Augmented Geological Service Characterization With Expert Workflows
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Solution Overview
Problem
Existing geological service characterization methods rely heavily on expert knowledge and experience, which is not retained effectively, leading to inconsistencies and loss of knowledge when experts change jobs, and require complex, time-consuming data analysis with non-standardized workflows.
Innovation Solution
A computer-implemented method and system that captures expert knowledge and experience in a training information set, using machine learning to augment the geological service characterization process, ensuring consistent and reliable results by retaining expertise and enabling automated data acquisition and analysis protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert knowledge is used to design data acquisition and analysis workflows, then analysis quality and insights are improved, but knowledge is lost when experts change jobs and results are not standardized
Solution Approach 1:
The patent captures expert knowledge by creating digital copies of workflows, data acquisition protocols, and analysis methods. These are stored as standardized templates that can be reused across different projects and experts, preventing knowledge loss when experts change jobs. The system replicates expert expertise through documented procedures rather than relying solely on individual human memory.
Solution Approach 2:
The patent transforms qualitative expert knowledge into quantifiable parameters and standardized metrics. By defining specific parameters for data acquisition and analysis workflows, the system enables precise measurement and consistent application of expert insights across different contexts and personnel.
2Measurement precision
If complex data analysis is performed manually by experts, then accurate interpretation is achieved, but time and resource consumption increase significantly
Solution Approach 1:
The patent prepares data acquisition protocols and analysis workflows in advance as standardized templates. Data processing steps are pre-configured and automated, so when actual analysis is needed, the system can quickly execute predefined procedures rather than requiring experts to manually design and perform each analysis step from scratch.
Solution Approach 2:
The patent replaces manual expert analysis with automated computer-based processing systems. Software algorithms and machine learning models perform data analysis tasks that previously required human experts, significantly reducing time consumption while maintaining or improving accuracy through consistent application of standardized methods.
3Reliability
If standardized workflows are implemented, then consistency and reliability are improved, but adaptability to different oil service activities decreases
Solution Approach 1:
The patent creates universal workflow templates and data acquisition protocols that can be applied across multiple oil service activities including exploration, drilling, production, and environmental monitoring. The standardized framework is designed to be activity-agnostic, allowing the same core system to adapt to different service types through configuration rather than fundamental redesign.
Solution Approach 2:
The patent implements dynamic workflows that can be customized and adjusted based on specific project requirements while maintaining core standardized elements. The system allows for flexible configuration of parameters, data sources, and analysis methods within the standardized framework, enabling adaptation to different oil service activities without sacrificing consistency in the underlying methodology.
Data Source
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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.