The Accurate Pore Pressure Prediction with Coupled Geomechanical and Thermodynamics Model

Abstract

Reservoir pressure and pore pressure coefficient are the key parameters for evaluating the preservation conditions of low permeability reservoirs and selecting different development processed and measures, as well as important input parameters for predicting ground stress. Due to the influence of unique geological characteristics such as ancient structure, current structure and rapid change of burial depth, the pore pressure in reservoir of the Upper Wuerhe Formation in the 53 east block of Junggar Basin has a large lateral change and is influenced by many factors. The conventional pore pressure prediction methods based on longitudinal wave velocity (such as Eaton method) have poor accuracy. Therefore, according to the geological characteristics of the reservoir in this area, based on the simultaneous inversion of P-wave and S-wave data before seismic stack, combined with the changes in formation lithology and the impact of denudation on pore pressure and pore pressure coefficient, this paper takes P-wave, S-wave, lithology, and denudation into account to predict pore pressure and pressure coefficient. The research results show that: ① the introduction of seismic inversion data improves the prediction accuracy and detail richness on the plane; ② the introduction of the lithology change factor improves the stability of the prediction of pressure coefficient in vertical direction; ③ for the area suffering from strong denudation, the introduction of denudation intensity help better predict the pressure coefficient of low pressure wells near the denudated area. The pressure data from more than 10 actual wells proves that the relative error of the prediction results of this method is less than 5%. It is concluded that the established prediction method has small error and high accuracy, and can be used to provide higher quality data support for the subsequent selection of good reservoirs, well location deployment, horizontal stress parameter prediction.