Hydrocarbon Facility Sustainability Tracking With Modular Action Plans
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Solution Overview
Problem
Challenges exist in tracking and improving sustainability parameters across hydrocarbon enterprise operations, including energy, carbon, waste, and water consumption, while identifying opportunities for enhancing sustainability, particularly in achieving net zero carbon emissions and reducing environmental impact.
Innovation Solution
A sustainability platform system that collects data from various sources, generates sustainability reports, and sends commands to adjust operations based on action plans to improve sustainability parameters, utilizing modular analysis to optimize computational efficiency and incorporate real-time feedback mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive data collection and analysis systems are implemented to track sustainability parameters across enterprise operations, then measurement precision and reliability of sustainability tracking improve, but device complexity and computational costs increase
Solution Approach 1:
The system segments sustainability tracking into modular components: data collection modules at facility level, aggregation modules at enterprise level, and analysis modules for specific sustainability parameters (carbon, energy, water, waste). This segmentation allows comprehensive tracking while managing system complexity through independent, manageable modules that can be implemented and scaled separately.
Solution Approach 2:
The patent introduces intermediary systems including sustainability platforms and action plan systems that mediate between raw operational data and sustainability metrics. These intermediaries standardize data formats, aggregate information from multiple sources, and transform operational data into actionable sustainability insights, reducing the complexity burden on the overall system.
2Productivity
If real-time feedback mechanisms and continuous monitoring are implemented to enable timely adjustments to operations, then productivity and responsiveness to sustainability goals improve, but use of energy and computational resources increase
Solution Approach 1:
The system implements periodic action through scheduled sustainability reporting cycles, phased action plan implementations, and interval-based monitoring frequencies. Rather than continuous real-time processing of all parameters, the system uses periodic assessments and updates, which maintain operational responsiveness while significantly reducing computational energy consumption compared to truly continuous monitoring.
Solution Approach 2:
The patent applies partial action by focusing computational resources on critical sustainability parameters and high-impact operational areas. The system identifies and prioritizes key sustainability drivers, applying intensive monitoring and analysis only where most needed, rather than uniformly across all operations. This selective approach maintains productivity in critical areas while reducing overall computational energy use.
3Manufacturing precision
If detailed action plans and workflows are developed to improve sustainability parameters, then manufacturing precision of sustainability goals is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
Action plans are segmented into discrete, manageable workflows with specific tasks assigned to different departments and timeframes. Each sustainability goal is broken down into actionable steps with clear ownership, making complex sustainability objectives easier to implement while maintaining precision through structured task management and tracking.
Solution Approach 2:
The system uses parameter changes to simplify action plan implementation by establishing standardized sustainability metrics and measurement protocols. By defining standard parameters for carbon emissions, energy consumption, water usage, and waste generation, the patent makes it easier to implement consistent tracking across diverse operations while maintaining precision in measuring sustainability performance.
4Loss of information
If extensive data collection from multiple sources is performed to generate comprehensive sustainability reports, then information completeness improves, but loss of time and computational costs increase
Solution Approach 1:
The system performs preliminary action by pre-configuring data collection protocols, establishing standardized data formats at the source, and implementing automated data aggregation workflows before reporting is needed. Sustainability metrics are pre-calculated and stored in standardized formats, enabling rapid report generation without requiring extensive data processing at report creation time, thus maintaining information completeness while reducing reporting time.
Data Source
AI summary
A method may include receiving production data and facility data associated with an enterprise, such that the production data includes operational tasks performed in a hydrocarbon production system and facility data includes utility operations within buildings associated with the enterprise. The method may involve receiving sustainability parameter data associated with corresponding to the enterprise based on the production data and the facility data. The method may include generating a sustainability report representative of sustainability parameters associated with the operations of the enterprise based on the sustainability parameter data, sending the sustainability report to engineering workflow systems that determine action plans associated with improving the sustainability parameters associated with the one or more utility operations, and sending commands to devices associated with the buildings based on the action plans, such that the commands cause the plurality of devices to adjust their respective operations.