Methods for Petroleum Well Optimization (eBook)

Drilling and production wells are becoming more digitalized as oil and gas companies continue to implement machine learning and big data solutions to save money on projects while reducing energy and emissions. Up to now there has not been one cohesive resource that bridges the gap between theory and application, showing how to go from computer modeling to practical use. Methods for Petroleum Well Optimization: Automation and Data Solutions gives today's engineers and researchers real-time data solutions specific to drilling and production assets. Structured for training, this reference covers key concepts and detailed approaches from mathematical to real-time data solutions through technological advances. Topics include digital well planning and construction, moving teams into Onshore Collaboration Centers, operations with the best machine learning (ML) and metaheuristic algorithms, complex trajectories for wellbore stability, real-time predictive analytics by data mining, optimum decision-making, and case-based reasoning. Supported by practical case studies, and with references including links to open-source code and fit-for-use MATLAB, R, Julia, Python and other standard programming languages, Methods for Petroleum Well Optimization delivers a critical training guide for researchers and oil and gas engineers to take scientifically based approaches to solving real field problems.

• Bridges the gap between theory and practice (from models to code) with content from the latest research developments supported by practical case study examples and questions at the end of each chapter
• Enables understanding of real-time data solutions and automation methods available specific to drilling and production wells, such as digital well planning and construction through to automatic systems
• Promotes the use of open-source code which will help companies, engineers, and researchers develop their prediction and analysis software more quickly; this is especially appropriate in the application of multivariate techniques to the real-world problems of petroleum well optimization

Rasool Khosravanian has worked as a postdoctoral fellow sponsored by Equinor and Aker BP, in the Department of Energy and Petroleum Engineering (IEP), University of Stavanger, Norway, since 2019. His focus has been on implementing digitalization in a drilling and wells organization. He holds MSc and PhD degrees in industrial engineering from the Iran University of Science and Technology in optimization techniques in the petroleum industry. Rasool received his BS degree in drilling and mining engineering from Kerman University. He was a faculty member and an assistant professor at Amirkabir University of Technology (Tehran Polytechnic) from 2011 to 2018. His research interests include large-scale optimization, data mining, artificial intelligence (AI), megaproject management, engineering economics, and risk and uncertainty analysis. He has published over 27 papers in international journals and 40 conference papers, with 10 years of drilling experience working both in academic research and with the petroleum industry. He has six years of professional experience from EPD companies and has also been a strategic planner in the implementing of business strategy for largesized companies. He is a member of the Society of Petroleum Engineers (SPE) and Tekna in Norway.

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Drilling and production wells are becoming more digitalized as oil and gas companies continue to implement machine learning andbig data solutions to save money on projects while reducing energy and emissions. Up to now there has not been one cohesiveresource that bridges the gap between theory and application, showing how to go from computer modeling to practical use. Methodsfor Petroleum Well Optimization: Automation and Data Solutions gives today's engineers and researchers real-time data solutionsspecific to drilling and production assets. Structured for training, this reference covers key concepts and detailed approaches frommathematical to real-time data solutions through technological advances. Topics include digital well planning and construction,moving teams into Onshore Collaboration Centers, operations with the best machine learning (ML) and metaheuristic algorithms,complex trajectories for wellbore stability, real-time predictive analytics by data mining, optimum decision-making, and case-basedreasoning. Supported by practical case studies, and with references including links to open-source code and fit-for-use MATLAB, R,Julia, Python and other standard programming languages, Methods for Petroleum Well Optimization delivers a critical training guidefor researchers and oil and gas engineers to take scientifically based approaches to solving real field problems. Bridges the gap between theory and practice (from models to code) with content from the latest research developments supported by practical case study examples and questions at the end of each chapter Enables understanding of real-time data solutions and automation methods available specific to drilling and production wells, suchas digital well planning and construction through to automatic systems Promotes the use of open-source code which will help companies, engineers, and researchers develop their prediction and analysissoftware more quickly; this is especially appropriate in the application of multivariate techniques to the real-world problems of petroleum well optimization