Unconventional resource development is increasing quickly in many places worldwide. For unconventional resources, multistage completions play a key role for both reservoir performance and well economics, which makes completion optimization a critical technical and commercial decision. This work integrates the reservoir modeling, fracture simulation, production forecast, and synthetic data pool generation via Monte Carlo methods, and it simplifies the final optimization process into a selection from multiple options. There are many approaches used to optimize completion parameters in shale gas development in the Sichuan basin. Although a trial and error method may work well with an adequate number of wells, this approach is not efficient with few wells because it would take many years to optimize the drilling and completion strategy. Also, such an approach may produce ambiguous results related to high uncertainty due to drilling quality and completion inconsistencies. An innovative workflow is defined in this work that combines reservoir modeling, fracture network simulation, production matching, regression analysis, and Monte Carlo methods. The procedure begins with modeling of the reservoir using the proper geological environment and reservoir properties. Based on this model, the hydraulic fracture network is simulated with varied compl etion parameter sets, including fluid volume, proppant volume, perforation spacing, and stage spacing. Production forecasting is then performed for each of the fracture network simulations, and the result is matched with previous offset well performance. Regression analysis is used to simplify the relationships between the input (completion parameters) and the output (production results). Finally, based on the regression results, a Monte Carlo method is used to generate a large number of input and output pairs creating a type of synthetic completion choice catalog. This catalog provides a pool of completion options, effectively reducing the optimization process to a choice of the best fit-for-purpose options. A synthetic model based on Sichuan shale gas is used in this study to validate the workflow on a single-well basis. It successfully produced many synthetic simulation results. With the large number of completion parameters—production result pairs—it is easy to filter the results and identify which combinations are preferred in terms of cost and production. This work also demonstrates that optimization is subject to the definition of purpose and duration of the objectives, which can be used as an important evidence to support different strategies.
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