Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

Abstract

Permeability is an important petrophysical parameter that controls the fluid flow within the reservoir. Estimating permeability presents several challenges due to the conventional approach of core analysis or well testing, which are expensive and time-consuming. On the contrary, artificial intelligence has been adopted in recent years in predicting reliable permeability data. Despite its shortcomings of overfitting and low convergence speed, artificial neural network (ANN) has been the widely used artificial intelligent method. Based on this, the present study conducted permeability prediction using the group method of data handling (GMDH) neural network from well log data of the West arm of the East African Rift Valley. Comparative analysis of GMDH permeability model and ANN methods of the back propagation neural network (BPNN) and radial basis function neural network (RBFNN) were further explored. The results of the study showed that the proposed GMDH model outperformed BPNN and RBFNN as it achieved R/root mean square error (RMSE) value of 0.989/0.0241 for training and 0.868/0.204 for predicting, respectively. Sensitivity analysis carried out revealed that shale volume, standard resolution formation density, and thermal neutron porosity were the most influential well log parameters when developing the GMDH permeability model.

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

“…Among several machine learning models explored over the literature, the group method of data handling type neural network (GMDHNN) is an dependent on the Rosenblatt's perceptron method introduced by Farlow [30]. GMDHNN is successfully applied in diverse engineering applications [31][32][33][34]. Within hydrology and water resources related research, Najafzadeh et al [35] developed the GMDHNN model for scour depth (SD) of pipelines estimation due to waves variability; the prediction of local SD at bridge abutments in coarse sediments with thinly armored beds was conducted by Najafzadeh et al [36]; simulation of flow discharge of straight compound channels was reported by Najafzadeh and Zahiri [37]; prediction of significant wave height was established by Shahabi et al [38]; prediction of turbidity considering daily rainfall and discharge data was determined by Tsai and Yen [39]; an improved modeling of the discharge coefficient for triangular labyrinth lateral weirs was described by Parsaie and Haghiabi [40]; an evaluation of treated water quality in a water treatment plant was carried out by Alitaleshi and Daghbandan [41]; a prediction of turbidity and the free residual aluminum of drinking water was tested by Daghbandan et al [42].…”

Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits

“…Also, they can be used to detect hidden patterns and dependencies controlled by such complicated functions that it would be very difficult or impossible to make a model using simple analytical methods. Neural networks can be also used for missing data prediction [29,30]. Neural networks have the basic ability to generalize, based on the fact that once taught on a certain set of data they can apply the acquired knowledge to completely new data with the same structure.…”

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

“…GMDH is an extended version of multivariable regression that contains non-linear interacting terms. 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].…”