From Data to Drilling: How AI/ML is Benefiting the Oil and Gas Industry

AI/ML

Authored by Abinandaraj Rajendran

Introduction

Working in AI/ML within the tech world, I’ve often been struck by how the same algorithms show up in places that seem completely unrelated. The models that help radiologists pick out faint patterns on a scan or allow banks to catch suspicious transactions are now being adapted for drilling rigs and pipeline monitoring. That overlap is what drew me to explore this space – to trace how AI and machine learning are beginning to reshape, and in some cases quietly disrupt, the oil and gas industry.

The oil and gas world is anything but static. Equipment runs in deserts, on offshore platforms, and inside far-flung refineries – places where the environment shifts faster than crews can realistically react: sudden pressure changes, temperature spikes, vibrations that hint at trouble. On top of that, the industry is drowning in data: seismic surveys, drilling logs, endless sensor readings, and maintenance records going back decades. Rather than leaving it to gather dust, many operators are now experimenting with learning algorithms that may help uncover patterns, anticipate failures before they escalate, and, with luck, keep production steadier [1], [2].

People like to say we’ve entered the Industry 4.0 era [3] – and a few are already talking about a coming “5.0,” where AI systems supposedly mesh seamlessly with human expertise. Whether that’s a bit ahead of itself is open to debate, but what isn’t in question is the growing pressure on operators to bring these tools into everyday practice.

The projections aren’t just hand-waving. One market analysis put the value of AI in oil and gas at about USD 3.79 billion in 2025, with forecasts that it could almost double by 2030 [4]. Other studies land on different numbers – sometimes a bit higher, sometimes lower – but they all seem to converge on the same takeaway: a steady climb, with annual growth rates hovering in the 6–7% range [5].

AI shows up in all sorts of corners of the business. On the office side, robotic process automation chips away at paperwork. Supply chain models try to keep tabs on critical parts and where to source them. Demand forecasting systems attempt to match output with market swings. There are even early experiments creeping into production planning and design. Some of these initiatives are more mature than others, but taken together they suggest AI in oil and gas isn’t one neat application. It’s starting to look more like a toolbox spread unevenly across the value chain.

Of all these use cases, one seems to draw the most attention and, at least so far, delivers the clearest payoffs: predictive maintenance.

Predictive Maintenance: The Core Use Case

Reactive maintenance is the most straightforward approach: let the equipment run until it breaks, then fix or replace it. That might be fine for cheap, non-critical gear, but in oil and gas, one failure can halt production and rack up losses quickly. Preventive maintenance takes the opposite tack – service on a set schedule, whether or not the machine actually needs it. It’s safer, no doubt, but also wasteful, since parts with plenty of life left often get pulled out too soon.

Predictive Maintenance (PdM) tries to walk the middle ground [6]. The idea is to keep equipment running for as long as it safely can, without nudging it into a costly breakdown. Rather than sticking to rigid service schedules, PdM leans on sensor data and past performance records to judge when attention is actually needed. In oil and gas, where something as ordinary as a compressor outage can ripple through and stall operations, that extra layer of precision makes a real difference.

Earlier versions of PdM leaned heavily on physics-based models – fatigue curves, corrosion equations – along with reliability stats and simple threshold rules for things like vibration or temperature. Those methods aren’t obsolete; they still carry weight in many settings. But they often stumble in messy, real-world conditions where operating environments shift constantly. Machine learning pushes things a bit further by picking up on subtle wear-and-tear signals that older models miss. Deep learning goes another step, fusing different inputs – pressure, vibration, acoustics, even thermal images – into a more complete picture of how equipment is actually aging [7], [8], [9].

Some researchers have started calling this next step “PdM 4.0” [3] – predictive maintenance wired into the broader Industry 4.0 stack, where IoT sensors, digital twins, and algorithms team up to estimate Remaining Useful Life (RUL). The attraction is pretty clear: repair a machine right before it’s about to fail, not months earlier when it still had life in it, and not hours later when the damage is already done.

Practical AI/ML Applications Across the Value Chain

Before getting into specific applications, it’s worth stepping back to ask: what exactly needs watching in oil and gas? The usual suspects are pipelines, reservoirs, drilling operations, and production equipment – the same areas where a single slip-up can spiral into financial losses, environmental harm, or safety incidents. Failures in these systems often fall into categories such as component breakdowns, environmental effects, human mistakes, and errors in handling procedures (e.g., in PdM frameworks like IIoT [10]).

Traditionally, the industry leaned on condition monitoring (CM) methods like nondestructive testing (NDT) – ultrasonic scans, radiography, eddy-current checks. These tools still matter, but they’re usually scheduled, hands-on, and not always sharp enough to catch problems early [11]. What AI brings to the table is a shift from periodic snapshots to something closer to continuous surveillance, pulling in data streams from sensors, images, and operational logs in real time.

A recent review points out that predictive maintenance may grab most of the headlines, but it’s hardly the only game in town. Researchers are also testing AI for seismic analysis, drilling optimization, and even environmental performance monitoring – though each of these sits at a different stage of maturity [12].

Pipeline Integrity

  • Magnetic Flux Leakage (MFL): In the past, inspections meant human experts staring at images and trying to judge defects by eye. These days, machine learning models can pick up those flaws automatically – and even estimate the size of metal-loss issues with a fair degree of accuracy [13].
  • Corrosion inspection: Armed with hundreds of thousands of field images, convolutional neural networks are now grading corrosion severity on the spot, often potentially in real time [14].
  • Internal corrosion rates: Hybrid approaches – for instance, support vector regression combined with optimization algorithms – have shown they can predict corrosion rates with surprising accuracy, even under messy multiphase flow conditions [15].
  • Knowledge-driven approaches: A few research groups are trying out knowledge graphs linked with neural networks – an approach that might sound a little academic at first, but could prove useful in cases where data is limited [16].
  • Leak detection via infrared/thermal imaging: More recently, thermal (FLIR-style) cameras have been paired with deep learning models to spot gas leaks earlier than traditional methods. One 2025 study tested a Gas Faster R-CNN on infrared imagery, detecting methane leaks at flow rates of 30, 100, and 300 mL/min, and reported strong precision across all three bands [17]. Another team combined thermal images with “electronic nose” gas sensors, achieving around 98–99% accuracy in distinguishing leak from no-leak scenarios under varied conditions [18].

Reservoir Engineering

  • Machine learning proxies are now being used to accelerate well placement decisions, potentially cutting down the long hours – sometimes days – that would normally go into running full-scale simulations [19].
  • Deep learning – especially LSTMs – has been hitting impressive accuracy in predicting porosity from well logs, with some studies reporting high accuracy [20].
  • Seismic interpretation is seeing gains too, with CNNs mapping subsurface structures at a pace that is potentially faster than human interpreters [21].

Seismic & Exploration

  • Outside of reservoir studies, CNNs are being applied to seismic datasets to pick out faults and sharpen prestack inversion tasks [21].
  • Researchers have even explored using deep learning to pinpoint induced earthquakes – a line of work that leans more toward environmental safety than immediate financial gain [22].

Drilling Operations

  • Rate of Penetration (ROP) has long been a headache for drillers. Empirical models have guided decisions for decades, but machine learning methods are now regularly beating them in accuracy and usefulness [23].
  • Reviews from SPE and other sources suggest ML has moved well past the academic stage. It’s already being applied in the field to trim non-productive time and fine-tune drilling parameters in live projects [24].

Production & Equipment

  • Dynamic risk assessment models – using machine learning techniques – are now helping offshore operators gauge drive-off scenarios and other high-stakes safety events [25].
  • Fault detection in centrifugal pumps, which used to depend mainly on vibration analysis, is increasingly turning to ML classifiers that can flag problems earlier [11].
  • Even gas turbines – an area where condition-based maintenance is already considered fairly advanced – are seeing gains from ML-enhanced diagnostics [26].
  • Broader reviews point to AI taking on an emerging role in operational risk management, tying predictive maintenance more closely to safety assurance in complex facilities [27].

Challenges

Predictive maintenance lives and dies by data, yet industrial datasets are notoriously tricky. True failure events are rare, so the bulk of what gets recorded reflects normal operation. Several earlier studies have flagged this gap – most available data is unlabeled and collected under shifting external conditions.

Techniques like SMOTE or broader synthetic data generation [9] can ease class imbalance, but not without caveats. Artificial failures don’t always capture the messy, context-specific ways machines actually break down, and engineers remain wary. The variability problem makes it worse: two assets built to identical specs may age differently thanks to tolerances, how they’re mounted, or quirks in the local environment – which means a model tuned at one site often falls flat at another [8].

Privacy concerns add yet another wrinkle. PdM often depends on pooling or sharing data across organizations, but operators are understandably cautious about handing over sensitive operational records to outside vendors or cloud services.

Explainability is another sticking point. A bare alert like “failure in 36 hours” rarely convinces a seasoned engineer without some sense of why. This is where explainable AI (XAI) methods may help – attribution scores or rule-based logic can provide the missing rationale, bridging the gap between black-box predictions and human judgment [7]. Without that layer of clarity, trust remains one of the biggest bottlenecks to adoption.

Cybersecurity presents a parallel challenge. Early surveys, such as Stergiopoulos et al. [28], documented dozens of confirmed cyber incidents across upstream, midstream, and downstream systems, exposing vulnerabilities in industrial control systems (ICS) that were never built with security in mind. As IT and OT networks have merged, those weak spots have only grown.

The nature of the threat has also shifted. What began with isolated incidents now leans heavily toward ransomware. The Colonial Pipeline attack made it painfully clear that the consequences stretch far beyond stolen data – they can grind operations to a halt. By 2024, Sophos reported that two-thirds of energy organizations had been hit by ransomware, while Trustwave recorded an 80% year-over-year surge [29], [30]. Elete [31] adds that such ransomware campaigns increasingly target ICS environments themselves, posing unique risks to both operational continuity and safety. These aren’t abstract statistics; they translate into downtime, recovery expenses, and, at times, safety risks.

Mitigation strategies exist – such as network segmentation and enhanced monitoring – but applying them to decades-old, fragile infrastructure often feels less like proactive defense and more like scrambling to keep up [32].

Case Snapshots

AI/ML

Future Directions: Industry 5.0 and Beyond

Looking ahead, chances are we’ll be hearing more about “agentic AI” – systems that don’t just forecast failures but go a step further, automatically kicking off maintenance workflows [3]. Digital twins are another favorite in the conversation. Some write them off as hype, but they might offer the kind of transparency PdM models still struggle to provide, as ops face ongoing maintenance challenges [37].

The framing is also shifting. AI isn’t being pitched only as a way to maximize uptime anymore. Companies are starting to test it for emissions monitoring, spill detection, and even worker safety [2], [39]. If Industry 4.0 was defined by automation, Industry 5.0 may end up being framed around balance – not just speed and efficiency, but responsibility.

Recent studies hint at what this future might look like in practice: LSTMs piloted in time-series failure prediction [38], CNNs tested for corrosion detection [14], and hybrid physics–ML models applied to seismic analysis [39]. Each example is still early, but together they sketch a picture of how adoption may gradually unfold across the sector.

Conclusion

From forecasting corrosion to flagging ransomware, AI in oil and gas sits in a strange middle ground: undeniably high-tech, yet inseparable from gritty, physical realities. The models are far from perfect – the data is patchy, true failures are rare, and legacy systems resist integration – but the trajectory feels hard to ignore.

This industry has always adapted under pressure, and AI may turn out to be just the latest addition to that long pattern. The harder question isn’t can it work, but whether operators, regulators, and the public are ready to place their trust in it.

References:

  1. Kuang, L., Liu, H., Ren, Y., Luo, K., Shi, M., Su, J., & Li, X. (2021). Application and development trend of artificial intelligence in petroleum exploration and development. Petroleum Exploration and Development, 48(1), 1–14. DOI: https://doi.org/10.1016/S1876-3804(21)60001-0
  2. Waqar, A., Othman, I., Shafiq, N., & Mansoor, M. (2023). Applications of AI in oil and gas projects towards sustainable development: a systematic literature review. Artificial Intelligence Review, 56, 10467. DOI: https://doi.org/10.1007/s10462-023-10467-7
  3. Sang, Y., Xu, H., & de Vrieze, P. (2021). A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0. Frontiers in Big Data, 4, 663466. DOI: https://doi.org/10.3389/fdata.2021.663466
  4. Mordor Intelligence (2025). AI in Oil and Gas Market Report. URL: https://www.mordorintelligence.com/industry-reports/ai-market-in-oil-and-gas
  5. Global Market Insights (2024). AI & ML in Oil & Gas Market Size Report. URL: https://www.gminsights.com/industry-analysis/ai-and-ml-in-oil-gas-market
  6. Baptista, M., Sankararaman, S., de Medeiros, I. P., et al. (2018). Forecasting fault events for predictive maintenance using data-driven techniques. Computers & Industrial Engineering, 115, 41–53. DOI: https://doi.org/10.1016/j.cie.2017.10.033
  7. Serradilla, O., Zugasti, E., Rodriguez, J., et al. (2022). Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects. Applied Intelligence, 52, 5291–5318. DOI: https://doi.org/10.1007/s10489-021-03004-y
  8. Keleko, A. T., Kamsu-Foguem, B., Ngouna, R. H., & Tongne, A. (2022). Artificial intelligence and real-time predictive maintenance in Industry 4.0: a bibliometric analysis. AI and Ethics, 2(4), 553–577. DOI: https://doi.org/10.1007/s43681-021-00132-6
  9. Serradilla, J., et al. (2020). Deep learning models for predictive maintenance: comparison, challenges, and prospects. arXiv preprint. URL: https://arxiv.org/abs/2009.09592
  10. Jia, Z., Wang, J., & Deng, C. (2022). IIoT-based predictive maintenance for oil and gas industry. Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering, 432–436. DOI: https://doi.org/10.1145/3573428.3573503
  11. Orrù, A., et al. (2020). Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry. Sustainability, 12(11), 4776. DOI: https://doi.org/10.3390/su12114776
  12. Ohalete, N., Aderibigbe, A. O., & Ohenhen, P. E. (2023). Advancements in predictive maintenance in the oil and gas industry: a review of AI and data science applications. ResearchGate. URL: https://www.researchgate.net/publication/376982511_Advancements_in_predictive_maintenance_in_the_oil_and_gas_industry_A_review_of_AI_and_data_science_applications
  13. Khodayari-Rostamabad, A., Reilly, J. P., Nikolova, N. K., Hare, J. R., & Pasha, S. (2009). Machine learning techniques for the analysis of magnetic flux leakage images in pipeline inspection. IEEE Transactions on Magnetics, 45(8), 3073–3084. DOI: https://doi.org/10.1109/TMAG.2009.2012898
  14. Bastian, A., et al. (2019). Visual inspection and characterization of external corrosion in pipelines using deep neural network. NDT&E International, 107, 102134. DOI: https://doi.org/10.1016/j.ndteint.2019.102134
  15. Peng, L., et al. (2021). Hybrid PCA-CPSO-SVR model for internal corrosion rate prediction in multiphase pipelines. Journal of Petroleum Science & Engineering, 196, 108887. DOI: https://doi.org/10.1016/j.petrol.2020.108887
  16. Xie, R., Zhang, F., Wang, Q., Liu, Y., & Zhang, X. (2024). Prediction model of corrosion rate for oil and gas pipelines based on knowledge graph and neural network. Processes, 12(11), 2367. DOI: https://doi.org/10.3390/pr12112367
  17. Zuo, J., Li, Z., Xu, W., Zuo, J., & Rong, Z. (2025). Automated detection of methane leaks by combining infrared imaging and a Gas-Faster Region-Based Convolutional Neural Network technique. Sensors, 25(18), 5714. DOI: https://doi.org/10.3390/s25185714
  18. Attallah, O. (2023). Multitask deep learning-based pipeline for gas leakage detection via E-Nose and thermal imaging multimodal fusion. Chemosensors, 11(7), 364. DOI: https://doi.org/10.3390/chemosensors11070364
  19. Nwachukwu, A., et al. (2018). Fast evaluation of well placements in heterogeneous reservoir models using machine learning. Journal of Petroleum Science & Engineering, 163, 463–475. DOI: https://doi.org/10.1016/j.petrol.2018.01.019
  20. Li, Z., Wang, Y., & Li, X. (2023). Digital construction of geophysical well logging curves using the long short-term memory deep learning network. Frontiers in Earth Science, 10, 1041807. DOI: https://doi.org/10.3389/feart.2022.1041807
  21. Zheng, S., et al. (2019). Seismic interpretation and inversion with deep learning. The Leading Edge, 38(7), 526–534. DOI: https://doi.org/10.1190/tle38070526.1
  22. Zhang, Y., et al. (2020). Locating induced earthquakes using deep learning. Scientific Reports, 10, 9897. DOI: https://doi.org/10.1038/s41598-020-67785-9
  23. Barbosa, R., et al. (2019). Machine learning for drilling rate of penetration: review. Journal of Petroleum Science & Engineering, 178, 559–573. DOI: https://doi.org/10.1016/j.petrol.2018.09.069
  24. Noshi, C. I., & Schubert, J. J. (2018). The role of machine learning in drilling operations: a review. SPE Eastern Regional Meeting. DOI: https://doi.org/10.2118/191823-18ERM-MS
  25. Paltrinieri, N., Comfort, L., & Reniers, G. (2019). Learning about risk: Machine learning for risk assessment. Safety Science, 118, 475–486. DOI: https://doi.org/10.1016/j.ssci.2019.06.001
  26. Tahan, M., Tsoutsanis, E., Muhammad, M., & Karim, Z. A. (2017). Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. Applied Energy, 198, 122–144. DOI: https://doi.org/10.1016/j.apenergy.2017.04.048
  27. Lee, S., Landucci, G., Reniers, G., & Paltrinieri, N. (2019). Validation of dynamic risk analysis supporting integrated operations across systems. Sustainability, 11(23), 6745. DOI: https://doi.org/10.3390/su11236745
  28. Stergiopoulos, G., Gritzalis, D., & Limnaios, E. (2020). Cyber-attacks on the oil & gas sector: a survey on incident assessment and attack patterns. IEEE Access, 8, 101389–101405. DOI: https://doi.org/10.1109/ACCESS.2020.3007960
  29. Sophos (2024). The State of Ransomware in Critical Infrastructure 2024. URL: https://news.sophos.com
  30. Trustwave (2025). Energy & Utilities Risk Radar 2025. URL: https://www.trustwave.com
  31. Elete, T. Y. (2024). Impact of ransomware on industrial control systems in the oil and gas sector: security challenges and strategic mitigations. Computer Science and Information Technology Research Journal, 4(2), 1–12.
  32. Jacobs, T. (2018). Shell picks a digital platform to build its AI future upon. Journal of Petroleum Technology, 70(12), 43–44. DOI: https://doi.org/10.2118/1218-0043-JPT
  33. C3.ai (2022). The Scale of Shell’s Global AI Predictive Maintenance Program. URL: https://c3.ai/blog/how-shell-scaled-ai-predictive-maintenance-to-monitor-10000-pieces-of-equipment-globally/
  34. SLB (2020). Permian Basin ESP Gas Lock Interventions | Lift IQ Service. Case Study. URL: https://www.slb.com/resource-library/case-study-with-navigation/al/lift-iq-esp-interventions-permian-basin-cs
  35. Harvard Business School (2016). How is Big Data Changing GE’s Oil and Gas Business? URL: https://d3.harvard.edu/platform-rctom/submission/how-is-big-data-changing-ges-oil-and-gas-business/
  36. Baker Hughes (2018). Buzzard Phase II Receives Full Sanction to Move Forward. Press Release. URL: https://www.bakerhughes.com/company/news/buzzard-phase-ii-receives-full-sanction-move-forward
  37. Shell (2025). Shell Lowers Gas Production Guidance After Unplanned Maintenance Hit. News Article. URL: https://illuminem.com/illuminemvoices/shell-lowers-gas-production-guidance-after-unplanned-maintenance-hit-volumes
  38. University of Leoben (2022). Artificial Intelligence-based Approach for Predicting Mud Pump Failures. PDF. URL: https://pure.unileoben.ac.at/files/24386598/AC17011574.pdf
  39. Alqahtani, A., et al. (2020). Artificial intelligence applications in oil and gas upstream activities: A scoping review. Journal of Cleaner Production, 275, 123139. DOI: https://doi.org/10.1016/j.jclepro.2020.123139

About the Author:

Abinandaraj Rajendran, Senior Software Engineer specializing in AI/ML, focused on operationalizing cutting-edge innovations into scalable, production-ready solutions.