Interpretation of Neural Network Models – New Insights of Estimating Static Bottom-Hole Pressures of Gas Wells in Nigerian Petroleum Provinces
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Udoma CE, Okon AN, Udoeyop SU, Okologume WC. Interpretation of Neural Network Models – New Insights of Estimating Static Bottom-Hole Pressures of Gas Wells in Nigerian Petroleum Provinces. Int. J. Petrol. Technol. [Internet]. 2023 Dec. 5 [cited 2024 Jul. 17];10:135-50. Available from: https://avantipublishers.com/index.php/ijpt/article/view/1458

Abstract

Methods to determine static bottom-hole pressure (BHP) from surface measurements include the average temperature and z-factor method, the Sukkar-Cornell method, the Cullender-Smith method, and the Poettmann method. Among these methods, the Poettmann method is preferable in the petroleum industry but with a concern for software developers, as the integral values to determine the static BHP are tabular. In this study, neural network-based models to predict the integral values using pseudo-reduced pressures and temperatures were developed. The 2-3-1, 2-4-1, and 2-5-1 neural-based models had overall correlation coefficients (R) of 0.9974, 0.99835, and 0.99745, respectively, for the maximum-minimum normalization method and R of 0.99745, 0.99805, and 0.9992 for the clip-scaling method. Comparing the models' predictions with the Lagrangian interpolated values resulted in R of 0.99895 and 0.9995 for the maximum-minimum and clip-scaling-based models. Thus, the developed models can predict Poettmann's integral values without table look-up to estimate static BHP in gas wells.

https://doi.org/10.15377/2409-787X.2023.10.10
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Copyright (c) 2023 Clement E. Udoma, Anietie N. Okon, Stella U. Udoeyop, Wilfred C. Okologume

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