Deep Oil and Gas Fault Characterization via Image Fusion
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Solution Overview
Problem
Conventional seismic interpretation methods face difficulties in accurately depicting complex faults due to diverse fault development stages, surface desert coverage, large burial depth of target layers, and low signal-to-noise ratio of seismic data, leading to high analysis complexity and controversy over tectonic evolution and formation mechanisms in deep oil and gas exploration.
Innovation Solution
A fault characterization method based on image fusion, which involves spectral shaping, diffusion filtering, dip filtering, amplitude gradient image calculation, ellipsoidal expansion, and hierarchical wavelet transform to enhance seismic data and accurately identify fault attributes, thereby refining fault characterization and navigation in deep oil and gas exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic interpretation methods are used, then the analysis process is simple, but the accuracy of fault identification is low due to low signal-to-noise ratio and large burial depth
Solution Approach 1:
The patent segments the fault identification process into multiple independent modules: spectral shaping module, diffusion filtering module, dip filtering module, amplitude gradient calculation module, and confidence region generation module. Each module processes specific aspects of the seismic data, allowing complex processing to be broken down into manageable steps that can be optimized independently.
Solution Approach 2:
The patent transitions from conventional 2D seismic interpretation to 3D fault characterization by calculating amplitude gradient vectors and generating three-dimensional confidence regions. This dimensional expansion allows for more comprehensive fault detection in complex subsurface structures, improving identification accuracy by considering spatial relationships in all three dimensions.
2Reliability
If conventional seismic interpretation is used, then the processing time is short, but the reliability of fault characterization is low due to noise interference and low resolution
Solution Approach 1:
The patent applies spectral shaping as a preliminary processing step before filtering and analysis. This pre-processing enhances the signal quality by optimizing the frequency spectrum of the seismic data, making subsequent filtering and fault detection more effective and reducing the need for iterative processing.
Solution Approach 2:
The patent combines multiple filtering techniques (diffusion filtering and dip filtering) with amplitude gradient calculation and confidence region generation into an integrated processing workflow. This merging of operations allows simultaneous noise suppression and feature enhancement, improving reliability without requiring separate sequential processing steps.
3Adaptability or versatility
If simple fault identification methods are used, then the analysis complexity is low, but the ability to handle diverse fault development stages and complex structures is insufficient
Solution Approach 1:
The patent applies different processing strategies to different fault types and development stages by calculating local amplitude gradient vectors and generating position-dependent confidence regions. This allows the system to adapt its analysis approach to local geological conditions, handling diverse fault characteristics with a unified framework that automatically adjusts to local requirements.
Solution Approach 2:
The patent creates a composite processing approach by combining multiple seismic attributes (amplitude, gradient magnitude, dip angle) and integrating them with geological prior knowledge to form a comprehensive fault characterization model. This composite methodology enables the system to handle complex, multi-stage fault development by synthesizing information from multiple sources.
4Measurement precision
If detailed fault characterization is performed, then the navigation accuracy is improved, but the signal-to-noise ratio requirement increases, making deep layer detection more difficult
Solution Approach 1:
The patent converts the challenge of low signal-to-noise ratio in deep layers into an advantage by using diffusion filtering, which enhances signal continuity and suppresses random noise. The method transforms noisy seismic data into enhanced images where fault features become more prominent, effectively converting the harmful noise condition into a beneficial signal enhancement opportunity.
Solution Approach 2:
The patent introduces confidence regions as an intermediary representation between raw seismic data and final fault interpretation. These confidence regions act as a mediator that integrates information from multiple seismic attributes and filtering results, providing a probabilistic framework that reduces the impact of noise while maintaining precise fault location information.
Data Source
AI summary
The present invention belongs to the field of geological exploration and specifically relates to the fault characterization method and system for precise navigation of deep oil and gas based on image fusion, aiming to solve the problem that faults in deep formations are difficult to characterize with conventional seismic interpretation methods. The present invention includes: obtaining amplitude gradient images by calculating amplitude gradient vectors; calculating dip attribute images based on the enhanced seismic data; fusing gradient amplitude attribute fault confidence region with dip angle attribute data volume that defines the fault position through a hierarchical wavelet transform method to obtain a superimposed fault attribute map; dividing bead-like structures based on superimposed fault attribute maps; calculating the score of branch fault data points based on the center point position of bead-like structures, and dividing and analyzing the dominant fault areas.