Machine learning in oil and gas; a SWOT analysis approach

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

Hardware Acceleration for Machine Learning

Zhao, R., Luk, W. W., Niu, X., Shi, H.-F., & Wang, H. (2017) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi.2017.127 · Cited by 56

Are Existing Knowledge Transfer Techniques Effective for Deep Learning with Edge Devices?

Sharma, R., Biookaghazadeh, S., Li, B., & Zhao, M. X. (2018) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge.2018.00013 · Cited by 41

A broadband full azimuth land seismic case study from Saudi Arabia using a 100,000 channel recording system at 6 terabytes per day: acquisition and processing lessons learned

Pecholcs, P. I., Al‐Saad, R., Al-Sannaa, M., Quigley, J., Bagaini, C., Zarkhidze, A., May, R., Guellili, M., Sinanaj, S., & Membrouk, M. (2012) · Second International Meeting for Applied Geoscience & Energy · doi:10.1190/segam2012-0438.1 · Cited by 23

Getting Value From Your Data Scientists

Harris, J. G., & Mehrotra, V. (2014) · International Linear Collider · Cited by 21

Machine Learning Application for Wellbore Damage Removal in the Wilmington Field

Kellogg, R. P., Chessum, W., & Kwong, R. R. (2018) · SPE Western Regional Meeting · doi:10.2118/190037-ms · Cited by 15

AVA classification as an unsupervised machine-learning problem

Bougher, B., & Herrmann, F. J. (2016) · Second International Meeting for Applied Geoscience & Energy · doi:10.1190/segam2016-13874419.1 · Cited by 11

ACG—20 Years of Downhole Surveillance History

BP, Sheydayev, A., Atakishiyev, F., Zett, A., & Thiruvenkatanathan, P. (2018) · Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description · doi:10.30632/pjv59v4-2018a6 · Cited by 11

A Hybrid Integrated Compositional Reservoir Simulator Coupling Machine Learning and Hard Computing Protocols

Putcha, V. B., & Ertekin, T. (2018) · SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition · doi:10.2118/192368-ms · Cited by 10

Casing Failure Using Machine Learning Algorithms: Five Case Studies

Noshi, C. I., Noynaert, S. F., & Schubert, J. J. (2018) · SPE Thermal Well Integrity and Design Symposium · doi:10.2118/193373-ms · Cited by 4

Application of machine learning and artificial intelligence in oil and gas industry

Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021) · Petroleum Research · doi:10.1016/j.ptlrs.2021.05.009 · Cited by 560

Adapting Universities for Sustainability Education in Industry 4.0: Channel of Challenges and Opportunities

Mian, S. H., Salah, B., Ameen, W., Moiduddin, K., & Alkhalefah, H. (2020) · Sustainability · doi:10.3390/su12156100 · Cited by 389

Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future

Koroteev, D., & Tekic, Z. (2021) · Energy and AI · doi:10.1016/j.egyai.2020.100041 · Cited by 285

Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage

Rangel-Martínez, D., Nigam, K. D. P., & Ricardez‐Sandoval, L. (2021) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2021.08.013 · Cited by 277

Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry

Orrù, P. F., Zoccheddu, A., Sassu, L., Mattia, C., Cozza, R., & Arena, S. (2020) · Sustainability · doi:10.3390/su12114776 · Cited by 235

A Detailed Survey on Machine Learning (ML) Techniques in Oil and Gas Industry

Dr. P. Sujatha, M. S. B. (2024) · Journal of Propulsion Technology

Machine learning and multi-agent systems in oil and gas industry applications: A survey

Hanga, K. M., & Kovalchuk, Y. (2019) · Computer Science Review · doi:10.1016/j.cosrev.2019.08.002 · Cited by 187

A systematic review of data science and machine learning applications to the oil and gas industry

Tariq, Z., Aljawad, M. S., Hasan, A., Murtaza, M., Mohammed, E., El-Husseiny, A., Alarifi, S. A., Mahmoud, M., & Abdulraheem, A. (2021) · Journal of Petroleum Exploration and Production Technology · doi:10.1007/s13202-021-01302-2 · Cited by 232

Ongoing revolution of Software Development in Oil and Gas IndustryPreprint

Vishwanath, M. (2023) · doi:10.31219/osf.io/vsyhk · Cited by 2

Potential Application of Generative Artificial Intelligence and Machine Learning Algorithm in Oil and Gas Sector: Benefits and Future ProspectsPreprint

Ochieng, E. G., Ominde, D., & Zuofa, T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5474887