Automated Sedimentologic Interpretation via Chronological Scenario Estimation
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Solution Overview
Problem
Current methods for reconstructing depositional conditions of sedimentary layers from seismic images are complex and largely dependent on interpreter expertise, with limited automation, especially when dealing with complex geological structures and large datasets.
Innovation Solution
A method that estimates chronological scenarios by assigning reflectors to discrete levels based on their position relative to other reflectors, using mathematical morphology and vectorization techniques to extract and classify reflectors, and interprets these scenarios to reconstruct depositional conditions, allowing for refinement of sedimentologic interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation methods are used by sedimentologists, then interpretation accuracy can be maintained through expert judgment, but interpretation time and complexity increase significantly when dealing with large datasets and complex geological structures
Solution Approach 1:
The patent replaces manual mechanical interpretation processes with automated computer-based image processing and analysis systems. The system automatically extracts geological features, classifies sedimentary structures, and generates chronological scenarios from seismic images, substituting human expert manual analysis with algorithmic processing that maintains accuracy while dramatically reducing time requirements.
Solution Approach 2:
The interpretation system performs self-service by automatically processing seismic images without requiring continuous human intervention. The computer-based system independently executes image processing, feature extraction, classification, and scenario generation tasks, enabling the system to serve its own interpretation needs while allowing experts to focus on validation and complex decision-making.
2Productivity
If automated image processing is implemented, then interpretation time is reduced and productivity increases, but device complexity and algorithm sophistication requirements increase
Solution Approach 1:
The patent segments the complex interpretation task into distinct modular components: image preprocessing, reflector extraction, sedimentary structure classification, chronological scenario generation, and validation. Each module handles a specific aspect of the analysis, making the overall complex system manageable through functional decomposition and enabling parallel processing to improve productivity.
Solution Approach 2:
The computer-based interpretation system is designed as a universal platform capable of handling multiple types of seismic images, various sedimentary structures, and different geological scenarios. The system integrates multiple functions including image processing, geological analysis, chronological modeling, and visualization within a single integrated environment, reducing the need for multiple specialized tools.
3Measurement precision
If multiple chronological scenarios are generated, then sedimentologic interpretation accuracy is improved through comprehensive analysis, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent implements dynamic scenario generation where the system automatically creates multiple chronological scenarios based on varying interpretations of the same seismic data. The system dynamically adjusts parameters, tests different depositional models, and generates alternative chronological arrangements, allowing comprehensive analysis of uncertainties while managing complexity through automated algorithmic exploration of scenario space.
Solution Approach 2:
The system incorporates feedback mechanisms where generated chronological scenarios are automatically evaluated against geological constraints and consistency criteria. Results from scenario analysis feed back into the processing pipeline, allowing iterative refinement and validation. This feedback loop improves interpretation accuracy by systematically testing and comparing multiple scenarios while managing complexity through automated evaluation rather than manual analysis of each scenario.
Data Source
AI summary
A method having application for the development of oil reservoirs for automatically extracting from a seismic image pertinent information for sedimentologic interpretation by using estimations of realistic chronological scenarios of sedimentary layers deposition. The method includes iterative estimation of a first and of a second chronological scenario of the deposition of sedimentary layers, assuming that each reflector settles at the earliest and at the latest possible moment during the sedimentary depositional process. A chronological level number is assigned to a group of initial reflectors. Then a chronological level number is incremented by one and decremented by one which numbers are assigned to the reflectors including pixels located above and respectively below the initial reflectors and above and respectively below no other reflector. An interpretation of these two chronological scenarios is eventually carried out so as to reconstruct the depositional conditions of the sedimentary layers.