Few industries operate under as much scrutiny as oil and gas: every valve, weld, and reading carries both physical and regulatory consequences. From worker safety standards and asset integrity assurance to environmental pollution reporting, regulation has long defined the rhythm of this industry. And because every element of operation must meet a code, a limit, a documented assurance, compliance is not an afterthought, it’s a mandatory engineering function.

As technology advances, regulatory thresholds evolve and new standards emerge, extending the demand for compliance in the industry. Each breakthrough in sensing, data analytics, or automation redefines what is possible to measure, and therefore, what must be measured.

In this article, we track some recent Artificial Intelligence breakthroughs that are expanding monitoring capabilities, enabling compliance, and their potential to influence regulatory standards in the oil & gas industry.

AI-powered autonomous vehicles for asset inspections and repairs:

Reliability engineers know that inspections are the frontline of compliance, and historically, these inspections have been carried out through scheduled downtime and manual reporting that are costly and time consuming. Today, autonomous systems are extending human oversight into places and times once inaccessible.

Aerial drones and autonomous underwater vehicles (AUVs) are increasingly equipped with computer models trained to detect corrosion, leaks, and structural anomalies. These systems can operate continuously, transmitting inspection data back to shore-based teams for near-instant analysis. And with more advancements in Generative AI capabilities, analyzed data can increasingly inform autonomous intervention and create compliance reports.

Saipem’s Hydrones, for instance, represent a step change in subsea operations. These vehicles can remain deployed for extended periods, performing visual inspections, collecting data, and even executing light interventions, all without a human pilot. Similarly, Oceaneering’s Freedom ROV, used by Shell and TotalEnergies, combines advanced navigation and machine-learning algorithms to detect early-stage structural wear on subsea assets.

Onshore, Chevron’s partnership with Percepto has demonstrated how autonomous aerial drones can be used to monitor large facilities, detect methane emissions, and automatically flag potential regulatory breaches. These systems allow operators to identify and correct noncompliance long before it escalates into risk.

Generative AI and the Living Digital Twin

Digital twins have already become indispensable for modeling asset behavior, and with the rise of generative AI, these models have been pushed into a new phase.

With large language models being integrated into industrial data platforms, engineers can query systems conversationally, simulate “what-if” scenarios, and synthesize data streams that previously required manual review.

Through its collaboration with Palantir, BP is using AI to power these capabilities. The partnership aims to utilize Palantir’s AIP platform in harnessing large language models “to improve and accelerate human decision-making with suggested courses of action based on automated analysis of the underlying data.” Instead of sifting through dashboards, engineers receive curated summaries; what changed, why it matters, and what actions to take. This system allows engineers to receive contextualized living model of compliance that updates as fast as conditions evolve.

However, this brings to mind the question of how the governance of this technology will take shape. What underlying standards of safeguards will regulators seek to enforce?

In practice, this could mean treating the model as a decision-support tool rather than a safety function, establishing clear thresholds for autonomy, defining logging and documentation standards for auditability, and ensuring model transparency through explainable outputs and traceable data sources.

Intelligent Cost-Efficiency in Asset Decommissioning

As assets reach the end of their lifecycle, they are expected to meet regulatory requirements for decommissioning which include plugging wells, clearing sites, and preventing contamination. Yet across many basins, including the United States, operators often fall short, leaving thousands of wells orphaned.

Although companies are required to post decommissioning bonds, these amounts lag far behind actual costs. A 2024 Government Accountability Office report found that the U.S. Bureau of Ocean Energy Management held roughly $3.5 billion in supplemental bonds to cover an estimated $40–70 billion in decommissioning liabilities. The gap highlights a pressing need to make end-of-life operations more efficient without compromising safety or environmental standards.

That possibility may be closer than expected as Australian-based Rahd AI is using machine learning to analyze decommissioning data and identify the most efficient plug-and-abandonment sequences. Trained on information from more than 15,000 wells worldwide, the platform’s 2023 pilot achieved a 10 percent cost reduction, and the company expects to reach 35 percent by 2027.

If proven at scale, these efficiencies could do more than reduce project cost. They could narrow the gap between financial guarantees and real obligations, cutting the risk of orphaned wells.

Toward Adaptive Regulation: When Technology Expands the Boundaries of Oversight

Across inspection, monitoring, and decommissioning, AI is doing more than improving efficiency, it is expanding the boundaries of what can be verified - and by extension, what regulators may consider demonstrable assurance.

For reliability engineers, this shift will feel familiar, as compliance has always followed capability. The difference today is pace: machine intelligence accelerates the feedback loop between innovation and oversight. The traditional model of episodic, document-based, and retrospective compliance is being challenged by technologies that enable continuous, data-driven verification.

In this emerging landscape, the relationship between operator and regulator could become less transactional and more collaborative, built on shared data rather than periodic audits.

About the Author:

Maayken Saegeman is a Sales Engineer at Nord-Lock Group, helping customers boost safety and reliability with smart bolt-securing solutions.