Improving prediction of Total Organic Carbon in prospective Australian basins by employing machine learning

Irina Emelyanova; Marina Pervukhina; M. Ben Clennell; David N. Dewhurst

doi:10.1071/ASEG2016ab164

25th International Conference and Exhibition – Interpreting the Past, Discovering the Future

ISSN: 2202-0586
E-ISSN:

oa Improving prediction of Total Organic Carbon in prospective Australian basins by employing machine learning
Authors Irina Emelyanova^a, Marina Pervukhina^b, M. Ben Clennell^c and David N. Dewhurst^d
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^a CSIRO Energy, 26 Dick Perry Avenue Kensington, WA, 6151, [email protected] ^b CSIRO Energy, 26 Dick Perry Avenue Kensington, WA, 6151, [email protected] ^c CSIRO Energy, 26 Dick Perry Avenue Kensington, WA, 6151, [email protected] ^d CSIRO Energy, 26 Dick Perry Avenue Kensington, WA, 6151, [email protected]
Publisher: Australian Society of Exploration Geophysicists
Source: ASEG Extended Abstracts, Volume 2016, Issue 25th International Conference and Exhibition – Interpreting the Past, Discovering the Future, Dec 2016, p. 1 - 5
DOI: https://doi.org/10.1071/ASEG2016ab164
- Published online: 01 Dec 2016

Abstract

Total organic carbon (TOC) is directly associated with total porosity and gas content and is a critical factor in assessing the potential of unconventional reservoirs. TOC content is only known at the depths where the laboratory measurements on recovered core samples are performed. However, reliable estimation of potential resources can only be based on information about vertical and lateral distribution of organic matter throughout the prospective gas shale reservoir. This information is commonly obtained from conventional wireline logs, such as gamma ray, density, transit time and resistivity. Due to the complexity of unconventional reservoirs, traditional methods based on distinct differences of resistivity, density and sonic velocity of organic matter from those of the inorganic matrix are not always successful. We investigate the best way to predict the TOC using gamma-ray, density, porosity, resistivity and sonic transit time log responses by applying machine learning methods such as Artificial Neural Network (ANN) and Support Vector Machine (SVM). The analysis is done on the data from seven wells drilled through onshore unconventional reservoirs in the McArthur Basin (Northern Territory) and Georgina Basin (Northern Territory and Queensland), Australia. The prediction quality of traditional, multiple liner regression (MLR) and machine learning methods was compared. The most accurate TOC estimates were generated by ANN- and SVM-based nonlinear predictors, followed by the MLR and traditional models. This indicates that geologic complexity affects the relationship between the log response and TOC in the area of interest.

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Article Type: Research Article

Keyword(s): ANN; regression; SVM; TOC; unconventional gas reservoir

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