Deep Learning Framework for Geophysical Data Interpretation
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Solution Overview
Problem
Current geophysical data processing methods face challenges in accurately interpreting subsurface structures and reservoir characterization due to limitations in data availability and noise interference, particularly in seismic data acquisition and interpretation.
Innovation Solution
A deep learning framework is employed to generate synthetic seismic data through data augmentation, using algorithms that simulate real seismic data to train neural networks, which can then interpret and classify structural features in geophysical data, enhancing the accuracy of subsurface analysis and reducing noise interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If synthetic seismic data is generated through data augmentation to train deep learning frameworks, then the accuracy of subsurface structure identification is improved, but the computational complexity and data processing time increase
Solution Approach 1:
The patent applies preliminary action by generating synthetic seismic data through data augmentation in advance to create training datasets for deep learning frameworks. This pre-processing step enables the neural networks to be trained beforehand with augmented data, improving their accuracy for subsurface structure identification while avoiding the need for real-time data processing during actual interpretation tasks.
2Measurement precision
If deep learning frameworks are used to interpret geophysical data, then the accuracy of reservoir characterization is improved, but the computational resources and algorithm complexity increase
Solution Approach 1:
The patent applies mechanics substitution by replacing traditional mechanical or manual interpretation methods with deep learning frameworks. Neural networks automatically learn complex patterns and features from seismic and well log data, substituting manual analysis processes with automated algorithms that can handle high-dimensional data and identify subtle geological features more accurately.
Solution Approach 2:
The patent applies copying by using synthetic seismic data generated through data augmentation as training copies. These synthetic datasets are created by transforming and augmenting existing real seismic data, providing multiple copies and variations for training the deep learning models without requiring additional field acquisitions, thus reducing costs while improving model robustness.
3Reliability
If synthetic data augmentation is applied to address data scarcity, then the training effectiveness of neural networks is improved, but the data generation computational load increases
Solution Approach 1:
The patent applies parameter changes by transforming existing seismic data through various data augmentation operations that modify parameters such as amplitude, frequency, time shifts, and spatial transformations. These parameter changes create diverse synthetic training samples from limited real data, improving neural network training effectiveness while avoiding the need for computationally intensive generation of entirely new synthetic datasets from scratch.
Data Source
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AI summary
A method can include selecting a type of geophysical data; selecting a type of algorithm; generating synthetic geophysical data based at least in part on the algorithm; training a deep learning framework based at least in part on the synthetic geophysical data to generate a trained deep learning framework; receiving acquired geophysical data for a geologic environment; implementing the trained deep learning framework to generate interpretation results for the acquired geophysical data; and outputting the interpretation results.