Hydrocarbon Network Contingency Planning With Real-Time Disruption Prediction

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Solution Overview

Problem

Existing contingency plan generation methodologies for hydrocarbon networks rely on static data and predefined scenarios, failing to accurately reflect real-time conditions, leading to insufficient, inaccurate, and prone-to-error responses to disruptions, which can exacerbate problems and cause prolonged downtime.

Innovation Solution

A tool that generates real-time contingency plans using a hydrocarbon network model, incorporating real-time data and machine learning algorithms to predict disruptions, forecast production and demand, and identify optimal switchover facilities, considering switchover difficulties and individual plant readiness.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If static data and predefined scenarios are used for contingency plan generation, then the system complexity is reduced and ease of operation is improved, but the accuracy and reliability of disruption response deteriorates

Engineering Contradiction:

Improveease of contingency plan generationVSAvoidaccuracy of disruption response

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system transitions from static contingency plans to dynamic, real-time plan generation by continuously monitoring network conditions through sensors and updating disruption predictions and contingency measures accordingly, ensuring plans reflect current operational states

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements feedback loops where disruption predictions trigger contingency plan generation, which is then executed and monitored, with results feeding back into the prediction model to continuously improve accuracy and adapt to actual network behavior

Inventive Principle:

Principle #23Feedback

2Reliability

If real-time data and machine learning algorithms are used to generate contingency plans, then the accuracy and reliability of disruption response is improved, but the system complexity and computational requirements increase

Engineering Contradiction:

Improveaccuracy of disruption responseVSAvoidcomplexity of contingency plan generation system

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system divides the hydrocarbon network into discrete segments or zones with specific monitoring points, allowing localized disruption detection and targeted contingency plan generation for affected areas rather than requiring system-wide analysis

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary processing layer that aggregates sensor data, applies machine learning algorithms, and translates complex predictions into actionable contingency plans, shielding operational complexity from end users while maintaining high accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If real-time monitoring and prediction systems are implemented, then the response time to disruptions is reduced and productivity is improved, but the energy consumption and operational costs increase

Engineering Contradiction:

Improveresponse speed to disruptionsVSAvoidenergy consumption of monitoring system

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system implements periodic monitoring at strategically selected points rather than continuous monitoring across the entire network, updating contingency plans at intervals based on disruption risk levels and criticality of monitored parameters

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system concentrates monitoring resources and computational power on high-risk areas and critical network nodes where disruptions are most likely to occur or have greatest impact, reducing overall energy consumption while maintaining effective coverage

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20260015930A1System and method for generating contigency plans for hydrocarbon network management

Publication Date: 2026.01.15 SAUDI ARABIAN OIL CO

AI summary

Systems and methods are disclosed for generating contingency plans for managing a hydrocarbon network. A hydrocarbon network model representative of the hydrocarbon network that can include one or more plants can be generated, and simulated to predict hydrocarbon demand, production, and potential disruptions for the hydrocarbon network. One or more contingency plans for responding to one or more disruptions in the hydrocarbon network model can be generated based on the predicted hydrocarbon demand, production, and potential disruptions, and a plant readiness of the one or more plants. The one or more contingency plans can be stored a contingency plan database, and one of the or more contingency plans can be retrieved from the contingency plan database based on a contingency plan request. The contingency plan request can identify a disruption in the hydrocarbon network. The retrieved contingency plan can be output to respond to the disruption in the hydrocarbon network.