Multi-Agent Based Automation of Offshore Oil Rig Monitoring and Safety Compliance

Manual monitoring of satellite feeds and inspection reports for safety violations (e.g., oil sheens, unauthorized vessel approaches) is slow and error-prone, leading to missed incidents, regulatory fines exceeding $1M per violation, and catastrophic event risk. A custom agentic workflow automates 24/7 surveillance, detecting and classifying anomalies in near-real-time, routing confirmed violations directly to the safety management system (e.g., SAP EHS, Intelex) for immediate corrective action. This shifts compliance from a periodic audit cost to a continuous control layer, directly protecting the operating license and avoiding major financial penalties.

Unplanned downtime from structural failures or safety-driven shutdowns can cost over $500k per day in lost production. A workflow using InSAR (satellite radar) and computer vision agents continuously monitors platform subsidence, corrosion hotspots, and structural anomalies, feeding predictive maintenance models. These agents correlate geospatial data with SCADA telemetry, prioritizing high-risk issues and automatically generating work orders in the CMMS (e.g., IBM Maximo, SAP PM) weeks before failure. This moves maintenance from calendar-based to condition-based, maximizing uptime and extending asset life.

Inefficient coordination of supply vessels and crew changes leads to excessive standby time for high-cost offshore support vessels (OSVs), which can burn $25k+ per day. An orchestration layer with specialized agents ingests AIS data, satellite-derived weather, and platform operational schedules to dynamically optimize marine logistics. It simulates routes, predicts delays, and automatically updates scheduling in the marine coordination system, ensuring just-in-time deliveries and minimizing vessel idle time. This turns marine ops from a fixed-cost center into a dynamically optimized variable cost.

Teams spend hundreds of hours monthly manually reviewing satellite imagery, drone footage, and sensor logs—a high-skill task prone to fatigue and oversight. The workflow automates this ingestion and triage using a LangGraph-orchestrated multi-agent system: a Sensor Fusion Agent normalizes data from SAR, optical satellites, and IoT sensors; a Detection Agent runs specialized CV models; and a Triage Agent scores and routes alerts based on severity. This liberates senior engineers and safety officers from routine surveillance to focus on high-value exception analysis and process improvement.

During regulatory audits, assembling evidence of continuous monitoring is a manual, frantic process that risks gaps and inconsistencies. The workflow automatically logs every detection, decision, and action in an immutable audit trail, linking raw satellite data, model inferences, agent rationale, and human approvals. This structured evidentiary record is generated on-demand for regulators (e.g., BSEE, PSA), dramatically reducing audit preparation time from weeks to days and strengthening the operator's regulatory posture by demonstrating systematic, technology-enabled diligence.

Reactive safety programs lead to higher insurance premiums and deductibles. By implementing a documented, automated monitoring workflow, operators can demonstrate superior risk management to underwriters. The system can be configured to generate standardized risk reports—showing reduced incident frequency and predictive maintenance coverage—that feed directly into the underwriting process for offshore energy packages. This can translate into 5-15% premium reductions and better policy terms, turning safety automation into a direct financial lever.

This agent orchestrates the continuous ingestion of SAR (Synthetic Aperture Radar) satellite imagery for oil sheen detection, optical satellite data for structural monitoring, and drone-based visual/thermal feeds for close-range inspection. It normalizes timestamps, geolocations, and data formats, fusing inputs into a unified geospatial data lake. The architecture is built on cloud-native pipelines (AWS/Azure) with event-driven triggers to process new data as it becomes available, ensuring a persistent, up-to-date operational picture.

Specialized, containerized agents run in parallel to detect specific risk indicators. A Sheen Detection Agent uses SAR backscatter analysis to identify potential hydrocarbon leaks. A Vessel Proximity Agent classifies and tracks all marine traffic within a defined safety perimeter, flagging unauthorized approaches. A Structural Anomaly Agent compares current optical imagery against a digital twin baseline to identify corrosion, debris, or structural shifts. These agents are built on PyTorch/TensorFlow, deployed via Kubernetes for scalable inference.

The central brain of the workflow, this orchestrator (built with LangGraph or Temporal) receives alerts from detection agents and evaluates them against a dynamic rules engine. Rules encode safety regulations (e.g., 500m exclusion zones), company SOPs, and environmental permits. It determines violation severity, correlates multiple alerts (e.g., a vessel proximity during high-wind operations), and routes the case to the appropriate approval or action queue. All logic is version-controlled and auditable for regulatory scrutiny.

This component is the critical interface to incumbent enterprise systems. It uses secure APIs and message queues (e.g., RabbitMQ, Apache Kafka) to push validated alerts and work orders. Key integrations include: Safety Management Systems (e.g., Intelex) for logging incidents and corrective actions, Offshore Control Centers (SCADA/Historian) to correlate with operational data, and Marine Traffic Management platforms to issue radio warnings. The hub ensures the AI workflow drives tangible operational actions without creating data silos.

Not all alerts can be auto-resolved. This console provides offshore safety officers and onshore engineers with a prioritized dashboard of incidents requiring review. It presents the raw imagery, agent confidence scores, and suggested actions. Users can approve, reject, or escalate actions, with all decisions feeding back into the model for continuous learning. The console includes audit trail logging and integration with communication tools (Slack, Teams) for rapid team coordination, preserving human oversight for high-stakes decisions.

A production-grade monitoring layer tracks system health, agent performance (precision/recall), and data pipeline latency. It detects model drift caused by seasonal sea-state changes or new vessel types. An automated retraining pipeline is triggered on performance decay, using newly labeled data from the review console to fine-tune detection models. This closed-loop system, managed via MLflow and Weights & Biases, ensures the workflow's accuracy and reliability degrade over time, protecting the operational investment.