Petroleum Composition Model Reconciliation for High-Boiling Resid
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Solution Overview
Problem
Current methods, such as FTICR-MS with APPI or N-, PESI ionization, fail to accurately model the composition of petroleum streams with boiling points above 677° C (1250° F) due to poor ionization efficiency, leading to under-prediction of high-boiling materials and inability to detect a significant portion of the resid fraction.
Innovation Solution
A method involving a reference model of composition (MoC) is developed, where molecular lumps are reconciled using a computer processor to match target properties, incorporating a Heavy Hydrocarbon Model of Composition (HHMoC) protocol, involving molecular formula distribution extrapolation and renormalization, and blending with initial distributions to generate a reconciled MoC that accurately represents the composition of petroleum streams with boiling points above 538° C (1000° F).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If FTICR-MS with APPI or N-, PESI ionization is used, then mass resolution is improved, but ionization efficiency deteriorates for high-boiling materials
Solution Approach 1:
The patent introduces an intermediary computational model (HHMoC) that bridges the gap between FTICR-MS measurements and actual composition. The model uses measured data as input but computationally reconstructs the full composition including high-boiling materials that were not directly detected, effectively using the model as a mediator between measurement and truth.
Solution Approach 2:
The patent performs preliminary computational reconstruction of the molecular formula distribution before final analysis. By pre-establishing the HHMoC framework and using it to predict high-boiling material composition before detailed analysis, the system compensates for the ionization efficiency limitation.
2Difficulty of detecting and measuring
If FTICR-MS is used to analyze petroleum streams, then detection capability is improved, but coverage of high-boiling materials deteriorates
Solution Approach 1:
The patent creates a computational copy (model) of the molecular formula distribution that represents the complete composition including high-boiling materials. This virtual copy allows analysis of materials that were not physically detected, effectively copying the information that would exist if complete detection were possible.
Solution Approach 2:
The patent changes the parameter space by extrapolating the molecular formula distribution to higher mass ranges beyond the direct detection limit. By transforming the data representation and using statistical extrapolation, the system recovers information about high-boiling materials that fall outside the direct measurement range.
3Loss of information
If molecular formula distribution extrapolation is performed, then representation of high-boiling materials is improved, but model accuracy deteriorates
Solution Approach 1:
The patent implements feedback by using measured data from FTICR-MS to constrain and validate the extrapolated model. The measured molecular formula distribution serves as feedback to adjust and refine the extrapolated high-boiling material composition, ensuring the model remains grounded in actual measurements while extending to undetected ranges.
Solution Approach 2:
The patent carefully controls parameter changes during extrapolation by using statistically rigorous methods that maintain uncertainty quantification. The model transforms parameters from the measured range to the extrapolated range while maintaining accuracy through proper statistical treatment and validation against known constraints.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate estimation of molecular property distributions for petroleum streams with higher boiling points, improving the detection and representation of high-boiling materials, thereby overcoming the limitations of existing techniques.
Implementation Method 1
Ionization methods used in conjunction with FTICR include Atmospheric Pressure Photoionization (APPI) and negative and positive ion electrospray (N-, PESI)
Implementation Method 2
Atmospheric Pressure Photoionization (APPI)
Data Source
Figure 1
AI summary
Method for determining the composition of a material, including obtaining a reference model of composition (MoC) of the material based on a molecular formula distribution of the material, and reconciling, using at least one computer processor, the reference MoC to match at least one target property of the material, is provided. The reference MoC can be expressed as a combination of molecular lumps with associated reference percent. The reconciliation can be carried out using by constrained optimization of information entropy, and the optimization can be performed on a more coarse-grained basis relative to the reference MoC.