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 21):
A computing network comprising an apparatus for testing unknown crude oil samples for predicting compatibility of unknown crude oils and optimizing crude oil blend for increasing heavy oil processing in refineries, the apparatus comprising:\n an API test cell configured to test the oil samples for determining API gravity of the oil samples; a Carbon Residue test cell configured to test the oil samples for determining carbon residue content of the oil samples; a Sulphur test cell configured to test the oil samples for determining Sulphur content of the oil samples; and a Kinematic Viscosity test cell configured to test the oil samples for determining Kinematic Viscosity of the oil samples; wherein the computing network further comprises a system for predicting the compatibility of the oil samples and optimizing an oil blend comprising at least one heavy oil from the oil samples, and wherein the apparatus is configured to provide the Kinematic Viscosity, API gravity, the Carbon Residue content, and the Sulphur content of the oil samples to the system; and wherein the system is to\n predict the compatibility for each unknown crude oil sample based on a compatibility prediction model by using the received values as an input to the compatibility prediction model, wherein the compatibility prediction model is to predict a compatibility parameter (K) for unknown crude oil samples based on a plurality of physical parameter ratios and 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 plurality of physical parameter ratios being based on Kinematic Viscosity (V), Sulphur (S), Carbon Residue (C), and American Petroleum Institute (API) gravity, 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); and \n determine the optimized oil blend for refinery processing by optimizing weight ratios of the at least one heavy oil and the one or more other crude oils based on a multi-objective optimization model comprising a constraint placed on the compatibility parameter (K) of the optimized blend determined by the compatibility prediction model, and on Kinematic Viscosity, Pour point, Vacuum residue yields, and Sulphur content.

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): 0.7285052906411459
- 35 USC 102 Novelty (BERT): 0.5585523131193117
- Combined Prediction Score: 0.7115099928889624
- Mean Citation Score: 337.4315660000001
- Max Citation Score: 444.97223
- Similarity Product: 377.22401808102137

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

Dataset: test