ML-Based Transient Object Tracking in Hydrocarbon Wells
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Solution Overview
Problem
Existing technologies lack the capability to accurately locate and track transient objects in hydrocarbon well conduits in real-time, which is crucial for remediation and operational efficiency.
Innovation Solution
A system utilizing a machine learning model trained on pressure measurements from a conduit monitoring system to predict the location and movement of transient objects within hydrocarbon well conduits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used in hydrocarbon well conduits, then the system structure remains simple, but the ability to locate and track transient objects in real-time is insufficient
Solution Approach 1:
The patent replaces traditional mechanical monitoring systems with a machine learning-based computational system. Pressure sensor data is processed through trained machine learning models to detect and track transient objects, substituting physical mechanical detection methods with intelligent algorithms that analyze pressure variations to identify blockages, pigs, or other transient objects in the conduit
Solution Approach 2:
The system monitors changes in pressure parameters over time and space to detect transient objects. By analyzing pressure differential changes and temporal pressure profiles, the machine learning model identifies characteristic signatures of different transient objects, enabling precise location and tracking without direct physical contact or complex mechanical sensors at multiple points
2Productivity
If real-time tracking of transient objects is implemented, then operational efficiency improves, but the loss of time for data processing and analysis increases
Solution Approach 1:
The machine learning models are trained in advance on historical pressure data from the specific conduit system. This preliminary training phase enables the models to rapidly classify and track transient objects in real-time operations, as the computational heavy lifting of model development is completed beforehand. The trained models can then process incoming pressure data quickly without requiring extensive real-time computation
Solution Approach 2:
The system continuously monitors pressure data and provides real-time feedback on transient object location and movement. This feedback loop enables operators to immediately respond to detected objects, adjusting operations to maintain productivity. The system updates tracking information continuously as new pressure data arrives, maintaining current knowledge of transient object positions without requiring batch processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate real-time location and tracking of transient objects, facilitating timely remediation and optimizing hydrocarbon well operations.
Implementation Method 1
A system utilizes a machine learning model trained on pressure measurements from a conduit monitoring system to predict the location and movement of transient objects within hydrocarbon well conduits
Data Source
AI summary
A machine learning-based system for automatically locating and tracking a transient object in a conduit of interest associated with a hydrocarbon well operation. The system may train a machine learning model using pressure data received from a conduit monitoring system that operates by introducing a pressure wave into the fluid within a conduit and using a sensor to measure the magnitude of pressure waves reflected by a transient object in the conduit. The pressure data can be filtered to remove noise and focus on a frequency range of interest prior to being used to generate a training dataset for training the machine learning model. The machine learning model may be trained to generate a predictive model that can predict the location and movement of a transient object in a conduit of interest based on new pressure measurements associated with the conduit of interest.