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Summary A Derivative-Free Trust-Region (DFTR) algorithm is proposed to solve for the well control optimization problem. Derivative-Free (DF) methods are often a practical alternative because gradients may not be available and/or are unreliable due to cost function discontinuities, e.g., caused by enforcement of simulation-based constraints. However, the effectiveness of DF methods for solving realistic cases is heavily dependent on an efficient sampling strategy since cost function calculations often involve time-consuming reservoir simulations. The DFTR algorithm samples the cost function space around an incumbent solution and builds a quadratic approximation model, valid within a bounded region (the trust-region). A minimization of the quadratic model guides the method in its search for descent. Crucially, because of the curvature information provided by the model-based routine, the trust-region approach is able to conduct a more efficient search compared to other sampling methods, e.g., direct-search approaches. DFTR is implemented within FieldOpt, an open-source framework for field development optimization that provides flexibility with respect to problem parameterization and parallelization capabilities. DFTR is tested in the synthetic case Olympus against two other type of methods commonly applied to production optimization: a direct-search (Asynchronous Parallel Pattern Search) and a population-based (Particle Swarm Optimization). Current results show DFTR has promising convergence properties. In particular, the method is seen to reach fairly good solutions using only a few iterations. This feature can be particularly attractive for practitioners who seek ways to improve production strategies while using full-fledged models. Future work will focus on wider application of the algorithm in more complex field development problems such as joint problems and ICD optimization, and extensions to the algorithm to deal with multiple geological realizations and output constraints.

Summary An Automatic Well Planner (AWP) is developed to efficiently adjust pre-determined well paths to honor near-well model properties and increase overall production. The AWP replicates a modern geo-steering decision-making process, where adjustments to pre-programmed well paths are driven by continuous integration of data obtained from logging-while-drilling and look-ahead technology. This work focuses on combining the AWP into a robust optimization scheme. AWP-determined well trajectories follow reservoir properties in a more realistic manner than common well representations; thus, they deal better with geological uncertainty. Specifically, the AWP creates custom trajectories that consider individual geological near-well conditions of each realization in an ensemble of models. Thus, for each well path calculated by the optimization procedure, the AWP creates one custom trajectory for each geological realization. The expected NPV, computed over the set of trajectories, is then used to assess the performance of the candidate well path. The core operation of the AWP relies on an artificial neural network for tailoring the trajectory to geological properties. The AWP embeds a geology-based feedback mechanism for the overall well placement search. Commonly, well placement searches are conducted using linear well path representations. Analog to realistic drilling operations, the AWP determines a custom trajectory by moving along such a path in a sequence of steps from the heel to the toe. Subsequent trajectory points are determined by the efficient processing of neighboring geological information through the AWP network. The proposed scheme is implemented within the open-source optimization framework FieldOpt, which provides a flexible interface for problem parameterization and parallelization. Tests are performed using two derivative-free algorithms: Asynchronous Parallel Pattern Search (APPS) and Particle Swarm Optimization (PSO). Both are applied to the Olympus ensemble. The results show that the AWP improved over a straight-line parametrization in a robust optimization scheme for both APPS and PSO.