四川盆地WR区块页岩气藏孔隙压力分布特征

Pore pressure distributional characterization of shale gas reservoir in WR block of Sichuan basin

页岩气储层孔隙压力是页岩气藏保存条件综合评价过程中的一项重要指标.常规基于欠压实成因的地层孔隙压力预测方法并不适用于存在大量有机质生烃增压过程的页岩气储层.本文在页岩储层岩石物理建模的基础上,通过在模型中添加有机相来间接考虑有机质生烃作用对泥页岩正常压实趋势的影响,结合有效应力原理,形成了面向页岩气储层的孔隙压力预测技术.该技术在四川盆地南部WR区块页岩气藏实际预测结果表明,研究区五峰组-龙马溪组页岩气藏属于典型的超压气藏.在纵向上,五峰-龙马溪组底部的有机碳含量高的优质页岩层段表现为明显的超压特征,压力系数介于1.6~2.1之间.在平面分布上,深凹区压力系数大,西部深凹区保存条件比东部隆起区要好.压力系数预测结果与钻井实测吻合率大于97%,该方法有效提高了钻前地层压力预测精度.

Pore pressure is an important index to evaluate the preservation condition of shale gas reservoir. The conventional pore pressure prediction method based on under-compaction cause is not suiTable for shale gas reservoir with lots of hydrocarbon generation and pressurization processes. In this paper, we develop the seismic pore pressure prediction method for shale gas reservoir, the key is to construct the reasonable pore prediction model of single well and get the accurate interval velocity volume. Firstly, with the constrain of Eaton constant, we construct the normal compaction trend with by different lithologies, and then construct the pore prediction model based on petrophysical modeling, which considers the influence of kerogen in hydrocarbon genesis. Secondly, we get the accurate interval velocity using high precision seismic inversion technology, and then predict the pore pressure in 3 D space based on effective stress principle. At last, we correct the prediction result of pore pressure by considering the structural factors. This method is successfully applied in WR block of south Sichuan basin, it shows that the Wufeng and Longmaxi formation is a typical overpressure shale gas reservoir, the pressure coefficient of high-quality reservoir section is between 1.6~2.1, the pressure coefficient of deep depression area is high, and the preservation condition of the western deep depression area is better than the uplift area. The coincidence rate between seismic prediction result and real drilling data is more than 97%, it means our method can improve the prediction accuracy of predrilling pore pressure effectively.