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1. |
Record Nr. |
UNINA9910481315803321 |
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Autore |
Patin Charles <1633-1693.> |
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Titolo |
Commentarius Caroli Patini, in antiquum cenotaphium Marci Artorii, medici Caesaris Augusti [[electronic resource]] |
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Pubbl/distr/stampa |
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Descrizione fisica |
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Online resource ([1] p., p. 428-462, [1] c. di tav., 4) |
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Lingua di pubblicazione |
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Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Reproduction of original in Biblioteca Nazionale Centrale di Firenze. |
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2. |
Record Nr. |
UNINA9911019402103321 |
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Autore |
Ouadfeul Sid-Ali |
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Titolo |
Unconventional Hydrocarbon Resources |
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Pubbl/distr/stampa |
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Newark : , : John Wiley & Sons, Incorporated, , 2023 |
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©2023 |
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ISBN |
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9781119389385 |
1119389380 |
9781119389378 |
1119389372 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (320 pages) |
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Soggetti |
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Artificial intelligence |
Shale gas |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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 |
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-- 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 |
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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 |
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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. |
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Sommario/riassunto |
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This comprehensive volume, edited by Dr. Sid-Ali Ouadfeul, explores the application of artificial intelligence in predicting and modeling hydrocarbon resources. The book delves into various methodologies, including seismic genetic inversion, neural networks, and fuzzy logic, to enhance the exploration and extraction processes in shale gas reservoirs. It presents case studies from notable shale formations like the Barnett Shale, examining aspects such as pore pressure estimation, wellbore stability, and lithofacies recognition. The text is intended for professionals and researchers in petroleum engineering and geosciences, aiming to provide innovative solutions and techniques for optimizing hydrocarbon resource management. |
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3. |
Record Nr. |
UNISANNIOMIL0043892 |
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Titolo |
12: Libertà costituzionali e limiti amministrativi |
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Pubbl/distr/stampa |
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ISBN |
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Descrizione fisica |
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Disciplina |
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Soggetti |
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Libertà politica |
Diritti di libertà - Italia - Diritto costituzionale |
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Collocazione |
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Materiale a stampa |
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