Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields

Abstract

The accurate determination of water saturation in shaly sandstone reservoirs has a significant impact on hydrocarbons in place estimation and selection of possible hydrocarbon zones. The available numerical equations for water saturation estimation are unreliable and depend on laboratory core analysis. Therefore, this paper attempts to use artificial intelligence methods in developing an artificial neural network model (ANN) for water saturation (Sw) prediction. The ANN model is developed and validated by using 2700 core measured points from the fields located in the Gulf of Suez, Nile Delta, and Western Desert of Egypt, with inputs including the formation depth, the caliper size, the sonic time, gamma rays (GRs), shallow resistivity (Rxo), neutron porosity (NPHI), the photoelectric effect (PEF), bulk density, and deep resistivity (Rt). The study results show that the optimization process for the ANN model is achieved by distributing the collected data as follows: 80% for training and 20% for testing processes, with an R 2 of 0.973 and a mean square error (MSE) of 0.048. In addition, a mathematical equation is extracted out of the ANN model that is used to estimate the formation water saturation in a simple and direct approach. The developed equation can be used incorporating with the existing well logs commercial software to increase the accuracy of water saturation prediction. A comparison study is executed using published correlations (Waxman and Smits, dual water, and effective models) to show the robustness of the presented ANN model and the extracted equation. The results show that the proposed correlation and the ANN model achieved outstanding performance and better accuracy than the existing empirical models for calculating the formation water saturation with a high correlation coefficient ( R 2 ) of 0.973, lowest mean-square error (MSE) of 0.048, lowest average absolute percent relative error (AAPRE) of 0.042, and standard deviation (SD) of 0.24. To the best of our knowledge, the current study and the proposed ANN model establish a novel base in the estimation of formation water saturation.

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

“…Yang et al proposed the construction and application of a comprehensive research digital platform for oil and gas exploration and development, which greatly improves the solving efficiency of artificial intelligence recognition. In recent years, the ensemble Kalman filter (EnKF), ensemble smooth multiple data assimilation (ESMDA), random gradient approximation algorithm, Markov process, and other non gradient methods for historical fitting have been widely applied. − At the same time, breakthroughs in machine learning and deep learning have also brought new ideas to artificial intelligence recognition. − Artificial intelligence uses the EnKF method to assist in automated history fitting, − and by establishing a reservoir model, set the parameters that need to be fitted. − Based on the EnKF method combined with production performance data, reservoir parameter inversion and reservoir simulation optimization are achieved, greatly improving the fitting accuracy. − This method greatly reduces the workload of reservoir engineering personnel and simplifies the history fitting workflow. However, the upstream development of China’s petroleum industry still faces challenges…”

Monitoring Methodology Study of a Reservoir Built on the Karst Depression: A Large Scale Laboratory Simulation Test

“…The relationship of the water saturation, porosity, and resistivity of porous material (rock and soil) was widely studied, especially in gas and oil exploration engineering. − The Archie equation was used F = ρ 0 ρ w = a ϕ m I = ρ t ρ 0 = b S w n where F is the stratigraphic factor ( F = ϕ – m ), ρ 0 is the resistivity of the rock when it contains water completely, ρ t is the resistivity of the rock, ρ w is the formation water resistivity, S is the water saturation, n is the saturation exponent (around 2.0), ϕ is porosity, m is the cementation factor, a and b are experimental constants. By establishing a theoretical model to quantitatively describe the relationship between moisture content and resistivity of rock and soil, the simplified Archie formula as ρ = ρ 0 S − β where ρ is the formation water resistivity (ρ = ρ w ), β is the parameters related to the characteristics of the medium.…”

Characterization of Lower Paleozoic Water and Gas Distribution in Yan’an Gas Field, Ordos Basin

“…According to the nature of the basement, geological evolution history, and tectonic features, the Ordos Basin can be divided into six tectonic units: the Yimeng Uplift, the Yishan Slope, the Tianhuan ore-seeking depressions, the Jinshi Belt, the Western Marginal Fold Belt, and the Weibei Uplift. − The Yishan Slope was formed at the end of the early Bailian period. − It is the largest primary tectonic unit in the basin, 250 km wide from east to west and 400 km long from north to south. − The current tectonic feature is a large, gently dipping monocline that dips to the west, with an average dip of about 1° and a dip of less than 1°. , The Ordos Basin was uplifted as a whole at the end of the Ordovician in the Early Paleozoic and underwent hundreds of millions of years of weathering stripping and candle stripping without Silurian, Devonian, and Lower Carboniferous. , During this period, the weathering crustal solution candle stripping zones formed by exposed weathering and buried karst are of great significance for the formation of ancient weathered crustal gas reservoirs in the Lower Paleoproterozoic. − Feng et al studied the geochemical characteristics of natural gas in the Paleozoic era. Zhang et al conducted a systematic study on pore structures such as primary pores, secondary pores, and microcracks.…”