Drill String Packoff Risk Prediction From Borehole Cleaning Signals
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Solution Overview
Problem
Borehole operations are inefficient due to friction and material buildup leading to packoff events, which can cause stuck drill strings and increase non-productive time, making it difficult to predict when such events will occur.
Innovation Solution
A method and system for predicting packoff events by analyzing borehole conditions using input parameters, executing hole cleaning models, estimating bed height, modifying drag and torque parameters, and determining a packoff risk index (PRI) to identify potential risks and adjust drilling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If borehole cleaning operations are performed to reduce material buildup, then friction and packoff risk are reduced, but operational time and complexity increase
Solution Approach 1:
The system performs preliminary hole cleaning assessments and packoff risk evaluations continuously during drilling operations, identifying potential packoff events before they occur. This allows operators to take preventive cleaning actions at optimal moments rather than reacting to severe buildup, reducing the frequency and intensity of cleaning operations while maintaining borehole efficiency
Solution Approach 2:
The system implements continuous feedback loops by monitoring borehole conditions, calculating bed height, and evaluating packoff risk in real-time. This feedback mechanism enables dynamic adjustment of drilling and cleaning parameters, optimizing the balance between preventing packoff events and minimizing unnecessary cleaning operations, thereby reducing overall operational time
2Productivity
If packoff events are allowed to develop naturally, then operational complexity is reduced, but drill string mobility and productivity decrease
Solution Approach 1:
The system replaces complex mechanical monitoring and intervention systems with computational models and algorithms. By using software-based hole cleaning models, bed height calculations, and packoff risk indices, the system achieves sophisticated monitoring and prediction capabilities without requiring additional complex mechanical sensors or intervention equipment, thus maintaining drill string mobility while managing operational complexity through digital means
Data Source
AI summary
The disclosure presents processes and methods for determining a packoff event at a location in a borehole undergoing a drilling operation. The packoff event can be represented by a packoff risk indicator (PRI) that presents, for example, a percentage risk of the packoff event occurring. The PRI can be utilized to initiate a remediation operation prior to the packoff event becoming more severe, such as a stuck drill string. In some aspects, the generation of the PRI can utilize an uncertainty model to provide a range of input parameters and an uncertainty parameter used by other systems to evaluate the risk of the potential packoff event has on borehole operations. In some aspects, the generation of the PRI can utilize machine learning algorithms or deep neural network algorithms to pre-process the input parameters to improve the accuracy of the PRI and of the models used to generate the PRI.