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Application of Physics Embedded Machine Learning Models to Optimize Downhole Pump Size and Reactivate Closed Wells

One of the key challenges in mature fields is to maintain economic viability of production under varying reservoir and operating conditions. Artificial lift is considered an essential component on both sides of the equation as it “lifts both the revenue and cost”. Reactivating closed wells is another economic approach to improve profitability. The objectives of the work presented here are two-fold. We apply a novel reservoir modeling technique called Data Physics to optimize production under the existing pump conditions and get recommendations for pump rightsizing when required. Furthermore, we utilize the same model to reactivate the most profitable closed wells in the field.

Data Physics combines conventional reservoir physics with machine learning, data assimilation and advanced optimization techniques. In this paper, a Data Physics model is trained for a mature field and evolutionary algorithms were used to find optimal water injection plan (unconstrained) maximizing long-term and short-term production and NPV as well as minimizing water injection capacity. The optimization was performed on the injector side and does not necessarily address the fact that the resulting gross production may fall outside the optimum operational ranges of the down-hole pumps. To address this constraint, the optimization tool was then modified to include bottom-hole pressure and down-hole pump operational parameters and a new plan (constrained) was generated. The new constraints are nonlinear functions of the optimization parameters and there are as many of these constraints as the number of wells with down-hole pumps which makes the problem challenging for the optimization algorithm.

The unconstrained redistribution plan (maintaining the same water capacity) led to an increase of 3.9% in production while the constrained plan resulted in 2.2% in production. This comparison allowed the operator to estimate the potential incremental oil and the added value created by replacing existing pumps by more appropriated pumps and to perform economic analysis of the NPV of such replacements. Additionally, a simplistic one-at-a-time reactivation optimization was performed to identify the top 10 wells in terms of production. Reactivating these wells would lead to an expected 11.7% further gain in production.

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