PATENT CLAIM ANALYSIS

Application Number: 15937397
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-08
Patent Classification: ["073", "152230"]

Abstract:
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.

Claim (Index 13):
A system for predicting compatibility of unknown crude oils and optimizing crude oil blend for increasing heavy oil processing in refineries, the system comprising:\n a processor; an interface; and a memory coupled to the processor, the memory comprising:\n a receiving module configured to receive values of a plurality of physical parameter ratios based on Kinematic Viscosity (V), Sulphur (S), Carbon Residue (C), and American Petroleum Institute (API) gravity for unknown crude oil samples; \n a regression module configured to compute coefficients of regression for a compatibility prediction model, wherein the compatibility prediction model is to predict a compatibility parameter (K) for unknown crude oil samples based on the plurality of physical parameter ratios and the coefficients of regression, the coefficients of regression being obtained from correlating the physical parameter ratios for known crude oils with respective composite compatibility measures, the composite compatibility measures being a composite of results obtained from a plurality of compatibility tests that are based on compositions of the known crude oils, wherein the plurality of compatibility tests are selected from colloidal instability index (CII), colloidal stability index (CSI), Stability Index (SI), Stankiewicz plot (SP), qualitative-qualitative analysis (QQA), stability cross plot (SCP), Heithaus parameter (or parameter P), toluene equivalence (TE) test, Heptane dilution (HD) test, and oil compatibility model (OCM); \n a prediction module configured to predict the compatibility for each unknown crude oil sample based on the compatibility prediction model by using the received values as an input to the compatibility prediction model; and \n an optimization module configured to determine an optimized blend for refinery processing, the optimized blend comprising at least one heavy oil from the unknown crude oils and one or more other crude oils, wherein the optimized blend is determined based on a multi-objective optimization model comprising a constraint on the compatibility parameter (K) of the optimized blend as an objective.

Metadata:
- Claim Count in Document: 32.0
- Percentile: 90.0
- Lexical Diversity: 2.02817
- Patent Class: 73.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['15272237', '14005425', '13740095', '15039481', '15239034']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.7300619255877944
- 35 USC 102 Novelty (BERT): 0.5546423636880289
- Combined Prediction Score: 0.7125199693978179
- Mean Citation Score: 337.4315660000001
- Max Citation Score: 444.97223
- Similarity Product: 346.17399199736894

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test