Field Development Optimization Under Uncertainty: Screening-Models for Decision Making

摘要

In this study we develop an integrated asset model (IAM) for a greenfield offshore oil development and demonstrate its use in an uncertainty analysis workflow. The proposed framework enables a systematic quantification of the uncertainties and provides users with an in-depth understanding of the impact of uncertainties on major design and operational decisions. The IAM is specified for a hypothetical deepwater field to investigate three decisions: optimal initial facility capacity, optimal number of pre-drilled wells, and the optimal number of drilling rigs. The uncertainty analysis addresses two critical variables: reservoir thickness and the transmissibility between the reservoir compartments. This work develops and demonstrates a fast-solving physics-based integrated optimization model where production, drilling, and facility expansion decisions are endogenous (that is, the model solves for these variables implicitly) and thus provides higher quality (and faster) guidance in many cases than the design-of-experiments and response surface workflows currently being used in the oil and gas industry.