Enabling New Frontiers: Machine Learning Applications Guiding Upstream Efficiencies

Mohamed Sidahmed

Abstract:

Pressured industry operating environment contributed to the acceleration of embracing innovative technologies for opening new exploration and operation frontiers, deriving efficiencies and reducing costs. Production surveillance and optimization is a paramount mission critical complex process inherent in smart oil field operations. Field-wide global surveillance strategy adopted more intelligent and prognostic scheme that evolved beyond near real-time monitoring. Increased leverage of advanced data-driven techniques and machine learning algorithms coupled with contextual physical representation offered reliable early alerting and lead time prediction of emerging trends and events. Static engineering models and process simulations often lag current operating state as a result of continuous system transient. Also, traditional sequential model training, verification, and testing process is largely driven by employed sampling plan of the universe, split ratio

representation, or constrained computational resources. From a practical perspective, scaling models beyond sub-space of universal operations profile is a desired business objective that stretches the boundaries for learning and maintaining validity of active models in a continuously evolving environment and growing big data streams.

Presenter:

Mohamed Sidahmed, Ph.D., SPE, IEEE Senior Member, is subject matter expert in machine learning, data- driven modeling and optimization at BP, Upstream Technology. He leads R&D and technology development transforming drilling, production and operations, reservoir predictions, and unconventionals applications. Mohamed is currently the outreach Chair and member of the board of the Society of Petroleum Engineers, Petroleum Data Driven Analytics (PD2A) technical section. He is also a technical advisory committee member of the SPE Reservoir Description and Dynamics discipline, and several other professional and non-profit organizations. Mohamed holds a Ph.D. in Computer Science from the University of Colorado and completed post-doctoral advanced training and leadership at MIT.

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