Seismic Facies Identification via Machine Learning Classification
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Solution Overview
Problem
Current methods for identifying seismic facies from seismic data are time-consuming and inefficient, often failing to accurately represent the true geometry and connectivity of geological features, leading to bottlenecks in subsurface characterization and hydrocarbon reservoir exploration.
Innovation Solution
The use of machine learning techniques, including unsupervised and supervised learning methods, to classify seismic data into distinct groups based on geometric and textural characteristics, generating 3-D digital images of seismic facies, which leverages computational power to uncover patterns difficult for human interpreters to spot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trace-by-trace classification methods are used, then seismic facies can be identified, but the process is time-consuming and creates bottlenecks in subsurface characterization
Solution Approach 1:
The patent segments the seismic data processing into distinct operational phases: initial trace-by-trace classification to identify candidate regions, followed by targeted analysis of those specific regions. This segmentation allows the time-intensive classification to be focused only where needed, rather than processing entire datasets uniformly, thus reducing overall processing time while maintaining identification accuracy.
Solution Approach 2:
The patent applies partial action by performing detailed facies classification only on selected traces or trace groups that meet specific criteria, rather than processing every trace in the dataset. This selective approach processes only the necessary portion of data at high detail, significantly reducing computation time while still achieving accurate facies identification in the most critical areas.
2Measurement precision
If trace-by-trace classification is performed, then seismic facies can be identified, but the true geometry and connectivity of geological features are not accurately represented
Solution Approach 1:
The patent merges multiple traces that exhibit similar facies characteristics into continuous facies bodies or zones. By combining adjacent traces with matching classification results, the method reconstructs the spatial continuity and geometric integrity of geological features, accurately representing their true shape and connectivity rather than treating each trace as an isolated unit.
Solution Approach 2:
The patent transitions from analyzing seismic data in a one-dimensional trace-by-trace manner to a multi-dimensional volumetric approach. By integrating information across multiple traces and depth levels, the method reconstructs three-dimensional geological features, preserving their spatial geometry and connectivity in the resulting facies models.
3Productivity
If machine learning techniques are used to classify seismic data, then processing efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary preprocessing of seismic data before applying machine learning algorithms, including data normalization, feature extraction, and initial filtering. This preliminary action prepares the data in an optimized format that reduces the computational burden on subsequent machine learning processing, improving overall efficiency while managing complexity through staged processing.
Data Source
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AI summary
A method is described for seismic facies identification including receiving a seismic dataset representative of a subsurface volume of interest; performing a machine learning algorithm on the seismic dataset to identify seismic facies and generate a classified seismic image; and identifying geologic features based on the classified seismic image. The method may be executed by a computer system.