3D Seismic Velocity Model Generation via Automated Inversion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for building reliable three-dimensional seismic velocity models of subsurface formations are inefficient and require significant manual intervention, especially in handling large 3D seismic datasets, which can be costly and time-consuming.
Innovation Solution
An automated process using a 4D median filter and 1.5D velocity inversion technique is applied to select and refine first arrival signals, generating a 3D velocity model by applying a quality control function and parallel processing for velocity inversion, reducing processing time and enhancing data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used to build 3D seismic velocity models, then model quality can be maintained, but processing time and cost increase significantly
Solution Approach 1:
The system performs self-service through automated quality control functions that evaluate and select first arrival signals without manual intervention. The automated picker identifies valid first arrivals, applies quality metrics, and constructs velocity models autonomously, eliminating the need for manual signal selection while maintaining model quality through systematic quality assessment criteria.
Solution Approach 2:
The system changes parameters by automatically adjusting quality control thresholds and selection criteria based on data characteristics. The automated process dynamically modifies picking parameters, signal quality thresholds, and inversion parameters to optimize model construction, replacing fixed manual procedures with adaptive parameter adjustment that maintains reliability while reducing processing time.
2Productivity
If automated processing is implemented, then processing time is reduced, but manual quality control is minimized
Solution Approach 1:
The system implements feedback through automated quality control functions that continuously evaluate first arrival signals against established criteria. The feedback mechanism assesses signal quality, validates picker selections, and adjusts processing parameters automatically, providing systematic quality assurance without requiring manual intervention. This closed-loop feedback system maintains productivity while enabling high-level automation through objective quality assessment and automatic parameter adjustment.
Data Source
AI summary
A method for building a three dimensional (3D) model of a subsurface formation includes selecting, from a set of seismic shots, a plurality of first arrival signals representing the seismic shots. The method includes applying a quality control function to the plurality of first arrival signals to obtain a set of remaining first arrival signals. For each remaining first arrival signals, the method includes applying a velocity inversion function to obtain a depth velocity value at a common-midpoint (CMP) location in a shot gather including the seismic shot associated with that remaining first arrival signal, the CMP location representing a lateral variation of the shot gather including that seismic shot. The method includes, based on the depth velocity value for the seismic shot associated with each remaining first arrival signal, generating a velocity model representing the 3D model of the subsurface formation.