NeoBramPlan an AI project
    Industrial AI solution

    AI Pipeline Leak Detection Faster, Quieter, More Accurate

    Fuse SCADA, fiber-optic DAS, satellite and acoustic data with AI to detect leaks in minutes - and slash false-alarm noise.

    Plan the use case Explore ROI tools
    AI pipeline leak detection for oil and gas

    Acceptance before scale

    Baseline, representative test set, failure conditions and a named human owner are defined before production approval.

    Direct answer

    NeoBram helps industrial teams evaluate and build ai pipeline leak detection for a defined workflow. The engagement starts with the operating decision, representative data, integration boundary, human owner and acceptance test. Deployment can be designed for offline, on-premises, edge or private-cloud operation when the selected components and licences support it.

    Private deployment available

    Why evaluate it

    Pipeline Leaks Are Costly, Public and Avoidable.

    The decision is whether this capability improves a defined workflow safely not whether an AI demo looks impressive.

    AI changes the economics. Fusing fiber-optic DAS, SCADA flow / pressure balance, satellite-based hyperspectral data and acoustic sensors gives operators detection in minutes and location accuracy in metres.

    We deploy leak detection AI that augments, not replaces, your existing CPM and SCADA - delivering API 1130-aligned performance with far less alarm fatigue.

    Uncited universal benchmarks from the legacy page were withheld. Define the baseline and acceptance threshold from customer evidence or a reviewable primary source.

    Candidate capabilities

    What a production solution may need to do.

    Each capability is validated against representative data and the customer's workflow. Product or model names describe possible components, not partnerships, certifications or guaranteed compatibility.

    Multi-modal leak detection

    AI models trained per sensor type and fused into a single confidence score per pipeline segment.

    • DAS acoustic event classification
    • Negative pressure wave + ML hybrid detection
    • Mass/volume balance with statistical models
    • Satellite confirmation for long-duration releases

    Alarm fusion & noise reduction

    Consolidates alarms across sensor types, dismisses known operational events (pig runs, valve moves) and presents one prioritised alarm queue.

    • Cross-source corroboration scoring
    • Operational-event filtering (pigs, valves, third-party works)
    • Operator-friendly explanations per alarm
    • Suppression of duplicate alarms across systems

    Location & size estimation

    Estimates leak location and size with confidence bands - speeding emergency response and shutdown decisions.

    • Location accuracy: typically ±10-50m on DAS-instrumented lines
    • Leak size estimate (small/medium/large) with rate band
    • Time-to-detect benchmarked against API 1130
    • Map view with response zone overlays

    Audit-ready, API 1130-aligned

    Every alarm, classification and operator action logged for regulator audit. Performance metrics aligned to API 1130 categories.

    • Reliability, sensitivity, accuracy, robustness metrics tracked
    • PHMSA / regulator export packs
    • Operator action audit trail
    • Annual performance review automation

    Architecture context

    Select components after the boundary and test are clear.

    The list is a design vocabulary. Final selection depends on licences, data location, latency, security, existing systems and customer approval.

    Sensing

    • Fiber-optic DAS (Silixa, OptaSense)

      Distributed acoustic

    • Acoustic emission sensors

      Discrete points

    • Satellite hyperspectral (Kayrros, Carbon Mapper)

      Wide-area

    • SCADA flow / pressure

      Balance & wave analysis

    ML & detection

    • PyTorch

      Acoustic / DAS classifiers

    • 1D CNN / Transformer

      Time-series detection

    • Negative pressure wave

      Hybrid physics + ML

    Operations integration

    • SCADA write-back

      Alarm consolidation

    • Control-room HMI

      Operator UX

    • Emergency response systems

      Auto-escalation

    Compliance

    • API 1130 alignment

      Performance benchmarks

    • PHMSA reporting

      Regulator output

    • Audit trail

      Every alarm logged

    Planning ranges

    A standard path from decision to operation.

    01

    Discovery and qualification

    1-2 weeks

    Named workflow, owner, baseline, risks and go/no-go questions.

    02

    Readiness assessment

    2-4 weeks

    Data, integration, security, value and operating-readiness findings.

    03

    Technical proof of value

    4-6 weeks

    A bounded test on representative data with documented limitations.

    04

    Production pilot

    8-12 weeks

    One controlled workflow, integrated and evaluated with real users.

    05

    Enterprise or multi-site rollout

    3-6+ months

    Phased scale-out, monitoring, support and change management.

    06

    AI capability or CoE programme

    3-6+ months

    Governance, delivery methods, reusable assets and team enablement.

    These are planning ranges, not guarantees. Readiness, validation, hardware, integration, access and change management affect the schedule.

    Production safeguards

    Private deployment is one control, not the whole control system.

    Data boundary

    • Select offline, edge, on-premises or private-cloud deployment from the real operating constraints.
    • Document data flows, storage, deletion, backups and support access before production.
    • The customer approves every interface and any permitted external connection.

    Model and application controls

    • Evaluate representative cases, uncertainty and harmful failure modes before use.
    • Use suitable access control, input handling and output guardrails for the selected risk.
    • Treat grounding and citations as testable behaviours, not as a promise of perfect answers.

    Traceability and operation

    • Define identity, roles, logs, monitoring, updates, backup and incident handling.
    • Keep the evidence needed to investigate outputs and reproduce important decisions.
    • Assign a named business and technical owner for production operation.

    Human authority

    • Domain, quality, safety and regulatory owners retain decision authority.
    • Compliance depends on the implemented system and the customer's validated controls.
    • Escalation and safe fallback are part of the acceptance criteria.

    Limitations to plan for

    Performance can change with data quality, equipment, process, product mix, documents, users or operating conditions. Third-party model and software licences still apply. AI output does not replace the responsible engineer, operator, quality owner, safety professional, legal adviser or regulator.

    Buyer questions

    Direct answers before you plan a pilot.

    How does this work with our existing CPM system?+
    AI runs alongside CPM, not in place. We typically subscribe to your CPM alarms and SCADA feeds, add additional detection on DAS / acoustic / satellite where available, and consolidate everything into a single alarm queue. Operators see fewer, better alarms. CPM continues to operate as the official compliance system.
    What about false alarms - the chronic CPM problem?+
    This legacy draft included a quantitative benchmark that has not been published with a reviewable source or customer evidence. NeoBram now treats it as an open validation question and defines the baseline, test method, acceptance threshold and limitations during discovery.
    Do we need fiber-optic DAS to benefit?+
    No. AI improves detection even on SCADA-only pipelines through better wave analysis and statistical balance models. DAS, acoustic sensors and satellite each add additional sensitivity - typically deployed on highest-risk segments first.
    What detection times can we expect?+
    On DAS-instrumented lines, large-leak detection in 30-90 seconds is realistic. SCADA-only pipelines typically 2-10 minutes for medium-to-large leaks. Satellite is hours-to-days but catches small chronic releases other sensors miss.
    Is this acceptable for API 1130 / PHMSA compliance?+
    Yes when deployed correctly. We document model performance against the API 1130 metrics (reliability, sensitivity, accuracy, robustness) and configure the system as a supplementary detection system to your primary CPM. We've supported clients through PHMSA inspections of similar architectures.
    Deployment timeline?+
    A focused production pilot is commonly planned over 8 12 weeks after scope, representative data, owners and acceptance tests are ready. Discovery or a technical proof of value may be shorter; integration, validation, hardware, site access and change management can extend the plan. This is a planning range, not a delivery guarantee.

    Bring one workflow

    Define the evidence, boundary and acceptance test together.

    Your team supplies process authority. NeoBram supplies AI architecture, engineering, evaluation and operating handover.

    Plan the first project