Abstract
The ability to monitor the flow bottom hole pressure in pumping oil wells provides important information regarding both reservoir and artificial lift performance. This paper proposes an iterative approach to optimize the spread constant and root mean square error goal of the radial basis function neural network. In addition, the optimized network is utilized to estimate this oil well pressure. Simulated experiments and qualitative comparisons with the most related techniques such as feedforward neural networks, neuro-fuzzy system, and the empirical model have been conducted. The achieved results show that the proposed technique gives better performance in estimating the flow of bottom hole pressure. Compared with the other developed techniques, an improvement of 7.14% in the root mean square error and 3.57% in the standard deviation of relative error has been achieved. Moreover, 90% and 95% accuracy of the proposed network are attained by 99.6% and 96.9% of test data, respectively.
Highlights
Flowing bottom-hole pressure is the pressure that can be measured or calculated nearby the producing formation while the well is producing hydrocarbons
Extensive experiments have been carried out using data collected from real oil wells to compare the performance of the developed radial basis function neural network with neurofuzzy system, two-layer feedforward neural network
Mean Square Error (RMSE), Standard Deviation (STD), Correlation Coefficient (R), and the accuracy have been used as performance metrics for the comparison
Summary
Flowing bottom-hole pressure is the pressure that can be measured or calculated nearby the producing formation while the well is producing hydrocarbons. To intervene the oil wells to measure FBHP is a tedious work, risky, and affects the wells production Because of all these difficulties to measure the flowing bottom-hole pressure, the most common problems in the field of petroleum engineering is how to predict FBHP. Radial basis function neural network is proposed to address this problem. Real data have been collected from different wells to be used as samples for learning and testing the developed network. To prove the effectiveness of the proposed radial basis function neural network in estimating FBHP, rigorous performance analysis have been conducted and a comparison with the most related approaches have been accomplished such as feedforward and neuro-fuzzy system, and the empirical model.
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