Proxy Capacitance- Resistance Modeling for Well Production Forecasts in Case of Well Treatments

We disclose a new -age field -scale production forecast model that handles complex treatment of wellbores during their life cycle. Predictive production models have been an object of increased interest and research for a long time due to the need for a fast tool for forecasting production rates or choosing an optimal field development scheme. The existing approaches based on the material balance equation have several limitations and are not very applicable for real objects. Full -scale reservoir modeling is relatively slow and requires large computing resources. In this paper, we propose a proxy model based on advanced capacitance-resistance approach. The model predicts multiphase flow rates based on the available historical data of field production and information about well treatments. In addition, it pro-vides preferable transmissibility trends, the presence of sealed or leaking faults, and the degree of dissipation between injector-producer well pairs. The advanced feature of the model is time-dependent weight coefficients, which have not been studied previously. They help in accounting the shut -in and workover periods and can be found during the optimization procedure simultaneously. Another feature is fast calculations due to a vectorized form of the model and application of modern optimization techniques. All these options allow modeling real oil fields with a large number of wells and a complex system of production control.

Automated summary

We introduce a new large-scale production forecast model that addresses the complex treatment of wellbores. The model predicts multiphase flow rates based on historical data and well treatment information, and provides preferred transmissibility trends and information about sealed or leaking faults. The model features time-dependent weight coefficients and fast calculations, making it suitable for modeling real oil fields with a large number of wells and complex production control systems.