GREG TROSTEL, ROCKWELL AUTOMATION  

Oil and gas operations have been automated for decades. The next step is different: moving from systems that execute predefined instructions to operations that can sense changing conditions, analyze what they mean, decide on the appropriate response and act within defined operating and safety constraints. That progression toward autonomy can help operators address some of the industry’s most persistent challenges, from maximizing production and uptime to reducing operating costs and limiting personnel exposure to hazardous or remote environments. 

Automation and digitalization already underpin safe, reliable production across wells, processing facilities, pipelines and offshore assets. But today’s operating environment is raising the stakes. Operators are managing both mature and new assets, geographically dispersed operations, workforce constraints, volatile economics and increasingly connected infrastructure. An increasingly digital operating environment is making it possible to move beyond visibility and automated control toward systems that can take on a greater share of operational decision-making. 



Fig. 1. Oil and gas operators are accelerating digital transformation and AI adoption to address a variety of market pressures, but must also address challenges, such as legacy infrastructure and fragmented systems that limit the impact of digital solutions.

That distinction matters. An automated system typically follows programmed logic or control strategies to perform a defined task. An autonomous system goes further. It uses real-time operational data, advanced process control, analytics and AI to interpret conditions, select among possible actions and execute or recommend a response within established boundaries. Autonomy is therefore not synonymous with removing people from operations. In most oil and gas applications, it means changing the operator’s role from managing routine actions to supervising higher-level performance, exceptions and critical decisions. 

Technology is only part of the equation. As operators connect more wells, process units, rotating equipment, safety systems and remote assets, they also expand the digital attack surface, Fig. 1. A secure, scalable digital foundation must connect new and legacy systems, contextualize operational data and make trusted information available where decisions are made. Such a foundation allows autonomy to advance without compromising reliability, process safety, or cybersecurity. Operators are already applying this model in targeted areas where the operating case is strongest. 

WHERE AUTONOMY IS DELIVERING VALUE TODAY 

Autonomy is not arriving as a single, plantwide deployment. It is developing application by application, with the level of independent decision-making increasing, as operators validate performance and build trust. In oil and gas, the most practical use cases emerge where conditions change quickly, assets are difficult or hazardous to access, or small improvements in production, uptime or energy use can create substantial value. 

Remote operations centers illustrate that progression. They centralize human expertise, operational data and digital tools to support geographically dispersed wells, platforms and facilities. Instead of requiring specialists to travel to every asset, these centers can monitor operating conditions, identify abnormal behavior and help field teams respond. As analytics and AI capabilities expand, more of that monitoring and diagnostic work can be performed continuously by the system, escalating to people when judgment or intervention is required. 

Model predictive control (MPC) solutions are another well-established example. They continuously assess current and predicted operational data against desired results and then adjust control targets to reduce process variability and improve performance while keeping equipment operating within constraints. 

AI can extend these capabilities by considering a broader set of variables and relationships than conventional control strategies alone. In an oil and gas setting, that can mean continuously evaluating changing process conditions, equipment health and production performance to identify anomalies or optimization opportunities earlier. The objective is not simply to provide more data for an operator to interpret, but to convert data into increasingly automated decisions and actions that reduce manual intervention while keeping the process within defined operating constraints. 

This is where the business case for autonomy becomes tangible: more stable production, higher asset availability, lower operating expenditure and less human exposure to hazardous work. The path to those outcomes, however, looks different across the oil and gas value chain. 

Four areas show how operators can apply autonomy to specific challenges today: 

PRODUCTION FACILITIES AND PROCESS AUTOMATION 

At a production facility, autonomy depends on making decisions close enough to the process to matter. As AI has become more deployable at the edge, operators can place data processing, advanced control and AI-driven decision support closer to wells, pumps, compressors and process equipment, rather than relying exclusively on remote or cloud-based computation. In time-sensitive control applications, even small communication delays can affect the ability to maintain stable operation, protect equipment and respond to changing process conditions. 

Field devices and Industrial Internet of Things (IIoT) devices are becoming smarter, too. Sensors that once relayed only simple yes/no or on/off status can now communicate process conditions, asset health and diagnostic information about their own performance, providing richer data that will ultimately define effectiveness and efficiency. 

AI-based decision-making can build on this control foundation, as closed-loop autonomy matures. MPC is designed to predict process behavior and optimize control actions against defined objectives and constraints. AI can complement those capabilities by evaluating larger and more complex data sets, identifying patterns that may not be captured in a fixed model and supporting decisions across a wider range of operating conditions. For critical oil and gas processes, the current model generally keeps an operator in the loop for consequential decisions while experience, governance and trust in autonomous capabilities continue to develop. 

That operating model is already taking shape in upstream operations. A leading national oil company in the Middle East, for example, set out to create a centralized digital operations hub capable of supporting future autonomous oilfield operations. Rockwell Automation and a leading energy technology provider assessed and designed a Centralized Digital Station that brings together real-time operational visibility, predictive analytics, AI-enabled decision-making, cybersecurity and process control across upstream assets. By transforming siloed operational data into centralized intelligence, the program established a roadmap for remote monitoring and control, reduced field intervention and created a more secure path toward autonomous operations. 

OFFSHORE AND REMOTE ASSET AUTONOMY 

The case becomes even more compelling in offshore operations, where distance magnifies the cost and risk of almost every intervention. On an FPSO or remote platform, an equipment problem is not simply a maintenance event. It can require marine or air transport, specialist mobilization, spare parts logistics and work in a hazardous environment, all while production may be constrained or offline. 

Autonomy also depends on modernizing the control architecture beneath it. In one LNG marine application, a leading LNG shipping operator modernized two tankers by replacing fragmented onboard systems with a unified operational platform integrating vessel automation, safety, monitoring and emergency shutdown functions. The architecture created real-time data acquisition, high-speed communications and centralized visibility across critical shipboard systems, improving situational awareness, reliability and energy efficiency while creating an AI-ready foundation for future predictive operations, remote monitoring and autonomous vessel capabilities. 

Part of the appraisal used to justify this and other investments is the “cost of change” axiom. If a change at the design stage costs $1, it may cost $10 during the building phase, while at commissioning it can rise to $100. And in offshore settings, it can be tens of thousands of dollars. This offshore cost scenario creates a strong case for the early adoption of digital infrastructures, automation and AI as operators build toward greater autonomy. 

That operating reality changes the economics of autonomy. If an AI-enabled system can detect degrading equipment performance earlier, diagnose the likely issue and recommend or initiate a corrective action within approved limits, the operator may be able to avoid an offshore intervention altogether or plan it before the problem affects production. The value is measured not only in maintenance cost, but also in avoided downtime, production continuity and reduced personnel exposure. 

Remote inspection is another natural fit. Robots, drones and other automated inspection technologies can collect visual, thermal or other condition data in areas that are difficult, repetitive or hazardous for people to access. When those inspection tools are connected to analytics and maintenance workflows, the operating model begins to shift from periodic manual inspection toward continuous condition awareness and earlier intervention. 

Safety is another key driver of autonomy. Reducing the need for personnel to enter hazardous or remote areas reduces exposure to potential incidents. It also reduces the time people spend traveling to and from an operating site or doing inspection and maintenance rounds.  

That shift is changing how operators approach inspection and maintenance. This year, a leading upstream energy producer in the Middle East worked with Rockwell Automation to establish a five-year robotics strategy spanning air, ground and marine technologies. The roadmap prioritized 40 high-value use cases and created 10 functional design specifications, while four quick-win projects were deployed to demonstrate advanced inspection and monitoring capabilities. The immediate objective was to reduce workforce exposure to hazardous environments and strengthen operational resilience. The longer-term objective is a scalable framework for enterprise-wide robotics adoption as part of autonomous operations. 

AUTONOMOUS WELLSITE OPERATIONS 

Automation, AI and autonomous operations can also change how operators manage wells and reservoirs. Well conditions and production constraints are dynamic: pressure, flow, injection performance and equipment condition can change over time, while actions taken at one point in the system can affect production elsewhere. The ability to continuously monitor and evaluate those conditions and make timely adjustments can help improve total production while protecting well, equipment and reservoir health. 

The value can also be seen at the individual wellsite. A leading energy producer modernized more than 130 rod-pump wellsites with regenerative drive technology that recovers energy normally lost during the pumping cycle while providing advanced control and real-time performance data. The deployment achieved 17% energy regeneration, with 95% recovered energy reused, and is projected to deliver $3 million in monthly energy savings at full deployment. The modernized wellsites now produce AI-ready data that can support predictive maintenance, production optimization, remote operations and adaptive control. 

The same principle applies to reservoir-support processes. In another application, a producer seeking to improve the stability and efficiency of its water-injection process deployed MPC technology across 35 water-injection pumps and three pool transfer units and pads. By automatically adjusting operations based on predicted conditions, the system increased water injection by nearly 36,000 bpd, helping the operator produce an additional 548 bopd while decreasing energy consumption by 3%. 

PREDICTIVE MAINTENANCE AND ASSET RELIABILITY 

Autonomy can also change how operators protect asset reliability. Many oil and gas assets still rely heavily on preventive schedules or reactive intervention. For rotating and other critical equipment on a drilling rig, production facility or offshore asset, identifying a developing issue before it becomes an alarm or failure can protect uptime, process continuity and safety.



Fig. 2. A structured approach helps operators scale autonomous technologies from initial assessment and pilot testing to broader asset deployment.

In one case, an operator deployed a predictive maintenance solution on a semi-submersible drilling rig, using a dynamic equipment health index, statistical anomaly detection and edge computing for real-time monitoring. The solution identifies and alerts maintenance teams of deviations from normal operating patterns, supporting rig availability and productivity. 

The larger opportunity emerges when operational, maintenance and performance data are connected across assets, rather than analyzed in isolation. Enterprise-level analytics can compare performance across geography, climate, operating environment, equipment age and other variables. AI can identify patterns and emerging risks. Autonomous systems can then translate approved recommendations into action. At that point, autonomy becomes more than a collection of point solutions. It becomes a way to continuously optimize asset performance. 

TWO PITFALLS TO AVOID 

The technology case for autonomy is strong, but oil and gas operators have learned that technology alone does not determine whether a deployment succeeds. Two pitfalls are especially important, as companies move from isolated use cases toward repeatable operating models.  

Pilot purgatory. New technologies are often evaluated on a small, isolated, offline test bed. Engineers test, analyze the results, conduct further reviews and eventually reach a decision. But if that process stretches too long, momentum can fade, the operating need can change, and no strategy may exist for scaling the solution across the enterprise. A formal testing and scaling framework can help address this challenge, Fig. 2. 



Fig. 3. Secure IT/OT architecture supports connected, data-driven operations across the energy value chain while managing cybersecurity risk.

A better approach is to design for scale from the outset: identify an operational problem with measurable value, run a focused test, define the technical and operational criteria for success, and establish how a successful application will be replicated across similar assets. For oil and gas operators with fleets of wells, multiple platforms or standardized process units, that repeatability is critical to turning a promising technology demonstration into enterprise value.  

Cybersecurity. With any digitally transformed operation, no matter its maturity level, cybersecurity must remain a core consideration. Threat vectors increase as more assets, network endpoints and data nodes are connected. Potential threats include disruption to operations, ransomware, intellectual property and data theft. Operators, therefore, need robust OT and IT solutions that deliver the highest level of cybersecurity without affecting day-to-day operations, Fig. 3. 

One global oil and gas producer lacked unified visibility and control of OT security for thousands of endpoints deployed across its operations on heavily segmented networks. By implementing centralized asset visibility and vulnerability management, the operator was able to identify and remediate risks while monitoring its security posture. Within just six months, it improved centralized visibility and vulnerability management while maintaining operational continuity. 

STARTING THE JOURNEY TO AUTONOMY 

The end state may be highly autonomous operations, but the practical starting point is rarely a fully autonomous facility. Oil and gas operators can build autonomy in layers, beginning with the processes where better sensing, prediction or automated response can solve a clear operating problem and where the risks can be managed. 

That starts with trustworthy process and asset data: collecting it consistently, contextualizing it, so systems understand what it represents, and making it available securely at the point of decision. From there, operators can select targeted use cases, establish operating and safety constraints, test performance, and define when a person must remain in, or return to, the decision loop. Early wins can then support the business case for broader deployment. Just as important, engineers, operators and maintenance teams must understand how the system reaches decisions and how their own roles change as autonomy increases. 

The oil and gas industry has always adopted new operating technologies with a premium on safety, reliability and disciplined risk management. Autonomy should be no different. The objective is not autonomy for its own sake, nor a future with people removed from operations. It is an operating model in which automation, advanced control and AI enable routine decisions to happen faster and more consistently, while human expertise is focused where judgment matters most. Operators that build that foundation now can move toward higher production efficiency, greater asset reliability and lower risk—one proven application at a time. 

GREG TROSTEL is Global Industry Development Manager at Rockwell Automation, where he leads initiatives across the FPSO, LNG and nuclear SMR markets. He has extensive experience in engineering, project development, operations, software and industrial automation, including 13 years with Kellogg/KBR. Mr. Trostel holds a degree in chemical engineering from Texas A&M University and an MBA from the University of Houston. He also hosts a World Oil podcast series on artificial intelligence in the energy industry. 

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