Crude Oil Chemical Composition Characterization via Probability Distribution Functions
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Solution Overview
Problem
Current crude oil assay methods are lengthy, costly, and limited by the need for extensive data, often relying on pseudocomponents that require inadequate empirical property estimation, and statistical methods face challenges with scarce, inconsistent, or complex data.
Innovation Solution
A method characterizing crude oil by selecting hydrocarbon constituent molecules and using probability distribution functions to determine their chemical composition, allowing for interpolation and extrapolation of physical and chemical properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional crude oil assay methods are used, then extensive hydrocarbon analysis data can be obtained, but the process becomes lengthy, tedious and costly
Solution Approach 1:
The patent extracts only the essential measurements needed for crude oil characterization (density, boiling point, refractive index, viscosity) rather than performing complete conventional assays. By selecting and measuring only these key properties, the method obtains sufficient data for process simulation and planning without the time-consuming comprehensive analysis of traditional methods.
Solution Approach 2:
The patent applies partial action by performing a limited set of measurements on the crude oil and its fractions rather than complete assay procedures. This selective measurement approach captures the critical characteristics needed for refinery planning and scheduling while significantly reducing the time and cost associated with full conventional assays.
2Adaptability or versatility
If pseudocomponents are used to represent petroleum mixtures, then crude oil can be characterized for planning and simulation, but physical and chemical properties require inadequate empirical estimation
Solution Approach 1:
The patent replaces the mechanical pseudocomponent approach with a molecular-based characterization system. Instead of using hypothetical pseudocomponents with empirically estimated properties, the method directly identifies actual hydrocarbon molecules (paraffins, naphthenes, aromatics) and their properties through measured data combined with group contribution methods, eliminating the need for inadequate empirical estimation.
Solution Approach 2:
The patent changes the fundamental parameters used for crude oil characterization from pseudocomponent-based empirical properties to molecule-specific properties derived from actual measurements and group contribution calculations. This parameter transformation enables direct estimation of physical and chemical properties based on real molecular characteristics rather than inadequate pseudocomponent estimates.
3Measurement precision
If statistical methods are used for predicting crude oil properties, then property estimation can be performed, but the methods fail when data are scarce, inconsistent, or of poor quality
Solution Approach 1:
The patent performs preliminary action by establishing a molecular-based characterization framework that can work with limited data. By pre-defining the molecular classes (paraffins, naphthenes, aromatics) and their segment structures, the system is prepared to predict properties even when assay data are scarce, eliminating the need for extensive consistent datasets required by statistical methods.
Solution Approach 2:
The patent introduces group contribution methods as an intermediary between limited measured data and property prediction. This intermediary approach allows the system to fill data gaps by using contribution values from molecular segments, enabling property estimation when direct measurement data are scarce or inconsistent, unlike statistical methods that require sufficient data quality.
Data Source
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AI summary
A computer method of characterizing chemical composition of crude oil and crude oil blends, includes determining respective segment type and segment number range of selected classes of hydrocarbon constituent molecules based on physical and chemical property data on each class of hydrocarbon constituent molecules and on crude oil physical and chemical property data. The method determines relative ratio of each class of hydrocarbon constituent molecules that forms a chemical composition representative of the subject crude oil, and therefrom characterizes chemical composition of the subject crude oil The method/system displays to an end-user, the characterized chemical composition of the subject crude oil. Based on the identified distribution functions and the relative ratio of each class of hydrocarbon constituent molecules, the method estimates chemical composition of the crude oil. Estimates of physical and chemical properties, such as boiling point, density, viscosity, paraffin content, naphthene content, aromatic content, carbon content, hydrogen content, C/H ratio, asphaltene content, carbon residue, sulfur content, nitrogen content, and total acid number of the crude oil are then based on the estimated chemical composition. The computer method is also used to optimise the properties of feedstock oils obtained by blending of crude oils from different origins.