Generalized structure of group method of data handling to prognosticate scour around various cross-vane structures

Shahbazbeygi, Ebrahim; Yosefvand, Fariborz; Yaghoubi, Behrouz; Shabanlou, Saeid; Rajabi, Ahmad

doi:10.1007/s12517-021-07483-8

Cited by 4 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the problems related to determining PP using petrophysical variables are non-linear and complex. As a result, in this study, a type of artificial intelligence algorithm called GS-GMDH is investigated, which can solve non-linear problems easily (Tsai and Yen, 2017;Shahbazbeygi et al, 2021). Among the other limitations of this algorithm is that it has only two variables as input for each neuron, and this algorithm has not been able to analyze patterns with complex nonlinear polynomial equations (López-Belchí et al, 2018).…”

Section: Gs-gmdh For Determination Of Ppmentioning

confidence: 99%

Application of GMDH model to predict pore pressure

et al. 2023

View full text Add to dashboard Cite

Pore pressure (PP) is one of the essential and very critical parameters in the oil and gas industry, especially in reservoir engineering, exploitation, and production. Forecasting this valuable parameter can prevent huge costs incurred by the oil and gas industry. This research aims to develop a algorithm to better predict PP in subsurface -formations. Based on this, information from three wells (F1, F2, and F3) representing one of the Middle East oil fields was used in this research. The input variables used in this research include; laterolog (LLS), photoelectric index (PEF), compressional wave velocity (Vp), porosity (NPHI), gamma ray (spectral) (SGR), density (RHOB), gamma ray (corrected) (CGR), shear wave velocity (Vs), caliper (CALI), resistivity (ILD), and sonic transit time (DT). Based on the results presented in the heat map (Spearman’s correlation), it can be concluded that the pairs of parameters RHOB-PEF, CGR-SGR, RHOB-CALL, DT-PEF, PP-RHOB, Vs-RHOB, ILD-LLS, DT-CGR, and DT-NPHI are connected. In this research the GS-GMDH methods is used for modeling which is based on the Group method of data handling (GMDH). The results of this research show that this algorithm has an average error of RMSE = 1.88 Psi and R2 = 0.9997, indicating its high-performance accuracy. The difference between this method and the conventional GMDH method is that it can use three or more variables instead of two, which can improve prediction accuracy. Furthermore, by using the input of each neuron layer, the proposed model can communicate with other adjacent and non-adjacent layers to solve complex problems in the simplest possible way.

show abstract

Section: Gs-gmdh For Determination Of Ppmentioning

confidence: 99%

Application of GMDH model to predict pore pressure

et al. 2023

View full text Add to dashboard Cite

show abstract

Scour downstream of stepped-baffle weirs in wide rivers

2022

View full text Add to dashboard Cite

Comparative study of soft computing models for prediction of scour below two symmetric crossing jets

2022

View full text Add to dashboard Cite

The scour estimation downstream of the dam's hydraulic structures is a serious issue and has long been considered an important topic by hydraulic engineers. The literature shows that the computational methods are good alternatives for the prediction of the scour in various hydraulic structures in which the conventional methods demonstrate shortcomings. In the present paper, the different machine learning techniques have been used for the first time to predict the scour below the two symmetric crossing jets. Four different artificial neural networks (ANNs), including feed forward back propagation (FFBP), cascade forward back propagation (CFBP), radial basis function (RBF), generalized regression neural network (GRNN) along with the adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR) were employed and evaluated. Two important scour hole characteristics, namely the maximum scour hole depth, and its location relative to the scour hole origin, are predicted at different crossing angles. The soft computing models are also compared with the traditional empirical methods. The results indicate that the applied computing techniques perform satisfactorily and improve the results remarkably. They can estimate the scour more precisely than the regression models and can be considered robust alternative tools. A detailed sensitivity analysis is also performed on the FFBP that shows the crossing angle parameter has a powerful effect on the predictions. The SVR improved the results to 47% and 31.71% for the scour hole depth and its location in terms of the coefficient of correlation.

show abstract

Generalized structure of group method of data handling to prognosticate scour around various cross-vane structures

Cited by 4 publications

References 27 publications

Application of GMDH model to predict pore pressure

Application of GMDH model to predict pore pressure

Scour downstream of stepped-baffle weirs in wide rivers

Comparative study of soft computing models for prediction of scour below two symmetric crossing jets

Contact Info

Product

Resources

About