Group Method of Data Handling (GMDH) Neural Network for Estimating Total Organic Carbon (TOC) and Hydrocarbon Potential Distribution (S1, S2) Using Well Logs

Mulashani, Alvin K.; Shen, Chuanbo; Asante-Okyere, Solomon; Kerttu, Paulus N.; Abelly, Elieneza Nicodemus

doi:10.1007/s11053-021-09908-3

Cited by 14 publications

(3 citation statements)

References 41 publications

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“…Routinely, the GMDH network connects the input and output layers through Volterra functional, series formula described by the Kolmogorov-Gabor polynomial, i.e., Eq. ( 6 ) 50 : here, M indicates the number of inputs, x is the input variables, and “ a ” is the coefficient. Afterward, the GMDH approach must be trained to minimize the square error ( SE ) between the real output ( y ) and the calculated output ( y cal ) according to Eq.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

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Applying feature selection and machine learning techniques to estimate the biomass higher heating value

Abdollahi,

Ranjbar,

Razeghi Jahromi

2023

Sci Rep

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The biomass higher heating value (HHV) is an important thermal property that determines the amount of recoverable energy from agriculture byproducts. Precise laboratory measurement or accurate prediction of the HHV is essential for designing biomass conversion equipment. The current study combines feature selection scenarios and machine learning tools to establish a general model for estimating biomass HHV. Multiple linear regression and Pearson’s correlation coefficients justified that volatile matter, nitrogen, and oxygen content of biomass samples have a slight effect on the HHV and it is better to ignore them during the HHV modeling. Then, the prediction performance of random forest, multilayer and cascade feedforward neural networks, group method of data handling, and least-squares support vector regressor are compared to determine the intelligent estimator with the highest accuracy toward biomass HHV prediction. The ranking test shows that the multilayer perceptron neural network better predicts the HHV of 532 biomass samples than the other intelligent models. This model presents the outstanding absolute average relative error of 2.75% and 3.12% and regression coefficients of 0.9500 and 0.9418 in the learning and testing stages. The model performance is also superior to a recurrent neural network which was recently developed in the literature using the same databank.

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Section: Machine Learning Methodsmentioning

confidence: 99%

“…Afterward, the GMDH approach must be trained to minimize the square error ( SE ) between the real output ( y ) and the calculated output ( y cal ) according to Eq. ( 7 ) 50 : …”

Section: Machine Learning Methodsmentioning

confidence: 99%

Applying feature selection and machine learning techniques to estimate the biomass higher heating value

Abdollahi,

Ranjbar,

Razeghi Jahromi

2023

Sci Rep

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show abstract

“…The successful application of computational intelligence (CI) in hydrocarbon exploration and exploitation in recent years, has seen the adoption of intelligence learning models in predicting TOC from well log data. [12][13][14][15][16][17][18][19][20][21][22][23] Computing intelligence is a captivating discipline that combines computational power with human intelligence to develop sophisticated and trustworthy solutions to stunningly nonlinear and complicated problems. The CI models have the advantage of being able to adapt and learn to the dynamic conditions of the reservoir such as depositional and formation environment whilst utilizing the entire suite of well logs for better prediction of TOC.…”

Section: Techniquesmentioning

confidence: 99%

An Improved Computational Learning-Based Model for Estimating Total Organic Carbon in Unconventional Shale Reservoirs

Guo¹

2023

TPE

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Unconventional resources have emerged as one of the crucial alternatives to the rapidly depleting of conventional hydrocarbon resources. The hydrocarbon potential of shale source rocks is assessed by the percentage of the organic index such as total organic carbon (TOC). Correct estimation of TOC is very important since minor deviations in anticipated results can lead to wastage of investments and time. A slight improvement in estimation practices, on the other hand, can increase the value of an exploration project. Therefore, the objective of this study is to present an improved classification and regression tree (CART) computational learning-based model as an improved alternative in estimating TOC from well logging data. Conventional well logs suite of bulk density, gamma-ray, deep resistivity, sonic transit time, spontaneous potential, and neutron porosity from Mihambia, Mbuo and, Nondwa, Formations of the Mandawa Basin Tanzania, were used as input variables. Results from the developed CART TOC model were compared with the random forest (RF) and backpropagation neural network (BPNN). It was observed that the proposed CART model trained better while generalizing better through unused testing data compared with RF and BPNN. CART model achieved R, RMSE, and MAPE values of 0.9615, 0.0840, and 0.5035 for training and 0.9703, 0.1162, and 0.3722 for testing respectively. The proposed model work with higher accuracy with the sensitivity analysis indicating that gamma-ray, deep resistivity, and sonic transit time significantly influenced the model outcome.

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An approach for total organic carbon prediction using convolutional neural networks optimized by differential evolution

Silva¹,

Saporetti²,

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬³

et al. 2023

Neural Comput & Applic

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Group Method of Data Handling (GMDH) Neural Network for Estimating Total Organic Carbon (TOC) and Hydrocarbon Potential Distribution (S1, S2) Using Well Logs

Cited by 14 publications

References 41 publications

Applying feature selection and machine learning techniques to estimate the biomass higher heating value

Applying feature selection and machine learning techniques to estimate the biomass higher heating value

An Improved Computational Learning-Based Model for Estimating Total Organic Carbon in Unconventional Shale Reservoirs

An approach for total organic carbon prediction using convolutional neural networks optimized by differential evolution

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