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Elsevier

Applications of Artificial Intelligence Techniques in the Petroleum Industry

  • 1st Edition - August 26, 2020
  • Latest edition
  • Authors: Abdolhossein Hemmati-Sarapardeh, Aydin Larestani, Nait Amar Menad, Sassan Hajirezaie
  • Language: English

Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve spe… Read more

Description

Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges.

Key features

  • Teaches how to apply machine learning algorithms that work best in exploration, drilling, reservoir or production engineering
  • Helps readers increase their existing knowledge on intelligent data modeling, machine learning and artificial intelligence, with foundational chapters covering the preprocessing of data and training on algorithms
  • Provides tactics on how to cover complex projects such as shale gas, tight oils, and other types of unconventional reservoirs with more advanced model input

Readership

Petroleum engineers; reservoir engineers; drilling engineers; production engineers; graduate-level petroleum engineering students; data scientists working in oil and gas

Table of contents

1. Introduction

2. Intelligent Models

3. Training and Optimization Algorithms

4. Application of Intelligent Models in Reservoir and Production Engineering

5. Application of Intelligent Models in Drilling Engineering

6. Application of Intelligent Models in Exploration Engineering

7. Weakness and Strength of Intelligent Models in Petroleum Industry

Product details

  • Edition: 1
  • Latest edition
  • Published: August 26, 2020
  • Language: English

About the authors

AH

Abdolhossein Hemmati-Sarapardeh

Abdolhossein Hemmati-Sarapardeh is currently an associate professor at Shahid Bahonar University of Kerman. He is also an adjunct professor at Jilin University and Northeast Petroleum University in China. He was previously a visiting scholar at the University of Calgary. He earned a PhD in petroleum engineering from Amirkabir University of Technology, an MSc in hydrocarbon reservoir engineering from the Sharif University of Technology, and a BSc in petroleum engineering from the Amirkabir University of Technology. His research interests include enhanced oil recovery processes, heavy oil systems, nanotechnology, and applications of intelligent models in the petroleum industry. Abdolhossein has been awarded as a distinguished graduate MSc student, was an honor PhD student, and a recipient of the National Elites Foundation Scholarship. He works as an associate professor in the Journal of Petroleum Science and Engineering. He has published over 150 journal articles, three books, several conference proceedings, and earned one patent in 2016.
Affiliations and expertise
Assistant Professor, Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Iran

AL

Aydin Larestani

Aydin Larestani is currently an MSc student at the University of Kerman and a member of the Iranian Oil Industry Youth Committee in the World Petroleum Council. He is the 1st ranked student in MSc in hydrocarbon reservoir engineering at the Shahid Bahonar University of Kerman and 1st ranked graduate in Bachelor of Science in drilling engineering. He was the secretary of petroleum engineering scientific association from 2015 to 2018. His research interests include applications of intelligent models in the petroleum industry, chemical enhanced oil recovery, thermal EOR, interfacial tension, and heavy oil.
Affiliations and expertise
Research Assistant, Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Iran

NM

Nait Amar Menad

Menad Nait Amar received the B.Sc. degree, the M.Sc. degree and the Ph.D. degree in Petroleum / reservoir Engineering at University M’hamed Bougara of Boumerdes, Algeria in 2013, 2015 and 2018 respectively. His research interests include machine learning, optimization and data mining and their applications in the oil industry. Menad Nait Amar is currently an Engineer Reacher at Sonatrach and an Assistant Professor within the Faculty of Hydrocarbons and Chemistry at the University M’hamed Bougara of Boumerdes in Algeria.
Affiliations and expertise
Assistant Professor, Faculty of Hydrocarbons and Chemistry, University M’hamed Bougara of Boumerdes, Algeria

SH

Sassan Hajirezaie

Sassan Hajirezaie is currently a PhD candidate at Princeton University studying civil and environmental engineering. He earned a Master of Science in petroleum engineering from the University of Oklahoma, and a Bachelor of Science in petroleum engineering from Sharif University of Technology. Sassan’s research focuses on carbon capture and storage (CCS), application of machine learning models in unconventional oil and gas production, and renewable energy sources. He has published many journal articles, peer reviewed at several journals, and is a member of the Society of Petroleum Engineers and the American Geophysical Union.
Affiliations and expertise
PhD Candidate, Princeton University

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