Geologic Encoder Framework for Accurate Subsurface Characterization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for characterizing subsurface reservoirs in geologic environments are limited by inaccuracies in data interpretation and modeling, leading to inefficiencies in resource extraction operations.
Innovation Solution
A geologic learning framework that utilizes an encoder to compress and process data files, generating groups that represent different types of content, allowing for improved characterization and modeling of subsurface regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used for subsurface geologic environments, then the process is simple and straightforward, but the accuracy of subsurface region characterization is insufficient
Solution Approach 1:
The patent segments the data processing task by dividing data files into different types (seismic data, well log data, core sample data, etc.) and processing each type separately through trained encoders. This segmentation allows the system to handle diverse data types with specialized processing methods, improving characterization accuracy while managing complexity through modular organization.
Solution Approach 2:
The patent applies preliminary action by training encoders in advance on various data types before actual subsurface characterization. The encoders are pre-trained to recognize patterns and relationships in different data formats, so when real data is processed, the system can immediately apply these learned transformations to improve accuracy without performing complex real-time analysis.
2Productivity
If diverse data types are processed together without compression, then all information is preserved, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent changes parameters by transforming diverse data types into a standardized compressed format through trained encoders. Each encoder learns the optimal parameter transformations for its specific data type, converting seismic, well log, and core sample data into comparable representations that reduce processing time while preserving essential geological information needed for accurate subsurface characterization.
3Manufacturing precision
If manual interpretation methods are used for geologic data, then the process is easy to understand and implement, but the accuracy and precision of drilling operations decrease
Solution Approach 1:
The patent replaces manual mechanical interpretation methods with automated encoder-based systems. Trained encoders automatically process and interpret geologic data, identifying subsurface features and characteristics that would be difficult for human interpreters to detect consistently. This substitution of automated intelligent systems for manual processes significantly improves drilling precision while the modular encoder architecture keeps system complexity manageable.
Data Source
AI summary
A method can include receiving data files, where the data files include different types of content; training an encoder using the data files to generate a trained encoder; compressing each of the data files using the trained encoder to generate a compressed representation of each of the data files; and processing the compressed representations of the data files to generate groups, where each of the groups represents one of the different types of content, where each of the groups includes members, and where each of the members is associated with a corresponding one of the data files.