Methods for Petroleum Well Optimization

Automation and Data Solutions

1st Edition - September 22, 2021
Imprint: Gulf Professional Publishing
Authors: Rasool Khosravanian, Bernt S. Aadnoy
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 2 3 1 - 1
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 2 3 2 - 8

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… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Preface
Acknowledgment I
Acknowledgment II
Chapter One. Introduction to digital twin, automation and real-time centers
1.1. Digital twin technology
1.2. Drilling automation
1.3. Real-time centers
1.4. Summary
1.5. Problems
Chapter Two. Petroleum well optimization
2.1. Mathematical optimization
2.2. Petroleum well optimization
2.3. Summary
2.4. Problems
Nomenclature
Chapter Three. Wellbore friction optimization
3.1. Elementary models for wellbore friction
3.2. Advanced models for wellbore friction
3.3. Application of friction models to wells
3.4. Design of oil wells using analytical friction models
3.5. Summary
3.6. Problems
Nomenclature
Chapter Four. Wellbore trajectory optimization
4.1. Introduction
4.2. Constraints potentially affecting the optimal well trajectory
4.3. Well path optimization
4.4. Well trajectory optimization for preventing wellbore instability
4.5. Summary
4.6. Problems
Nomenclature
Chapter Five. Wellbore hydraulics and hole cleaning: optimization and digitalization
5.1. Hydraulic optimization
5.2. Hole cleaning
5.3. Real-time assessment of the hole cleaning efficiency
5.4. New methods for drilling hydraulics
5.5. Summary
5.6. Problems
Nomenclature
Chapter Six. Mechanical specific energy and drilling efficiency
6.1. Introduction to mechanical specific energy
6.2. Mechanical specific energy: next-generation digital drilling optimization
6.3. Rock drillability assessments
6.4. Drilling system energy beyond mechanical specific energy
6.5. Summary
6.6. Problems
Nomenclature
Chapter Seven. Data-driven machine learning solutions to real-time ROP prediction
7.1. Introduction
7.2. Data piping in real time
7.3. Drilling rate of penetration optimization workflow
7.4. Statistical and data-driven rate of penetration model
7.5. Summary
7.6. Problems
Nomenclature
Chapter Eight. Advanced approaches and technology for casing setting depth optimization
8.1. Introduction
8.2. Problem statement
8.3. Mathematical approach: casing string placement optimization under uncertainty
8.4. Multiple criteria approach: casing seat selection method
8.5. Real-time approach: casing seat optimization using remote real-time well monitoring
8.6. Technological approach: reduced number of casings using unconventional drilling methods
8.7. Summary
8.8. Problems
Nomenclature
Chapter Nine. Data mining in digital well planning and well construction
9.1. Data mining techniques
9.2. Data mining application in digital drilling engineering
9.3. Summary
9.4. Problems
Chapter Ten. Well completion optimization by decision-making
10.1. Basic concepts
10.2. Well completion optimization by decision-making
10.3. Summary
10.4. Problems
Nomenclature
Chapter Eleven. Monte Carlo simulation in wellbore stability optimization
11.1. Basic multivariate statistics
11.2. Uncertainty assessment of wellbore stability
11.3. Numerical examples
11.4. Summary
11.5. Problems
Nomenclature
Chapter Twelve. Case-based reasoning (CBR) in digital well planning and construction
12.1. Basic concepts
12.2. Application of case-based reasoning in digital well construction planning
12.3. Summary
12.4. Problems
Nomenclature
Index

Rasool Khosravanian

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.

Affiliations and expertise

Post-doctoral fellow in the Department of Petroleum Engineering, University of Stavanger, Norway

Bernt S. Aadnoy

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.

Affiliations and expertise

Professor of Petroleum Engineering, University of Stavanger, Norway

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