Data Mining/Machine Learning in the Oil and Gas Sector: Applications in the Area of Petroleum Engineering
According to a newly published white paper from industry analysts IDC, 49% of upstream energy organizations are prioritizing data capitalization and monetization as preexisting challenges and 2020 turbulence has elevated digital transformation from priority to imperative status. Even about five years earlier, expectations from the digital transformation was already high, especially in the context of bottomline impact. However, with respect to these expectations the progress has been quite slow in the oil and gas industry. For most early adopters, this journey began with real-time monitoring and remote operation of equipment. In this talk, we will highlight the current environment in terms of “Distruptive”/Fast Changes where Data- Process-Software Integration and how it is almost becoming a must. In the second part of the the talk, we will have specific machine learning techniques that we have applied successfully to a certain class of problem in recent years. One of the learnings from these studies is that data quality check coupled the content knowledge was proven to be instrumental for the successful application of the machine learning and/or correlative techniques.
Dr. Birol Dindoruk is currently American Association of Drilling Engineers Endowed Professor of Petroleum Engineering at University of Houston, previously he was the Chief Scientist of Reservoir Physics and the Principal Technical Expert of Reservoir Engineering in Shell.
His technical contributions have been acknowledged with many awards during his career, including SPE Lester C. Uren Award (2014), Cedric K. Ferguson Medal (1994), and Distinguished Membership. In 2017, he was elected as a member of the National Academy of Engineering for his significant theoretical and practical contributions to enhanced oil recovery and CO2 sequestration.
He was one of the Distinguished Lecturers of SPE for 2010-2011 term.
Dr. Dindoruk was Data Science and Engineering Analytics Technical Director of the SPE and a member of the Advisory Committee of the SPE Reservoir Dynamics and Description Technical Discipline. He has been active in various editorial positions under SPE and also Elsevier. Currently he is the Editor In Chief for all SPE Journals (five journals in total) and as well as Editor In Chief of Journal of Natural Gas Science and Engineering.
Dr. Dindoruk is well-known for his extensive work on thermodynamics of phase behavior/EOS development and experimental work, interaction of phase behavior and flow in porous media, enhanced oil recovery and CO2 sequestration, and correlative methodologies.
Recently, Dr. Dindoruk has also been working in the area of data analytics, artificial intelligence, and machine learning and focusing on effective incorporation of data sciences into the oil and natural gas industry practices and energy systems. In recent years, he has authored/co-authored various articles on CO2 solutions, hydrogen, geothermal systems and adsorptive storage.
Dindoruk has 28 years of industrial experience, holds a BSc Degree from Technical University of Istanbul in Petroleum Engineering, MSc Degree from The University of Alabama in petroleum engineering and also a PhD from Stanford University in Petroleum Engineering and Mathematics, and an MBA from University of Houston.