Machine Learning Drilling Limit Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Drilling operations face inefficiencies due to data transmission issues, such as noise and lost connections, which affect the accuracy of determining the technical limit of a drilling operation, leading to invisible lost time and incomplete data analysis.
Innovation Solution
A machine-learning algorithm is applied to real-time data from drilling operations to determine the technical limit by analyzing drilling connections, correlations with historical data, and identifying lost connections, thereby reducing noise and improving data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data transmission is performed during drilling operations, then real-time monitoring capability is improved, but data transmission reliability deteriorates due to noise and lost connections
Solution Approach 1:
The system performs preliminary actions by establishing baseline performance metrics and technical limits before drilling operations begin. Historical data is collected and analyzed in advance to create reference models for normal operation patterns, enabling the system to detect anomalies and data quality issues during actual drilling operations.
Solution Approach 2:
The system implements continuous feedback loops where drilling data is monitored in real-time, compared against established technical limits and historical patterns, and used to adjust operations dynamically. Feedback mechanisms identify data quality issues and trigger corrective actions to maintain reliable data transmission throughout the drilling process.
2Measurement precision
If traditional data analysis methods are used, then system complexity is reduced, but measurement precision deteriorates due to noise and missing data
Solution Approach 1:
The system introduces intermediary processing layers including data filtering mechanisms, noise reduction algorithms, and intermediate validation steps between raw data collection and final analysis. These intermediaries clean and prepare data before it reaches the analytical models, improving measurement precision without requiring overly complex end-to-end systems.
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with computational methods including machine learning algorithms and automated statistical analysis. This substitution enables sophisticated precision in technical limit determination while managing complexity through software-based solutions rather than complex hardware systems.
3Productivity
If drilling operations continue without technical limits, then productivity is maintained, but loss of time increases due to invisible lost time from data issues
Solution Approach 1:
Technical limits and performance thresholds are established before drilling operations begin, creating predefined criteria for optimal operation parameters. This preliminary setup enables the system to immediately identify when operations deviate from optimal performance, allowing for rapid corrective actions that prevent time loss during critical drilling phases.
Solution Approach 2:
The system implements mechanisms to quickly skip through or bypass problematic operational phases by identifying data quality issues and transmission problems in real-time. When invisible lost time is detected, the system can rapidly adjust operations or prioritize critical data collection, minimizing the duration of unproductive periods.
Data Source
AI summary
A system can determine an accurate technical limit for a wellbore drilling operation using machine learning. A computing device can receive real-time data of the wellbore drilling operation; apply a machine-learning algorithm to the real-time data to determine a lost connection of the wellbore drilling operation; apply the machine-learning algorithm to determine correlations between the real-time data and historic drilling reports; and determine the technical limit for the wellbore drilling operation based on the correlations.