Emulsion Composition Sensor Using Iterative Density Modeling
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Solution Overview
Problem
Current hydrocarbon recovery processes face challenges in quickly estimating the composition of emulsion streams, which can take days to analyze in laboratories, and existing methods fail to accurately detect the presence of solids or gas, leading to potential damage and operational inefficiencies.
Innovation Solution
A system and method that utilize sensors to measure temperature, flow rate, pressure, and pump speed data to generate estimated compositions of produced fluids, including the presence of bitumen, water, and phantom components, using iterative convergence tools and neural networks to account for dynamic characteristics and contaminants like solids or gas, and generate alerts for undesirable conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laboratory analysis is used to determine emulsion stream composition, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The patent creates a virtual model (copy) of the emulsion stream using a process simulation model that replicates the physical separation process. This digital twin allows composition estimation without physical laboratory analysis, achieving both speed and accuracy by comparing simulated separator outputs with actual measured separator compositions
Solution Approach 2:
The patent replaces the mechanical/physical laboratory analysis system with a computational model-based system. Instead of physically separating and analyzing emulsion samples in a lab, the system uses a process simulation model with iterative solvers to calculate composition from operational data, substituting physical measurement with computational analysis
2Loss of time
If existing composition estimation methods are used, then loss of time is reduced, but measurement precision deteriorates due to inability to detect solids or gas
Solution Approach 1:
The patent implements a dynamic process simulation model that continuously adapts to changing emulsion compositions and operational conditions. The model uses iterative solvers that dynamically adjust calculations based on current separator performance and emulsion characteristics, enabling accurate detection of solids and gas content that static methods miss
Solution Approach 2:
The patent incorporates feedback loops where actual measured separator compositions are continuously compared with simulated separator outputs. This feedback mechanism allows the model to self-correct and refine composition estimates, improving precision by validating predictions against real-world measurements and adjusting for undetected contaminants
3Measurement precision
If process simulation model with iterative solver is used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal process simulation model that handles multiple functions: composition estimation, separator performance prediction, contaminant detection, and operational optimization. This single multi-functional model reduces overall system complexity compared to having separate specialized systems for each function
Solution Approach 2:
The iterative solver in the process simulation model is self-correcting, automatically adjusting composition estimates based on feedback from actual separator measurements. The model self-calibrates without requiring external intervention or complex calibration procedures, reducing operational complexity while maintaining high precision
Data Source
AI summary
A system for sensing an estimated composition of a produced fluid being conducted from a reservoir includes: at least one device for measuring temperature data; at least one device for obtaining flow rate data, pressure data, pump speed data and valve travel data; a first produced fluid density generator; a second produced fluid density generator; and a composition generator. The first produced fluid density generator is configured to generate a first produced fluid density based on the obtained flow rate, pressure, pump speed and valve travel data. The second produced fluid density generator is configured to generate a second produced fluid density based at least in part on the measured temperature data. The composition generator is configured to: iteratively generate a phantom component content, a bitumen content and a water content for the produced fluid based on at least in part on: a material balance of the produced fluid.