Adaptive Material Balance Method for Reserve Evaluation: A Combination of Machine Learning and Reservoir Engineering
Publication: Journal of Energy Engineering
Volume 148, Issue 3
Abstract
In the era of automation and artificial intelligence, some problems in the petroleum industry can be efficiently solved by machine-learning models based on big data. Previous researchers have addressed truly precise methods to evaluate reserves from accessible well-test data, although enormous time, effort, and cost were required. However, due to the lack of sufficient conventional material balance (CMB) test data for a large number of wells in the Su Dong (SD) gas field, only 12 wells (0.99%) were generally suitable for previous methods. We substituted the fully built-up reservoir pressures used in CMB with those converted from shut-in casing-pressure data. Suffering from data noise caused by pressure fluctuation, only 22.91% of the plots were linear. The nonlinear plots were enhanced into linear plots by denoising and smoothening according to engineering knowledge. Machine-learning models were developed to perform the enhancement automatically, which constructed an adaptive and feasible workflow. The model improved the percentage of applicable wells to 68.16% (824 out of 1,209) and achieved considerable precision for reserve evaluation. The proposed method is much more suitable than CMB in gas fields featuring unstable production status and provides feasible reserve evaluation. This paper presents an exemplary demonstration of the combination of petroleum engineering and machine learning.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to thank sponsors of the paper: the National Science Foundation for Distinguished Young Scholars of China (Grant No. 51304032), the National Natural Science Foundation of China (Grant No. 51674044), and the National Science and Technology Major Project (Grant No. 2016ZX05023-001). Some support came from the State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Chengdu University of Technology, China) Open Fund (Characterization of Shale Microstructural and Mechanical Properties, Grant No. PLC201607).
References
Al-Marhoun, M. A., S. Nizamuddin, A. A. Abdul Raheem, S. S. Ali, and A. A. Muhammadain. 2012. “Prediction of crude oil viscosity curve using artificial intelligence techniques.” J. Pet. Sci. Technol. 86–87 (May): 111–117. https://doi.org/10.1016/j.petrol.2012.03.029.
Cullender, M. H., and R. V. Smith. 1956. “Practical solution of gas-flow equations for wells and pipelines with large temperature gradients.” Trans. AIME 207 (1): 281–287. https://doi.org/10.2118/696-G.
El-Sebakhy, E. A., O. Asparouhov, A. A. Abdulraheem, A. A. Al-Majed, D. Wu, K. Latinski, and I. Raharja. 2012. “Functional networks as a new data mining predictive paradigm to predict permeability in a carbonate reservoir.” Expert Syst. Appl. 39 (12): 10359–10375. https://doi.org/10.1016/j.eswa.2012.01.157.
Kalantari-Dahaghi, A., S. Mohaghegh, and S. Esmaili. 2015. “Coupling numerical simulation and machine learning to model shale gas production at different time resolutions.” J. Nat. Gas Sci. Eng. 25 (Jul): 380–392. https://doi.org/10.1016/j.jngse.2015.04.018.
Kubat, M., R. C. Holte, and S. Matwin. 1998. “Machine learning for the detection of oil spills in satellite radar images.” Mach. Learn. 30 (2–3): 195–215. https://doi.org/10.1023/A:1007452223027.
Langley, P., and H. A. Simon. 1995. “Applications of machine learning and rule induction.” Commun. ACM 38 (11): 54–64. https://doi.org/10.1145/219717.219768.
Mattar, L., and R. McNeil. 1998. “The ‘flowing’ gas material balance.” J. Can. Pet. Technol. 37 (2): 52–55. https://doi.org/10.2118/98-02-06.
Oden, R. D., and J. W. Jennings. 1988. “Modification of the cullender and smith equation for more accurate bottomhole pressure calculations in gas wells.” In Proc., Permian Basin Oil and Gas Recovery Conf. Richardson, TX: OnePetro.
Peffer, J. W., M. A. Miller, and A. D. Hill. 1988. “An improved method for calculating bottomhole pressures in flowing gas wells with liquid present.” SPE Prod. Eng. 3 (4): 643–655. https://doi.org/10.2118/15655-PA.
Russel, S., J. Binder, D. Koller, and K. Kanazawa. 1995. Local learning in probabilistic networks with hidden variables[C]. Berkeley, CA: Berkeley Electrical Engineering and Computer Sciences.
Schilthuis, R. J. 1936. “Active oil and reservoir energy.” Trans. AIME 118 (1): 33–52. https://doi.org/10.2118/936033-G.
Smith, R. V. 1950. “Determining friction factors for measuring productivity of gas wells.” J. Pet. Sci. Technol. 2 (3): 73–82. https://doi.org/10.2118/950073-g.
Sukkar, Y. K., and D. Cornell. 1955. “Direct calculation of bottom-hole pressures in natural gas wells.” Trans. AIME 204 (1): 43–48. https://doi.org/10.2118/439-G.
Swets, J. A. 1988. “Measuring the accuracy of diagnostic systems.” Science 240 (4857): 1285–1293. https://doi.org/10.1126/science.3287615.
Takacs, G., and C. G. Guffey. 1989. “Prediction of flowing bottomhole pressures in gas wells.” In Paper Presented at the SPE Gas Technology Symp. Richardson, TX: OnePetro. https://doi.org/10.2118/19107-ms.
Young, K. L. 1967. “Effect of assumptions used to calculate bottom-hole pressures in gas wells.” J. Pet. Sci. Technol. 19 (4): 547–550. https://doi.org/10.2118/1626-pa.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Apr 20, 2021
Accepted: Dec 9, 2021
Published online: Apr 13, 2022
Published in print: Jun 1, 2022
Discussion open until: Sep 13, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.