Abstract:

With the recent boom in US shale gas production, optimal development of these assets has become a topic of significant interest. In addition to the complex physics typically associated with shale plays, field development optimization can be a challenging problem itself when binary, integer and continuous variables are concurrently present. For complex problems of this nature, it is appropriate to use simulation-based optimization to search the solution space.

Prior work has shown that shale gas reservoirs can be simplified, through application of a history-matching-like tuning procedure, from a locally refined, dual-porosity, dual-permeability model that considers desorption and non-Darcy effects, to a surrogate model without desorption, non-Darcy corrections or refined grids. This surrogate model is simply a single-porosity, single-permeability model with tuned parameters in the stimulated reservoir volume (SRV). The two tuning parameters, SRV porosity and permeability multipliers, are determined through a history matching process that minimizes the difference in production between the full and surrogate models. With an appropriately tuned surrogate model, optimization function evaluations can be performed at a low computational cost. Here we utilize PSO-MADS (particle swarm optimization - mesh adaptive direct search), a hybrid global-local optimization algorithm, to find the optimal tuning parameters for the surrogate model and to find an optimal field development plan. During the asset development optimization we consider five decision variables per well: well location, lateral length, number of fracture stages, bottomhole pressure, and finally whether or not to drill that well at all.

In this work, we integrate PSO-MADS, coupled-geomechanics, and new decision variables into the workflow. The results demonstrate the efficiency and utility of using proxy-based optimization for shale gas asset development. The results also indicate that geomechanics can have an effect on the optimal development plan and should be considered during optimization. The overall optimization framework developed in this study should be applicable for a wide range of shale development projects.

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Copyright 2016, Jamal Cherry: Please note that the reports and theses are copyright to their original authors. Authors have given written permission for their work to be made available here. Readers who download reports from this site should honor the copyright of the original authors and may not copy or distribute the work further without the permission of the author, Jamal Cherry.