Multimodal Deep Learning for Well Log Interpretation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complexity and time-consuming nature of well log interpretation, especially with increasing numbers of logs and complications from reservoir complexity and mud invasion, hinder accurate petrophysical property estimation and reservoir characterization in oil and gas exploration.
Innovation Solution
A cognitive method and system using multimodal deep learning to automatically identify reservoir layers and petrophysical properties by processing well log data, combining geometric features, specific values, and well attributes into fusion vectors for enhanced characterization and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual well log interpretation is performed by experts, then accuracy of petrophysical property estimation is maintained, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system enables self-service by implementing automated well log interpretation through machine learning models that independently analyze well log data, extract features, and generate reservoir layer interpretations without requiring continuous expert intervention, thereby reducing time consumption while maintaining accuracy
Solution Approach 2:
The patent replaces the manual mechanical interpretation process with an automated computational system using deep learning models (CNN, LSTM, Attention mechanisms) that process well log data electronically, substituting human expert analysis with algorithmic processing to reduce time while preserving measurement precision
2Reliability
If the number of well logs to be analyzed increases, then comprehensive reservoir characterization is improved, but interpretation complexity and time consumption increase
Solution Approach 1:
The system segments the complex interpretation task into distinct processing stages: data preprocessing, feature extraction (geometric and specific values), model processing (CNN for spatial features, LSTM for temporal sequences), and result generation. This segmentation allows the system to handle increasing numbers of well logs systematically without proportionally increasing overall complexity
Solution Approach 2:
The patent implements a universal deep learning framework that can process multiple types of well log data simultaneously using the same architectural components (Attention mechanisms, fusion layers), enabling the system to handle comprehensive reservoir characterization across diverse datasets without requiring separate specialized systems for each log type
3Productivity
If automated interpretation methods are implemented, then productivity and time efficiency are improved, but measurement precision and reliability may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the Attention mechanism dynamically weights and adjusts feature importance based on learned patterns, and the model continuously refines its interpretations through the fusion of multiple processing streams (CNN spatial features, LSTM temporal features), enabling automated processing to maintain measurement precision comparable to expert analysis
Solution Approach 2:
The patent combines multiple processing approaches into a composite interpretation system: geometric feature extraction, specific value analysis, CNN-based spatial pattern recognition, LSTM-based sequential modeling, and Attention-based feature weighting. This composite approach integrates diverse processing strengths to maintain high measurement precision while achieving automated productivity
Data Source
AI summary
A cognitive well log analysis using a computer includes receiving, by one or more processors, well log data from a plurality of well logs. One or more processors identify geometric features, specific values, and well attributes from the received well log data and embed the identified features to generate a plurality of intermediate vectors arranged based on a relevance for identifying petrophysical properties. The intermediate vectors are combined to create a fusion vector based on which the one or more processors identify reservoir layers.