A Novel Semianalytical Model for the Relationship between Formation Pressure and Water Saturation in Coalbed Methane Reservoirs

Abstract

The accuracy of the relationship between formation pressure and water saturation has a direct impact on predicting the production performance of coal reservoirs. As a result, researchers are becoming more interested in this connection. The most commonly used method to evaluate this connection is the semianalytic method, but it disregards the impact of coal matrix shrinkage on pore compressibility, resulting in inaccurate water saturation estimations for coal reservoirs. A material balance equation that considers the effect of coal matrix shrinkage on cleat porosity and pore compressibility, as well as the gas–water relative permeability curve, is used for the first time in this study to establish a model between pressure and water saturation. Furthermore, this study extends the proposed pressure–saturation model to predict cumulative gas production and gas recovery, resolving the difficult problem of calculating recovery for coalbed methane reservoirs. To verify its accuracy, this study compares the proposed method with numerical simulations and previous methods; the results of the comparison show that the water saturation under formation pressure calculated by the method proposed in this study is closer to the results of the numerical simulation. Sun’s model ignores the effect of matrix shrinkage on pore compressibility, resulting in larger calculation results. The findings of this study indicate that the effect of coal matrix shrinkage on pore compressibility cannot be ignored, and that the proposed method can replace numerical simulation as a simple and accurate method for water saturation evaluation, which can be applied to predict cumulative gas and recovery estimation for closed coalbed methane reservoirs. The proposed method increases the accuracy of the semianalytical method and broadens its application. It is critical for the prediction of coal reservoir production performance and forecasting of production dynamics.