1.5D Full Waveform Inversion with Uphole Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional seismic data processing methods struggle with the increased complexity and volume of data from modern seismic acquisition systems, particularly in integrating uphole velocity measurements with inversion-based methods for accurate near-surface velocity modeling, leading to unsatisfactory calibration results and lack of automation.
Innovation Solution
The implementation of 1.5-dimensional full waveform inversion (FWI) methods, which involve generating enhanced virtual shot gathers, calibrating common-midpoint velocity models using uphole data, and applying machine learning techniques such as conditional image-to-image mapping networks or Generative Adversarial Networks to integrate uphole and inversion-based velocity modeling, reducing the need for human intervention and improving data processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional seismic data processing methods are used, then the processing can be performed with standard tools, but the handling of increased data volume and complexity from modern seismic acquisition systems becomes less useful and requires time-consuming human intervention
Solution Approach 1:
The patent replaces manual, interactive velocity modeling procedures with automated full waveform inversion (FWI) algorithms. The computer system automatically performs velocity model building by iteratively adjusting velocity parameters to minimize the difference between observed and synthetic seismic waveforms, eliminating the need for time-consuming human intervention in velocity picking and model calibration.
Solution Approach 2:
The patent transforms the processing approach by changing from traditional event-based seismic analysis to waveform-based full waveform inversion. This parameter change enables the system to utilize the complete seismic signal information including amplitude, phase, and timing across the entire waveform, thereby efficiently handling increased data volume and providing more accurate velocity models.
2Reliability
If interactive procedures with human intervention are used for quality control, then the analysis can be performed with traditional methods, but the process becomes time-consuming
Solution Approach 1:
The patent implements self-service quality control through automated FWI algorithms that inherently perform quality assessment and model validation. The system automatically evaluates the fit between observed and synthetic waveforms, iteratively refines the velocity model, and provides quality metrics without requiring manual quality control steps, thereby maintaining reliability while eliminating time-consuming human intervention.
Solution Approach 2:
The patent incorporates feedback mechanisms where the FWI algorithm continuously compares observed seismic data with synthetic waveforms generated from the current velocity model. The misfit between observed and synthetic data provides feedback that drives iterative model updates, automatically ensuring quality control while reducing processing time through systematic refinement rather than manual inspection.
3Measurement precision
If uphole velocity data is integrated with inversion-based methods, then the calibration accuracy can be improved, but the integration process becomes more complex and less automated
Solution Approach 1:
The patent merges uphole velocity measurements with inversion-based FWI methodology into a unified automated workflow. The uphole data serves as constraints or initial conditions for the FWI process, and the integration is performed automatically by the computer system through standardized data assimilation techniques, thereby improving calibration accuracy while maintaining automation and avoiding complex manual integration procedures.
Solution Approach 2:
The patent creates a universal integrated framework that can accommodate multiple data sources including uphole velocity measurements, surface seismic data, and well log information within a single FWI workflow. This multi-functional approach allows diverse data types to be combined systematically, improving velocity model calibration while maintaining process automation through a unified algorithmic framework rather than separate complex integration steps.
Data Source
AI summary
Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving for a plurality of common midpoint-offset bins each comprising a respective plurality of seismic traces, respective candidate pilot traces representing the plurality of common midpoint-offset bins; generating, based on the respective candidate pilot traces, a respective plurality of corrected seismic traces for each of the plurality of common midpoint-offset bins; grouping the respective pluralities of corrected seismic traces into a plurality of enhanced virtual shot gathers (eVSGs); generating, based on the plurality of common midpoint-offset bins, a common-midpoint (CMP) velocity model; calibrating the CMP velocity model using uphole velocity data to generate a pseudo-3 dimensional (3D) velocity model; performing, based on the plurality of enhanced virtual shot gathers and the pseudo-3D velocity model, a 1.5-dimensional full waveform inversion (FWI); and determining the subsurface velocity model based on the 1.5 dimensional FWI.