BHC3 Hydrocarbon Loss Analytics

Reducing transportation and processing loss of crude oil

BHC3 Hydrocarbon Loss Analytics

Reducing transportation and processing loss of natural gas

BHC3 Hydrocarbon Loss Analytics

Reducing transportation and processing loss of refined oil

Identify Theft, Reduce Leakage, and Verify Material Movement

BHC3 Hydrocarbon Loss Analytics™ pinpoints patterns in data streams that highlight mass balance issues across hydrocarbon transportation networks so operations teams can respond in a timely manner to ensure physical safety and safeguard revenues.

Hydrocarbon transportation is among the most critical elements in the oil and gas supply chain, involving vast pipeline networks and spanning highly variable geographies. Complete and in-full delivery of supply requires coordination and verification. Any discrepancies found in mass balances between supply and delivery will not only impact revenue capture, but also generate considerable safety risk in the areas surrounding pipeline networks. Among the challenges in identifying those discrepancies include theft, leakage, and sensor fidelity.

By ingesting data from numerous sources in the BHC3 AI Suite and applying an ensemble of advanced AI algorithms, BHC3 Hydrocarbon Loss Analytics™ highlights areas of concern, quantifies the potential loss amount, and categorizes the risk for effective operator intervention and analysis. Issues are tracked and managed through the life cycle of analysis, review, confirmation and intervention, enabling maintenance engineers and production planners to identify trends and develop long-term investment plans.

Features

Prioritization of hydrocarbon loss risks

Comprehensive risk analysis that enables operators to address issues with appropriate resources.

Visual reconstruction of surface network

Assess deviations between as-designed and as-operated configurations, including categorizing discrepancies as true deviations or faulty sensors.

Advanced AI algorithms

Use AI models to distinguish between fraud and safety risks, equipment or pipeline leakage, and faulty sensors.

Ad-hoc mass balance analysis capabilities

Enable maintenance and reliability engineers to conduct historical review of trouble areas and plan for asset and network upgrades.

Accurate and near real time assessment of material movement

Support invoicing and internal value accounting by verifying actual supplied and delivered quantities.

Configurable user interface and application logic

Integrate with existing operational business processes and enable fine-tuned access controls.
Coordinate

Comprehensive closed-loop workflow support

Enable operators to construct maintenance packages based on risk categorization. Create work orders directly from the application by leveraging bidirectional integration with work order management and dispatch systems.

Alerts and notification functionality

Facilitate effective coordination through configurable alerts and thresholds. Alerts are summarized and presented within the application and can be configured for delivery to designated contacts via SMS and/or email.

Benefits

Reduce

Reduce hydrocarbon losses through timely identification of pipeline and associated network risks.

Lower

Lower unplanned operational expense due to high precision in loss-issue isolation within geographic and network infrastructure, leading crews directly to high-risk area.

Enhance

Enhance safety risk by quickly highlighting and isolating emergent theft and leakage issues.

Improve

Improve assurance of delivered product through identification and replacement of faulty sensors, enabling more accurate invoicing.

Increase

Increase uptime through early identification and resolution of equipment at high risk of failure.

Optimize

Optimize planned maintenance costs through streamlined workflow that enables maintenance planners to effectively bundle and schedule high-priority work across the network.

Reduce

Reduce capital expenditures by driving sensor replacement decisions using sensor fidelity risk scores.

Proven results in weeks, not years

timeline
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