Drilling Sensor Data Cleansing via Bayesian Validation
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Solution Overview
Problem
The challenge in oil and gas drilling is the difficulty in processing the vast volume of sensor measurements, which can lead to masked detrimental situations and false alarms due to sensor faults, especially since rapid sensor recalibration or replacement is often not possible during drilling operations.
Innovation Solution
The proposed systems and methods identify and remedy sensor faults by cleansing sensor readings using a combination of preprocessing to remove missing and outlier data, validation with Bayesian network models to detect errors, and repopulation with probabilistically derived values to replace faulty readings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor measurements are collected during drilling operations, then drilling performance can be monitored and improved, but sensor faults may provide erroneous data that harms drilling operations
Solution Approach 1:
The system performs preliminary validation of sensor measurements against expected physical relationships and operational constraints before the faulty data can harm drilling operations. By checking measurements in advance against known physical laws and operational parameters, the system identifies and flags potentially erroneous readings before they are used for decision-making, thus preventing harm while maintaining continuous monitoring.
Solution Approach 2:
The system implements feedback mechanisms where sensor measurements are continuously validated against expected ranges and relationships. When measurements deviate from expected patterns, the system generates alerts and can trigger corrective actions. This closed-loop feedback ensures that erroneous data is detected and addressed, maintaining both monitoring reliability and measurement accuracy throughout the drilling operation.
2Productivity
If complex computerized systems are used to manage large volumes of sensor measurements, then data processing capability is improved, but detrimental situations may be masked or false alarms generated
Solution Approach 1:
The complex validation system is segmented into multiple independent validation modules, each responsible for specific aspects of measurement verification. These include checks for physical impossibilities, operational constraint violations, and statistical anomalies. By dividing the validation process into discrete, transparent segments, the system maintains high processing capacity while improving reliability through layered verification that reduces false alarms and prevents masking of detrimental situations.
Solution Approach 2:
Different validation rules and thresholds are applied to different sensor types, measurement parameters, and operational contexts. Rather than using a single uniform validation approach, the system tailors validation criteria to the specific characteristics of each measurement stream and the current drilling conditions. This localized validation approach improves detection accuracy for each specific measurement type while maintaining overall system productivity.
3Productivity
If sensor faults are not detected and remedied, then drilling operations may continue without interruption, but incorrect sensor readings will compromise drilling performance and safety
Solution Approach 1:
The system performs preliminary detection of sensor faults by validating measurements against expected physical relationships and operational constraints before erroneous data can significantly impact drilling operations. By identifying faulty sensors early through continuous validation checks, the system can trigger alerts and initiate remediation procedures while maintaining operational continuity, thus preventing both safety incidents and prolonged disruption.
Solution Approach 2:
The system implements real-time feedback loops that continuously monitor sensor measurements for signs of fault conditions. When faults are detected, the system immediately generates alerts and can trigger automated responses or notify operators. This feedback mechanism ensures that sensor faults are detected and addressed promptly, maintaining both drilling continuity and safety by preventing erroneous data from compromising operations.
Data Source
AI summary
Drilling rig operations may be monitored using a variety of sensors and/or other data sources. Erroneous, faulty, and/or missing data may be cleansed prior to using the data for modeling and/or monitoring drilling operations. Erroneous, faulty, and/or missing data may be identified by comparing received data to anticipated values based on historical operations, other physically related sensor readings, and known operating ranges. Cleansing may comprise replacing erroneous, faulty, and/or missing data with a modeled value or omitting a reading entirely.