A Bayesian Optimization Workflow for Field Development Planning Under Geological Uncertainty
This article proposes a Bayesian Optimization workflow comprising a Stochastic Bayes Linear proxy model and a combination of experimental and sequential design techniques. The workflow is demonstrated by optimizing several field development strategies in a synthetic North Sea reservoir model. The performance and practical implications of the approach are important in designing an accurate and computationally efficient optimization workflow under geological uncertainty, and ultimately are factors in developing decision support tools for field development.
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