Machine learning in oil and gas; a SWOT analysis approach

Digital Transformation Strategies

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A Survey on Deep Transfer Learning

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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

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Seven V's of Big Data understanding Big Data to extract value

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Firms’ innovation strategy under the shadow of analyst coverage

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ATM: A distributed, collaborative, scalable system for automated machine learning

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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.

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IEEE Computer Society Annual Symposium on VLSI

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

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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

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

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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

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 233

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

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

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

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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

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

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Potential Assessment of PRISMA Hyperspectral Imagery for Remote Sensing Applications

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

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Agent-based manufacturing — review and expert evaluation

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Factors influencing readiness for artificial intelligence: a systematic literature review

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

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

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

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

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

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

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

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Artificial general intelligence for the upstream geoenergy industry: A review

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

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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

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

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Forecasting the abnormal events at well drilling with machine learning

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

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The role of data management in the Industrial Internet of Things

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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

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

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

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

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

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

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 18

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

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 16

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

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

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

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

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

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

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

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

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

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

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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

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

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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

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

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

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

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

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

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

Artificial intelligence and its performance impacts in the oil and gas industry: Challenges, insights, and evaluation approaches

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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

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

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

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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

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

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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

NEFT-QAZ SƏNAYESİ ÜÇÜN RƏQƏMSAL ƏKİZLƏRİN KONSEPTUAL MODELİ

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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

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

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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

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

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

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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 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