A Water Saturation Prediction Using Artificial Neural Networks and an Investigation on Cementation Factors and Saturation Exponent Variations in an Iranian Oil Well

Mardi, M.; Nurozi, H.; Edalatkhah, S.

doi:10.1080/10916460903452033

Cited by 22 publications

(9 citation statements)

References 7 publications

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Section: Methodsmentioning

confidence: 99%

“…The application of the artificial neural network in the oil and gas industry can be seen in several instances, namely, petroleum reservoir characterization, 41 multiwell field development, 42 and prediction of water saturation. 43 The intelligent techniques presented previously, namely, the FL and ANN, are suited for particular problems but not for others when acting individually. For instance, the ANN excels in identifying patterns but fails to explain how the specific decision was made.…”

Section: Methodsmentioning

confidence: 99%

“…Added on by 40 on the ANN “is an information-computing system with particular performance characteristics in conjunction with biological neural networks”. The application of the artificial neural network in the oil and gas industry can be seen in several instances, namely, petroleum reservoir characterization, multiwell field development, and prediction of water saturation …”

Section: Methodsmentioning

confidence: 99%

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Determination of the Gas–Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS)

et al. 2022

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Determining the solution gas–oil ratio ( R s ) below the bubble point is a vital requirement that aids in multiple production engineering and reservoir analysis issues. Currently, there are some models available for the determination of the solution gas–oil ratio under the bubble point. However, they still may prove unreliable due to the applied assumptions and their specification to operate only under a particular range of data. In this study, the neuro-fuzzy, i.e., the adaptive neuro-fuzzy inference system (ANFIS) approach, is utilized to develop an accurate and dependable model for determining the R s below the bubble point pressure. A total of 376 pressure–volume–temperature datasets from Sudanese oil fields were used to establish the proposed ANFIS model. The trend analysis was applied to affirm the proper relationships between the inputs and outputs. Furthermore, using different statistical error analyses, the developed model was benchmarked against widely used empirical methods to evaluate the proposed method’s performance in predicting the R s at pressures below the bubble point. The proposed ANFIS model performs with an average absolute percent relative error of 10.60% and a correlation coefficient of 99.04%, surpassing the previously studied correlations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Determination of the Gas–Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS)

et al. 2022

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“…The results showed that the model is precise for the forecasting process with the correlation factor R 2 of 0.97. An artificial neural network model is proposed 25 to evaluate the saturation of water in gas reservoirs based on competitive algorithm. A total of 2200 data points taken from 12 wells have been collected to build the model.…”

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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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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“…Mais tarde, Winsauer et al (1952) propuseram uma modificação no modelo de Archie através da introdução do fator de tortuosidade (a). A forma atualmente mais conhecida da equação de Archie é dada por: O expoente de cimentação m e o expoente de saturação n são fontes de incerteza no cálculo da saturação fluida em reservatórios carbonáticos heterogêneos (Mardi et al, 2012). O expoente de cimentação seria igual a um se os poros de uma rocha fossem tubos cilíndricos retos e paralelos na direção da corrente elétrica (Olsen et al, 2008;Glover, 2010).…”

Section: Introductionunclassified

Avaliação dos parâmetros de Archie através de simulações numéricas em modelos digitais de rochas

Moraes¹,

SOARES²

2021

Proceeding of the 17th International Congress of the Brazilian Geophysical

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A Water Saturation Prediction Using Artificial Neural Networks and an Investigation on Cementation Factors and Saturation Exponent Variations in an Iranian Oil Well

Cited by 22 publications

References 7 publications

Determination of the Gas–Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Determination of the Gas–Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS)

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

Avaliação dos parâmetros de Archie através de simulações numéricas em modelos digitais de rochas

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