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A CORRELATION FOR RESERVOIR CHARACTERIZATION USING RECORDED REAL-TIME SURFACE DRILLING PARAMETERS AND WELL LOG DATA by Simone Steinecker c Copyright by Simone Steinecker, 2014 All Rights Reserved A thesis submitted to the Faculty and the Board of Trustees of the Colorado School of Mines in partial fulfillment of the requirements for the degree of Master of Science (Petroleum Engineering). Golden, Colorado Date Signed: Simone Steinecker Signed: Dr. Alfred Eustes Thesis Advisor Signed: Dr. Mark Miller Thesis Advisor Golden, Colorado Date Signed: Dr. Will Fleckenstein Professor and Head Department of Petroleum Engineering ii ABSTRACT Recent economic developments of the US gas market and enhanced technological im- provements lead towards an increase of future operations in the sector of shale gas plays. The Eagle Ford field in Texas, being amongst the youngest US shale plays, serves as a good example of how correlating recorded real-time surface drilling parameters and well log data can be used to improve reservoir characterization. Variations of properties occurring hor- izontally and vertically, across the entire play or even along the wellbore are regarded as a major challenge directly affecting the economic development of shale gas reservoirs. An enormous amount of data is collected at present but not analyzed and evaluated in detail. Instead the trend is evolving that more data is generated, resulting in the incapability to integrate the data. Regression analysis is used to determine quantitative relationships be- tween a real-time surface drilling parameter and petrophysical logging data for wells located in the same geographic and geologic area. This research describes how the rate of penetration correlates with the gamma and acoustic log (slowness of elastic waves) for the predominant shale section of each well and how the regression outputs contribute to optimize reservoir characterization. Within the shale formation, the gamma log (GR) shows a good correlation with the rate of penetration. Information from mudlogs and daily drilling reports is used to identify possible reasons for misfits between the actual and the calculated rate of pen- etration. Studying a defined set of data in depth has proven to be a reliable indicator for comparing and categorizing wells. The results depict similarities and differences amongst the wells based on the properties of the formation they were drilled in. It is expected that additional real-time surface drilling parameters besides the rate of penetration are useful to obtain improved results. They can be used to normalize the rate of penetration to optimize the comparison between wells and to detect misfits between the regression output and the actual rate of penetration measured on a real-time base. iii TABLE OF CONTENTS ABSTRACT . iii LIST OF FIGURES . vii LIST OF TABLES . xi LIST OF SYMBOLS . xiv LIST OF ABBREVIATIONS . xv ACKNOWLEDGMENTS . xviii CHAPTER 1 INTRODUCTION . 2 1.1 General Aspects of the Problem . 2 1.2 Geological Setting . 4 1.3 Motivation of Study . 9 1.4 Objectives . 10 1.5 Thesis Outline . 11 CHAPTER 2 LITERATURE REVIEW . 13 2.1 Lithological Classification . 13 2.2 Shale Reservoir Characterization . 15 2.3 Openhole Petrophysical Measurements . 19 CHAPTER 3 METHODOLOGY . 22 3.1 Petrophysical Interpretation . 22 3.1.1 Nuclear/Radioactive Properties . 22 3.1.2 Elastic Properties . 23 iv 3.2 Real-Time Surface Drilling Data . 24 3.3 Data Analysis . 26 3.3.1 Comparison of Studied Wells . 28 3.3.2 Regression Analysis . 30 3.3.3 Data Correlations . 33 CHAPTER 4 RESULTS . 40 4.1 Crossplots . 40 4.1.1 Internal Log Crossplots . 40 4.1.2 Log to ROP Crossplots . 41 4.2 Regression Analysis . 41 4.2.1 Well BC 1-1 . 48 4.2.2 Well GE A1H . 53 4.2.3 Well PE A2H . 59 4.2.4 Well LK B1H . 65 4.2.5 Well PE A1H . 69 4.2.6 Well PE A2H ST . 70 4.2.7 Well RR A3H . 75 4.2.8 Well WL A1H . 76 4.2.9 Well WL A1H ST . 81 4.3 Inverted Regressions for GR as a Function of ROP . 84 4.4 Empirical Relationships Between VP and VS - Castagna Equation . 84 4.4.1 Well BC 1-1 . 87 4.4.2 Well GE A1H . 88 v 4.4.3 Well PE A2H . 90 4.5 Summarizing Results . 90 CHAPTER 5 CONCLUSION . 94 REFERENCES CITED . 98 APPENDIX A - PYTHON SCRIPT . 102 A.1 Python Script for Crossplots . 102 APPENDIX B - WELL LOGGING TOOLS . 103 B.1 Gamma Log . 103 B.2 Acoustic/Sonic Log . 105 APPENDIX C - CROSSPLOTS . 107 C.1 Crossplots Not Used for Regression Analysis . 107 APPENDIX D - REGRESSION ANALYSIS . 110 D.1 Simple Linear Regression Analysis . 110 APPENDIX E - INFORMATION PROVIDED BY COMPANIES . 112 E.1 Areas of Interest Stated by Companies . 112 vi LIST OF FIGURES Figure 1.1 US total natural gas production by source between 1990-2035 . 3 Figure 1.2 Map of lower 48 states shale plays in the US . 4 Figure 1.3 Stratigraphic column and correlation in the upper Cretaceous interval, US Gulf Coast . 5 Figure 1.4 Eagle Ford shale map showing the wells permitted and completed in the field........................................7 Figure 1.5 Eagle Ford shale map including producing oil and gas wells and showing the three sections of the field . 7 Figure 1.6 Eagle Ford shale map showing the geologic structure of the shale play . 8 Figure 1.7 Lateral extent of Eagle Ford shale play in South Texas showing the wells used in this research . 8 Figure 2.1 Anisotropy and heterogeneity . 17 Figure 2.2 Measured permeability anisotropy in shales with permeability measured parallel and perpendicular to bedding and SEM image of Kimmeridge shale . 18 Figure 3.1 P-wave and S-wave velocities for clay, quartz, calcite and dolomite . 25 Figure 3.2 Overview of the wells studied located in the Eagle Ford field in McMullen country in Texas. Scale of the map 1:326,670. 26 Figure 3.3 Wellbore schematic for horizontal wells illustrating information obtained from daily drilling reports. 29 Figure 3.4 Comparing information from daily drilling reports with information from mudlogs . 31 Figure 3.5 Internal log crossplot for well GE A1H showing DTC versus DTS and MD as a third dimension . 37 Figure 3.6 Crossplot for well GE A1H showing GR versus ROP and MD as a third dimension . 38 vii Figure 3.7 Simple linear regression correlating ROP and GR for well GE A1H . 39 Figure 4.1 Internal log crossplots illustrating the relationship between the compressional slowness DTC and the shear slowness DTS. 42 Figure 4.2 Internal log crossplots illustrating the relationship between gamma ray GR and compressional slowness DTC . 43 Figure 4.3 Internal log crossplots illustrating the relationship between gamma ray GR and shear slowness DTS . 44 Figure 4.4 Correlations illustrating the relationship between the compressional slowness DTC and the rate of penetration ROP . 45 Figure 4.5 Correlations illustrating the relationship between the shear slowness DTS and the rate of penetration ROP . 46 Figure 4.6 Correlations illustrating the relationship between gamma ray GR and the rate of penetration ROP . 47 Figure 4.7 Data set used for regression analysis for well BC 1-1 . 49 Figure 4.8 Simple linear regression analysis for well BC 1-1 . 50 Figure 4.9 Multiple linear regression analysis for well BC 1-1 . 52 Figure 4.10 Data set used for regression analysis for well GE A1H . 54 Figure 4.11 Simple linear regression analysis for well GE A1H . 56 Figure 4.12 Multiple linear regression analysis for well GE A1H . 58 Figure 4.13 Data set used for regression analysis for well PE A2H . 60 Figure 4.14 Simple linear regression analysis for well PE A2H . 61 Figure 4.15 Multiple linear regression analysis for well PE A2H . 63 Figure 4.16 Data set used for regression analysis for well LK B1H . 66 Figure 4.17 Simple linear regression analysis for well LK B1H . 67 Figure 4.18 Data set used for regression analysis for well PE A1H . 69 Figure 4.19 Simple linear regression analysis for well PE A1H . 71 viii Figure 4.20 Data set used for regression analysis for well PE A2H ST . 72 Figure 4.21 Simple linear regression analysis for well PE A2H ST . 73 Figure 4.22 Data set used for regression analysis for well RR A3H . ..

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