Neural Networks for Flow Bottom Hole Pressure Prediction

2016 ◽  
Vol 6 (4) ◽  
pp. 1839 ◽  
Author(s):  
Medhat Awadalla ◽  
Hassan Yousef
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Data Sets ◽  
Mean Square ◽  
Layer Model ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole ◽  
Hidden Layer

Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure (FBHP) is a dominant process especially in wells lifted with electrical submersible pumps.  However, intervening a well occasionally is an exhaustive task, associated with production risk, and interruption. The previous empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper aims to find the optimum parameters of Feed-Forward Neural Network (FFNN) with back-propagation algorithm to predict the flowing bottom-hole pressure in vertical oil wells.  The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and for the sake of qualitative comparison, Radial Basis neural and network and the empirical modes have been developed. The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 12.5% of the total data sets to give 3.4 RMSE and 97% of the test data achieved 90% accuracy.

scholarly journals Neural Networks for Flow Bottom Hole Pressure Prediction

2016 ◽  
Vol 6 (4) ◽  
pp. 1839
Author(s):  
Medhat Awadalla ◽  
Hassan Yousef
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Data Sets ◽  
Mean Square ◽  
Layer Model ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole ◽  
Hidden Layer

Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure (FBHP) is a dominant process especially in wells lifted with electrical submersible pumps.  However, intervening a well occasionally is an exhaustive task, associated with production risk, and interruption. The previous empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper aims to find the optimum parameters of Feed-Forward Neural Network (FFNN) with back-propagation algorithm to predict the flowing bottom-hole pressure in vertical oil wells.  The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and for the sake of qualitative comparison, Radial Basis neural and network and the empirical modes have been developed. The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 12.5% of the total data sets to give 3.4 RMSE and 97% of the test data achieved 90% accuracy.

scholarly journals Artificial Intelligent techniques for Flow Bottom Hole Pressure Prediction

2016 ◽  
Vol 15 (12) ◽  
pp. 7263-7283
Author(s):  
M Awadalla ◽  
H Yousef ◽  
A Al-Shidani ◽  
A Al-Hinai
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Network Models ◽  
Data Sets ◽  
Mean Square ◽  
Layer Model ◽  
Neural Network Models ◽  
Bottom Hole ◽  
Hidden Layer

This paper proposes Radial Basis and Feed-forward Neural Networks to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and the standard statistical analysis has been  accomplished on the achieved results to validate the models’ prediction accuracy. For the sake of qualitative comparison, empirical modes have been developed. The obtained results show that the proposed Feed-Forward Neural Network models outperforms and capable of estimating the FBHPaccurately.The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 375 sets only to give a 3.4  RMES and 97% of the test data achieved 90% accuracy.

scholarly journals Prediction of the Bicarbonate Amount in Drinking Water in the Region of Médéa Using Artificial Neural Network Modelling

2020 ◽  
Vol 69 (11-12) ◽  
pp. 595-602
Author(s):  
Hichem Tahraoui ◽  
Abd Elmouneïm Belhadj ◽  
Adhya Eddine Hamitouche
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Drinking Water ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Mean Square ◽  
Ann Model ◽  
Network Modelling ◽  
Data Set ◽  
Artificial Neural

The region of Médéa (Algeria) located in an agricultural site requires a large amount of drinking water. For this purpose, the water analyses in question are imperative. To examine the evolution of the drinking water quality in this region, firstly, an experimental protocol was done in order to obtain a dataset by taking into account several physicochemical parameters. Secondly, the obtained data set was divided into two parts to form the artificial neural network, where 70 % of the data set was used for training, and the remaining 30 % was also divided into two equal parts: one for testing and the other for validation of the model. The intelligent model obtained was evaluated as a function of the correlation coefficient nearest to 1 and lowest mean square error (RMSE). A set of 84 data points were used in this study. Eighteen parameters in the input layer, five neurons in the hidden layer, and one parameter in the output layer were used for the ANN modelling. Levenberg Marquardt learning (LM) algorithm, logarithmic sigmoid, and linear transfer function were used, respectively, for the hidden and the output layers. The results obtained during the present study showed a correlation coefficient of <i>R</i> = 0.99276 with root mean square error RMSE = 11.52613 mg dm<sup>–3</sup>. These results show that obtained ANN model gave far better and more significant results. It is obviously more accurate since its relative error is small with a correlation coefficient close to unity. Finally, it can be concluded that obtained model can effectively predict the rate of soluble bicarbonate in drinking water in the Médéa region.

scholarly journals Pengenalan Karakter Tulisan Menggunakan Metode Backpropagation Neural Network

2019 ◽  
Vol 1 (2) ◽  
pp. 14
Author(s):  
Ni’mah Moham ◽  
Felix Andika Dwiyanto ◽  
Herman Santoso Pakpahan ◽  
Islamiyah Islamiyah ◽  
Hario Jati Setyadi
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Learning Rate ◽  
Parameter Learning ◽  
Mean Square ◽  
Backpropagation Neural Network ◽  
Hidden Layer

Artikel ini bertujuan untuk menjelaskan langkah-langkah kerja metode Backpropagation Neural Network (BPNN) dalam mengenali pola Aksara Lontara Bugis Makassar dan menjelaskan seberapa akurat dalam mengenali pola aksara Lontara Bugis Makassar. Dari hasil pengujian, diperoleh tingkat akurasi sebesar 76.08%, dengan parameter learning rate sebesar 0,02, epoch maksimum sebesar 50 epoch dan hidden layer sebanyak 90 neuron berdasarkan ciri 8. Adapun, performa mean square error (MSE) sebesar 0.00424 telah diperoleh. Namun demikian, waktu yang dibutuhkan saat proses pembelajaran terbilang cukup lama yaitu 16 menit 56 detik. Berdasarkan hasil pengujian metode BPNN dapat direkomendasikan untuk mengenali pola aksara Lontara Bugis Makassar dalam rangka menunjang pembelajaran kepada masyarakat.

scholarly journals ANALYSIS OF POWER TRANSFORMATOR CONDITIONS USING DGA METHOD USING ARTIFICIAL NEURAL NETWORK IN KRAKATAU ELECTRICAL POWER COMPANY

2020 ◽  
Vol 7 (6) ◽  
pp. 77-88
Author(s):  
Hartono ◽  
Muharni ◽  
Adipura ◽  
Martiningsih ◽  
Otong ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Test Data ◽  
Mean Square ◽  
Neural Network Method ◽  
Analysis Process ◽  
Network Method ◽  
Artificial Neural ◽  
Hidden Layer

Test method that can be done for transformer oil with DGA method. In identifying early transformer conditions, one of them is using IEC 60599 Standards. The artificial neural network training process used 341 data in the presence of nine conditions based on the IEC standard. The best network architecture configuration is a configuration with 3 neurons in the input layer, 10 neurons in the first hidden layer, 20 neurons in the second hidden layer, 20 neurons in the third hidden layer and 4 neurons in the output layer with the transfer logic. The results of the training give a regression value of 0.95216 and MSE (Mean Square Error) is worth 0.000216. Testing of artificial neural networks is done 19 first test data is performed to determine the number of transformer conditions that can be diagnosed by each method. From the test data obtained the accuracy value for artificial neural network models is 94.7%. The following will guide the structure of your abstract: Motivation/Background: Using the neural network method in this study is expected to improve accuracy and improve the transformer analysis process. Transformer to make one effective and fast way for transformers. Method: The IEC method is an effective method for implementing transformers. The way this method works is by comparing the concentration of solute, then the results are represented into nine kinds of conditions. However, this method has a weakness that is the length of time in the analysis process. Therefore, to overcome these deficiencies, this study uses the Artificial Neural Network (ANN) method with a comparison of the use of gas as its input and the condition transformer as its target. Results: The results of the training give a regression value of 0.95216 and MSE (Mean Square Error) is worth 0.000216. Conclusions: This study uses 460 data from existing data into 2 namely data for training that brings 341 data and data for testing to get 19 data. In this study using a neural network resolves the problem in this study. in this study obtained an accuracy of 94.4%, so this artificial neural network method has good potential to assist in this study.

scholarly journals PRAKIRAAN CURAH HUJAN BANDARA SYAMSUDIN NOOR DENGAN ALGORITMA NEURAL NETWORK BACKPROPAGATION

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Ir. H. M. Muflih ◽  
Nur Alamsyah ◽  
Wagino Wagino
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Mean Square ◽  
Hidden Layer

Data curah hujan bulanan merupakandata yang djadikan tujuan untukmemprakirakan curah hujan di BandaraSyamsudin Noor Banjarbaru denganmenggunakan neural network multilayerdan algoritma backpropagation. UntukSimulasi algoritma menggunakansoftware Matlab R2013a.Parameter yang digunakan dalampenelitian menggunakan metodepembelajaran backpropagation denganmomentum, laju pemahaman (learningrate) dan MSE (Mean Square Error)melihat selisih error yang dilakukan olehjaringan saat pelatihan maupumpengujian data, dengan arsitekturjaringan berupa 12 input, 5 Neuronlapisan layar tersembunyi (hidden layer)dan 1 output.Dari output yang dihasilkan sudahmendekati nilai target dan telah berhasilmelakukan proses dengan baik dalammengenali target dengan pola data yangditentukan.Kata Kunci: Curah Hujan, JaringanSyaraf Tiruan, backpropagation

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