[Retracted] Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray Article Swipe
Osama Siddig
,
Ahmed Abdulhamid Mahmoud
,
Salaheldin Elkatatny
,
Pantelis Soupios
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/2486046
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/2486046
Due to high oil and gas production and consumption, unconventional reservoirs attracted significant interest. Total organic carbon (TOC) is a significant measure of the quality of unconventional resources. Conventionally, TOC is measured experimentally; however, continuous information about TOC is hard to obtain due to the samples’ limitations, while the developed empirical correlations for TOC were found to have modest accuracy when applied in different datasets. In this paper, data from Devonian Duvernay shale were used to develop an optimized empirical correlation to predict TOC based on an artificial neural network (ANN). Three wells’ datasets were used to build and validate the model containing over 1250 data points, and each data point includes values for TOC, density, porosity, resistivity, gamma ray and sonic transient time, and spectral gamma ray. The three datasets were used separately for training, testing, and validation. The results of the developed correlation were compared with three available models. A sensitivity and optimization test was performed to reach the best model in terms of average absolute percentage error (AAPE) and correlation coefficient ( R ) between the actual and predicted TOC. The new correlation yielded an excellent match with the actual TOC values with R values above 0.93 and AAPE values lower than 14%. In the validation dataset, the correlation outperformed the other empirical correlations and resulted in less than 10% AAPE, in comparison with over 20% AAPE in other models. These results imply the applicability of this correlation; therefore, all the correlation’s parameters are reported to allow its use on different datasets.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2021/2486046
- https://downloads.hindawi.com/journals/cin/2021/2486046.pdf
- OA Status
- hybrid
- Cited By
- 17
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3184800324
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3184800324Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2021/2486046Digital Object Identifier
- Title
-
[Retracted] Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma RayWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Osama Siddig, Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Pantelis SoupiosList of authors in order
- Landing page
-
https://doi.org/10.1155/2021/2486046Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/cin/2021/2486046.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/cin/2021/2486046.pdfDirect OA link when available
- Concepts
-
Oil shale, Devonian, Artificial neural network, Total organic carbon, Carbon fibers, Pattern recognition (psychology), Shale gas, Geology, Environmental science, Computer science, Artificial intelligence, Biological system, Paleontology, Chemistry, Environmental chemistry, Algorithm, Biology, Composite numberTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 5, 2023: 5, 2022: 3, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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