Abstract
Computer-aided approach to interpretation have become common. In automated type-curve matching, the selection of an appropriate reservoir model and the initial parameter estimation are essential for obtaining reliable results.
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References
Al-Kaabi, A.U., Lee, W.J.: Using artificial neural networks to identify the well test interpretation model. In: Paper SPE 20332 presented at the 1990 5th SPE Petroleum Computer Conference, Denver, Colorado, 25–28 June (1990)
Allain, O.F., Horne, R.N.: Use of artificial intelligence in well test interpretation. J. Petroleum Tech. pp. 342–349 (1990)
Allain, O.F., Houźe O.P.: A practical artificial intelligence application in well test interpretation. In: SPE 24287 presented at the 1992 SPE European Petroleum Computer Conference, Stavanger, Norway, 25–27 May (1992)
Anraku, T., Horne, R.N.: Discrimination between reservoir models in well test analysis. In: SPE Formation Evaluation (June), pp. 114–121 (1995)
Athichanagorn, S., Horne, R.N.: Automated parameter estimation of well test data using artificial neural networks. In: SPE 30556, presented at the 70th Annual Technical Conference and Exhibition, Dallas, TX, 22–25 October (1995)
Dastan, A., Horne, R.N.: Robust well-test interpretation by using nonlinear regression with parameter and data transformation. SPE J. (2011). https://doi.org/10.2118/132467-PA
Ershaghi, I., Li, X., Hassibi, M.: A robust neural network model for pattern recognition of pressure transient test data. In: SPE 26427 presented at the 1993 SPE Annual Technical Conference and Exhibition, Houston, Texas, 3–6 October (1993)
Mattar, L.: Critical evaluation and processing of data before pressure-transient analysis. SPE Form Eval. 11, 120–127 (1996). https://doi.org.libproxy1.usc.edu/10.2118/24729-PA
Rosa, A.J., Horne, R.N.: Automated type curve matching in well test analysis using Laplace space determination of parameter gradients. In: Paper SPE 12131, presented at the 1983 SPE Annual Technical Conference, San Francisco (1983)
Tian, C., Horne, R.: Applying machine-learning techniques to interpret flow-rate, pressure, and temperature data from permanent downhole gauges. SPE Res. Eval. & Eng. 22(02), 386–401 (2019). https://doi.org/10.2118/174034-PA
Zhao, Y., Reynolds, A.C.: Estimation and removal of tidal effects from pressure data. SPE J. 14, 144–152 (2009). https://doi.org.libproxy2.usc.edu/10.2118/103253-PA
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Ershaghi, I. (2023). Computer Aided Methods and AI. In: Solved Problems in Well Testing. Springer, Cham. https://doi.org/10.1007/978-3-031-47299-2_13
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DOI: https://doi.org/10.1007/978-3-031-47299-2_13
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