Application of GMDH model to predict pore pressure - Gao, Guozhong; Hazbeh, Omid; Rajabi, Meysam; Tabasi, Somayeh; Ghorbani, Hamzeh; Seyedkamali, Reza; Shayanmanesh, Milad; Radwan, Ahmed E.; Mosavi, Amir H. (Frontiers in Earth Science, 2023)
Pore pressure (PP) is one of the essential and very important parameters in the oil and gas industries, as well as reservoir engineering, exploitation, and production. Forecasting this valuable parameter can prevent huge costs incurred by the oil and gas industries. This research aims to develop a new algorithm to better predict pore pressure in subsurface formations. Based on this, information from three wells (F1, F2 and F3) for one of the oil fields in Middle East field was used in this research. The input variables used in this research include; laterolog (LLS), photoelectric index (PEF), compressional wave velocity (Vp), porosity (NPHI), gamma ray (spectral) (SGR), density (RHOB), gamma ray (corrected) (CGR), shear wave velocity (Vs), caliper (CALI), resistivity (ILD), and sonic transit time (DT). Based on the results presented in the heat map (Spearman's correlation), it can be concluded that the pairs of parameters RHOB-PEF, CGR-SGR, RHOB-CALL, DT-PEF, PP-RHOB, Vs-RHOB, ILD-LLS, DT-CGR, and DT-NPHI have been connected. In this research, a new method called GMDH-GS was developed from the group data processing method (GMDH). The results of this research show that this algorithm has an average error of RMSE = 1.88 Psi and R2 = 0.9997, indicating its high-performance accuracy. The difference between this method and the conventional GMDH method is that it can use three or more variables instead of two, which can improve prediction accuracy. Furthermore, by using the input of each neuron layer, this new algorithm can communicate with other adjacent and non-adjacent layers to solve complex problems in the simplest possible way.