Experimental study of a multimechanism formation pressure prediction model influenced by shale content

Abstract

The precise prediction of abnormally high-pressure formations has always been a hot topic in oil and gas exploration and development. In the Yingqiong Basin of the South China Sea, abnormally high-pressure formations are widespread, with narrow drilling safety windows and frequent accidents caused by the limited accuracy of formation pressure prediction. To improve the prediction accuracy of formation pressure, based on the characterization and classification method of the pressure forming mechanism of sediment and mechanics and considering the influence of shale content on pore pressure, in this study, soil consolidation acoustic experiments were carried out using real rock debris soil in a block of strata in this area. We obtained the response laws of parameters such as acoustic velocity and density under different pressure-forming mechanisms and established an acoustic density intersection chart exhibiting the influence of shale content. The experimental results show that under the same loading mechanism and conditions, the higher the shale content in the core is, the smaller the density, the smaller the acoustic velocity, and the smoother the trend line of the acoustic density intersection chart. When the shale content ≥0.5, under the unloading mechanism, the higher the shale content of the rock is, the further away the unloading trend line is from the loading trend line, and the greater the difference in the changes in the acoustic properties of the rock compared to the density properties. Moreover, the experimental conclusion also verified why the pressure formation mechanism in non-shale formations is difficult to determine. Based on the experimental results, the traditional Bowers model was improved, and a multimechanism formation pressure prediction model for the influence of shale content was established. The established model was validated and compared. The evaluation results showed that compared to the traditional model, the maximum prediction error of the multimechanism prediction model was reduced by approximately 20%, and the average prediction error was approximately 3%. This model effectively improved the prediction accuracy of the formation pore pressure.