Seismic Data Tile Analysis for Automated Fluid Contact Detection
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Solution Overview
Problem
Current methods for identifying fluid contacts in subsurface reservoirs are time-consuming, labor-intensive, and prone to missing many possible fluid contacts, making them economically inefficient for analyzing all AVO data sets.
Innovation Solution
An automated method that analyzes seismic data in a tile-wise manner by defining data tiles from a seismic data cube, testing each tile for local amplitude maxima and dips, and applying correlation and cluster analysis to identify possible fluid contacts, which can be performed by a computer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual qualitative comparison of fluid and lithology stacks is used to identify fluid contacts, then measurement precision can be maintained, but productivity is significantly reduced and loss of time increases
Solution Approach 1:
The seismic data cube is divided into multiple data tiles that can be processed independently and in parallel. Each tile represents a specific spatial region and depth range, allowing the analysis system to process multiple regions simultaneously, thereby increasing overall productivity while maintaining analysis precision for each individual tile.
Solution Approach 2:
The manual visual inspection process is replaced with automated computer-based analysis using AVO attributes and machine learning algorithms. The system automatically calculates fluid and lithology stacks, identifies amplitude anomalies, and detects fluid contacts without human intervention, dramatically increasing productivity while maintaining or improving measurement precision through consistent algorithmic application.
2Productivity
If automated tile-wise analysis with multiple AVO attributes is applied, then productivity and measurement precision are improved, but device complexity increases
Solution Approach 1:
The complex analysis task is segmented into distinct processing stages: data tile generation, AVO attribute calculation, fluid stack computation, lithology stack computation, anomaly detection, and fluid contact identification. Each stage handles a specific aspect of the analysis, making the overall complex system manageable and allowing parallel processing of multiple tiles through each stage.
Solution Approach 2:
The processing system is designed to handle multiple AVO attributes (fluid stack, lithology stack, impedance attributes) and perform multiple functions (amplitude analysis, anomaly detection, fluid contact identification) within a unified automated framework. This multi-functional approach increases productivity by analyzing multiple parameters simultaneously while managing complexity through integrated processing.
3Measurement precision
If comprehensive AVO attribute analysis is performed on all data tiles, then measurement precision improves, but use of energy and computational resources increases
Solution Approach 1:
The system performs comprehensive AVO attribute analysis on all data tiles to ensure no fluid contacts are missed, accepting the higher computational energy consumption as necessary to achieve complete and accurate detection. The automated processing ensures that every region of the seismic data cube is thoroughly analyzed with multiple attributes, providing exhaustive coverage that manual methods cannot match.
Solution Approach 2:
The automated system continuously processes data tiles through the complete analysis pipeline without interruption, maintaining constant computational activity to analyze all AVO attributes across the entire seismic volume. This continuous processing ensures comprehensive coverage and high measurement precision while utilizing computational resources efficiently through sustained operation rather than intermittent analysis.
Data Source
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AI summary
A method of analysing seismic data to detect possible hydrocarbons comprises: determining a set of data tiles from a seismic data cube of seismic data; and testing each data tile in the set of data tiles to determine whether it corresponds to a possible fluid contact.