Prediction of free flowing porosity and permeability based on conventional well logging data using artificial neural networks optimized by Imperialist competitive algorithm – A case study in the South Pars gas field

Jamshidian, Majid; Hadian, Mohsen; Zadeh, Mostafa Mansouri; Kazempoor, Zohreh; Bazargan, Pouya; Salehi, Hoorash

doi:10.1016/j.jngse.2015.02.026

Cited by 55 publications

(10 citation statements)

References 22 publications

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“…The results show that the prediction performance of the WOA-Elman model can achieve relatively high prediction accuracy. The RMSE of the model proposed in this paper is 0.1457, which is smaller than the back propagation neural network model proposed by P. M. Wong [13] in 1995 (RMSE = 2.700), fuzzy logic and neural network technology proposed by Wafaa El Shahat Afify [16] in 2010 (RMSE = 0.3896), feedforward directional propagation neural network proposed by Majid Jamshidian [32] in 2015 (RMSE = 0.1621) and deep learning model by Peng An [33] in 2018 (RMSE = 1.2688).…”

Section: Analysis Of Prediction Resultsmentioning

confidence: 67%

“…Energies 2022, 15, x FOR PEER REVIEW 10 of 14 network model proposed by P. M. Wong [13] in 1995 (RMSE = 2.700), fuzzy logic and neural network technology proposed by Wafaa El Shahat Afify [16] in 2010 (RMSE = 0.3896), feedforward directional propagation neural network proposed by Majid Jamshidian [32] in 2015 (RMSE = 0.1621) and deep learning model by Peng An [33] in 2018 (RMSE = 1.2688).…”

Section: Analysis Of Prediction Resultsmentioning

confidence: 99%

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WOA (Whale Optimization Algorithm) Optimizes Elman Neural Network Model to Predict Porosity Value in Well Logging Curve

Sun

Zhang

et al. 2022

Energies

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Porosity is a vital parameter in reservoir research. In the process of oil exploration, reservoir research is very important for oil and gas exploration. Because it is necessary to take cores for indoor test in order to accurately obtain the porosity value of cores, this process consumes significant manpower and material resources. Therefore, this paper introduces the method of machine learning to predict the porosity by using logging curves. This paper creatively develops a WOA (whale optimization algorithm) optimized Elman neural network model to predict porosity through logging parameters PE, DEN, M2R1, AC, GR, R25, R4 and CNL. Porosity measurement is constructed by taking cores for indoor experiments. It contains a total of 328 sample points. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity measured in the laboratory are used as the output parameter. In order to evaluate the performance of the model, RMSE, R2, MAE and VAF evaluation indexes are introduced to evaluate. This paper also introduces the non-optimized Elman neural network and BP neural network to compare with this optimization model. The research shows that the WOA algorithm optimizes the super parameters of the Elman neural network, so that the performance of the WOA–Elman model is better than the Elman neural network model and the BP neural network model.

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Section: Analysis Of Prediction Resultsmentioning

confidence: 67%

Section: Analysis Of Prediction Resultsmentioning

confidence: 99%

WOA (Whale Optimization Algorithm) Optimizes Elman Neural Network Model to Predict Porosity Value in Well Logging Curve

Sun

Zhang

et al. 2022

Energies

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“…After excluding certain factors, such as algorithms and parameters, they are considered related to the input data structure. In the process of the log data acquisition, noise will be inevitably generated due to interference from the environment and random factors, which brings errors to the calculation of geological parameters [60]. In addition to the TOC variation, the factors that affect the changes in log values include abnormal fluid pressure, hydrocarbons, tight reservoirs, overmature organic The measured TOC (%) The predicted TOC (%) = 7.9 = 6.242 R 2 = 0.8997 _init =100.00, λ_init = 0.001 Figure 4: Training and testing results of the Bayesian linear regression model with different hyperparameters.…”

Section: Hyperparameter Selection and Model Establishmentmentioning

confidence: 99%

Machine Learning Method for TOC Prediction: Taking Wufeng and Longmaxi Shales in the Sichuan Basin, Southwest China as an Example

et al. 2021

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The total organic carbon content (TOC) is a core indicator for shale gas reservoir evaluations. Machine learning-based models can quickly and accurately predict TOC, which is of great significance for the production of shale gas. Based on conventional logs, the measured TOC values, and other data of 9 typical wells in the Jiaoshiba area of the Sichuan Basin, this paper performed a Bayesian linear regression and applied a random forest machine learning model to predict TOC values of the shale from the Wufeng Formation and the lower part of the Longmaxi Formation. The results showed that the TOC value prediction accuracy was improved by more than 50% by using the well-trained machine learning models compared with the traditional Δ Log R method in an overmature and tight shale. Using the halving random search cross-validation method to optimize hyperparameters can greatly improve the speed of building the model. Furthermore, excluding the factors that affect the log value other than the TOC and taking the corrected data as input data for training could improve the prediction accuracy of the random forest model by approximately 5%. Data can be easily updated with machine learning models, which is of primary importance for improving the efficiency of shale gas exploration and development.

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“…Saljooghi & Hezarkhani (2015) used a method based on wavelet theory and artificial neural networks for the prediction of permeability [20]. The prediction of NMR logging parameters was made by Jamshidian et al (2015) [21]. For this purpose, they utilized a multilinear perceptron network being optimized by the imperialist competitive algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs

Talebkeikhah

Sadeghtabaghi

Shabani

2021

J. Hum. Earth Future

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Permeability is a vital parameter in reservoir engineering that affects production directly. Since this parameter's significance is obvious, finding a way for accurate determination of permeability is essential as well. In this paper, the permeability of two notable carbonate reservoirs (Ilam and Sarvak) in the southwest of Iran was predicted by several different methods, and the level of accuracy in all models was compared. For this purpose, Multi-Layer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBF), Support Vector Regression (SVR), decision tree (DT), and random forest (RF) methods were chosen. The full set of real well-logging data was investigated by random forest, and five of them were selected as the potent variables. Depth, Computed gamma-ray log (CGR), Spectral gamma-ray log (SGR), Neutron porosity log (NPHI), and density log (RHOB) were considered efficacious variables and used as input data, while permeability was considered output. It should be noted that permeability values are derived from core analysis. Statistical parameters like the coefficient of determination ( ), root mean square error (RMSE) and standard deviation (SD) were determined for the train, test, and total sets. Based on statistical and graphical results, the SVM and DT models perform more accurately than others. RMSE, SD and R2values of SVM and DT models are 0.38, 1.63, 0.97 and 0.44, 2.89, and 0.96 respectively. The results of the best-proposed models of this paper were then compared with the outcome of the empirical equation for permeability prediction. The comparison indicates that artificial intelligence methods perform more accurately than traditional methods for permeability estimation, such as proposed equations. Doi: 10.28991/HEF-2021-02-02-01 Full Text: PDF

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Prediction of free flowing porosity and permeability based on conventional well logging data using artificial neural networks optimized by Imperialist competitive algorithm – A case study in the South Pars gas field

Cited by 55 publications

References 22 publications

WOA (Whale Optimization Algorithm) Optimizes Elman Neural Network Model to Predict Porosity Value in Well Logging Curve

WOA (Whale Optimization Algorithm) Optimizes Elman Neural Network Model to Predict Porosity Value in Well Logging Curve

Machine Learning Method for TOC Prediction: Taking Wufeng and Longmaxi Shales in the Sichuan Basin, Southwest China as an Example

A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs

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