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Umfang: 1 Online-Ressource (187 Seiten)

Ausgabe: 1st ed

ISBN: 9781000995138

Inhalt: This book covers aspects of data science and predictive analytics used in oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital well, value chain integration, crude basket forecasting and so forth

Anmerkung: Description based on publisher supplied metadata and other sources , Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- About the Editors -- Contributors -- Acknowledgments -- Chapter 1 Understanding the Oil and Gas Sector and Its Processes: Upstream, Downstream -- 1.1 Introduction -- 1.2 Identification of the Geological Origins of Petroleum Reservoirs and Reservoir Fluids -- 1.3 History of the Oil and Gas Industry -- 1.3.1 Ancient Oil and Gas Industry -- 1.3.2 Modern Oil and Gas Industry -- 1.3.3 The Role of the Russian Oil and Gas Industry -- 1.3.4 Royal Dutch Shell in the East Indies -- The Structure of the Modern Oil and Gas Industry -- 1.4.1 Upstream -- 1.4.2 Midstream -- 1.4.3 Downstream -- 1.5 Differences between the Conventional and Unconventional Reservoirs -- 1.5.1 Conventional Reservoirs -- 1.5.2 Unconventional Reservoirs -- 1.6 A List of the Various Disciplines that Make Up Petroleum Engineering -- 1.6.1 Exploration Stage -- 1.6.2 The Appraisal Stage -- 1.6.3 The Development Stage -- 1.6.4 Production Stage -- 1.7 Analyzing Rudimentary Engineering Methods in Exploration and Production -- 1.7.1 Role of Geoscientists -- 1.7.2 Methods of Exploration -- 1.7.3 Methods of Production -- 1.8 Interpretation of Cross-Plots -- Acknowledgement -- References -- Chapter 2 IT Technologies Impacting the Petroleum Sector -- 2.1 Introduction -- 2.2 GIS and Remote Sensing -- 2.2.1 Case Study of GIS in the Petroleum Industry 1: OMV Enterprise GIS -- 2.2.2 GIS Case Study 2: Assessment of Hurricane Effects on Gulf of Mexico Oil and Gas Production Predictions -- 2.3 Image Processing -- 2.4 SCADA and Telemetry -- 2.4.1 Telemetry -- 2.5 Geological and Geophysical Parameters -- 2.6 Introduction to ANN and Automation -- 2.6.1 ANN in the Petroleum Industry -- 2.6.2 Exploration -- 2.6.3 Drilling -- 2.6.4 Production -- 2.6.5 Reservoir -- 2.7 Different Types of Surveys -- 2.7.1 Seismic Surveys , 2.7.2 3D Seismic Survey -- 2.8 Cloud Technologies -- 2.8.1 Cloud Technologies in the Petroleum Industry -- 2.8.2 Cloud Storage Application of Petroleum Industry -- 2.9 Conclusion -- Bibliography -- Chapter 3 Data Handling Techniques in the Petroleum Sector -- 3.1 Introduction -- 3.2 BigData in O& -- G -- 3.3 BigData Administration in the Oil and Gas Sector -- 3.3.1 Data Attainment -- 3.3.2 Data Processing -- 3.4 Contemporary Frameworks in the Oil and Gas Sector -- 3.5 BigData Prospects and Challenges in the Oil and Gas Sector -- 3.5.1 Asset Enactment Controlling -- 3.5.2 Asset Risk Valuation -- 3.5.3 Virtual Operational Drill -- 3.5.4 Disaster Reaction Training -- 3.5.5 Lack of Standardization -- 3.5.6 Data Ownership and Sharing -- 3.5.7 Functionality -- 3.6 Rami Dossier in the Case of the Oil and Gas Industry -- 3.6.1 Requirement of Flexibility in the Upstream Oil and Gas Business -- 3.6.2 Identification of Mobility -- 3.6.3 Readiness Assessment Framework -- 3.7 Major Risks to Companies -- 3.8 Risk Analysis /Assessment for Organization -- Bibliography -- Chapter 4 Predictive Modelling Concepts in Petroleum Sector -- 4.1 Overview -- 4.2 Statistical Methods -- 4.2.1 Parametric vs Non-Parametric Methods -- 4.2.2 Regression and Classification -- 4.2.3 Performance Metrics for Classification and Regression -- 4.2.3.1 Performance Metrics for Classification-Confusion Matrix -- 4.2.3.2 Performance Metrics for Regression Model -- 4.3 Machine Learning Concepts -- 4.3.1 Background -- 4.3.2 Machine Learning Methods -- 4.3.2.1 Linear Regression -- 4.3.2.2 Support Vector Machine -- 4.3.2.3 Strength Weakness Opportunities Threats (SWOT) Analysis -- 4.4 The Artificial Neural Network and its Application in the Oil and Gas Sector -- 4.4.1 Introduction -- 4.4.2 Generic Development of ANN Models -- 4.4.3 Applications in the Oil and Gas Industry -- 4.4.3.1 Exploration , 4.4.3.2 Reservoir -- 4.4.3.3 Drilling -- 4.5 Concepts of Deep Neural Networks -- 4.5.1 Introduction -- 4.5.2 DNN Models -- 4.5.3 Model Evaluation Metrics -- 4.6 Case Study -- Bibliography -- Chapter 5 Supply Chain Management in the Oil and Gas Business -- 5.1 Introduction -- 5.2 Challenges in SCM -- 5.2.1 Inventory Management -- 5.2.2 Warehouse Management -- 5.2.3 Logistics -- 5.2.4 Reduction of Transport Costs -- 5.2.5 Processing a Large Amount of Information -- 5.2.6 Delay in Delivery -- 5.3 Opportunities in the Supply Chain -- 5.3.1 Customer Requirement Process -- 5.3.2 Sourcing and Supplier Management -- 5.4 Analysis -- 5.5 Introduction to ERP for SCM -- 5.5.1 Main Features of ERP -- 5.5.2 Role of ERP in SCM -- 5.5.3 Advantages of ERP in SCM -- 5.6 Case Study -- 5.6.1 Geographic Information System (GIS) Solution Enhances Inventory Control -- Bibliography -- Chapter 6 Prescriptive Analysis and Its Application in Oil and Gas Business -- 6.1 Introduction -- 6.2 Introduction to Different Types of Analytics -- 6.3 Basics of Prescriptive Analysis -- 6.4 Reservoir Simulator (Petrel) -- 6.5 Refining Simulator (Aspen-HySYS) -- 6.6 AI and Its Application in Simulation -- 6.7 Case Study on Upstream -- 6.7.1 Case Study 1: Optimizing the Exploration of Shale Oil -- 6.7.2 Case 2: Permian Basin Unconventional Hydrocarbon Extraction -- 6.8 Case Study on Downstream -- 6.8.1 Case Study 1: Supply Chain 4.0 -- 6.8.2 Supply Chain and Predictive Analytics: -- 6.8.3 Supply Chain and Big Data Analytics: -- 6.8.4 Case Study: The Norwegian Continental Shelf -- Bibliography -- Chapter 7 Future Challenges in Petroleum Sector and IT Solutions -- 7.1 Introduction -- 7.2 Challenges in the Oil and Gas Industry -- 7.2.1 Reduce Cost of Production to Remain Competitive in the Market -- 7.2.2 Improving Performance to Ensure the Valourization of Assets , 7.2.3 Reducing Carbon Footprint to Meet Stringent Governmental Standards -- 7.3 Data as a New Oil -- 7.3.1 Data Refining -- 7.3.2 Data Quality -- 7.3.3 Data Requires Infrastructure -- 7.3.4 Difference between Data and Oil -- 7.4 Challenges of Data Integration in the Oil and Gas Sector -- 7.5 Reducing Production Cost through IT Technologies -- 7.6 Case Study -- Conclusion -- Bibliography -- Chapter 8 Oil and Gas Industry in Context of Industry 4.0 -- 8.1 Introduction -- 8.2 Concepts of the Oil and Gas Industry 4.0 -- 8.3 Concepts of Digital Oilfields -- 8.4 Cloud Integration in the Oil and Gas Domain -- 8.5 Digital Transformation -- 8.6 Agile Business Transformation -- 8.7 Digital Leadership -- 8.8 Case Study -- 8.8.1 List of Case Studies on the Oil and Gas Industry -- 8.8.2 Analyzing the Barriers to Adopt Industrial Revolution in India -- References -- Index

Weitere Ausg.: Erscheint auch als Druck-Ausgabe Srivastava, Kingshuk Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry Milton : Taylor & Francis Group,c2023 ISBN 9781032413891

Sprache: Englisch

UID:

Umfang: 1 online resource (xviii, 168 pages)

ISBN: 9781003357872 , 1003357873 , 9781000995114 , 1000995119 , 9781000995138 , 1000995135

Inhalt: This book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies. Features: Provides an understanding of the basics of IT technologies applied in the oil and gas sector Includes deep comparison between different artificial intelligence techniques Analyzes different simulators in the oil and gas sector as well as discussion of AI applications Focuses on in-depth experimental and applied topics Details different case studies for upstream and downstream This book is aimed at professionals and graduate students in petroleum engineering, upstream industry, data analytics, and digital transformation process in oil and gas.

Anmerkung: 1. Understanding the Oil & Gas Sector and its Processes: Upstream and Downstream 2. IT technologies Impacting the Petroleum Sector 3. Data Handling Techniques in Petroleum Sector 4. Predictive Modelling Concepts in Petroleum Sector 5. Supply Chain Management in Oil and Gas Business 6. Prescriptive Analytics and its Application in Oil and Gas Business 7. Future Challenges in Petroleum Sector 8. Oil & Gas Industry in context of Industry 4.0

Sprache: Englisch

Schlagwort(e): Statistics.

DOI: 10.1201/9781003357872

URL: https://www.taylorfrancis.com/books/9781003357872