Using Formation Resistivity Discontinuities to Test The

USING FORMATION RESISTIVITY DISCONTINUITIES TO TEST THE

HYDROLOGIC SEALING NATURE OF A FAULT

A Thesis

Presented to the faculty of the Department of Geology

California State University, Sacramento

Submitted in partial satisfaction of the requirements for the degree of

MASTER OF SCIENCE

Geology

Zachary David Levinson

SPRING 2020

Zachary David Levinson

USING FORMATION RESISTIVITY DISCONTINUITIES TO TEST THE

HYDROLOGIC SEALING NATURE OF A FAULT

A Thesis

Zachary David Levinson

Approved by:

______, Committee Chair Dr. David Shimabukuro

______, Second Reader Dr. Steven Skinner

______, Third Reader Mr. Michael Stephens

______Date

iii

Student: Zachary David Levinson

I certify that this student has met the requirements for format contained in the University format manual, and this thesis is suitable for electronic submission to the library and credit is to be awarded for the thesis.

______, Graduate Coordinator ______Dr. David Shimabukuro Date

Department of Geology

Abstract

USING FORMATION RESISTIVITY DISCONTINUITIES TO TEST THE

HYDROLOGIC SEALING NATURE OF A FAULT

Zachary David Levinson

Water co-produced with petroleum—termed produced water—is often disposed of through subsurface injection. Regulations require that the injected fluid stays in the target zone and does not migrate into usable groundwater aquifers. While impermeable rock provides the primary barrier to fluid migration, faults have also been asserted to act as barriers to fluid flow, often without evidence of their degree of seal. In previous studies, discontinuities in salinity, measured in ppm TDS (total dissolved solids) were used as a method to determine if a fault seals. Here, discontinuities in formation resistivity (Rt) were used as proxy for TDS discontinuities to examine the sealing ability of the Kern Front and West Premier Faults on the east side of Kern County, California.

A publicly-available online database of borehole geophysical logs and historical well records from oil and gas wells, maintained by the California Geologic Energy

Management Division (CalGEM), was utilized to determine fault throw patterns and investigate the sealing ability of the Kern Front and West Premier Faults. Historical well records from wells in Kern Front and Kern River and geophysical logs from wells within

the Poso Creek field were used to map variations in fault throw. After fault throw patterns were determined, Rt data was collected from borehole geophysical logs from wells on either side of the fault. When a Rt measurement is taken in a 100% water saturated sand unit, free of any clay or hydrocarbon, it is denoted as Ro. A multiple linear regression model was used to test the statistical significance of the observed trends.

Results show apparent Ro discontinuities across the Kern Front Fault, which vary in magnitude along-strike of the fault. A multiple linear regression analyses indicate the side of the fault in which the Ro measurement was taken was statistically significant and heavily impacted the prediction of log Ro (coefficient= 0.386, p=<0.001). Prediction of log Ro was also strongly dependent on an interaction term that measured the difference in

Ro moving along strike of the fault (coefficient= -0.563, p=<0.001). The R-squared of this model was 0.38. Discontinuities in Ro suggest the Kern Front Fault creates a hydrologic seal in the Kern River Formation. Results from Poso Creek show only slight Ro trends near the West Premier Fault. These apparent trends suggest the West Premier fault does not create a lateral seal above the Macoma Claystone and groundwater can migrate across the fault plane. These observations were supported by a multiple linear regression model that showed no statistical difference in Ro from one side of the fault to the other Ro

(coefficient= 0.0015, p=0.997). The R-squared of this model was 0.210.

______, Committee Chair Dr. David Shimabukuro

______Date vi

ACKNOWLEDGEMENTS

I would like to recognize everyone that has supported me throughout graduate school and the development of this thesis. First and foremost, I would like to express my deepest gratitude to my advisor Dr. Dave Shimabukuro for providing me with all the tools and guidance I needed throughout my graduate career. Dave went out of his way to ensure my family and I were comfortable after our move to Sacramento and has provided an endless amount of support throughout this project. I would also like to thank Dr.

Steven Skinner, who always had an open door when I had needed help along the way. I would also like to extend my gratitude to Michael Stephens for his guidance throughout this project. Michael has provided endless hours of advice and discussion that have not only benefited this project but helped me develop as a scientist.

I would also like to say thank you to the California State Water Resources Board and the U.S. Geologic Survey for providing funding for this project. I truly appreciate the opportunity for collaboration with these organizations and I am thankful for the breadth of knowledge they provided. I also had the great pleasure of working with Water

Resources Group at CSUS and I would like to thank all that supported my research.

Special thanks to my parents, Harlan and Cecile, who have always supported me in all my endeavors. I cannot begin to express enough thanks to my wife, Kimberly, who has supported me throughout my academic career and always encouraged me to follow my passion. Finally, I would like to thank my children, Makenzie and Liam, for providing the motivation I needed to prosper in every step of my academic career.

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TABLE OF CONTENTS Page

Acknowledgements ...... vii

List of Tables ...... x

List of Figures ...... xi

Chapter

1. INTRODUCTION ……..……………………………………………………….. 1

Project Background ...... 1

Fault Zone Architecture and Permeability ...... 3

Hydrocarbon Fault Seal Analysis ...... 4

Groundwater Fault Seal Analysis ...... 6

Recent Advances in Fault Zone Hydrology ...... 8

Formation Resistivity as a Proxy for TDS ...... 8

2. REGIONAL GEOLOGY ...... 11

Regional Sedimentation History ...... 11

Regional Hydrology ...... 12

Regional Tectonic and Structural Development ...... 14

Stratigraphy of Study Area ...... 15

Important Stratigraphic Relationships within Study Area ...... 18

Structural Development of Study Area ...... 19

3. METHODS ...... 22

viii

Data for Formation Resistivity and Fault Offset Analysis...... 23

Formation Resistivity ...... 24

Ro Data Collection...... 24

Dataset A: Formation Resistivity near the Kern Front Fault ...... 26

Dataset C : Formation Resistivity near the Premier Fault ...... 26

Geologic Markers...... 28

Dataset B: Geologic Marker Data ...... 28

Dataset D: Clay Mapping near the Premier Fault ...... 29

4. RESULTS AND DISCUSSION ...... 31

Kern Front Fault in Kern Front and Kern River ...... 31

Kern Front Fault Offset ...... 31

Resistivity Near the Kern Front Fault ...... 35

Interpretation of Ro Trends Near the Kern Front Fault ...... 40

Premier Faults in Poso Creek ...... 43

Fault Offset in Poso Creek ...... 43

Resistivity Near the Premier Faults ...... 45

Interpretation of Ro Trends Near the Premier Faults ...... 51

Comparison of Kern Front and Premier Fault Systems ...... 53

5. CONCLUSIONS...... 55

Appendix A. Resistivity Measurements near Kern Front Fault ...... 57

References ...... 63

LIST OF TABLES Tables Page

1. Multiple Linear Regression Result from Kern Front Fault.……………………...……. 41

2. Multiple Linear Regression Result from West Premier Fault……………………...…. 51

LIST OF FIGURES Figures Page

1. Sealing Fault……………………………………….……………………………. 5

2. Map of the San Joaquin Valley……………………………….………………....13

3. Map of the study area……...………………………….………………………... 17

4. Stratigraphic columns for the oil fields in the study area…………….…….…... 19

5. Resistivity measurement locations ………………..…………………………….27

6. Fault markers in map view ……………………………………..………...……. 33

7. Fault markers projected on latitude …………..………….……………..……….34

8. Resistivity measurements near the Kern Front Fault .….…………………...…. 37

9. Fault relay ………………………………………...…………………....…...…. 45

10. Resistivity measurements near the West Premier Fault ..…………………..…. 47

Introduction Project Background The accumulation of hydrocarbon in fault traps has been well documented over the past half century (Weber, 1978; Smith, 1980; Allan, 1989; Yielding et al., 1997); however, it is commonly asserted that faults which trap oil within hydrocarbon fields also prevent the migration of water (California Geologic Energy Management (CalGEM),

2017, 2018a). As focus on fault-zone hydrology continues to increase, faults are being recognized as structurally-complex features that develop and change over time

(Antonellini and Aydin, 1994; Caine et al., 1996; Heynekamp et al., 1999; Bense et al.,

2013). Yet few faults have been investigated in enough detail to understand the sealing mechanisms or controls (Knipe et al., 1998).

Recent drought has resulted in an increased demand of groundwater resources in

California. These circumstances have pushed regulators to take important steps to protect valuable groundwater resources. To ensure these finite resources are being properly protected Senate Bill 4 (SB4, 2013) revised the protecting practices of underground sources of drinking water (USDW). The Environmental Protection Agency (EPA) Safe

Water Drinking Act (1974) and SB 4 define a USDW as

“An aquifer or portion of an aquifer that supplies any public water system or that contains a sufficient quantity of ground water to supply a public water system, and currently supplies drinking water for human consumption, or that contains fewer than

10,000 mg/l total dissolved solids and is not an exempted aquifer.” (40 CFR § 146.3).

As groundwater demand increases, the importance of protecting current and future sources of drinking water is paramount.

In California from 2014-2018 over 16 billion barrels (Gbbl) of water were produced as a byproduct of oil production (CalGEM, 2018b). Water produced with oil, which is typically saline, has higher concentrations of total dissolved solids (TDS) and contains compounds associated with hydrocarbon, thus limiting its potential for future use (Clark and Veil, 2009). Most produced water is reinjected into the subsurface for enhance oil recovery (EOR) or disposal (CalGEM, 2018a). Over the past five years nearly 15 Gbbl of produced water have been injected into the subsurface (CalGEM,

2018b). Although the importance of sealing faults for hydrocarbon production has received significant attention (Smith 1966,1980; Downey, 1984; Knipe et al., 1998;

Aydin, 2000), the role these faults play in groundwater resources is still poorly understood. This is because lithological heterogeneity and along-strike variability of fault zone structure have not received enough attention (Faulkner et al., 2010).

Few faults have been studied in enough detail to identify controls on permeability or describe the sealing capacity throughout the entire fault plane (Knipe et al., 1998). A fault's ability to seal to oil is often used as evidence that it can prevent injected fluids from migrating to nearby USDW. The objective of this thesis is to use discontinuities in formation resistivity (Ro) to test whether faults known to seal hydrocarbon also have the ability to seal groundwater. This was done using borehole geophysical logs to identify and map along-strike variations in fault throw and collecting Rt (denoted as Ro if taken in

100% water-saturated and free of any hydrocarbon) measurements from the geophysical

logs from either side of the fault to evaluate potential Ro discontinuities across the fault.

Using Ro gradients to establish the hydrologic sealing ability of a fault assumes freshwater recharge pathways are known and consistent. Freshwater recharge can create discontinuities in Ro gradients near a fault zone by flushing out relatively saline connate water from one side while leaving the other side unimpacted. This could create a discontinuity in groundwater TDS on either side of the fault. Here, we use Ro as a proxy for TDS and interpret discontinuities in Ro as a fault creating a hydrologic seal.

Fault Zone Architecture and Permeability

Fault zones create lithological diverse and structurally complex discontinuities

(Caine et al., 1996; Evans et al., 1997) that can act as fluid conduits, barriers or a combination of both, thus having the ability to promote or impede fluid flow within a system (Antonellini and Aydin, 1994; Bense and Person, 2006; Bense et al., 2013). Fault zones are made up of three components; the fault core, where displacement is accommodated by gouge and cataclasite; the surrounding damage zone, which is associated with the growth mechanics of the fault; and the relatively undisturbed parent rock (Caine et al., 1996). The volume, intensity, and spatial arrangement of each component can be used to describe the permeability structure of a fault (Caine et al.,

1996; Evans et al., 1997; Rawling et al., 2001). This is important when describing the impact of faulting on a hydrologic system because permeability structure and damage zone geometry, both of which are heavily influenced by fault throw (Balsamo and Storti,

2010), are the main controlling factors of fluid flow through a fault zone (Antonellini and

Aydin, 1994; Heynekamp et al., 1999; Aydin, 2000; Bense and Person, 2006).

Hydrocarbon Fault Seal Analysis

In a mature oil production field, the distribution and production history of the oil wells near a fault can provide evidence to its sealing nature. If there are numerous wells on one side of a fault that have produced large quantities of oil and few wells on the adjacent side that have failed to produce oil, assuming these wells are completed in the same formation, this could be evidence that fault prevented oil from migrating across is plane and thus seals to hydrocarbon. More precise methods of fault seal analysis can be used to evaluate the ability of a fault to seal one reservoir from another (Antonellini and

Aydin, 1994; Yielding et al., 1997; Cerveny et al., 2005), and have been extensively explored by the petroleum industry because of the role faults often play in trapping hydrocarbon (Knipe et al., 1998; Aydin, 2000). As hydrocarbons and groundwater interact within a reservoir rock, buoyancy causes the hydrocarbons to migrate upward.

Most hydrocarbon accumulations occur because vertical movement is stopped or slowed by a less permeable cap-rock (Watts, 1987). In most cases fault-related traps or seals are the result of a fault preventing lateral migration below a cap-rock that has already stopped vertical migration (Smith, 1966, 1980). When this type of seal is created, the fault must only seal directly adjacent to the hydrocarbon column because a confining geologic unit is required to impede vertical flow (Downey, 1984; Allan, 1989; Gibson, 1994) (Figure

1).

Figure 1. Sealing Fault

A cartoon depiction of a normal fault creating a hydrocarbon trap. The local stratigraphy is offset by a fault with normal displacement indicated by the thick red line and red arrows. The grey unit represents a vertical flow barrier. A hydrocarbon accumulation is shown in the hanging wall of the fault which is acting as a lateral seal. The portion of the fault known to seal (known by the existence of the hydrocarbon accumulation) is highlighted by a black bracket. There is no evidence that the rest of the fault plane seals.

There are techniques which can be applied to fault seal analysis that allow for a clearer understanding of the fault in question. Juxtaposition of adjacent stratigraphic units creates displacement pressure differences across a fault plane which are thought to be the primary mechanism responsible for creating hydrocarbon seals (Smith, 1966, 1980;

Allen, 1989). More recently interpretive framework has been developed to simplify interpretation by establishing fault plane properties and juxtaposition of adjacent units

6 caused by fault displacement (Knipe, 1992, 1997). There are several secondary mechanisms that occur as a result of the fault displacement and subsequent juxtaposition of adjacent units such as clay smear (Yielding et al., 1997; Cerveny et al., 2005), fault gouge or cataclasis in the fault core (Weber, 1978; Harris, 2002), and particulate flow or sediment mixing. Clay smear is determined by percentage of clay in a rock unit relative to the total amount of fault displacement and is generally found in rocks high clay content

(Yielding et al., 1997). Fault gouge and cataclasis are more difficult to predict but typically occurs in sandstones with low clay content at deep burial depths (Engelder,

1974; Fisher and Knipe et al., 1998). Particulate flow is the mixing of heterogeneous fine-grained sediments in the fault core and is more prevalent at lower burial depths in rocks with a moderate clay content (Heynekamp et al., 1999; Rawling and Goodwin,

2006) but receives relatively little attention in hydrocarbon fault seal literature. Although the oil and gas industry recognize the importance of sealing faults for hydrocarbon production, the role these faults play in groundwater resources is frequently overlooked

(Knipe et al., 1998).

Groundwater Fault Seal Analysis

It is well accepted that fault zones are structurally complex systems that can act as fluid conduits or barriers to groundwater flow both laterally and vertically (Antonellini and Aydin, 1994; Caine et al., 1996; Heynekamp et al., 1999; Bense and Person, 2006).

Unlike with hydrocarbon, simple water production history cannot be used identify a fault that creates a hydrologic seal. There are several methods hydrologists use to assess the permeability of fault zones including mapping hydraulic head (Haneberg, 1995; Bense

7 and Van Balen, 2004), measuring temperature patterns (Fairley and Hinds, 2004; Bense et al., 2008), and artificial or environmental tracers (Poreda et al., 1988; Leray et al.,

2012). It can be difficult to assess a faults ability to create a hydrologic seal because lithologies and mechanisms that control permeability are often heterogeneous and complex, leading to variable fault architecture and hydrologic behavior (Faulkner et al.,

2010).

The sealing capacity or hydrologic behavior of a fault zone cannot be determined without a comprehensive description of its permeability structure (Bense et al., 2013).

There are several processes that are known impact a fault’s permeability, many of which were discussed in the previous section. One area that receives considerably more attention in fault zone hydrology is particulate flow which is the product of sediment mixing in a fault zone (Heynekamp et al., 1999; Rawling and Goodwin, 2006). This may be because many important aquifers in the United States, including the research area of this study, are made up of heterogeneous, poorly lithified sediment (Anderson et al.,1988;

Farrar and Bertoldi, 1988) where particulate flow is a common control on fault zone permeability (Heynekamp et al., 1999; Rawling and Goodwin, 2006). Unlike hydrocarbon, groundwater is present in most subsurface rocks; therefore, a faults hydrologic sealing ability must be defined at a unique zone and cannot be inferred by its ability to seal elsewhere within the plane. Instead, fault permeability and subsequently a faults ability to create a hydrologic seal must be investigated at a particular location and depth of interest.

Recent Advances in Fault Zone Hydrology

Recent efforts to understand impacts of subsurface injection and the subsequent fluid flow and displacement (Taylor et al., 2014; McMahon et al., 2018) have highlighted need for a better understanding of fault zone hydrology. A review paper (Bense et al.

2013) compiles several techniques to approach fault zone hydrogeology; however, most of these techniques share a similar hardship, when working in or near an oil field access to land or equipment is rare. Recent efforts to use publicly available geophysical logs to map groundwater TDS has presented an opportunity to evaluate groundwater quality

(Gillespie et al., 2017, 2019; Stephens et al., 2019; Stephens et al., in review). TDS mapping efforts in Poso Creek oil field near Bakersfield, California showed TDS gradients across a fault zone can be used to evaluate along-strike variations in a faults ability to create a hydrologic seal (Stephens et al., in review). TDS mapping techniques provide a method of evaluating a fault’s ability to create a hydrologic seal between a hydrocarbon reservoir and a groundwater aquifer (Stephens et al., in review) from publicly available borehole geophysical logs.

Formation Resistivity as a Proxy for TDS

To estimate TDS from Ro requires a series of parameters. The resistivity-porosity

(RP) method (Lyle,1988) has been successfully used to estimate TDS in several recent groundwater studies (Gillespie et al., 2017, 2019; Stephens et al., 2019; Stephens et al., in review). This method is based on a discovery G.E. Archie made while measuring the

9 resistivity of water-saturated rock core. Archie (1942) established an empirical relationship between Ro and Rw using the following equations:

Ro = F × Rw. (1) where,

F= Formation resistivity factor (dimensionless),

Ro = Resistivity of 100% water-saturated formation (ohm-m),

Rw = Resistivity of formation water (ohm-m).

This equation is rearranged to isolate Rw,

Rw = Ro/F. (2)

Archie later showed the formation factor is related to rock porosity using,

m F = a/φ .. (3) where, a = Pore geometry coefficient (dimensionless), m = Cementation factor (dimensionless),

φ = Porosity (decimal).

Ro and φ can be estimated from geophysical well logs; however, the values of a and m are not easily obtainable without access to fresh drilling core material. Archie defines these parameters as a = 1.0 and m = 2.0 (Archie 1942). Other parameter values have been

10 empirically determined, such as the Humble parameters a = 0.62 and m = 2.15 (Winsauer et al., 1952). These values can be further refined by tuning the empirical parameters using aqueous geochemistry samples described by Stephens et al. (2019). Once Rw has been determined, TDS can be estimated using an empirical chart created by Schlumberger

(1997) or a temperature adjustment can be made to Rw and an additional equation

(Bateman and Konen, 1978) can be utilized.

Collecting and estimating all these data can be difficult and time consuming. This study investigates the possibility of using Ro as a proxy for TDS, thus simplifying the analysis. The empirical relationships discussed in this section can be summarized into a single statement, TDS is a function of Ro, temperature, porosity, a and m. Ro measurements will be taken from either side of a fault within similar geologic units and in relatively close proximity to each other; thus, the lithological parameters of porosity, a and m should remain constant. Additionally, measurements will only be taken in wells in which temperature represents a natural geothermal gradient, which will ensure temperature at which the measurement was taken behaves uniformly. This leaves the estimation of TDS a function of only Ro; thus, Ro can be used as a proxy for groundwater

TDS on either side of a fault. Using this relationship, Ro discontinuities near a fault can be interpreted as a fault creating a hydrologic seal if freshwater recharge has flushed out relatively higher TDS (lower Ro) connate water from only one side of the fault.

Regional Geology

Regional Sedimentation History

The sedimentary history in the San Joaquin Basin is particularly complicated because of multiple tectonic regimes including the development of San Andreas fault, uplift and rotation of the Sierra Nevada basement, lateral changes in depositional facies caused by developing fluvial sediment discharge points, and temporal changes in relative sea level (Reid, 1995; Scheirer and Magoon, 2007). Eocene sedimentation of the southern

San Joaquin Basin began with a major sea level transgression event which resulted in deposition of large sand and silt packages (Bartow, 1991). Sea level cycles continued into the Early Miocene as shelf sands covered the basin margins while deep marine shales and clays covered the center of the basin (Bartow, 1991; Reid, 1995). This trend continued until the Late Miocene when rapid sea level decline combined with activity on the San

Andreas Fault and changing drainage patterns from the Sierra Nevada Mountains (Figure

2) allowed deposition of sandstones near the center of the basin and the formation of diatom blooms in organic rich shales (Reid, 1995). Large quantities of sand continued to enter the basin from the Kern River (Figure 2) throughout the Late Miocene into the

Early Pliocene and although some continued to reach the center of the basin, most was confined to the margins. Uplift surrounding the basin in the Early Pliocene resulted in increased sediment loads which led to the development of prograding tidal facies influenced by eustatic sea level changes (Reid, 1995).

Sedimentation of the southwestern San Joaquin Basin was mostly controlled by northward transit of the Salinian block caused by the development of the San Andreas

Fault (Reid, 1995). Similarly, uplift and rotation of the Sierra Nevada basement- controlled sedimentation in the Bakersfield Arch area in the southeastern portion of the basin (Bartow and McDougall, 1984). Sedimentation of the Bakersfield Arch began in the Eocene on the Great Valley Ophiolite to the south and Sierra Nevada granitoid to the north (Bartow and McDougall, 1984). To the north of the Bakersfield Arch, the Eocene sedimentary rocks are conformably overlain by largely marine Oligocene and Miocene rocks, which are then unconformably overlain by shallow marine to non-marine sediments ranging from Miocene to Pleistocene in age (Bartow and McDougall, 1984;

Scheirer and Magoon, 2007). South of the Bakersfield Arch similar sequences are present with the addition of a terrestrial gravel that dominated the middle Miocene.

Regional Hydrology

The southern portion of the San Joaquin Basin is the largest groundwater subbasin in California which is known as the Kern subbasin (Figure 2) (California Department of

Water Resources (DWR), 2003). The Kern subbasin has a long history land subsidence caused by groundwater withdrawal starting in the mid 1920’s (Poland et al., 1975). This trend was slowed by surface water importation by the Friant Kern Canal the early 1950’s and the California Aqueduct in the 1960’s; however, groundwater levels did not begin to rise again until the 1980’s (Poland et al., 1975). Precipitation is scarce in this region, with rainfall averaging ~6.5 inches per year. The primary sources of recharge are from the

Figure 2. Map of the San Joaquin Valley The Southern San Joaquin Valley is the southern portion of the Central Valley of California. Regional mountain ranges are labeled. The study area which consists of Poso Creek, Kern Front, and Kern River oil fields are outlined in grey. The Kern Subbasin is outlined in red and the Kern River is shown in blue. Kern River (Dale, 1966) and return flows from agricultural and municipal irrigation

(DWR, 2003). Secondary sources of recharge include artificial recharge at ground water banking facilities and infiltration of flows from intermittent streams along the edge of the subbasin (DWR, 2003).

Together the Tulare and Kern River Formations form the principal aquifer for the

Kern County groundwater subbasin. Freshwater-bearing deposits in the Kern subbasin are primarily non-marine, Tertiary and Quaternary age sediments with an interval thickness of 175 to 2,900 feet and averaging about 600 feet (DWR, 2003). The Miocene

Olcese and Santa Margarita Formations are both sandy confined aquifers that provide drinking water in the northeastern portion of the basin (Bartow and McDougall, 1984;

DWR, 2003). The unconfined aquifer in the Kern River and Tulare Formations includes a mixture of clay, sand, and gravel that are highly permeable and supply the highest quantities of water to wells in the region (DWR, 2003). Additional alluvium and stream deposits composed of Pleistocene deposits of clay, silt, sand, and gravel that are poorly to well consolidated, and are exposed mostly at the subbasin margins (DWR, 2003). The alluvium is highly permeable and frequently indistinguishable from the Tulare and Kern

River Formations.

Regional Tectonic and Structural Development

The Great Valley developed as the forearc basin of the Sierra Nevada continental arc during the Late Mesozoic and Early Cenozoic beneath an east-dipping subduction zone (Dickinson and Seely, 1979). The southern San Joaquin Valley is an asymmetrical structural trough with a westward-dipping east flank, a narrow belt of folds and faults in the west and is filled with late Mesozoic and Cenozoic marine and non-marine clastic sediments that reach a maximum thickness of nearly thirty thousand feet in the southern part of the valley (Bartow, 1991). The valley is bounded by the Sierra Nevada Mountains

15 to the east, the San Emigdio and Tehachapi Ranges to the south and the Temblor Range to the west (Figure 2).

Uplift of the Sierra Nevada Mountains began in the early Cenozoic and happened in three phases, the last of which began in the early Pliocene with uplift still occurring

(Unruh, 1991). During the Oligocene uplift of the San Emigdio Range has been proposed to be the result of a south verging thrust fault system (Davis, 1983) which along with the

Tehachapi Range are the southern bound of the San Joaquin Basin. In the mid-Oligocene collision of the East Pacific Rise caused subduction to halt and established a transform plate boundary to the west of the present San Andreas Fault trace; however, slip on the present San Andreas Fault trace did not begin until the mid-Miocene (Atwater, 1970;

Bartow, 1991). Slip rates on the San Andreas Fault increased in the late Miocene and are likely correlated with increased deformation and rapid subsidence in the west side of the

San Joaquin Basin (Bartow, 1991). Changes in relative plate motion in the early Pliocene resulted in increased northeast-southwest compressive stress normal to the San Andreas

Fault (Page and Engebretson, 1984). The northeast-southwest stress regime continued into the Pleistocene and caused major deformation and uplift in the Temblor Range to the west, which completed the enclosing of the southern San Joaquin Basin (Reid, 1995).

Stratigraphy of Study Area

The stratigraphy of the southeastern San Joaquin Basin mostly consists of Upper

Miocene to Pleistocene units; however, horizonal variations and stratigraphic pinch outs play an important role in the present study. This section will start by discussing the

16 general stratigraphic relations between Poso Creek, Kern Front, and Kern River oil fields

(Figure 3) and then describe details regarding important variations. The Santa Margarita

Formation is a shallow marine sequence of sand and shale with an average thickness of about 500 feet. Conformably overlying the Santa Margarita Formation is the Chanac

Formation, a continental unit of sand, clay, and conglomerates deposited in a meandering stream sequence on coastal plain (Link et al., 1990). The Chanac Formation is confined to a narrow band in the southeastern portion of the San Joaquin Basin (Scheirer and

Magoon, 2007) and the major hydrocarbon-bearing unit in Poso Creek and Kern Front oil fields. The Etchegoin Formation consists of marine sands and shales deposited by advancing and receding seas during the Miocene and Pliocene. The Etchegoin Formation unconformably overlays the Chanac Formation and has three distinct members; the Basal

Etchegoin, a fine to coarse grain sandstone with common black phosphatic nodules that characterize the unit; the Macoma Claystone, a blue-green shale unit deposited during a sea level transgression which commonly acts as a confining layer in this area; and the

Upper Etchegoin. The Pliocene/Pleistocene fluvial Kern River Formation is the youngest rock unit in the Tertiary sequence and is comprised of conglomerates, sands, mudstones, and clays sourced from the Sierra Nevada Batholith and deposited in a braided stream system (Bartow and Pittman, 1983).

Figure 3. Map of the study area. In the upper right corner, a map of California with Kern Country highlighted in red. Below that a map of the western portion of Kern County with oil and gas production fields highlighted in black. Poso Creek oil field is highlighted in yellow, Kern Front oil field in blue and Kern River oil Field in green. The large portion of the map is focused on the three oil fields in which geophysical well logs were used for this research. Poso Creek oil field is outlined in yellow, Kern Front oil field in blue and Kern River oil Field in green. Faults are shown with thick black lines, with the Poso-Pond and East and West Premier faults were georeferenced from Weddle (1959) and the Kern Front fault was georeferenced from Park (1965).

Important Stratigraphic Relationships within Study Area

Varying depositional environments and sedimentation patterns cause stratigraphy to change laterally within the study area, thus impacting the location of local hydrocarbon accumulations and groundwater aquifers. The basal Kern River Formation interfingers with the Etchegoin to the west and the Chanac to the east making the contacts between these units difficult to define (Figure 4) (Bartow and McDougall, 1984; Reid, 1995). In

Poso Creek and Kern Front the Chanac is the main hydrocarbon producing interval throughout the field; however, the Basal Etchegoin is also a significant producing unit in

Poso Creek (Weddle, 1959; Park, 1965). The Macoma Claystone commonly acts as a confining layer to oil accumulation in the Basal Etchegoin sands within Poso Creek, where it is easily identifiable on borehole geophysical logs. The ability to recognize the

Macoma becomes increasingly difficult moving eastward in Kern Front (Figure 4) because the facies changes to alternating clay and sandstone beds (Park, 1965). In Kern

Front the Etchegoin Formation thins from southwest to northeast and no longer has three distinct members because the thinning Macoma Claystone becomes difficult to identify

(Figure 4). Near the western edge of the Kern River field, the Etchegoin Formation has completely pinched out leaving the Kern River Formation directly in contact with the underlying Chanac Formation (Figure 3) (Crowder, 1952; Park, 1965). This is an important stratigraphic relationship because oil accumulation is no longer trapped under the marine Etchegoin, instead it has been free to migrate into the Kern River Formation which is the youngest producing formation in the state (Crowder, 1952). These

19 stratigraphic relationships play a vital role in the present study and must be taken into consideration when investigating groundwater in this area.

Figure 4. Stratigraphic columns for the oil fields in the study area. Stratigraphic columns from Poso Creek, Kern Front and Kern River oil fields. Blue lines represent the decreasing thickness and subsequent pinch out of the Etchegoin Formation from west to east. The solid black wedge represents the Macoma claystone pinching out near the border of Poso Creek and Kern Front oil fields. These stratigraphic columns were recreated from Summary of Operations reports from each field.

Structural Development of Study Area

The east side of the San Joaquin Basin is divided into the southern Sierran Block and Maricopa-Tejon subbasins which are separated by the crest of the Bakersfield Arch

(Bartow, 1991). The Bakersfield Arch is a southwest plunging ridge of Sierra Nevada basement rock which causes exposed Tertiary units to dip an average of 4°-6° (Bartow and McDougall, 1984; Scheirer and Magoon, 2007; Saleeby et al., 2013). The present

20 study utilized borehole geophysical data from oil production and injection wells from three oil production fields, Poso Creek, Kern Front, and Kern River (Figure 3) which are north of the city of Bakersfield and the crest of the structural Bakersfield arch. North- and northwest-trending normal faulting is common in the research area with generally west- east and southwest-northeast extension (Bartow and McDougall, 1984; Bartow, 1991).

However, there are notable exceptions including the Poso Creek Fault which strikes to the west and later links to the subsurface Pond Fault, also referred to as the Poso-Pond

Fault. This study will focus on two fault groups: the Premier Fault group, which consists of the Eastern Premier Fault and the Western Premier Fault (Stephens et al., in review), and the Kern Front Fault, which has 2 unnamed splays, the southern most of which is significant to this study (Figure 3).

The origin of these faults is uncertain, they are thought to be related to movement on the Garlock Fault at the southern end of the San Joaquin Valley (Bartow, 1991), the collapse of the southern Sierra Nevada (Saleeby et al., 2004, 2013; Sousa et al, 2016), or some combination of both. The East and West Premier Faults strike north and have hanging wall down to the west displacement, though the West Premier Fault has a shallower dip (approximately 45°-50°) and accommodates more offset (approximately

150 feet) (Weddle, 1959; Bartow, 1991). The Kern Front fault separates Kern Front and

Kern River oil fields (Figure 3) and along with the stratigraphic pinch out of the Chanac oil sands, creates a structural hydrocarbon trap along the east side of the Kern Front field

(Park, 1965). The Kern Front Fault has an average offset of roughly 150 feet in the center of the field (Park, 1965), although the offset can be difficult to define in certain areas

21 because of insufficient well control. This study will focus on the southern portion of the

Kern Front fault because the northern portion lacks sufficient well control on the eastern side of the fault.

Methods

Fault zones have complex internal structure that can act as a conduit and/or barrier to fluid flow (Antonellini and Aydin, 1994; Bense and Person, 2006). To investigate lateral fluid flow across the fault will require the collection of two data sets.

First, the location and geometry of the fault zone must be determined to ensure other data can be interpreted accurately. This will be done by collecting geologic markers (depth to a certain geologic unit) from wells near the fault zone. Second, Ro measurements from borehole geophysical logs from wells on either side of the fault was collected and compared. Here, knowing the location and geometry of the fault zone will ensure Ro data can grouped properly to reflect each side of the fault individually. Ro will be used as a proxy for groundwater TDS near the fault zone. If the fault acts as a hydrologic barrier, it would prevent any possible freshwater recharge from reaching the adjacent side of the fault, thus leaving relatively lower Ro (higher TDS) connate water on the other side. This discontinuity in Ro values across the fault suggest the fault acts as a hydrologic barrier and will be used as the criteria for determining if a fault creates a hydrologic seal throughout this study. However, continuous Ro values across the fault zone suggest hydrologic communication across the fault zone in which freshwater recharge was able to move laterally across the fault zone and clear out relatively lower Ro (higher TDS) connate water on either side leaving similar TDS groundwater on either side.

The California Geologic Energy Management Division (CalGEM) (formally named California Division of Oil, Gas, and Geothermal Resources (DOGGR)) maintains a publicly available online database of borehole geophysical logs, historical well records,

23 and other various information reported by producers from oil and gas wells in California

(CalGEM, 2020). Here, data from the CalGEM database was used to investigate the Kern

Front Fault in Kern Front oil field and the Premier Fault system in Poso Creek oil field.

The first step in any fault investigation is to determine offset and geometry of the fault zone. Well engineering records from wells in Kern Front and Kern River and geophysical logs from wells within Poso Creek were used to map variations in offset near the fault zones. Once fault location and offset were determined, Ro data was collected using borehole geophysical logs from wells on either side of the fault. The Ro data was collected from 100% water saturated sands units that were free of any clay or hydrocarbon in wells that borehole temperatures were representative of a natural geothermal gradient. A spatial and statistical analysis of Ro measurements was then conducted to evaluate the behavior of Ro across the fault zones.

Data for Formation Resistivity and Fault Offset Analysis

Kern Front and Kern River Oil Fields

Dataset A: Ro measurements from 24 oil and gas geophysical well logs (Appendix A).

Dataset B: Geologic marker data for 747 oil and gas wells from well history reports.

Poso Creek Oil Field

Dataset C: Ro measurements from 55 oil and gas geophysical well logs.

Dataset D: Borehole geophysical logs for clay/fault mapping from 646 oil and gas wells

Formation Resistivity

Geophysical logs have evolved over time; however, Rt is one of the oldest and most important tools for petroleum exploration, so Rt data is available on nearly every geophysical log (Asquith and Krygowski, 2004). Historically, Rt has been represented on geophysical logs by shallow and deep curves, with the former representing the resistivity

(denoted as Rxo) of the invaded zone (invaded by drilling mud filtrate) and the latter the uninvaded zone. Deep Ro measurements are representative of the fluid that fills the pore space and the formation rock. If Ro is to be interpreted as a proxy for groundwater TDS, data can only be taken from rocks that are 100% water-saturated and free of any hydrocarbon.

Ro Data Collection

Several considerations were taken when choosing wells for Ro data collection.

First, only wells in which formation temperature near the borehole represented a natural geothermal gradient were chosen. To avoid wells impacted by EOR processes, wells were considered acceptable for data collection if they were drilled and logged before steaming practices began which was 1961 in Kern River and 1964 in Kern Front and Poso Creek

(CalGEM, 1998). Data from wells drilled after steam injection began were only used if the bottom hole temperature (BHT) and maximum recorded temperature (MRT) found on the well log header were representative of a natural geothermal gradient.

Second, side wall core data was used to assure data was being collected in water saturated sands and not influenced by hydrocarbons. Core data is necessary in this situation because groundwater in the area is generally fresh (<3000 mg/L TDS), which makes it difficult to use resistivity to differentiate between hydrocarbon and groundwater.

In most cases core data describes the thickness of a sand package, presence of hydrocarbon shows, petroleum odor, staining and fluorescence, and describes an area that showed none of these things as “wet”. This indicates it was water saturated or “NSOF” which stands for no show, odor or fluorescence. Wells were considered acceptable for Ro data collection when the top of the hydrocarbon zone could be identified using core data.

Lastly, 100% water saturated clean sands (free of clay) were chosen by utilizing relationships between shallow and deep resistivity curves (Asquith and Krygowski, 2004) and SP curves. Clean sands were considered acceptable for Ro data collection when an individual sand package was at least ten feet thick and did not have any problematic changes in resistivity or SP. Abrupt changes in resistivity or SP could indicate the presence of clay within a larger sand package or a gradual increase or decrease could suggest a upward or downward coarsening sequence. Within wells where temperature represented a natural geothermal gradient and the top of the hydrocarbon zone could be defined, Ro measurements were taken from 100% water saturated clean sands and recorded along with depth of the measurement, longitude, and latitude.

Dataset A: Formation Resistivity near the Kern Front Fault

Using CalGEM’s online database (CalGEM, 2020) 206 Ro measurements from 24 wells (Appendix A) (Figure 5) were collected on either side of the Kern Front Fault. Data was only collected near the southern half of the Kern Front Fault because there is a lack of well coverage in the northwestern portion of Kern River oil field. Each data point is accompanied by a unique identifying number (American Petroleum Institute (API)), x

(longitude), y (latitude), and z (elevation) for spatial analysis. Elevation was obtained from the well log header and recorded as depth below Kelly bushing or Derrick floor and converted to elevation by subtracting recorded depth from well elevation.

Dataset C: Formation Resistivity near the Premier Fault

Following the method described in Dataset A, Ro data were collected from borehole geophysical well logs in Poso Creek oil field. This is a subset of data that was collected as part of the TDS mapping efforts described in Stephens et al., (in review) and contains 897 measurements of Ro from 55 oil and gas geophysical well logs (Figure 5).

Wells in which Ro data had been previously collected and were with 1500 m of the East or West Premier Fault were used in this analysis. Wells within 50 meters of the West

Premier Fault were excluded because this fault is known to have a low dip angle and it would be difficult to determine where the borehole crossed the fault plane, which could add uncertainty when investigating Ro changes across the fault. Wells that are separated from the Premier Fault system by other previously mapped faults were also excluded.

Figure 5. Resistivity measurement locations

Map of resistivity pick locations. Each well in which resistivity measurements were taken is shown here. Colors represent the side of the fault in which the measurement was taken.

Data points below the Macoma claystone were excluded from this analysis because this claystone is known to be a regional confining layer and data below would be from a different aquifer system. These exclusions were done to minimize the number of factors that could impact Ro trends near the Premier Fault zone. Each data point is accompanied by a x (longitude), y (latitude), and z (elevation) for spatial analysis.

Geologic Markers

When oil and gas wells are drilled it is common practice to identify important changes in lithology using mud logs, sidewall core analysis, or geophysical logs. These changes in lithology can often represent transitions between stratigraphic units and can be used as reference points when correlating geologic units between wells or establishing the geologic history of an area. These changes in lithology are commonly referred to as geologic markers. Geologic markers that are important to hydrocarbon production, such as a confining clay or oil rich sand are commonly recorded in well engineering records.

In mature oil fields like Kern Front previously recorded geologic markers can be an important tool to help determine fault offset patterns.

Dataset B: Geologic Marker Data

Geologic marker data found in well engineering records from CalGEM’s online database (CalGEM, 2020) were used to map the geometry of the Kern Front Fault.

Specifically, well engineering records from 747 wells that contained geologic marker data for the Chanac Formation were utilized in this analysis. The previously mapped

Kern Front Fault was georeferenced from a structural contour map describing the top of the Chanac Formation (Park, 1965) (Figure 3) and was used as a reference in the well selection process. All wells within 500 meters of the Kern Front Fault were selected for further analysis in which well engineering records were searched for geologic marker data. To optimize the process of searching and recording the geologic maker data, Adobe

Acrobat recognize text feature was used to convert scanned documents in searchable

PDF’s. The term “marker” was then searched using Adobe Acrobat “find” feature and the depth were recorded. This process yielded results from 943 well engineering records that contained at least one geologic marker data point from wells within 500 meters of the

Kern Front Fault. These data were collected in feet below ground surface. Surface elevation values from CalGEM were used to convert them into elevation. Chanac

Formation geologic marker data were the most prevalent and had the best spatial distribution amongst all the geologic makers data collected. From the 943 well engineering records that contained at least one geologic marker data point, 747 well engineering records included a geologic marker for the Chanac Formation; therefore, geologic marker data for the Chanac Formation were selected to define changes in fault offset along strike of the Kern Front Fault. These data along with corresponding API numbers were compiled to create a x (longitude), y (latitude), and z (elevation) database.

Dataset D: Clay Mapping near the Premier Fault

Geologic markers (n=646) for the top and base of the Macoma claystone were collected from wells near the Premier Fault system. Using CalGEM’s online database

(CalGEM, 2020) all wells within 200 m of the previously mapped Premier Faults

(Weddle, 1959) were identified and selected for further investigation. The top and base of the Macoma Claystone was identified in 646 geophysical well logs by analyzing spontaneous potential (SP), deep and shallow resistivity, porosity, and driller’s notes when available. Each geologic marker depth was recorded in depth below Kelly bushing or Derrick floor and converted to feet below mean sea level by subtracting recorded depth from the Kelly bushing or Derrick floor elevation noted on the well log header. Each marker is then associated with x (longitude) and y (latitude). Excellent well coverage near the Premier Faults allowed for a precise depiction of fault geometry and offset.

Results and Discussion

The ability of a fault zone to can act as a conduit or barrier to lateral fluid flow was investigated using Ro as a proxy for groundwater TDS near the fault zone. Geologic marker data was used to determine offset and geometry of the Kern Front Fault (Dataset

B) and Premier Fault (Dataset D) zones in Kern Front and Poso Creek oil fields.

Structural maps and north to south projections were created using these data and used to analyze and interpret offset patterns. Once fault location and offset were determined, Ro data collected from either side of the Kern Front Fault (Dataset A) and the Premier Faults

(Dataset C) was analyzed to identify Ro discontinuities across the fault zones. Two multiple linear regression models were used to identify along-strike and across fault relationships in Ro measurements. Here, fault offset patterns of the Kern Front Fault and

Ro measurements taken near the fault zone will be discussed first, followed by the

Premier Fault system in Poso Creek.

Kern Front Fault in Kern Front and Kern River

Kern Front Fault Offset

Chanac markers (n=747) were collected within 500 meters of the Kern Front Fault but only five were on the east side of the fault (Figure 6a), which is not ideal for defining the geometry of the fault. These data show a maximum fault throw near the center of the fault of ~170 feet, then a decrease to ~70 feet near the southern end of the mapped fault

(Figure 7a). Offset trends are unknown for the northern portion of the fault because of a lack of well coverage on the east side of the fault.

It is difficult to assess the detailed geometry of the Kern Front Fault with the data collected because pinch out of the marine Etchegoin Formation near the Kern Front Fault leaves the Kern River Formation overlying the Chanac in Kern River oil field. The contact between the Kern River and Chanac Formations is almost indistinguishable and thus, these units are often grouped together. The primary hydrocarbon reservoir in Kern

River field is the Kern River Formation, so most of the geologic makers noted on well engineering records are smaller oil sands within the formation while the Chanac marker is rarely noted. These changes in stratigraphy and different hydrocarbon production zones make correlating stratigraphic units between Kern River and Kern Front oil fields difficult using marker data from well engineering records. Although a detailed geometry was not defined, a general trend in offset was found to decrease toward the south.

Vertical separation of geologic marker elevation (Figure 7a) indicate discontinuities in the rock units which is interpreted as offset along the fault.

Along-strike variations in the elevation of the Chanac Formation are interpreted to be mostly controlled by regional dip of the stratigraphic units (Figure 6a), which is between 4-6° to the southwest (Bartow, 1991). When the elevation of top Chanac is

Figure 6. Fault markers in map view a) Map of all wells that had available Chanac Formation markers within 500 meters of the Kern Front Fault and elevation values are represented in feet below mean sea level. Colors show elevation of the Chanac formation. Faults georeferenced from a Summary of Operations structural map of top Chanac Formation (Park, 1965) are shown as thin black lines. Administrative field boundaries are shown by thick grey lines. b) Map of all wells with 500 meters of the Premier Faults in which geophysical logs were used to map fault geometry and offset. Colors show elevation of the bottom of the Macoma claystone. Faults georeferenced from a Summary of Operations structural map of top Basal Etchegoin (bottom Macoma claystone) (Weddle, 1959) are shown as thin black lines. Administrative field boundaries are shown by thick grey lines.

Figure 7. Fault markers projected on latitude a) A north-south cross-section of top Chanac Formation elevation markers within 500 meters of the Kern Front fault. These are the same markers shown in figure 5a projected onto latitude. A thin black line was drawn to show the interpreted position of the southern fault splice. b) A north- south cross-section of top Macoma elevation markers within 500 meters of the Premier faults. An interpreted fault relay is circled in black . plotted against latitude to create a north-south projection a gradual decline in Chanac elevation is controlled shows the regional dip while the separation in markers from either

35 side of the fault can be interpreted at fault offset (Figure 7a). An additional discontinuity can be observed (Figure 7a) beginning near latitude of ~35.485° where separation of markers indicates the location of the southern fault splice. This fault splice contributes to the total offset on the Kern Front Fault in that area.

Resistivity Near the Kern Front Fault

Ro measurements were used as a proxy for groundwater TDS to identify trends along strike near- and across the Kern Front Fault. Ro trends on either side of the fault will be discussed first followed by a discussion of trends observed across the fault. The stratigraphy near the Kern Front Fault system varies on either side of the fault. On the west side of the fault, in Kern Front, the primary hydrocarbon reservoir is the Chanac, which is unconformably overlain by the marine Etchegoin (Figure 4). On the east side of the Kern Front Fault system, in Kern River oil field, the primary hydrocarbon reservoir is found in the Kern River Formation. A pinch out of the marine Etchegoin near the Kern

Front Fault unit has left the Kern River Formation overlying the Chanac, the contact of which is almost indistinguishable and thus these units are often grouped together (Figure

4). Due to these changes in stratigraphy and the presence of hydrocarbon, data was not available lower than ~300 feet below mean sea level on the east side of the fault, except for two wells in the northern portion of the research area. Therefore, analysis across the

Kern Front Fault will only consider Ro measurements taken above 300 feet below mean sea level.

On the westside of the Kern Front fault it is difficult to distinguish any trends in

Ro. Relatively higher Ro pockets were found in the deeper measurements, but they were not consistent throughout the research area (Figure 8a). The cause of these higher Ro pockets is unknown, but they could be small confined aquifers that do not communicate with the larger aquifer system. Ro data is not available at these depths on the east side of the fault, so these pockets do not impact analysis across the fault.

On the eastside of the Kern Front Fault a decreasing Ro trend was observed from north to south (Figure 8b). In the northern portion of the field, the first two wells contain similar and relatively higher Ro values. Moving southward the next three wells show similar and slightly lower Ro values compared to the two northern wells. In the southern half, Ro measurements are similar to each other but generally lower than those in the northern portion.

Three distinct trends can be observed when visualizing data from both sides of the fault together (Figure 8c). In the north Ro is generally higher on the eastside of the fault, but then generally lower in the southern portion of the research area. The middle portion acts as a transition zone where Ro is similar on either side of the fault. While the trends in the north and south portions of the research area have an inverse relationship, their magnitudes are similar. The differences in Ro across the fault are mostly controlled by the decreasing Ro trend from north to south on the eastside of the fault because as previously mentioned as there is no discernable Ro trend on the westside.

Figure 8. Resistivity measurements near the Kern Front Fault

a) Ro measurements west of the Kern Front Fault projected on latitude creates a north-south cross-section. Elevation of the measurement is shown on the Y axis and color represents the Ro value at the point. The black circles show high Ro pockets. b) Ro measurements west of the Kern Front Fault. The arrow represents the observed change in Ro from north to south. c) Ro measurements from both sides of the Kern Front Fault. Black boxes highlight the different trend observed across the fault. The color scale is normalized to ensure proper representation of the Ro.

To determine if apparent Ro along-strike trends and apparent Ro differences the

Kern Front Fault are statistically significant, two multiple linear regression models were created. The first model (Model A) was used to test the ability of the measurement elevation and latitude to predict a response in log Ro. Ro was log transformed in both models because the predictor variables show a stronger correlation with log Ro resulting in a higher model R-squared. Model A used Ro data from each side of the Kern Front

Fault separately to identify along-strike relationships between the predictor variables and log Ro. Different along strike behavior could indicate groundwater does not communicate across the fault. The second model (Model B) tests the ability of four independent variables to predict a response in log Ro. The four independent variables are: the elevation of the measurement, latitude, the side of the fault in which the measurement was taken, and an interaction term which latitude and side were multiplied together. Model B used

Ro data from both side of the Kern Front Fault to identify trends across the fault.

Ro data was separated into two groups by using the previously mapped Kern Front

Fault as the divide (Park, 1965). Using Model A, Ro measurements from either side of the fault tested the ability of elevation and latitude to predict a response in log Ro. The elevation was used to identify any depth dependent trends impacting Ro.

Latitude and elevation values were normalized to values from 0 to 1, with 0 representing the lowest value in the dataset and 1 representing the highest. Latitude and elevation normalization were done to make values closer to the other variables and make result easier to interpret. The normalized latitude and elevation variables were used to identify changes in Ro along the strike of the fault.

Model A (Table 1) was used predict a response in log Ro on both the westside and eastside of the fault separately. On the westside of the Kern Front Fault measurement elevation a statistically significant impact on the prediction of log Ro (coefficient= -0.401, p=<0.001). However, normalized latitude did not have a statistically significant impact on the prediction of log Ro (coefficient= -0.0151, p=0.849). The westside had a weak R- squared of 0.122 which indicates the predictor variables could only explain a small portion of the variability of log Ro. On the eastside of the fault, prediction of log Ro had a strong dependence on latitude (coefficient= 0.667, p=<0.001). While elevation did not have statistically significant impact on log Ro (coefficient= -0.0898, p=0.483). The eastside model had an R-squared of 0.471 which shows the predictor variables were able to explain nearly half of the variation in the response of log Ro. Here, the multiple linear regression analysis supports the apparent along-strike variability in log Ro, in which there is significant along-strike variability on the eastside and very little on the westside.

Model B (Table 1) uses Ro measurements from both sides of the Kern Front Fault to test the ability of four independent variables to predict a response in log Ro. The first two independent variables are the same as those used in Model A, the elevation of the measurement and normalized latitude. The additional two independent variables are the side of the fault in which the measurement was taken, and an interaction term which latitude and side were multiplied together. A categorical variable was used to identify the side of the fault in which the measurement was taken. In this case the dichotomous variable was coded as 0 for a measurement on the east side of the fault and 1 for a measurement on the west side. The dichotomous predictor variable allows the model to

identify Ro trends across the Kern Front Fault. An interaction term multiplied normalized latitude by the categorical variable which represented the side of the fault in which the measurement was taken. This interaction term was used to evaluate if the predicted change in Ro across the Kern Front Fault varied moving along-strike.

Model B (Table 2) used Ro measurements from both sides of the Kern Front Fault to predict a response in log Ro using four independent variables. Measurement elevation had a statistically significant impact on the prediction of log Ro (coefficient= 0.333, p=<0.001). Prediction of log Ro had a strong dependence on latitude (coefficient= 0.554, p=<0.001) and the side of the fault the measurement was taken (coefficient= 0.386, p=<0.001). Prediction of Ro was also strongly dependent on the interaction term

(coefficient= -0.563, p=<0.001). The R-squared of this model was 0.38 which indicates the predictor variable can explain a moderate amount of the variability in log Ro. Here, the multiple linear regression analysis supports the assertion of apparent across-fault discontinuities in Ro because the side variable had a relatively high coefficient and low P- value. Additionally, the high coefficient and low P-value of the interaction term suggests the magnitude of the Ro discontinuity across the fault varies along strike.

Interpretation of Ro Trends Near the Kern Front Fault

A visual and statistical analysis of Ro measurements near the Kern Front Fault were used to identify Ro discontinues across the fault in order to establish the faults hydrologic sealing ability. Results from the visual analyses show a combination of higher

Ro gradients across the Kern Front Fault in the north, less or no gradient in the center and

41 then a higher but inverse gradient in the south. A multiple linear regression model found a statistically significant difference in Ro across the Kern Front Fault. That model

Table 1. Results for multiple linear regression analysis from data near Kern Front Fault. showed the side of the fault in which the measurement was taken strongly impacted the prediction of log Ro, indicating Ro values are discontinuous on either side of the fault.

Using the criteria set for this study, Ro discontinuities across the fault suggest the Kern

Front Fault creates a hydrologic seal between Kern Front and Kern River oil fields in the

Kern River Formation.

Differences in the magnitude of the gradient may indicate some variation in sealing ability along-strike of the fault. This along strike variation in the difference of Ro across fault was also recognized in the multiple linear regression analysis. An interaction term which multiplied the side and latitude of the Ro measurements showed a statistically significant change in the difference of Ro across the fault moving along-strike. These along-strike Ro differences could be influenced by the fault splice that begins near the latitude of ~35.485° (Figure 6a). A lower Ro gradient was observed across the fault near the splice, this could indicate the splice is possibly acting as a conduit to across-fault fluid flow. Further definition of the fault geometry in this area could help determine if this fault splice could act as a potential fluid pathway. Coburn and Gillespie (2002) conducted a hydrologic study to optimize steamflood in Kern River oil field in which they noted the

Kern Front Fault slows or stops downdip drainage of fluids. The interpretation of this study at least partially agrees with previous assertions that the Kern Front Fault creates a hydrologic seal (Coburn and Gillespie, 2002).

Using Ro gradients to establish the hydrologic sealing ability of a fault assumes freshwater recharge pathways are known and consistent. Freshwater recharge can create discontinuities in Ro gradients near a fault zone if the recharge pathway is to some extent perpendicular to the strike of a sealing fault. In this study area subsurface inflow from the

Sierra Nevada mountains, the Kern River and other streams are recharge sources. Local

Precipitation is scarce and does not contributes to groundwater recharge; however, precipitation in the Sierra Nevada mountains is thought to contribute (Dale et al., 1966).

The amount of subsurface flow this area receives is unknown, but flow direction is asserted to follow the regional structural dip to the west-southwest (Dale et al., 1966).

An additional limitation to this analysis is the assumption that lithologic parameters (a, m, and porosity) remains consistent throughout the Kern River Formation.

This fluvial environment is known for lateral facies changes (Reid, 1995) which could control changes in lithology. These facies changes can cause clay content and porosity to vary, both of which can potentially impact the relationship between Ro and Rw (Equation

2 and 3) and the ability to use Ro as a proxy for groundwater TDS. Care was taken during the data collection process to ensure sands were free of clay by utilizing relationships between shallow and deep resistivity curves (Asquith and Krygowski, 2004), SP curves and avoiding any packages with abrupt changes in resistivity or SP. The impacts of porosity changes are unknown in this study; however, future work could use geophysical logs to estimate changes in porosity and relative clay content through the study area.

Premier Faults in Poso Creek

Fault Offset in Poso Creek

Using geologic marker data collected from geophysical logs in wells within Poso

Creek oil field (Dataset D), a structural map was created to show the base of the Macoma

Claystone near the Premier Fault (Figure 5b). This is a subset of data collected during

TDS mapping of Poso Creek oil field (Stephens et al. in review). Offset on the West

Premier Fault is at its maximum (~150 feet) near the northern end of the fault and decreases southward leading into an apparent fault tip where it was previously mapped to

44 have a slight bend to east (Weddle, 1959) (Figure 5b). The East Premier Fault appears to have very little offset in the northern portion, followed by increasing offset up to ~130 feet moving southward and then decreasing again to ~50 feet in the south. The offset patterns presented here indicate that the geometry of this fault zone is more complex than shown by the original mapping of these faults (Weddle, 1959).

The Premier Fault system was previously mapped as two single strand faults that have their northern terminus where they intersect with the Poso-Pond Fault to the north and combined into a single fault toward the southern portion of the field. Offset patterns indicate the East Premier Fault’s northern tip is south of the Poso-Pond Fault, from there offset increases southward. At that point the offset begins to decrease and well control is lost before a southern tip can be identified. The West Premier Fault has a slightly more simple offset pattern, in that maximum offset can be identified in the northern most portion of the fault before it intersects with the Poso-Pond Fault and then offset decreases to the south where a southern fault tip can be identified near the previously mapped eastward bend.

Although faults are commonly thought to be continuous linear features, they are often a series of smaller fault segments that link up as the fault evolves (Peacock and

Sanderson, 1991). The offset patterns uncovered during clay mapping reflect a structure commonly found in extensional settings known as a fault relay, a term that gained popularity after it was used to describe extensional fault structures in East Greenland

(Larsen, 1988) (Figure 9). This structural feature can be seen in offset patterns between the East and West Premier faults near the center of the research area (Figure 6b).

Elevation of base Macoma claystone projected on latitude in a north to south cross- sectional view allowed for the further identification of the fault relay structure (Figure

7b). Toward the northern portion of the north-south projection, the separation in elevation is interpreted to be the offset along the West Premier Fault (Figure 7b), this separation closes moving southward and then increases again in the southern portion of the field.

The middle portion with the least separation is interpreted to be the ramp structure

(Figure 7b).

Figure 9. Fault relay A cartoon representation of a fault relay showing fault tip where fault throw in near zero, area of high fault throw and the ramp feature that can connect fault blocks.

Resistivity Near the Premier Faults

Using Ro data collected from geophysical well logs near the Premier Faults

(Dataset C), along strike and across fault Ro trends were used to evaluate the sealing ability of the West Premier Fault. Stratigraphy is very similar on either side of the

Premier faults and hydrocarbon is contained underneath the Macoma claystone; thus, all data analyzed here was gathered above the Macoma. For along strike and across fault analysis the West Premier Fault was chosen for the separation point because of spatial data distribution. Along-strike and across-fault Ro trends near the West Premier Fault will be discussed first followed by a discussion of a statistical analysis of the data using a multiple linear regression model.

Ro data from either side of the West Premier Fault were investigated to identify depth dependent or along strike trends. Measurements taken from the east side of the fault have a general trend of having a lower Ro in the higher elevations, Ro increasing in some places between ~0- and -1000-feet elevation and then decreasing again in the lower portion (Figure 10a). When evaluating trends from north to south it appears there could be a subtle trend of decreasing Ro moving southward on the eastside of the West Premier

Fault. Ro measurements from the westside of the West Premier Fault also show a general trend of having a lower Ro in the higher elevations, but here Ro increase in some areas between ~0- and -1500-feet elevation and then decrease again in the lower elevations

(Figure 10b). West of the West Premier Fault overall resistivity seems to decrease toward the southern end of the fault, similar to the trend on the east side. When measurements

Figure 10. Resistivity measurements near the West Premier Fault

a) Ro measurements projected on latitude creates a north-south cross-sectional view of Ro values on the eastside of the West Premier Fault. Elevation of the measurement is shown on the Y axis and color represents the Ro value at the point. b) North-south cross-sectional view of Ro values on the westside of the West Premier Fault. Elevation of the measurement is shown on the Y axis and color represents the Ro value at the point. c) Ro measurements projected on latitude creates a north-south cross-section of points near the West Premier Fault. Elevation of the measurement is shown on the Y axis and color represents the Ro value at the point. The color scale is normalized to ensure proper representation of the Ro.

48 are compared from both sides of the West Premier fault, there is no apparent trend that separates one side from the other (Figure 10c).

To investigate whether Ro trends along strike and similarities across the West

Premier Fault are statistically significant, two multiple linear regression models were created. These models are similar to those used near the Kern Front Fault and were utilized to test the ability of the measurement elevation, side of the fault and latitude to predict a response in log Ro. The first Poso Creek model (Model C) used Ro data from either side of the West Premier Fault separately to identify along strike relationships between the predictor variables and log Ro. The second Poso Creek model (Model D) investigates the ability of four independent variables to predict a response in log Ro. The four independent variables are: the elevation of the measurement, latitude, the side of the fault in which the measurement was taken, and an interaction term which latitude and side were multiplied together. Model D uses Ro data from both sides of the West Premier

Fault to identify trends across the fault.

Ro data was separated into two groups by using interpreted discontinuities in the elevation of the top of the Macoma claystone (Figure 6b) as the divide. Model C used Ro measurements from either side of the fault separately to test the ability of the measurement elevation and latitude to predict a response in log Ro. The measurement elevation variable is used to identify the impact of measurement depth on log Ro. Latitude and elevation values were normalized to give latitude values a range closer to the other variables and make result easier to interpret. Normalized latitude and elevation values from 0 to 1 were used to identify changes Ro along the strike of the fault. Ro

49 measurements near the West Premier Fault also show a general trend of having a lower

Ro in the higher elevations, increase in some intermediate areas and then decrease again in the lower elevations; thus, a third-degree polynomial fit was used for the elevation variable.

Model C (Table 2) was used estimate a response in log Ro using elevation and latitude as predictors on both the westside and eastside of the fault separately. On the westside of the West Premier Fault measurement elevation had a statistically significant impact on the prediction of log Ro (coefficient= 3.727, p=<0.001). However, normalized latitude did not have a statistically significant impact on the prediction of log Ro

(coefficient= 0.0813, p=0.129). The westside model had a fairly weak R-squared of

0.263, indicating these predictors variables explain a relatively small portion of the variability of log Ro. On the eastside of the fault, prediction of log Ro also had a dependence on elevation (coefficient= 3.91, p=<0.001). While latitude was statistically significant, it only had a moderate impact on the prediction of log Ro (coefficient= 0.186, p=0.056). The eastside model also had a weak R-squared (0.125) indicating these predictors only control a small part of the variability in Ro. Here, the multiple linear regression analysis somewhat agree with the apparent along-strike variability in log Ro, in which there is slight along-strike variability on the eastside but less on the westside.

However, weak R-squared values indicates these results do not provide strong support of apparent trends.

Similar to Model B, Model D (Table 2) uses Ro measurements from both sides of the West Premier Fault to test the ability of four independent variables to predict a

response in log Ro. The first two independent variables are the elevation of the measurement and latitude. The additional two independent variables are the side of the fault in which the measurement was taken, and an interaction term which latitude and side were multiplied together. Here, the categorical variable was coded as 0 for a measurement on the east side of the fault and 1 for a measurement on the west side. An interaction term which multiplied normalized latitude by the categorical variable which represented the side of the fault in which the measurement was taken was used to evaluate if the predicted change in Ro across the West Premier Fault varied moving northward.

Model D used Ro measurements from both side of the West Premier Fault to predict a response in log Ro using four independent variables. Measurement elevation was statistically significant in the prediction of log Ro (coefficient= 4.441, p=<0.001).

Prediction of log Ro had a slight dependence on latitude (coefficient= 0.143, p=0.061); however, the side of the fault the measurement was taken was not statistically significant

(coefficient= 0.0015, p=0.997). The interaction term which multiplied normalized latitude by the categorical variable which represented the side of the fault was also not statistically significant in this scenario (coefficient= -0.0591, p=0.527). The R-squared of this model was weak (0.210) which indicates these predictors can only explain a small portion of the variation in log Ro near the West Premier Fault. Here, the multiple linear regression analysis supports the apparent similarities in across-fault Ro values because the side variable had a relatively low coefficient and high P-value. However, weak R-squared values indicates these results do not provide strong support of apparent trends.

Table 2. Results for multiple linear regression analysis from data near West Premier Fault

Interpretation of Ro Trends Near the Premier Faults

Ro behavior near the West Premier Fault was analyzed using graphical and statistical methods in order to evaluate the hydrologic sealing ability of the fault. Ro trends near the West Premier Fault system show no statistical difference in Ro from one side of the fault to the other. Using the criteria set for this study, a lack of Ro discontinuities across the fault suggest the West Premier fault does not create a lateral

52 seal above the Macoma claystone and groundwater is free to flow across the fault plane.

This behavior could be explained by displacement of the West Premier Fault decreasing toward the surface. The Macoma claystone is the only continuous clay unit in Poso Creek oil field that can be used to map fault throw. However, it is difficult to determine if the fault has a similar degree of displacement throughout the Kern River Formation, which could impact its ability to create a hydrologic seal above the Macoma claystone. Another possible mechanism controlling the non-sealing behavior of the West Premier Fault is the structural feature interpreted to be a fault relay between the East and West Premier Faults near the center of the research area (Figure 6b). This fault relay could create a potential pathway for fluid flow across the fault system (Bense and Van Balen, 2004) because it creates a ramp structure that makes a link between the adjacent fault tips (Figure 8)

(Crider and Pollard, 1998; Fossen and Rotevatn, 2016). However, the ramp structure has limited along-strike extent so its ability to provide recharge to the adjacent side of the fault may be limited.

There are other factors that could support the hypothesis the West Premier Fault does not create a hydrologic barrier above the Macoma claystone. Lack of statistically significant difference in Ro along strike of the fault, both when each side was evaluated separately and combined with the side variable, could be evidence of fluid flow across the fault plane. Further support could be seen historic hydrocarbon production records because production from both the Etchegoin and Chanac from either side of the fault

(Weddle, 1959) contradict the idea of the fault creating a hydrocarbon trap. However, historical hydrocarbon production is only evidence of sealing capacity below the

Macoma claystone and provides little insight to the sealing behavior above. Sealing nature in the hydrocarbon zone is only mentioned here because it is consistent with the trend seen near the Kern Front Fault in which that fault historically sealed to hydrocarbon production and this study show it also creates a hydrologic seal in the stratigraphic units above.

There are limitations when using Ro gradients to establish the hydrologic sealing ability of a fault. First, using Ro gradients assumes freshwater recharge pathways are known because discontinuities in Ro gradients near a fault zone are assumed to be cause by recharge being stopped or dramatically slowed by the fault. Second, is the assumption that lithologic parameters (a, m, and porosity) remains consistent throughout the study area. Changes in these parameters can potentially impact the relationship between Ro and

Rw (Equation 2 and 3) and the ability to use Ro as a proxy for groundwater TDS. Future work could improve this technique by investigating changes in porosity and clay content throughout the study area.

Comparison of Kern Front and Premier Fault Systems

The comparison of these two study areas provides an example of a fault that is interpreted to create a hydrologic seal (Kern Front Fault) and a fault that is interpreted to allow fluid flow across the fault plane (West Premier Fault). These fault zones are relatively close to each other and have similar stratigraphic throw. The fault relay that is interpreted as a potential fluid path across the West Premier Fault could potentially be the difference in sealing behavior (Figure 6b and 8). As fault zones develop, individual fault

54 strand can link as these ramp structures breach to accommodate additional throw (Crider and Pollard, 1998; Fossen and Rotevatn, 2016). Therefore, if the Kern Front Fault is further developed, smaller fault strand could have linked together to create one larger fault plane. Conversely, if the Kern Front Fault splice also acts as a fluid pathway, indicated by the changing Ro gradient near the splice, then it could be lessening displacement of the West Premier Fault toward the surface which is causing the different between the two fault zones. The mechanisms that control fault development in this area are beyond the scope of this study; however, this hypothesis could provide a pathway for future work on the tectonic development of the east side of the San Joaquin Basin.

Conclusions

Ro was used as a proxy for groundwater quality to assess the sealing ability of the

Kern Front and West Premier Faults. Ro discontinuities across the Kern Front Fault provide evidence the fault creates a hydrologic seal between Kern Front and Kern River oil fields. The magnitude of these discontinuities changes along strike of the fault, which could indicate the sealing ability of the fault varies. These results were supported using a multiple linear regression model which prediction of log Ro had a strong dependence on the side of the fault the measurement was taken (coefficient= 0.386, p=<0.001) and the interaction term which multiplied latitude and side (coefficient= -0.563, p=<0.001). The

R-squared of this model was 0.38. This conclusion also generally agrees with a previous assertion that potentiometric surfaces in regional unconfined aquifer and perched aquifers in Kern River oil field flatten as they approach the fault, indicating the Kern Front Fault is slowing or stopping flow (Coburn and Gillespie, 2002).

Ro gradients were also used to assess the sealing ability of the West Premier Fault.

Consistent Ro values across the West Premier Fault provide evidence this fault does not create a hydrologic seal above the Macoma claystone in Poso Creek oil field. Near the

West Premier Fault there are no apparent along strike variations in Ro gradients across the fault. These results were also supported by multiple linear regression model that showed no statistically significant difference in Ro across the fault (coefficient= 0.0015, p=0.997).

The interaction term which multiplied side by latitude, showed no statistically significant change in Ro across the fault moving along strike. The R-squared of this model was

0.210. Ro trends near the Premiers Fault system are similar to trends found during recent

TDS mapping (Stephens et al., in review) which showed no gradients across the West

Premier Fault above the Macoma claystone.

The overall objective of this study was to assess the ability to use Ro as a proxy for groundwater quality to evaluate a faults ability to seal groundwater flow. Using the criteria set for this study, the results presented here answered this question; however, further investigation into fluid recharge pathways and sedimentation patterns could be done to more accurately interpret results. The findings of this study are significant because they have shown that Ro can be used to investigate the sealing nature of a fault.

These findings could be important for future groundwater protection because they present an additional method that uses publicly available data to investigate a fault ability to contain injected fluids. Future work could be directed at finding more efficient methods of collecting Ro data near a desired fault zone, identifying recharge pathways and investigate changes in lithology. These improvements could advance the spatial distribution of the data and the ability to interpret the results.

Appendix A

Resistivity Measurements near Kern Front Fault

API Latitude Longitude Elevation (ft) Depth (ft) Resistivity 2910728 35.47844 -119.030554 854 1560 67 2910728 35.47844 -119.030554 854 1518 81 2910728 35.47844 -119.030554 854 1420 60 2910728 35.47844 -119.030554 854 1385 50 2910728 35.47844 -119.030554 854 1325 44 2910728 35.47844 -119.030554 854 1280 56 2910728 35.47844 -119.030554 854 1110 37 2910728 35.47844 -119.030554 854 880 32 2910728 35.47844 -119.030554 854 795 40 2910728 35.47844 -119.030554 854 750 43 2910728 35.47844 -119.030554 854 655 45 2910737 35.459969 -119.031704 745 1645 65 2910737 35.459969 -119.031704 745 1525 61 2910737 35.459969 -119.031704 745 1410 61 2910737 35.459969 -119.031704 745 1350 61 2910737 35.459969 -119.031704 745 1140 30 2910737 35.459969 -119.031704 745 960 35 2910737 35.459969 -119.031704 745 920 34 2910737 35.459969 -119.031704 745 851 42 2910737 35.459969 -119.031704 745 741 42 2910737 35.459969 -119.031704 745 710 37 2910737 35.459969 -119.031704 745 625 38 2910746 35.462513 -119.031683 782 1625 70 2910746 35.462513 -119.031683 782 1560 78 2910746 35.462513 -119.031683 782 1515 62 2910746 35.462513 -119.031683 782 1425 56 2910746 35.462513 -119.031683 782 1365 44 2910746 35.462513 -119.031683 782 1260 45 2910746 35.462513 -119.031683 782 1114 43 2910746 35.462513 -119.031683 782 1005 41 2910746 35.462513 -119.031683 782 835 38

2910746 35.462513 -119.031683 782 749 45 2910746 35.462513 -119.031683 782 675 40 2910746 35.462513 -119.031683 782 515 41 2940300 35.455077 -119.020188 789 1080 26 2940300 35.455077 -119.020188 789 1050 29 2940300 35.455077 -119.020188 789 920 24 2940300 35.455077 -119.020188 789 845 21 2940300 35.455077 -119.020188 789 785 28 2940300 35.455077 -119.020188 789 680 28 2940300 35.455077 -119.020188 789 605 33 2940300 35.455077 -119.020188 789 565 36 2940300 35.455077 -119.020188 789 530 35 2940466 35.476046 -119.027704 898 1640 52 2940466 35.476046 -119.027704 898 1560 58 2940466 35.476046 -119.027704 898 1480 54 2940466 35.476046 -119.027704 898 1275 38 2940466 35.476046 -119.027704 898 1170 32 2940466 35.476046 -119.027704 898 1100 33 2940466 35.476046 -119.027704 898 790 32 2940466 35.476046 -119.027704 898 660 72 2940466 35.476046 -119.027704 898 610 50 2940614 35.466049 -119.019114 923 1130 28 2940614 35.466049 -119.019114 923 1110 28 2940614 35.466049 -119.019114 923 1050 18 2940614 35.466049 -119.019114 923 1020 23 2952908 35.486158 -119.032781 911 1550 36 2952908 35.486158 -119.032781 911 1470 46 2952908 35.486158 -119.032781 911 1325 32 2952908 35.486158 -119.032781 911 1272 26 2952908 35.486158 -119.032781 911 1162 28 2952908 35.486158 -119.032781 911 925 29 2952908 35.486158 -119.032781 911 855 36 2952908 35.486158 -119.032781 911 663 43 2956475 35.464695 -119.031696 804 1628 38 2956475 35.464695 -119.031696 804 1525 45

2956475 35.464695 -119.031696 804 1470 48 2956475 35.464695 -119.031696 804 1390 40 2956475 35.464695 -119.031696 804 1240 35 2956475 35.464695 -119.031696 804 1080 32 2956475 35.464695 -119.031696 804 1050 26 2956475 35.464695 -119.031696 804 975 32 2956475 35.464695 -119.031696 804 900 32 2956475 35.464695 -119.031696 804 835 20 2956475 35.464695 -119.031696 804 770 26 2956475 35.464695 -119.031696 804 620 35 2956475 35.464695 -119.031696 804 560 35 2956790 35.470692 -119.017603 890 1100 20 2956790 35.470692 -119.017603 890 1035 32 2956790 35.470692 -119.017603 890 945 29 2956790 35.470692 -119.017603 890 875 24 2956790 35.470692 -119.017603 890 805 24 2956790 35.470692 -119.017603 890 755 21 2957471 35.481795 -119.035621 878 1650 30 2957471 35.481795 -119.035621 878 1590 45 2957471 35.481795 -119.035621 878 1500 39 2957471 35.481795 -119.035621 878 1440 35 2957471 35.481795 -119.035621 878 1400 30 2957471 35.481795 -119.035621 878 1350 33 2957471 35.481795 -119.035621 878 1180 29 2957471 35.481795 -119.035621 878 950 39 2957471 35.481795 -119.035621 878 760 30 2957471 35.481795 -119.035621 878 570 34 2957797 35.466889 -119.020417 844 835 21 2957797 35.466889 -119.020417 844 745 28 2957797 35.466889 -119.020417 844 635 33 2957797 35.466889 -119.020417 844 605 31 2957797 35.466889 -119.020417 844 555 28 2961548 35.480093 -119.016325 991 1000 25 2961548 35.480093 -119.016325 991 945 32 2961548 35.480093 -119.016325 991 832 30

2961548 35.480093 -119.016325 991 685 36 2962686 35.460081 -119.023775 779 1035 27 2962686 35.460081 -119.023775 779 960 21 2962686 35.460081 -119.023775 779 900 24 2962686 35.460081 -119.023775 779 790 20 2963676 35.463015 -119.023748 785 1000 24 2963676 35.463015 -119.023748 785 922 25 2963676 35.463015 -119.023748 785 865 28 2963676 35.463015 -119.023748 785 815 25 2963675 35.461988 -119.023759 773 1073 24 2963675 35.461988 -119.023759 773 1005 28 2963675 35.461988 -119.023759 773 970 20 2963675 35.461988 -119.023759 773 935 20 2963675 35.461988 -119.023759 773 857 26 2963675 35.461988 -119.023759 773 820 23 2965522 35.472665 -119.026463 846 1680 71 2965522 35.472665 -119.026463 846 1610 61 2965522 35.472665 -119.026463 846 1570 32 2965522 35.472665 -119.026463 846 1530 26 2965522 35.472665 -119.026463 846 1390 28 2965522 35.472665 -119.026463 846 1268 24 2965522 35.472665 -119.026463 846 1150 24 2965522 35.472665 -119.026463 846 930 26 2965522 35.472665 -119.026463 846 840 24 2965522 35.472665 -119.026463 846 735 35 2965522 35.472665 -119.026463 846 640 35 2967781 35.455236 -119.031232 699 1640 40 2967781 35.455236 -119.031232 699 1550 39 2967781 35.455236 -119.031232 699 1430 55 2967781 35.455236 -119.031232 699 1380 30 2967781 35.455236 -119.031232 699 1345 43 2967781 35.455236 -119.031232 699 1300 25 2967781 35.455236 -119.031232 699 1130 23 2967781 35.455236 -119.031232 699 1030 22 2967781 35.455236 -119.031232 699 985 29

2967781 35.455236 -119.031232 699 810 31 2967781 35.455236 -119.031232 699 740 23 2967948 35.47678 -119.016304 867 970 32 2967948 35.47678 -119.016304 867 930 40 2967948 35.47678 -119.016304 867 750 35 2967948 35.47678 -119.016304 867 700 34 2967948 35.47678 -119.016304 867 1685 39 2967948 35.47678 -119.016304 867 1640 38 2967948 35.47678 -119.016304 867 1500 45 2967948 35.47678 -119.016304 867 1380 30 2967948 35.47678 -119.016304 867 1338 30 2967948 35.47678 -119.016304 867 1165 32 2967948 35.47678 -119.016304 867 900 26 2967948 35.47678 -119.016304 867 772 32 2974308 35.472149 -119.031557 887 1800 30 2974308 35.472149 -119.031557 887 1700 33 2974308 35.472149 -119.031557 887 1560 43 2974308 35.472149 -119.031557 887 1475 38 2974308 35.472149 -119.031557 887 1335 38 2974308 35.472149 -119.031557 887 1195 32 2974308 35.472149 -119.031557 887 1145 30 2974308 35.472149 -119.031557 887 1080 25 2974308 35.472149 -119.031557 887 1015 30 2974308 35.472149 -119.031557 887 935 25 2974308 35.472149 -119.031557 887 815 32 2974308 35.472149 -119.031557 887 515 40 2981535 35.48437 -119.031265 860 1555 56 2981535 35.48437 -119.031265 860 1535 56 2981535 35.48437 -119.031265 860 1495 44 2981535 35.48437 -119.031265 860 1475 60 2981535 35.48437 -119.031265 860 1375 32 2981535 35.48437 -119.031265 860 1330 35 2981535 35.48437 -119.031265 860 1280 29 2981535 35.48437 -119.031265 860 1230 34 2981535 35.48437 -119.031265 860 1105 27

2981535 35.48437 -119.031265 860 1055 42 2981535 35.48437 -119.031265 860 880 33 2981535 35.48437 -119.031265 860 790 27 2981535 35.48437 -119.031265 860 570 36 3009548 35.468485 -119.021525 828 1180 48 3009548 35.468485 -119.021525 828 1120 52 3009548 35.468485 -119.021525 828 1060 48 3009929 35.475228 -119.018908 844 970 40 3009929 35.475228 -119.018908 844 932 60 3009929 35.475228 -119.018908 844 870 35 3009929 35.475228 -119.018908 844 800 33 3009929 35.475228 -119.018908 844 760 41 3009929 35.475228 -119.018908 844 740 46 3010794 35.485166 -119.024833 924 1546 51 3010794 35.485166 -119.024833 924 1452 45 3010794 35.485166 -119.024833 924 1390 65 3010794 35.485166 -119.024833 924 1210 41 3010794 35.485166 -119.024833 924 1050 40 3010794 35.485166 -119.024833 924 1010 42 3010794 35.485166 -119.024833 924 855 35 3010794 35.485166 -119.024833 924 605 56 3010795 35.487946 -119.026603 946 1638 63 3010795 35.487946 -119.026603 946 1505 52 3010795 35.487946 -119.026603 946 1405 45 3010795 35.487946 -119.026603 946 1350 40 3010795 35.487946 -119.026603 946 1250 46 3010795 35.487946 -119.026603 946 1220 51 3010795 35.487946 -119.026603 946 1135 50 3010795 35.487946 -119.026603 946 1032 41 3010795 35.487946 -119.026603 946 880 45 3010795 35.487946 -119.026603 946 705 34 3010795 35.487946 -119.026603 946 595 66 3010795 35.487946 -119.026603 946 530 53

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