How to Cite
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.
References
Akinsete O, Adesiji BA. Bottom-hole pressure estimation from wellhead data using artificial neural network. SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, 5-7 August 2019. https://doi.org/10.2118/198762-MS
Akintola SA, Olakunle F. Pressure drop determination for multiphase flow in a vertical well tubing. Trends Pet Eng. 2021; 1(2): 1-7. https://doi.org/10.53902/TPE.2021.01.000510
Ebrahimi A, Khamehchi E. A robust model for computing pressure drop in vertical multiphase flow. J Nat Gas Sci Eng. 2015; 26: 1306-16. https://doi.org/10.1016/j.jngse.2015.08.036
Faraji F, Santim C, Chong PL, Hamad F. Two-phase flow pressure drop modelling in horizontal pipes with different diameters. Nuclear Eng Des. 2022; 395: 111863. https://doi.org/10.1016/j.nucengdes.2022.111863
Akinseye CA, Omotara OO, Giwa A. Determination of flowing bottom-hole pressure of a well using modified Guo’s model. J Environ Sci Comput Sci Eng Technol. 2020; 9(2): 270-86. https://doi.org/10.24214/jecet.C.9.2.27086
El-Saghier RM, Abu El-Ela M, El-Banbi A. A model for calculating bottom-hole pressure from simple surface data in pumped wells. J Petrol Explor Prod Technol. 2020; 10: 2069-77. https://doi.org/10.1007/s13202-020-00855-y
Zhang C, Zhang R, Zhu Z, Song X, Su Y, Li G, et al. Bottom-hole pressure prediction based on hybrid neural networks and Bayesian optimization. Pet Sci. 2023. https://doi.org/10.1016/j.petsci.2023.07.009
Yue P, Yang H, He C, Yu GM, Sheng JJ, Guo ZL, et al. Theoretical approach for the calculation of the pressure drop in a multibranch horizontal well with variable mass transfer. ACS Omega. 2020; 5(45): 29209-21. https://doi.org/10.1021/acsomega.0c03971
Osman EA, Ayoub MA, Aggour MA. Artificial neural network model for predicting bottom-hole flowing pressure in vertical multiphase flow. SPE Middle East Oil and Gas Show and Conference, Kingdom of Bahrain: SPE; 12-15 March 2005. https://doi.org/43.1098/11262-MS
Mohammadpoor M, Shahbazi K, Torabi F, Qazvini AR. A new methodology for prediction of bottomhole flowing pressure in vertical multiphase flow in Iranian oil fields using Artificial Neural Networks (ANNs). SPE Latin American and Caribbean Petroleum Engineering Conference, Lima, Peru: SPE; 1-3 December 2010. https://doi.org/10.2418/1009-MS
Pastorek N, Young KR, Eustes A. Downhole sensors in drilling operations. Proceedings of the 44th Workshop on Geothermal Reservoir Engineering Stanford University, Stanford, California: 11-13 February 2019, pp. 1-9.
Ashena R, Moghadasi J, Ghalambor A, Bataee M, Ashena R, Feghhi A. Neural networks in BHCP prediction performed much better than mechanistic models. International Oil and Gas Conference and Exhibition, Beijing, China, 8-10 June 2010. https://doi.org/10.2118/130095-MS
Lee J, Wattenbarger RA. Gas reservoir engineering. Richardson, Texas, USA: SPE; 1996; p. 5. https://doi.org/10.2118/9781555630737
Poettmann FH. The calculation of pressure drop in the flow of natural gas through pipe. Trans AIME. 1951; 192: 100-13.
Cullender MH, Smith RV. Practical solution of gas flow equations for wells and pipelines with large temperature gradients. Trans AMIE. 1956; 207: 281-7.
Ikoku CU. Natural Gas Engineering. Tulsa, Oklahoma: PennWell Publishing Company; 1980, pp. 317-55.
Ikoku CU. Natural Gas Reservoir Engineering. Malabar, Florida: Krieger Publishing Company, 1992.
Brar GS, Aziz K. Analysis of modified isochronal tests to predict the stabilized deliverability potential of gas wells without using stabilized flow data. J Pet Technol. 1978; 30(2): 297-304. https://doi.org/10.2118/6134-PA
Ikoku CU. Natural gas production engineering. New York: John Wiley & Sons; 1984.
Feng Q, Gao S, Zhang J, Ye X. Well bottom-hole flowing pressure evaluation method in 48 Block of S Gas Field. Paper presented at the 5th International Conference on Energy and Environmental Protection, Shenzhen, China, 17-18 September 2016.
Sukkar YK, Cornell D. Direct calculation of bottom-hole pressures in natural gas wells. Trans AIME. 1955; 204: 43-8.
Cullender MH, Smith RV. Practical Solution of Gas flow equations for wells and pipelines with large temperature gradients. Trans AIME. 1956; 207: 281-7.
Barjouei HS, Ghorbani H, Mohamadian N, Wood DA, Davoodi S, Moghadasi J, et al. Prediction performance advantages of deep machine learning algorithms for two-phase flow rates through wellhead chokes. J Pet Explor Prod. 2021; 11: 1233-61. https://doi.org/10.1007/s13202-021-01087-4
Okon AN, Ansa IB. Artificial neural network models for reservoir-aquifer dimensionless variables: influx and pressure prediction for water influx calculation. J Pet Explor Prod Technol. 2021; 11(4): 1885-1904. https://doi.org/10.1007/s13202-021-01148-8
Okon AN, Effiong AJ, Daniel DD. Explicit neural network-based models for bubble point pressure and oil formation volume factor prediction. Arab J Sci Eng. 2023; 48: 9221-57. https://doi.org/10.1007/s13369-022-07240-3
Mahmoudi S, Mahmoudi A. Water saturation and porosity prediction using back-propagation artificial neural network (BPANN) from well log data. J Eng Technol. 2014; 5(2): 1-8.
Song S, Wu M, Qi J, Wu H, Kang Q, Shi B, et al. An intelligent data-driven model for virtual flow meters in oil and gas development. Chem Eng Res Design. 2022; 186: 398-406. https://doi.org/10.1016/j.cherd.2022.08.016
Al-Bulushi N, King PR, Blunt MJ, Kraaijveld M. Development of artificial neural network models for predicting water saturation and fluid distribution. J Pet Sci Eng. 2009; 68: 197-208. https://doi.org/10.1016/j.petrol.2009.06.017
Tugwell KW, Livinus A. Predictive models for oil in place for oil rim reservoirs in the Niger Delta using machine learning approach. Pet Petrochem Eng J. 2023; 7(3): 1-16. https://doi.org/10.23880/ppej-16000361
Sircar A, Yadav K, Rayavarapu K, Bist N, Oza H. Application of machine learning and artificial intelligence in oil and gas industry. Pet Res. 2021; 6: 379-91. https://doi.org/10.1016/j.ptlrs.2021.05.009
Effiong AJ, Etim JO, Okon AN. Artificial intelligence model for predicting formation damage in oil and gas wells. SPE Nigeria Council 45th Nigeria Annual International Conference and Exhibition, 2-4 August 2021. http://dx.doi.org/10.2118/207129-MS
El-Bouchefry K, de Souza RS. Learning in big data: Introduction to machine learning. In: Skoda P, Adam F, Eds. Knowledge discovery in big data from astronomy and earth observation. Elsevier; 2020; pp. 225-49. https://doi.org/10.1016/B978-0-12-819154-5.00023-0
Taye MM. Understanding of machine learning with deep learning: architectures, workflow, applications, and future directions. Computers. 2023; 12(5): 91. https://doi.org/10.3390/computers12050091
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2023 Clement E. Udoma, Anietie N. Okon, Stella U. Udoeyop, Wilfred C. Okologume