Multi-Sensor Fluid Contamination Evaluation in Formation Testing
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Solution Overview
Problem
Existing methods for sampling subterranean formation fluids often result in contamination due to drilling fluid filtrate, making it difficult to obtain representative samples, especially in mixed-phase systems like oil-based mud (OBM) where fluid properties and phase behavior are altered, leading to challenges in determining contamination levels and sample quality.
Innovation Solution
The use of multiple sensors, including optical, resistivity, and density sensors, to measure fluid properties in real-time, allowing for the estimation of contamination levels and optimizing the sampling process by automatically selecting suitable sensors based on fluid type, and implementing algorithms to model fluid behavior and contamination over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to measure fluid properties in real-time, then measurement precision and contamination detection accuracy are improved, but device complexity increases
Solution Approach 1:
The system divides contamination detection into multiple independent measurement dimensions by deploying different sensor types (optical, resistivity, density, NMR) that each measure specific fluid properties. This segmentation allows comprehensive contamination assessment while maintaining modular sensor components that can be independently selected and calibrated.
Solution Approach 2:
The sensor system is designed with multi-functionality where a single downhole tool integrates multiple sensor types that can detect various contamination scenarios (OBM, WBM, hybrid mud) and fluid properties simultaneously. This universal approach allows one system to handle diverse measurement needs without requiring separate specialized tools.
2Productivity
If automated sensor selection and algorithms are implemented, then productivity and sampling efficiency are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system pre-establishes contamination detection algorithms and sensor selection criteria before downhole deployment. The algorithms are programmed to automatically interpret sensor data patterns and determine contamination levels, eliminating the need for real-time manual analysis and enabling rapid sampling decisions based on pre-configured logic.
Solution Approach 2:
The system implements continuous feedback loops where sensor measurements are immediately processed by algorithms that compare readings against contamination thresholds. This feedback mechanism automatically adjusts sampling parameters and provides real-time contamination status, enabling adaptive optimization of the sampling process without manual intervention.
Data Source
AI summary
A method of evaluating fluid sample contamination is disclosed. A formation tester tool is introduced into a wellbore. The formation tester tool comprises a sensor. Sensor data is acquired from the sensor and a contamination estimation is calculated. A remaining pump-out time required to reach a contamination threshold is then determined.