Unconventional Reservoir Characterization: Permeability Prediction in Shale Gas Reservoirs from Well-Logs Data Using Neural Network. Application to the Barnett Shale (USA)
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-73026-0_99
Reference10 articles.
1. Aliouane, L., Ouadfeul, S, Djarfour, D., Boudella, A.: Permeability prediction using artificial neural networks. A comparative study between back propagation and Levenberg–Marquardt learning algorithms. In: Pardo-Igúzquiza, E., et al. (eds.) Mathematics of Planet Earth, Lecture Notes in Earth System Sciences. Springer, Berlin (2014)
2. Aliouane, L., Ouadfeul, S.: Sweet spots discrimination in shale gas reservoirs using seismic and well-logs data. A case study from the Worth basin in the Barnett shale. Energy Procedia (2013)
3. Aliouane, L., Ouadfeul, S.D., Boudella, A.: Back propagation and hidden weight optimization algorithms neural network for permeability estimation from well-logs data in Shaly Sandstone petroleum reservoirs: application to Algerian Sahara. In: Advances in Petroleum Engineering and Petroleum Geochemistry, Proceedings of the 1st Springer CAGJ, pp. 25–27. Tunisia (2018)
4. Aminian, K., Ameri, S.: Application of artificial neural networks for reservoir characterization with limited data. J. Petrol. Sci. Eng. 49, 212–222 (2005)
5. Civan, F., Rai, C.S., Sondergeld, C.H.: Shale permeability determined by simultaneous analysis of multiple pressure-pulse measurements obtained under different conditions. SPE paper 144253 presented at SPE North American Unconventional Gas Conference and Exhibition, The Woodlands, Texas (2011)
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