AI Workflow for Offshore Platform Structural Inspection

Offshore platform integrity management is a high-cost, high-risk operational bottleneck. Manual inspections of jacket legs, risers, and helidecks require specialized crews, costly vessel time, and expose personnel to hazardous environments. A custom automated workflow replaces this with autonomous drones equipped with visual, thermal, and LiDAR sensors. The business value is direct: it eliminates tens of thousands of man-hours in high-risk environments, accelerates inspection cycles from months to days, and provides auditable, quantitative data for predictive maintenance, directly protecting asset value and operational safety.

Implementation requires robust orchestration, typically using frameworks like LangGraph, to manage the autonomous flight in harsh environments, synchronize sensor data, and route findings. The AI analysis layer employs specialized vision models for corrosion quantification and marine growth classification, with confidence scoring to route low-confidence detections for human review. Integration with platform integrity management software (e.g., ABS Nautical Systems, Oracle) and CMMS is critical to close the loop, automatically generating API 570/653-compliant reports and prioritized work orders. Controls include geofenced flight plans, real-time telemetry monitoring, and mandatory engineering review gates for high-severity findings before any maintenance action is dispatched.

The workflow begins with an autonomous drone mission triggered by a scheduled maintenance calendar or an anomaly alert from IoT sensors. The orchestrator agent manages flight parameters for harsh marine environments, ingests raw visual, thermal, and LiDAR data, and dispatches it to specialized analysis agents. These agents, trained on historical defect libraries, concurrently scan for marine growth on jacket legs, corrosion on risers, and cracks on helideck structures, outputting geotagged findings with confidence scores.

Aggregated, scored findings are routed through a mandatory human review gate for engineering sign-off before the orchestrator creates a structured work package. This package is pushed via API into the platform's integrity management software (e.g., SAP PM, Aveva), generating a prioritized work order with parts forecasting. The final step is a closed-loop verification, where post-repair drone data confirms resolution, updating the digital twin and asset health record. This end-to-end automation reduces inspection-to-repair cycles from weeks to days, directly lowering operational risk and deferring major capital expenditure.

Phase 1 establishes the core data pipeline, focusing on autonomous drone mission execution in harsh environments and secure ingestion of visual, thermal, and LiDAR data into a cloud data lake. This initial stage de-risks the project by validating sensor fusion and basic data integrity before any AI analysis, ensuring the foundational capture system is reliable. Integration with platform SCADA or historian systems for environmental context (e.g., wave height, wind) is also configured.

Phase 2 deploys and validates the AI analysis layer, training vision models on historical inspection data to detect marine growth, coating breakdown, and corrosion on jacket legs and risers. A critical parallel deliverable is the orchestration logic that converts AI findings into prioritized work orders in the platform's Integrity Management System (e.g., SAP PM, Oracle EAM), with human-in-the-loop approval gates for high-severity defects. This phase proves the workflow's accuracy and operational utility before full-scale rollout.

Operational rollout begins with a phased pilot on a single jacket leg or helideck, integrating the drone data pipeline with the platform's Integrity Management System (IMS) like Aveva or SAP EAM. This controlled environment validates the AI's defect detection accuracy against certified surveyor reports, establishing a baseline for marine growth and corrosion classification. The governance model must define explicit approval gates for work order generation, ensuring any high-severity finding triggers a mandatory human review by a structural engineer before dispatch, preserving safety-critical oversight.

Full-scale deployment follows a asset-criticality-based sequence, prioritizing high-risk components like risers and subsea supports. The control plane must include real-time monitoring of drone fleet status, data pipeline health, and model performance drift, with alerts routed to a dedicated offshore operations console. Exception handling for poor weather, GPS denial, or sensor failure requires predefined fallback to manual inspection protocols. This structured rollout, governed by clear RACI matrices and embedded within the platform's existing management of change (MOC) process, transforms inspection from a periodic, labor-intensive event into a continuous, controlled, and capital-efficient operational stream.

The workflow's value hinges on data quality and model precision. Autonomous drones capture terabytes of visual, thermal, and LiDAR data in harsh marine environments. This raw data must be geotagged, synchronized, and validated before analysis. Specialized computer vision models, trained on domain-specific datasets of marine growth, coating breakdown, and fatigue cracks, then process this data. These models must output not just detections but quantifiable metrics—corrosion area, crack length, NACE rust grades—with calibrated confidence scores to support engineering decisions and avoid false positives that waste field crew time.

Integration is where automation delivers ROI. Quantified defects must flow seamlessly into platform integrity management software like SAP Plant Maintenance or Aveva to trigger maintenance work orders, update digital twins, and feed capital planning models. This requires robust APIs and orchestration logic to handle exceptions, such as low-confidence findings routed for engineer review. The architecture must also embed audit trails for regulatory compliance (e.g., API 570), proving the lineage from raw image to maintenance action, which is critical for safety and auditability in regulated offshore operations.