Hybrid Drilling Event Detection System Using Dependency Matcher and Machine Learning
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Solution Overview
Problem
Current methods for event detection in drilling operations rely heavily on manual interpretation of unstructured remarks, which is time-consuming and prone to errors, and lack efficient automated solutions for identifying unscheduled events like stuck pipe, lost circulation, and gas influx.
Innovation Solution
A hybrid system combining a dependency matcher and a machine learning model to process drilling remarks, where the dependency matcher identifies patterns and the ML model detects events, with a weakly-supervised learning approach to handle unlabeled data and reduce reliance on annotated datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual interpretation of unstructured remarks is used, then flexibility in handling diverse event descriptions is maintained, but time consumption increases and detection accuracy decreases
Solution Approach 1:
The patent replaces manual mechanical interpretation with an automated hybrid system comprising a dependency matcher for pattern recognition and a machine learning model for event detection. This substitution eliminates time-consuming manual analysis while improving detection accuracy through automated processing of unstructured remarks.
Solution Approach 2:
The patent introduces an intermediary processing layer that combines dependency matching and machine learning models between the raw unstructured remarks and the final event detection output. This intermediary system automatically bridges the gap between manual interpretation flexibility and automated processing efficiency.
2Productivity
If automated event detection systems are implemented, then time consumption is reduced, but detection accuracy and reliability may deteriorate without proper training data
Solution Approach 1:
The patent applies preliminary action through the dependency matcher that performs pattern matching and grammatical analysis before the machine learning model processes the data. This preliminary processing prepares the unstructured remarks in advance, improving both efficiency and accuracy of the subsequent event detection.
Solution Approach 2:
The patent merges two detection approaches: dependency matching for syntactic pattern recognition and machine learning for semantic event identification. This combination leverages the strengths of both methods to achieve high productivity and reliability simultaneously.
3Speed
If machine learning models are used for event detection, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent segments the event detection system into distinct functional components: a dependency matcher module for syntactic analysis and a machine learning model module for event classification. This segmentation reduces overall system complexity by making each component's function explicit and manageable while maintaining high processing speed.
Data Source
AI summary
A method can include receiving remarks associated with one or more field operations; processing the remarks for event detection using a dependency matcher and a machine learning model, where, responsive to the dependency matcher failing to detect an event, the processing implements the machine learning model to detect the event; and outputting at least the detected event.