AI Dew Point Pressure Prediction from Constant Composition Expansion
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Solution Overview
Problem
Existing methods for determining dew point pressure in reservoir fluids are subjective, time-consuming, and costly due to human observation and the subtlety of phase changes in wet gas fluids, making it difficult to quantify dew points accurately.
Innovation Solution
Utilizing isothermal constant composition expansion data and machine learning models trained on a library of reservoir fluid data to predict saturation pressures without human bias, determining dew point pressure through central tendencies of predicted values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human observation methods are used to determine dew point pressure, then the process can be performed with simple equipment, but the results are subjective and time-consuming
Solution Approach 1:
The patent replaces manual visual observation with automated detection systems including pressure transducers, temperature sensors, and optical detection systems that automatically identify phase transition points, eliminating human subjectivity and reducing measurement time
Solution Approach 2:
The patent introduces computer-controlled data acquisition systems and software algorithms as intermediaries between the experimental setup and human analysts, automatically processing sensor data to determine dew point pressure with consistent, objective criteria
2Measurement precision
If traditional experimental methods are used to determine dew point pressure, then the measurement can be obtained, but the process is costly and time-consuming
Solution Approach 1:
The patent employs preliminary automated data collection and preprocessing steps that prepare measurement data for rapid analysis, allowing multiple samples to be processed efficiently through pre-programmed experimental sequences and automated parameter optimization
Solution Approach 2:
The patent utilizes automated control of experimental parameters such as pressure ramp rates, temperature conditions, and measurement intervals to optimize the balance between measurement accuracy and time consumption, enabling rapid yet precise dew point determination
3Measurement precision
If human observation is used to detect phase changes in wet gas fluids, then the equipment can be simple, but the subtle phase changes are difficult to quantify accurately
Solution Approach 1:
The patent replaces subjective human visual assessment with automated optical detection systems, pressure sensors, and temperature monitoring systems that objectively detect subtle phase transitions through measurable physical parameter changes
Solution Approach 2:
The patent implements real-time feedback systems where sensors continuously monitor pressure, temperature, and phase state, with computer algorithms analyzing the data streams to automatically identify and record phase transition points with high precision
Data Source
AI summary
A computer-implemented method that enables dew point pressure prediction using isothermal constant composition expansion and artificial intelligence is described herein. The method includes obtaining constant composition expansion data of a reservoir fluid, and predicting saturation pressures for the reservoir fluid at the constant composition expansion data points using a trained machine learning model selected from multiple machine learning models that are trained using data from a generated constant composition expansion library. The method also includes determining a central tendency of the predicted saturation pressures to obtain a final saturation pressure corresponding to the constant composition expansion data for the reservoir fluid. In examples, the final saturation pressure is a dew point pressure of the reservoir fluid