Bottom-Hole Pressure Estimation from Wellhead Data Using Artificial Neural Network

2019 ◽  
Author(s):  
Oluwatoyin Akinsete ◽  
Blessing Adetoye Adesiji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hole Pressure ◽  
Bottom Hole Pressure ◽  
Bottom Hole ◽  
Artificial Neural ◽  
Pressure Estimation

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 A novel approach based on artificial neural network for calibration of multi-hole pressure probes

2020 ◽  
Vol 73 ◽  
pp. 101739
Author(s):  
Homam Nikpey Somehsaraei ◽  
Magnus Hölle ◽  
Herwart Hönen ◽  
Mohsen Assadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hole Pressure ◽  
Novel Approach ◽  
Artificial Neural
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