Geological Service Characterization Workflows Using Machine Learning
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Solution Overview
Problem
Existing geological service characterization methods rely heavily on expert knowledge and experience, leading to inconsistencies, loss of knowledge when experts change jobs, and a lack of standardized workflows, making data analysis complex and resource-intensive.
Innovation Solution
A method and apparatus that utilize machine learning to generate and augment geological service characterization processes, including data acquisition protocols and analysis workflows, by capturing expert knowledge in a training information set and applying it to automate and standardize these processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expert knowledge is used to design data acquisition and analysis workflows, then the analysis can be performed, but the knowledge is lost when experts change jobs and inconsistencies arise between different experts' workflows
Solution Approach 1:
The patent creates digital copies of expert knowledge through automated workflow generation. The system captures expert decisions, methodologies, and workflows, then replicates them as standardized digital templates that can be reused without relying on the original experts. This prevents knowledge loss when experts change jobs while maintaining consistency across different projects.
Solution Approach 2:
The system enables self-service through automated workflow generation. Instead of requiring continuous expert intervention to create and maintain workflows, the automated system generates workflows independently by processing input data and applying learned patterns, reducing dependency on expert availability while maintaining quality and consistency.
2Measurement precision
If manual analysis of large volumes of geological data is performed, then comprehensive analysis can be achieved, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated computational systems. The automated workflow generation and execution systems perform data analysis tasks that would otherwise require manual expert intervention, significantly reducing processing time while maintaining analytical quality through standardized algorithms and consistent application of methodologies.
3Adaptability or versatility
If different experts design workflows for different projects, then project-specific requirements can be met, but standardization and comparability between projects are compromised
Solution Approach 1:
The system maintains standardization through parameter changes. It uses a standardized core framework and template structure that ensures consistency, while allowing flexible parameter adjustments to accommodate project-specific requirements. The automated system processes different input parameters and generates customized workflows that all adhere to the same standardized structure, enabling both standardization and adaptability.
Data Source
AI summary
Methods and systems for augmented geological service characterization are described. An embodiment of a method includes generating a geological service characterization process in response to one or more geological service objectives and a geological service experience information set. Such a method may also include augmenting the geological service characterization process by machine learning in response to a training information set. Additionally, the method may include generating an augmented geological service characterization process in response to the determination information.