The amount of information available for field development planning is limited, forcing the Production Strategy to be designed under great amounts of uncertainty. During its implementation, new information allows the adaptation of the strategy for economic gain. This work reproduces the field development process under geological uncertainty in case study UNISIM-I-D (benchmark case based on Namorado Field in Brazil). The main objectives are to evaluate the process and observe the evolution of risk curves, all in a controlled environment with real field features.

The methodology generates new geostatistical images based on new well logs, assimilates production data using an ensemble-based method and re-optimizes the production strategy using a hybrid algorithm. The field development is carried out by repeatedly applying this framework under human supervision. Each step is customized using algorithms to simplify the implementation and to reduce computational effort, making this methodology more appealing for practical use. New data are collected from a high-resolution reference model that does not belong to the ensemble of models. The process starts with a production strategy, previously optimized under the uncertainties of the case study, which yields the real economic outcome within the original uncertain range.

Results show high quality history matching that excessively reduced the risk range and the variability of the updated model sets. Optimizations on the production strategy, based on the updated ensembles, consistently increased the expected monetary value of the project without guaranteeing an increment in the real NPV. Applying the methodology repeatedly throughout the field development increased the EMV by 29% (from 1.532 to 1.975 billion USD) while the real NPV decreased 2% (from 1.346 to 1.319 billion USD), falling out of the expected range and revealing that the model sets did not fully represent the real field. The lack of good representation is aggravated by heterogeneities inherent to the unknown reservoir, which are difficult to identify using only well logs and production data.

The results from the application of a closed-loop reservoir development process in a controlled environment warn against similar hidden mechanisms happening on a real field development under similar circumstances. They reveal intrinsic pitfalls in reservoir modeling that may contribute to production forecast problems and call for a reflection on how reservoir uncertainty assessment is performed. We prove that large sets of models do not guarantee coverage of geologic uncertainties as the field development process naturally changes the risk curves.

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