Events

Dr. Shahab D. Mohaghegh, Professor at West Virginia University; President and CEO of Intelligent Solutions will give a talk entitled " Fact-Based Modeling; Analysis, Prediction and Optimization of Hydraulic Fracturing Performance in Shale ” as part of the Claude R. Hocott Graduate Seminar Series.

About:

Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry,  is the president and CEO of Intelligent Solutions, Inc. (ISI) and Professor of Petroleum and Natural Gas Engineering at West Virginia University. He holds B.S., MS, and PhD degrees in petroleum and natural gas engineering. He has authored more than 150 technical papers and carried out more than 50 projects with NOCs and IOCs. He is a SPE Distinguished Lecturer (2007-08) and has been featured in the Distinguished Author Series of SPE’s Journal of Petroleum Technology (JPT) four times (2000 – 2004). He is the program chair of Petroleum Data-Driven Analytics, SPE’s Technical Section dedicated to data mining. He has been honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and is a member of U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources (2008-present). He represents the United States in the International Standard Organization (ISO) on Carbon Capture and Storage.

Lecture Abstract:

Advanced Data-Driven Analytics provides much needed insight into hydraulic fracturing practices in Shale. Unlike traditional analytical and/or numerical modeling, Advanced Data-Driven Analytics incorporates “Hard Data” rather than “Soft Data”. Using this technology synthetic geo-mechanical well logs can be generated, impact of reservoir quality can be meaningfully assessed and contribution of completion and hydraulic fracturing practices to production from shale can be modeled and optimized. Advanced Data-Driven Analytics is a unique and innovative implementation of Artificial Intelligence, Machine Learning and Data Mining in the upstream E&P. Data-Driven predictive models are trained, calibrated and validated using “Hard Data” and are used to design optimum frac jobs in new wells, identify the best locations to place the next pads, design optimum distance between laterals, and between stages and clusters of hydraulic fractures. Application of this technology is demonstrated using case studies in multiple Shale assets. “Hard Data” refers to field measurements such as well logs (Gamma ray, density, sonic, etc.), lateral and stage lengths, fluid type and amount, proppant type and amount, ISIP, breakdown and closure pressures, and corresponding injection rates, etc. “Soft Data” refers to variables that are interpreted, estimated or guessed, such as hydraulic fracture half length, height, width and conductivity or the extent of the Stimulated Reservoir Volume (SRV).