Stochastic Discrete Well Affinity (DiWA) Model for Data Quality Diagnostic and Production Forecast

Abstract

In this study, we present a history matching framework for oil production forecast based on synthetic and real production data developed using the stochastic Discrete Well Affinity (DiWA) model. With the increase in the complexity of the geological model and the uncertainty in the geological data, it becomes more difficult (sometimes infeasible) to conduct model inversion and production based on conventional technique. To address this problem, we proposed a stochastic DiWA model with unstructured low-resolution mesh to represent the location of wells and reservoir fluid dynamics. With this method, we can efficiently train the forward model based on production data and a stochastic ensemble of property realization. The performance of forward evaluation benefits from the Operator-Based Linearization (OBL) technique and the adjoint method for gradient calculation. Before the model training, a large ensemble size of stochastic DiWA models is generated based on the permeability statistics of the real reservoir, and those models are then filtered using the misfit between the true production data and the DiWA model response. The filtered models have the best fit with the production history of the real reservoir, while they also contain the basic geological information of the real field. The proposed method is tested first on a synthetic data ensemble for production forecast and then applied to a real field. Based on real observations, we use the DiWA model for data quality diagnostic and identify certain flaws in the collected data and model assumptions. Based on these findings, the original assumptions and data observations have been adjusted and the resulting DiWA model was successfully trained. The prediction quality of the trained DiWA model is comparable to conventional simulation techniques based on detailed geological models and has the advantage of a much more efficient and faster ability to update and maintain the subsurface model when continuous updates in production data become available. This study shows that the proposed method can provide the history matching results with high accuracy and low computational costs. Furthermore, the performance of the stochastic DiWA model can be further improved using more comprehensive and geologically constrained initial and boundary conditions.