Seismic Volatility Model for Anomaly Detection
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Solution Overview
Problem
Current seismic surveying methods face challenges in accurately detecting geological anomalies due to obstructions like gas clouds and water pockets, which distort signal returns and lead to incorrect depth estimations of oil deposits, resulting in incomplete data sets and costly drilling errors.
Innovation Solution
A system and method utilizing a volatility measurement model, such as a GARCH model, to generate correlations from seismic data, update estimates of density and velocity, and perform PCA to enhance anomaly detection, incorporating core samples and move-out phase adjustments for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional P-wave or S-wave seismic surveying is used, then geological formations can be surveyed, but signal returns are distorted or blocked by obstructions like gas clouds and water pockets, leading to incomplete data sets
Solution Approach 1:
The patent changes the fundamental parameters of the seismic surveying approach by using multiple wave types (P-waves, S-waves, and surface waves) with different physical properties. Each wave type interacts differently with geological obstructions, allowing the system to overcome signal distortion and blocking issues that plague traditional single-wave methods. The processor analyzes correlations across these different wave parameters to reconstruct complete geological information.
2Difficulty of detecting and measuring
If cross-section transformation methods are used to detect geologic anomalies, then anomaly detection capability is provided, but accuracy is reduced when gas clouds or other obstructions are present
Solution Approach 1:
The patent segments the seismic data analysis into multiple independent correlation analyses, each focusing on different wave types and different physical parameters (density, velocity). By dividing the complex anomaly detection problem into separate correlation studies of P-waves, S-waves, and surface waves, the system can identify anomalies more accurately even in the presence of obstructions, as each wave segment provides complementary information.
Solution Approach 2:
The system implements feedback through iterative correlation analysis, where the processor continuously refines anomaly detection by comparing results across multiple wave types and adjusting interpretations based on consistent patterns. The feedback loop allows the system to distinguish between signal distortions caused by obstructions and actual geological anomalies, improving measurement precision.
3Measurement precision
If density and velocity estimates are updated based on seismic data, then better geological modeling is achieved, but errors persist when signal returns are distorted by obstructions
Solution Approach 1:
The patent applies a universal correlation analysis framework that works across multiple wave types (P-waves, S-waves, surface waves) and multiple physical parameters (density, velocity). This multi-functional approach allows the same processing methodology to reliably estimate geological properties regardless of which wave type is being analyzed, even when some waves are distorted or blocked by obstructions. The universality of the method across different wave parameters enhances overall model reliability.
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
The system provides enhanced accuracy in determining non-linear velocity parameters and density estimates, reducing errors in anomaly interpretation and location, thereby improving the precision of seismic surveys and reducing the risk of drilling into incorrect depths.
Implementation Method 1
direct compressional or 'P' waves at the geological surface and measure the returns from the waves reflecting off of different materials in the ground. Another related approach is to use shear or 'S' waves for this same purpose, which propagate through solids only
Data Source
AI summary
A system for processing seismic data for a geologic formation generated by an array of acoustic transducers responsive to an acoustic source may include a seismic data storage device and a processor. The processor may cooperate with the seismic data storage device to use a volatility measurement model to generate current correlations of data from the array of acoustic transducers based upon a current estimate for at least one of density and velocity of the geologic formation, and compare the current correlations to a threshold. When the current correlations are below the threshold, the processor may update the current estimate for at least one of density and velocity of the geologic formation, and repeat use of the volatility measurement model to generate updated correlations of data from the array of acoustic transducers based upon the updated estimate for at least one of density and velocity of the geologic formation.