Porosity and permeability prediction from wireline logs using artificial neural networks: a North Sea case study

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 a priori knowledge of the rock material and pore fluids is required. Real‐time conversion based on measurements while drilling (MWD) is thus an obvious application.

Artificial Neural Network Models for Prediction of Ozone Concentrations in Guadalajara, Mexico

“…The artificial neural network employed was a multilayer backpropagation network, which has been used successfully in several studies (Garcia & Shigidi, 2005, Kuo et al, 2003, Helle et al, 2001Yesilnacar et al, 2007;Yetilmezsoy & Demirel, 2007). The important feature of this network is its ability to self-adapt the weights of neurons in intermediate layers to learn the relationship between a set of patterns given as examples and their corresponding outputs, so that after having been trained, it can apply the same relationship to new input vectors and produce appropriate outputs from inputs that the system has never seen before, a feature known as the generalizability of an ANN (Mehrotra et al, 1997).…”

“…ANN models generally have acceptable performance with three layers; input, hidden and output (Del Brio & Sanz, 2001, Yetilmezsoy & Demirel et al, 2008, Helle et al, 2001. Deciding the number of neurons in the hidden layer is usually not so obvious, so the decision was based on the rules suggested by Goethals et al, (2007).…”

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“…Sabe-se que existe uma dependência adicional sobre a textura da rocha, a forma dos poros e a sua distribuição, juntamente com o teor de argila, tornando-a mais complicada do que para a porosidade (Helle et al, 2001). Da mesma forma, pesquisadores como Nie et al (2012), mostraram em seus resultados numéricos das simulações, que há diferenças óbvias entre permeabilidade dupla e modelos de permeabilidade individuais.…”

Tecnologías constituidas, innovaciones en proceso y tecnologías introducidas en el mercado internacional: caso de estudio

“…Otros enfoques incluyen el uso de algoritmos genéticos (Dorrington & Link, 2004) o redes neuronales (Helle et al, 2001;Bhatt & Helle, 2002). Estas últimas han sido utilizadas exitosamente para estimar la porosidad y permeabilidad a partir de registros sísmicos.…”