Artificial Neural Network (ANN) to estimate reservoir parameters in Naturally Fractured Reservoirs using well test data
Author:
Publisher
Elsevier BV
Subject
Geotechnical Engineering and Engineering Geology,Fuel Technology
Reference19 articles.
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2. Al-Kaabi, A.U., Lee, W.J., 1990. An artificial neural network approach to identify the well test interpretation model: applicationts. SPE 20552, Paper Presented at the 65th Annual Technical Conference and Exhibition of the Society of Petroleum Engineers held in New Orleans, LA, 23−26 September.
3. Allian, O., Houze, O.P. 1992. A practical Artificial Intelligence Application in Well Test Interpretation. SPE 24287. Paper Presented at the SPE European Petroleum Conference held in Stavanger, Norway, 25–27 May.
4. Athichanagorn, S., Horne, R., 1995. Automatic parameter estimation from well test data using artificial neural network. SPE 30556. Paper Presented at the SPE Annual Technical Conference & Exhibition held in Dallas, USA, 22−25 October.
5. Boerner, S., Gray, D., Todorovic-Marinic, D. et al., 2003. Employing neural networks to integrate seismic and other data for the prediction of fracture intensity. SPE 84453. Paper Presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, U.S.A., 5–8 October.
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