Application of GMDH model to predict pore pressure

Abstract

Pore pressure (PP) is one of the essential and very critical parameters in the oil and gas industry, especially in reservoir engineering, exploitation, and production. Forecasting this valuable parameter can prevent huge costs incurred by the oil and gas industry. This research aims to develop a algorithm to better predict PP in subsurface -formations. Based on this, information from three wells (F1, F2, and F3) representing one of the Middle East oil fields 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 are connected. In this research the GS-GMDH methods is used for modeling which is based on the Group method of data handling (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, the proposed model can communicate with other adjacent and non-adjacent layers to solve complex problems in the simplest possible way.

Predictive Analytics Performance on Oil and Gas: A Significant Review

“…In the domain of non-temporal well analysis in the Middle East utilized the oil fields, Gao et al [134] utilized the group method of data handling (GS-GMDH) model with 2,748 samples. The researcher predicted pore pressure based on various parameters such as gamma-ray (spectral) (SGR), density (RHOB), gamma-ray (corrected) (CGR), and sonic transit time (DT).…”

A Review of Predictive Analytics Models in the Oil and Gas Industries

“…In the domain of non-temporal well analysis in the oil fields in the Middle East, Gao et al [ 136 ] utilized the group method of data handling (GS-GMDH) models with 2748 samples. The researchers predicted pore pressure based on various parameters such as gamma ray (spectral) (SGR), density (RHOB), gamma ray (corrected) (CGR), and sonic transit time (DT).…”

Field Telemetry Drilling Dataset Modeling with Multivariable Regression, Group Method Data Handling, Artificial Neural Network, and the Proposed Group-Method-Data-Handling-Featured Artificial Neural Network

“…Among others, GMDH has been utilized for accurate log interval value estimation (Mohammed Ayoub (2014) [34]), permeability prediction by Alvin K. Mulashani (2019) [35] and Lidong Zhao (2023) [36], as well as permeability modeling and pore pressure analysis by Mathew Nkurlu (2020) [37]. Additionally, GMDH finds applications in cement compressive strength design (Edwin E. Nyakilla, 2023 [38]), rock deformation prediction (Li et al, 2020 [39]), bubble point pressure estimation by Fahd Saeed Alakbari (2022) and Mohammad Ayoub (2022) [40,41], gas viscosity determination, CO 2 emission modeling (Rezaei et al, 2020 and2018 [42,43]), the prediction of CO 2 adsorption by Zhou L. (2019) [44] and Li (2017) [45], forecasting stock indices, and modeling power and torque as demonstrated by Ahmadi (2015) [46] and Gao Guozhong (2023) [47], and the prediction of pore pressure by Mgimba (2023) [48].…”