Flow-Path Tracking Strategy in a Data-Driven Interwell Numerical Simulation Model for Waterflooding History Matching and Performance Prediction with Infill Wells

Abstract

Summary Recently, we have developed two computationally efficient data-driven models—interwell numerical simulation model (INSIM) and INSIM with front tracking (INSIM-FT)—for history matching, prediction, characterization, and optimization of waterflooding reservoirs. Then, stemming from the INSIM family, we derived a new data-driven model referred to as INSIM with flow-path tracking (FPT), for more-accurate interwell connectivity calculations, dynamic flow-path tracking, and waterflooding predictions. The model is a connection-based simulation model that is developed on the basis of a two-phase-flow material-balance equation. With the new model, we can characterize a reservoir by history matching the historical well flow-rate data without the detailed petrophysical properties of the reservoir. In INSIM-FPT, we provide an automatic and systematic workflow that incorporates Delaunay triangulation and imaginary wells to construct the model connection map. We apply a modified depth-first searching method to track all influential flow paths between an injector/producer pair for more-accurate calculations of dynamic allocation factors and control pore volumes (PVs). In addition, we provide a method to visualize a saturation field for a history-matched INSIM-FPT model. On the basis of the saturation map, we design a workflow to evaluate possible drilling locations and future performance of infill wells. For application, we create a synthetic reservoir with two different scenarios to demonstrate the reliability of INSIM-FPT. The results show that the dynamic allocation factors and control PVs between injector/producer pairs in the history-matched INSIM-FPT models are consistent with those obtained from the true streamline simulation model. Furthermore, the oil-saturation field generated with INSIM-FPT reasonably matches that obtained with the true model. It shows that the future predictions of infill wells on the basis of history-matched INSIM-FPT models are reasonably accurate but can be improved if more observed data are collected from near the planned infill wells. We also test a large-scale field problem with 65 wells, which shows INSIM-FPT can reasonably match and predict the field data.