Prediction of decline in shale gas well production using stable carbon isotope technique

Abstract

Prediction of shale gas production is a challenging task because of the complex fracture-pore networks and gas flow mechanisms in shale reservoirs. Empirical methods, which are used in the industry to forecast the future production of shale gas, have not been assessed sufficiently to warrant high confidence in their results. Methane carbon isotopic signals have been used for producing gas wells, and are controlled by physical properties and physics-controlling production; they serve as a unique indicator of the gas production status. Here, a workable process, which is combined with a gas isotope interpretation tool (also known as a numerical simulator), has been implemented in Longmaxi shale gas wells to predict the production decline curves. The numerical simulator, which takes into account a convection-diffusion-adsorption model for the matrix and a convection model for fractures in ¹³CH₄ and ¹²CH₄ isotopologues, was used to stabilize the carbon isotope variation in the produced gas to elucidate gas recovery. Combined with the production rates of the four developing wells, the total reserves ranged from 1.72 × 10⁸ to 2.02 × 10⁸ m³, which were used to constrain the trend of two-segment production decline curves that exhibited a transition from a hyperbolic equation to an exponential one within 0.82–0.89 year. Two-segment production decline curves were used to forecast future production and estimate ultimate recovery.

Graphical abstract

Keywords

shale gas / production decline / Longmaxi formation / carbon isotope

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Acknowledgments

We are indebted to Prof. Rixiang Zhu for his unremitting support and encourage during this work. We thank Dr. Gaohui Cao for help in technical assistance of the isotope fractionation model and numerical model calculation, also, Prof. Biao Jin, Kun He and Qisheng Ma for their insightful scientific comments and language improvements that significantly enhanced the quality of the original manuscript. This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA14050201).

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