Abstract
Economic constraints impose stringent limits on the number of wells that can be drilled in deepwater developments. Thus, optimal placement and operation of wells have a major impact on the project rewards. Well-placement in deepwater developments is a challenging optimization problem. Manual approaches to its solution can be cumbersome even with good use of engineering judgment: (a) There often exist many combinations of well locations subject to investigation; (b) There is need to optimize operational constraints for every well-placement scenario; (c) The optimization process has to be repeated for a variety of geologic realizations; (d) Presence of complex sub-seismic geologic architecture may render workflows that solely rely on seismic data obsolete. We developed an adjoint-based optimization algorithm that rapidly identifies alternative optimal well-placement scenarios for a given geologic realization. Adjoint-based gradients approximate the sensitivities of a suitable objective function with respect to well locations. These sensitivities guide the iterative search with improving directions that progressively maximize the ultimate recovery. The main advantage of the adjoint method is that it provides sensitivities with only one forward (reservoir) and one backward (adjoint) simulation, rendering computational costs affordable for field applications. The adjoint method in our implementation operates in local search mode.
Our algorithm is applied to two different geologically complex channelized turbidite reservoirs to identify alternative optimal locations for a new injector-producer pair given existing operational and well constraints. Ensuing results demonstrate that the adjoint-based sensitivities can be effectively used to find optimal well locations. In the field simulation models, the optimization derived well locations lead to a higher ultimate oil recovery compared to a manual optimization approach. The algorithm yields multiple alternative well-placement scenarios within a shorter amount of time compared to a single optimal location found through manual optimization.
