Abstract

For conventional black oil reservoirs, oil production rate decreases rapidly as cumulative oil production increases under the condition of constant sand-face pressure. Normally, conventional decline curve analysis (DCA) is very good at predicting decline trend. However, after producing for a few years, decrease in oil production rate of unconventional reservoirs may be much slower than conventional black oil reservoirs. Actual production data of liquid-rich unconventional reservoirs (e.g., in Permian) also shows significant deviation from the typical decline curves. The traditional DCA cannot explain such abnormal behaviors of unconventional reservoirs. In this paper, a simulation-based DCA for unconventional volatile oil reservoirs is proposed to address the limitations of conventional DCA particularly in low GOR fluid type. First, data analytics is applied to select a representative well for dynamic modeling. Second, with input from fluid PVT, well-log, geology, and production data, a reservoir model for a well is built to simulate multi-phase flow behavior in the reservoir condition. As the subsurface uncertainty is very large, we applied multi-realization history matching to characterize the rock and geomechanical properties. Third, with all the combinations of subsurface parameters, we can simulate the full range of forecasting uncertainty and the rate decline trend. The results are analyzed carefully to understand if drive mechanism is primary phase depletion or secondary phase driven. The material balance approach is also used as a quality check procedure to make sure recovery factor is reasonable. The representative forecast curves are then used to derive a new formulation of DCA. Results of numerical simulations are consistent with both theoretical analysis and actual production data, which further validates the new formulation of DCA for unconventional reservoirs proposed in this paper. We observed one case where oil rate declines to a plateau for an extended time. This phenomenon could be explained by solution-gas drive and captured by simulation model. The proposed method considers physics and real production data, and hence it is very useful when conventional DCA fails to predict the trend. For example, when reservoir pressure decreases below bubble point, solution gas comes out and traps in the matrix when gas saturation is smaller than the critical gas saturation. Solution gas is now a big player in driving the production. Physics-based simulation can capture oil rate decline due to solution gas driven mechanism, which normally happens later in the producing life of a well.

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