Stuck Pipe Adapter Predicts Stuck Pipe Location
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Solution Overview
Problem
Stuck pipe situations during well operations, where drill strings become frozen or restricted, pose significant challenges due to various well parameters and can lead to severe complications, including loss of the drill string or well, and existing technologies lack effective diagnostic and remediation methods to address these issues efficiently.
Innovation Solution
A system comprising a stuck pipe adapter and a stuck pipe manager, utilizing machine-learning algorithms and diagnostic assemblies to determine the location and type of stuck pipe events, allowing for predictive analytics and appropriate remediation procedures, including the use of a stuck pipe adapter that applies tension to diagnose and a communication interface to transmit data for real-time analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods are used for stuck pipe events, then the process is simple and equipment is minimal, but prediction accuracy and diagnostic capability are insufficient
Solution Approach 1:
The system segments the diagnostic process into multiple independent modules: sensor assembly for data collection, communication interface for data transmission, and machine-learning algorithm for analysis. This segmentation enables high prediction accuracy through specialized functions while managing system complexity through modular design.
Solution Approach 2:
The system performs preliminary diagnostic actions by continuously collecting well operation data, drill string data, and stuck pipe data before actual stuck pipe events occur. The machine-learning algorithm processes this preliminary data to predict potential stuck pipe locations and types in advance, improving prediction accuracy while maintaining manageable system complexity through proactive rather than reactive diagnostics.
2Productivity
If real-time diagnostic data collection is implemented, then prediction capability is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The communication interface acts as an intermediary between the sensor assembly and the machine-learning algorithm. It receives raw data from sensors, transmits it to the algorithm for processing, and receives commands in return. This intermediary layer simplifies the overall system architecture by providing a standardized interface, reducing data processing complexity while enabling real-time diagnostic capabilities that improve operational efficiency.
Solution Approach 2:
The machine-learning algorithm performs self-service by automatically analyzing collected data, identifying patterns, and generating diagnostic conclusions without requiring complex external processing systems. The algorithm autonomously determines stuck pipe locations and types based on input data, reducing the computational burden on external systems while maintaining high productivity through rapid automated analysis.
3Measurement precision
If machine-learning algorithms are used for prediction, then diagnostic accuracy is improved, but computational requirements and time for analysis increase
Solution Approach 1:
The system continuously collects and pre-processes well operation data, drill string data, and stuck pipe data before actual events occur. This preliminary data preparation reduces the computational burden during actual analysis, allowing the machine-learning algorithm to quickly generate accurate diagnostic results when needed, thereby improving diagnostic accuracy while minimizing analysis time.
Solution Approach 2:
The system implements feedback mechanisms where the machine-learning algorithm continuously refines its predictions based on actual stuck pipe events and diagnostic outcomes. This feedback loop improves diagnostic accuracy over time while optimizing analysis speed, as the algorithm learns from past performance and adjusts its processing efficiency accordingly.
Data Source
AI summary
A method may include determining a stuck pipe event in a well operation for a well. The method may further include obtaining well data regarding the well operation and stuck pipe data regarding the stuck pipe event. The method may further include determining, using the well data and the stuck pipe data, a diagnostic action for the stuck pipe event. The method may further include transmitting a command to a stuck pipe adapter to perform the diagnostic action. The stuck pipe adapter may be a substitute adapter for a drill string or a work string in the well. The method may further include obtaining diagnostic data regarding the stuck pipe event in response to the stuck pipe adapter performing the diagnostic action. The method may further include determining a predicted location in the well of the stuck pipe event based on the diagnostic data and a machine-learning algorithm.