Data-Driven Inversion-Free Workflow of Well Performance Forecast Under Uncertainty for Fractured Shale Gas Reservoirs

Abstract

Well performance prediction and uncertainty quantification of fractured shale reservoir are crucial aspects of efficient development and economic management of unconventional oil and gas resources. The uncertainty related to the characterization of fracture topology is highly difficult to be quantified by the conventional model-based history matching procedure in practical applications. Data-space inversion (DSI) is a recently developed inversion-free and rapid forecast approach that directly samples the posterior distribution of quantities of interest using only prior model simulation results and historical data. This paper presents some comparative studies between a recent DSI implementation based on iterative ensemble smoother (DSI-IES), model-based history matching, and conventional decline curve analysis (DCA) for shale gas rate forecast. The DSI-IES method treats the shale gas production rate as target variables, which are directly predicted via conditioning to historical data. Dimensionality reduction is also used to regularize the time-series production data by low-order representation. This approach is tested on two examples with increasing complexity, e.g., a fractured vertical well and a multistage fractured horizontal well in the actual fractured Barnett shale reservoir. The results indicate that compared with the traditional history matching and DCA methods, the DSI-IES obtains high robustness with a high computational efficiency. The application of data-space inversion-free method can effectively tap the potential value directly from historical data, which provides theoretical guidance and technical support for rapid decision-making and risk assessment.