Multi-Task Neural Network Salt Modeling for Focused Seismic Imaging
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Solution Overview
Problem
Salt model building in subsurface hydrocarbon exploration is challenging due to strong diffraction and poor image focus, leading to complex and time-consuming manual interpretation, especially in salt-dominated environments, which complicates the detection of geological structures and hydrocarbon presence.
Innovation Solution
A computer-implemented method using machine learning to generate a salt feature model by training a neural network with multiple output channels for salt features and other correlated geological features, such as TOS, BOS, and P-wave velocity, to improve image quality and automate the interpretation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation methods are used for salt model building, then the process can handle complex geological structures, but the time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual mechanical interpretation processes with an automated neural network system. The neural network is trained to automatically detect salt boundaries, top-of-salt, and bottom-of-salt features from seismic data, substituting human experts' manual picking and interpretation work with machine learning algorithms that process data automatically and consistently.
Solution Approach 2:
The system enables self-service by allowing the neural network to autonomously perform salt model building without requiring continuous human intervention. The network learns from training data and independently identifies geological features, making the process self-sufficient while maintaining high accuracy through automated feature detection and boundary identification.
2Device complexity
If traditional single-task neural networks are used, then the model structure is simpler, but the training data utilization is insufficient
Solution Approach 1:
The patent implements a multi-task neural network that performs multiple functions simultaneously: detecting top-of-salt, bottom-of-salt, salt boundaries, and other geological features in a single unified model. This multi-functional approach allows the network to leverage diverse training data for multiple objectives, improving data utilization while maintaining a cohesive model structure that handles various detection tasks together.
3Reliability
If human experts perform salt model building, then the interpretation quality is high, but the process becomes labor intensive and ergonomically challenging
Solution Approach 1:
The patent replaces human experts' manual interpretation work with automated neural network processing. The system maintains high interpretation quality by training the network on expert-labeled data, while eliminating the labor-intensive aspects of manual picking, tracking, and boundary identification, thereby improving ease of operation without sacrificing reliability.
4Measurement precision
If iterative manual procedures are used for salt model building, then the model accuracy can be improved, but the process takes several months to a year
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on extensive training data before deployment. The network learns from labeled examples during an offline training phase, preparing it to perform accurate salt model building rapidly when deployed. This preliminary training eliminates the need for time-consuming iterative manual refinement during actual model building operations.
Solution Approach 2:
The patent replaces the iterative manual refinement process with automated neural network processing. The network rapidly processes seismic data and generates accurate salt models in minutes or hours rather than months, substituting the slow iterative human review and adjustment cycles with efficient machine learning inference while maintaining high model accuracy through the network's learned capabilities.
Data Source
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AI summary
A method and a system for a multi-task neural network for salt model building is disclosed. Imaging salt in the subsurface may be challenging because salt may be associated with strong diffraction and poor focused image, thereby making it difficult to interpret sediments underneath salt body or near salt flanks. To better image salt in the subsurface, the method and system trains, in combination, multiple aspects related to the subsurface, one of which is the target salt feature, in order to generate a salt feature model. The multiple aspects may include the target salt feature, such as the predicted salt mask, and at least one other salt feature, and one or more subsurface features, such as reconstruction of the input image and P-wave velocity. Thus, the salt model may better image salt, thereby making the seismic migration image more focused and easier to identify geological structures.