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 · see citing works

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 · see citing works

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 · see citing works

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 329 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

The Effects of Data Quality on Machine Learning Algorithms.

Sessions, V., & Valtorta, M. (2006) · ICIQ · Cited by 64 · see citing works

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 · see citing works

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 52 · see citing works

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 · see citing works

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 · see citing works

Getting Value From Your Data Scientists

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

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 265 · see citing works

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 228 · see citing works

Machine learning methods applied to drilling rate of penetration prediction and optimization - A review

Barbosa, L. F. F. M., Nascimento, A., Mathias, M. H., & de Carvalho, J. A. (2019) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2019.106332 · Cited by 227 · see citing works

Machine Learning in Oil and Gas Exploration: A Review

Lawal, A., Yang, Y., He, H., & Baisa, N. L. (2024) · IEEE Access · doi:10.1109/access.2023.3349216 · Cited by 102 · see citing works

MACHINE LEARNING'S INFLUENCE ON SUPPLY CHAIN AND LOGISTICS OPTIMIZATION IN THE OIL AND GAS SECTOR: A COMPREHENSIVE ANALYSIS

Odimarha, A. C., Ayodeji, S. A., & Abaku, E. A. (2024) · Computer Science & IT Research Journal · doi:10.51594/csitrj.v5i3.976 · Cited by 90 · see citing works

A Review of Modern Approaches of Digitalization in Oil and Gas Industry

Al-Rbeawi, S. (2023) · Upstream Oil and Gas Technology · doi:10.1016/j.upstre.2023.100098 · Cited by 87 · see citing works

Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting

Asif, M., Kuglitsch, M. M., Pelivan, I., & Albano, R. (2025) · Water Resources Management · doi:10.1007/s11269-025-04093-x · Cited by 78 · see citing works

On the feasibility of using physics-informed machine learning for underground reservoir pressure management

Harp, D. R., O’Malley, D., Yan, B., & Pawar, R. J. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115006 · Cited by 70 · see citing works

Potential Assessment of PRISMA Hyperspectral Imagery for Remote Sensing Applications

Shaik, R. U., Periasamy, S., & Zeng, W. (2023) · Remote Sensing · doi:10.3390/rs15051378 · Cited by 64 · see citing works

The Application of Analytical Hierarchy Process in Combination with PESTEL-SWOT Analysis to Assess the Hydrocarbons Sector in Cyprus

Tsangas, M., Jeguirim, M., Limousy, L., & Zorpas, A. A. (2019) · Energies · doi:10.3390/en12050791 · Cited by 62 · see citing works

Agent-based manufacturing — review and expert evaluation

Pulikottil, T., Estrada-Jimenez, L. A., Rehman, H. U., Mo, F., Nikghadam-Hojjati, S., & Barata, J. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-11517-8 · Cited by 50 · see citing works

Factors influencing readiness for artificial intelligence: a systematic literature review

Ali, W., & Khan, A. Z. (2024) · Data Science and Management · doi:10.1016/j.dsm.2024.09.005 · Cited by 53 · see citing works

Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods

Islamov, S., Grigoriev, A., Белоглазов, И. И., Savchenkov, S. А., & Gudmestad, O. T. (2021) · Symmetry · doi:10.3390/sym13071293 · Cited by 53 · see citing works

Half a century experience in rate of penetration management: Application of machine learning methods and optimization algorithms - A review

Najjarpour, M., Jalalifar, H., & Norouzi-Apourvari, S. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109575 · Cited by 52 · see citing works

Strategic view on the current role of AI in advancing environmental sustainability: a SWOT analysis

Greif, L., Kimmig, A., Bobbou, S. E., Jurisch, P., & Ovtcharova, J. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00146-z · Cited by 51 · see citing works

Machine learning-assisted production data analysis in liquid-rich Duvernay Formation

Kong, B., Chen, Z., Chen, S., & Qin, T. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.108377 · Cited by 43 · see citing works

Data driven-based model for predicting pump failures in the oil and gas industry

Mohammed, A. (2022) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2022.107019 · Cited by 42 · see citing works

Data-driven digital twin method for leak detection in natural gas pipelines

Jing, L., Ma, L., Liang, S., Zhang, H., Zuo, Z., & Dai, J. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108833 · Cited by 42 · see citing works

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

Pachalieva, A. A., O’Malley, D., Harp, D. R., & Viswanathan, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-22832-7 · Cited by 39 · see citing works

Artificial general intelligence for the upstream geoenergy industry: A review

Li, J. X., Zhang, T., Zhu, Y., & Chen, Z. (2024) · Gas Science and Engineering · doi:10.1016/j.jgsce.2024.205469 · Cited by 37 · see citing works

Prediction of sonic log and correlation of lithology by comparing geophysical well log data using machine learning principles

Joshi, D., Patidar, A. K., Mishra, A., Mishra, A. P., Agarwal, S., Pandey, A., Dewangan, B. K., & Choudhury, T. (2021) · GeoJournal · doi:10.1007/s10708-021-10502-6 · Cited by 37 · see citing works

A data-driven pipeline pressure procedure for remote monitoring of centrifugal pumps

Giro, R. A., Bernasconi, G., Giunta, G., & Cesari, S. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.108845 · Cited by 33 · see citing works

Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review

Aliyu, R., Mokhtar, A. A., & Hussin, H. (2022) · Applied Sciences · doi:10.3390/app122211691 · Cited by 33 · see citing works

Forecasting the abnormal events at well drilling with machine learning

Gurina, E., Klyuchnikov, N., Antipova, K., & Koroteev, D. A. (2022) · Applied Intelligence · doi:10.1007/s10489-021-03013-x · Cited by 29 · see citing works

Diagnostic of Operation Conditions and Sensor Faults Using Machine Learning in Sucker-Rod Pumping Wells

do Nascimento, J. M. A., Maitelli, A. L., Maitelli, C. W. S. D. P., & Cavalcanti, A. L. D. O. (2021) · Sensors · doi:10.3390/s21134546 · Cited by 28 · see citing works

The role of data management in the Industrial Internet of Things

AlSuwaidan, L. (2020) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6031 · Cited by 26 · see citing works

Data Science Application for Failure Data Management and Failure Prediction in the Oil and Gas Industry: A Case Study

Arena, S., Manca, G., Murru, S., Orrù, P. F., Perna, R., & Recupero, D. R. (2022) · Applied Sciences · doi:10.3390/app122010617 · Cited by 23 · see citing works

Evaluation of transfer learning in data-driven methods in the assessment of unconventional resources

Ashayeri, C., & Jha, B. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109178 · Cited by 22 · see citing works

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

Vuttipittayamongkol, P., Tung, A., & Elyan, E. (2021) · IEEE Access · doi:10.1109/access.2021.3117891 · Cited by 21 · see citing works

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool

Celecia, A., Figueiredo, K., Rodríguez, C., Vellasco, M. M. B. R., Maldonado, E., Silva, M. A., Rodrigues, A. R., do Nascimento, R. G. L., & OUROFINO, C. G. (2021) · Sensors · doi:10.3390/s21196347 · Cited by 19 · see citing works

The analysis of agricultural Internet of things product marketing by deep learning

Liu, Q., Zhao, X., & Shi, K. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04817-5 · Cited by 18 · see citing works

Oil and Gas Upstream Sector: The Use of IEC-61499 and OPC

García, C. A., Caiza, G. J., & García, M. V. (2022) · Springer eBooks · doi:10.1007/978-3-030-84205-5_24 · Cited by 17 · see citing works

Improving predictive models for rate of penetration in real drilling operations through transfer learning

Pacis, F. J., Ambrus, A., Alyaev, S., Khosravanian, R., Kristiansen, T. G., & Wiktorski, T. (2023) · Journal of Computational Science · doi:10.1016/j.jocs.2023.102100 · Cited by 17 · see citing works

SWOT analysis in the General Organization of Labor, Cooperation and Social Welfare of East Azerbaijan Province with a scientific and technological approach

Gharachorloo, N., Ghahremani-Nahr, J., & Nozari, H. (2021) · International Journal of Innovation in Engineering · doi:10.59615/ijie.1.4.47 · Cited by 17 · see citing works

CLUE-S model based on GIS applied to management strategies of territory with oil wells—Case study: Santa Elena, Ecuador

Herrera-Franco, G., Escandón-Panchana, P., Montalván, F. J., & Velástegui-Montoya, A. (2022) · Geography and sustainability · doi:10.1016/j.geosus.2022.11.001 · Cited by 15 · see citing works

Development and performance assessment of a novel multigeneration clean energy system based on biomass-LNG using a dynamic model and supervised learning algorithms

Ghiyami, M., Bahramian, A., & Sedaee, B. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.04.111 · Cited by 14 · see citing works

Modeling the strategies to accelerate the natural gas business market growth in a developing country

Rawat, A., & Garg, C. P. (2022) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-03-2022-0135 · Cited by 14 · see citing works

Application of machine learning algorithm in the sheet metal industry: an exploratory case study

Shamsuzzoha, A., Kankaanpää, T., Nguyen, H. Q., & Nguyen, H. (2021) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2021.1972469 · Cited by 13 · see citing works

Pore pressure prediction assisted by machine learning models combined with interpretations: A case study of an HTHP gas field, Yinggehai Basin

Zhao, X., Chen, X., Lan, Z., Wang, X., & Yao, G. (2023) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2023.212114 · Cited by 12 · see citing works

Transforming Islamic Education: Madrasah-Based Management Strategies for Academic Excellence

Fatihin, M. K., Asnawi, N., & Tharaba, M. F. (2023) · AL-TANZIM JURNAL MANAJEMEN PENDIDIKAN ISLAM · doi:10.33650/al-tanzim.v7i4.6746 · Cited by 11 · see citing works

Digital Transformation of Historical Data for Advanced Predictive Maintenance

Giunta, G., Bernasconi, G., Giro, R. A., & Cesari, S. (2020) · Abu Dhabi International Petroleum Exhibition & Conference (ADIPEC) · doi:10.2118/202906-ms · Cited by 11 · see citing works

Identification and selection of stakeholder engagement strategies: case study of an Iranian oil and gas construction project

Khalilzadeh, M., Kebriyaii, O., & Rezaei, R. (2021) · International Journal of Construction Management · doi:10.1080/15623599.2021.1889749 · Cited by 11 · see citing works

Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review

Danyaro, K. U., Hussain, H. H., Abdullahi, M., Liew, M. S., Shawn, L. E., & Abubakar, M. Y. (2022) · Applied Sciences · doi:10.3390/app12115690 · Cited by 11 · see citing works

Intelligent oil well identification modelling based on deep learning and neural network

Sun, L., Shi, H., & Bai, M. (2020) · Enterprise Information Systems · doi:10.1080/17517575.2020.1722252 · Cited by 11 · see citing works

Risk assessment for groundwater in the region of municipal landfill systems in Tychy-Urbanowice (Southern Poland)

Rykała, W., & Dąbrowska, D. (2020) · Environmental & Socio-economic Studies · doi:10.2478/environ-2020-0002 · Cited by 11 · see citing works

TOWARDS AN EFFICIENT APPROACH FOR IDENTIFICATION AND SELECTION OF STAKEHOLDER ENGAGEMENT STRATEGIES: A CASE STUDY

Khalilzadeh, M., Kebriyaii, O., Šaparauskas, J., & Lepkova, N. (2021) · E+M Ekonomie a Management · doi:10.15240/tul/001/2021-4-004 · Cited by 9 · see citing works

New approach for interpreting pressure and flow rate data from permanent downhole gauges, least square support vector machine approach

Khazali, N., & Sharifi, M. (2019) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2019.04.091 · Cited by 9 · see citing works

Applying transfer learning to address data scarcity: A case study on LWD gamma ray depth-lag remediation from Volve to another gasfield

Zhang, J., Liu, Y., & Cao, J. (2024) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2024.213231 · Cited by 8 · see citing works

Digital transformations of the urban – carbon – labor nexus: A research agenda

Tretter, E. M., & Burns, R. (2023) · Digital Geography and Society · doi:10.1016/j.diggeo.2023.100062 · Cited by 8 · see citing works

New Model for Predicting Production Capacity of Horizontal Well Volume Fracturing in Tight Reservoirs

Lei, Z., & Gu, D. (2024) · ACS Omega · doi:10.1021/acsomega.3c09051 · Cited by 8 · see citing works

A Productivity Prediction Method of Fracture-Vuggy Reservoirs Based on the PSO-BP Neural Network

Tian, K., Kang, Z., & Kang, Z. (2024) · Energies · doi:10.3390/en17143482 · Cited by 8 · see citing works

Chemofacies characterization of lacustrine shale based on machine learning classification: A case study from the Dongying depression, Bohai bay basin, China

Li, G., Lin, C., Ma, P., Dong, C., Wu, Y., Guan, Q., Liu, W., Du, X., Zhao, Z., & Yang, Y. (2024) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2024.213154 · Cited by 8 · see citing works

Crude-Oil Scheduling Network in Smart Field Under Cyber-Physical System

Qin, H., & Han, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2927529 · Cited by 8 · see citing works

Comparative analysis of digitalization programs for strategic industries of the Russian Federation

Shiboldenkov, V. A., Vanyashkina, A. Y., & Pakhomova, O. O. (2023) · AIP conference proceedings · doi:10.1063/5.0107742 · Cited by 7 · see citing works

Artificial Intelligence and Its Performance Impacts in the Oil and Gas Industry: Challenges, Insights, and Evaluation Approaches

AlAbdouli, M. A., & Al-Shihabi, S. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107636 · Cited by 6 · see citing works

Hybrid Machine Learning for Modeling the Relative Permeability Changes in Carbonate Reservoirs under Engineered Water Injection

Reginato, L. F., Gioria, R. D. S., & Sampaio, M. A. (2023) · Energies · doi:10.3390/en16134849 · Cited by 7 · see citing works

Strengths, Weaknesses, Opportunities, and Threats (SWOT) of Implementing Teleconsultation: A Systematic Review

Khoshsirat, S., Soleimanpour, H., & Rezaei‐Hachesu, P. (2025) · Health Science Reports · doi:10.1002/hsr2.70645 · Cited by 6 · see citing works

Towards Real-Time Edge Analytics - A Survey Literature Review of Real-Time Data Acquisition Evolution in the Upstream Oil and Gas Industry

Djamaluddin, B., Jovin, T., & Nugraha, B. (2019) · Abu Dhabi International Petroleum Exhibition & Conference (ADIPEC) · doi:10.2118/197670-ms · Cited by 6 · see citing works

Artificial Lift Selection Methods in Conventional and Unconventional Wells: A Summary and Review from Old Techniques to Machine Learning Applications

Mahdi, M. A. A., Amish, M. B., & Oluyemi, G. F. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24mar2108 · Cited by 6 · see citing works

Towards Machine Learning-Driven Practices for Oil and Gas Decommissioning – Introduction of a New Offshore Pipeline Dataset

Vuttipittayamongkol, P., Tung, A., & Elyan, E. (2021) · International Conference on Computer and Communications Management (ICCCM) · doi:10.1145/3479162.3479179 · Cited by 6 · see citing works

Recent Advances and Challenges of the Application of Artificial Intelligence to Predict Wellbore Instabilities during Drilling Operations

Lenwoue, A. R. K., Li, Z., Tang, C., Zhang, W., Ding, S., Hu, P., & Sun, W. (2023) · SPE Drilling & Completion · doi:10.2118/215830-pa · Cited by 5 · see citing works

Artificial Intelligence (AI) Application in Process Safety Cumulative Risk Visualization for Petroleum Operations: Conceptual Framework

Maduabuchi, E. (2024) · International Journal of Engineering and Advanced Technology Studies · doi:10.37745/ijeats.13/vol12n13349 · Cited by 5 · see citing works

Modeling Apparent Viscosity, Plastic Viscosity and Yield Point in Water-Based Drilling Fluids: Comparison of Various Soft Computing Approaches, Developed Correlations and a Committee Machine Intelligent System

Jafarifar, I., & Najjarpour, M. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06224-z · Cited by 5 · see citing works

Oil and Gas Industries in India- A Case Study

Desikan, J., & Devi, A. J. (2022) · International Journal of Case Studies in Business IT and Education · doi:10.47992/ijcsbe.2581.6942.0154 · Cited by 5 · see citing works

Smart Real Time Data Transfer Surveillance with Edge Computing and Centralized Remote Monitoring System

Nugraha, B., Nair, R., & Muhammad, K. (2020) · International Petroleum Technology Conference · doi:10.2523/iptc-19588-ms · Cited by 4 · see citing works

Transfer Learning Approach to Prediction of Rate of Penetration in Drilling

Pacis, F. J., Alyaev, S., Ambrus, A., & Wiktorski, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08754-7_44 · Cited by 4 · see citing works

Real-Time Estimation of Production Rates in Gas Condensate Wells Using a Machine Learning Model

Raslan, K., Elnaggar, H., Shahin, A., Owusu, I., Elagab, O., Aboushanab, M., Sobhy, M., Nafea, B., & Adam, A. M. (2026) · IPTC Summit on AI for the Energy Industry · doi:10.2523/iptc-25142-ms · Cited by 3 · see citing works

Application of Explainable Data-Driven Methods to Predict Minimum Miscibility Pressure for EOR and CCUS Applications

Lidong, Z., Guo, Y., Mohammadian, E., Azdarpour, A., & Karami, A. A. (2026) · Energy & Fuels · doi:10.1021/acs.energyfuels.5c05352 · Cited by 3 · see citing works

Management as a Technical Discipline in Energy

Capello, M. A., Howes, C. S., Kirkham, P., Calleberg, A., Nandurdikar, N. S., & Swami, G. (2024) · SPE Annual Technical Conference and Exhibition · doi:10.2118/220719-ms · Cited by 3 · see citing works

Development of machine learning model for determination of contamination length in a multi-product pipeline

Uwaezuoke, N., Obiora, C. F., Igwilo, K. C., Kerunwa, A., & Nwanwe, O. I. (2023) · Upstream Oil and Gas Technology · doi:10.1016/j.upstre.2022.100085 · Cited by 3 · see citing works

Predictive Maintenance of Produced Water Re-injection Pump Failure in the Field of Oil and Gas: A Review

Azrag, M. A. K., Khatir, S. K., Zain, J. M., & Odili, J. B. (2023) · IEEE International Conference on Cognitive Computing and Complex Data (ICCD) · doi:10.1109/iccd59681.2023.10420542 · Cited by 3 · see citing works

An Analysis of Arab Undergraduate Students’ Writing Performance: Applying SWOT Framework

Al-Mutawa, A. S., Al-Kandari, H. S., & Fayez, F. (2024) · Journal of Language Teaching and Research · doi:10.17507/jltr.1502.12 · Cited by 3 · see citing works

Testing Machine Learning Algorithms for Drilling Incidents Detection on a Pilot Small-Scale Drilling Rig

Løken, E. A., Løkkevik, J., & Sui, D. (2021) · Journal of Energy Resources Technology · doi:10.1115/1.4052284 · Cited by 3 · see citing works

Application of soft computing approaches for modeling fluid transport ratio of slim-hole wells in one of Iranian central oil fields

Jafarifar, I., & Simi, A. (2023) · Earth Science Informatics · doi:10.1007/s12145-023-00947-3 · Cited by 3 · see citing works

Risk Assessment for Groundwater: A Case Study from a Municipal Landfill in Southern Poland

Dąbrowska, D., & Rykała, W. (2021) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-030-59320-9_56 · Cited by 2 · see citing works

A Theoretical Study on Implementing Machine Learning for Safety and Security of Oil and Gas Pipelines

A., S. K. K., & Raj, A. (2023) · Computology Journal of Applied Computer Science and Intelligent Technologies · doi:10.17492/computology.v3i1.2303 · Cited by 2 · see citing works

Enhancing economic sustainability in mature oil fields: Insights from the clustering approach to select candidate wells for extended shut-in

Lobut, B., & Artun, E. (2024) · Artificial Intelligence in Geosciences · doi:10.1016/j.aiig.2024.100082 · Cited by 2 · see citing works

Predicting System Surface Parameters Using Artificial Neural Network

Eltrissi, M., & Yousef, O. (2024) · Abu Dhabi International Petroleum Exhibition & Conference (ADIPEC) · doi:10.2118/222605-ms · Cited by 2 · see citing works

Generative Adversarial Neural Network and Genetic Algorithm To Predict Oil and Gas Pipeline Defect Length

Aldosari, H., Rajasekaran, S., & Ammar, R. A. A. (2021) · EPiC series in computing · doi:10.29007/w663 · Cited by 2 · see citing works

Review of the Application of Social Media Data in Disaster Research

Tang, J., Yang, S., & Wang, W. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200642 · Cited by 2 · see citing works

A Review of Strategies for Managing Uncertainty in Crude Oil Prices by Indonesian Oil and Gas Companies and the Government

Maarif, S., Susanti, D. A., Rezalti, D. T., Irmaya, A. I., Yunita, L., Damayanti, D., & Wahyuningtyas, Y. F. (2022) · Jurnal Offshore Oil Production Facilities and Renewable Energy · doi:10.30588/jo.v6i2.1449 · Cited by 2 · see citing works

NEFT-QAZ SƏNAYESİ ÜÇÜN RƏQƏMSAL ƏKİZLƏRİN KONSEPTUAL MODELİ

İmamverdiyev, Y. (2020) · Problems of Information Technology · doi:10.25045/jpit.v11.i2.04 · Cited by 1 · see citing works

A predictive model for oil well maintenance: a case study in Kazakhstan

Aktaukenov, D., Alshaalan, M., Omirbekova, Z. Z., & Pinsky, E. (2024) · Proceedings of OilGasScientificResearchProjects Institute SOCAR · doi:10.5510/ogp20240100939 · Cited by 1 · see citing works

Enhancing Data Governance with Real-Time Analytics in Oil Production Forecasting

Sallapalli, N. (2024) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit24106187 · Cited by 1 · see citing works

NEFT VƏ QAZ 4.0: MÖVCUD HƏLLƏR, PERSPEKTİVLƏR VƏ PROBLEMLƏR

İnstitutu, A. İ. T., & İmamverdiyev, Y. (2021) · Problems of Information Society · doi:10.25045/jpis.v12.i1.03 · Cited by 1 · see citing works

Oil and Gas Upstream Sector: The use of IEC-61499 and OPC

García, C. A., Caiza, G. J., & García, M. V. (2021) · Springer eBooks · doi:10.1007/978-3-030-58675-1_24-1 · Cited by 1 · see citing works

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure managementPreprint

Pachalieva, A. A., O’Malley, D., Harp, D. R., & Viswanathan, H. (2022) · Research Square · doi:10.21203/rs.3.rs-1782030/v1 · Cited by 1 · see citing works

References

(2022) · Wiley eBooks · doi:10.1002/9781119879893.refs · Cited by 1 · see citing works

Organizational Readiness for Machine Learning: Exploring the key readiness factors for business adoption of machine learning

Yoon, A. (2019) · Research Repository (Delft University of Technology)

Data Science Adoption and Operationalization in the O&G Industry: Challenges and Solutions

Zouch, M. (2022) · International Petroleum Technology Conference · doi:10.2523/iptc-22172-ms

ИССЛЕДОВАНИЕ СОВРЕМЕННОГО СОСТОЯНИЯ ПРИМЕНЕНИЯ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ В НЕФТЕГАЗОВОЙ ОТРАСЛИ

Şıxəliyev, R. (2020) · Problems of Information Technology · doi:10.25045/jpit.v11.i2.05

Application of machine learning techniques for modeling of relative permeability in engineered water injection in carbonate reservoirs.

Reginato, L. F. (2022) · doi:10.11606/d.3.2022.tde-26082022-083727

Determination of drilling mud weight using deep learning techniques

Khazaei, A., Radfar, R., & Eshlaghy, A. T. (2022) · Petroleum Science and Technology · doi:10.1080/10916466.2022.2092637

Strategies for Designing Intellectual Property-based Financing Business Model within The Creative Economy Sector: An Indonesian Context

Santoso, M. G., Dachyar, M., & Nurcahyo, R. (2023) · International Conference on Industrial Engineering and Operations Management (IEOM) · doi:10.46254/an12.20220159

Business Improvement Strategy for Local ERP Provider Company in Indonesia with SWOT-Fuzzy AHP-TOPSIS Method

Nabila, K. R., & Dachyar, M. (2023) · International Conference on Industrial Engineering and Operations Management (IEOM) · doi:10.46254/an12.20220188

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 180 · see citing works

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 · see citing works

Ongoing revolution of Software Development in Oil and Gas IndustryPreprint

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

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