A Novel Hierarchical Global-Local Model Calibration Method for Deep Water Reservoirs Under Depletion and Aquifer Influence

Abstract

Abstract An ensemble of rigorously history matched reservoir models can help better understand the interactions between heterogeneity and fluid flows, improve forecasting reliability, and locate infill-drilling opportunities to support field development plans. However, developing efficient calibration methods for complex, multi-million cell deep-water models remains a challenge. This paper presents a hierarchical global-local assisted-history matching (AHM) approach with new elements, applied to a complex deep-water reservoir. The new AHM method consists of two stages: global and local. In the global stage, the reservoir energy is matched using an evolutionary approach to calibrate the model parameters with build-up and average reservoir pressures. Instead of using regional/box multipliers, we use parameters that are in line with geologic and engineering data across the reservoir. In the local stage, local updates are made to reservoir heterogeneity to match water cut in a geologically continuous manner. The permeability field is calibrated to production data using a novel streamline-based sensitivity-driven AHM method to ascertain the spatial variability and geologic continuity of local updates. The sensitivity for each streamline is weighted by the water fraction and constrained by a time-of-flight cutoff to focus on water intrusion regions within the near wellbore region. The resulting method is physically intuitive and easy to implement in practice. The hierarchical AHM method is field-tested in a complex deep-water reservoir. Associated challenges from model-calibration perspective are multiple saturation-function/PVT regions, uncertain sand connectivity, multi-sand well penetrations, a long reservoir history, and depletion-driven recovery under the influence of an aquifer. The method is applied to match data including build-up/reservoir pressures, oil production rates, and water cut. The evolutionary approach generates an ensemble of models with well-matched oil production rates and build-up/reservoir pressure using global model parameters. Local updates using streamline-based gradients are then conducted to match the water cut for each ensemble member while maintaining overall pressure match quality. Results show that the hierarchical AHM method significantly reduces the data misfit and is well-suited to primary recovery in a deep-water setting with few producers and under the influence of mild/weak aquifers. The new developments in the local stage make the entire workflow more robust because ensuing variations do not disrupt the global match quality for problems without a strong coupling between pressure and saturation physics. The novelty of the proposed method lies in the streamline-based sensitivity computation method modified for use in history matching deep-water reservoirs undergoing depletion under mild/weak aquifer influence. Using a two-stage global-local AHM workflow, the proposed method is robust, efficient, and straightforward to implement and deploy.