Automated Fault Modeling Workflow for Seismic Interpretation Uncertainty
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Solution Overview
Problem
Traditional seismic fault interpretation methods are manual, time-consuming, inefficient, and error-prone, lacking integration into automated workflows and proper data consumption, and fail to handle uncertainty or scenario variations, leading to suboptimal fault modeling.
Innovation Solution
An integrated and automated fault interpretation-to-fault modeling solution using machine learning techniques, which includes formatting, gridding, and sensitivity/uncertainty analysis to generate a multi-dimensional fault model, enabling real-time visualization and efficient fault framework construction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual seismic fault interpretation methods are used, then interpretation accuracy can be maintained through expert judgment, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical interpretation processes with automated machine learning algorithms and computer vision systems. Neural networks and deep learning models analyze seismic data to automatically detect and characterize faults, substituting human expert judgment with automated computational systems that maintain accuracy while dramatically reducing interpretation time
Solution Approach 2:
The system enables self-service through automated fault detection and characterization capabilities. The machine learning models independently process seismic data, identify fault structures, and generate interpretation results without requiring continuous human intervention, allowing the system to serve itself in the interpretation process while maintaining high accuracy through trained algorithms
2Reliability
If traditional separate interpretation and modeling workflows are used, then each step can be performed with dedicated tools, but integration and data flow management become complex and error-prone
Solution Approach 1:
The patent merges fault interpretation and fault modeling workflows into a single integrated system. The machine learning interpretation module directly feeds processed fault data to the modeling module, eliminating the need for separate tools and manual data transfer. This integration reduces workflow complexity and minimizes errors associated with data handoff between separate systems
Solution Approach 2:
The system implements multi-functionality by creating a unified platform that performs both interpretation and modeling tasks. The same system architecture handles data processing, fault detection, and model generation, providing universal functionality that reduces the need for multiple specialized tools and simplifies the overall workflow while maintaining reliability
3Productivity
If automated interpretation tools focus on output objects, then interpretation speed improves, but data preparation and optimization for downstream workflows become additional burden
Solution Approach 1:
The system performs preliminary action by pre-processing and optimizing interpretation data during the automated interpretation phase. The machine learning models not only detect faults but also prepare the data in the appropriate format and structure for downstream modeling workflows, eliminating the need for separate data optimization steps and reducing overall workflow complexity
Solution Approach 2:
The integrated system acts as an intermediary that bridges interpretation and modeling workflows. The standardized data interface and automated data transformation capabilities serve as a mediator between the interpretation output and modeling input requirements, seamlessly transferring processed data without requiring additional preparation steps or complex data formatting operations
4Ease of manufacture
If deterministic fault models are generated, then model construction is straightforward, but uncertainty and scenario variations cannot be analyzed
Solution Approach 1:
The system implements dynamics by transitioning from static deterministic models to dynamic probabilistic models. The machine learning frameworks generate not only single fault models but also multiple scenario variations with associated uncertainty quantification. This allows the system to adapt to different interpretations and scenarios while maintaining ease of model construction through automated probabilistic modeling capabilities
Solution Approach 2:
The system applies parameter changes by varying model parameters to explore different scenarios and uncertainty ranges. The probabilistic framework allows parameters such as fault location, orientation, and properties to be expressed as distributions rather than fixed values, enabling uncertainty analysis and scenario comparison while maintaining straightforward model construction through automated parameter sampling and analysis
Data Source
AI summary
Methods and systems for geological fault modeling are presented. The methods comprise: receiving interpretation data associated with one or more geological faults based on extracted data from a geological site; optimizing the interpretation data using one or more formatting operations associated with a signal processing module; generating one or more gridding representations of the interpretation data using the formatted interpretation data; executing, using the one or more gridding representations of the interpretation data, one or more inference operations based on one or more fault relationships associated with the formatted data to generate a fault model; executing, using the fault model, one or more of a sensitivity operation or an uncertainty analysis operation based on one or more parametric configurations of the fault model during a simulation to generate output data; and initiating generation of a visualization associated with the one or more geological faults based on the output data.