Machine Learning Model for Lost Circulation Detection in Drilling
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Solution Overview
Problem
Current wellbore drilling operations face challenges in early detection and prevention of lost circulation events, which are costly and time-consuming due to the difficulty in identifying these events in real-time, leading to significant fluid loss and equipment damage.
Innovation Solution
A machine-learning based system that monitors real-time drilling data and historical information to identify precursors of lost circulation events, providing early warnings and enabling preventive measures by training models using semi-supervised learning and spectral clustering techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual monitoring methods are used to detect lost circulation events, then operators can identify some circulation issues, but detection is delayed and occurs much later than the beginning of the event due to the large amount of data to be analyzed
Solution Approach 1:
The patent replaces manual data analysis with an automated machine learning system that processes drilling data in real-time. The system uses trained models to automatically detect lost circulation events and their precursors, eliminating the delays associated with manual monitoring while maintaining high detection accuracy through sophisticated algorithms.
Solution Approach 2:
The patent introduces an intermediate machine learning processing layer between raw drilling data and operator decision-making. This intermediary system pre-processes and analyzes large volumes of data, identifying patterns and anomalies that indicate lost circulation events, thereby reducing the time and effort required for manual analysis while improving detection precision.
2Loss of time
If real-time analysis of all drilling data is performed to enable early detection, then detection timing improves, but the complexity of the monitoring system increases due to the large amount of data to be processed
Solution Approach 1:
The patent applies preliminary action by training machine learning models offline using historical drilling data before deployment. These pre-trained models capture patterns and relationships in the data, enabling them to perform real-time detection with simplified processing requirements. This approach allows early detection without requiring complex real-time analysis of all raw data.
Solution Approach 2:
The patent segments the data processing task into distinct stages: offline model training using historical data, and online real-time inference using the trained model. This segmentation allows complex pattern recognition to be performed during the offline training phase, while the real-time system only needs to evaluate pre-processed features against the trained model, reducing real-time computational complexity.
3Measurement precision
If machine learning models are trained using extensive historical drilling data to improve detection accuracy, then identification precision improves, but the time and computational resources required for model training and updates increase
Solution Approach 1:
The patent performs preliminary model training using extensive historical drilling data before deployment to real-time systems. This offline training phase allows the model to learn from large datasets without impacting real-time operational time. Once trained, the model can be deployed for immediate use, achieving high identification precision without consuming real-time resources during inference.
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning model continuously learns from new drilling data and detected events. This allows the model to improve its precision over time while adapting to specific well conditions, reducing the need for extensive retraining from scratch and optimizing the balance between model accuracy and training time.
Data Source
AI summary
A wellbore drilling system can generate a machine-learning model trained using historic drilling operation data for monitoring for a lost circulation event. Real-time data for a drilling operation can be received and the machine-learning model can be applied to the real-time data to identify a lost circulation event that is occurring. An alarm can then be outputted to indicate a lost circulation event is occurring for the drilling operation.