Subsurface Analytics; Digital Transformation of Reservoir Management with Artificial Intelligence and Machine Learning


Shahab D. Mohaghegh

IN CONJUNCTION WITH QLD AND SA SECTIONS

HYBRID ONLINE(no charge for members) AND FACE TO FACE (registration fees apply and include lunch)

Following DL and lunch, the Annual General Meeting will be held

Abstract:

Subsurface Analytics is an alternative to traditional reservoir modeling and res. management.

Positively influencing subsurface related decision making is the most important contribution of any new technology. Subsurface Analytics is the application of Artificial Intelligence and Machine Learning (AI&ML) in Reservoir Engineering, Characterization, Modeling, and Management. Applicable to both conventional and unconventional plays, Subsurface Analytics goes far beyond the traditional statistical algorithms that use only production data and fail to take into consideration the important field measurements such as well trajectories, well logs, seismic, core data, PVT, well test, completion, and operational constraints. Subsurface Analytics is the manifestation of Digital Transformation in Reservoir Engineering, Modeling, and Management.

Subsurface Analytics is a new and innovative technology that has been tested and validated in a large number of real life cases in North and Central America, North Sea, Middle East, and Southeast Asia. It has been successfully applied in several highly complex mature fields where conventional commercial reservoir simulators were unable to simultanuously history match multiple dynamic variables for large number of wells. Results and field validations from multiple case studies are included in the presentation.

Subsurface Analytics addresses realistic and useful applications of AI&ML in the upstream Exploration and Production Industry. The technology has been validated and confirmed for (a) prediction of well behavior under different operational conditions, (b) modeling and forecasting pressure and saturation distribution throughout the reservoir, (c) infill well location optimization for both producers and injectors, (d) choke optimization for production improvement, and (e) completion optimization for production enhancement.

Biography:

Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Machine Learning in the Exploration and Production industry, is Professor of Petroleum Engineering at West Virginia University and founder of Intelligent Solutions, Inc. He has authored three books (Shale Analytics – Data Driven Reservoir Modeling – Application of Data-Driven Analytics for the Geological Storage of CO2), more than 200 technical papers and carried out more than 60 projects for independents, NOCs and IOCs. He has been featured as the Distinguished Author in SPE’s Journal of Petroleum Technology (JPT 2000 and 2004). He is the founder of SPE’s Petroleum Data-Driven Analytics Technical Section. He has been honored by the U.S. Secretary of Energy for his AI-based technical contribution in the aftermath of the Deepwater Horizon and was a 
member of U.S. Secretary of Energy’s Technical Advisory Committee (2008-2014).He represented the United States at ISO on Carbon Capture and Storage (2014-2016).


This meeting will be live streamed via Zoom and members can participate in person with lunch, or online.

Registrations to participate on line here

Registrations close June 20, 2021
Early bird registrations close June 8