| Popis: |
As shale gas development is advancing continuously and rapidly, how to deeply analyze the production performance of shale gas wells and evaluate their production characteristics has become an urgent problem in the evaluation of shale gas productivity construction zone, the formulation of new area development scheme and the preparation of planning program. Some scholars have applied the Logical Growth Model (LGM) in the production decline analysis of unconventional gas wells, but the influences of shale gas reservoir and development characteristics are not taken into consideration. Therefore, this method still has some space of further development and improvement. In this paper, a Logistic Growth Model considering shale gas reservoirs and development characteristics (RB-LGM) was established based on the previous research results. Then, it was applied to the shale gas development wells in the Changning Block of the Sichuan Basin to analyze their production performance, and the analysis results were compared with the fitting and prediction results provided by the Arps hyperbolic decline model. Finally, the optimal well spacing of horizontal wells was determined using RB-LGM. And the following research results were obtained. First, shale gas is produced by deploying horizontal wells in the clustered pattern in a large number, so on the basis of LGM, RB-LGM takes shale gas reservoir parameters (thickness, shale density, gas content) and development parameters (horizontal section length, well spacing and recovery factor) as the logic control factors of horizontal-well gas production fitting, so that the production prediction result of gas well is more reasonable. Second, RB-LGM can not only well fit the early production data of gas well, but ensure the convergence of the later prediction results under the control of logical conditions. Third, RB-LGM takes into account the influence of shale gas reservoir and development characteristics so as to optimize the horizontal well pattern and analyze the change trend of reservoir parameters in the development area through data inversion. |
