Automation Workflow for Oil and Gas Reservoir Monitoring and Subsidence Detection

This workflow automates the detection of reservoir subsidence or uplift caused by extraction or injection, a critical operational risk. It replaces manual, batch-based InSAR analysis with a continuous pipeline that ingests Sentinel-1 or commercial SAR data, processes time-series interferograms, and extracts deformation vectors. The business value is direct: preventing costly well failures, ensuring license-to-operate by demonstrating regulatory compliance, and optimizing injection strategies to protect asset integrity and field economics.

Implementation integrates this pipeline with reservoir engineering platforms like Petrel and OSDU data hubs. The orchestration layer, built with LangGraph, manages data flow, triggers reprocessing on new imagery, and routes alerts based on configurable velocity and cumulative displacement thresholds. Critical controls include geomechanical model validation of alerts, mandatory engineer review for high-risk zones, and immutable audit logs for regulatory submissions. This creates a closed-loop system where geospatial intelligence directly informs field operations and compliance reporting.

This custom pipeline automates the continuous monitoring of reservoir-induced subsidence, a critical operational risk and regulatory requirement. It replaces manual, batch-based InSAR analysis with a production-grade system that ingests raw satellite data, processes time-series interferograms, and flags deformation exceeding safety thresholds. The business value is direct: preventing costly wellbore failures, ensuring license-to-operate compliance, and optimizing injection/extraction strategies by providing reservoir engineers with near-real-time geomechanical feedback, reducing manual analyst workload by over 70%.

Implementation requires orchestrating specialized agents within a framework like LangGraph, each handling a discrete pipeline stage: data prep, interferometric processing, and geospatial alerting. The architecture must integrate with enterprise systems like Petrel for reservoir simulation and regulatory portals for automated reporting. Critical controls include human-in-the-loop review gates for high-consequence alerts, automated quality checks on input data, and a feedback loop to retrain detection models, ensuring the system adapts to new geological signatures and maintains auditability for compliance evidence.

This workflow automates the continuous monitoring of reservoir-induced subsidence or uplift using time-series InSAR satellite data. It eliminates the manual, batch-based analysis that delays risk identification, directly addressing operational safety and regulatory compliance bottlenecks. The business value is measured in prevented wellbore failures, avoided regulatory fines, and optimized injection strategies, translating millimeter-scale detection into millions in risk mitigation and operational savings. Implementation requires integrating with SCADA, reservoir simulation software, and regulatory reporting systems like Enverus or custom E&P platforms.

Phase 1 establishes the foundational data pipeline, processing raw SAR data into displacement maps with validation against ground control points. Phase 2 introduces the alerting agent and a mandatory human review gate, ensuring operational trust before automated actions. Phase 3 integrates the validated workflow with engineering and compliance systems, enabling closed-loop control. This staged approach de-risks deployment, allows for calibration against historical incidents, and builds the observability and audit trails required for high-consequence environments. Final orchestration is managed via LangGraph, with agents handling data fusion, threshold logic, and API calls to enterprise systems.

A production-grade reservoir monitoring workflow automates the ingestion and processing of time-series InSAR data to detect millimeter-scale ground movement, triggering alerts for potential well integrity issues or regulatory non-compliance. The operational upside comes from preventing catastrophic failures, optimizing injection/extraction rates, and automating compliance reporting, which directly protects asset value and reduces manual geoscience labor. Implementation requires integrating satellite data feeds with reservoir simulation models (like Petrel) and SCADA systems, governed by confidence thresholds and review gates.

Rollout begins with a shadow mode, comparing AI-generated alerts against manual analysis for a pilot asset group. Upon validation, the system transitions to a co-pilot phase where alerts require geotechnical engineer approval before action. Full autonomy is granted only for high-confidence, low-risk detections. Governance mandates immutable audit trails linking satellite data, model inferences, and human decisions, with continuous monitoring for model drift against ground-truth GPS and tiltmeter data to ensure the system's reliability under evolving field conditions.