Subsurface Structural Modeling With Fault Constraints and QC
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Solution Overview
Problem
Traditional seismic interpretation and subsurface modeling operations are inefficient, prone to human errors, and lack integration of automated workflows, leading to data inconsistencies, neglect of geological constraints, and insufficient incorporation of modeling uncertainty, which hampers the generation of accurate geological models.
Innovation Solution
An integrated and automated subsurface structural interpretation and modeling solution that combines machine learning techniques with automated modeling operations, utilizing fault models and wellbore data to generate consistent, high-resolution geological models, enabling real-time quality control and geological uncertainty assessment through cloud-based platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual interpretation techniques are used, then geoscientists can perform seismic interpretation and subsurface modeling, but the process is time-consuming and prone to human errors
Solution Approach 1:
The system enables automated seismic interpretation and subsurface modeling where the computational system performs interpretation tasks autonomously using machine learning algorithms, eliminating the need for manual geoscientist intervention in routine interpretation work while maintaining high accuracy through algorithmic consistency
Solution Approach 2:
The patent replaces manual mechanical interpretation processes with automated computational systems using machine learning and artificial intelligence algorithms, substituting human cognitive processes with algorithmic decision-making to reduce human error and increase processing speed
2Ease of manufacture
If traditional sequential processing steps are used, then horizon interpretation and fault modeling can be performed separately, but data inconsistencies occur between interpretation data points and resulting models
Solution Approach 1:
The system merges horizon interpretation and fault modeling into a unified automated workflow where both processes operate simultaneously with shared data structures and consistent coordinate systems, eliminating the data inconsistency problems that arise from separate sequential processing steps
Solution Approach 2:
The system implements feedback loops where interpretation results are continuously validated against geological constraints and model consistency requirements, with automated correction mechanisms that adjust interpretation data points to ensure they conform to the final structural model
3Ease of operation
If traditional interpretation processes are used, then operations can be performed with basic tools, but geological constraints and rules are neglected
Solution Approach 1:
The system incorporates geological constraints and rules as preliminary conditions before interpretation begins, with pre-defined geological knowledge bases and constraint systems that guide the automated interpretation process to ensure geological consistency from the outset rather than requiring post-processing validation
4Productivity
If individual automated technologies are used, then interpretation speed is accelerated, but the technologies cannot be chained together into efficient automated workflows
Solution Approach 1:
The system creates a universal integrated platform that combines multiple automated interpretation technologies with standardized data interfaces and common processing frameworks, enabling different interpretation modules to chain together efficiently while maintaining high productivity through unified workflow management
5Reliability
If traditional approaches are used, then manual quality control can be performed, but full 3-dimensional consistency verification is extremely difficult or impossible
Solution Approach 1:
The system replaces manual quality control processes with automated computational verification algorithms that can perform complete 3-dimensional consistency checks across the entire subsurface model, using computer-based geometric validation and topological analysis to verify model integrity at scales and dimensions impossible for manual review
Data Source
AI summary
Methods and systems for subsurface modeling are disclosed. The methods include: generating a set of fault models using fault interpretation data derived from one or more of geological data captured by one or more sensors at a geological site or aggregated historical geological data generated from a plurality of geological sites; executing a filtering operation on the set of fault models to select one or more fault models with a shared property; applying one or more geometry constraints on the one or more fault models to generate a constrained set of fault models; generating a subsurface framework model using the constrained set of fault models, the subsurface framework model indicating consistent horizon data for the geological formation; testing the subsurface framework model based on one or more simulations to generate output data; and initiating generation of one or more visualizations based on the output data for viewing on a graphical display device.