Drilling Data Correction via Hybrid ML and Rules-Based Models
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Solution Overview
Problem
Current predictive models for drilling operations often fail to accurately correct high-importance drilling data due to flaws in data entries, lacking a comprehensive approach that combines machine learning and domain-level rules effectively.
Innovation Solution
A drilling data quality engine that utilizes a combination of machine learning models trained on impactful features and rules-based models determined by domain experts to identify and correct high-importance drilling data, incorporating a data importance analyzer and prediction analyzer to determine confidence values and improve data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to correct drilling data, then prediction capability is improved, but reliability of correction is worsened due to data flaws and lack of domain expertise integration
Solution Approach 1:
The patent combines machine learning models with rules-based models into a hybrid system. The machine learning component captures complex patterns in drilling data, while the rules-based component incorporates domain expertise and ensures physically plausible corrections. This merging allows the system to achieve both high prediction accuracy and reliable corrections by leveraging the strengths of both approaches.
Solution Approach 2:
The patent introduces a confidence value as an intermediary mechanism that bridges machine learning predictions and rules-based corrections. When confidence is low or data quality is poor, the system defaults to rules-based predictions, which serve as a reliable intermediary when machine learning outputs are uncertain.
2Reliability
If a hybrid model combining machine learning and rules-based approaches is used, then correction reliability is improved, but device complexity is worsened
Solution Approach 1:
The patent segments the correction system into distinct components: a machine learning model for pattern recognition, a rules-based model for domain knowledge application, and a confidence value mechanism for decision-making. This segmentation allows each component to be developed, validated, and maintained independently, reducing overall system complexity while maintaining reliability.
Solution Approach 2:
The patent implements a dynamic switching mechanism where the system adapts between machine learning and rules-based approaches based on confidence values and data quality assessments. This dynamic behavior allows the system to use the most appropriate model for each specific correction task, managing complexity by activating only the necessary components.
3Productivity
If high-importance data segments are identified and corrected selectively, then productivity is improved, but measurement precision is worsened due to focused attention on only critical data
Solution Approach 1:
The patent applies local quality by identifying and prioritizing high-importance data segments (such as formation evaluation parameters) for correction. By focusing computational resources and domain expertise on these critical segments, the system achieves high correction accuracy where it matters most, while maintaining acceptable performance on less critical data, thus improving overall productivity without sacrificing essential precision.
Data Source
AI summary
A drilling data correction system corrects drilling data entries in high-importance drilling data segments using machine learning and rules-based drilling models. A data importance analyzer identifies high-importance data segments in incoming drilling data. The drilling data correction system inputs features of drilling data into machine learning drilling models and rules-based drilling models trained to predict the high-importance data segments. Predictions from the machine learning drilling models and rules-based drilling models are presented to a user based on drilling data prediction criteria. The machine learning drilling data predictions are used to automatically correct the high-importance data segments, or the user chooses between machine learning drilling data predictions and rules-based drilling data predictions to correct the high-importance drilling data segment.