Adaptive Detection of Abnormal Optical Channels for Downhole Fluid Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current subsurface formation fluid measurement techniques are hindered by mud filtrate contamination and the inefficiency of pre-designed optical filters, leading to inaccurate composition analysis and mud contamination assessment due to the inability to differentiate between mud and formation fluids, especially in multiphase fluid flows.
Innovation Solution
An adaptive detection method for abnormal optical channels is implemented, using a system with a downhole optical tool that includes a light source, filters, and detectors to identify ineffective channels based on optical data, allowing for real-time correction and improved analysis of formation fluid composition and mud contamination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-designed optical filters are used for fluid analysis, then the device complexity is reduced and ease of operation is improved, but the measurement precision deteriorates due to inability to differentiate between mud and formation fluids
Solution Approach 1:
The system performs self-evaluation of optical channel effectiveness using the optical data itself. The processor automatically identifies ineffective channels by analyzing variance and information content without requiring external calibration or manual intervention, allowing the system to self-optimize its measurements
Solution Approach 2:
The system dynamically changes the parameter of channel selection based on evaluated effectiveness. By adjusting which channels are deemed effective versus ineffective based on real-time optical data analysis, the system adapts to different fluid conditions and maintains measurement precision across varying scenarios
2Measurement precision
If all optical channels are used for data analysis, then the productivity is improved by processing all available data, but the measurement precision deteriorates due to inclusion of ineffective channels contaminated by mud filtrate
Solution Approach 1:
The system extracts and removes ineffective optical channels from the data analysis process. By identifying channels with low variance or high mud contamination and excluding them from final fluid composition calculations, the system improves measurement precision without significantly impacting overall productivity
Solution Approach 2:
The system applies different quality assessments to different optical channels individually. Rather than treating all channels uniformly, each channel is evaluated based on its specific effectiveness metrics, allowing selective use of high-quality channels while discarding problematic ones
3Reliability
If adaptive channel evaluation is implemented, then the measurement precision is improved by removing ineffective channels, but the device complexity increases due to additional processing requirements
Solution Approach 1:
The reliability improvement is achieved through self-service evaluation where the optical data itself is used to determine channel effectiveness. The system automatically identifies and weights channels based on their informational content without requiring external intervention or complex additional hardware
Solution Approach 2:
The system implements feedback by using the optical measurement data to evaluate channel effectiveness and then using that evaluation to improve subsequent measurements. The processed effectiveness information feeds back into the analysis algorithm to enhance overall measurement reliability
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of formation fluid composition analysis and mud contamination assessment by removing ineffective channels, providing reliable data for decision-making in real-time without the need for surface laboratory analysis.
Implementation Method 1
collecting optical data associated with a plurality of simultaneous channel groups, where each of the simultaneous channel groups includes data collected from sensing one or more beams of light that has interacted with a sample of a fluid collected downhole
Implementation Method 2
each of the one or more beams of light having been passed through a respective light filter configured to filter one or more wavelengths of light from the beam of light
Data Source
AI summary
Light from a light source that has interacted with a sample of downhole fluid provided in a downhole optical tool is sequentially passed through a plurality of groups of light filters, each of the groups of light filters including of one or more light filters, to generate a data set for each of the groups of light filters, also referred to as a simultaneous channel group. The data generated for each of the simultaneous channel groups is then analyzed to determine if the data from that simultaneous channel groups is effective in providing information useful for the analysis of the sample of downhole fluid.