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January 2005

ShaleQuant coming to MPath

Permedia and the Newcastle Research Group are collaborating to bring the ShaleQuant artificial neural networks to MPath.

Developed by the University and industry partners, ShaleQuant will be rewritten in C++ and integrated into MPath. ShaleQuant will also be available as a stand-alone package to non-MPath users.

About ShaleQuant

ShaleQuant is a set of artificial neural networks (ANNs) used to estimate the clay content (proportion of particles smaller than 2 micrometer diameter), grain density and total organic content (TOC) of mudstones from standard wireline log data. Initially developed using a dataset of 530 analyzed mudstone samples from 19 North Sea and 9 Gulf of Mexico wells, the ShaleQuant ANNs were trained to estimate the clay content (proportion of particles smaller than 2 micrometer diameter), grain density and total organic content (TOC) of mudstones from standard wireline log data (gamma, resistivity, sonic, density, caliper). ANNs have also been trained to discriminate carbonates from clastic mudstones and give a preliminary indication of the extent to which mudstones are lithified or cemented.

Results show that for clay content, 85% of predictions are within ±10% of the measured value; for TOC, 92% of predictions are within ±1% of the measured value; for grain density, 91% of predictions are within ±0.07 g cm-3 of the measured value; for the discrimination of carbonates from clastics, 98.3% of carbonate samples and 99.9% of non-carbonate samples are classified correctly.

The ANNs work well not only in the areas from where training data were measured, but also (as an example) in offshore West Africa. Potential applications include defining the 3D sedimentary architecture of mudstone sequences from wireline data and, because both the porosity-effective stress and porosity-permeability relationships of mudstones are strongly influenced by clay content, more accurate, basin-scale fluid flow modeling.

In 2002, a set of Visual Basic (VB) programs was created to facilitate the application of the ANNs to real-world problems.

For more information on ShaleQuant, contact Professor Andy Aplin (A.C.Aplin at newcastle.ac.uk) at the University of Newcastle upon Tyne.

References

Quantitative assessment of mudstone lithology using geophysical wireline logs and artificial neural networks
PDF, 352 KB

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