考虑页岩气储层及开发特征影响的逻辑增长模型

随着页岩气开发工作的持续快速推进,如何深入分析页岩气井生产动态、评价气井生产特征,成为页岩气建产区评价、新区开发方案制订和规划方案编制等工作中亟待解决的问题。已有学者将逻辑增长模型(以下简称LGM模型)应用于非常规气藏气井的产量递减分析中,但未考虑页岩气储层及开发特征的影响,该方法仍有进一步完善和发展的空间。为此,基于前人的研究成果,建立了考虑页岩气储层及开发特征的逻辑增长模型(以下简称RB-LGM模型),并且以四川盆地长宁区块页岩气开发井为例对页岩气井生产动态进行了分析,将分析结果与Arps双曲递减模型的拟合、预测结果进行了对比;在此基础上,采用RB-LGM模型来确定水平井的最优井距。研究结果表明:①在LGM模型的基础上,RB-LGM模型根据页岩气开发采取大批量集群式部署水平井的特点,将页岩气储层参数(厚度、页岩密度、含气量)及开发参数(水平段长度、井距、采收率)相结合,作为水平井产气量拟合的逻辑控制因素,从而使气井的产量预测结果更加合理;②采用RB-LGM模型既能够对气井早期生产数据进行良好拟合,又能够保证后期预测结果在逻辑条件控制下收敛;③由于RB-LGM模型考虑了页岩气储层和开发特征的影响,因而既可以用于水平井井网优化,又可以通过数据反演来分析开发区域内储层参数的变化趋势。

A logical growth model considering the influence of shale gas reservoirs and development characteristics

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 model) 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 model) 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 Arps hyperbolic decline model. Finally, the optimal well spacing of horizontal wells was determined using the RB-LGM model. 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 the LGM model, RB-LGM model 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, the RB-LGM model 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, the RB-LGM model 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.