Reservoir management in mature waterflooded fields demands accurate dynamic modeling and agile optimization under complex heterogeneity and ever-changing conditions. This paper presents a novel reservoir engineering reservoir simulation (REROSIM) approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China. The methodology represents the reservoir as a network of connection units linking injectors and producers, characterized by transmissibility and connected volume parameters instead of traditional gridblocks. History-matched with production and injection data by automated machine-learning calibration, the model captures time-varying well connectivity and flow paths with high speed and reasonable accuracy.
Introduction
Given the limitations of traditional modeling approaches, a clear need exists for a model that captures essential interwell connectivity physics with more fidelity than lumped capacitance-resistance models (CRMs), yet is significantly faster and more data-driven than full simulations.
