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Volume 49, Issue 4
  • E-ISSN: 1365-2478

Abstract

Estimations of porosity and permeability from well logs are important yet difficult tasks encountered in geophysical formation evaluation and reservoir engineering. Motivated by recent results of artificial neural network (ANN) modelling offshore eastern Canada, we have developed neural nets for converting well logs in the North Sea to porosity and permeability. We use two separate back‐propagation ANNs (BP‐ANNs) to model porosity and permeability. The porosity ANN is a simple three‐layer network using sonic, density and resistivity logs for input. The permeability ANN is slightly more complex with four inputs (density, gamma ray, neutron porosity and sonic) and more neurons in the hidden layer to account for the increased complexity in the relationships. The networks, initially developed for basin‐scale problems, perform sufficiently accurately to meet normal requirements in reservoir engineering when applied to Jurassic reservoirs in the Viking Graben area. The mean difference between the predicted porosity and helium porosity from core plugs is less than 0.01 fractional units. For the permeability network a mean difference of approximately 400 mD is mainly due to minor core‐log depth mismatch in the heterogeneous parts of the reservoir and lack of adequate overburden corrections to the core permeability. A major advantage is that no knowledge of the rock material and pore fluids is required. Real‐time conversion based on measurements while drilling (MWD) is thus an obvious application.

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2001-12-21
2024-04-26

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Porosity and permeability prediction from wireline logs using artificial neural networks: a North Sea case study
Geophysical Prospecting 49, 431 (2001); https://doi.org/10.1046/j.1365-2478.2001.00271.x
/content/journals/10.1046/j.1365-2478.2001.00271.x
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  • Article Type: Research Article

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