Application of Various Machine Learning Techniques in Predicting Water Saturation in Tight Gas Sandstone Formation

Abstract

Water saturation (Sw) is a vital factor for the hydrocarbon in-place calculations. Sw is usually calculated using different equations; however, its values have been inconsistent with the experimental results due to often incorrectness of their underlying assumptions. Moreover, the main hindrance remains in these approaches due to their strong reliance on experimental analysis which are expensive and time-consuming. This study introduces the application of different machine learning (ML) methods to predict Sw from the conventional well logs. Function networks (FN), support vector machine (SVM), and random forests (RF) were implemented to calculate the Sw using gamma-ray (GR) log, Neutron porosity (NPHI) log, and resistivity (Rt) log. A dataset of 782 points from two wells (Well-1 and Well-2) in tight gas sandstone formation was used to build and then validate the different ML models. The data set from Well-1 was applied for the ML models training and testing, then the unseen data from well-2 was used to validate the developed models. The results from FN, SVM and RF models showed their capability of accurately predicting the Sw from the conventional well logging data. The correlation coefficient (R) values between actual and estimated Sw from the FN model were found to be 0.85 and 0.83 compared to 0.98, and 0.95 from the RF model in the case of training and testing sets, respectively. SVM model shows an R-value of 0.95 and 0.85 in the different datasets. The average absolute percentage error (AAPE) was less than 8% in the three ML models. The ML models outperform the empirical correlations that have AAPE greater than 19%. This study provides ML applications to accurately forecast the water saturation using the readily available conventional well logs without additional core analysis or well site interventions.

RETRACTED ARTICLE: Data-driven EUR for multistage hydraulically fractured wells in shale formation using different machine learning methods

“…forest (RF), decision tree (DT), gradient boosting regression (GBR), function networks (FN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) can be applied to predict specific parameters from readily accessible data (Abdelgawad et al 2019;Al Dhaif et al 2022;Alarifi and Miskimins 2021;Ibrahim et al 2022;Moussa et al 2018).…”

Prediction of fracture density in a gas reservoir using robust computational approaches

“…Due to their powerful capability to map correlation among data and find solution for different problems, ML models' application has become much popular in various fields of science and engineering over the last few decades (Choubin et al, 2019;Ghalandari et al, 2019;Qasem et al, 2019;Torabi et al, 2019;Ahmadi et al, 2020;Band et al, 2020;Mosavi et al, 2020;Shabani et al, 2020;H Ghorbani and Davarpanah, 2021). For instance, ML methods have been applied for tackling a variety of challenges in petroleum engineering such as petrophysical (Rajabi et al, 2022c;Jafarizadeh et al, 2022;Tabasi et al, 2022;Zhang et al, 2022), reservoir characterization (Hassanpouryouzband et al, 2020;Abad et al, 2021a;Hassanpouryouzband et al, 2021;Zhang et al, 2021;Kamali et al, 2022;Kamali et al, 2022;Rajabi et al, 2022d;Hassanpouryouzband et al, 2022;Ibrahim et al, 2022;Zhang et al, 2022), production (Mirzaei-Paiaman andSalavati, 2012;Ghorbani et al, 2020;Abad et al, 2021b) drilling (Soares and Gray, 2019;Syah et al, 2021;Beheshtian et al, 2022;Pang et al, FIGURE 5 Flowchart for LSSVM-GA/PSO models used for prediction of fracture density. 2022).…”

Data-driven models to predict shale wettability for CO2 sequestration applications

“…SVMs are widely used in various fields such as bioinformatics, natural language processing, computer vision, and finance. SVM has different applications in oil and gas industry for classification and regression problems [38][39][40] .…”