An Automatic Well Planner for Efficient Well Placement Optimization Under Geological Uncertainty

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.