Shale Asset Management via Advanced Data-Driven and Predictive Analytics
Recorded on November 13, 2013 (90 minutes)

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Advanced Data-Driven Analytics provides much needed insight into hydraulic fracturing practices in Shale. Unlike analytical and numerical modeling that are based on “Soft Data”, Advanced Data-Driven Analytics incorporates “Hard Data”. “Hard Data” refers to field measurements (facts) such as drilling information, well logs (Gamma ray, density, sonic, etc.), fluid type and amount, proppant type, amount and concentration, ISIP, breakdown and closure pressures, and rates, while “Soft Data” refers to variables that are interpreted, estimated or guessed (and never measured), such as hydraulic fracture half length, height, width and conductivity or the extent of the Stimulated Reservoir Volume (SRV).

Advanced Data-Driven Analytics incorporates pattern recognition capabilities of Artificial Intelligence, Machine Learning and Data Mining in order to understand and rank the contribution and influence of rock characteristics and design parameters during the production from shale. Data-Driven predictive models are trained and calibrated using “Hard Data” and are used to predict shale well productivity as a function of measured data. The predictive model is validated using data from newly completed wells. The inverse model is then used to perform analysis such as, looking back at the performance of the previous frac jobs (data-driven best practices), evaluating service companies’ performance, developing type curves for the entire or specific locations in the asset, optimizing distance between laterals and stages and designing optimum frac jobs in new wells. Application of this technology is demonstrated in case studies from multiple shale assets involving more than 400 wells.


Shahab D. Mohaghegh
is the president and CEO of Intelligent Solutions, Inc. (ISI) and Professor of Petroleum and Natural Gas Engineering at West Virginia University. A pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry, 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 many of them with major international companies. He is a SPE Distinguished Lecturer and has been featured in the Distinguished Author Series of SPE’s Journal of Petroleum Technology (JPT) four times. 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. Shahab is the designated U.S. liaison (WG4) representing the Unites States in the International Organization for Standardization (ISO) for CO2 capture and storage.