A hybrid spatial model based on identified conditions for 3D pore pressure estimation

Accurate spatial modeling of pore pressure helps to provide extensive and continuous information regarding the insightful downhole geological environment for safe drilling plans, performance analysis, and efficient reservoir modeling. Considering the varying drilling depth and pore pressure of study wells, a hybrid spatial modeling method is developed to estimate the pore pressure of an area. The method comprises three parts. In the first part, an appropriate number of pressure conditions is determined adaptively based on quantization error modeling (QEM), and similar pressure conditions are identified based on fuzzy c-means (FCM) method. In the second part, a kriging interpolation (Kriging) algorithm is introduced to produce profiles of different pressure subspaces. Afterward, random forest (RF) submodels are built separately according to the pressure profiles. In the last part, all of these submodels coalesced into one whole model. Verification of the proposed method is carried out on real case studies using field data from Songliao basin. The results show that the proposed method achieves the best prediction accuracy than the other five well-known methods for six wells, especially for well XS3, the mean absolute error and mean absolute percentage error of the proposed method achieve 0.717 and 6.063, which are at least 31% less than those of other five well-known methods.

Automated summary

The study developed a hybrid spatial modeling method to accurately estimate pore pressure of an area. The method outperforms five other well-known methods in prediction accuracy, especially excelling in well XS3.