A new model for production variation patterns and decline prediction of SZ shale gas reservoir in Sichuan basin
Production characteristics of shale gas wells are significantly different from those of conventional oil and gas wells,and studying the variation patterns of shale gas production and decline prediction is of vital importance to guide the adjustment of shale gas field development measures,improve the ultimate recoverable reserves,and recovery degree.Taking the SZ shale gas reservoir in the Sichuan basin as an example,this study investigated the variation patterns of production decline for different types of shale gas wells based on the change characteristics of daily gas production and cumulative gas production of shale gas wells in different production stages.Firstly,a dimensionless index of production decline rate was proposed to quantitatively describe the variation patterns of production decline rates for different types of shale gas wells;Then,by utilizing the quantitative change between production and decline rates,a novel prediction model for shale gas production decline was developed,leading to the derivation of a mathematical expression for cumulative production.Considering the variability of key parameters in the new model,the study calculated the variation patterns of daily gas production under different initial decline rates and decline rate amplitudes.Finally,by comparing and analyzing the adaptability of the new model with commonly used production decline models in historical fitting and dynamic prediction of different types of wells,the results demonstrate that the new model exhibits higher fitting accuracy and better predictive performance for production changes of different types of shale gas wells in SZ shale gas reservoir.This study provides valuable insights for effectively predicting production and evaluating the dynamic utilization of reserves in shale gas reservoirs.
shale gas reservoirproduction declinenon-dimensional decline ratedecline amplitudeprediction modelreserve utilization effect
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