Modified CR-Type Material Balance Model for Well Production Forecasts in Case of Well Treatments

To reduce inefficient costs and environmental risks, oil companies strive to optimize the process of hydrocarbon production at all stages of field development, including geological and technical works at wells. In particular, it is important to predict fluid production with high accuracy. 3D hydrodynamic modeling is a generally accepted technique for solving this problem. It provides reliable results but requires many input data, computational resources, and time for calculations. Since the decision-making process has to be reactive, it is necessary to develop a simultaneously precise and prompt predictive instrument for quick forecasts of liquid production. The most promising tools for these purposes are proxy models based on solving the material balance equation. They adapt to the existing historical data even without PVT properties and reservoir data. Some of the most popular approaches are proxy models such as Capacitance Resistance Models (CRM). CR-type model is a material balance-based flow model, which provides preferable transmissibility trends, the presence of sealing or leaking faults with compressibility effects in consideration, and dissipation between injector-producer pairs. It is a data-driven model with adjustable time constants and interwell connectivity parameters. Before the model tuning, all parameters must be initialized with analytical or random approximations, and then they can be found by an appropriate optimization procedure. Historical-based Capacitance Models can be applied to poorly studied fields. Besides, they give an opportunity to rapidly optimize field development strategy by making calculations with different well exploitation parameters. They only require historical data of hydrocarbon production volumes, injection profiles, and bottom-hole pressure dynamics as input data. One of the main is that properties in the interwell space are estimated approximately and considered to be constant throughout the entire development history. However, this is a weak assumption in the case of including well interventions and stimulations. Thus, the main goal of this work is to adjust coefficients online to changes in well operation modes, introducing new wells or shut-in the existing ones. Since the governing equation includes the considered CRM improvement, users can perform optimization over different timespans, including "special" intervals. As a result, weighting connectivity parameters of the model can be depicted on a map of well interactions versus time.