LS-FDR Depth Migration for High-Resolution Seismic Imaging
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Solution Overview
Problem
Current seismic imaging technologies in the oil and gas industry are time-consuming, subjective, and prone to errors, limiting the accuracy of reservoir modeling and operational efficiency.
Innovation Solution
A method involving a least-squares FWI-derived reflectively (LS-FDR) depth migration workflow is used to refine an earth model, generating high-fidelity, high-resolution seismic images, which can inform wellsite actions such as drilling decisions and trajectory adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic imaging methods are used, then the process is simpler and faster, but the image precision and reliability are lower
Solution Approach 1:
The method performs preliminary actions by recovering an initial estimation of the earth model before conducting the depth migration workflow. This preliminary earth model recovery enables the subsequent LS-FDR workflow to operate more efficiently with better initial conditions, reducing iterative processing time while maintaining high image precision through pre-prepared velocity models and traveltime information.
Solution Approach 2:
The method implements feedback by repeatedly revising the initial estimation of the earth model based on results from the depth migration workflow. The LS-FDR workflow generates updated earth model estimations that feed back into subsequent migration iterations, continuously improving seismic image precision through iterative refinement while managing processing time through efficient convergence.
2Reliability
If conventional seismic imaging methods are used, then the workflow is simpler, but the results are more subjective and error-prone
Solution Approach 1:
The method replaces subjective manual interpretation with an automated least-squares FWI-derived reflectivity (LS-FDR) depth migration workflow. This computational approach substitutes human judgment with mathematically rigorous algorithms that objectively process seismic data through iterative optimization, eliminating subjectivity and reducing errors while managing complexity through systematic automated procedures.
Solution Approach 2:
The method changes key parameters by using least-squares optimization to minimize the difference between observed and modeled seismic data. The LS-FDR workflow adjusts earth model parameters (velocity, impedance) through iterative parameter optimization, transforming the imaging process from static conventional methods to dynamic parameter-driven reconstruction, thereby improving reliability through quantitative optimization.
3Measurement precision
If high-resolution seismic imaging is achieved, then better subsurface information is obtained, but the processing becomes more time-consuming
Solution Approach 1:
The method recovers an initial estimation of the earth model as a preliminary step before high-resolution imaging. This preliminary earth model provides accurate velocity structures and traveltime information that enable subsequent high-resolution depth migration to converge faster, achieving better subsurface imaging detail while reducing the computational burden of iterative processing through pre-prepared initial models.
Solution Approach 2:
The method uses feedback loops where the LS-FDR depth migration workflow repeatedly revises the earth model estimation to progressively improve image resolution. Each iteration refines the subsurface model using feedback from previous results, allowing the system to achieve high-resolution imaging with optimized computational resources by stopping iterations when convergence criteria are met, thus balancing resolution with operational efficiency.
Data Source
AI summary
A method for producing a high-fidelity, high-resolution seismic image of a subsurface of a wellsite. The method includes receiving seismic data from wellsite equipment that is disposed at a wellsite. An initial estimation of an earth model is recovered from the received data and a depth migration workflow is performed that is based on the initial estimation of the earth model. The depth migration workflow may be a least-squares FWI-derived reflectively (LS-FDR) workflow. The method also includes revising the initial estimation of the earth model based on the results of the depth migration workflow in order to produce an optimal estimation of the earth model. A seismic image may then be generated from the optimal estimation of the earth model and displayed on a screen for a user. The user may then perform a wellsite action that is based on the generated seismic image.