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Multiscale evaluation of a thin-bed reservoir

Lis-Śledziona Anita

Geology, Geophysics and Environment

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2021

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Vol. 47, no. 1

5--20

EN

A thin-bed laminated shaly-sand reservoir of the Miocene formation was evaluated using two methods: high resolution microresistivity data from the XRMI tool and conventional well logs. Based on high resolution data, the Earth model of the reservoir was defined in a way that allowed the analyzed interval to be subdivided into thin layers of sandstones, mudstones, and claystones. Theoretical logs of gamma ray, bulk density, horizontal and vertical resistivity were calculated based on the forward modeling method to describe the petrophysical properties of individual beds and calculate the clay volume, porosity, and water saturation. The relationships amongst the contents of minerals were established based on the XRD data from the neighboring wells; hence, the high-resolution lithological model was evaluated. Predicted curves and estimated volumes of minerals were used as an input in multimineral solver and based on the assumed petrophysical model the input data were recalculated, reconstructed and compared with the predicted curves. The volumes of minerals and input curves were adjusted during several runs to minimalize the error between predicted and recalculated variables. Another approach was based on electrofacies modeling using unsupervised self-organizing maps. As an input, conventional well logs were used. Then, the evaluated facies model was used during forward modeling of the effective porosity, horizontal resistivity and water saturation. The obtained results were compared and, finally, the effective thickness of the reservoir was established based on the results from the two methods.

2

Classification of transformer oil using self-organizing networks and bayesian neural networks

Mokhnache L., Boubakeur A.

Prace Naukowe Instytutu Podstaw Elektrotechniki i Elektrotechnologii Politechniki Wrocławskiej. Konferencje

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2004

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Vol. 40, nr 15

196-199

EN

In this paper supervised and unsupervised neural networks have been applied. To help the inexperienced transformer oil analyst to make good diagnosis, by classifying the transformer oil in classes giving its dielectric and cooling state; a Bayesian network a self organization map and a competitive layer have been applied. They present a high quality of classification in generalization phase.

3

A neurocomputing approach to the correspondence problem in stereo vision based upon an unsupervised neural network

El-Henawy I., Fouda Y. M., Enab Y. M.

Machine Graphics and Vision

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2001

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Vol. 10, No. 1

29-46

EN

The stereo matching problem is one of the most widely stidied problems in stereo vision. In this paper we introduce a neurocomputing approach to the local stereo matching problem using edge segments as features with several attributes. Most classical local stereo matching techniques use features representing objects in both images and compute the minimum values of attribute differences. pajares et al ([21]) had verified that the differences in attributes, for the true matches, cluster in a cloud around a center. We used the self-organizing neural network to get the best cluster center. Based on the similarity constraint, we compute the minimum Mahalaobis distances between the differences of the attributes for a new pair of features and the cluster center to classify this new pair as true or false match. Experimental results with two real pairs of images are shown.