Knowledge Graph Logic for Reservoir Simulation Accuracy
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Solution Overview
Problem
Calibration of reservoir simulation models to dynamic field production data, known as history matching, is a time-consuming and computationally intensive process, especially with reservoir structural complexities and intrinsic subsurface uncertainty, and is further complicated by numerous variables in field development planning.
Innovation Solution
A method involving the examination of knowledge graph logic for completeness, generation of updated decision information, and execution of the reservoir simulation model based on this information to improve the efficiency and accuracy of reservoir simulation and field development planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reservoir simulation models are calibrated to dynamic field production data through history matching, then model accuracy is improved, but computational time and complexity increase significantly
Solution Approach 1:
The system performs preliminary analysis by examining knowledge graph logic for completeness before executing the full reservoir simulation model. This preliminary check identifies incomplete decision information in advance, allowing the system to generate updated knowledge graph logic beforehand, thereby avoiding repeated computational iterations and reducing overall computational time while maintaining model accuracy
Solution Approach 2:
The patent introduces knowledge graph logic as an intermediary layer between field production data and the reservoir simulation model. This knowledge graph serves as a mediator that structures decision information and governs model execution, enabling more efficient calibration by organizing complex relationships before full simulation runs, thus reducing computational complexity while preserving accuracy
2Reliability
If knowledge graph logic is examined for completeness and updated, then decision information quality is improved, but processing time increases
Solution Approach 1:
The system performs self-service by automatically examining knowledge graph logic for completeness and generating updated logic without requiring external intervention. The examination process autonomously identifies incomplete decision information and triggers updates, reducing the need for manual review and iteration, thereby improving decision information quality while minimizing additional processing time
Data Source
AI summary
A method for reservoir simulation involves examining a knowledge graph logic associated with a reservoir simulation model for completeness. The knowledge graph logic contains decision information that governs an execution of the reservoir simulation model. The method further involves making a determination, based on the examination, that the knowledge graph logic is incomplete, based on the determination, generating an updated knowledge graph logic, obtaining the decision information from the updated knowledge graph, and executing the reservoir simulation model as instructed by the decision information.