Multi-Asset Decision Optimization Under Uncertainty
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Solution Overview
Problem
The oil and gas industry faces challenges in making optimal decisions regarding asset exploitation due to uncertainties in factors like oil and gas reserves, reservoir properties, and market conditions, which affect production volume, revenue, and profit.
Innovation Solution
A decision management system that optimizes decisions by receiving information on decision and uncertainty variables, generating decision vectors, executing an evaluation process, and using optimizers like tabu search or scatter search to update a reference set of decision vectors, while respecting functional dependencies and constraints, to optimize global objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional decision-making methods are used for asset exploitation, then simplicity and ease of operation are maintained, but the ability to handle uncertainties and optimize decisions across multiple assets is insufficient
Solution Approach 1:
The system segments the complex decision-making process into distinct modular components: uncertainty management module, optimization module, and evaluation module. Each module handles specific aspects of the decision process independently, allowing the system to manage multiple assets and uncertainties without becoming unmanageably complex.
Solution Approach 2:
The patent introduces an intermediary optimization system that mediates between uncertain input parameters and decision outcomes. This intermediary layer processes uncertainties through structured algorithms and provides optimized recommendations, shielding decision-makers from the full complexity of underlying uncertainties while improving adaptability.
2Measurement precision
If comprehensive analysis of multiple assets and uncertainties is performed, then decision quality and optimization are improved, but computational time and processing requirements increase
Solution Approach 1:
The system performs preliminary structuring of uncertainty parameters and asset data before full optimization analysis. By pre-organizing input data, defining uncertainty relationships, and establishing evaluation criteria in advance, the system reduces computational overhead during the actual optimization process, achieving high accuracy without excessive time consumption.
3Reliability
If detailed evaluation of each asset is conducted, then measurement precision and reliability are improved, but the quantity of data processing and system complexity increase
Solution Approach 1:
The patent merges individual asset evaluations into a unified multi-asset optimization framework. By combining asset-specific data with cross-asset relationships and shared uncertainties, the system achieves comprehensive reliability improvement while avoiding the inefficiency of processing each asset completely separately. The evaluation module integrates results across assets to reduce redundant data processing.
Data Source
AI summary
A client-server based system for building and executing flows (i.e., interconnected systems of algorithms). The client allows a user to build a flow specification and send the flow specification to the server. The server assembles the flow from the flow spec and executes the flow. A decision flow builder allows the user to build a flow targeted for the analysis/optimization of decisions (modeled by decision variables) regarding a plurality of assets in view of various underlying uncertainties (modeled by uncertainty variables). The user may specify a global objective (as a function of asset level statistics) as well and one or more constraints for the optimization. The flow may account for inter-asset correlations and inter-asset dependencies between uncertainty variables, and, inter-asset constraints between decision variables.