Today’s push behind machine learning has led to some pretty high expectations for performance and deliverables. When it comes to business value and ROI, can it really live up to the claims? Evaluating the value of a full machine learning project requires moving past the hype and managing the realities of this evolving technology, one example at the time. We’ll look at the realities of a pure machine learning approach through the lens of applied field cases relevant to seismic interpretation, geological studies, formation evaluation, reservoir engineering, and production forecasting. Using examples and experiences, the panelist will discuss how machine learning has helped the O&G industry in a few of the following areas:
- Breaking silo and integrating various disciplines
- Using the ever-growing data being generated using multiple sources at a high rate.
- Is machine learning really a great tool to have at our disposal, like computers and mobile phones?
- Challenges of data constraints in terms of quality and quantity.
- Domain knowledge is needed to build robust machine learning methods.
- Can machine learning handle complex subsurface problems?
- Machine learning helps improve productivity and efficiency of geologists, geophysicists, and engineers.
Dr. Dingzhou Cao
Dingzhou Cao is currently the Data Science Manager in WPX Energy Inc. In this role, he focuses on building the real-time analytics systems within WPX, which include the drilling, completion and production. Prior to joining WPX, he worked for Anadarko Petroleum Corporation as a data science manager, BP America Inc as a Reliability Specialist, ReliaSoft Corporation as a Research Scientist (the solo person in charge of the simulation engine development), and Ford Motor Company as a Machine Learning Researcher. His background is on machine learning, operation research, optimization and real-time stream analytics. With his past roles in Anadarko, he first led a team to work with drilling engineers to build Anadarko’s real-time drilling analytics from scratch, and later he led the data science efforts in the real-time drilling and real-time completion domains. He got his Ph.D. in Industrial Engineering from Wayne State University, Detroit, Michigan and his bachelor degree in Applied Math from Jinan University, Guangzhou, China.
Dr. Birol Dindoruk
Birol Dindoruk is a Professor of Petroleum Engineering at University of Houston. Recently retired after 23 years of a career with Shell International E&P Inc. as a Chief Scientist and a Principal Technical Expert in Reservoir Engineering. In this position, he had many opportunities in working worldwide projects and project teams and as well as provided expert advice on numerous projects, especially in the areas of advanced recovery processes, complex phase behavior, Gas Injection and CO2 Sequestration, and lifecycle data handling & standards and data analytics. He also serves a consulting professor at Stanford University. He holds PhD degree in petroleum engineering and mathematics from Stanford University, and an MBA degree from University of Houston. He is a recipient of SPE’s Cedric K. Ferguson Medal and Lester C. Uren Awards and is a member of National Academy of Engineering. He has also served as co-executive Editor of SPE, Editor-in-Chief for JPSE and currently for JNGSE. Dindoruk is a member of the SPE board and currently serving as the Technical Director of Data Science and Engineering Analytics (DSEA).
Dr. Arvind Sharma
Dr. Arvind Sharma is VP of Data & Analytics at TGS. In this role he is responsible for Machine Learning initiatives as well as broader Digital transformation. He has 15+ years of experience in various subsurface and software related work. Arvind has bachelors and masters degrees in Applied Geology and Exploration Geophysics respectively from the Indian Institute of Technology (IIT) Kharagpur. He has a Ph.D. from Virginia Tech (VT) in Geophysics.
Arvind has a broad background in the oil and gas industry as well as outside the industry. He has worked in jobs ranging from software engineering (Infosys) to efficient seismic acquisition design (PGS) to developing seismic image algorithms (BP) to prospecting and drilling exploration wells (BP). Most recently he was Chief Geophysicist at PGS and held a similar role at TGS before his current position where he led the Industry’s first crowdsourcing challenge “TGS-Kaggle Salt Identification Challenge”. Additionally, he holds several patents, has been keynote speaker at major conferences, has been featured on several ML podcasts and is listed among E&P’s 2020 Energy Innovators.
At TGS, his mission is to create a platform to integrate and analyze all available sub-surface information for risking and decision making. Arvind believes that data integration and machine learning will be pivotal to this industry’s future success.
Dr. Satyam Priyadarshy
Satyam Priyadarshy, Ph.D., is a Technology Fellow and the Chief Data Scientist at Halliburton, a global oilfield service company, where he leads the oil and gas industry’s first Center of Excellence for Big Data and Data Science. Dr. Priyadarshy is a globally recognized leader for his expertise in leveraging disruptive technologies and strategies to increase business value. Prior to joining Halliburton, Dr. Priyadarshy held various leadership positions at AOL, Network Solutions and Acxiom Corporation. He serves as an adjunct professor at Virginia Tech, Oklahoma State University and is a senior fellow of cybersecurity at George Mason University. Dr. Priyadarshy obtained his Ph.D. from IIT Bombay and MBA from The Pamplin School of Business at Virginia Tech.
Dr. Siddharth Misra
Prof. Siddharth Misra is an Associate Professor in Harold Vance Department of Petroleum Engineering at Texas A&M University. Misra holds a Ph.D. in Petroleum Engineering from The University of Texas at Austin. Prior to that, from 2007 to 2010, he worked as a Wireline Field Engineer in Saudi Arabia, Egypt, and USA with Halliburton. He received his undergraduate degree in Electrical Engineering from Indian Institute of Technology Bombay, India, in 2007. Recently, he was awarded the prestigious Department of Energy Early Career Award, American Chemical Society New Investigator Award, and SPE Mid-Continent Formation Evaluation Award. His research interest include subsurface characterization, machine learning, sensing and sensors, and inverse problems.