Methods for Petroleum Well Optimization : Automation and Data Solutions

Rasool (Post-doctoral fellow in the Department of Petroleum Engineering Khosravanian

ISBN 10: 0323902316 / ISBN 13: 9780323902311

Edité par Gulf Publishing Company Sep 2021, 2021

A propos de cet article

Neuware - 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.

N° de réf. du vendeur 9780323902311

Détails bibliographiques

• Titre: Methods for Petroleum Well Optimization : Automation and Data Solutions
• Éditeur: Gulf Publishing Company Sep 2021
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 0323902316
• ISBN à 13 chiffres: 9780323902311
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 191 H x 0 P (en millimètres) 235 millimètres de largeur par 191 millimètres de hauteur par 0 millimètres de profondeur
• Poids: 1130 grammes

A propos de ce titre

Synopsis

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.

À propos des auteurs

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.

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.