The turnaround process in oil and gas industries is the most complex and high‑stakes events in an asset’s lifecycle. A single extra day of delay during a major shutdown can cost millions in lost production, impact safety, and strain already stretched teams. Yet many organizations still manage turnarounds with fragmented data, manual monitoring, and reactive decision‑making.
A structured five‑phase turnaround process provides a strong foundation. When combined with AI‑powered visibility and analytics, it becomes a powerful way to run safer, faster, and more predictable turnaround operations. This is exactly where platforms like Detect Technologies’ TA Orchestrator create measurable value.
Contents In This Blog
Phase 1: Scoping the turnaround
Scoping defines what will be included in the turnaround: the units that will be shut down, the assets that will be worked on, the outage window, and the high‑level objectives for safety, cost, and reliability. In practice, many scopes are still driven by intervals and experience rather than hard evidence, which can result in both missed risk and unnecessary work.
AI helps scope more intelligently by analysing maintenance history, failure trends, condition monitoring data, and previous production losses. It highlights assets with elevated risk profiles and surfaces patterns that may not be obvious from spreadsheets alone. When planners understand which equipment is most likely to fail before the next cycle, they can focus scope where it matters most and avoid overloading the turnaround process with low‑value tasks.
Phase 2: Planning and preparation
Once scope is agreed, teams move into detailed planning—developing work packs, confirming isolations, planning access and scaffolding, booking contractors, and ordering materials. This phase often spans months and crosses multiple disciplines, which makes coordination difficult if information is scattered across systems and email threads.
AI‑assisted planning tools can pull together data from CMMS, engineering systems, and past turnarounds to support more realistic plans. They help planners understand labour curves, craft requirements, and material demand, and can flag where long‑lead items or constrained resources are likely to create bottlenecks. Search and knowledge features make it easier to reuse proven job plans and lessons from earlier outages. The outcome is a more achievable baseline that reduces the risk of late surprises once the plant goes offline.
Phase 3: Execution in the field
Execution is where turnaround operations are most exposed. Multiple contractors, dense work fronts, hot work, work at height, and confined space entries all happen under tight timelines and high pressure. Traditional oversight in turnaround process depends on manual supervision, radio calls, and periodic progress updates—which can leave blind spots in both safety and schedule.
Detect Technologies’ AI‑powered TA Orchestrator is designed to close those blind spots through proactive hazard identification. Using data from field devices, planning systems, and digital workflows, it continuously tracks task status, work‑front readiness, permit compliance, and execution progress across the site. Intelligent alerts and prioritization help supervisors focus on jobs that are blocking the critical path, while risk signals highlight areas where congestion, non‑compliance, or repeated delays are emerging. This combination of real‑time orchestration and safety context helps teams maintain control during the most intense phase of the turnaround process.
Phase 4: Inspection, testing, and start‑up
Once mechanical work is finished, assets must be inspected and tested before the units can be restarted. This includes NDT, pressure and leak testing, loop checks, and functional testing of safety and control systems. The start‑up period is particularly sensitive, as equipment conditions and operating envelopes may have changed, and teams are executing revised or unfamiliar procedures.
AI can support this stage by enhancing both inspection and early operations. Digital workflows managed through TA Orchestrator ensure that inspection and test records are complete, traceable, and linked to specific work orders and systems. During start‑up, AI models can watch process and equipment data streams to identify abnormal trends at an early stage, giving operators more time to respond. This reduces the risk of incidents and rework after the plant is back in service.
Phase 5: Review and continuous improvement
After the turnaround process, organizations need to capture learning: did they meet targets for safety, schedule, and cost; where did delays come from; which contractors, work fronts, or systems caused the most friction? Too often, this review is limited by incomplete data and disconnected reports, making it difficult to build a clear picture.
TA Orchestrator, backed by AI‑driven analytics, can aggregate data from maintenance systems, project controls, permits, and execution logs into a unified view. With this, leaders can see recurring causes of delay, patterns of non‑compliance, and structural issues such as chronic congestion or frequently reworked tasks. These insights can be fed back directly into scoping, planning, and execution strategies for the next event, turning each turnaround process into a learning engine rather than a one‑off project.
Making turnarounds a strategic advantage with Detect
Turnarounds will always be complex, but they no longer have to be opaque or purely reactive. A clear five‑phase turnaround process provides structure; Detect Technologies adds AI‑driven visibility, orchestration, and analytics that help oil and gas operators run turnaround process with greater confidence and control.
By embedding TA Orchestrator into each phase—from planning and execution to post‑event analysis—operators can reduce non‑productive time, strengthen their safety performance, and bring critical units back online faster. For organizations under pressure to deliver more uptime with fewer resources, AI‑enabled turnaround operations are becoming a strategic differentiator.
