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

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 551 ◽  
Author(s):  
Baraka Mathew Nkurlu ◽  
Chuanbo Shen ◽  
Solomon Asante-Okyere ◽  
Alvin K. Mulashani ◽  
Jacqueline Chungu ◽  
...  
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Group Method ◽  
Well Testing ◽  
Well Log ◽  
Data Handling ◽  
Permeability Model ◽  
Log Data ◽  
Artificial Neural Network Ann

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.

Meteorological Tidal Predictions in the Mekong Estuary Using an Evolved ANN Time Series

2019 ◽  
Vol 53 (6) ◽  
pp. 27-34
Author(s):  
Tim Chen ◽  
C.Y.J. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Variation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Systems ◽  
Modeling And Forecasting ◽  
Physically Based ◽  
Artificial Neural Network Ann ◽  
Meteorological Tsunamis

AbstractThe reproduction of meteorological waves utilizing physically based hydrodynamic models is very difficult in light of the fact that it requires enormous amounts of information, for example, hydrological and water-driven time arrangement limits, stream geometry, and balance coefficients. Accordingly, an artificial neural network (ANN) strategy utilizing a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is perceived as a viable option for modeling and forecasting the maximum and time variation of meteorological tsunamis in the Mekong Estuary in Vietnam. The parameters, including both the nearby climatic and breeze field factors, for finding the most extreme meteorological waves are first examined, depending on the preparation of the evolved neural systems. The time series for meteorological tsunamis are used for training and testing the models, and data for three cyclones are used for model prediction. This study finds that the proposed advanced ANN time series model is easy to utilize with display and prediction tools for simulating the time variation of meteorological tsunamis.

Well-log correlation using a back-propagation neural network

1997 ◽  
Vol 29 (3) ◽  
pp. 413-425 ◽  
Author(s):  
Stefan M. Luthi ◽  
Ian D. Bryant
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Well Log

scholarly journals Different ANN Models for Short Term Electricity Price Forecasting

2019 ◽  
Vol 8 (3) ◽  
pp. 6706-6712
Keyword(s):  
Neural Network ◽  
Market Price ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Price Forecasting ◽  
Market Prices ◽  
Electricity Price Forecasting ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Pool Market

In a deregulated electiricity market, price forecasting is gaining demand with application of Artificial Neural Network (ANN). The paper deals with price forecasting with different ANN models.like Back Propagation Neural Network( BPNN), Radial Bias Function Neural Network (RBFNN) and Genectic Algorithm based Neural Network (GANN). A contextual investigation is made with the downloaded data of the day-ahead pool market prices of the California Pool Market using the above four different ANN models and the results are compared.

Offline Signature Recognition using Back Propagation Neural Network

2016 ◽  
Vol 4 (3) ◽  
pp. 678
Author(s):  
Asyrofa Rahmi ◽  
Vivi Nur Wijayaningrum ◽  
Wayan Firdaus Mahmudy ◽  
Andi Maulidinnawati A. K. Parewe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recognition Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Learning Rate ◽  
Signature Recognition ◽  
Artificial Neural Network Ann ◽  
Number Of Iterations

The signature recognition is a difficult process as it requires several phases. A failure in a phase will significantly reduce the recognition accuracy. Artificial Neural Network (ANN) believed to be used to assist in the recognition or classification of the signature. In this study, the ANN algorithm used is Back Propagation. A mechanism to adaptively adjust the learning rate is developed to improve the system accuracy. The purpose of this study is to conduct the recognition of a number of signatures so that can be known whether the recognition which is done by using the Back Propagation is appropriate or not. The testing results performed by using learning rate of 0.64, the number of iterations is 100, and produces an accuracy value of 63%.

Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data

Energy ◽  
2022 ◽  
Vol 239 ◽  
pp. 121915
Author(s):  
Alvin K. Mulashani ◽  
Chuanbo Shen ◽  
Baraka M. Nkurlu ◽  
Christopher N. Mkono ◽  
Martin Kawamala
Keyword(s):  
Group Method ◽  
Well Log ◽  
Data Handling ◽  
Log Data ◽  
Permeability Prediction ◽  
Levenberg Marquardt

scholarly journals Various Techniques Involving Plant Leaf Diseases Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 1544-1550
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Disease Classification ◽  
Biological Research ◽  
Support Vector ◽  
Agricultural Field ◽  
Plant Leaf ◽  
Artificial Neural Network Ann

In agricultural field, paddy development assumes an imperative job. Be that as it may, their developments are influenced by different diseases. There will be diminish in the plant growth, if the illnesses are not recognized at an early arrange. There are several image processing methods we can custom such as Genetic algorithm, Probabilistic Neural Network (NN), Back propagation Neural Network (BNN), Artificial-Neural-Network(ANN), and Support vector machine(SVM). Choosing an organization technique is continuously a tough task since the worth of outcome can differ for unlike input data. Plant leaf infection categorizations have wide-ranging applications in several fields such as in biological research, in Agriculture etc. This survey affords a summary of dissimilar organisation systems used for plant leaf disease classification. Also we have discoursed prevailing segmentation technique beside with classifiers for exposure of plant leaves.

Experimental Study and Mathematical Modeling of Propane-SCR-NOx Using Group Method of Data Handling and Artificial Neural Network

2016 ◽  
Vol 14 (2) ◽  
pp. 559-569 ◽  
Author(s):  
N. Ghasemian ◽  
H. Nourmoradi
Keyword(s):  
Neural Network ◽  
Mathematical Modeling ◽  
Artificial Neural Network ◽  
Volumetric Flow Rate ◽  
Mesh Size ◽  
Group Method ◽  
Data Handling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Scr No

Abstract In this study, the catalytic behavior of protonated clinoptilolite in propane-SCR-NOx was investigated. The experiments were carried out in the temperature range of 200–500 °C as a function of zeolite mesh size 20, 35 and 70 at different weights of zeolite (0.45–1 g) and flow rates (300–600 ml/min) and consequently at various gas hourly space velocities (GHSV). Group method of data handling (GMDH) and artificial neural network (ANN) system were applied for mathematical modeling of NOx conversion to N2 in propane-SCR-NOx. The operating temperature (T), volumetric flow rate (F) and the weight of clinoptilolite zeolite (W) and the conversion of NOx to N2 (X) were considered as the inputs and output, respectively. In order to evaluate the models performance, conversions of NOx obtained from the GMDH and ANN systems were compared with those obtained from the experimental method. It is concluded that the ANN could successively estimate the conversion and the results were in a good agreement with the experimental data.

Using a GMDH-type neural network and ARIMA model to forecasting GDP in Algeria during the period of 1990-2019

2020 ◽  
Vol 3 (4) ◽  
pp. 37-47
Author(s):  
Abdelkader Sahed
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Group Method ◽  
Data Handling ◽  
Performance Indices ◽  
Promising Technique ◽  
Using Data ◽  
Artificial Neural Network Ann

Forecasting is a method to predict the future using data and the last information as a tool assists in planning to be effective. GMDH-Type (Group Method of Data Handling) artificial neural network (ANN) and Box-Jenkins method are among the know methods for time series forecasting of mathematical modeling. in the present study  GMDH-type neural network and ARIMA method has been used to forecasted GDP in Algeria during the period 1990 to2019 (Time series of quarterly observations on Gross Domestic Product (GDP) is used). Root mean square error (RMSE) was used as performance indices to test the accuracy of the forecast. The empirical results for both models showed that the GMDH model is a powerful tool in forecasting GDP and it provides a promising technique in time series forecasting methods.

scholarly journals Design of an Artificial Neural Network (BPNN) to Predict the Content of Silicon Oxide (SiO2) based on the Values of the Rock Main Oxides: Glass Factory Feed Case Study

2022 ◽  
Vol 11 (02) ◽  
pp. 41-44
Author(s):  
Hamed Nazerian ◽  
Adel Shirazy ◽  
Aref Shirazi ◽  
Ardeshir Hezarkhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Silicon Oxide ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Artificial Neural Network Algorithm

Artificial neural network (ANN) is one of the practical methods for prediction in various sciences. In this study, which was carried out on Glass and Crystal Factory in Isfahan, the amount of silica purification used in industry has been investigated according to its analyses. In this discussion, according to the artificial neural network algorithm back propagation neural network (BPNN), the amount of silica (SiO2) was predicted according to rock main oxides in chemical analysis. These studies can be used as a criterion for estimating the purity for use in the factory due to the high accuracy obtained.

Method of Wind Power Prediction Based on ANN and Improvements

2014 ◽  
Vol 521 ◽  
pp. 143-146 ◽  
Author(s):  
Shao Shuai Song ◽  
Ran Li
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Good Effect ◽  
Power Prediction ◽  
Learning Speed ◽  
Wind Power Prediction ◽  
Artificial Neural Network Ann

This paper puts emphasis on studying on artificial neural network (ANN) method. Following that a model of wind power prediction is established based on back-propagation neural network. In order to improve the learning speed of ANN, a revised BP algorithm is adopted by using variable step and the combination of GA and BP algorithm. This method has a good effect in practice.

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