compaction, permeability, and fluid flow in brent-type reservoirs under depletion

8/10/2019 Compaction, Permeability, and Fluid Flow in Brent-type Reservoirs Under Depletion

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A CORRELATION FOR RESERVOIR CHARACTERIZATION

USING RECORDED REAL-TIME SURFACE DRILLING

PARAMETERS AND WELL LOG DATA

by

Simone Steinecker


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c Copyright by Simone Steinecker, 2014

All Rights Reserved


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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 HeadDepartment of Petroleum Engineering

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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.

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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 BetweenVP andVS- Castagna Equation . . . . . . . 84

4.4.1 Well BC 1-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

4.4.2 Well GE A1H . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

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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

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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 thefield. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Figure 1.5 Eagle Ford shale map including producing oil and gas wells andshowing 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 thewells 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 Kimmeridgeshale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 inMcMullen country in Texas. Scale of the map 1:326,670. . . . . . . . . . . 26

Figure 3.3 Wellbore schematic for horizontal wells illustrating informationobtained from daily drilling reports. . . . . . . . . . . . . . . . . . . . . . 29

Figure 3.4 Comparing information from daily drilling reports with informationfrom mudlogs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Figure 3.5 Internal log crossplot for well GE A1H showing DTC versus DTS andMD as a third dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Figure 3.6 Crossplot for well GE A1H showing GR versus ROP and MD as a thirddimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

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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 thecompressional slowness DTC and the shear slowness DTS.. . . . . . . . . 42

Figure 4.2 Internal log crossplots illustrating the relationship between gamma rayGR and compressional slowness DTC . . . . . . . . . . . . . . . . . . . . 43

Figure 4.3 Internal log crossplots illustrating the relationship between gamma rayGR and shear slowness DTS . . . . . . . . . . . . . . . . . . . . . . . . . 44

Figure 4.4 Correlations illustrating the relationship between the compressionalslowness DTC and the rate of penetration ROP . . . . . . . . . . . . . . 45

Figure 4.5 Correlations illustrating the relationship between the shear slownessDTS and the rate of penetration ROP . . . . . . . . . . . . . . . . . . . . 46

Figure 4.6 Correlations illustrating the relationship between gamma ray GR andthe 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

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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 . . . . . . . . . . . 75

Figure 4.23 Simple linear regression analysis for well RR A3H . . . . . . . . . . . . . 77

Figure 4.24 Data set used for regression analysis for well WL A1H . . . . . . . . . . . 78

Figure 4.25 Simple linear regression analysis for well WL A1H . . . . . . . . . . . . . 80

Figure 4.26 Data set used for regression analysis for well WL A1H ST . . . . . . . . . 81

Figure 4.27 Simple linear regression analysis for well WL A1H ST . . . . . . . . . . . 83

Figure 4.28 Gamma ray GR estimate from rate of penetration ROP indicating shaleand non-shale sections for the wells. . . . . . . . . . . . . . . . . . . . . . 85

Figure 4.29 Simple linear regression analysis for well PE A1H calculating GR fromROP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Figure 4.30 Correlation of the compressional and shear slowness with influence ofgamma ray for well BC 1-1.. . . . . . . . . . . . . . . . . . . . . . . . . . 87

Figure 4.31 Correlation of the compressional and shear slowness with influence of

gamma ray for well GE A1H. . . . . . . . . . . . . . . . . . . . . . . . . . 89

Figure 4.32 Correlation of the compressional and shear slowness with influence ofgamma ray for well PE A2H. . . . . . . . . . . . . . . . . . . . . . . . . . 90

Figure 4.33 Summary for simple linear regressions.. . . . . . . . . . . . . . . . . . . . 92

Figure 4.34 Categories for simple linear regressions using gamma ray. . . . . . . . . . 93

Figure 5.1 Splitting up the well into three sections for enhanced regression analysis.. 97

Figure A.1 Crossplot analysis based on Excel database using Python script. . . . . 102

Figure B.1 Gamma ray logging tool . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Figure B.2 Acoustic/sonic logging tool . . . . . . . . . . . . . . . . . . . . . . . . . 106

Figure C.1 Internal log crossplots illustrating the relationship between gamma rayGR and the ratio of the compressional to the shear slowness DTC/DTS 107

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Figure C.2 Internal log crossplots illustrating the relationship between gamma rayGR and compressional DTC2 or shear DTS2 slowness . . . . . . . . . . 108

Figure C.3 Correlations illustrating the relationship between the compressionalshear slowness DTC2 and the rate of penetration ROP. . . . . . . . . . 109

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LIST OF TABLES

Table 2.1 Common sources for reservoir properties . . . . . . . . . . . . . . . . . . . 16

Table 2.2 Key reservoir parameters and their data source . . . . . . . . . . . . . . . 16

Table 2.3 Logging tool response in shale gas reservoirs . . . . . . . . . . . . . . . . . 20

Table 2.4 Summary of methods to determine TOC from logs . . . . . . . . . . . . . 21

Table 3.1 Measured distances between wells in McMullen country, Texas . . . . . . . 27

Table 3.2 Measurement intervals for each well. . . . . . . . . . . . . . . . . . . . . . 28

Table 3.3 Information on drilling parameters from daily drilling reports . . . . . . . 32

Table 3.4 Formula map of regression statistics output . . . . . . . . . . . . . . . . . 33

Table 3.5 Formula map of ANOVA output . . . . . . . . . . . . . . . . . . . . . . . 34

Table 3.6 Various combinations for crossplot analysis using well log parametersand ROP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Table 4.1 Output for regression 1 for well BC 1-1 . . . . . . . . . . . . . . . . . . . . 51




Table 4.5 Output for regression 1 for well GE A1H . . . . . . . . . . . . . . . . . . . 55




Table 4.9 Output for regression 1 for well PE A2H . . . . . . . . . . . . . . . . . . . 62

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Table 4.13 Summary of simple linear regression curve misfits for well LK B1H . . . . 65

Table 4.14 Regression output for the lower section of well LK B1H. . . . . . . . . . . 68

Table 4.15 Regression output for the upper section of well LK B1H . . . . . . . . . . 68

Table 4.16 Regression output for well PE A1H . . . . . . . . . . . . . . . . . . . . . . 70

Table 4.17 Regression output for well PE A2H ST . . . . . . . . . . . . . . . . . . . . 74

Table 4.18 Summary of simple linear regression curve misfits for well PE A2H ST . . 7 4

Table 4.19 Regression output for well RR A3H . . . . . . . . . . . . . . . . . . . . . . 76

Table 4.20 Summary of simple linear regression curve misfits for well RR A3H . . . . 76

Table 4.21 Regression output for well WL A1H . . . . . . . . . . . . . . . . . . . . . 79

Table 4.22 Summary of simple linear regression curve misfits for well WL A1H . . . . 7 9

Table 4.23 Regression output for well WL A1H ST . . . . . . . . . . . . . . . . . . . 82

Table 4.24 Summary of simple linear regression curve misfits for well WL A1H ST . . 8 2

Table 4.25 Parameters c and d for GR estimate from ROP . . . . . . . . . . . . . . . 84

Table 4.26 DTC correlated with DTS and the resulting equations for well BC 1-1. . . 8 8

Table 4.27 DTC correlated with DTS and the resulting equations for well GE A1H. . 89

Table 4.28 DTC correlated with DTS and the resulting equations for well PE A2H. . 91

Table B.1 Volume of investigation for gamma log . . . . . . . . . . . . . . . . . . . 103

Table B.2 Operational constraints for gamma log . . . . . . . . . . . . . . . . . . . 103

Table B.3 Volume of investigation for acoustic/sonic log . . . . . . . . . . . . . . . 105

Table B.4 Operational constraints for acoustic/sonic log . . . . . . . . . . . . . . . 105

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Table D.1 Summary of simple linear regression outputs correlating ROP with DTC 110

Table D.2 Summary of simple linear regression outputs correlating ROP with GR . 111

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LIST OF SYMBOLS

Bulk compressional modulus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . k

Bulk density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Capillary pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pc

Compressional interval transit time . . . . . . . . . . . . . . . . . . . . . . . . . . . . tP

Compressional wave modulus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M

Compressional wave velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VP

Lames first parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Poissons ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Shear interval transit time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . tS

Shear modulus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Shear wave velocity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VS

Youngs modulus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E

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LIST OF ABBREVIATIONS

American Petroleum Institute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . API

Analysis of variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ANOVA

Barns per electron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . b/e

Compressional wave travel time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DTC

Condensate gas ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CGR

Dipole shear imager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DSI

Feet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ft

Flow rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Q

Fourier transform infrared spectroscopy. . . . . . . . . . . . . . . . . . . . . . . . . FTIR

Gallons per minute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . gpm

Gamma ray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GR

Inch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . in

Inductively coupled plasma mass spectrometry. . . . . . . . . . . . . . . . . . . . ICP-MS

Injection fall-off test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IFOT

Kickoff point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . KOP

Log ASCII standard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LAS

Measured depth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD

Measuring while drilling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MWD

Mechanical specific energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MSE

Mercury injection capillary pressure. . . . . . . . . . . . . . . . . . . . . . . . . . . MICP

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Millimeter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mm

Million cubic feet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . mcf

Nanometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . nm

Normalized rate of penetration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NROP

Nuclear magnetic resonance log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NMR

Oil based mud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . OBM

Overhead lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . OHL

Parts per million . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ppm

Photo-luminescence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PL

Photodiode array detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PDA

Photoelectric index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pe

Pound-force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . lbf

Pressure volume temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PVT

Rate of penetration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ROP

Revolutions per minute. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RPM

Rotations per minute. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RPM

Scanning electron microscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SEM

Shear wave travel time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DTS

Sidetrack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ST

Standard pipe pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SPP

Standard resolution formation density . . . . . . . . . . . . . . . . . . . . . . . . . RHOZ

Steady state & unsteady state permeability test . . . . . . . . . . . . . . . . . . SS&USS

Total organic carbon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TOC

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Trillion cubic feet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tcf

United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . US

United States Dollars. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . USD

Vitrinite reflectance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ro

Water based mud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WBM

Weight on bit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WOB

X-Ray diffraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XRD

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ACKNOWLEDGMENTS

First of all, I would also like to thank Dr. Alfred Eustes. He made it possible for

me to write this thesis as part of a cooperation between the Colorado School of Mines

and Montanuniversitat Leoben. His way of not making anything more complicated than

necessary and challenging students to think beyond the book, makes him an honorable

professor. Although Dr. Eustes was on his SPE distinguished lecture tour during my second

semester as graduate student at the Colorado School of Mines, he kept in touch with me in

order to support me whenever necessary. I really appreciate his help throughout my time

at CSM and beyond. He is a great adivsor. My thanks also go to my co-advisor Dr. Mark

Miller. His input helped me to keep on track with my work . Especially when it comes to

the correct layout and format of my thesis. From the Colorado School of Mines, I would

also like to thank Dr. Azra Tutuncu for letting me be part of her research group within

the Unconventional Natural Gas Institute (UNGI). She granted me the stipend I needed to

be able to finish my graduate studies in the USA. Tom Bratton played an important part

while I was writing my thesis. He helped a lot with his input and ideas. Tom Bratton

has the experience from many year in the industry and the petrophysical background that I

needed. It was nice that I could come by his office any time he was there to ask questions.

I would also like to thank the UNGI sponsors. Without their data and feedback at the

sponsoring meetings, this research would have not been possible. At last, Denise and Terri

have my deepest respect for the daily work they do to help make a students life easier and

less bureaucratic.

From the Montanuniversity in Leoben, I would like to thank my advisor Dr. Gerhard

Thonhauser and my co-advisor Dr. Jurgen Schon.

This project would never have succeeded without the almost daily support from my

family, my hosts Ann and Dave, my closest friends Margit, Mike, Nihal and Mathias and

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CHAPTER 1

INTRODUCTION

The projected growth of the United States (US) energy demand is dominated by the

industrial and electric power sectors with an increase of natural gas consumption of 5.1 Tcf

between 2011 and 2040 (EIA, 2013). To meet the present and future developments of US

energy demands the production of natural gas from fine-grained clastic sedimentary rocks

known as shale is of significant importance. This rock type is composed mainly of clay

and silt-sized particles and in some cases even contains sand-sized particles as well (Schon,

2011). Developments in horizontal drilling technology and improved techniques of hydraulic

fracturing over the last 15 years contribute to an enhanced recovery of shale gas and thus to

an economically justifiable production (Bazan, Lattibeaudiere and Palisch,2012). According

to Holditch(2003), the production from shale gas reservoirs is the exploitation of natural

gas from so-called unconventional reservoirs. These unconventional reservoirs are the largest

source of US natural gas supplies, estimated to make up 49 % of US total natural gas

production in 2035 (Figure 1.1). The shale gas production is supposed to increase from 5.0

Tcf in 2010 to 13.6 Tcf in 2035. Recent developments will enable the US to change its role

of being a net importer to becoming an exporter in 2020. In 2010, 11 % of US natural gas

supply came from imports. By 2035 the expected US net natural gas exports are 1.4 Tcf

(EIA,2012). Although the optimistic projections with regard to future shale gas production

remain promising throughout the next two decades, the EIA (2012) states that achieving

economic gas-flow rates for commercial purposes continues to be a critical issue looking atthe historic development of the natural gas market prices and future developments.

1.1 General Aspects of the Problem

The economic development of shale resources offers opportunities but also faces numer-

ous challenges (Bustin et al.,2008). According toKennedy, Gupta and Kotov(2012) finding

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Figure 1.1: US total natural gas production by source between 1990-2035. Figure fromEIA(2012).

and targeting the so-called sweet spots in shale gas reservoirs is the key issue to produce

the natural gas at economic rates. The widespread diversity of reservoir specific properties

requests the use of a minimum set of techniques to characterize the fields individually and

identify the unique properties of each shale gas reservoir. Understanding the nature of shale

gas reservoirs by improving reservoir characterization helps to reduce existing uncertainties

and develop the field economically (Mullen,2010). Martin et al. (2011) describe shale for-

mations as low permeability formations that act as both, the source for the natural gas and

the reservoir. Compared to the production from conventional reservoirs, more data from un-

conventional reservoirs is needed to address the issue of reservoir characterization. Holditch

(2006) mentiones that due to their high diversity, identifying and characterizing these types

of reservoirs is complex. Unconventional reservoirs do not show a typical trend as proven

byHayden and Pursell (2005) who compared organic shale properties of six different shale

plays.

The first well producing directly from a shale formation was drilled in 1821 ( Sondergeld

et al., 2010). Since then almost 200 years have passed. In the 1980s the Barnett shale started

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Figure 1.3: Stratigraphic column and correlation in the upper Cretaceous interval, US GulfCoast. Figure fromSondhi(2011).

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thickness ranges between 50 ft and up to approxiametely 350 ft. Although the Eagle Ford

formation is regarded as a shale formation, up to 70 % can be carbonate content which makes

the formation more brittle and easier to stimulate using hydraulic fracturing. With a total

organic carbon (TOC) of 1-7 %, the Eagle Ford shale is split into an Upper and Lower Eagle

Ford formation whereas the Lower Eagle Ford has more organically rich material and thus

produces more hydrocarbons (Bazan, Lattibeaudiere and Palisch, 2012). Due to the large

extent of the Eagle Ford formation, the field is split into three areas depending on the type of

hydrocarbons. In the north of the Eagle Ford field the reservoir produces oil (green), whereas

the middle part of the field is referred to as the condensate (yellow) window. Here, both

condensates and natural gas can be found. The area in the south is a dry gas (red) producer

(Figure 1.5). Mullen, Lowry and Nwabuoku(2010) agree that characterizing the Eagle Ford

shale bears challenges. Besides the different hydrocarbons produced in different geographic

areas, the shale formations structure changes across the entire play indicated by Figure 1.6

showing the diverse geologic structure of the play. The gross height and the formations

depth can vary significantly. For successful developments of shale plays, it is important that

each shale is characterized individually. Shale formations can even vary significantly across

the play itself and therefore studying und understanding the local reservoir is recommended.

By way of example, Ramurthy et al. (2011) show that the successful surface-area fracture

treatment for Barnett shale does not achieve the same results for other shales. As the Eagle

Ford field is one of the youngest shale gas reservoirs in the US, characterizing the field is still

in progress with issues and challenges related to it. Examples of topics companies regard as

important to learn about are petrophysical and geomechanical characterization, production

and completion design and optimization, drilling design and rock mechanics, etc. (AppendixE).

For this research, multiple wells from the Eagle Ford field were selected. The wells

are all located in the area of McMullen County, approxiametely 60-70 miles south of San

Antonio, Texas. Figure 1.7 gives an overview of an excerpt of the Eagle Ford field. The

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Figure 1.4: Eagle Ford shale map showing the wells permitted and completed in the field.Figure fromRailroad Commission of Texas (2013).

Figure 1.5: Eagle Ford shale map including producing oil and gas wells and showing thethree sections of the field, the oil window (green), the wet gas/condensate window (yellow)and the dry gas window (red). Figure from EIA (2010).

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Figure 1.6: Eagle Ford shale map showing the geologic structure of the shale play. FigurefromChesapeake Energy(2013).

Figure 1.7: Lateral extent of Eagle Ford shale play in South Texas showing the wells usedin this research. Figure modified by Statoil taken fromRoberson(2013).

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wells for this research are located in the central part. The selection is based on wells to

be in the same geographic and geologic area, i.e. reduced variations in terms of organic

matter, mineralogy, rock composition and depositional environment are expected. This is

important when studying the shale reservoirs characteristics and potential relationships

between the wells. Pointing out similarities as well as differences amongst these wells cannot

solve the complexity involved when trying to characterize an entire shale play. However, by

picking out a pre-defined number of parameters from the data sets available and analyzing

them in more detail, an improved or even new approach can enhance shale gas reservoir

characterization. Increased knowledge and improved understanding of the area studied is

expected to contribute to future drilling operations.

1.3 Motivation of Study

Considering the significant amount of data collected by operators drilling in the same

geographic and geologic area from the wells on a daily basis justifies the presumption that

selected data can be extracted for the purpose of analyzing it in more detail. The results

obtained can improve real-time drilling operations and enhance reservoir characterization in

the area of study. A relationship between petrophysical properties obtained from well logsand drilling parameters can be expected. The comparison of operational drilling parameters

to the known properties from the reservoir can be applied for future real-time analysis during

drilling operations. Both groups are controlled by porosity, clay content or rock composition,

water content and the internal rock structure implementing the bonding forces between

constituents. The weight of these properties influences on the individual parameters is

different. Therefore the expected correlation is dominated by a complexity of these factors.

A simple correlation as detected for sandstone between velocity and drilling parameters or

strength will not be successful and will result in great scatter of data points. Therefore, as an

additional parameter, a property describing lithological effects like the clay influence should

be considered by implementing the results of the gamma ray log. In a comparable direction

could be a variation of organic content. Thus, as a mathematical instrument, simple and

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multiple linear regressions are to be applied. The development/derivation of correlations is -

from the present knowledge - directed by two dominant input parameters: elastic properties

(e.g. acoustic log, density) and lithology or shale typing properties (e.g. natural gamma,

density-neutron) as stated by Fjaer et al. (2008), Schon (2011) and Somerton, Esfandiari

and Singhal (1969). The motivation of this research is to use available data, analyze it in

detail and give recommendations of how this approach can be realized and applied to future

drilling operations.

1.4 Objectives

The main task of this proposed thesis research is to develop a log-to-drilling parameters

model represented in form of simple and multiple linear regressions to present correlations

for the wells drilled in the same geologic and geographic region of the Eagle Ford field. At

this stage, no research has been conducted to show if drilling parameters, as for example,

the rate of penetration (ROP) can be described as a variable, dependent on several other

independent variables for this particular field of study. The proposed research is aimed to

achieve the following objectives as part of three phases:

1. Gathering and extracting relevant logging data and drilling parameters from the data

sets received for each well for a defined depth interval. For various combinations of

measured well log data and drilling parameters, the data is processed, analyzed and

presented in crossplots. Two different correlations will be studied: correlations of

selected well log data with a drilling parameter and internal correlations using well log

data only to prove internal consistency of petrophysical properties. The primary goal

is to determine the best set of parameters where significant correlations can be found.

2. Developing a correlation for the drilling parameter and the well log data using sim-

ple and multiple linear regressions to obtain a best-fit curve that represents the

relationship of the data analyzed.

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3. Determining possible reasons for a mismatch of the regression model and the actual

drilling parameter in the form of a misfit occurring for a certain depth interval. Inte-

grating information from daily drilling reports, mud log reports and additional logging

data will be used.

4. At last, the conclusion obtained from this study will be used to give recommendations

for future operations and also state how the outcome of this study can be improved by

taking additional data in the form of other real-time surface drilling parameters into

account.

1.5 Thesis Outline

This thesis covers four main topics:

crossplots using log-to-log correlations and log-to-drilling parameters correlations to

study the relationships between the parameters;

regression analysis using a drilling parameter as the dependent variable and log pa-

rameters as the independent variables to predict trends for the area of interest;

inverted regression analysis for estimating gamma ray as a function of the rate of

penetration;

empirical relationships between VP andVS - Castagnas equation.

The crossplots are used to compare and interpret the data measurements. Internal cor-

relations of log data help to understand internal cosistency and show the quality of the data

used. Additionally, the influence of a third parameter is used to determine the dependency

of the correlated parameters on it, e.g. measured depth, gamma ray or rate of penetra-

tion. Empirical relationships (Castagna, Batzle and Eastwood, 1985) used for seismic can

be studied and verified using these internal log-log correlations. Next, correlations using

one drilling and one log parameter show the relationship between the two. The crossplots

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serve as a qualitive measure to determine which parameters are correlating strongly with the

drilling parameter. The regression analysis quantify the relationships between the drilling

parameter and the log data based on the results obtained from the crossplots. Comparing

the regressions of the wells assist in verifying that they are located in the same geologic area

and that they were drilled under comparable drilling conditions. For this research it was

expected to receive additional real-time surface drilling data for enhanced research in the

sense of improved interpretation of the results and further comparisons. As no additional

data was delivered in the time frame of this study, the recommendations will include how

additional drilling data can be integrated to improve the results of this research.

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CHAPTER 2

LITERATURE REVIEW

The economic development of shale gas reservoirs, the opportunities as well as the chal-

lenges going along with the characterization of these reservoirs have been subject to numerous

studies carried out by multiple authors. Kennedy, Gupta and Kotov(2012) say that target-

ing the so-called sweet spots in shale gas reservoirs as the key issue to produce the natural

gas at economic rates. By September2013, Baker Hughes recorded 85 % of the wells drilled

in the Eagle Ford field in Texas to be horizontal, 11 % to be directional and only 4 % to be

vertical wells. In the second quarter of 2013, a total of 1,050 wells were drilled. The majority

of wells drilled have low flow rates ranging between 560-8,400 m3/day or 20-300 mcf/day.

Wells producing at these rates show a slow decline trend with an average production duration

of 20-40 years. Initially higher flow rates usually experience a fast decline within the first

months or years (Rokosh et al., 2009). These numbers already show that many horizontal

wells have to be drilled in order to achieve economic rates of hydrocarbon production.

2.1 Lithological Classification

Throughout the literature, authors use different approaches to define shale, shale gas and

sweet spots. Some use the terms very loosely while others describe them based on a more

lithological attempt. Arthur, Langhus and Alleman(2008) define shale to be a sedimentary

rock type with particles the size of fine-grained clay particles rich in organic content. Shale

gas is described to be a predominantely dry natural gas containing>90 % methane although

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an unconventional reservoir system. Attempts to describe shale can cause confusion. For

this research a more systematic sedimentological approach is used to provide a thoughtful

description of shale (Fjaer et al., 2008;Schon, 2011). Shale belongs to the clastic sedimentary

rocks which come from pre-existing rocks that formed into smaller fragments by mechanical

and/or chemical weathering and erosion processes. Water and wind are the main transport

mechanisms for depositing the fragments. Sedimentation is followed by compaction and dia-

genesis of the material. At this stage, the sediment transforms into a rock, i.e. clay to shale.

According to the classification schematic of clastic sediments, clay particles are smaller than

0.002 mm in diameter. Shale is a mixture of primarily these clay-sized particles and silt-sized

particles (0.002-0.063 mm). It may also contain some sand-sized particles (0.063-2.0 mm).

Due to its high clay content, shale is dominated by the clay minerals and these influence

the rocks properties significantly, e.g. permeability, porosity, gamma radiation. Pore sizes

in shales range between 5-25 nm in diameter, although this changes depending on the clay

minerals existant. We also distinguish between dispersed, laminated and structural clay dis-

tribution in shales and this fact is often neglected by many authors. In the case of the Eagle

Ford shale, up to 70 % of the formation can come from carbonates, consisting primarily of

the mineral dolomite or calcite. In comparison to shale, carbonates are chemically not as

stable and are highly complex in their pore space and geometry. Carbonates can occur as

limestone or dolomite. Taking into account postdepositional processes like dissolution or

dolomitization increasing porosity and stress fracturing resulting in areas of high permeabil-

ity, carbonates are regarded as high quality reservoirs. They are estimated ...to hold 60 %

of the worlds petroleum reserves and presently account for 40 % of the worlds total hy-

drocarbon production. (Chopra, Chemingui and Miller,2005). The simultaneous presenceof both, shale and limestone or dolomite in the Eagle Ford field increases the complexity

involved when studying this area. The physical rock properties are influenced with a greater

diversity by its parameters and dependencies. In order to maintain consistency through-

out this research, the term shale is used when describing the Eagle Ford play although

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carbonates are present in the depth intervals of interest too.

2.2 Shale Reservoir Characterization

Interestingly many authors use the term sweet spot but only few describe it in more

detail like Baihly et al. (2010) who see it as a zone of high production. For this research

the study carried out by Mullen (2010) on the petrophysical properties of the Eagle Ford

shale offers an interesting approach. He found out that the zone of interest, namely the

shale formation, can be detected from mudlogs that show an increase in the rate of pene-

tration influenced by existing natural fractures and high amounts of total gas. His objective

is to integrate data from various sources like mudlogs, well logs and core analysis into a

petrophysical model. The model should help to characterize the reservoir by locating zones

with high total organic content and zones with geomechanical properties ideal for fracturing.

Other authors likeChen et al. (2011) point out that the characterization typically includes

geological, geochemical and mineralogical as well as petrophysical and petrographic analy-

sis. Thus, numerous tools, techniques and methods to address the need for integrated shale

gas reservoir characterization are in use nowadays. Additionally to core tests and well logs,

measurements to obtain reservoir properties also come from different pressure test methods.Table 2.1andTable 2.2give an overview of reservoir properties considered as key reservoir

parameters and how they can be obtained from various data sources.

Again, studying the literature with respect to shale reservoir characterization, it is noted

that authors use different terms that relate to the same concept. For this research, the

focus lies on the mineralogical (nuclear/radioactive properties like gamma ray) and geophys-

ical characteristics (elastic properties like wave velocities) coming from surface and borehole

measurements. Both, mineralogical and geophysical properties originate from the physical

properties of the rock, called petrophysics. According toSchon(2011), the shale content

from the natural gamma log is a scalar underlying no directional dependence. Elastic prop-

erties do have a directional depence and are described by a tensor and discussed by Melaku

(2008). This is important knowing that anisotropy in shales can differ at high magnitudes as

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Table 2.1: Common sources for reservoir properties. Table fromSondergeld et al. (2010)

Reservoir properties Data source

Elastic properties DSI (dynamic), core-based compression

test (static)Fluid properties Mud log, PVT, PDA, pressure gradients

Fracture and closure stress IFOT, Frac job, log-based (DSI)Free and sorbed gas Visual Ro, maserals, RockEval (calc)

Maturity SS&USS, IFOT, MICP, PDA, NMR (calc)Permeability IFOT, PDA, log-based, dip-inPore pressure Gas expansion, MICP, NMR, log-based

Porosity XRD, TS point counts, FTIR, ICP-MS,

EDAS (SEM)

Rock composition XRD, TS point counts, FTIR, ICP-MS,

EDAS (SEM)Temperature OHL, PL, frac job, IFOT

TOC Leco TOC, RockEval (calc)Water saturation Core extraction, Pc, log-based

Table 2.2: Key reservoir parameters and their data source. Table from Kennedy(2010)

Reservoir parameters Data Source

Brittle rock DSI (dynamic), core-based compression

test (static)

Stress regime Mud log, PVT, PDA, pressure gradientsOver-pressure IFOT, frac job, log-based (DSI)

Local lithology variations Visual Ro, maserals, RockEval (calc)faults, karsts, water SS&USS, IFOT, MICP, PDA, NMR (calc)

Organic content IFOT, PDA, log-based, dip-inMicro-porosity Gas expansion, MICP, NMR, log-based

Thermal Mmaturity XRD, TS point counts, FTIR, ICP-MS,

EDAS (SEM)

mentioned byKing(2010) andWaters, Lewis and Bentley(2011). Figure 2.1gives a graphi-

cal overview of what is meant by the terms isotropic-homogeneous (Figure 2.1(a)), isotropic-

inhomogeneous (Figure 2.1(b)), anisotropic-homogeneous (Figure 2.1(c)) and anisotropic-

inhomogeneous (Figure 2.1(d)). In comparison to an isotropic material, an anisotropic ma-

terial shows a change in magnitude dependent on its direction. Homogeneity refers to no

change of property at different locations within the volume.

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Variations of properties occur horizontally and vertically, across the entire play or even

along the wellbore. Shale is characterized typically by its widespread textural anisotropy re-

sulting from depositional differences of clay, other minerals and organic components creating

laminations. These differences have an impact on several rock properties like permeability,

electrical resistivity, elastic wave velocities, derived properties (Youngs modulus, shear mod-

ulus and Poissons ratio) and strength properties. Regardless of how the anisotropic fabric

of gas shales impacts its mechanical properties, it undoubtedly can be considered as one of

the major components for shale gas reservoir characterization (Sondergeld et al.,2010). Fig-

ure 2.2shows the permeability anisotropy measured parallel and perpendicular to bedding

on core plugs and a SEM image of a shale with clay and silt particles.

Figure 2.2: Measured permeability anisotropy in shales with permeability measured paralleland perpendicular to bedding. Figure fromSondergeld et al.(2010). SEM (scanning electronmicrophotograph) image of Kimmeridge shale. Figure fromMelaku(2008).

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2.3 Openhole Petrophysical Measurements

The question that arises after studying researches carried out by various authors is the

availability and practicability of the tools discussed and methods described. In order to

achieve comparable results amongst wells in the same geologic area, the same data sets

should be used. Taking the example of interpreting well log data from wireline or measuring

while drilling (MWD) with the purpose to characterize the near wellbore area in order to

obtain information about the formations properties of interest was and still is of signifi-

cant importance for the development of hydrocarbon bearing fields. Existing technologies

are continuously improved and measurements become more accurate and precise for differ-

ent applicationsLuthi(2001). Whether it is to estimate the depth for formation and rock

boundaries, to create a lithological profile, to identify rock properties or even the change of

properties, logging is considered to be a reliable measurement tool. Schon(2011) mentions

the importance of the procedure how the target formation is evaluated. Logging is followed

by the data processing including corrections and inversions. He describes the analysis and

interpretation as a critical stadium as it shows if additional information is needed for any

further investigations of the data. Characterizing a shale gas reservoir makes it clear that it

is a highly integrated process. Consequently, studying one single logging tool is not meet-

ing the complex demands and challenges when it comes to characterizing unconventional

reservoirs. The continuing development of methods and combined tools proves this ongoing

trend. Passey et al. (2010) consider a variety of logs essential for shale gas reservoir char-

acterization. Gamma ray and spectral gamma ray, resistivity, density, sonic, neutron and

NMR logs are listed to be valuable sources for shale gas reservoir characterization. These

logs are requested as a minimum standard for possible correlations. For the purpose of cal-

ibrating logs to TOC the combined porosity/resistivity methods as well as elemental and

mineralogical logs and borehole image logs can be applied. The complexity of problems to

characterize shale and the progress in integrating techniques results in the development of

specific work flows implementing log data. Table 2.3lists logging tools useful for obtaining

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measured data for shale gas reservoirs. The data is used to derive meaningful information

based on the response of each tool in gas shales. Authors especially note that each method

requires rock-to-logs calibration for validation (Sondergeld et al.,2010). Table 2.4refers to

Fertl and Rieke(1980) andPassey et al. (2010) who summarize the response in gas shales

from different logging tools. In comparison to Table 2.3,Table 2.4focuses on methods how

TOC can be estimated from logs.

Table 2.3: Logging tool response in shale gas reservoirs. Table from Passey et al.(2010)

Logging tool Response in gas shales

Natural gamma ray Type II kerogen has anamalously high

Uranium content.

Total gamma rayHigh gamma ray in intensity is related to

anomalously high Uranium content inorganic matter.

Bulk densityBulk density will read light because

organic matter is less dense than matrixminerals in source rocks.

Sonic Organic matter increases the apparent

transit time of acoustic logs.

Neutron Organic matter increases apparent

neutron porosity.

Resistivity

Organic matter is non-conductive.Resistivity increases with the presence ofTOC. With maturation and conversion of

kerogen to hydrocarbons, resistivityincreases dramatically.

It can be concluded that logging is a reliable method for petrophysical characterization.

However, only for a minority of wells log measurements suggested by authors are available

due to cost reasons. The objective of this study is to work with common logs and use them

together with the rate of penetration to find possible correlations. If a relationship between

the drilling parameter and the log measurement exists, it can be used as an indicator to

identify organic-rich zones with a high organic content on a real-time basis while drilling.

This work is carried out on behalf of the claim stated byKennedy, Gupta and Kotov(2012)

that especially US operators rely too much on information obtained from the high number

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Table 2.4: Summary of methods to determine TOC from logs. Table taken in part fromSondergeld et al.(2010)

Method Description

Spectral GR - Uranium enrichment Linear relationship of Uranium (ppm) toTOC for Appachian Devonian blackshales.

Gamma ray intensity Derivation of TOC volume from total GR

intensity.

Bulk density Empirical relationship of bulk density to

TOC weight %.

Gamma ray intensity - formation density

Derivation of a TOC volume from therelationship of GR intensity and

formation density in a Devonian blackshale of the Appalachian basin.

Delta log R Scaled porosity log resistivity overlay

method.

Neural networks Usage of conventional well logs to predict

TOC.

Pulsed neutron - spectral gamma rayPulsed neutron mineralogy and spectral

gamma ray methodology used todiscriminate excess carbon.

of offset wells being drilled in the major plays. They tend not to invest in any additional

methods of reservoir characterization. In comparison, countries like China, Latin America

or Saudi Arabia beginning to enter the area of shale gas development with only limited

access to offset wells seem to pay more attention to new technologies. The amount of

data sets generated from each single drilling rig including drilling data records, mudlogs

and completion and work-over reports on a daily basis raises the question of how this data

treasury can contribute to optimize reservoir characterization of shale gas reservoirs. Lagreca

et al.(2008),Staveley and Thow(2010) andIyoho et al.(2004) state that it is still unclear

how and for what purposes the data should be used for. Considering the amount of drilling

data and the complexity of shale gas reservoir characterization evokes the idea to search for

a missing link that provides useful information for future real-time drilling operations and

improved methods for reservoir characterizations.

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CHAPTER 3

METHODOLOGY

This chapter gives a brief outline of the physical rock properties and their related petro-

physical interpretation techniques used in this research to convey an integrated work. The

data analysis part describes the method of the data selection process and how the data was

processed. It also contains a short theoretical background on why crossplots and regression

analysis are used to generate the results.

3.1 Petrophysical Interpretation

To describe the fundamentals and the theory of nuclear/radioactive and elastic properties

and their petrophysical interpretation techniques, reference is made to Schon (2011) and

Krygowski(2003) respectively. Specific technical details about each openhole petrophysical

measurement introduced in this study are given in B.

3.1.1 Nuclear/Radioactive Properties

Considerable and measurable amounts of natural gamma radiation in rocks originate

from the emitted radiation when the elements uranium, thorium and potassium undergo a

decaying process. The unit of measurement for the gamma radiation is API units. All three

elements in sedimentary rocks are found in shale (or clay minerals), particularly uranium.

Potassium, for example, is found in micaceous clay, thorium in shales and uranium is a good

indicator for the presence of organic material. Under the assumption that no other radioac-

tive materials besides shale or clay are present, radiation increases with higher shale content

in sedimentary rocks. The gamma log is thus regarded as a reliable shale indicator. It

helps to distinguish between shale/clay and sand layers but also helps to determine the clay

content and the clay types. In general, the gamma ray measurement is used for correlating

formations, determining gross lithologies and identifying source rocks. The purpose of using

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the gamma log in this research is to study the correlation between the gamma ray measure-

ment for the formation of interest and the rate of penetration for this particular interval. To

determine the interaction of the emitted gamma radiation from an additional source as part

of the probe with the rock the photoelectric effect (PE), a supplementary measurement by

modern density logging tools is used. PE gives information about the mineralogy, i.e. the

mineral composition and is measured in barns per electron (b/e). Rock forming minerals like

quartz show typical values of PE=1.81 b/e, calcite of PE=5.08 b/e and dolomite of PE=3.15

b/e. If the mud contains barite, high PE values of approxiametly 267 b/e are recorded. The

PE log can give information on whether a change of lithology occured, e.g. dolomite to

limestone.

3.1.2 Elastic Properties

In the context of this study, the theory of elastic wave velocities known as elastic wave

propagation is described. Equation3.1shows how the compressional or P-wave velocity is

expressed using elastic moduli like Youngs modulus E, the compressional wave modulusM,

the bulk compressional modulusk , the shear modulus or Poissons ratio . Equation3.2

shows likewise for the relationship between the shear or S-wave and the elastic moduli. and are known as the Lame parameters, constants for isotropic material. is the bulk

density.

VP =

M

=

E

1

(1 +)(12)=

+ 2

=

k+ (4/3)

(3.1)

VS=

=E

12(1 +)

(3.2)

The inverse of the compressional velocity and the shear velocity is used to evaluate the

formation. The inverted velocity is the compressional wave slowness, DTC (3.3) and the

shear wave slowness, DTS (3.4).

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tP =DT C=V1P (3.3)

tS=DT S=V

1

S (3.4)

Velocities are measured in m/s or ft/s whereas the slowness is defined by time over

distance usually given in s/m or s/ft. The interval transit time, DT is obtained from

sonic or acoustic logs. As elastic properties are a direct indicator of the rocks properties

influenced by minerals and fluids present, they are regarded to have an effect on drilling

parameters measured. However, in the case of sedimentary rocks velocities show a great

diversity ranging from high for dense rocks with low porosities to low velocities for more

porous, most likely gas-bearing rocks. For shales the range can go from 1,000-5,000 m/s

or approxiametly 3,300-16,500 ft/s. In general, velocities tend to decrease with increasing

clay content influenced by clay-water bonds. It can be concluded that mechanically compact

material with high elastic moduli results in high velocities and low slowness values and vice

versa. The relationship betweenVP/VS (3.5) discussed byCastagna, Batzle and Eastwood

(1985) for seismic applications introducing the concept of the mudrock line (Figure 3.1)

will also be discussed here. These correlations are purely empirical and formation specific,

i.e. influenced by the compositon of the rock, porosity and shale content. As both, DTC

and DTS are available from the sonic/acoustic logs, linear regressions for VP andVScan be

shown for the wells studied.

VPVS

=

2

1

12 (3.5)

3.2 Real-Time Surface Drilling Data

During the drilling process, a great data treasury is collected and if analysed in detail,

useful information from drill cuttings, drilling mud and most important of all, drilling pa-

rameters is obtained. It is essential to understand how surface drilling data, in this case,

surface drilling parameters are measured. Conclusions on how drilling parameters as for ex-

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Figure 3.1: P-wave and S-wave velocities for clay, quartz, calcite and dolomite. Figure fromCastagna, Batzle and Eastwood(1985).

ample the rate of penetration or weight on bit are effected by the rock penetrated can assist

in interpreting physical properties of the formation a well is drilled in. Different sensors are

used to gather surface data at the rig site. Primarily, the rate of penetration is studied as it

is the only drilling parameter available.

A depth-tracking sensor is an electronic device that counts the number of rotational

movements of the sheave when the drilling line moves up or down. Two sensors in an ap-

proximate distance of 178 cm or 7 in apart, track the movement the draw-work drums make.

Every count that is recorded by the sensor equals a certain distance traveled and represents

increasing or decreasing depth movement. In comparison to a depth-based sensor, the time-

based counter gives either an instantaneous or an average rate of penetration measure in feet

per hour (ft/hr) or meters per hour (m/hr). No data from drill-monitoring sensors like the

electronic hook load sensor or the hydraulic torque sensor to measure revolutions per minute

(RPM), rotary torque and hook load are provided for the studied wells.

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3.3 Data Analysis

Out of nine wells, seven wells are presented in this research and all wells studied have

encrypted names due to confidentiality reasons (Figure 3.2). Two wells, namely AY 1 and

YR 1-1 are not part of this research as both wells do not have any ROP measurements. Wells

LK B1H and PE A2H have well log data for the sidetracks (ST). Distances between wells are

given in (Table 3.1). After Cronquists classification of hydrocarbon fluids (Whitson,1993),

wells GE A1H, PE A1H and PE A2H are in the black oil window with a condensate/gas

ratio (CGR) of 570 and above. Well BC 1-1 is in the volatile oil window with a CGR

range between 313-570, whereas well LK B1H lies between the volatile oil and retrograde

gas window with 67-312 CGR. RR A3H is in the wet gas window with 10-67 CGR.

Figure 3.2: Overview of the wells studied located in the Eagle Ford field in McMullen countryin Texas. Scale of the map 1:326,670.

Well log data were measured by MWD or wireline logging, stored and exchanged as a Log

ASCII Standard (LAS) file. The log data and the ROP from the depth-tracking sensor were

merged into one single Excel database to overcome the high number of LAS files for various

depth intervals displaying measurements taken at varying measurement intervals for each

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Table 3.1: Measured distances between wells in McMullen country, Texas

Wells Distance (miles) Distance (kilometers)

BC 1-1 - GE A1H 24.80 40.0

BC 1-1 - LK B1H 22.70 36.50BC 1-1 - PE A1H 22.10 35.60BC 1-1 - PE A2H 22.10 35.60BC 1-1 - RR A3H 15.90 25.50BC 1-1 - WL A1H 25.20 40.50

GE A1H - LK B1H 2.41 3.89GE A1H - PE A1H 3.23 5.20GE A1H - PE A2H 3.23 5.20GE A1H - WL A1H 1.37 2.21LK B1H - PE A1H 2.72 4.38

LK B1H - PE A2H 2.72 4.38LK B1H - RR A3H 10.80 17.50LK B1H - WL A1H 2.50 4.00RR A3H - PE A1H 8.76 14.00RR A3H - PE A2H 8.76 14.00WL A1H - PE A1H 4.28 6.88WL A1H - PE A2H 4.28 6.88WL A1H - RR A3H 13.00 20.90

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about changes in drilling parameters are obtained. Additionally, the mudlogs are used for

comparative reasons (Figure 3.4). As for example, the kickoff point of each well is given

by both, the daily drilling reports and the mudlogs. Additionally the mudlogs indicate

graphically with remarks from the geologist whether the well was drilled in rotating or

sliding mode as indicated byFigure 3.4(b). The daily drilling reports also include comments

on rotating and sliding mode (Figure 3.4(a)). This information is useful for identifying

any possible curve misfits between the actual ROP measured and the ROP calculated as a

function of well log data. Table 3.3summarizes the operational drilling parameters from the

daily drilling reports. These parameters are reported for the depth intervals considered for

the regression analysis. The top section of well PE A2H and LK B1H is drilled with a 12

1/4 in drill bit and water based mud (WBM) whereas the other wells are drilled with oil

based mud (OBM) for the entire depth interval investigated for the regression analysis.

(a) Well BC 1-1 (b) Well GE A1H (c) Well PE A2H

(d) Well LK B1H (e) Well RR A3H (f) Well WL A1H

Figure 3.3: Wellbore schematic for horizontal wells illustrating information obtained fromdaily drilling reports.

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3.3.2 Regression Analysis

Regression analysis is used to quantify the relationship between the rate of penetration

and various well log measurements. According to Orlov (1996) regression models are an

accepted method in modern science to study the relationship between variables. To be more

accurate, the relationship of an independent variable y on numerous independent variables

xi expressed by the regression function is studied. Unknown parameters bi in the regression

function are also existant. We distinguish between linear and non-linear regression models

in case the regression function is non-linear in its parameters. If a regression model has

multiple independent variables, it is referred to as a multiple linear model, otherwise it is a

simple linear model. The following notations are directly taken from Orlov(1996).

y dependent variable (predicted by a regression model)

y* dependent variable (experimental value)

p number of independent variables (number of coefficients)

xi(i=1,2,...p) ith independent variable from total set of p variables

bi(i=1,2,...p) ith coefficient corresponding to xi

b0 intercept (or constant)

k=p+1 total number of parameters including intercept (constant)

n number of observations ( experimental data points)

i=1,2. . . p independent variables index

j=1,2,. . . n data points index

As we study ROP as a function of different log parameters, the simple linear and the multiple

linear model are used. Expressing ROP as a function of more than one independent variable,

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(a) Information from daily drilling reporting on drilling mode (rotating and sliding)

for the depth interval between 11,654-12,138 ft MD

(b) Mudlog shows graphically that the wellwas drilled in sliding mode starting at 11,700ft MD

Figure 3.4: Comparing information from daily drilling reports with information from mud-logs. Figure 3.4(a)shows that the well was drilled in rotating and sliding mode from 11,654-12,138 ft MD.Figure 3.4(b)shows where exactly the well was drilled in sliding mode withinthe depth interval given by the daily drilling report.

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Table 3.3: Information on drilling parameters from daily drilling report

Well Depth interval

(ft MD)

Bit size (in)/

Bit model

Nozzles

(/32 in)

WOB

(1,000 lbf)

RP

(rp

BC 1-1

5,585-6,486 9 7/8 in, FX 55D 16/16/16/16/16 16 12

6,486-8,626 9 7/8 in, FX 55D 16/16/16/16/16 10 128,626-10,500 9 7/8 in, FX 55D 16/16/16/16/16 16 12

10,500-10,954 8 3/4 in, FX 63 16/16/16/16/16 5 0

10,954-11,434 8 3/4 in, FX 63 16/16/16/16/16 18 5

11,434-11,947 8 3/4 in, FX 63 16/16/16/16/16 11 5

11,947-12,042 8 3/4 in, FX 63 16/16/16/16/16 4 5

PE A2H

3,003-5,244 12 1/4 in, MD 519 15/15/15/15/15/15/15 7 10

5,254-6,916 8 1/2 in, SDi 513 no information 10 7




GE A1H 3,342-5,041 12 1/4 in, MDi 519 13/13/13/13/13/13/13 5 7

10,617-11,109 8 1/2 in, SDi 513 14/14/14/14/14/14/14 8 6

LK B1H

2,174-4,919 12 1/4 in, MSi 616 no information 10 20

4,919-6,088 12 1/4 in, MSi 616 no information 10 20



6,805-11,231 8 1/2 in, SDi 513 no information 8/12 0/



RR A3H

5,598-7,332 8 1/2 in, SDi 413 15/15/15/15/15/15 8 19

7,332-10,067 8 1/2 in, SDi 413 15/15/15/15/15/15 12 2010,067-12,285 8 1/2 in, SDi 413 15/15/15/15/15/15 14 20

12,285-12,580 8 1/2 in, SDi 413 15/15/15/15/15/15 9 20

WL A1H

9,369-9,445 8 1/2 in, SDi 513 20/22/20/20 0 0

9,690-9,708 8 1/2 in, SDi 513 20/22/20/20 10 2

10,165-10,670 8 1/2 in, SDi 513 20/22/20/20 15 2

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Equation3.7or Equation3.8is used. For determining ROP as a function of only one simple

independent variable, Equation3.6 is used.

y= b0+b1x1 (3.6)

y= b0+b1x1+b2x2+bpxp (3.7)

y= b0+i

bixi, i= 1, 2,...p (3.8)

The objective of using linear regression models is to predict the experimental or actual

ROP tracked in the field for each well. By determining the unknown parameters, the calcu-

lated ROP values should display values close to experimental ROP values. The regression

analysis is carried out using Microsoft Excel following the steps explained by Orlov(1996).

In the case of a misfit of the regression curve, possible reasons and the origin of this mismatch

have to be identified and evaluated.

To test the accuracy of the regression model representing the experimental data, several

output options are taken into account, e.g. regression statistics and the analysis of variance

(ANOVA). Table 3.4 and Table 3.5 are used as a reference to create an overview of the

outputs generated by Microsoft Excel. The numbers in the parenthesis are not part of the

output but refer to equations to calculate the regression output variables not stated in this

research. For further explainations,Gupta(2000) can be used.

Table 3.4: Formula map of regression statistics output. Table fromOrlov(1996)

Multiple R (36)R Square (R2) (34)

Adjusted R Square (Radj2) (35)

Standard Error (Sy) (33)Observations (n)

3.3.3 Data Correlations

To define the set of well log parameters for the regression analysis, two-dimensional

crossplots are used as a qualitative interpretation tool. These so-called scatter plots are

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Table 3.5: Formula map of ANOVA output. Table from Orlov(1996)

Coefficients(bi)

StandardError (se(bi))

t Stat(ti)

P-value

(Pi)

Lower 95%(bL,(1Pi))

Upper 95%(bU,(1Pi))

Intercept (b0) (26) (25)

(27) (31) (31a)

X Variable 1(x1)

(26) (25)

(27) (31) (31a)

X Variable 2(x2)

(26) (25)

(27) (31) (31a)

widely accepted for petrophysical and engineering data interpretation purposes. One variable

is plotted on the vertical axis (y-axis) and the other on the horizontal axis (x-axis). Any

third dimension is represented using a color legend. Identical scales for plotting the same

parameter are used to ensure that comparisons between the plots can be made. To determine

meaningful relationships, linear and quadratic functions as well as ratios between parameters

are plotted. As a primary selection criteria, standard logs are used. The gamma log and

acoustic log was measured for the majority of wells. Both log parameters, gamma ray

and slowness are expected to correlate well with the rate of penetration. The formation

density (RHOZ) was only available for three wells whereas well AY 1 did not have ROP

measurements. RHOZ was therefore not considered for further investigations. For three

wells - BC 1-1, GE A1H and PE A2H - a complete data set including ROP, GR, DTC and

DTS is available. Here, multiple linear regression analysis is used. For the wells LK B1H,

PE A1H, PE A2H ST (sidetrack), RR A3H, WL A1H and WL A1H ST (sidetrack) only

ROP and GR is available. Simple linear regression is calculated. Table 3.6gives an overview

of all combinations for each well.

First, internal log crossplots using two well log parameters like for example DTC on

the x-axis and DTS on the y-axis to study the quality and internal cosistency of the data

are generated (Figure 3.5). Although not considered to be directly part of this research,

these correlations are expected to serve as reference values for any correlations with ROP.

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Table 3.6: Various combinations for crossplot analysis using well log parameters and ROP

Parameter on

x-axis

Parameter on

y-axis Color code Well names

DTC ROP MD BC 1-1, GE A1H, PE A2HDTC ROP GR BC 1-1, GE A1H, PE A2H

DTS ROP MD BC 1-1, GE A1H, PE A2H

DTS ROP GR BC 1-1, GE A1H, PE A2H

DTC2 ROP MD BC 1-1, GE A1H, PE A2H

DTC2 ROP GR BC 1-1, GE A1H, PE A2H

DTS2 ROP MD BC 1-1, GE A1H, PE A2H

DTS2 ROP GR BC 1-1, GE A1H, PE A2H

DTC DTS MD BC 1-1, GE A1H, PE A2H

DTC DTS GR BC 1-1, GE A1H, PE A2H

DTC DTS ROP BC 1-1, GE A1H, PE A2H

DTC/DTS ROP MD BC 1-1, GE A1H, PE A2H

DTC/DTS ROP GR BC 1-1, GE A1H, PE A2H

GR DTC MD BC 1-1, GE A1H, PE A2H

GR DTC GR BC 1-1, GE A1H, PE A2H

GR DTC2 MD BC 1-1, GE A1H, PE A2H

GR DTC2 ROP BC 1-1, GE A1H, PE A2H

GR DTS MD BC 1-1, GE A1H, PE A2H

GR DTS GR BC 1-1, GE A1H, PE A2H

GR DTS2 MD BC 1-1, GE A1H, PE A2H

GR DTS2 ROP BC 1-1,

compaction, permeability, and fluid flow in brent-type reservoirs under depletion

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