AI-Driven Diagnostics for O&G Reservoirs
Presented by: Dr. Hamed Darabi, Chief Technology Officer, QRI
The main goal in reservoir diagnostics is to leverage all existing data and information generated in an O&G field to identify its key recovery obstacles (KROs) and, from here, to facilitate identification of field development opportunities (FDOs). However, the actual process of “diagnosing” an O&G reservoir is not really standardized in our industry, leading to very ad-hoc approaches to the problem. In reality, while we accept every reservoir is certainly unique, the number of ways you can look at an existing reservoir system is limited (so it is the number of tools/techniques that are available to analyze it). A key (and difficult) factor here is how to properly infuse the so-called “domain knowledge” or expertise into this diagnostics process.
We propose a framework for a smart (knowledge-based and data-driven) automation of the reservoir diagnostics problem. We’ll use one or two case studies to demonstrate our proposal and results.
Dr. Hamed Darabi is the Chief Technology Officer at Quantum Reservoir Impact (QRI). His current responsibility is to maintain key QRI technologies, expand QRI’s intellectual property, and ensure high-quality delivery of QRI technologies to clients. Since 2013, Dr. Darabi has served as team lead and project manager for multiple field studies, and has been involved in the development of some of QRI's proprietary products. He also worked on several giant fields in the Middle East to implement QRI technologies and perform reservoir studies.
Prior to QRI, Dr. Darabi worked as reservoir engineer at Occidental Oil & Gas Corporation and various companies in the Middle East. His experience spans reservoirs in California, Kuwait, Partitioned Zone, Iran, UAE, Mexico, and Iraq.
Dr. Darabi received his Ph.D. in Petroleum Engineering from the University of Texas at Austin, where he extensively studied reservoir simulator development and mathematical modeling. For his dissertation, he developed a non-isothermal compositional simulator to model asphaltene precipitation, flocculation, and deposition in oil reservoirs and near wellbore. Moreover, he studied the condition of asphaltene precipitation in the Asab Field in UAE during CO2 Injection.