Reflection Seismic Q Tomography via Depth-Domain Kernel Matrix

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing ray-based Q tomography algorithms rely heavily on transmission seismic data, which are limited in availability and depth penetration, making it difficult to reconstruct deep subsurface Q models, especially when reflection seismic data is more readily available but complex to process.

Innovation Solution

A multi-domain approach is adopted, constructing the kernel matrix in the depth domain using common image gathers, converting data to time and frequency domains, and employing a frequency weighted exponential function and box-constrained optimization to apply the centroid frequency shift method to post-migration reflection seismic data, allowing for the reconstruction of subsurface Q profiles.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If transmission seismic data Q tomography is used, then the kernel matrix construction is straightforward and shallow Q models can be reconstructed, but deep Q models cannot be recovered due to limited depth penetration of transmission seismic rays

Engineering Contradiction:

Improvekernel matrix construction simplicityVSAvoiddeep Q model reconstruction accuracy

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent combines transmission seismic data and reflection seismic data into a unified Q tomography framework. The kernel matrix is constructed to incorporate both data types, allowing the algorithm to leverage the shallow coverage of transmission rays and the deep penetration of reflection rays simultaneously, thereby resolving the depth limitation while maintaining computational feasibility.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent develops a universal Q tomography algorithm that can process both transmission and reflection seismic data through a single unified framework. The measurement vector and kernel matrix formulation are designed to accommodate multiple data types, enabling the same algorithm to reconstruct Q models at various depths depending on the available data, thus achieving multi-functional capability.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Measurement precision

If reflection seismic data is used for Q tomography, then deep Q models can be reconstructed, but the kernel matrix construction becomes complex due to multiple raypaths and difficult event picking

Engineering Contradiction:

Improvedeep Q model reconstruction accuracyVSAvoidkernel matrix construction complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent performs preliminary migration of reflection seismic data to depth domain before constructing the kernel matrix. This pre-processing step simplifies the raypath geometry by mapping complex time-domain reflection events to their depth-domain counterparts, making the subsequent kernel matrix construction more tractable while preserving the deep penetration capability of reflection rays.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediate depth domain representation as a mediator between the complex time-domain reflection data and the Q tomography inversion. By transforming reflection seismic data to the depth domain through migration, the algorithm creates an intermediate form that retains deep imaging capability while simplifying the ray tracing and kernel matrix construction process.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Ease of operation

If transmission seismic data is used, then the procedure is simple with pre-migration pre-stack data, but the number of traces is limited making the inverse problem underdetermined

Engineering Contradiction:

Improveprocedure simplicityVSAvoidinverse problem determination

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent merges transmission and reflection seismic data into a single integrated measurement vector for the Q tomography inversion. This combination increases the total number of independent measurements available, transforming the underdetermined inverse problem into a well-determined or overdetermined system, thereby improving the reliability of the Q model reconstruction while maintaining procedural simplicity through the unified framework.

Inventive Principle:

Principle #5Merging (Combining)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This method enables reliable reconstruction of deep Q models using reflection seismic data, overcoming the limitations of transmission seismic data by simplifying kernel matrix construction and improving Q estimation accuracy through iterative optimization.

Implementation Method 1

employing a frequency weighted exponential function and box-constrained optimization to solve the Q tomography optimization problem... relating the kernel matrix, the Q distribution profile, and the measurement vector

Methodology Applied

Scientific EffectCentroid frequency shift:

Data Source

PatentUS10317548B2Reflection seismic data Q tomography

Publication Date: 2019.06.11 EXXONMOBIL UPSTREAM RESEARCH COMPANY(US)

AI summary

Method for reconstructing subsurface Q depth profiles from common offset gathers (92) of reflection seismic data by performing migration (40), ray tracing (100), CDP-to-surface takeoff angle finding (96, 98), kernel matrix construction (110), depth-to-time conversion and wavelet stretching correction (80), source amplitude spectrum fitting, centroid frequency shift calculation (90), and box-constrained optimization (120).