Shale volume estimation using ANN, SVR, and RF algorithms compared with conventional methods

Mohammadinia, Fatemeh; Ranjbar, A.A.; Kafi, Moein; Shams, Mohsen; Haghighat, Fariborz; Maleki, Mahdi

doi:10.1016/j.jafrearsci.2023.104991

Cited by 8 publications

(2 citation statements)

References 43 publications

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“…Each decision tree is made separately and based on the division of input variables and their values, as a decision regression. This means that each leaf of the decision tree estimates the regression value that is extracted based on the training data for that leaf 44 .…”

Section: Methodsmentioning

confidence: 99%

“…Among the advantages of the random forest algorithm, the following can be mentioned 44 , 45 : Interpretability: according to the structure of the decision tree, it is easy to understand which features are important for prediction. Resistance to noisy data: random forest has a good ability to deal with noisy data and shows good performance for new data.…”

Section: Methodsmentioning

confidence: 99%

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Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran

Dehghani,

Jahani,

Ranjbar

2024

Sci Rep

Self Cite

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Shear wave transit time is a crucial parameter in petroleum engineering and geomechanical modeling with significant implications for reservoir performance and rock behavior prediction. Without accurate shear wave velocity information, geomechanical models are unable to fully characterize reservoir rock behavior, impacting operations such as hydraulic fracturing, production planning, and well stimulation. While traditional direct measurement methods are accurate but resource-intensive, indirect methods utilizing seismic and petrophysical data, as well as artificial intelligence algorithms, offer viable alternatives for shear wave velocity estimation. Machine learning algorithms have been proposed to predict shear wave velocity. However, until now, a comprehensive comparison has not been made on the common methods of machine learning that had an acceptable performance in previous researches. This research focuses on the prediction of shear wave transit time using prevalent machine learning techniques, along with a comparative analysis of these methods. To predict this parameter, various input features have been employed: compressional wave transit time, density, porosity, depth, Caliper log, and Gamma-ray log. Among the employed methods, the random forest approach demonstrated the most favorable performance, yielding R-squared and RMSE values of 0.9495 and 9.4567, respectively. Furthermore, the artificial neural network, LSBoost, Bayesian, multivariate regression, and support vector machine techniques achieved R-squared values of 0.878, 0.8583, 0.8471, 0.847 and 0.7975, RMSE values of 22.4068, 27.8158, 28.0138, 28.0240 and 37.5822, respectively. Estimation analysis confirmed the statistical reliability of the Random Forest model. The formulated strategies offer a promising framework applicable to shear wave velocity estimation in carbonate reservoirs.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran

Dehghani,

Jahani,

Ranjbar

2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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Petrophysical properties evaluation and reservoir characterization of a giant gas field: Bangladesh case study

Hasan,

Mondol,

Chen

et al. 2023

Arab J Geosci

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Estimation of porosity and volume of shale using artificial intelligence, case study of Kashafrud Gas Reservoir, NE Iran

Ardebili,

Jozanikohan,

Moradzadeh

2023

J Petrol Explor Prod Technol

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The key problem in oil exploration and engineering is the lack of accurate and reliable data about the reservoir parameters of a field. Having a precise assessment of petrophysical properties can provide the ability to make decisions with a high degree of confidence about planning for production, exploitation, and further field development scenario. In this research, an artificial intelligence (AI)-based approach was developed to improve the estimation of reservoir parameters including porosity and volume of shale, which has a significant role in different stages of hydrocarbon exploration, in the Kashafrud Gas Reservoir in the northeast of Iran. For this purpose, we measured the petrophysical properties of 27 samples of the Kashafrud Formation. To increase the amount of data for employing a multilayer perceptron (MLP) artificial neural network (ANN), a geostatistical algorithm was used to increase the amount of laboratory measured data of porosity and volume of shale to 686 and 702, respectively. In addition, 2263 well-logging data from the same well were provided. The optimal MLP network with the topology of 6-7-1, and 6-8-1 was selected to estimate the porosity and shale volume with mean squared error (MSE) of 2.78731E−4, and 1.28701E−9, respectively. The training process was performed using two different sets of input data. In the first approach, all available well-logging data were used as input, ending up in high MSE. In the second approach, some selected well logs were used based on the results of sensitivity analysis which clearly improved the estimations. The ability of MLP networks made great improvements in the estimation of the both parameters up to 99.9%. The presence of valuable core data in this study significantly improved the process of comparison and conclusion. The final results prove that AI is a trusted method, also the potential of the ANN method for the reservoir characterization and evaluation associated problems should be taken into consideration. Due to the unavailability of core data along the whole wells, the application of intelligent methods, such as machine learning (ML) can be used to estimate the parameters in other oil or gas fields and wells.

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Shale volume estimation using ANN, SVR, and RF algorithms compared with conventional methods

Cited by 8 publications

References 43 publications

Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran

Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran

Petrophysical properties evaluation and reservoir characterization of a giant gas field: Bangladesh case study

Estimation of porosity and volume of shale using artificial intelligence, case study of Kashafrud Gas Reservoir, NE Iran

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