Prediction of Water Saturation in Tight Gas Sandstone Formation Using Artificial Intelligence

Ahmed Farid Ibrahim; Salaheldin Elkatatny; Mustafa Al Ramadan

doi:10.1021/acsomega.1c04416

Prediction of Water Saturation in Tight Gas Sandstone Formation Using Artificial Intelligence

ACS Omega. 2022 Jan 3;7(1):215-222. doi: 10.1021/acsomega.1c04416. eCollection 2022 Jan 11.

Authors

Ahmed Farid Ibrahim^{1

2}, Salaheldin Elkatatny^{1

2}, Mustafa Al Ramadan^{1

2}

Affiliations

¹ Department of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
² Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.

Abstract

Water saturation (S _w) is a vital factor for the original oil and gas in place (OOIP and OGIP). Numerous available equations can be used to calculate S _w, but their values have been unreliable and strongly depend on core analyses, which are costly and time-consuming. Hence, this study implements artificial intelligence (AI) modules to predict S _w from the conventional well logs. Artificial neural networks (ANNs) and the adaptive neuro-fuzzy inference system (ANFIS) were applied to estimate S _w using gamma-ray (GR) log, neutron porosity (NPHI) log, and resistivity (R _t) log. A data set of 782 points from two wells (Well-1 and Well-2) in tight gas sandstone formation was used to develop and test the different AI modules. Well-1 was used to construct the AI models, then the hidden data set from Well-2 was applied to validate the optimized models. The results showed that the ANN and ANFIS models were able to accurately estimate S _w from the conventional well logging data. The correlation coefficient (R) values between the actual and estimated S _w from the ANN model were found to be 0.93 and 0.91 compared to 0.95 and 0.90 for the ANFIS model during the training and testing processes. The average absolute percentage error (AAPE) was less than 5% in both models. A new empirical correlation was established using the biases and weights from the developed ANN model. The correlation was validated with the unseen data set from Well-2, and the correlation coefficient between the actual and the estimated S _w was 0.91 with an AAPE of 6%. This study provides AI application with an empirical correlation to estimate the water saturation from the readily available conventional logging data without the requirement for experimental analysis or well site interventions.