Abstract

In field development, many decision-make processes are associated with uncertainties, which sometimes can expose the project to significant engineering and economic risks. Accurate uncertain assessment on field-scale production forecast requires multidisciplinary approach, such as Integrated Asset Modeling (IAM), to accommodate subsurface response, well performance, and surface facilities simultaneously. However, the added complexity can make IAM prohibitively and computationally expensive and time-consuming, particularly for uncertainty analysis and optimization studies, which normally demand thousands of simulation runs.

This paper presents a workflow that effectively integrates all the production components to an IAM then generates its proxy model for faster deployment. First, to properly configure the model, some practical sensitivity analyses were performed on how reservoir model selection and forecast period affect the uncertainty assessments. Second, in order to generate the proxy model, two-level screening design was first used to reduce the number of variables. Third, simulations through experimental design were conducted. The resulting response surface was used to fit a multiple regression model to reduce simulation runtime and maintain prediction accuracy at the same time.

The results suggested that in order to build accurate IAM and avoid misleading uncertainty results, it is recommended to take careful considerations to select proper tools to model different components of integrated production system. It was also found that the relative importance or influence of some parameters will not remain constant but evolve through the life of field. Thus, it is necessary to keep the production forecast period of uncertainty analysis consistent with the development period associated with field strategies and economic objectives. To reduce the computational cost and accelerate decision-making process, a proxy model was generated to approximate this physical IAM model with high correlation coefficient of 99.65%. In case study, the high impact factors that can explain most variations in response were identified from totally 29 uncertain variables through screening design, including water-oil contact, thickness, oil density, initial reservoir pressure, and porosity. The validation results showed that proxy model predictions matched closely with simulation results from actual IAM with correlation coefficient of 95.70%. Based on a synthetic production forecast uncertainty study using Monte Carlos simulation, the proxy model can reduce the runtime for five-thousand simulations to seconds from hundreds of hours if using fully physical IAMs.

Although using proxy model generated through experimental design for reservoir simulation studies is common approach, it has not been applied to integrated asset modeling which couples reservoir simulator with well and surface facilities. This workflow allows efficient and accurate uncertainty estimation for production forecast and field-scale optimizations.

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