Decision-Driven Subsurface Surrogate Model for Development Optimization Under Uncertainties

Abstract

AbstractWith ever-increasing complexity in Upstream project planning, to ensure decision quality, the dynamics of subsurface resources need to be embedded into concept screening to maintain consistency between the production forecast and development plan. We developed a decision-driven subsurface surrogate model that encapsulates key reservoir dynamics into the machine augmented mathematical technologies for holistic decision recommendation in concept selection and development planning under uncertainties. The surrogate model replicates the essential subsurface dynamics by using a hybrid-approach that takes into accounts both reservoir simulation data and physical first principle. In addition to standalone usage on production forecast for rapid profile screening under resource uncertainties, the subsurface surrogate model is incorporated into mathematical optimization models that simultaneously consider surface network, commercial obligation and project economics etc. to provide alternative concepts under various uncertainties. Our subsurface surrogate model has been applied for decision making on gas gathering system design, field development optimization, field-management timing and sequencing, and field tie-back study etc. Results not only show the capability of surrogate models to enable large scale rapid decision screening, but also bear a close resemblance between the predicted production profiles and the reservoir simulation results when fed with the field operating strategy recommended by our decision models with surrogate dynamics. The study demonstrates the reliability of our surrogate modeling technology on ensuring decision quality and helps build business’ confidence on technology adoption. By further incorporating subsurface uncertainties into surrogate models, the decision makers are provided with: probabilistic analysis of the outcomes, value of information analysis, cost of optionality and flexibility, and holistic project outlook etc. Our decision-driven surrogate modeling technology incorporated in mathematical decision models is the first of its kind for holistic decision support in concept selection and development planning in oil and gas industry, and has full potentials in a variety of asset lines with reliable subsurface performance prediction under uncertainties.