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Large-Scale Field Development Optimization Using a Two-Stage Strategy
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, ECMOR XVII, Sep 2020, Volume 2020, p.1 - 24
Abstract
Summary
The optimization of the locations of a large number of wells represents a challenging computational problem. This is because the number of optimization variables scales with the maximum number of wells considered, and some of these variables may be categorical if the determination of the number and types of wells is part of the optimization problem. In this work, we develop and test a two-stage strategy for large-scale field development optimization problems. In the first stage, wells are constrained to lie in repeated patterns, and the optimization variables define the pattern type and geometry (e.g., well spacing, orientation). This component of the optimization follows a previous procedure ( Onwunalu and Durlofsky, 2011 ), though several important modifications, including optimization of the drilling sequence, are introduced. The solution obtained in the first stage is used as an initial guess for the second stage. In this stage we apply comprehensive field development optimization, where the well location, type, drill/do not drill decision, completion interval (for 3D models), and drilling time variables are determined for each well. Pattern geometry is no longer enforced in this stage. Specialized treatments (consistent with actual drilling practice) are introduced for cases where multiple geomodels, used to capture geological uncertainty, are considered.
The two-stage procedure is applied to 2D and 3D models corresponding to different geological scenarios. Both deterministic and geologically uncertain settings are considered. All optimizations are performed using a derivative-free particle swarm optimization – mesh adaptive direct search hybrid algorithm. Our most challenging example involves optimization over multiple realizations of the Olympus model, which we simulate using a GPU- based commercial flow simulator. In all cases, results using the two-stage procedure are compared to those from a standard single-stage approach. We achieve consistently better optimizer performance using the two-stage approach. For example, in one case, the optimum achieved after 17,500 flow simulations using the standard approach is found after only 4400 flow simulations using the two-stage approach. In another case, for the same computational effort, the NPV achieved using the two-stage approach exceeds that of the standard approach by 4.7%. These results suggest that this optimization strategy may indeed lead to improved results in practical problems.
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