Liner Hanger Event Inference for Real-Time Job Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional liner hanger job installations in subterranean environments rely heavily on human expertise, leading to inefficiencies and increased risks due to the need for manual interpretation of subtle data patterns, and existing monitoring systems struggle with real-time accuracy and timeliness.
Innovation Solution
A method and system utilizing machine learning models, specifically deep learning neural networks, to monitor and control liner hanger jobs in real-time by receiving data from field equipment, generating inferences about job events, and adjusting operations accordingly, thereby reducing reliance on human expertise and enhancing operational reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are implemented for real-time monitoring, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
A machine learning inference system acts as an intermediary between field equipment data and human operators. The system receives data from sensors and equipment, processes it through trained machine learning models to generate inferences about liner hanger job events, and presents these inferences to operators for decision-making. This intermediary layer automates the complex pattern recognition task while maintaining human oversight, thereby improving measurement precision without fully automating the entire system.
2Device complexity
If manual interpretation methods are used, then device complexity is reduced, but measurement precision and timeliness deteriorate
Solution Approach 1:
The system implements feedback by continuously monitoring field equipment data, comparing actual observations against machine learning model predictions, and adjusting operations based on the inferences generated. The system provides real-time feedback to operators about detected events and anomalies, enabling timely corrections and improvements in event detection accuracy without requiring fully automated complex systems.
3Productivity
If real-time machine learning inference is implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The system applies partial automation by using machine learning models to generate inferences about specific critical events during liner hanger jobs, rather than attempting to fully automate all monitoring and control functions. The system processes data in real-time for high-value events while allowing manual handling for less critical aspects, thereby improving productivity through targeted automation while limiting energy consumption to only the computational resources needed for the most impactful inferences.
Data Source
AI summary
A method may include receiving data from field equipment during performance of a liner hanger job at a wellsite; generating an inference as to an occurrence of an event associated with the performance of the liner hanger job based on at least a portion of the data using one or more machine learning models; and controlling the performance of the liner hanger job based at least in part on the inference.