data. A distinctive method is waveform classification which identifies regions of similar waveform along a horizon (Barnes, 2016), resulting in waveform maps.

It is widely known that similar waveforms represent similar geology and patterns (Barnes, 2016). Thus, the interpreter can identify stratigraphic features such as flood plains, channels and point bars that cannot be distinguished using other attributes. Each waveform represents a different class number in an interval, they range from 5 to 25 classes, each one with a characteristic waveform (Barnes, 2016). There are two classification methods for the waveform classes, also known as seismic facies.

7.7.1.1. UNSUPERVISED CLASSIFICATION

There are two common methods for unsupervised classification, in which Petrel automatically determines the template waveform (Barnes, 2016): • K-means clustering • Kohonen self-organizing feature map (Kohonen SOFM)

The workflow consists in making a first guess of the template waveforms, followed by a trial and error in a small subset to refine these waveforms, and finally selecting the full data set with the final waveforms (Barnes, 2016). The main difference is that K-means produces a random order of the waveforms, while Kohonen SOFM orders waveform that

34 have similar shapes. Thus, the second method is more accurate than the first(Barnes, 2016). The unsupervised method used was the SOFM Kohonen. It determines if there are lateral changes in order to proceed to the supervised method. The main input is the Gachetá surface map, the size of the waveform is determined by the user as well as the waveform shift relative to the surface. The surface needs to be at the beginning of the waveform that will be analyzed, so that the interval of interest has full coverage, and the number of classes is determined by the number of wells in the area of study (Barnes, 2016).

7.7.1.2. SUPERVISED CLASSIFICATION

According to Barnes (2016) supervised classification is controlled by the geophysicist, which supplied the template waveforms. Waveforms are obtained from the interval of interest on each corresponding well. Both unsupervised and supervised methods proceed in the same way. An observed waveform is compared to the template waveforms and is assigned to a class number as show in Figure 19.

7.8. VELOCITY MODEL FOR DEPTH CONVERSION

A velocity model describes the vertical and lateral changes of the velocity in a geological sequence. It is defined by two parameters: the geometry of the reflectors and the velocity of the layers in the study area. To have precise depth conversion, the appropriate methodology must be used depending on the interpreted geological model and on the available data. The main objectives of the depth conversion are to predict depths in areas where there are no wells and to calibrate the vertical seismic data. For the purpose of this project average velocity was applied, but in Appendix B there is an additional methodology for a time-depth conversion using intervallic velocities.

Average velocity is the total depth divided by vertical one-way time. For this work the following calculations were undertaken (Table 1):

Seismic Datum 500 Well head 321.025 KB 584.6 Gachetá TWT 2296.68 Gachetá OWT TWT/2 Gachetá MD 9236.31 Gachetá TVDSS 8651.73 Gachetá TVD TVDSS +KB Prof Sismica datum (Seismic Datum-KB)+TVD

Velocity Prof Sismica Datum/OWT Conversion 9151.73 TVDSS 8651.73 Table 1 Average velocity calculation.

8. PROCEDURES

In this section the procedures used to obtain the results will be described and illustrated.

8.1. WELL SEISMIC CALIBRATION

The synthetic seismogram generated allowed correlation of geological well tops to seismic reflectors. The formational tops were checked with results obtained from previous internal reports made in CEPSA using lithological markers. Additionally, quality control was based on seismic reflection characters of each interval, for example, the characteristic frequencies content, amplitudes and lateral continuity of every formation. The synthetic-seismic trace correlation was used to reduce the uncertainty in interpretation by tying geological markers to seismic horizons. Depending on the interpreter needs, Petrel gives a variety of inputs to improve correlation. An example is shown in Figure 20, it illustrates a suggested time shift. This procedure allowed an accurate interpretation of the interval of interest, both structurally and stratigraphically. For this project, the most relevant markers are composed of: Mirador Fm (top of the interval), Guadalupe Fm, Gachetá Fm, Basal Gachetá Fm and top of the Paleozoic (or base of the interval of interest).

Figure 21 illustrates the representative extracted wavelet of the seismic cube, a minimum- phase wavelet. This wavelet has the greatest concentration of energy close to the start time (Bacon et al., 2003). The first wavelet represents the range in amplitude that varies between -50 and 50 ms. The second is the power spectrum that describes the relative amplitudes of the waveform at each frequency (Bacon et al., 2003), with values from 0 to -50 dB and frequencies from 0 to 200 Hz respectively. Finally, the bottom is the Phase spectrum ranging between 0° and -150° with a similar frequency variation. Moreover, the synthetic trace shows considerable similarity and concordance with the seismic data, tying strong peaks (red amplitudes). The Mirador Fm and Gachetá Fm have the strongest amplitudes and are the easiest to correlate for most of the wells.

After the template is complete, the interpreter must superpose the synthetic trace on a seismic section corresponding to the well location as shown in Figure 22. This figure shows the reliability of the data due to a very good fit between the seismic cube data and the

37 synthetic seismogram for the main events. Figure 23 shows that there is a good visual match for the stronger reflector but not for the weaker ones in the middle of the section. Overall, the well tying is reliable, except for a few wells where there might be synthetic seismogram defects, according to Bacon et al. (2003) due to defective logs, hydrocarbon effects or inadequate spatial sampling.s

Figure 20 Seismic Well tie. Sonic Calibration and synthetic generation are integrated in the image. Each column represents a different result.

Figure 21 Extracted wavelet of the seismic data. Correlation between the synthetic trace and the reference seismic data showing concordance between peaks (red amplitudes) and troughs (blue amplitudes).

Figure 22 Synthetic trace generated that matches the seismic trace. Positive wiggles (red) in the synthetic are correlated to positive reflectors and vice versa. Zoomed in the interval of interest.

Figure 23 Example of synthetic seismogram superposed on seismic section at a well location. Peaks in the synthetic trace match peaks in the seismic data, and troughs in the synthetic traces correlate to their corresponding throughs on the seismic data. This figure covers the section from package II to package V.

8.2. HORIZON INTERPRETATION

Horizons are significant for seismic interpretation workflow and are the pillar for structural and stratigraphic interpretation. For this work five (5) horizons were identified and each of them was associated with a seismic event or seismic reflector within the seismic volume. Subsequently, these horizons were picked by the use of inlines and crosslines sections, followed by a combination of seed detection and line based interpretation. In Petrel E&P (2016) seeded autotracking is a method for interpeting horizons where points will sbe tracked in the direction of the selected line intersection. Guided autotracking uses two points as the input, then the tracking will find the best route from one to the other. This process was undertaken in order to acquire a regional understanding of the subsurface geology. Next, a brief explanation of the methods used for each seismic event is given (Figure 24):

• Mirador Fm top: Seeded autotracking every 10 inlines and 50 crosslines, where needed manual tracking was used. • Gachetá Fm top: Every 10 crosslines and 50 inlines using seeded autrotracking and manual tracking. • Basal Gachetá Fm top: The reflector corresponding to this formation had good continuity, so the appropriate method was seeded 3-D auto tracking. • Paleozoic Fm top: Similar to Basal Gachetá. • Basement top: Easy to recognize in seismic lines because of a strong reflector, thus 3-D auto tracking was an adequate method.

8.3. SEISMIC ATTRIBUTES

Attributes are very important for structural and stratigraphic interpretation. They allow the interpreter to visualize the fault planes and some stratigraphic characteristics more easily. Figure 25 shows the cosine phase that represents a time derivative for post-stack analysis, where structural delineation is enhanced. Figure 26 portrays seismic discontinuities or similarity between traces in an inline and enhanced them by using Coherence/Variance, characterized by a yellow/red color bar that represent the fault planes of the area (Figure 27). Variance is a time derivate attribute for post-stack interpretation. The faults were picked by using a section where the fault plane was perpendicular to the bedding.

Figure 24 Horizons interpreted by using different methods. A) Mirador Fm (seeded autotracking) B) Gachetá Fm (seeded autotracking) C) Paleozoic rocks ( 3-D autotracking D) Basement (3-D autotracking).

Furthermore, amplitudes exhibit lateral changes so it is easy to recognize faults as shown in Figure 28. Curvature is useful to recognize fractures, changes in dip and azimuth that highlights channels structures and faults (Figure 29). Similarly, depositional systems show up better in time slices where the window is a constant flat time interval. Flattening the cube to a horizon appropriately displays the features or geoforms by eliminating the effect of superimposed deformation. Time slices in the coherency cube allows visualization of the horizontal extension of the faults and, allows the interpreter to reconstruct tectonic and sedimentary events (Figure 30 and Figure 31).

Figure 25 Cosine of phase enhances faults polygons and structural delineation. Top- Raw attribute. Bottom- Faults interpreted overlaid on the attribute.

Figure 32a is a horizon slice to Gachetá Fm, it enhances the channels edge, fault edges, and certain incised valley fills. Figure 32b exemplifies the fault trend delineated by the interpreter according to the coherence volume. Once the fault polygons are interpreted they are merged with the horizons to create surface that have the resulting faults (Figure 33, Figure 34).

Figure 26 Variance/Coherence attribute (a) Inline 620 with no interpretation and (b) with the interpreted faults (c) time slice showing the faults

Figure 27 Time slice at t=2300 ms through coherence volume showing the interpreted fault polygons.

Figure 28 An inline of the original amplitudes portraying changes in amplitudes that represent faults (left). The faults and main horizons interpreted (right). Yellow-Mirador Fm, Green-Gachetá Fm and Purple- Paleozoic beds. Red represents high amplitudes and blue low amplitudes.

Figure 29 a) Most Negative Curvature, b) Most Positive Curvature and c) blended a) and b) enhancing channels (purple arrows) and faults (yellow circles)

a) b)

Figure 30 Time Slices of the amplitude cube illustrating a sedimentary reconstruction in 8 ms TWT window. a) Time slice to Guadalupe Fm, b) time slice to Gachetá Fm and c) time slice to Une Fm. The Yellow line represents inline 620 shown in the coherence volume.

Figure 31 Variance blended with RMS exhibiting the same sedimentary reconstruction in 12 ms TWT window, 36 ft or 10m there is a change from marine to non-marine environment. The three images represent Guadalupe Fm (Top left), Gachetá Fm (top right) and Basal Gachetá (Bottom)

Figure 33 Opacity of 50% in the variance cube to show the fault lines more clearly (left). The fault lines and fault polygons intercepted and applied to the horizon that results in a surface with their respective faults (right).

Figure 34 In order to do an adequate interpretation, faults need to be incorporated into the surface to differentiate the hanging wall and foot wall.

9. RESULTS

9.1. STRUCTURAL INTERPRETATION

This chapter summarizes the analysis of the fault systems that composes the area of study and its relevance to the description of geological bodies. By comparing the information obtained using the seismic data, the area of study is located within the Casanare domain, mentioned in section 4 (structural framework). Along with the seismic attributes, the main faults and horizons were interpreted (Figure 26).

Seismic attributes were essential for defining the structural control in the area. Figure 35 displays an amplitude time slice of the Gachetá Fm where the contrast between high and low amplitudes is clear. These lateral changes in amplitudes are not smooth, thus, there cannot be a stratigraphic feature controlling sedimentation. On the contrary, the change is abrupt as expected for a fault, so it is possible to conclude that the sedimentation of this Formation is controlled by structural deformation.

Figure 35 Amplitude (left) and Sweetness (right) time slices to Gachetá Fm. Magenta arrow shows a channel who’s deposition was controlled by faults (abrupt limit).

Two main trends of faults are visible in the area of study. One of them is a fault trend with a predominantly N-S direction and east or west-dipping. These faults are often called antithetic and synthetic, and are the focus of this project. Also, some faults are present

50 with a NE trend due to the compressive tectonic setting. The section affected by the deformation of these faults comprises the Mirador Fm, Gachetá Fm, Une Fm, Paleozoic rocks, and the Basement as shown in Figure 36. The second trend observed has a NE-SW direction, parallel to the deformation front of the orogen and it corresponds to characteristic normal faults of the Casanare Area mentioned in the literature by Moretti, Mondragon et al. (2009) and Sarmiento (2011)(Figure 37). They correspond to faults with an origin within basement and extent to Eocene times.

On the structural maps of the basement, three fault trends can be seen, and the main trend is the NW-SE. This trend affected the sedimentation and the deformation in the sedimentary cover. The magenta arrow in Figure 38 illustrates the structural change due to an abrupt lateral discontinuity highlighted with seismic amplitudes and Root-mean Square cubes. Another structural figure is the plunge of the basement high, easily identified in Figure 38 (see Figure 5). This high has a SE direction that controlled the Paleozoic sedimentation of the study area (Figure 38).

It is possible to appreciate the configuration of the contour lines as a basement high in the map. This high is immediately preserved by an event of the Paleozoic sedimentation. Nonetheless, the effect of this high is noticeable in the re-activation structures that affect the Cretaceous – Palaeogene +Neogene sequence.

Figure 36 Xline of faults affecting the interval of interest, from the Basement up to Mirador Fm. Each vertical line represents a fault.

The basement was divided into three groups of faults with different orientations. The first group is illustrated on Figure 39 with a NW-SE orientation, limiting the basement high. The basement presents a typical configuration of an extensional regime, and presents a domino effect (green circle). Some faults propagate through the sedimentary cover, and some of them do not, but understanding the propagation of these fault planes is essential for reconstruction of the tectonic history of the area.

Figure 37 Inline of the amplitude seismic cube showing how the stratigraphic sequence has been affected by the second fault trend. Each vertical line or color represents a fault.

The second group, is a set of faults with a N-S domain (Figure 40) and is the structure from where the faults reactivated creating a pop up in the inverted blocks. Furthermore, the faults control the sedimentation from Precambrian times to Eocene times, and affect the deformation of the subsequent sedimentary sequence. The third group consists of faults with a preferential E-W direction and produces relays in the faults of the other two domains, indicating that the faults are older (Figure 41).

Figure 38 Basement´s structural map showing three fault trends: N-S, NE-SW and NW-SE. The magenta arrow shows the gravimetric high (orange color) mention in Figure 5 that controls the sedimentation in the area, the contours enclosed the high. The circles, each with a different color represent wells in the area.

Figure 39 Structural map showing the basement high, blue arrows are the NW-SE faults and green circle represent a domino effect.

Figure 40 Second group of faults within the basement portraying a N-S trend.

Figure 41 Third group of faults with a SE-NW trend, similar to the trend in the Gachetá Fm.

The structural map of the Gachetá Fm shows a fault intersection to the south of the area (Figure 42), this feature is in the apex of the basement high. The contour lines represent a monocline with a 2-degree angle, dipping to the west. The direction of the contours of the top of Gachetá Fm is different from the basement contour lines that delineated the structural high. The change in direction of the contours is because of the accommodation

55 of the basin floor after the Paleozoic rocks were deposited. Also, there are two fault blocks delimited by the N-S trend, described before.

Figure 42 Structural map of the Gachetá Formation. The magenta arrow shows the N-S fault trend that is the most important for this project, NW-SE and NE-SW trends are present. The contours have different direction, NE-SW, than the basement map due to Paleozoic sedimentation, which created a horizontal surface that was overlapped by Cretaceous sediments.

Figure 43 shows the structural high limited by the green fault to the west, which is an inverted fault that is interpreted from the basement, dipping is towards the east. The red fault in Figure 43 represents a fault with a dip to the west forming a pop up structure. Also, it is rooted in the basement or propagates from the basement. It is possible to observe the fault heave around 10 ms. Figure 44 and 45 show an overall tendency of an inversion block located between the green and red faults. The faults in the area have high dip angles and they originate in the basement; most of these fault domains still affect the entire sedimentary sequence of the area.

Mirador

Gachetà

Figure 43 Inverted structure due to tectonic load affecting the sedimentary section from the basement to Carbonera Fm in the northern part of the area. The inverted block is limited by the green fault (west) and the red fault (east). The inverted structure is delimited by the N-S fault trend mention in Figure 41.

Mirador

Gachetà

Figure 44 Inverted structure to the south of the area (purple line) affecting from the basement to Mirador Fm. This structure is limited to the east by the green fault and to the west by the red fault. Each vertical line represents a fault. 58

Mirador

Gacheta

Figure 45 Xline in the southern part of the area, where the inverted structure is limited to the west by the green fault and to the east by the red fault and affects the interval from the basement up to Mirador Fm. Each vertical line represents a fault. The domino effect mentioned above can be appreciated by the red amplitude in the bottom part of the figure. 59

9.2. STRATIGRAPHIC INTERPRETATION

9.2.1. DEPOSITIONAL ENVIRONMENTS

The stratigraphic description focuses on the interval that includes the Basement up to Mirador Formation. In order to understand the depositional environments seismic attributes and well correlation were used as guide to interpret and describe new geological bodies.

Considering the previous seismic characterization, the interval analyzed is package V that comprises from Paleozoic rocks up to the top of the Mirador Fm (Figure 46). In this section the paleo-sedimentary configuration of each formation and the seismic image of each sedimentary environment will be described. The Datum used was Mirador flattened, which means that the different cubes were all flattened to this Formation (Figure 47).

Seismic attributes help to find changes in amplitudes or character anomalies when a vertical seismic section intersects a stratigraphic feature. And the horizontal slide shows the extent of the feature. Figure 48 shows a channel of the Basal Gachetá Fm, corresponding to its depositional environment, the attributes used were amplitudes (black and white) and RMS. The inline and crosslines have the same red color, and when compared to the time slice the features are shown completely. Another stratigraphic attribute is sweetness, which identifies features where the overall energy signatures change in the seismic data (Petrel E&P, 2016). Figure 49 illustrates the same channel, the brown arrow is an onlap, and the grey arrows represent possible changes in lithology.

From Figure 50-Figure 57 it is possible to distinguish different sedimentary cycles or associations between the base of Cretaceous and Paleocene times. The first cycle corresponds to a non-marine sequence composed by the Barcos-Cuervos-Guadalupe formations, and the second cycle, formed by Gachetá Fm and Basal Gachetá (Une) Fm,

60 with a marine influence. The depositional environment of the different formations found in literature will be described as follows: • Carbonera Fm. (Figure 50): fluvial and estuarine channels (Bayona, Jaramillo, Rueda, Reyes-harker, & Torres, 2007). • Mirador Fm. (Figure 51): fluvial channels and mouth bar sands (Bayona et al., 2007) in a coastal plain. The channels are large and thin, without sinuosity. • Barcos-Cuervo Fm (Figure 52): comprises a fluvial-coastal plain package (Bayona et al., 2007). The main characteristic is the presence of Incised Valley Fill (IVF) distributed across most of the area.

Mirador

Gacheta

Basement

Figure 46 Stratigraphic intervals used in this work

Figure 47 Close up of the interval of interest flattened to Mirador (50 ms TWT)

• Guadalupe Fm. (Figure 53): Lower shore face, estuarine channels, tidal to wave- influenced marine channels and bars, and fluvial with estuarine influence (Sarmiento, 2011).Similar characteristics to Barco-Cuervos formations, but IVF are less common. • Gachetá Fm. (Figure 54): Corresponds to a flood pain or deltaic plain with marine influence, or inner shelf influenced (Sarmiento, 2011). Most of the structures are controlled by faults. • Basal Gachetá (Une) Fm. (Figure 55): Fluvial channels at the base to estuarine channels or bay deposits and marine shelf deposits in the upper part of the unit (Sarmiento, 2011). • Paleozoic Fm. (Figure 56): Thick stratigraphic wedge (Bayona et al., 2009). • Basement (Figure 57): Extensional regime (Bayona et al., 2009).

The depositional environment changed from dominantly marine (Une Fm., Gachetà Fm.) during the Latest Cretaceous to marginal and continental during the Paleocene (Guadalupe Fm, Barcos y Cuervos Fm)(Bayona et al., 2007) , and Early Eocene units correspond to fluvial sandstones of the Mirador Formation (Figure 50-Figure 55). The changes in the sequence of deposition are associated with exhumation and denudation of the Central Cordillera (Sarmiento, 2011).

a b ) )

)

Figure 48 Amplitudes blended with RMS showing a) the red circle is a high amplitude and high RMS characteristic of channels b)the Basal Gachetá Fm Channel in a time slice with their respective inline and crossline. c)The red circle represents the main channel within Gachetá Formation.

a b

Figure 49 Sweetness attribute. a) Inline and b) intersection between an inline and a time slice. The grey arrows show the changes in color, and the brown is the onlap of the channel.

a) b)

Figure 50 a) Variance/Coherence, b) Root Mean Square and c) The coherence volume blended with RMS showing the deposition environment of Carbonera Formation. Depositional Environment: fluvial and estuarine channels (Bayona et al., 2007).

a) b)

Figure 51 a) Variance/Coherence, b) Root Mean Square and c) The coherence volume blended with RMS showing the deposition environment of Mirador Formation. Depositional Environment: fluvial channels and mouth bar sands (Bayona et al., 2007).

a) b)

d) c)

Figure 52 The Barcos-Cuervos Fm. assemblage comprises a fluvial-coastal plain package (Bayona et al., 2007) . a) Coherence seismic cube flattened to the Mirador Fm., as well as b) RMS volume. Both attributes were blended as shown in c). d) Incised valley fill (IVF) examples, red arrows show the direction of sedimentation inside the IVF.

a) b)

Figure 53 The depositional environment of Guadalupe Fm. are lower shore face, estuarine channels, tidal to wave- influenced marine channels and bars, and fluvial with estuarine influence (Sarmiento, 2011). a) Variance, b) RMS and c) blended between a) and b).

a) b)

Figure 54 Gachetá Fm. corresponds to a flood plain or deltaic plain with marine influence (inner shelf)(Sarmiento, 2011) . a) Variance, b) RMS and c) mixed image of the two input attributes.

b) a)

Figure 55 Strata slice through Basal Gachetá Fm., showing evidence of the depositional environment. a) Variance and b) root-mean square. c) Blended of a) and b). The depositional environment corresponds to fluvial channels from the base to estuarine channels or bay deposits and marine shelf deposits in the upper part of the unit. (Sarmiento, 2011)

a) b)

Figure 56 Time slice through Paleozoic sediment, in this area there is no metamorphism (Bayona et al., 2009). The orange arrows represent the onlap over the basement. a) Variance/Coherence, b) Root Mean Square and c) The coherence volume blended with RMS.

a) b)

Figure 57 Time slice showing the basement. a) Coherence seismic cube flatten to Mirador Fm, b) RMS volume. Both attributes were blended as shown in c). Yellow arrow portrays the domino effect due to extension (Bayona et al., 2009).

9.2.2. ANALYSIS OF THE GACHETÁ FORMATION

This section is an approximation to the methodology used in the quantitative analysis. If applied in detail, the geological disciplines used might be: sequence stratigraphy, waveform analysis, petrophysical analysis, and detailed reservoir analysis. The waveform and other complementary attributes are constrained to the well data. Nevertheless, for the adequate use of this methodology different attributes must be compared and interpreted jointly. The objectives of this chapter are to apply the methodology and calibrate the seismic attributes with the facies analysis. The initial amount of wells was 27, but only 10 of them were correlated and analyzed.

In order to run the supervised spectral decomposition, the analyzed window had to be larger than the seismic resolution, which is approximately 20 ms (TWT) equivalent to 70- 80 ft. Gachetá Fm has a total thickness of 320 ft. approximately 50 ms TWT. The formation was divided into two sequences for the facies classification based on a sequence boundary, which corresponds to two tectono-sequences (Figure 58). The first is Gachetá A with a total thickness of approximately 150 ft. equivalent to 23 ms TWT. And the second part is Gachetá B with 170 ft. approximately corresponding to 27 ms TWT (Figure 58). Both sequences are in time, greater than the seismic resolution. Each sequence is equivalent to the three events mentioned in section 5.2.1. For each sequence with the help of the petrophysics the net/gross ratio was calculated using the 푉푐푙푎푦 and Effective porosity logs for each well (Figure 58).

Figure 59 is a close up of the interval of study where the top is a peak in the seismic data which is interpreted as Gachetá Fm top. The top of the second interval is a shift of Gachetá horizon that was located at the top of sequence B. The whole interval is shown from Mirador Fm base to the base of Gachetá B. In the GR log it is possible to observe the lower sequences inside the whole interval. Seismic amplitudes were tied to lithology (Figure 60), with positive amplitudes (red) indicating sandstones and negative amplitudes (blue)

72 representing shales. The Mirador Formation is the most prolific reservoir in the Llanos Basin, and it is represented by the brightest red color in the area.

Unsupervised facies analysis is applied in the preliminary phase when the reservoir properties depend mostly on seismic data (Matos, Osório, & Johann, 2005). The first step for mapping facies was to start with unsupervised classifications that validated a geological model of the area. Each map reproduces the distribution of clusters (Figure 61,Figure 63), for example the seismic response belonging to class 2 (purple) share the same characteristic all over the area and are distinct from other classes. These changes can be interpreted to represent changes in rock physics, fluid content, or lithology.

Gachetá A employed 10 classes with a 25 ms TWT window for each map. Figure 62 and Figure 64 illustrate two examples of wavelets in the area, each one with a distinguishing amplitude, frequency, and phase. Once the 10 main waveforms are determined, the rest of the seismic traces are compared to make a final association. There was not any prior information used, the results show that there are lateral changes in the entire area in both intervals that were used to make a classified supervision. Each color variation represents a change in waveform. The unconstrained classification results show a channel dominance in the central part of the area, bounded by red arrows (Figure 61). C in different colors. The different stages of the Gachetá are more visible in these maps. The channels in Gachetá A show more continuity than in Gachetá B, as well as the IVF. Supervised classification was tested several times with different cluster associations until the best spatial distribution of seismic facies was determined based on the lithology and depositional environments described on section 5.

Figure 58 Subdivision within the Gachetá Formation. Gachetá A and Gachetá B are composed of three main divisions as mention in Section 5.2. Each interval has its own lithological characteristics. Based on this division, different facies associations were undertaken. The tables on the right represent the Net-Gross of each well: well 4 in Gachetá A and Gachetá B has the highest N-G ratio, thus has the highest sand content.

Figure 59 Zoomed into the subdivision within the Gachetá Fm. The Gamma Ray is shown as well as the 6 division of the sequence stratigraphy. Gachetá A top is a strong red amplitude reflector. The interval presents chaotic patterns that represent complex lithologies or stratigraphy.

Figure 60 Interval of interest divided in three Formations: Mirador-Guadalupe fms, Gachetá A and Gachetá B. Gamma Ray log on the back shows that the yellow interval has the highest sand content thus the reflector have good lateral continuity. Shaly intervals are more chaotic and harder to interpret than sandy intervals.

The facies map on Figure 65 illustrates the influence of ten (10) cluster, each corresponding to a different well, resulting in 4 associations/facies. All wells were associated according to similar lithologies, for example, Facies 1 has three similar wells (1,4 and 5). There is an aggradation event towards the base of the sequence, followed by a coarsening upward event, another coarsening upward event, and an event finning upward only in two of the three wells. Another criteria used was the 푉푐푙푎푦 and effective porosity cut-offs (Table 2) represented in a log called Facies next to the GR log in the correlated wells. With this cut-offs the Net-Gross ratio, “the ratio of the net pay thickness to the total (or gross) thickness of the reservoir” (Bouffin, 2007, p. 1) was calculated. Facies 4 for Gachetá A has two wells, 7 and 8, with an aggradational event towards the base of the sequence and a coarsening upward to the top, the resulting log for wells 7 and 8 is similar.

Figure 61 Unsupervised facies classification for Gachetá A. There are lateral changes in the interval, thus is possible to obtain a supervised classification. Each color represents a distinctive cluster with a characteristic wavelet. The red arrows show a pattern interpreted as a channel according to evidence in previous maps.

In the supervised facies maps each color corresponds to different facie. Different depositional stages and geological features are highlighted in the maps and compared to other seismic

76 attributes. Both maps allow the recognition of major depositional domains of the initial geological model. The first is characterized by three coarsening upward marine parasequences and of incised valleys fills and channels. The IVF are the consequence of accommodation space, due to erosional events that started in the Late Cretaceous, corresponding to the top of the Gachetá Formation, and began to fill during the Guadalupe and Barcos-Cuervos depositional phase (Facies 1). Therefore, stratigraphic attributes such as RMS show these geoforms where high values represent sand fill (IVF). Moreover, the channels are the result of the sediment disposition controlled by the faults and the basement high in the area (Facies 3). The evidence for this assumption are the sharp edges of the channels that suggest abrupt geological boundaries.

Figure 62 Resultant wavelets of two classes from the waveform classification in Gachetá A. Each wavelet has a distinct amplitude (top), frequency (bottom left) and phase (bottom right).

Figure 66 has some limitations due to the selection of colors but some distinctive characteristics, such as the channel controlled by faults in the middle of the area. To the left there are a few braided channels (orange arrow) (Facies 2). Also, there are two fault trends in the north-west part of the area. Facies 3 and Facies 4 have low content of sand, they are mostly shales.

Even though some features can be interpreted in the maps, this method has some difficulties. First, the cluster association might not be adequate since the response of each waveform to the 77 seismic data is not well known. The correlation between wells/lithologies might vary depending on the ability of the interpreter or there might be errors in the logs used. Another limitation is the selection of colors for the area, if they do not contrast correctly some features cannot be easily appreciated. However, if the colors are adequate, complex patterns can be identified as well as the correlation of the sedimentary environment.

Figure 63 Unsupervised facies classification of Gachetá A. There are lateral changes in the interval, thus it is possible to obtain a supervised classification.

Figure 64 Resultant wavelets from the waveform classification in Gachetá B. Each wavelet has a distinct amplitude (top), frequency (bottom left) and phase (bottom right).

Volume of Clay Effective porosity Facies 1 0-0.28 0.17-0.23 Facies 2 0.28-0.38 0.14-0.17 Facies 3 0.38-0.65 0.07-0.14 Facies 4 >0.65 <0.07 Table 2 Volume of clay and effective porosity cut-offs for defining the facies and Net-Gross of each well.

Figure 65 Image showing the lateral stratigraphic changes in Gachetá A. Each facies represents a color and they area associated to clusters (wavelets), for example facies 1 has an aggradational events at the base of the interval, follow by three progradational events. There is a N-S control of faults shown by red arrows, some channels to the west part of the area corresponding to facies 2.

Figure 66 Image showing the lateral stratigraphic changes in Gachetá B. The orange arrows channels overlapping. Each color represents a facies or wavelet with a respective lithology, for example Facies 4 has the least sand content.

9.3. DEPTH CONVERSION

A competent and thoroughly velocity model permitted a depth conversion of the structural map for the Gachetá Fm using well calibration. Table 3 shows the data used to find the velocities for each well, as more wells are used, more detail can be added to the model and there will be a more accurate depth conversion. The velocity calculated for each well was an average velocity. This method is just one example of how a depth conversion can be made, for another method (see also APPENDIX B).

Well-1 Well-2 Well-3 Well-4 Well-5 Surface TWT 329.89 335.55 334.42 349 321.025 KB 593 593 576.1 581 584.6 Gachetá TWT 2449 2440 2428 2397 2466 Gachetá OWT 1224.5 1220 1214 1198.5 1233 Gachetá MD 9125.55 9138.61 9105.88 8918.1 9236.31 Gachetá TVDSS 8530 8509 8455.3 8337.14 8651.73 Gachetá TVD 9123 9102 9031 8918.14 9236.33 Prof Sismica datum 8780 8759 8705 8587.14 8901.73

Velocity 7.170 7.180 7.171 7.165 7.220 Conversion 8780 8759 8705.3 8587.14 8901.73 TVDSS 8530 8509 8455.3 8337.14 8651.73

Well-6 Well-7 Well-8 Well-9 Surface TWT 481.64 338.93 392.48 435.47 KB 552 556 552.4 592 Gachetá TWT 2263 2270.57 2240 2543 Gachetá OWT 1131.5 1135.285 1120 1271.5 Gachetá MD 8063.21 8098.82 8000.13 9617.61 Gachetá TVDSS 7510.07 7542.9 7447.02 9018.89 Gachetá TVD 8062.07 8098.9 7999.42 9610.89 Prof Sismica datum 7760.07 7792.9 7697.02 9268.89

Velocity 6.858 6.864 6.872 7.290 Conversion 7760.07 7792.9 7697.02 9268.89 TVDSS 7510.07 7542.9 7447.02 9018.89 Table 3 Average Velocity calculation for 9 wells.

Figure 67 Average velocity map, Gachetá Fm top. The map shows smooth lateral changes that will help to prove the model proposed in the area. The table on the right are the velocities for each well that vary from 6800 ft/s to 7300 ft/s, not all wells are shown on the map because of confidential information.

From the velocity maps, it is possible to distinguished that there are smooth lateral changes (Figure 67,Figure 69). Additionally, the velocity and depth maps evidenced that there are no complex structures in the area (Figure 68). Figure 69 shows the same velocity gradient but adjusted to a structural map of Gachetá Fm Figure 70 shows a new depth conversion and the results are similar to the previous map, there are no abrupt lateral changes and the high values represent a high within the horizon, that correspond to the plunge.

Figure 68 Depth Conversion map, the gradient of average velocity Gachetá Fm top.

Figure 69 Average velocity map adjusted to fit the Gachetá Fm. There are no lateral abrupt changes that invalidated the geological model proposed. The table on the right shows average velocities for each well

Figure 70 Depth conversion adjusted to Gachetá Fm top.

9.4. GEOFORMS

The interpretation of subsurface geology is relevant to the analysis of depositional environments and to the prediction of facies distribution. Stratigraphic interpretation helps to determine stratal architecture and the development of depositional systems. One important attribute used by geophysicists is the spectral decomposition that unravels the seismic signal into its constituent frequencies (Hall & Trouillot, 2004). Each stratigraphic feature has a response to different combination of frequencies, therefore stratigraphic relationships will be determined and geoforms will be created.

Figure 71 and Figure 72 compare the facies distribution of the Gachetá Fm and the Gachetá Basal Fm with spectral decomposition (SD). Comparison of both facies models maps, and two SD maps prove the structural control in the area as mentioned before. The facies model for Interval A

85 represents characteristics of different depositional environments. Channels shown in orange lines evidence marine deposition on a slope or clastic ramp associated to facies 1 where there is a high content of sand. According to the literature (Bayona et al., 2009; CEPSA, 2013), the Gachetá Fm was exposed to a period of subaerial exposure that caused erosion in the area, creating Incised Valleys (yellow polygons). These features were then filled with sediments from the Guadalupe Fm and later Barcos and Cuervos fms in similar depositional events.

Figure 71 Supervised facies map (left) with different geological bodies such as IVF (yellow), channels associated to facies 1 (orange forms) and structural control with a N-S trend. Spectral decomposition image (right) that represents the geological bodies on Gachetá A. Frequencies used: 20Hz, 50 Hz and 80 Hz

Gachetá B sediments record deposition in fluvial channels, represented by yellow lines in the middle and eastern part of the area where they are overlapping; those channels correspond to Facies 1 and they also have a distinct color in the SD map. Additionally, there is evidence of flood plain event represented by the chaotic patterns in the western part of the area on both maps (Figure 71 and Figure 72).

10. DISCUSSION

According to Bayona et al. (2008) the Eastern Cordillera (EC) was the result of the interaction of the Nazca, Caribbean, South American, and Farallon plates. The EC is a doubly vergent thrust belt placing Cretaceous and Paleogene rocks over a thick Cenozoic succession in the Llanos Basin to the east (Bayona et al., 2013) . Reactivation of pre-existing structures on the Llanos Basin occured

86 as reponse to changes in tectonic events on the EC and the maximum horizontal stress. The orogenic contractional deformation is trasnferred into previously undeformed continental forelands causing normal faults to move (Delgado et al., 2012). One fault trend in the area of study are parallel to the deformation front of the Easter Cordillera (NE-SW).

Figure 72 High- resolution spectral decomposition (right), showing similarities with the facies map (left) extracted before to Gachetá B. The frequencies used were 20,30 and 50 Hz.

Delgado et al. (2012) states there are two causes for reactivation of normal faults. First, far field tectonic stresses are transferred into the foreland plate impacting inherited structures, which are reactivated depending on the horizontal stress. Second, bending forces due to foreland flexure under tectonic loading in the hinterland facilitate faulting due to buckling. Flexural deformation in the Llanos Basin started during the Maastrichtian as the result of the uplift and exhumation of the Central Cordillera, and a compressional regime developed until Late Paleocene (Sarmiento- Rojas et al., 2006). The tectonic event affected the entire sequence deposited until Late Paleocene creating normal faults that were reactivated later, as shown in Figure 36. Before Maastrichtian time there is no evidence or record of any tectonic subsidence in the Basin (Bayona et al., 2009). The differences in dip along the same fault plane along strike could be explained by an oblique component during the collision of the plates that produced a component of strike-slip movement. This phenomena can be see in a seismic cube by using arbitrary lines perpendicular to the fault.

The Paleocene-Lower Miocene is characterized by two episodes of eastward migration of tectonic loads and a flexural wave interrrupted by an episode of westward migration in Eocene times (Bayona et al., 2009). The exhumation of the EC took place during Paleocene time, but the EC did not advance to the east until the Late Oligocene. During the Eocene, tectonic loads and the flexural wave migrated westward. The absence of most of the Lower-Middle Eocene succession can be explained by the westward migration of a subaerial forebulge (Bayona et al., 2009). From Late Eocene to Oligocene, flexural uplift of the EC affected normal fault geometry in the area creating inverted structures. The forebulge of the basin migrated eastward far from the deformation front due to the orogenic load. It was only by mid to late Miocene when the frontal thin-skinned thrust of the EC emerged (Delgado et al., 2012). The frontalmost structure of the orogen activated, causing the faults to contract and invert during the depositon of the sediments (Figure 43-Figure 45).

Figure 73 illustrates the reconstruction of the tectonic events in the area of study as follows: the crystaline basement has been affected by normal faults that create a characteristic domino effect, these faults are interpreted on the map as N-S lineaments. During the Paleozoic (Cambrian-Devonian) the basin was a shallow epicontinental sea developed over the South American Guyana Shield. The structural configuration formed from E-W stresses due to continental separation and it is typical of the extensive regime that took place from Triassic to Jurassic times. The Early Cretaceous in the area correspond to an extensional rift basin related to the break-up of Gondwana (Sarmiento, 2011). During the Late Cretaceous the accreation of an oceanic plateau in western Colombia and the orogenic growth of the Central Cordillera drove normal faults to reactivate and produce inverse faults of high angle. The oblique collision is responsible for the strike-slip component in the faults and the lithostatic load creates flexure in the plate reactivating the faults. This started a compressional regime from the Late Cretaceous to Paleocene.

Figure 73 Reconstruction of tectonic history in the area of study: the crystaline basement has been affected by normal faults that create a characteristic domino effect, these faults are interpreted on the map as N-S lineaments. During the Paleozoic (Cambrian-Devonian) the basin was a shallow epicontinental sea developed over the South American Guyana Shield. The structural configuration formed from E-W stresses due to continental separation and it is typical of the extensive regime that took place from Triassic to Jurassic times. The Early Cretaceous in the area correspond to an extensional rift basin related to the break-up of Gondwana (Sarmiento, 2011). During the Late Cretaceous the accreation of an oceanic plateau in western Colombia and the orogenic growth of the Central Cordillera drove normal faults to reactivate and produce inverse faults of high angle. The oblique collision is responsible for the strike-slip component in the faults and the lithostatic load creates flexure in the plate reactivating the faults. This started a compressional regime from the Late Cretaceous to Paleocene. a) inverted structure as result of compression events and flexural wave.

Figure 74 Cartoon of the depositional environment of Gachetá. Each color dot is a well, the red arrow represents a regressive event and the blue arrow a transgressive event. Redrawn after Catuneanu, 2006

Based on the logs, facies map, lithologies, and seismic images the Gachetá Fm has different depositional successions. Figure 74 shows a cartoon summarizing the depositional environments from Albian to Santonian age. To the west of the area, the Gachetá Fm records deposition in a clastic ramp setting (CEPSA, 2013; Sarmiento, 2011) and is close to the axis of sand transport. The succession is dominated by argillaceous sandstones (wells 1, 2, 4, 5) interbedded with mudstones. The more bedded intervals correspond to sediments from gravity flows on a slope.

Wells 9 and 6 have mud-dominated successions with some intervals of argillaceous and clean sands towards the top (CEPSA, 2013). This succession is far from the axis of sand supply and the deposition is in an inner shelf environment that comprises the coastal, and transitional to marine environments, explaining the presence of sandstones (east of Figure 74). Finally, wells 3, 7 and 8 consist of mudstones with thick intervals of relatively clean sands that correspond to deposition in channels or lobes on more stable areas of the slope controlled by a topographic high.

11. CONCLUSIONS

• The methodology of this project is based on current prospect interpretation in the oil industry: well to seismic tie, log interpretation, structural and stratigraphic interpretation, 3-D seismic interpretation, facies classification, extraction of seismic attributes, time- depth conversion, acoustic impedance inversion, and geoform identification. • A structural consistency was identified between the configuration of the structural maps of the Gachetá Fm, Basal Gachetá Fm, and Basement, and the presence of a gravimetric basement high, which controlled the geological development (structural and stratigraphic) of the area. These highs might have favored hydrocarbon migration during the geological history of the basin. • In the analysis of the sedimentary environment, maps of the Cretaceous formations, resemblance was found in the configuration of the geoforms in two packages: o Gachetá and Une formations: fluvial channels in an estuarine or shelf domain. o Guadalupe and Barcos-Cuervos formations: fluvial channels crossing a coastal plain. • The study area has a structural domain with a preferential North-South direction, formed by high-angle antithetic and synthetic normal faults that control the sedimentation for the interval of interest. • In the facies analysis, the Gachetá Fm was divided into two intervals. Gachetá A presents the main geoforms identified and it has the greatest content of sandstones. • The wells corresponding to facies 1 have the greatest sandstones content and they are characterized by coarsening upward subintervals. Considering this analysis, the distribution of the previously mentioned sandstones can be predicted. • The vertical faults of the sedimentary cover are noticeable. There may exist vertical relays and dip changes that evidence the effect of strike-slip movements and flexural deformation episodes.

• The overall dip (2 degrees) of the Llanos basin was calculated by using the seismic data of the area. This favours hydrocarbon migration from the generation area towards the west in the hinterland. • The geoforms in the maps of facies distribution and seismic attributes for the formations of study represent the depositional environment described in the geological literature. • The (wedge-shaped) Palaeozoic sedimentation alters the pre-Cretaceous basin configuration, leaving a planar surface where deposition occured in a uniform way. • The deformation events in the basin mentioned in the literature are present in the area (three structural domains): N-S, NW-SE, and NE-SW. • The appropriate application and selection of the seismic attributes, and the adequate management of the visualization allowed the interpretation of the depositional environments and facies distribution. • The methodology used was adequate due to the consistency of the results with the sedimentary environments mentioned in the geological literature.

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evolution of the linked Eastern Cordillera orogen and the Llanos foreland basin of Colombia. Bulletin of the Geological Society of America, 120(9–10), 1171–1197. https://doi.org/10.1130/B26187.1 Bayona, G., Jaramillo, C., Rueda, M., Reyes-harker, A., & Torres, V. (2007). Paleocene-Middle Miocene Flexural-Margin Migration Of The Nonmarine Llanos Foreland Basin Of Colombia. CT&F - Ciencia, Tecnología Y Futuro, 3(3), 51–70. Retrieved from http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0122- 53832007000100003&lng=en&tlng=en. Bayona, G., Villamarin, P., Mora, A., Ojeda, G., Cortes, M., Valencia, A., Mahecha, H., & Torres, V. (2009). Exploratory Implications of Forebulge Geometry and Migration in the Llanos Basin. AAPG, 10th Simposio Bolivariano - Exploracion Petrolera En Las Cuencas Subandinas. Retrieved from http://www.earthdoc.org/publication/publicationdetails/?publication=44518 Bouffin, N. (2007). Net Pay Evaluation : a Comparison of Methods To Estimate Net Pay and Net- To-Gross Ratio Using Surrogate Variables Net Pay Evaluation : a Comparison of Methods To Estimate Net Pay and Net-To-Gross Ratio Using Surrogate Variables. Master Thesis from the Texas A&M University. Campos, H., & Mann, P. (2015). Tectonostratigraphic Evolution of the Northern Llanos Foreland Basin of Colombia and Implications for Its Hydrocarbon Potential. In C. Bartolini & P. Mann (Eds.), Petroleum geology and potential of the Colombian Caribbean margin (1st ed., pp. 517–546). American Association of Petroleum Geologists. https://doi.org/10.1306/13531948M1083651 Catuneanu, O. (2006). Principles of Sequence Stratigraphy. (1st ed.). Alberta, Canada: Elsevier Science. https://doi.org/10.1017/S0016756807003627 Causey, G. L. (1991). Computer determination and comparison of volume of clay derived from petrophysical and laboratory analysis. Master Thesis from Texas Tech University. Retrieved from https://ttu-ir.tdl.org/ttu-ir/handle/2346/15744 CEPSA. (2013). Integrated Seismic and Sequence Stratigraphic Interpretation Report Eastern Llanos Basin, Colombia. Bogotá. CEPSA. (2017). Petrel. Bogotá, Colombia. Chopra, S., & Marfurt, K. J. (2007). Seismic Attributes for Prospect Identification and Reservoir Characterization. (S. J. Hill, Ed.). Society of Exploration Geophysicists and European Association of Geoscientists and Engineers. https://doi.org/10.1190/1.9781560801900 Cooper, M. a, Addison, F. T., Alvares, R., Hayward, A. B., Howe, S., Pulham, A. J., & Taborda, A. (1995). Basin development and tectonic history of the Llanos basin, Colombia. Petroleum Basins of South America. AAPG. Memoir No. 62, 10(10), 659–666. https://doi.org/10.1306/7834D9F4-1721-11D7-8645000102C1865D Dasilva, A., Gomez, Y., Villa, M. A., Yoris, F., & Morales, D. (2014). Oil Distribution in the Carbonera Formation , Arenas Basales Unit . A Case Study in the Quifa and Rubiales Fields ,

Eastern Llanos Basin , Colombia. American Association of Petroleum Geologists International Conference Abstracts, International Conference & Exhibition, Cartagena, Colombia, July 29, 2013. American Association of Petroleum Geologists, International., 1–5. Delgado, A., Mora, A., & Reyes-Harker, A. (2012). Deformation partitioning in the Llanos foreland basin during the Cenozoic and its correlation with mountain building in the hinterland. Journal of South American Earth Sciences, 39, 228–244. https://doi.org/10.1016/j.jsames.2012.04.011 Edgar, J. A., & van der Baan, M. (2011). How reliable is statistical wavelet estimation? Geophysics, 76(4), V59. https://doi.org/10.1190/1.3587220 El Tiempo. (2017). En 20 campos se produce el 66 % del petróleo del país. Retrieved from ttp://www.eltiempo.com/economia/sectores/los-20-campos-petroleros-de-colombia-con- mayor-produccion-84750 García González, M., Mier Umaña, R., Cruz Guevara, L. E., & Vásquez, M. (2009). Informe ejecutivo: evaluación del potencial hidrocarburífero de las cuencas colombianas. ANH. Retrieved from http://www.oilproduction.net/cms3/files/cuencas petroleras de colombia- 2009.pdf Glover, P. (2011). The Total Gamma Ray Log. University of Leeds. Leeds. Retrieved from http://homepages.see.leeds.ac.uk/~earpwjg/PG_EN/CD Contents/GGL-66565 Petrophysics English/Chapter 11.PDF Gobernación de Casanare. (2017). Mapa Hidrográfico. Retrieved July 5, 2017, from http://www.casanare.gov.co/index.php?idcategoria=1204 Hall, M., & Trouillot, E. (2004). Predicting stratigraphy with spectral decomposition. Great Explorations – Canada and Beyond, 1–3. Retrieved from https://cseg.ca/assets/files/resources/abstracts/2004/046S0131- Hall_M_Trouillot_Modelling_Spectral_Decomposition.pdf Luthi, S. M. (2001). Geological Well Logs. Their Use in Reservoir Modeling. Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-04627-2 Matos, M. C., Osório, P. L. M., & Johann, P. R. S. (2005). Vertical Seismic Facies Detection Through Unsupervised 3D Voxel Based Seismic Facies Classification Applied to a Turbidite Field in Campos Basin, Brazil. In 9th International Congress of the Brazilian Geophysical Society & EXPOGEF, Salvador, Bahia, Brazil, 11-14 September 2005 (pp. 1222–1225). Society of Exploration Geophysicists and Brazilian Geophysical Society. https://doi.org/10.1190/sbgf2005-242 Miall, A. D. (2010). The geology of stratigraphic sequences: Second edition. The Geology of Stratigraphic Sequences: Second Edition. Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-05027-5 Moretti, I., Mondragon, J. C., Garzon, J. C., Bosio, G., & Daniel, J. M. (2009). Structural style and decollement levels in the Llanos Orientales basin (Colombia). American Association of Petroleum Geologists Bulletin. Retrieved from

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APPENDIX A – 3-D MERGE SEISMIC REPROCESSING

Gather Conditioning Workflow (Information obtained from CEPSA):

1. Review, Crops and Merge the Gathers from Tape 2. Gathers Loading and QC 3. Mute or Preliminary Angle Mute 4. Amplitude Spectrum 5. Band Pass Filter (High Cut) 6. Angle Mute 40° to 50° 7. Randome Noise Filter (Parabolic Radon Transform) 8. Multiple Suppression (Coherent Noise, Parabolic Radon Transform) 9. Trims Statics (Corrects for residual move out errors and aligns events) 10. Angle Mute 11. CDP Stack for QC 12. Angle Gather Generation using RMS Velocities

APPENDIX B – ALTERNATE METHODOLOGY FOR THE VELOCITY MODEL IN PETREL

According to (Alaminiokuma, Ugbor, Harcourt, & State, 2010), the different types of analytical models whose velocities vary in a continuous and systematic way with depth are:

Figure B 1 Types of analytical velocity models. Taken from Alaminiokuma et al., 2010

a) For this type of project, two functions of linear intervallic velocity were used:

➢ Instantaneous linear velocity: This model assumes that the velocity varies linearly with depth.

푉(푧) = 푉0 + 푘푧

푉0 = Initial velocity 푘 = Velocity gradient 푧 = Depth

➢ Advanced linear velocity: This model uses specific intervals that are defined by two surfaces.

푉(푧) = 푉0 + 푘(푧 − 푧0)

푉0 = Instantaneous velocity in the upper limit of the velocity interval.

푧0 = Initial velocity 푧 = Final velocity

b) After defining the function, the interpreter must have the following inputs to obtain a depth conversion: ➢ Horizons. • Determine the different layers of the model. ➢ Faults: • Delimit the layers laterally. ➢ Surfaces: • Define the different layers for a model and are the interpolation of the horizons. ➢ Sonic, Gamma Ray and Density logs: • Determine the properties of the rocks and fluids to be an indirect resource to calculate the velocity of the layers. • The sonic logs are a direct measurement of the interval velocity ➢ Tops: • Real depth data of each formation that allows the calibration of the model. ➢ Vertical Seismic Profile (VSP) or Checkshots: • From this log, time-depth curves are generated (T-Z), giving direct values of the medium velocity. Additionally, it is a quality control used to predict depths. ➢ Well correlation: ➢ Creation of synthetic seismograms:

➢ Digital Elevation Model (DEM) • Enables the calculation of the topographic corrections to take the model to a seismic datum

c) The necessary processes before the development of the model are:

Time-depth table The interval velocity vs. depth graph will allow the interpreter to distinguish the changes in lithology to recognize the changes in velocity for an adequate model. The data is obtained from the VSP of each well. In Figure B 2, the changes/jumps (green arrows) in the slope represent the limits of the lithological changes. These limits denote the top of the formation interest that will be interpreted in the seismic data. In this example, data from one well was used, but it is recommended to use the data from all the wells together to notice trends.

Figure B 2 Time-depth curve showing changes in lithology that correspond to the top of a formation. These changes represent the changes in velocity and changes in slope of each layer 100

Meters to feet conversion

➢ The replacement velocity is shown in meters (m) in the VSP logs. It must be converted into feet (ft) by making the following calculation:

2300 푚 (푟푒푚푝푙푎푐푒푚푒푛푡 푣푒푙표푐푖푡푦) 푥 = 1 푚 3.2808 푓푡

푥 = 7546 푓푡

➢ The well data in terms of depth should be converted into time. This process must be repeated individually with all the wells in the area:

푑 푡 = 푣

퐷푎푡푢푚 − 퐺퐿 푡 = 푣푒푙표푐푖푡푦

1640.4 푓푡 − 634 푓푡 푡 = 7546 푓푡 ∗ 푠−1

푡 = 0.13 푠

The result is multiplied by two because the seismic is in TWT and then it is also multiplied by 1000 to convert it into milliseconds (ms):

푡 = 0.13 ∗ 2 ∗ 1000 푡 = 266 푚푠

101

The actual measured depth is:

푀퐷 = 퐾퐵 − 퐺퐿 푀퐷 = 669 푓푡 − 634 푓푡 푀퐷 = 35 푓푡 Consequently, for a time of:

푡 = 0 푚푠

The measured depth is:

푀퐷 = 퐾퐵 − 퐷푎푡푢푚 푀퐷 = 669 − 1640.4 푀퐷 = −971.4

These two results should be included in the Checkshots previously loaded from Petrel for each well. MD TWT -971.4 0 35 266

Table B 1 Results obtained from doing the conversion

d) Use the Petrel modulus to obtain the velocity model. Once the model is well calibrated a depth conversion can be made by using another modulus.

102

Figure B 3 Velocity model window in Petrel with the main inputs

APPENDIX C - SEISMIC INVERSION

The seismic inversion was not included in the body of the thesis because it did not affect the results. But it was part of the methodology used.

One approach for lithofacies identification is seismic impedance inversion that considers the entire waveform of the seismic trace and not just the amplitudes (Avseth, Mukerji, & Mavko, 2005). Inversion is useful to link seismic attributes to rock physics models to identify lithological changes and the response to main reservoir characteristic interfaces. This is done by the inversion 103 of the seismic cube into an Acoustic Impedance (AI) cube (Veeken, 2007). The correlation between both cubes is the seismic wavelet, which is used to transform seismic amplitudes into reflection coefficients. A wavelet is established to compare synthetic traces at the wells and the seismic traces, and to transform reflection coefficient traces into seismic traces; this wavelet is then used to perform the seismic inversion (Veeken, 2007). The limits of the lithological changes are expressed by the limits in the AI log.

There are various techniques to make a seismic inversion. The method used for this project was the colored inversion (CI): the input is a post-stack time-migrated seismic data cube out/out. The CI method is a trace integration in the frequency domain. The amplitude spectrum of the well log is compared to the seismic data (Veeken, 2007).

Figure C 1 Colored inversion method. A) the input seismic cube B) colored inversion cube C) Comparison of the seismic trace and synthetic traces at the well.

An inversion operator is design to compare seismic amplitudes of the frequencies in correspondence with those seen in the well. This operator is determined by a cross plot made between the amplitude and the logarithm of the frequency. An equivalence is made from the

104 seismic trace into an assumed acoustic impedance by using the resulting operator (Veeken, 2007).

The inversion made in Hampson and Russell Software required different inputs as followed: • 3-D out/out seismic cube • Density and P-wave logs for 5 wells • Interpreted logs

The processes used are similar to the ones in Petrel: • Create project with available data • Seismic-Well calibration • Stratigraphic/Structural interpretation • Seismic inversion

After gathering all the data and interpreting the horizons of interest, an initial model was built as shown below.

Figure C 2 Initial model for defining the layers that are going to be used in the inversion.

The next step is to perform the Inversion Analysis to the model

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Figure C 3 After all the data is gathered, an inversion analysis was performed.

The final step is to apply the model and the inversion analysis to the actual seismic volume.

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Figure C 4 Colored inversion applied to the seismic cube. Each color represents a impedance that helped to interpret the area.

The changes in color represent changes in impedance according to different lithologies.

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