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    Industrial AI solution

    AI Emissions Monitoring For Oil & Gas Operations

    Fuse satellite, drone, fixed-sensor and process data into one AI platform for methane detection, attribution and OGMP 2.0 reporting.

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    AI methane and emissions monitoring 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 builds AI emissions monitoring for oil & gas that fuses satellite, drone, OGI camera and fixed-sensor data with process data to detect, quantify and attribute methane and other emissions - meeting OGMP 2.0 L4/L5 and EPA OOOOb requirements.

    Private deployment available

    Why evaluate it

    Methane Regulation Is Tightening Fast.

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

    OGMP 2.0 Levels 4 and 5, EPA OOOOb, EU Methane Regulation and the Global Methane Pledge all demand measured (not estimated) methane data with site-level attribution and quantification.

    Operators face a sprawl of point solutions: satellite providers, drone vendors, fixed sensors, OGI cameras. None of them alone deliver a defensible site-level inventory.

    We build the integration layer: an AI platform that ingests every emissions data source, reconciles with process data, attributes events to equipment, quantifies leak rate and produces audit-grade reports.

    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-source detection fusion

    Single platform that ingests satellite, drone, fixed-sensor and OGI data and reconciles events across sources to eliminate double-counting.

    • Per-asset emissions timeline across all sources
    • Cross-source corroboration logic
    • False-positive filtering against process events
    • API ingest for any provider format

    Site-level attribution & quantification

    AI attributes detected plumes to specific equipment (tank, separator, compressor, flare) and quantifies leak rate with calibrated uncertainty.

    • Equipment-level attribution
    • Quantified leak rate (kg/hr or scf/hr) with uncertainty
    • Reconciliation with bottom-up inventory
    • Top-emitter ranking by site and basin

    Process-side root cause

    Joins emissions events with SCADA, DCS and CMMS data to identify root cause: blowdown, malfunction, equipment leak, maintenance event.

    • Auto-classification: routine, abnormal, malfunction
    • Correlation with operating mode and equipment state
    • Maintenance event filtering
    • Recommended LDAR follow-up

    OGMP 2.0 / EPA OOOOb reporting

    Audit-grade reporting packages aligned to OGMP 2.0 L4/L5, EPA OOOOb and EU Methane Regulation - with full data lineage.

    • OGMP 2.0 L4 and L5 templates
    • EPA OOOOb compliance reports
    • EU Methane Regulation dashboards
    • Auditor-mode data lineage view

    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.

    Detection sources

    • Satellite (MethaneSAT, GHGSat, Carbon Mapper)

      Wide-area screening

    • Aerial / drone (Bridger, SeekOps)

      Site-level survey

    • Fixed sensors (Project Canary, Qube, Sensirion)

      Continuous monitoring

    • OGI cameras (FLIR GFx, OPGAL)

      Inspector workflow

    AI & fusion

    • PyTorch

      Detection & attribution models

    • Gaussian plume modelling

      Quantification

    • Anomaly detection

      Process-side events

    Process data

    • OSIsoft PI / historian

      Operating context

    • SCADA / DCS

      Equipment state

    • Maintenance / CMMS

      Known-event filtering

    Reporting

    • OGMP 2.0 templates

      L4/L5 reporting

    • EPA OOOOb workflows

      Compliance reporting

    • Power BI dashboards

      Executive & ESG

    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.

    Can this satisfy OGMP 2.0 Level 5 reconciliation?+
    Yes. The platform produces a measured (top-down) inventory from your detection sources and reconciles it with a bottom-up inventory built from your equipment list and emissions factors. Variance analysis, attribution and uncertainty are all included in the OGMP-aligned reporting.
    Which detection providers do you support?+
    All major satellite providers (MethaneSAT, GHGSat, Carbon Mapper, Kayrros), aerial / drone (Bridger Photonics, SeekOps, ChampionX, Scientific Aviation), and fixed sensors (Project Canary, Qube, LongPath, Sensirion). New providers are added as needed - the platform is source-agnostic.
    How does AI attribution work without ground-truth labels?+
    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.
    Can this also handle CO2, NOx, SOx and VOCs?+
    Yes. The fusion and attribution stack is gas-agnostic; methane is the primary focus today because of regulatory urgency. We have deployments tracking CO2 equivalents, NOx, SOx and VOCs alongside methane.
    How does it integrate with existing LDAR programmes?+
    AI augments rather than replaces LDAR. It prioritises which sites and assets need physical inspection, classifies events to skip known operational releases, and feeds LDAR outcomes back to improve attribution accuracy over time.
    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