Downhole Fluid Composition Analysis Using Machine Learning
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Solution Overview
Problem
Current downhole fluid identification technologies face challenges in achieving real-time accuracy and efficiency for analyzing the composition and properties of formation fluids, particularly in measuring components like oil, gas, water phase fractions, and gas-oil ratios, with limitations in undisturbed formation fluid sampling and real-time sampling guidance.
Innovation Solution
A method and system utilizing a pre-trained machine learning model, integrated with downhole sensors, to process real-time data for accurate composition and property analysis of formation fluids, involving data acquisition, preprocessing, and outputting results on fluid composition and properties, with the model trained on big data from various reservoir fluids and sensor measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional downhole fluid identification technology is used, then hardware capabilities are improved, but real-time analysis efficiency and accuracy of formation fluid composition and properties deteriorate
Solution Approach 1:
The patent replaces traditional mechanical and chemical analysis systems with a machine learning-based computational system. Sensors collect downhole fluid data, which is then processed by pre-trained machine learning models to predict composition and properties in real-time, eliminating the need for complex physical analysis equipment and enabling rapid accurate measurements.
Solution Approach 2:
The patent transforms the approach from direct physical measurement to predictive parameter estimation. By training machine learning models on extensive fluid composition data, the system learns to predict multiple fluid properties (composition, density, viscosity, etc.) from limited sensor inputs, fundamentally changing how downhole fluid analysis is performed.
2Quantity of substance
If traditional fluid sampling methods are used, then sampling capability is improved, but efficiency of undisturbed formation fluid sampling and real-time sampling guidance deteriorates
Solution Approach 1:
The machine learning model enables the system to self-guide the sampling process by continuously analyzing real-time sensor data and predicting fluid composition. This allows automatic determination of optimal sampling points and timing without external intervention, improving both the quality of undisturbed sampling and overall sampling efficiency.
Solution Approach 2:
The system implements real-time feedback by continuously monitoring sensor readings and using the machine learning model to predict fluid properties. This feedback loop provides real-time sampling guidance, allowing the system to adjust sampling parameters dynamically to ensure undisturbed formation fluid collection while maximizing sampling efficiency.
Data Source
AI summary
Provided are a method and system for measuring a composition and property of a formation fluid. The method includes: acquiring a measuring model used for measuring a composition and a property of a formation fluid; using a signal measured by a sensor on a downhole hydrocarbon formation tester in real-time as input data and inputting the input data into the measuring model; processing the input data by the measuring model; and directly outputting a processing result as data on the composition and the property of the real-time formation fluid, or parsing the data on the composition and the property of the real-time formation fluid according to the processing result.