Unconventional Hydrocarbon Resources

Unconventional Hydrocarbon Resources

Prediction and Modeling Using Artificial Intelligence Approaches
Herausgegeben:Ouadfeul, Sid-Ali

• Gebundenes Buch

Produktbeschreibung

Unconventional Hydrocarbon Resources

Enables readers to save time and effort in exploring and exploiting shale gas and other unconventional fossil fuels by making use of advanced predictive tools

Unconventional Hydrocarbon Resources highlights novel concepts and techniques for the geophysical exploration of shale and other tight hydrocarbon reservoirs, focusing on artificial intelligence approaches for modeling and predicting key reservoir properties such as pore pressure, water saturation, and wellbore stability. Numerous application examples and case studies present real-life data from different unconventional hydrocarbon fields such as the Barnett Shale (USA), the Williston Basin (USA), and the Berkine Basin (Algeria).

Unconventional Hydrocarbon Resources explores a wide range of reservoir properties, including modeling of the geomechanics of shale gas reservoirs, petrophysics analysis of shale and tight sand gas reservoirs, and prediction of hydraulic fracturing effects, fluid flow, and permeability.

Sample topics covered in Unconventional Hydrocarbon Resources include:
_ Calculation of petrophysical parameter curves for non-conventional reservoir modeling and characterization
_ Comparison of the Levenberg-Marquardt and conjugate gradient learning methods for total organic carbon prediction in the Barnett shale gas reservoir
_ Use of pore effective compressibility for quantitative evaluation of low resistive pays and identifying sweet spots in shale reservoirs
_ Pre-drill pore pressure estimation in shale gas reservoirs using seismic genetic inversion
_ Using well-log data to classify lithofacies of a shale gas reservoir

Unconventional Hydrocarbon Resources is a valuable resource for researchers and professionals working on unconventional hydrocarbon exploration and in geoengineering projects.

Produktdetails

• Verlag: American Geophysical Union / Wiley & Sons
• Artikelnr. des Verlages: 1W119389360
• 1. Auflage
• Seitenzahl: 320
• Erscheinungstermin: 22. August 2023
• Englisch
• Abmessung: 260mm x 183mm x 22mm
• Gewicht: 826g
• ISBN-13: 9781119389361
• ISBN-10: 1119389364
• Artikelnr.: 63337672

Autorenporträt

Sid-Ali Ouadfeul, Professor, Department of Geophysics, Geology and Reservoir Engineering, Algerian Petroleum Institute-IAP Corporate University, Algeria.

Inhaltsangabe

Preface xiii

1 Predrill Pore Pressure Estimation in Shale Gas Reservoirs Using Seismic Genetic Inversion with an Example from the Barnett Shale 1
Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Leila Aliouane

1.1 Introduction 1

1.2 Methods and Application to Barnett Shale 2

1.2.1 Geological Setting 2

1.2.2 Methods 3

1.3 Data Processing 6

1.4 Results Interpretation and Conclusions 7

References 9

2 An Analysis of the Barnett Shale's Seismic Anisotropy's Role in the Exploration of Shale Gas Reservoirs (United States) 11
Sid-Ali Ouadfeul, Leila Aliouane, Mohamed Zinelabidine Doghmane, and Amar Boudella

2.1 Introduction 11

2.2 Seismic Anisotropy 12

2.3 Application to Barnett Shale 14

2.3.1 Geological Setting 14

2.3.2 Data Analysis 15

2.4 Conclusions 18

References 18

3 Wellbore Stability in Shale Gas Reservoirs with a Case Study from the Barnett Shale 21
Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Leila Aliouane

3.1 Introduction 21

3.2 Wellbore Stability 22

3.2.1 Mechanical Stress 22

3.2.2 Chemical Interactions with the Drilling Fluid 22

3.2.3 Physical Interactions with the Drilling Fluid 22

3.3 Pore Pressure Estimation Using the Eaton's Model 23

3.4 Shale Play Geomechanics and Wellbore Stability 24

3.5 Application to Barnett Shale 26

3.5.1 Geological Context 26

3.5.2 Data Processing 28

3.6 Conclusion 28

References 30

4 A Comparison of the Levenberg-Marquardt and Conjugate Gradient Learning Methods for Total Organic Carbon Prediction in the Barnett Shale Gas Reservoir 31
Sid-Ali Ouadfeul, Mohamed Zinelabidine Doghmane, and Leila Aliouane

4.1 Introduction 31

4.2 Levenberg-Marquardt Learning Algorithm 32

4.3 Application to Barnett Shale 33

4.3.1 Geological Setting 33

4.3.2 Data Processing 33

4.3.3 Results Interpretation 36

4.4 Conclusions 39

References 40

5 Identifying Sweet Spots in Shale Reservoirs 41
Susan Smith Nash

5.1 Introduction 41

5.2 Materials and Methods 41

5.3 Data for Two Distinct Types of Sweet Spot Identification Workflows 42

5.3.1 Workflow 5.1: Early-Phase Workflow Elements: Total Petroleum System Approach 42

5.3.2 Workflow 5.2: Smaller-Scale Field-Level Tools and Techniques 43

5.4 Results: Two Integrative Workflows 45

5.4.1 Early-Phase Exploration Workflow 45

5.4.2 Later Phase Developmental, Including Refracing Workflow 45

5.5 Case Studies 46

5.5.1 Woodford Shale: Emphasis on Chemostratigraphy 46

5.5.2 Barnett Shale: Emphasis on Seismic Attributes 46

5.5.3 Eagle Ford Shale: Pattern Recognition/Deep Learning 47

5.6 Conclusion 47

References 47

6 Surfactants in Shale Reservoirs 49
Susan Smith Nash

6.1 Introduction 49

6.2 Function of Surfactants 49

6.2.1 Drilling 50

6.2.2 Completion (Hydraulic Fracturing) 50

6.3 Materials and Methods 50

6.4 Characteristics of Shale Reservoirs 50

6.4.1 High Clay Mineral Content 51

6.4.2 Nano-Sized Pores 51

6.4.3 Mixed-Wettability Behavior 51

6.4.4 High Capillary Pressures 51

6.5 The Klinkenberg Correction 51

6.5.1 Klinkenberg Gas Slippage Measurement 52

6.6 Completion Chemicals to Consider in Addition to the Surfactant 52

6.6.1 Enhanced Oil Recovery (EOR) 52

6.6.2 Liquids-Rich S