Physics-Based Data-Driven Model for Short-Term Production Forecast

Abstract

Before performing a field production forecast, an inverse problem has to be solved. Resulting in an ensemble of models that include the integration of real data with a complex physical and geological data describing subsurface processes. For large models, this approach can be very time and computationally expensive, therefore we propose an alternative approach for reservoir forecasting. In this work, we develop a physics-based data-driven model that purely relies on production data of the field and does not require any in-depth knowledge of the reservoir geology and physics. In the proposed approach, we utilize Delft Advanced Reservoir Terra Simulator (DARTS) as a base for our reservoir simulations. DARTS uses an Operator-Based Linearization technique for the approximation of exact physics. It allows us to encounter a more realistic interpretation of physics and is computationally efficient. The physics-based data-driven approach uses sequential regression to the data to increase the fidelity of the model forecast and encounter any significant changes in reservoir dynamics and physics over its history. The model was examined and validated for synthetic and real field production models. We demonstrate that the developed approach is capable of providing accurate and reliable production forecast on a daily basis.