Well-testing model identification using time-series shapelets
Author:
Publisher
Elsevier BV
Subject
Geotechnical Engineering and Engineering Geology,Fuel Technology
Reference73 articles.
1. Principal component analysis;Abdi;Wiley Interdiscip. Rev. Comput. Stat.,2010
2. Agarwal, R.G., 1980. A new method to account for producing time effects when drawdown type curves are used to analyze pressure buildup and other test data. In: Paper SPE 9289-MS Presented at the Annual Technical Conference and Exhibition, Dallas, Texas. doi: 〈http://doi.org/10.2118/9289-MS〉.
3. Alajmi, M.Ertekin, T., 2007. The development of an artificial neural network as a pressure transient analysis tool for applications in double-porosity reservoirs. In: Paper SPE 108604 Presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, Jakarta, Indonesia. doi: 〈http://doi.org/10.2118/108604-MS〉.
4. Al-Kaabi, A.U.Lee, W.J., 1990. Using artificial neural networks to identify the well test interpretation model. In: Paper SPE 20332-PA presented at the SPE Petroleum Computer Conference, Denver, pp. 25–28. doi: 〈http://doi.org/10.2118/20332-PA〉.
5. Use of artificial intelligence in well-test interpretation;Allain;JPT,1990
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