New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs

Gomaa, Sayed; Soliman, Ahmed Ashraf; Mohamed, Abdulrahman; Emara, Ramadan; Attia, Attia

doi:10.1021/acsomega.1c06044

Cited by 15 publications

(9 citation statements)

References 29 publications

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“…Table 3 shows a comparison of the statistical information of the proposed correlation with other empirical correlations. It can be seen from Table 3 that the proposed ANN empirical correlation gives AAPRE of 0.048 and MSE of 0.042, less than that obtained from Sayed et al (2022) 31 (AAPRE of 0.13 and MSE of 0.066) and Hamada et al 19 (AAPRE of 0.15 and MSE of 0.064). Statistically, the proposed ANN model is consistent and robust, based on the presented statistical analysis in Table 4 .…”

Section: Resultsmentioning

confidence: 56%

“…The input data used in their model include density, resistivity, and sonic logs, and saturation was the target. Numerous studies have been presented for water saturation calculation based on the regression method. − Gomaa et al (2022) proposed an ANN model for SW estimation using 383 core samples. The input data used in the model include porosity, permeability, and resistivity index.…”

Section: Application Of Ann To Predict Water Saturationmentioning

confidence: 99%

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

Azim¹,

Hamada²

2022

ACS Omega

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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.

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Section: Resultsmentioning

confidence: 56%

Section: Application Of Ann To Predict Water Saturationmentioning

confidence: 99%

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

Azim¹,

Hamada²

2022

ACS Omega

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“…Several researchers utilized artificial neural networks in different petroleum engineering applications. In reservoir engineering, ANN is used to estimate and predict the vertical sweep efficiency ( E V ) in terms of mobility ratio, water–oil ratio, and reservoir permeability variation; estimate the areal sweep efficiency ( E A ) at different well patterns; estimate gas condensate dew point pressure in the reservoir condition; and predict the water saturation in carbonate formations using the nonlinear multiple regression (NLMR) and ANN model . In drilling engineering applications, ANN helps in calculating the characteristics of the invert emulsion mud, such as yield point (YP) and plastic viscosity (PV) …”

Section: Methodsmentioning

confidence: 99%

New Models for Predicting Pore Pressure and Fracture Pressure while Drilling in Mixed Lithologies Using Artificial Neural Networks

et al. 2022

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Precise prediction of pore pressure and fracture pressure is a crucial aspect of petroleum engineering. The awareness of both fracture pressure and pore pressure is essential to control the well. It helps in the elimination of the problems related to drilling, waterflooding project, and hydraulic fracturing job such as fluid loss, kick, differential sticking, and blowout. Avoiding these problems enhances the performance and reduces the cost of operation. Several researchers proposed many models for predicting pore and fracture pressures using well log information, rock strength properties, or drilling data. However, some of these models are limited to one type of lithology such as clean and compacted shale formation, applicable only for the pressure generated by under compaction, and some of them cannot be used in unloading formations. Recently, artificial intelligence techniques showed a great performance in petroleum engineering applications. Hence, in this paper, two artificial neural network models are developed to estimate both pore pressure and fracture pressure through the use of 2820 data sets obtained from drilling data in mixed lithologies of sandstone, carbonate, and shale. The proposed artificial neural network (ANN) models achieved accurate estimation of pore and fracture pressures, where the coefficients of determination ( R 2 ) for pore and fracture pressures are 0.974 and 0.998, respectively. Another data set from the Middle East was used to validate the developed models. The models estimated the pore and fracture pressures with high R 2 values of 0.90 and 0.99, respectively. This work demonstrates the validity and reliability of the developed models to calculate pore and fracture pressures from real-time surface drilling parameters by considering the formation type to overcome the limitation of previous models.

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“…Therefore, we can depend on the new technique to characterize reservoir rock from only welllogging data instead of core analysis to save time and money. 7. Relation between permeability (K) calculated by using the Amaefule equation eq 4 versus permeability (K) obtained by using eq 22.…”

Section: Relations Between the New Technique And The Conventional Res...mentioning

confidence: 99%

Electrical Tortuosity Index: A New Approach for Identifying Rock Typing to Enhance Reservoir Characterization Using Well-Log Data of Uncored Wells

et al. 2023

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The world is gradually moving toward a severe energy crisis, with an ever-increasing demand for energy overstepping its supply. Therefore, the energy crisis in the world has shed important light on the need for enhanced oil recovery to provide an affordable energy supply. Inaccurate reservoir characterization may lead to the failure of enhanced oil recovery projects. Thus, the accurate establishment of reservoir characterization techniques is required to successfully plan and execute the enhanced oil recovery projects. The main objective of this research is to obtain an accurate approach that can be used to estimate rock types, flow zone indicators, permeability, tortuosity, and irreducible water saturation for uncored wells based on electrical rock properties that were obtained from only logging tools. The new technique is obtained by modifying the Resistivity Zone Index (RZI) equation that was presented by Shahat et al. by taking the tortuosity factor into consideration. When true formation resistivity (R t) and inverse porosity (1/Φ) are correlated on a log–log scale, unit slope parallel straight lines are produced, where each line represents a distinct electrical flow unit (EFU). Each line’s intercept with the y-axis at 1/Φ = 1 yields a unique parameter specified as the Electrical Tortuosity Index (ETI). The proposed approach was validated successfully by testing it on log data from 21 logged wells and comparing it to the Amaefule technique, which was applied to 1135 core samples taken from the same reservoir. Electrical Tortuosity Index (ETI) values show marked accuracy for representing reservoir compared with Flow Zone Indicator (FZI) values obtained by the Amaefule technique and Resistivity Zone Index (RZI) values obtained by the Shahat et al. technique, with correlation coefficients of determination (R 2) values equal to 0.98 and 0.99, respectively. Hence, by using the new technique, the Flow Zone Indicator, permeability, tortuosity, and irreducible water saturation were estimated and then compared with the obtained results from the core analysis, which showed a great match with the R 2-values of 0.98, 0.96, 0.98, and 0.99, respectively.

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New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs

Cited by 15 publications

References 29 publications

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

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

New Models for Predicting Pore Pressure and Fracture Pressure while Drilling in Mixed Lithologies Using Artificial Neural Networks

Electrical Tortuosity Index: A New Approach for Identifying Rock Typing to Enhance Reservoir Characterization Using Well-Log Data of Uncored Wells

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