Automated Fault Uncertainty Analysis in Seismic Exploration
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Solution Overview
Problem
Manual interpretation of seismic data for fault detection in hydrocarbon exploration is time-consuming, labor-intensive, and prone to human bias, leading to uncertainties in fault location, connectivity, and orientation, which can result in drilling hazards and inefficiencies in hydrocarbon resource development.
Innovation Solution
An automated fault uncertainty analysis system that processes multiple seismic image volumes to compute fault attribute volumes, derive uncertainty and confidence measures, and build a non-linear interpretation-confidence-index prediction model, enabling proactive risk assessment and visualization of qualitative fault uncertainties, thereby facilitating more accurate and efficient hydrocarbon exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of seismic data is performed, then fault detection can be conducted, but it is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical interpretation with automated computer-based processing. The system uses algorithms to automatically detect faults, calculate attributes, and assess uncertainties in seismic data, eliminating the need for manual analysis while maintaining or improving accuracy.
Solution Approach 2:
The system enables self-service by allowing the seismic data to speak for itself through automated attribute calculations and uncertainty assessments. The computer system independently processes the data without requiring continuous human intervention, making the interpretation process autonomous and efficient.
2Measurement precision
If manual fault interpretation is performed, then fault location can be identified, but human bias and pre-processing errors lead to significant differences in interpretations
Solution Approach 1:
The patent replaces human interpretation with automated computer-based analysis to eliminate human bias. The system applies consistent algorithms and criteria across all seismic data, ensuring that fault location and characterization are determined objectively without subjective influences.
Solution Approach 2:
The system changes the parameters of analysis by using multiple fault attributes (such as dip, strike, curvature, and uncertainty metrics) rather than relying on single subjective judgments. This multi-parameter approach provides a more comprehensive and consistent characterization of faults.
3Reliability
If comprehensive fault analysis is performed to reduce uncertainties, then drilling hazards can be identified, but the process becomes more complex
Solution Approach 1:
The patent segments the complex fault analysis into distinct computational components: fault detection, attribute calculation, uncertainty assessment, and risk evaluation. Each component handles a specific aspect of the analysis independently, making the overall complex process manageable and systematic.
Solution Approach 2:
The system achieves universality by using a unified automated framework that handles multiple fault analysis tasks simultaneously. The same computer-based system performs detection, characterization, and uncertainty analysis, reducing operational complexity despite the comprehensiveness of the analysis.
4Measurement precision
If multiple fault attribute volumes are computed to derive uncertainty measures, then confidence in fault predictions is improved, but computational effort increases
Solution Approach 1:
The system applies partial action by computing fault attributes selectively rather than exhaustively analyzing every possible parameter. It focuses on the most critical attributes that contribute significantly to uncertainty assessment, achieving good confidence levels without unnecessary computational expenditure.
Data Source
AI summary
A system includes a processor and a memory. The memory includes instructions that are executable by the processor to access a plurality of seismic images of a subterranean formation in a first geological area. The instructions are also executable to generate a plurality of fault estimates from each of the plurality of seismic images. Further, the instructions are executable to generate a processed seismic image of the first geological area by normalizing and merging the plurality of seismic images and the plurality of fault estimates. Additionally, the instructions are executable to generate a statistical fault uncertainty volume of the first geological area using the processed seismic image. Furthermore, the instructions are executable to control a drilling operation in the first geological area using the statistical fault uncertainty volume of the first geological area.