一种改进的页岩气地震约束多因素孔隙压力预测方法

孔隙压力和孔隙压力系数是评价页岩气保存条件和选取不同开发工艺措施的关键参数,同时也是预测地应力的重要输入参数。四川盆地蜀南地区页岩气田下志留统龙马溪组由于受古构造、现今构造、埋深变化快等独特地质特点的影响,孔隙压力横向变化大、影响因素多,采用常规基于纵波速度的孔隙压力预测方法(如Eaton法等)对其预测的准确性欠佳。为此,根据该区页岩气田的地质特点,基于地震叠前同时反演纵横波数据,结合地层岩性变化以及剥蚀作用对孔隙压力和孔隙压力系数的影响情况,形成了考虑纵横波、岩性以及剥蚀作用的多因素孔隙压力及压力系数预测方法。研究结果表明:①地震反演数据的引入,提高了平面上的预测精度和细节丰富程度;②通过引入岩性变化这一因素,改善了纵向上压力系数预测的稳定性;③针对遭受较强剥蚀作用的地区,剥蚀作用强度的引入能够很好地预测剥蚀区附近低压井的压力系数。源自于10余口实钻井的压力数据证明,该方法预测结果的相对误差小于5%。结论认为,所建预测方法得到的结果误差小、精度高,可以为后续页岩气"甜点"区选择、井位部署、水平应力参数预测等提供更高质量的数据支持。

An improved seismic-constrained multi-factor pore pressure prediction method for shale gas reservoirs

Pore pressure and pore pressure coefficient are key parameters for the evaluation of shale gas preservation conditions and the selection of development technologies and measures. They are also important input parameters for geostress prediction. Affected by unique geological characteristics (such as ancient and current structures and rapid change of burial depth), the pore pressure of Lower Silurian Longmaxi Formation in the Shunan shale gas field of the Sichuan Basin varies greatly in the lateral direction and is influenced by many factors, so the conventional pore pressure prediction methods based on P-wave velocity (e.g. Eaton method) cannot provide accurate prediction. In this paper, a multiple-factor pore pressure and pressure coefficient prediction method considering P-wave, S-wave, lithology and denudation was developed based on the geological characteristics of the Shunan shale gas field and the seismic prestack simultaneous inversion data of P-wave and S-wave, combined with the influences of lithological change and denudation on pore pressure and pore pressure coefficient. And the following research results were obtained. First, the introduction of seismic inversion data improves prediction accuracy and detail richness on the plane. Second, the introduction of lithological change improves the vertical prediction stability of pressure coefficient. Third, for the reservoirs with stronger denudation, the introduction of denudation intensity can well predict the pressure coefficient of low-pressure wells nearby. The pressure data of more than 10 actual wells shows that the relative error of the prediction results by this method is less than 5%. In conclusion, this newly developed prediction method has small errors and high accuracy and can provide higher-quality data support for subsequent "sweet spot" area selection, well location deployment, horizontal stress parameter prediction and so on.