The DOI matches its Crossref record; 19 of the 20 references it cites were found in a registry or an index.
Machine learning in oil and gas; a SWOT analysis approach
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The oil and gas industry is very data-rich [48] .”
From Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future (2021) · cites this paper as [48]
In the citing paper
“While open innovation is becoming standard in the tech sector, oil and gas companies are not famous for their joint industry projects, especially between competitors and especially not in strategic domains such as AI [48] .”
From Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future (2021) · cites this paper as [48]
In the citing paper
“This may be one of the core problems for the oil and gas industry famous for its risk-averse culture and poor innovation management practices [48] .”
From Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future (2021) · cites this paper as [48]
In the citing paper
“Still, it is not a guaranty for success as there are known issues with the quality and accuracy of field data and overall lack of large volumes of labeled data in the oil and gas industry [48] .”
From Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future (2021) · cites this paper as [48]
Citations
Cites
Digital Transformation Strategies
Matt, C., Heß, T., & Benlian, A. (2015) · Business & Information Systems Engineering · doi:10.1007/s12599-015-0401-5 · Cited by 3,172
A Survey on Deep Transfer Learning
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01424-7_27 · Cited by 3,165
An Overview of Concept Drift Applications
Žliobaitė, I., Pechenizkiy, M., & da Gama, J. M. P. (2015) · Studies in big data · doi:10.1007/978-3-319-26989-4_4 · Cited by 396
Simple Black-Box Adversarial Attacks on Deep Neural Networks
Narodytska, N., & Kasiviswanathan, S. P. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.172 · Cited by 345
Seven V's of Big Data understanding Big Data to extract value
Khan, M. A.-U.-D., Uddin, M. F., & Gupta, N. (2014) · Proceedings of the 2014 Zone 1 Conference of the American Society for Engineering Education · doi:10.1109/aseezone1.2014.6820689 · Cited by 290
Firms’ innovation strategy under the shadow of analyst coverage
Guo, B., Pérez‐Castrillo, D., & Simats, A. T. (2018) · Journal of Financial Economics · doi:10.1016/j.jfineco.2018.08.005 · Cited by 262
ATM: A distributed, collaborative, scalable system for automated machine learning
Swearingen, T., Drevo, W., Cyphers, B., Cuesta‐Infante, A., Ross, A. A., & Veeramachaneni, K. K. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8257923 · Cited by 120
Status of Data-Driven Methods and their Applications in Oil and Gas Industry
Balaji, K., Rabiei, M., Suicmez, V. S., Canbaz, C. H., Agharzeyva, Z., Tek, S., Bulut, U., & Temizel, C. (2018) · EAGE Conference and Exhibition incorporating SPE EUROPEC · doi:10.2118/190812-ms · Cited by 79
The Effects of Data Quality on Machine Learning Algorithms.
Sessions, V., & Valtorta, M. (2006) · ICIQ · Cited by 64
IEEE Computer Society Annual Symposium on VLSI
(2004) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2004.823917 · Cited by 59
Cited by
Sorted by citations
Items 101–112 of 112
Lobut, B., & Artun, E. (2023) · EAGE Conference and Exhibition incorporating SPE EUROPEC · doi:10.2118/214353-ms
al., D. D. E. (2024) · Egyptian Journal of Aquatic Biology and Fisheries · doi:10.21608/ejabf.2024.369450
Zhao, Z. (2024) · Highlights in Business Economics and Management · doi:10.54097/jcbzp725
Gong, S., & Wang, Y. (2024) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2024-3432
Kamath, M. (2025) · International Journal of Management Technology and Social Sciences · doi:10.47992/ijmts.2581.6012.0386
Nishat, M. M., Aarvold, M. O., Hartvig, W. J., & Olsson, N. O. E. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-07986-2_2
Dadwani, A. D., & Hazarika, G. (2025) · Abu Dhabi International Petroleum Exhibition & Conference (ADIPEC) · doi:10.2118/229380-ms
Geoenvironmental zoning of the Santa Elena Peninsula-Ecuador for sustainable management
Herrera-Franco, G., Bravo-Montero, L., Montalván, F. J., Aguilar-Aguilar, M., & Carrión-Mero, P. (2025) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss55030.2025.11242589
Corporate social responsibility and SWOT analysis: The case of NIS company
Radišić, B., Stanisavljev, S., Gluvakov, V., Prvulović, S., Kavalić, M., & Kovač, D. (2025) · International Conference on Industrial Engineering and Environmental Protection (IIZS) · doi:10.5937/iizs25242r
Deng, K., Azmy, K., Lu, Y., Liu, Z., Liu, Z., Meng, M., Sun, W., Yang, F., & Gou, Q. (2026) · Marine Geoscience and Energy Resources · doi:10.1016/j.marger.2026.207688
Husseiny, A. A., Attia, A. M., & Hagag, A. G. (2026) · Journal of Engineering and Applied Science · doi:10.1186/s44147-026-00980-1
Sun, Q., Wang, J., Li, X., & Li, J. (2025) · Energy Foresight · doi:10.1016/j.engfor.2026.03.005
Metadata
- Authors
- Hajizadeh, Y.
- Affiliations
- Microsoft (United States) (US)
- Corresponding author
- Hajizadeh, Yasin
- Venue
- Journal of Petroleum Science and Engineering
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 176
- Pages
- 661-663
- ISSN
- 0920-4105 (ISSN-L)
- OpenAlex
- W2914176825
- Cited by
- 111
- References
- 20
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 21 · 2023: 18 · 2022: 18 · 2021: 24 · 2020: 11 · 2019: 6
- FWCI
- 6.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Petroleum Science and Engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Energy › Fuel Technology › Oil and Gas Production Techniques
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- machine learning, oil and gas industry, digitalization, business value, model deployment, technology adoption, strategic management, data science teams, domain experts, agile teams, return on investment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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