Opportunities and challenges for gas coproduction from coal measure gas reservoirs with coal‐shale‐tight sandstone layers: A review

Abstract

AbstractThe extraction of coal measure gas has been shifted toward thin gas reservoirs due to the depletion of medium‐thick gas reservoirs. The coproduction of coalbed gas, shale gas, and tight sandstone gas (called a multisuperposed gas system) is a key low‐cost technology for the enhancement of natural gas production from thin gas reservoirs in coal measure. As an emerging engineering exploitation technology at its early stage of development, gas coproduction confronts various engineering challenges in hydraulic fracturing, bottom‐hole pressure regulation, well network arrangement, and extraction sequence. The current understanding of the opportunities and challenges in the gas coproduction from the multisuperposed gas system is not comprehensive enough. In this case, the previous achievements in the field of gas coproduction should be urgently reviewed to provide valuable guidance and recommendations for further development. This review first discusses the regional and spatial distribution characteristics and possible reservoir combinations of gas reservoirs in coal measure. Then, the basic properties of different reservoirs, engineering challenges, and interlayer interference are comparatively analyzed and discussed. The current simulation models for gas coproduction and potential future research directions are further explored. The results indicate that the coupling effects of reservoir heterogeneity, interwell interference, and geological structure for increasing coproduction prediction accuracy should be included in future simulation models for gas coproduction. Careful investigation is required to explore the mechanisms and their further quantifications on the effects of interlayer interference in gas coproduction. The fractal dimension as a scale can play an important role in the characterization of the gas and water transport in different reservoirs. The machine learning methods have tremendous potential to provide accurate and fast predictions for gas coproduction and interlayer interference.