Causal AI for Oil & Gas
Envisioning a green future
Causal AI can significantly advance the oil and gas industry’s efforts to enhance combustion efficiency and to achieve decarbonization goals.
By precisely identifying the root causes of inefficiencies and emission sources, causal AI enables the optimization of combustion processes, ensuring that fuel is used more efficiently and emissions are minimized.
By leveraging these capabilities, the oil and gas industry can make substantial progress in improving operational performance, reducing environmental impact, and meeting stringent decarbonization targets.
Total economic benefit
Energy management
Advanced process
control strategy
Production intelligence
Mass balancing
Pipeline leakage
detection
Energy management & de-carbonization
A global oil & gas major implemented a Causal AI based energy optimization agent using Parabole’s TRAIN. Enhancing fired heater performance (energy efficiency, carbon emission and compliance) amidst varying fuel composition and changing process requirements was an unsolved challenge.
The AI agent utilizes causal knowledge to provide unit and equipment specific guidance for the operators and pinpoint the root causes of excessive energy consumption and carbon emissions from the heater.
The causal analysis identified a set of critical process variables from local, upstream and downstream equipment along with quantified setpoint range recommendations.
This implementation resulted in discovering sub-optimal air-fuel ratio and heater fouling as key factors driving poor combustion efficiency, leading to high fuel consumption and increased emissions (COX, SOX, and NOX).
Total economic benefit
2% – 5%
Improved combustion efficiency
5%
Reduction in carbon emission
15 seconds
Root cause analysis
Advanced process control
A Fortune 50 oil & gas major implemented a Causal AI based decision optimization engine to improve operational efficiency and environmental compliance using Parabole’s TRAIN.
The traditional siloed approach to optimizing operational processes (planning, scheduling, and APC) leads to suboptimal operational efficiencies. The isolated loops don’t take the real-word iteration of other process variables into consideration. The absence of plant condition knowledge in the planning and scheduling process, combined with the APC layer’s lack of visibility into supply constraints, results in unmet demand, revenue loss, and damage to brand reputation.
The AI agent integrates planning, scheduling and APC layer attributes to improve operational efficiency and environmental compliance. This implementation has reduced unscheduled asset downtime, significantly minimized productivity losses, revenue declines, and asset life-cycle costs.
Total Economic benefit
3%
Increase in yield
12%
Reduction in un-planned shut downs
6%
Reduction in product quality variations
Mass balancing
A Fortune 50 oil & gas major implemented a Causal AI based production accounting agent to automate mass balance processes critical for monitoring yield using Parabole’s TRAIN.
The AI agent utilizes causal knowledge to autonomously recommend corrections for process measurements by analyzing multiple scenarios at once. The AI agent serves as a digital companion for process engineers by significantly reducing cognitive burden enabling, quick and informed decision-making.
This implementation helped reduce accounting inaccuracies, stem revenue leakage in custody transfer applications.
Total Economic benefit
2.5%
Reduction in revenue leakage
1-2%
Increase in yield
Rapid
Reconciliation
Pipeline leakage detection and remediation
Energy companies transport crude oil through hundreds of miles of pipeline network. They strategically place pumping stations in the pipeline network to sustain pressure and ensure a continuous oil flow. These pipelines traverse through potentially hostile areas susceptible to sabotage and are exposed to extreme weather conditions. As a result, energy companies often encounter pipeline leakages caused by natural and human factors.
Any such leakage, if not remediated promptly, results in significant operational losses and creates serious safety and environmental issues.
TRAIN’s leak detection agent autonomously formulates hypotheses, identifies flow anomalies, and localizes leak location in minutes.
Total Economic benefit
15 seconds
Root cause analysis
1.3%-2%
Maximize throughput
Simulations
Reduced Cognitive burden
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Learn more about how Causal AI can empower your teams to
- Maximize touchless order processing and reduce OTIF penalties.
- Optimize Procurement planning and maximizing mill throughput.
- Minimize shop floor incidents to ensure workers safety.
- Minimize energy consumption and achieve de-carbonization goals.
- Harmonize technical and customer facing information for product/system design optimization.