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Oil and Gas Wells / / Sid-Ali Ouadfeul, Leila Aliouane
Oil and Gas Wells / / Sid-Ali Ouadfeul, Leila Aliouane
Autore Ouadfeul Sid-Ali
Pubbl/distr/stampa London : , : IntechOpen, , 2020
Descrizione fisica 1 online resource (116 pages) : illustrations
Disciplina 622.338
Soggetto topico Oil wells
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910688274803321
Ouadfeul Sid-Ali  
London : , : IntechOpen, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Unconventional Hydrocarbon Resources
Unconventional Hydrocarbon Resources
Autore Ouadfeul Sid-Ali
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica 1 online resource (320 pages)
ISBN 1-119-38938-0
1-119-38937-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Predrill Pore Pressure Estimation in Shale Gas Reservoirs Using Seismic Genetic Inversion with an Example from the Barnett Shale -- 1.1 Introduction -- 1.2 Methods and Application to Barnett Shale -- 1.2.1 Geological Setting -- 1.2.2 Methods -- 1.3 Data Processing -- 1.4 Results Interpretation and Conclusions -- References -- Chapter 2 An Analysis of the Barnett Shale's Seismic Anisotropy's Role in the Exploration of Shale Gas Reservoirs (United States) -- 2.1 Introduction -- 2.2 Seismic Anisotropy -- 2.3 Application to Barnett Shale -- 2.3.1 Geological Setting -- 2.3.2 Data Analysis -- 2.4 Conclusions -- References -- Chapter 3 Wellbore Stability in Shale Gas Reservoirs with a Case Study from the Barnett Shale -- 3.1 Introduction -- 3.2 Wellbore Stability -- 3.2.1 Mechanical Stress -- 3.2.2 Chemical Interactions with the Drilling Fluid -- 3.2.3 Physical Interactions with the Drilling Fluid -- 3.3 Pore Pressure Estimation Using the Eaton's Model -- 3.4 Shale Play Geomechanics and Wellbore Stability -- 3.5 Application to Barnett Shale -- 3.5.1 Geological Context -- 3.5.2 Data Processing -- 3.6 Conclusion -- References -- Chapter 4 A Comparison of the Levenberg-Marquardt and Conjugate Gradient Learning Methods for Total Organic Carbon Prediction in the Barnett Shale Gas Reservoir -- 4.1 Introduction -- 4.2 Levenberg-Marquardt Learning Algorithm -- 4.3 Application to Barnett Shale -- 4.3.1 Geological Setting -- 4.3.2 Data Processing -- 4.3.3 Results Interpretation -- 4.4 Conclusions -- References -- Chapter 5 Identifying Sweet Spots in Shale Reservoirs -- 5.1 Introduction -- 5.2 Materials and Methods -- 5.3 Data for Two Distinct Types of Sweet Spot Identification Workflows -- 5.3.1 Workflow 5.1: Early-Phase Workflow Elements: Total Petroleum System Approach.
5.3.2 Workflow 5.2: Smaller-Scale Field-Level Tools and Techniques -- 5.4 Results: Two Integrative Workflows -- 5.4.1 Early-Phase Exploration Workflow -- 5.4.2 Later Phase Developmental, Including Refracing Workflow -- 5.5 Case Studies -- 5.5.1 Woodford Shale: Emphasis on Chemostratigraphy -- 5.5.2 Barnett Shale: Emphasis on Seismic Attributes -- 5.5.3 Eagle Ford Shale: Pattern Recognition/Deep Learning -- 5.6 Conclusion -- References -- Chapter 6 Surfactants in Shale Reservoirs -- 6.1 Introduction -- 6.2 Function of Surfactants -- 6.2.1 Drilling -- 6.2.2 Completion (Hydraulic Fracturing) -- 6.3 Materials and Methods -- 6.4 Characteristics of Shale Reservoirs -- 6.4.1 High Clay Mineral Content -- 6.4.2 Nano-Sized Pores -- 6.4.3 Mixed-Wettability Behavior -- 6.4.4 High Capillary Pressures -- 6.5 The Klinkenberg Correction -- 6.5.1 Klinkenberg Gas Slippage Measurement -- 6.6 Completion Chemicals to Consider in Addition to the Surfactant -- 6.6.1 Enhanced Oil Recovery (EOR) -- 6.6.2 Liquids-Rich Shale Plays After Initial Decline -- 6.7 Mono-Coating Proppant -- 6.7.1 Zwitterionic Coating -- 6.8 Dual-Coating Proppant -- 6.8.1 Outside Coating -- 6.8.2 Inner Coating -- 6.9 Dual Coating with Porous Proppant -- 6.9.1 Zwitterionic Outer Coating -- Inorganic Salt Inner Coating, Porous Core -- 6.10 Data -- 6.10.1 Types of Surfactants -- 6.11 Examples of Surfactants in Shale Plays -- 6.11.1 Bakken (Wang and Xu 2012) -- 6.11.2 Eagle Ford (He and Xu 2017) -- 6.11.3 Utica (Shuler et al. 2016) -- 6.12 Results -- 6.13 Shale Reservoirs, Gas, and Adsorption -- 6.14 Operational Conditions -- 6.15 Conclusions -- References -- Chapter 7 Neuro-Fuzzy Algorithm Classification of Ordovician Tight Reservoir Facies in Algeria -- 7.1 Introduction -- 7.2 Neuro-Fuzzy Classification -- 7.3 Results Discussion -- 7.4 Conclusion -- References.
Chapter 8 Recognition of Lithology Automatically Utilizing a New Artificial Neural Network Algorithm -- 8.1 Introduction -- 8.2 Well-Logging Methods -- 8.2.1 Nuclear Well Logging -- 8.2.2 Neutron Well Logging -- 8.2.3 Sonic Well Logging -- 8.3 Use of ANN in the Oil Industry -- 8.4 Lithofacies Recognition -- 8.5 Log Interpretation -- 8.5.1 Methodology of Manual Interpretation -- 8.5.2 Results of Manual/Automatic Interpretation -- 8.6 Conclusion -- References -- Chapter 9 Construction of a New Model (ANNSVM) Compensator for the Low Resistivity Phenomena Saturation Computation Based on Logging Curves -- 9.1 Introduction -- 9.2 Field Geological Description -- 9.2.1 Conventional Interpretation -- 9.2.2 Reservoir Mineralogy -- 9.3 Low-Resistivity Phenomenon -- 9.3.1 Cross Plots Interpretation -- 9.3.2 NMR Logs Interpretation -- 9.3.3 Comparison Between Well-1 and Well-2 -- 9.3.4 Developed Logging Tools -- 9.3.5 Proposed ANNSVM Algorithm -- 9.4 Conclusions -- References -- Chapter 10 A Practical Workflow for Improving the Correlation of Sub-Seismic Geological Structures and Natural Fractures using Seismic Attributes -- 10.1 Introduction -- 10.2 Description of the Developed Workflow -- 10.3 Discussion -- 10.4 Conclusions -- References -- Chapter 11 Calculation of Petrophysical Parameter Curves for Nonconventional Reservoir Modeling and Characterization -- 11.1 Introduction -- 11.2 Proposed Methods -- 11.3 Results and Discussion -- 11.4 Conclusions -- References -- Chapter 12 Fuzzy Logic for Predicting Pore Pressure in Shale Gas Reservoirs With a Barnett Shale Application -- 12.1 Introduction -- 12.2 The Fuzzy Logic -- 12.3 Application to Barnett Shale -- 12.3.1 Geological Context -- 12.3.2 Data Processing -- 12.4 Results Interpretation and Conclusions -- References.
Chapter 13 Using Well-Log Data, a Hidden Weight Optimization Method Neural Network Can Classify the Lithofacies of a Shale Gas Reservoir: Barnett Shale Application -- 13.1 Introduction -- 13.2 Artificial Neural Network -- 13.3 Hidden Weight Optimization Algorithm Neural -- 13.4 Geological Context of the Barnett Shale -- 13.5 Results Interpretation and Conclusions -- Bibliography -- Chapter 14 The Use of Pore Effective Compressibility for Quantitative Evaluation of Low Resistive Pays -- 14.1 Introduction -- 14.2 Low-Resistivity Pays in the Studied Basin -- 14.3 Water Saturation from Effective Pore Compressibility -- 14.4 Discussion -- 14.5 Conclusions -- Bibliography -- Chapter 15 The Influence of Pore Levels on Reservoir Quality Based on Rock Typing: A Case Study of Quartzite El Hamra, Algeria -- 15.1 Introduction -- 15.2 Quick Scan Method -- 15.3 Results -- 15.4 Discussion -- 15.5 Conclusions -- Bibliography -- Chapter 16 An Example from the Algerian Sahara Illustrates the Use of the Hydraulic Flow Unit Technique to Discriminate Fluid Flow Routes in Confined Sand Reservoirs -- 16.1 Introduction -- 16.2 Regional Geologic Setting -- 16.3 Statement of the Problem -- 16.3.1 Concept of HFU -- 16.3.2 HFU Zonation Process -- 16.4 Results and Discussion -- 16.4.1 FZI Method -- 16.4.2 FZI Method -- 16.5 Conclusions -- References -- Chapter 17 Integration of Rock Types and Hydraulic Flow Units for Reservoir Characterization. Application to Three Forks Formation, Williston Basin, North Dakota, USA -- 17.1 Introduction -- 17.2 Petrophysical Rock-Type Prediction -- 17.3 Rock Types' Classification Based on R35 Pore Throat Radius -- 17.3.1 Upper Three Forks -- 17.3.2 Middle Three Forks -- 17.3.3 Lower Three Forks -- 17.4 Determination of Hydraulic Flow Units -- 17.4.1 Upper Three Forks -- 17.4.2 Middle Three Forks -- 17.4.3 Lower Three Forks -- 17.5 Conclusion.
References -- Chapter 18 Stress-Dependent Permeability and Porosity and Hysteresis. Application to the Three Forks Formation, Williston Basin, North Dakota, USA -- 18.1 Introduction -- 18.2 Database -- 18.3 Testing Procedure -- 18.3.1 Core Samples Cleaning and Drying -- 18.3.2 Permeability and Porosity Measurements -- 18.3.3 Mineral Composition Analysis -- 18.3.4 Scanning Electron Microscope -- 18.4 Results and Discussions -- 18.4.1 Stress-Dependent Permeability and Hysteresis -- 18.4.2 Permeability Evolution with Net Stress -- 18.4.3 Stress-Dependent Porosity and Hysteresis -- 18.4.4 Porosity Evolution with Net Stress -- 18.4.5 Permeability Evolution with Porosity -- 18.5 Conclusion -- References -- Chapter 19 Petrophysical Analysis of Three Forks Formation in Williston Basin, North Dakota, USA -- 19.1 Introduction -- 19.2 Petrophysical Database -- 19.2.1 Curve Editing and Environmental Correction -- 19.2.2 Preanalysis Processing -- 19.3 Methods and Background -- 19.3.1 Wireline Logs -- 19.3.2 Petrophysical Analysis Challenges -- 19.4 Petrophysical Analysis Results and Discussion -- 19.4.1 Upper Three Forks -- 19.4.2 Middle Three Forks -- 19.4.3 Lower Three Forks -- 19.5 Conclusion -- References -- Chapter 20 Water Saturation Prediction Using Machine Learning and Deep Learning. Application to Three Forks Formation in Williston Basin, North Dakota, USA -- 20.1 Introduction -- 20.2 Experimental Procedure and Methodology -- 20.2.1 Support Vector Machine Concepts -- 20.2.2 Preprocessing of the Dataset -- 20.2.3 Building SVR Model -- 20.2.4 Building Random Forest Regression Model -- 20.2.5 Building Deep Learning Model -- 20.2.6 Curve Reconstruction Using K.Mod -- 20.3 Results and Discussion -- 20.4 Conclusion -- References -- Appendix Hysteresis Testing and Mineralogy -- Index -- EULA.
Record Nr. UNINA-9910829939803321
Ouadfeul Sid-Ali  
Newark : , : John Wiley & Sons, Incorporated, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Unconventional Hydrocarbon Resources
Unconventional Hydrocarbon Resources
Autore Ouadfeul Sid-Ali
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica 1 online resource (320 pages)
ISBN 1-119-38938-0
1-119-38937-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Predrill Pore Pressure Estimation in Shale Gas Reservoirs Using Seismic Genetic Inversion with an Example from the Barnett Shale -- 1.1 Introduction -- 1.2 Methods and Application to Barnett Shale -- 1.2.1 Geological Setting -- 1.2.2 Methods -- 1.3 Data Processing -- 1.4 Results Interpretation and Conclusions -- References -- Chapter 2 An Analysis of the Barnett Shale's Seismic Anisotropy's Role in the Exploration of Shale Gas Reservoirs (United States) -- 2.1 Introduction -- 2.2 Seismic Anisotropy -- 2.3 Application to Barnett Shale -- 2.3.1 Geological Setting -- 2.3.2 Data Analysis -- 2.4 Conclusions -- References -- Chapter 3 Wellbore Stability in Shale Gas Reservoirs with a Case Study from the Barnett Shale -- 3.1 Introduction -- 3.2 Wellbore Stability -- 3.2.1 Mechanical Stress -- 3.2.2 Chemical Interactions with the Drilling Fluid -- 3.2.3 Physical Interactions with the Drilling Fluid -- 3.3 Pore Pressure Estimation Using the Eaton's Model -- 3.4 Shale Play Geomechanics and Wellbore Stability -- 3.5 Application to Barnett Shale -- 3.5.1 Geological Context -- 3.5.2 Data Processing -- 3.6 Conclusion -- References -- Chapter 4 A Comparison of the Levenberg-Marquardt and Conjugate Gradient Learning Methods for Total Organic Carbon Prediction in the Barnett Shale Gas Reservoir -- 4.1 Introduction -- 4.2 Levenberg-Marquardt Learning Algorithm -- 4.3 Application to Barnett Shale -- 4.3.1 Geological Setting -- 4.3.2 Data Processing -- 4.3.3 Results Interpretation -- 4.4 Conclusions -- References -- Chapter 5 Identifying Sweet Spots in Shale Reservoirs -- 5.1 Introduction -- 5.2 Materials and Methods -- 5.3 Data for Two Distinct Types of Sweet Spot Identification Workflows -- 5.3.1 Workflow 5.1: Early-Phase Workflow Elements: Total Petroleum System Approach.
5.3.2 Workflow 5.2: Smaller-Scale Field-Level Tools and Techniques -- 5.4 Results: Two Integrative Workflows -- 5.4.1 Early-Phase Exploration Workflow -- 5.4.2 Later Phase Developmental, Including Refracing Workflow -- 5.5 Case Studies -- 5.5.1 Woodford Shale: Emphasis on Chemostratigraphy -- 5.5.2 Barnett Shale: Emphasis on Seismic Attributes -- 5.5.3 Eagle Ford Shale: Pattern Recognition/Deep Learning -- 5.6 Conclusion -- References -- Chapter 6 Surfactants in Shale Reservoirs -- 6.1 Introduction -- 6.2 Function of Surfactants -- 6.2.1 Drilling -- 6.2.2 Completion (Hydraulic Fracturing) -- 6.3 Materials and Methods -- 6.4 Characteristics of Shale Reservoirs -- 6.4.1 High Clay Mineral Content -- 6.4.2 Nano-Sized Pores -- 6.4.3 Mixed-Wettability Behavior -- 6.4.4 High Capillary Pressures -- 6.5 The Klinkenberg Correction -- 6.5.1 Klinkenberg Gas Slippage Measurement -- 6.6 Completion Chemicals to Consider in Addition to the Surfactant -- 6.6.1 Enhanced Oil Recovery (EOR) -- 6.6.2 Liquids-Rich Shale Plays After Initial Decline -- 6.7 Mono-Coating Proppant -- 6.7.1 Zwitterionic Coating -- 6.8 Dual-Coating Proppant -- 6.8.1 Outside Coating -- 6.8.2 Inner Coating -- 6.9 Dual Coating with Porous Proppant -- 6.9.1 Zwitterionic Outer Coating -- Inorganic Salt Inner Coating, Porous Core -- 6.10 Data -- 6.10.1 Types of Surfactants -- 6.11 Examples of Surfactants in Shale Plays -- 6.11.1 Bakken (Wang and Xu 2012) -- 6.11.2 Eagle Ford (He and Xu 2017) -- 6.11.3 Utica (Shuler et al. 2016) -- 6.12 Results -- 6.13 Shale Reservoirs, Gas, and Adsorption -- 6.14 Operational Conditions -- 6.15 Conclusions -- References -- Chapter 7 Neuro-Fuzzy Algorithm Classification of Ordovician Tight Reservoir Facies in Algeria -- 7.1 Introduction -- 7.2 Neuro-Fuzzy Classification -- 7.3 Results Discussion -- 7.4 Conclusion -- References.
Chapter 8 Recognition of Lithology Automatically Utilizing a New Artificial Neural Network Algorithm -- 8.1 Introduction -- 8.2 Well-Logging Methods -- 8.2.1 Nuclear Well Logging -- 8.2.2 Neutron Well Logging -- 8.2.3 Sonic Well Logging -- 8.3 Use of ANN in the Oil Industry -- 8.4 Lithofacies Recognition -- 8.5 Log Interpretation -- 8.5.1 Methodology of Manual Interpretation -- 8.5.2 Results of Manual/Automatic Interpretation -- 8.6 Conclusion -- References -- Chapter 9 Construction of a New Model (ANNSVM) Compensator for the Low Resistivity Phenomena Saturation Computation Based on Logging Curves -- 9.1 Introduction -- 9.2 Field Geological Description -- 9.2.1 Conventional Interpretation -- 9.2.2 Reservoir Mineralogy -- 9.3 Low-Resistivity Phenomenon -- 9.3.1 Cross Plots Interpretation -- 9.3.2 NMR Logs Interpretation -- 9.3.3 Comparison Between Well-1 and Well-2 -- 9.3.4 Developed Logging Tools -- 9.3.5 Proposed ANNSVM Algorithm -- 9.4 Conclusions -- References -- Chapter 10 A Practical Workflow for Improving the Correlation of Sub-Seismic Geological Structures and Natural Fractures using Seismic Attributes -- 10.1 Introduction -- 10.2 Description of the Developed Workflow -- 10.3 Discussion -- 10.4 Conclusions -- References -- Chapter 11 Calculation of Petrophysical Parameter Curves for Nonconventional Reservoir Modeling and Characterization -- 11.1 Introduction -- 11.2 Proposed Methods -- 11.3 Results and Discussion -- 11.4 Conclusions -- References -- Chapter 12 Fuzzy Logic for Predicting Pore Pressure in Shale Gas Reservoirs With a Barnett Shale Application -- 12.1 Introduction -- 12.2 The Fuzzy Logic -- 12.3 Application to Barnett Shale -- 12.3.1 Geological Context -- 12.3.2 Data Processing -- 12.4 Results Interpretation and Conclusions -- References.
Chapter 13 Using Well-Log Data, a Hidden Weight Optimization Method Neural Network Can Classify the Lithofacies of a Shale Gas Reservoir: Barnett Shale Application -- 13.1 Introduction -- 13.2 Artificial Neural Network -- 13.3 Hidden Weight Optimization Algorithm Neural -- 13.4 Geological Context of the Barnett Shale -- 13.5 Results Interpretation and Conclusions -- Bibliography -- Chapter 14 The Use of Pore Effective Compressibility for Quantitative Evaluation of Low Resistive Pays -- 14.1 Introduction -- 14.2 Low-Resistivity Pays in the Studied Basin -- 14.3 Water Saturation from Effective Pore Compressibility -- 14.4 Discussion -- 14.5 Conclusions -- Bibliography -- Chapter 15 The Influence of Pore Levels on Reservoir Quality Based on Rock Typing: A Case Study of Quartzite El Hamra, Algeria -- 15.1 Introduction -- 15.2 Quick Scan Method -- 15.3 Results -- 15.4 Discussion -- 15.5 Conclusions -- Bibliography -- Chapter 16 An Example from the Algerian Sahara Illustrates the Use of the Hydraulic Flow Unit Technique to Discriminate Fluid Flow Routes in Confined Sand Reservoirs -- 16.1 Introduction -- 16.2 Regional Geologic Setting -- 16.3 Statement of the Problem -- 16.3.1 Concept of HFU -- 16.3.2 HFU Zonation Process -- 16.4 Results and Discussion -- 16.4.1 FZI Method -- 16.4.2 FZI Method -- 16.5 Conclusions -- References -- Chapter 17 Integration of Rock Types and Hydraulic Flow Units for Reservoir Characterization. Application to Three Forks Formation, Williston Basin, North Dakota, USA -- 17.1 Introduction -- 17.2 Petrophysical Rock-Type Prediction -- 17.3 Rock Types' Classification Based on R35 Pore Throat Radius -- 17.3.1 Upper Three Forks -- 17.3.2 Middle Three Forks -- 17.3.3 Lower Three Forks -- 17.4 Determination of Hydraulic Flow Units -- 17.4.1 Upper Three Forks -- 17.4.2 Middle Three Forks -- 17.4.3 Lower Three Forks -- 17.5 Conclusion.
References -- Chapter 18 Stress-Dependent Permeability and Porosity and Hysteresis. Application to the Three Forks Formation, Williston Basin, North Dakota, USA -- 18.1 Introduction -- 18.2 Database -- 18.3 Testing Procedure -- 18.3.1 Core Samples Cleaning and Drying -- 18.3.2 Permeability and Porosity Measurements -- 18.3.3 Mineral Composition Analysis -- 18.3.4 Scanning Electron Microscope -- 18.4 Results and Discussions -- 18.4.1 Stress-Dependent Permeability and Hysteresis -- 18.4.2 Permeability Evolution with Net Stress -- 18.4.3 Stress-Dependent Porosity and Hysteresis -- 18.4.4 Porosity Evolution with Net Stress -- 18.4.5 Permeability Evolution with Porosity -- 18.5 Conclusion -- References -- Chapter 19 Petrophysical Analysis of Three Forks Formation in Williston Basin, North Dakota, USA -- 19.1 Introduction -- 19.2 Petrophysical Database -- 19.2.1 Curve Editing and Environmental Correction -- 19.2.2 Preanalysis Processing -- 19.3 Methods and Background -- 19.3.1 Wireline Logs -- 19.3.2 Petrophysical Analysis Challenges -- 19.4 Petrophysical Analysis Results and Discussion -- 19.4.1 Upper Three Forks -- 19.4.2 Middle Three Forks -- 19.4.3 Lower Three Forks -- 19.5 Conclusion -- References -- Chapter 20 Water Saturation Prediction Using Machine Learning and Deep Learning. Application to Three Forks Formation in Williston Basin, North Dakota, USA -- 20.1 Introduction -- 20.2 Experimental Procedure and Methodology -- 20.2.1 Support Vector Machine Concepts -- 20.2.2 Preprocessing of the Dataset -- 20.2.3 Building SVR Model -- 20.2.4 Building Random Forest Regression Model -- 20.2.5 Building Deep Learning Model -- 20.2.6 Curve Reconstruction Using K.Mod -- 20.3 Results and Discussion -- 20.4 Conclusion -- References -- Appendix Hysteresis Testing and Mineralogy -- Index -- EULA.
Record Nr. UNINA-9910877289503321
Ouadfeul Sid-Ali  
Newark : , : John Wiley & Sons, Incorporated, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui