Enterprise Sustainability Platform for Greenhouse Gas Action Plans
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Solution Overview
Problem
Challenges exist in efficiently tracking and improving sustainability parameters across hydrocarbon enterprise operations, including greenhouse gas emissions, energy consumption, waste, and water usage, while identifying opportunities for enhancing sustainability.
Innovation Solution
A sustainability platform system that collects data from various sources, simulates action plans, and adjusts operations to reduce greenhouse gas emissions, using IoT devices, engineering workflow systems, and real-time data to optimize sustainability parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual tracking methods are used for sustainability parameters, then implementation simplicity is maintained, but measurement precision and productivity deteriorate
Solution Approach 1:
The sustainability platform serves multiple functions including data collection from diverse sources (production systems, facility operations, utility operations), simulation of action plans, generation of GHG footprint reports, and identification of engineering workflow systems. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single unified platform, improving measurement precision without proportionally increasing complexity.
Solution Approach 2:
The platform acts as an intermediary layer between various data sources (IoT devices, production systems, facility systems) and the sustainability analysis functions. This intermediary architecture standardizes data collection and processing, enabling precise tracking of sustainability parameters while abstracting the complexity from end users and simplifying implementation.
2Measurement precision
If comprehensive data collection from multiple sources is implemented, then measurement precision improves, but loss of time and device complexity worsen
Solution Approach 1:
The platform performs preliminary actions by continuously collecting and preprocessing sustainability data from multiple sources in real-time, maintaining ready-to-analyze data buffers. Action plans are pre-simulated with predicted outcomes stored for quick retrieval. This preliminary preparation eliminates the need for time-consuming data aggregation and analysis when sustainability assessments are needed, reducing loss of time while maintaining comprehensive data collection.
Solution Approach 2:
The system implements feedback mechanisms where simulation results and predicted outcomes are continuously compared with actual measured data. This feedback loop enables the platform to learn from discrepancies and improve its data processing efficiency over time, reducing the time required to process comprehensive data while maintaining or improving measurement precision.
3Productivity
If simulation of action plans over time is performed, then productivity improves, but use of energy and computational cost worsen
Solution Approach 1:
The platform applies partial simulation approaches by focusing computational resources on the most critical action plans and key sustainability parameters rather than exhaustively simulating all possible scenarios. The system identifies and prioritizes high-impact action plans for detailed simulation while using simplified models for less critical areas, improving productivity in achieving sustainability goals while reducing computational energy consumption.
Solution Approach 2:
The simulation methodology dynamically adjusts computational parameters based on the specific action plan being evaluated and the current state of the enterprise. Computational resolution, time step granularity, and model complexity are adapted according to the importance and characteristics of each simulation, optimizing the balance between productivity improvement and energy consumption.
4Reliability
If real-time monitoring and adjustment of operations is implemented, then sustainability goal achievement improves, but device complexity and loss of time worsen
Solution Approach 1:
The sustainability platform implements self-service capabilities by automatically collecting data from connected systems, performing simulations, generating reports, and identifying recommended actions without requiring manual intervention. The system autonomously monitors sustainability parameters and adjusts operations based on simulation outcomes, improving reliability of goal achievement while presenting a simplified interface that masks the underlying complexity.
Data Source
AI summary
A method may include receiving a sustainability model indicative of a plurality of sustainability parameters associated with enterprise operations corresponding to production data for operational tasks performed in a hydrocarbon production system, facility data corresponding to utility operations within buildings associated with the enterprise, or both. The method involves simulating implementing action plans via devices that correspond to the enterprise operations over time to determine an amount of greenhouse gas (GHG) emissions associated with the enterprise over time, generating a GHG footprint evolution report based on the amount of GHG emissions, and identifying at least one of a plurality of engineering workflow systems to reduce the amount of GHG emissions based on the GHG footprint evolution report. The method involves sending commands to devices of the plurality of devices based on the action plan, such that the commands are cause the devices to adjust their operations.