The future of maintenance in Oil & Gas: Artificial intelligence and technological innovation  

Amidst extreme environments, dispersed assets, and growing demands for safety, efficiency, and sustainability, the Oil & Gas sector is radically transforming its approach to infrastructure maintenance. Technologies such as artificial intelligence (AI), the Internet of Things (IoT), Digital Twins, drones, and autonomous robots are enabling a more connected, predictive, and data-driven approach.

This article explores how these solutions are being applied in real-world operations, the challenges they help overcome, and the results achieved in terms of reliability, cost, and operational performance. More than just a technological upgrade, this represents a paradigm shift—placing maintenance at the core of the sector’s future competitiveness.

The new maintenance landscape in the Oil & Gas sector

The Oil & Gas sector is undergoing a profound transformation, driven by technological, economic, and environmental changes. Traditionally reliant on large-scale assets, technical complexity, and high operational costs, the industry now faces a pivotal moment, where maintenance is becoming a strategic driver of competitiveness, safety, and sustainability.

In a context of energy price volatility, regulatory pressure, and growing concern over environmental impact, companies are seeking smarter, more proactive ways to manage their assets. It is in this scenario that artificial intelligence (AI) and other emerging technologies are gaining prominence, enabling a more predictive, efficient, and safer approach to maintenance.

Traditional challenges in industrial asset maintenance

Maintenance within the Oil & Gas sector has long been shaped by several structural challenges:

• Extreme environments and critical equipment: Offshore platforms, refineries, and pipelines operate under harsh conditions, where asset failures can pose serious human, environmental, and financial risks. Additionally, the need to use certified equipment in specialized environments, such as EX (explosive zones), significantly increases the cost of field devices.
• Costly corrective interventions: The lack of failure predictability often leads to unplanned shutdowns, disrupting production and resulting in high emergency repair costs.
• Limited access to reliable real-time data: The geographic dispersion of infrastructure and lack of digital integration make it challenging to continuously monitor and control assets.
• Shortage of qualified labor: The retirement of experienced technicians, combined with the growing need for digital skills, presents an additional risk to operational continuity.

These challenges clearly highlight the need for a new maintenance model—more intelligent, automated, and data-driven.

The pressure for efficiency, safety, and sustainability

Maintenance has evolved beyond its traditional technical function to become a critical performance factor:

• Operational efficiency: As profit margins shrink, companies are striving to reduce operating costs without compromising asset reliability. Predictive maintenance enables more effective resource allocation and prioritization of interventions, leading to improved operational efficiency.
• Industrial safety: In high-risk sectors, maintaining equipment in optimal condition is crucial to prevent serious accidents. Data-driven prevention reduces unexpected failures and protects on-site teams.
• Sustainability and environmental compliance: Leaks, compressor failures, or pipeline breaks can lead to severe environmental damage and legal penalties. Continuous monitoring and early intervention are vital to mitigating these risks and aligning operations with ESG goals.

These three pressures—efficiency, safety, and sustainability—are converging to accelerate the adoption of advanced maintenance technologies.

The role of emerging technologies in operational transformation

Emerging technologies are redefining the maintenance paradigm in the Oil & Gas sector:

• Artificial intelligence (AI): Enables the analysis of large volumes of data from sensors, failure histories, and operating conditions, generating predictive models that accurately anticipate maintenance needs.
• Internet of Things (IoT): The deployment of sensors connected to assets allows for continuous, real-time data collection, improving visibility into equipment status, even in remote locations.
• Digital Twins: By creating digital replicas of physical assets, companies can simulate equipment behavior under various conditions, test failure scenarios, and optimize maintenance plans.
• Automation and robotics: Inspections in hazardous environments can now be carried out by drones, underwater robots, or autonomous systems, reducing human risk and increasing analysis accuracy.

These technologies not only improve operational performance but also represent a new asset management model—smarter, safer, and better aligned with the sector’s future challenges.

Artificial intelligence applied to maintenance

Applying artificial intelligence to industrial maintenance marks a clear departure from traditional models based on fixed schedules or reactive interventions. In the Oil & Gas sector, where assets are critical and operate under extreme conditions, AI offers a transformative approach. It enables early fault detection, resource optimization, and increased system availability, directly impacting operational profitability and safety.

What is predictive maintenance with AI

Predictive maintenance powered by AI uses advanced algorithms to accurately forecast when an asset is likely to fail or begin degrading. Unlike traditional preventive maintenance, which is based on fixed intervals, the predictive approach relies on real-time equipment data collected by sensors and analyzed through machine learning models.

These models are trained on historical failures, operational conditions, and environmental variables, identifying patterns and anomalies that signal potential issues. This enables timely intervention, preventing unexpected breakdowns, avoiding wasted parts, and reducing unnecessary labor hours.

AI use cases in Oil & Gas

Leading companies in the sector are already adopting AI-driven maintenance, with several real-world applications:

• Early fault detection in pumps, compressors, and valves: Algorithms detect minor changes in vibration, temperature, or pressure—often early indicators of wear or impending failure.
• Continuous monitoring of pipelines and offshore infrastructure: AI analyzes real-time sensor data across distributed networks, flagging potential leaks, corrosion, or structural instability.
• Anomaly detection in engines and turbines: Predictive models combine operational data with environmental factors (such as humidity and ambient temperature) to assess performance risks under adverse conditions.
• Maintenance planning optimization: AI-generated forecasts help prioritize tasks, allocate teams more efficiently, and minimize downtime.
• Integration with Digital Twins: Digital replicas use AI to simulate future asset behavior across different scenarios, enabling more informed decisions about maintenance and replacement.

Benefits of AI: Lower costs, higher availability, and fewer unplanned downtimes

AI adoption in maintenance brings clear, measurable benefits to operations:

• Reduced operating costs: Fewer unnecessary interventions, lower consumption of spare parts, and more efficient use of technical teams.
• Greater asset availability: By intervening only when needed and before failures occur, equipment stays operational longer and with improved reliability.
• Fewer unplanned downtimes: AI’s predictive capabilities significantly reduce unexpected interruptions, directly improving productivity and safety.
• Improved planning for resources and procurement: Reliable forecasts allow for earlier preparation of materials, tools, and teams—avoiding emergency purchases and minimizing waste.
• Increased safety and lower environmental risk: Catastrophic failures with potential human or environmental impact can be avoided through early alerts and targeted intervention.

By transforming raw data into actionable insights, artificial intelligence is fundamentally reshaping the maintenance paradigm in Oil & Gas—from a reactive, costly function to an intelligent, efficient, and strategic process.

Other breakthrough technologies transforming maintenance

The transformation of maintenance in the Oil & Gas sector extends far beyond artificial intelligence. Other disruptive technologies are gaining traction, enabling unprecedented levels of monitoring, analysis, and intervention in industrial assets. When integrated, these technologies provide a holistic view of equipment health, reduce response times, and make operations safer and more efficient.

Internet of Things (IoT) and smart sensors

The Internet of Things is the foundation of digital maintenance. It involves the integration of real-time connected sensors into industrial systems and equipment, continuously collecting data such as temperature, pressure, vibration, flow rates, or oil levels.

In the Oil & Gas context, IoT enables:

• Remote monitoring of assets in extreme environments, such as offshore platforms or remote production fields.
• Real-time anomaly detection, instantly alerting technical teams to potential issues.
• Detailed operational history creation, essential for feeding AI models and supporting predictive maintenance decisions.

Today’s sensors are increasingly intelligent, with edge computing capabilities that allow data preprocessing at the source—reducing latency and increasing the reliability of alerts.

Digital Twins and real-time simulations

Digital Twins represent a significant leap in how industrial assets are managed. A Digital Twin is a virtual replica of a physical asset, continuously updated with real-time data from IoT sensors.

In the Oil & Gas sector, Digital Twins are used to:

• Simulate operational and failure scenarios, allowing teams to predict behaviors and plan responses before real-world incidents occur.
• Test changes or interventions without risk, ensuring decisions are optimized and evidence-based.
• Visualize asset status comprehensively, combining sensor data, historical information, and predictive models.

This technology enables a more proactive management approach, grounded in continuously updated insights into asset behavior, delivering measurable gains in efficiency, reliability, and safety.

Augmented reality and remote assistance for field teams

Augmented reality (AR) is revolutionizing how maintenance technicians access information and perform tasks. Using smart glasses, tablets, or mobile devices, operators can overlay operational data, technical manuals, and step-by-step instructions directly onto the physical equipment they’re inspecting.

Key applications include:

• Real-time remote assistance: Experts onshore can guide field technicians by viewing what they see and providing step-by-step instructions.
• On-the-job training: AR enables immersive simulations of complex or hazardous procedures in a controlled environment.
• Faster and more accurate task execution: Immediate access to accurate, up-to-date information allows technicians to work with greater confidence and precision.

This technology is especially valuable in hard-to-reach or high-risk environments, where quick response times and accuracy are critical, and expert presence is limited.

Drones and autonomous robots for inspections in hazardous environments

The use of drones and autonomous robots in maintenance is becoming increasingly important in the Oil & Gas sector, especially for offshore operations, refineries, and hard-to-access areas where technician safety is a top priority.

These technologies allow for:

• Visual and thermal inspections in dangerous or inaccessible locations—such as flare stacks, elevated tanks, pipelines, and offshore platforms—without scaffolding or production shutdowns.
• High-precision data collection—including visual, thermal, and ultrasonic inputs—for integration with AI systems or Digital Twins for deeper analysis and diagnostics.
• Significant reduction in human risk by eliminating the need for operators to enter explosive zones, extreme temperature areas, or high-risk locations.

In addition to aerial drones, ground-based and underwater robots are used to inspect inside pipelines, tanks, or submerged infrastructure.

The combination of autonomous mobility, advanced sensors, and integration with analytical systems makes these tools essential for intelligent, safe, and data-driven maintenance.

Strategies for adopting intelligent maintenance

The transition toward smarter, more digital maintenance in the Oil & Gas sector requires more than just acquiring new technology. It demands a strategic approach that integrates people, processes, and systems. Successful implementation hinges on data quality, team readiness, and the organization’s ability to integrate new tools with existing systems—while fostering a culture of continuous improvement and innovation.

Data collection and quality as the foundation

Reliable and relevant data is the foundation of intelligent maintenance. Without high-quality data, even the most advanced algorithms cannot deliver meaningful insights.

Organizations must ensure:

• Proper sensor deployment on critical assets to monitor key variables such as vibration, temperature, pressure, and wear.
• Robust communication infrastructure that supports continuous data collection and transmission, even in remote or extreme environments.
• Data validation and cleansing processes, ensuring that the information analyzed is accurate and reflects actual operating conditions.
• Strong data governance, with clearly defined responsibilities, standards, and access rules to ensure the security and integrity of the information.

Data quality directly impacts the effectiveness of predictive maintenance and the automation of operational decision-making.

Workforce development and change management

The introduction of intelligent technologies requires a shift in the skillsets and mindsets of maintenance teams. Installing sensors or deploying algorithms is not enough—people must be prepared for a new way of working.

This includes:

• Training technicians and engineers in data analysis, interpreting automated diagnostics, and using digital tools.
• Promoting cross-functional collaboration between IT, maintenance, operations, and engineering to align technology implementation with operational goals.
• Managing organizational change, addressing resistance, and fostering a culture of innovation and continuous learning.
• Engaging teams from the outset, ensuring they understand the benefits and actively contribute to the transformation’s success.

Technology is an enabler, but real impact comes from the people who use it.

Technology partnerships and system integration

Adopting intelligent maintenance requires a collaborative approach with partners that specialize not only in technology but also in digital transformation and organizational change. This shift goes far beyond deploying sensors or AI platforms—it involves rethinking processes, culture, and decision-making models.

Companies should consider:

• Selecting partners with Oil & Gas expertise, who understand the sector’s operational challenges and the requirements for safety, robustness, and compliance.
• Collaborating with reliable technology providers—including system integrators, IoT vendors, analytics platforms, AI solutions, and Digital Twin developers—ensuring scalable, secure, and interoperable solutions.
• Ensuring seamless integration between new tools and existing platforms like SCADA, ERP, CMMS, or asset management systems—avoiding duplication and building a cohesive digital ecosystem.
• Avoiding technological silos by choosing open solutions with well-defined APIs that enable communication across multiple data sources and management tools.

Equally important is working with partners who bring organizational transformation expertise, supporting:

• Cultural and operational change management, helping teams adapt to new working models and aligning technical and business units.
• Employee upskilling, with training programs focused on developing the digital competencies needed for intelligent maintenance.
• Process and governance redesign, revisiting workflows, responsibilities, and KPIs to maximize the impact of smart maintenance.

These strategic and technological partnerships are crucial to ensure that the transition to intelligent maintenance is not only technically feasible but also sustainable and fully integrated into the organization’s operating model.

Practical cases and results

The shift toward smart maintenance models is already underway in several companies across the sector, delivering measurable, real-world results. Technologies such as artificial intelligence, the Internet of Things (IoT), digital twins, and drones are being deployed in both pilot projects and large-scale operations. These solutions are clearly improving asset reliability, operational safety, and resource efficiency.

Real-world examples of data-driven maintenance in Oil & Gas

Predictive systems powered by AI are generally capable of detecting abnormal patterns through sensors installed on pumps, compressors, and valves, helping to prevent unplanned shutdowns. For example, Shell currently monitors around 10,000 critical assets using AI, resulting in a 35% reduction in unplanned downtime and a 20% decrease in maintenance costs. Similarly, BP has achieved a 25% reduction in maintenance costs at its refineries through the use of AI-based predictive analytics.

IoT enables the deployment of smart sensors across entire facilities, allowing for continuous collection of critical data 24/7. Shell, for instance, has installed wireless sensors on offshore platforms to monitor variables such as pressure and flow in real time—tasks that were previously performed manually. While it used to take an operator about two hours to gather these readings, the system now sends automatic alerts only when significant deviations occur. This constant monitoring has freed up teams to focus on strategic interventions and has helped prevent severe damage. In one case, the digital model detected a misconfigured valve, avoiding repair costs that could have reached hundreds of thousands of dollars.

Digital twins create exact virtual replicas of physical assets by integrating sensor data, operating conditions, and engineering models. By combining digital twins with AI, Shell has reduced asset downtime by 20%, translating into estimated annual savings of $2 billion. Additionally, through technological partnerships, Shell extended the lifecycle of a North Sea platform by 20 years by digitally simulating the structural integrity of its equipment. These examples show how digital twinning not only optimizes maintenance programs—reducing MTTR—but also improves OEE and postpones the need for new asset investments.

Meanwhile, autonomous drones and robotics are accelerating inspections and repairs, particularly in hard-to-reach areas. Chevron, for example, uses smart drones to inspect pipelines and platforms, leveraging computer vision to detect leaks or structural faults in real time. This approach has significantly reduced the need for human inspections in high-risk zones, sped up incident response, and decreased operational disruptions.

Shell has also implemented augmented reality (AR) using devices like HoloLens on its Gulf of Mexico platforms. This technology allows onshore inspectors to view, in real time, what the offshore operator is seeing, eliminating the need for helicopter travel. In one case, AR prevented a multi-day delay during a gas system inspection on the Ursa platform, unlocking hundreds of thousands of dollars in production value by enabling the rapid reactivation of the well.

Impact on performance indicators (OEE, MTTR, MTBF)

The introduction of these technologies has a direct impact on key maintenance performance metrics:

• OEE (Overall Equipment Effectiveness): Improves due to fewer unexpected failures and greater asset availability.
• MTTR (Mean Time to Repair): Decreases as early detection and remote support accelerate the maintenance team’s response.
• MTBF (Mean Time Between Failures): Increases thanks to predictive maintenance and more stable operations.
• Maintenance costs: Drop significantly due to the elimination of unnecessary interventions, better spare parts management, and fewer unplanned outages that typically require emergency repairs or external services.

Together, these improvements contribute to more reliable, safer, and more efficient operations—with reduced environmental impact, more controlled operating costs, and greater budget predictability. Organizations that adopt these practices in a structured manner are gaining a competitive edge while moving toward more resilient and sustainable asset management.

Maintenance as a competitive advantage in Oil & Gas and future trends

In a highly competitive, heavily regulated, and technically demanding sector like Oil & Gas, maintenance has evolved from a purely operational function into a strategic driver of value creation. Companies investing in digital technologies and artificial intelligence to modernize their maintenance models are reducing costs, increasing asset availability, improving safety, and mitigating environmental risks—benefits that translate into a clear and sustainable competitive advantage.

Smart maintenance enables organizations to turn data into actionable decisions, empower technical teams with digital tools, and intervene at the right time, rather than reacting to costly failures. As assets age and the challenges of energy transition intensify, the ability to maintain operational reliability efficiently will become a key differentiator in organizational performance.

Looking ahead, several trends are expected to continue shaping the sector:

• Full integration of AI, digital twins, and asset management platforms, creating connected and autonomous ecosystems.
• Increased automation of inspections and interventions using robots and drones, further minimizing human risk.
• Expanded use of augmented and virtual reality for training, remote support, and assisted maintenance.
• Growth of real-time, data-driven models with self-learning capabilities and continuous improvement.
• Alignment with ESG and sustainability goals through more efficient operations and reduced environmental impact.

The digital transformation of maintenance is not merely a technological evolution—it’s a paradigm shift that’s redefining how the industry manages its assets, enhances resilience, and prepares for the future of energy.