Equipment Failure Prediction Using Flowback and Real-Time Well Data
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Solution Overview
Problem
Equipment failures at hydrocarbon wells, such as choke valves, result in costly and time-consuming repairs due to damage from sand particles and pressure surges, posing risks to downstream equipment and pipelines.
Innovation Solution
A machine learning-based method using flowback data and real-time data to predict equipment failures, extracting engineered features that correlate with failure frequencies, allowing for preemptive measures and optimized production planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If equipment operates continuously without interruption, then productivity is improved, but reliability deteriorates due to accumulated damage from sand particles and pressure surges
Solution Approach 1:
The system performs preliminary actions by continuously monitoring equipment parameters and predicting failures before they occur. The machine learning model analyzes historical and real-time data to identify early signs of equipment degradation, enabling maintenance to be scheduled before actual failure happens, thus maintaining continuous operation while preventing reliability deterioration.
Solution Approach 2:
The system implements feedback by continuously collecting operational data from sensors, feeding it to the machine learning model, and using the predictions to adjust maintenance schedules. This closed-loop feedback mechanism allows the system to adapt to changing equipment conditions and optimize the balance between continuous operation and reliability maintenance.
2Reliability
If equipment is monitored continuously to detect failures early, then reliability is improved, but device complexity increases due to additional sensors and data processing systems
Solution Approach 1:
The system applies universality by designing a multi-functional monitoring platform that not only detects equipment failures but also performs predictive analytics, generates maintenance recommendations, and stores historical data. The machine learning model serves multiple purposes: detecting anomalies, predicting failure timing, and identifying root causes, thereby improving reliability without proportionally increasing complexity.
Solution Approach 2:
The system implements self-service by enabling the equipment to monitor its own health status through integrated sensors and the machine learning model to automatically assess its condition. This self-diagnostic capability reduces the need for external monitoring infrastructure while maintaining high reliability through continuous self-assessment.
3Reliability
If maintenance is performed frequently to prevent failures, then reliability is improved, but productivity deteriorates due to increased downtime for maintenance activities
Solution Approach 1:
The system performs preliminary maintenance actions by predicting equipment failures in advance and scheduling maintenance during planned downtime rather than responding to unexpected failures. The machine learning model provides lead time for maintenance planning, allowing operations to be coordinated to minimize disruption to productivity while maintaining high reliability through proactive maintenance.
Solution Approach 2:
The system applies dynamics by making maintenance schedules flexible and adaptive rather than fixed. The machine learning model continuously updates failure predictions based on real-time equipment conditions, allowing maintenance timing to be optimized dynamically. This enables maintenance to be performed just before predicted failures without unnecessary early interventions, thus maximizing operational time while maintaining reliability.
Data Source
AI summary
A method for predicting equipment failure at a site. The method may include receiving data related to the site by a data gathering platform and then generating a failure prediction model based on the data by a data science platform. The failure prediction model may then be deployed to the data gathering platform so that a failure prediction related to the equipment using the failure prediction model may be generated. The generated failure prediction may be displayed on a display and may include graphical visualization which assist a user in interpreting the prediction of failure. The failure prediction model may be based or trained on flowback data obtained from when a wellbore may have been established at the site and/or real-time data that has been received from equipment disposed at the site and connected to the data gathering platform.