Applying Machine-Learning Techniques To Interpret Flow-Rate, Pressure, and Temperature Data From Permanent Downhole Gauges
- Chuan Tian (Stanford University) | Roland Horne (Stanford University)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- May 2019
- Document Type
- Journal Paper
- 386 - 401
- 2019.Society of Petroleum Engineers
- permanent downhole gauges, machine learning, pressure transient analysis, well test analysis, deconvolution
- 95 in the last 30 days
- 323 since 2007
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Permanent downhole gauges (PDGs) provide a continuous record of pressure, temperature, and, sometimes, flow rate during well production. The continuous record provides rich information about the reservoir and makes PDG data a valuable source for reservoir analysis (e.g., pressure-rate deconvolution for reservoir-model identification). It has been shown in previous work that the convolution-kernel (CK) -based data-mining approach is a promising tool to interpret flow-rate and pressure data from PDGs. The CK method denoises and deconvolves the pressure signal successfully without an explicit-breakpoint detection. However, the bottlenecks of computational efficiency and the incomplete recovery of reservoir behaviors limit the application of the method to interpret real-PDG data.
In this paper, three different machine-learning techniques were applied to flow-rate/pressure interpretation. We formulated the machine-learning techniques into a linear regression (LR) on parameters that connect the nonlinear flow-rate features with pressure targets. Such a formulation leads to a closed-form solution, which speeds up the computation dramatically. The machine-learning algorithms that were formulated using LR were shown to have the same learning quality as the CK method, and they outperformed it with much less computational effort. Next, the kernel method was applied to address the issue of the incomplete recovery of reservoir behaviors, because it efficiently expanded the dimension of the feature space without an explicit representation of the features, but it led to overfitting. Finally, kernel ridge regression (KRR) used the expanded features given by the kernel function to capture the more detailed reservoir behaviors, while controlling the prediction error using ridge regression (RR). It was shown that KRR recovers the full reservoir behaviors successfully (e.g., wellbore-storage effect, skin effect, infinite-acting radial flow, and boundary effect).
Some potential uses of temperature data from PDGs are also discussed in this paper. Machine learning was shown to be able to model the temperature and pressure data recorded by PDGs, even if the actual physical model is complex. This originates from the fact that, by using features as an approximation of model characteristics, machine learning does not require a perfect knowledge of the physical model. The modeling of pressure using temperature data was extended to two promising applications: pressure-history reconstruction using temperature data, and the cointerpretation of temperature and pressure data when flow-rate data are not available.
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Chorneyko, D. M. 2006. Real-Time Reservoir Surveillance Using Permanent Downhole Pressures—An Operator’s Experience. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24–27 September. SPE-103213-MS. https://doi.org/10.2118/103213-MS.
Duru, O. O. and Horne, R. N. 2010. Modeling Reservoir Temperature Transients and Reservoir-Parameter Estimation Constrained to the Model. SPE Res Eval & Eng 13 (6): 873–883. SPE-115791-PA. https://doi.org/10.2118/115791-PA.
Friedman, J., Hastie, T., and Tibshirani, R. 2010. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software 33 (1): 1–22. http://www.jstatsoft.org/v33/i01/.
Hastie, T., Tibshirani, R., and Friedman, J. 2009. The Elements of Statistical Learning, second edition. New York: Springer.
Horne, R. N. 2007. Listening to the Reservoir—Interpreting Data From Permanent Downhole Gauges. J Pet Technol 59 (12): 78–86. SPE-103513-JPT. https://doi.org/10.2118/103513-JPT.
Horne, R. N. 1995. Modern Well Test Analysis, second edition. Palo Alto, California: Petroway.
Liu, Y. 2013. Interpreting Pressure and Flow Rate Data From Permanent Downhole Gauges Using Data-Mining Approaches. PhD dissertation, Stanford University, Stanford, California (March 2013).
Liu, Y. and Horne, R. N. 2011. Interpreting Pressure and Flow Rate Data From Permanent Downhole Gauges Using Data-Mining Approaches. Presented at the SPE Annual Technical Conference and Exhibition, Denver, 30 October–2 November. SPE-147298-MS. https://doi.org/10.2118/147298-MS.
Liu, Y. and Horne, R. N. 2012. Interpreting Pressure and Flow-Rate Data From Permanent Downhole Gauges by Use of Data-Mining Approaches. SPE J. 18 (1): 69–82. SPE-165346-PA. https://doi.org/10.2118/165346-PA.
Liu, Y. and Horne, R. N. 2013a. Interpreting Pressure and Flow Rate Data From Permanent Downhole Gauges Using Convolution-Kernel-Based Data-Mining Approaches. Presented at the SPE Western Regional & AAPG Pacific Section Meeting 2013 Joint Technical Conference, Monterey, California, 19–25 April. SPE-165346-MS. https://doi.org/10.2118/165346-MS.
Liu, Y. and Horne, R. N. 2013b. Interpreting Pressure and Flow Rate Data From Permanent Downhole Gauges With Convolution-Kernel-Based Data-Mining Approaches. Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, 30 September–2 October. SPE-166440-MS. https://doi.org/10.2118/166440-MS.
Nomura, M. 2006. Processing and Interpretation of Pressure Transient Data From Permanent Downhole Gauges. PhD dissertation, Stanford University, Stanford, California (September 2006).
Ouyang, L. B. and Kikani, J. 2002. Improving Permanent Downhole Gauge (PDG) Data Processing via Wavelet Analysis. Presented at the European Petroleum Conference, Aberdeen, 29–31 October. SPE-78290-MS. https://doi.org/10.2118/78290-MS.
Shawe-Taylor, J. and Cristianini, N. 2004. Kernel Methods for Pattern Analysis. Cambridge, UK: Cambridge University Press.
The MathWorks, Inc. 2014. MATLAB Statistics and Machine Learning Toolbox, Release 2014b. The MathWorks, Inc. Natick, Massachusetts.
Tian, C. 2014. Applying Machine Learning and Data-Mining Techniques To Interpret Flow Rate, Pressure, and Temperature Data From Permanent Downhole Gauges. MS thesis, Stanford University, Stanford, California (June 2014).
von Schroeter, T., Hollaender, F., and Gringarten, A. C. 2004. Deconvolution of Well-Test Data as a Nonlinear Total Least-Squares Problem. SPE J. 9 (4): 375–390. SPE-77688-PA. https://doi.org/10.2118/77688-PA.