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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
Academics (scientists, researchers, MSc. PhD. students) from the fields of oil and gas, optimization, simulation, big data analysis, real-time technology, automation in operations, and decision-making.
Industry: different oil and gas companies that want to improve their organization's drilling and production performance, oil and gas training companies, oil and gas consultants, innovative drilling companies, drilling engineers, operation engineers, production engineers, asset managers, project managers and digitalization managers
Chapter 1. Introduction to Digital Twin, Automation and Real-Time Centers
Chapter 2. Petroleum Well Optimization
Chapter 3. Wellbore Friction Optimization
Chapter 4. Wellbore trajectory optimization
Chapter 5. Wellbore Hydraulics and Hole Cleaning: Optimization and digitalization
Chapter 6. Mechanical Specific Energy (MSE) & Drilling efficiency
Chapter 7. Data-driven Machine Learning Solutions to Real-Time ROP Prediction
Chapter 8. Advanced Approaches and Technology for Casing Setting Depth Optimization
Chapter 9. Data Mining in Digital Well Planning and Well Construction
Chapter 10. Well Completion Optimization by Decision-Making
Chapter 11. Monte Carlo Simulation in Wellbore Stability Optimization
Chapter 12. Case-Based Reasoning (CBR) in Digital Well Planning & Construction
- No. of pages:
- © Gulf Professional Publishing 2021
- 22nd September 2021
- Gulf Professional Publishing
- Paperback ISBN:
- eBook ISBN:
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.
Post-doctoral fellow in the Department of Petroleum Engineering, University of Stavanger, Norway
Bernt Sigve Aadnøy is a Professor of Petroleum Engineering at the University of Stavanger, specializing in all aspects of well engineering, including geomechanics. He is also an Adjunct Professor at NTNU—the Norwegian University of Science and Technology in Trondheim. He worked for major operators in the oil industry from 1978 until 1994, when he transitioned to academia. Aadnøy has published more than 300 papers, holds 15 patents, and has authored or co-authored seven books, among them Modern Well Design, Petroleum Rock Mechanics, and Mechanics of Drilling. He was also one of the editors of the SPE book Advanced Drilling and Well Technology (Society of Petroleum Engineers). Aadnøy holds a BS degree in mechanical engineering from the University of Wyoming, an MS in control engineering from the University of Texas, and a PhD in petroleum rock mechanics from the Norwegian Institute of Technology. He was a recipient of the 1999 SPE International Drilling Engineering Award and is also a 2015 SPE/AIME Honorary Member and a 2015 SPE Distinguished Member. He was named SPE Professional of the Year 2018 in Norway.
Professor of Petroleum Engineering, University of Stavanger, Norway
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