Crude Oil Blend Compatibility Prediction for Heavy Oil Processing
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Solution Overview
Problem
Refineries face challenges in predicting the compatibility of crude oil blends, particularly when mixing heavy and light oils, leading to issues like incompatibility, high viscosity, high pour point, high sulfur content, and high vacuum residue yields, which can cause asphaltene precipitation and equipment fouling, and existing laboratory tests are time-consuming and unreliable.
Innovation Solution
A method using physical parameter ratios such as Log(S)/C, API/S, and V/C to predict crude oil blend compatibility, allowing for quick determination of optimal blends that minimize operational issues through a K model developed via regression analysis, enabling simultaneous optimization of viscosity, pour point, and vacuum residue yields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple laboratory compatibility tests (CII, CSI, SP, QQA, SCP, OCM) are conducted to accurately predict crude oil blend compatibility, then prediction reliability improves, but testing time and operational complexity increase significantly
Solution Approach 1:
The patent extracts the essential compatibility prediction function from complex laboratory testing by identifying and utilizing only the most critical physical parameters (asphaltene content, resin content, API gravity, viscosity) that govern blend stability. This extraction allows compatibility prediction through simple calculations rather than comprehensive laboratory testing, directly resolving the time-reliability contradiction.
Solution Approach 2:
The patent creates a simplified computational model that copies the essential behavior of complex laboratory tests. By developing prediction equations based on fundamental physical parameters, the invention replicates the compatibility assessment function of multiple laboratory tests in a rapid computational format, eliminating the need for time-consuming physical testing while maintaining prediction reliability.
2Measurement precision
If comprehensive laboratory testing is performed to determine crude oil blend compatibility, then prediction accuracy improves, but operational efficiency deteriorates
Solution Approach 1:
The patent performs preliminary assessment of blend compatibility by calculating prediction indices using readily available physical parameters before actual blending operations. This preliminary action allows refiners to screen crude oil combinations for compatibility issues in advance, preventing problematic blends from being processed and enabling rapid decision-making without compromising prediction accuracy.
Solution Approach 2:
The patent transforms the complexity of comprehensive laboratory testing into simple computational parameters (compatibility prediction indices) based on fundamental physical properties. By changing the assessment parameters from complex chemical analysis to straightforward calculations using asphaltene content, resin content, API gravity, and viscosity, the invention simultaneously maintains accuracy and dramatically improves operational efficiency.
3Device complexity
If physical parameter ratios (asphaltene/resin, API/viscosity) are used to predict blend compatibility, then testing complexity reduces, but prediction reliability may compromise
Solution Approach 1:
The patent identifies and utilizes the most critical physical parameters (asphaltene content, resin content, API gravity, viscosity) that fundamentally govern crude oil blend compatibility. By focusing on these key parameters and their ratios, the invention simplifies the testing complexity while maintaining prediction reliability, as these parameters directly control the thermodynamic stability and phase behavior of crude oil blends.
Solution Approach 2:
The patent segments the compatibility prediction problem into distinct computational steps: calculating individual physical parameters, determining their ratios (asphaltene/resin, API/viscosity), and evaluating compatibility indices. This segmentation transforms a complex holistic assessment into manageable computational components, reducing testing complexity while preserving the reliability of the overall prediction.
Data Source
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AI summary
Methods and systems for predicting crude oil blend compatibility and optimizing blends for increasing heavy crude oil processing are described. The method includes receiving ratios of physical parameters of crude oils for optimization of crude oil blend. The physical parameter ratios are based on Kinematic Viscosity (V), Sulphur (S), Carbon Residue (C), and American Petroleum Institute (API) gravity. The crude oil blend compatibility (K model) is determined and generated using the physical parameter ratios. The K model is developed by coefficients obtained by regression analysis between the ratios of physical parameters of known crude oils and composite compatibility measure determined from multiple compatibility test results of the known crude oils. The predicted crude oil blend compatibility can be used for optimizing heavy crude oil processing.