Interactive Fault Modeling Automation for Reservoir Characterization
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Solution Overview
Problem
The traditional process of generating fault frameworks in reservoir modeling is cumbersome and requires manual intervention, making it inefficient for characterizing oil and gas reservoirs and drilling wellbores.
Innovation Solution
An interactive automation method for fault modeling that senses fault-fault relationships by computing models of faults as if they were unrelated, detecting proximity, presenting potential connections to an interpreter for confirmation, and generating a final model illustrating interconnected faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used to generate fault frameworks, then accuracy of fault interpretation can be maintained, but the process becomes cumbersome and inefficient
Solution Approach 1:
The system performs self-service by automatically detecting fault-fault relationships and generating interconnected fault models without requiring manual intervention. The computer system autonomously senses proximity relationships between faults and constructs the final model, eliminating the cumbersome manual process while maintaining interpretability through selective confirmation.
Solution Approach 2:
The system performs preliminary actions by pre-computing fault models as if they were unrelated, then detecting proximity conditions before presenting potential relationships to the interpreter. This preliminary processing automates the initial analysis work, reducing the burden on operators during the actual fault framework generation.
2Productivity
If automated sensing of fault relationships is implemented, then efficiency of reservoir characterization is improved, but complexity of the modeling system increases
Solution Approach 1:
The fault modeling process is segmented into distinct automated and interactive components. The system automatically performs fault detection, proximity sensing, and preliminary model construction, then segments the confirmation and finalization steps for interpreter review. This segmentation allows complex automated processing without overwhelming the user with unnecessary complexity.
Solution Approach 2:
The system acts as an intermediary by providing potential fault relationships to the interpreter for confirmation rather than making definitive automated decisions. This intermediary role balances automation efficiency with human expertise, allowing the complex sensing algorithms to operate while maintaining interpretability through selective user confirmation.
3Measurement precision
If faults are modeled as unrelated independently, then individual fault accuracy is maintained, but interconnections and relationships are missed
Solution Approach 1:
The system merges individual fault models by detecting spatial proximity relationships and combining them into interconnected fault frameworks. After initially modeling faults independently to preserve individual accuracy, the system merges related faults based on sensed relationships, maintaining both individual precision and relational completeness in the final model.
Solution Approach 2:
The system uses feedback by continuously monitoring the spatial relationships between independently modeled faults and identifying when proximity conditions indicate potential connections. This feedback mechanism allows the system to detect missed relationships and present correction opportunities to interpreters, ensuring complete fault-fault relationship information is captured.
Data Source
AI summary
A method is disclosed for sensing fault-fault relationships, comprising: automatically sensing interrelationships among faults, and presenting a final model including a fault-fault intersection curve and one fault truncated at the curve to an interpreter representing the interrelationships among faults.