Application of Group Method of Data Handling to Strean-Way Transition

Ma, Shih-Wei; Kou, Chang‐Huan; Chen, Li; Wang, An-Pei

doi:10.1109/jcai.2009.128

Cited by 4 publications

(3 citation statements)

References 6 publications

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“… 47 , 48 GMDH also performs by connecting neurons linked to quadratic polynomials, which results in neurons in the successive layers. 49 The polynomial neural network has some benefits. Initially, the polynomial neural network can choose the most appropriate inputs in a set of applicants.…”

Section: Methodsmentioning

confidence: 99%

“…GMDH could be a multi-layer algorithm amalgamation. GMDH has been used to identify and recognize nonlinear relationships between the dependent and independent variables. , GMDH also performs by connecting neurons linked to quadratic polynomials, which results in neurons in the successive layers . The polynomial neural network has some benefits.…”

Section: Methodsmentioning

confidence: 99%

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An Accurate Reservoir’s Bubble Point Pressure Correlation

et al. 2022

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Bubble point pressure ( P b ) is essential for determining petroleum production, simulation, and reservoir characterization calculations. The P b can be measured from the pressure–volume–temperature (PVT) experiments. Nonetheless, the PVT measurements have limitations, such as being costly and time-consuming. Therefore, some studies used alternative methods, namely, empirical correlations and machine learning techniques, to obtain the P b . However, the previously published methods have restrictions like accuracy, and some use specific data to build their models. In addition, most of the previously published models have not shown the proper relationships between the features and targets to indicate the correct physical behavior. Therefore, this study develops an accurate and robust correlation to obtain the P b applying the Group Method of Data Handling (GMDH). The GMDH combines neural networks and statistical methods that generate relationships among the feature and target parameters. A total of 760 global datasets were used to develop the GMDH model. The GMDH model is verified using trend analysis and indicates that the GMDH model follows all input parameters’ exact physical behavior. In addition, different statistical analyses were conducted to investigate the GMDH and the published models’ robustness. The GMDH model follows the correct trend for four input parameters (gas solubility, gas specific gravity, oil specific gravity, and reservoir temperature). The GMDH correlation has the lowest average percent relative error, root mean square error, and standard deviation of 8.51%, 12.70, and 0.09, respectively, and the highest correlation coefficient of 0.9883 compared to published models. The different statistical analyses indicated that the GMDH is the first rank model to accurately and robustly predict the P b .

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

An Accurate Reservoir’s Bubble Point Pressure Correlation

et al. 2022

View full text Add to dashboard Cite

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“…According to Ayoub et al [28], GMDH may also be represented as a polynomial neural networks as well as an algorithm modeling method to define nonlinear input-output variable relationships. Ma et al [29] explained that GMDH algorithm operates by linking sets of neurons connected to quadratic polynomials, resulting in new sets of neurons in the subsequent layer.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

et al. 2020

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Permeability is an important petrophysical parameter that controls the fluid flow within the reservoir. Estimating permeability presents several challenges due to the conventional approach of core analysis or well testing, which are expensive and time-consuming. On the contrary, artificial intelligence has been adopted in recent years in predicting reliable permeability data. Despite its shortcomings of overfitting and low convergence speed, artificial neural network (ANN) has been the widely used artificial intelligent method. Based on this, the present study conducted permeability prediction using the group method of data handling (GMDH) neural network from well log data of the West arm of the East African Rift Valley. Comparative analysis of GMDH permeability model and ANN methods of the back propagation neural network (BPNN) and radial basis function neural network (RBFNN) were further explored. The results of the study showed that the proposed GMDH model outperformed BPNN and RBFNN as it achieved R/root mean square error (RMSE) value of 0.989/0.0241 for training and 0.868/0.204 for predicting, respectively. Sensitivity analysis carried out revealed that shale volume, standard resolution formation density, and thermal neutron porosity were the most influential well log parameters when developing the GMDH permeability model.

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Application of genetic algorithm combining operation tree (GAOT) to stream-way transition

Chen

Kou

Chen

et al. 2012

2012 International Conference on Machine Learning and Cybernetics

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The main purpose of this paper is to predict stream-way transition with genetic algorithm (GA) combined with the Operation Tree (OT), called GAOT. Therefore, the downstream stream-way transition according to the upstream conditions is forecasted by GAOT. Five main factors affect the stream-way transition including inflow position, inflow angle, slope, flow discharge, and sand content of suspended sediment were chosen as input variables. We selected two important cross sections nearby a damaged bridge of Ta-Chia River in Taiwan as a case study. The results show that GAOT has better performance than the traditional linear regression (LR) method.

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Application of Group Method of Data Handling to Strean-Way Transition

Cited by 4 publications

References 6 publications

An Accurate Reservoir’s Bubble Point Pressure Correlation

An Accurate Reservoir’s Bubble Point Pressure Correlation

Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

Application of genetic algorithm combining operation tree (GAOT) to stream-way transition

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