Downhole Prediction Error Source Detection in Wellbore Operations
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Solution Overview
Problem
Accurately predicting wellbore operation parameters, such as pressure, is challenging due to discrepancies between predicted and measured values, often caused by erroneous data, equipment failures, or environmental changes, leading to inefficiencies and potential operational risks.
Innovation Solution
A system that compares predicted and measured values using sensors and models to identify sources of discrepancies, allowing for real-time adjustments and error notifications, and implements processes to correct these differences by modifying model parameters, prompting users for new data, or alerting to equipment issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If predictive models are used to forecast wellbore operation parameters, then operational planning is improved, but prediction accuracy deteriorates due to discrepancies between predicted and measured values
Solution Approach 1:
The system continuously compares predicted parameter values against actual measured values from sensors, identifies discrepancies, and feeds this information back to update the predictive models. This closed-loop feedback mechanism enables the models to learn from actual operational data and improve their accuracy over time while maintaining efficient operational planning.
Solution Approach 2:
The system dynamically adjusts model parameters based on the comparison between predicted and measured values. When discrepancies are detected, the system modifies parameters such as drilling rate, mud weight, or pump pressure in the predictive models to better align with actual downhole conditions, thereby improving prediction accuracy without sacrificing planning efficiency.
2Measurement precision
If multiple sensors and models are deployed to improve prediction accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system employs a multi-functional architecture where a single computing device performs multiple functions: it runs predictive models, processes sensor data, compares predictions with measurements, identifies discrepancies, and updates model parameters. This universal system approach improves prediction accuracy without proportionally increasing complexity, as one integrated platform handles all these tasks rather than requiring separate dedicated systems for each function.
3Measurement precision
If real-time comparison and error identification are implemented, then prediction accuracy is improved, but loss of time increases due to processing requirements
Solution Approach 1:
The system performs preliminary comparisons between predicted and measured values continuously in the background during normal operations. By proactively identifying discrepancies as they occur rather than waiting for scheduled analysis, the system maintains high prediction accuracy while minimizing processing delays. The computing device is pre-configured with the predictive models and comparison algorithms, enabling rapid real-time analysis without significant time loss.
Data Source
AI summary
A system usable in a wellbore can include a processing device and a memory device in which instructions executable by the processing device are stored for causing the processing device to: generate multiple predicted values of a first parameter associated with a well environment or a wellbore operation; determine a first trend indicated by the multiple predicted values; receive, from a sensor, multiple measured values of a second parameter associated with the well environment or the wellbore operation; determine a second trend indicated by the multiple measured values; determine a difference between the first trend and the second trend or a rate of change of the difference; and in response to the difference exceeding a threshold or the rate of change exceeding another threshold, determine a source of the difference including at least one of an erroneous user input, an equipment failure, a wellbore event, or a model error.