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

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.

Deep learning algorithm-enabled sediment characterization techniques to determination of water saturation for tight gas carbonate reservoirs in Bohai Bay Basin, China

“…Both models achieved accurate Sw estimation with R values exceeding 0.90 and AAPE less than 5%. The study introduced a new empirical correlation derived from ANN biases and weights, validated with unseen data, providing a reliable method for Sw prediction without costly core analyses 42 . Markovic and colleagues (2022) conducted a thorough examination using the XGBoost technique to ascertain inter-pore SW within Canadian sandstone oil resources through the analysis of LF-NMR and density information 43 .…”

Applications of AI in oil and gas projects towards sustainable development: a systematic literature review

“…The safety factor is highly important because, as the existing research has shown, many safety-related accidents occur on the sites where the oil and gas rigs are operational (Kuang et al 2021 ). The risk factors depend entirely on operational process efficiency because, ultimately, there is always a need for an effective working method to bring sustainability to a higher level while increasing product life cycle efficiency (Mehta et al 2021 ; Ibrahim et al 2022 ). The day-to-day operations in the operational phase of an oil and gas project have the major objective of producing more oil and gas so that the clients can be satisfied.…”

Hydrocarbon Potential Assessment of Carbonate-Bearing Sediments in a Meyal Oil Field, Pakistan: Insights from Logging Data Using Machine Learning and Quanti Elan Modeling

“…The hydrocarbon potential assessment through the well-logging evaluation of MOF, Pakistan, is the major goal of this research. Determination of the physical characteristics of the rocks is carried out by the well-known approach of formation evaluation. , The specific aims of the well-log evaluation are to determine the presence or lack of commercial hydrocarbon quantities in wellbore formations and to identify small amounts of hydrocarbons that are nonetheless very relevant from an exploration point of view. − Wireline log analysis techniques are generally aimed to stimulate primary log data into the approximate quantities of petroleum and water throughout the formation. − Physical rock characteristics, which include the pore geometry, lithology, porosity, and permeability, are clarified using geophysical logs. − The fundamental objective of the petrophysical analysis is the transformation of log data into petrophysical properties, including porosity, permeability, shale volume, water saturation, and possible hydrocarbon saturation. ,, Determining the correct petrophysical properties for a hydrocarbon deposit is very important for all results, including lithofacies identification, evaluation of the pay zone, hydrocarbon volume estimation, and geophysically interpreting the reservoir. − …”

“… 4 − 8 Wireline log analysis techniques are generally aimed to stimulate primary log data into the approximate quantities of petroleum and water throughout the formation. 9 − 12 Physical rock characteristics, which include the pore geometry, lithology, porosity, and permeability, are clarified using geophysical logs. 13 − 16 The fundamental objective of the petrophysical analysis is the transformation of log data into petrophysical properties, including porosity, permeability, shale volume, water saturation, and possible hydrocarbon saturation.…”