Abstract
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.
Highlights
Petroleum engineers are always interested in finding appropriate and reliable tools to predict the productivity of horizontal well as accurate predictions seem very important to conduct technical and economical feasible studies before drilling the wells which is very costly[1]
Designing a radial basis network often takes much less time than training a sigmoid/linear feed forward with back propagation network. This is because the required number of sigmoid neurons for FFNN is much less those required for radial basis
The data analysis is performed for the data collected from 15 wells in the Field-A, below are the results for the FFNN with two hidden layer with 22 neurons in the first layer and 24 neurons in the second layer {22 24} and 0.001 training mean square error goal
Summary
Petroleum engineers are always interested in finding appropriate and reliable tools to predict the productivity of horizontal well as accurate predictions seem very important to conduct technical and economical feasible studies before drilling the wells which is very costly[1]. Neural networks (NNs) estimated the flow pattern Bottom Hole Pressure with less than 5% error and frictional pressure drop with less than 30%. Single-stage pumps are mainly used when low to medium discharge pressure is required, while multi-stage pumps are designed to overcome higher discharge pressures This is the case of ESP used in the petroleum industry where fluids must be lifted from deep formations. Bottom-hole pressure surveys of the field will provide data which will assist in making a more accurate estimate, much earlier in the life of the field, of the time when wells must be produced by artificial lift and of the amount of fluid that will have to be handled. It is of considerable value to know within reasonable limits when the wells will have to be pumped
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