# US 2014/0156241 A1 · Prediction of refining characteristics of oil > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0156241 A1 - Document ID: US20140156241A1 - Status: Published application - Kind: A1 - Application number: 14/005,425 - Filed: 2012-10-31 - Published: 2014-06-05 - Earliest claimed: 2012-01-06 - Assignees: Bharat Petroleum Corporation Limited - Applicants: Bharat Petroleum Corporation Ltd. - Inventors: Rajeev Kumar; Mohammad Muzaffar Ahsan; Prashant Udaysinh Parihar; Ravi Kumar Voolapalli - Web page: https://patentgrep.com/patent/US20140156241A1 ## CPC - G01N 33/28: Oils {, i.e. hydrocarbon liquids} (edible oils or edible fats G01N33/03) - G01N 33/2823: {Raw oil, drilling fluid or polyphasic mixtures} - G01N 33/2835: {Specific substances contained in the oils or fuels} - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) ## Abstract Method(s) and a system for predicting the refining characteristics of an oil sample are described. The method of predicting the refining characteristics, such as distillate yield profile, processability, product quality or refinery processing cost, may include development of a prediction model based on regression analysis. The method may further include determining the physical properties of the oil sample and predicting the refining characteristics based on the developed prediction model. The determination of the physical properties of the oil sample includes determining at least one of Conradson Carbon Residue (CCR) content, Ramsbottom Carbon Residue (RCR) and Micro Carbon Residue (MCR). ## Claims ### Claim 1 A method for predicting a refining characteristic of an oil sample of an unknown oil for planning, controlling, and optimizing refinery operation of the unknown oil, the method comprising: receiving values for a plurality of physical properties of the oil sample, wherein the plurality of physical properties includes at least one of Conradson Carbon Residue (CCR) content, Ramsbottom Carbon Residue (RCR) and Micro Carbon Residue (MCR); and determining the refining characteristic of the oil sample based on a prediction model by using the received values as an input to the prediction model, wherein the prediction model is based on coefficients of regression obtained from correlating the refining characteristic with the plurality of physical properties for known crude oils and wherein the refining characteristic is a characteristic of the unknown oil used for planning, controlling, and optimizing refinery operation of the unknown oil. ### Claim 2 (depends on 1) The method as claimed in claim 1, wherein the unknown oil is at least one of a crude oil, synthetic crude oil, an unknown hydrocarbon mixture, and a combination thereof. ### Claim 3 (depends on 1) The method as claimed in claim 1, wherein the refining characteristic is at least one of distillate yield profile, residue yield profile, processability, product qualities, hydrogen consumption in hydro processing, refinery processing cost, and ranking of the unknown oil. ### Claim 4 (depends on 1) The method as claimed in claim 1, wherein the determining the refining characteristic further comprises determining potential of production of at least one of bitumen, Fuel Oil and Low Sulphur Heavy Stock from the oil sample. ### Claim 5 (depends on 1) The method as claimed in claim 1, wherein the refining characteristic comprises at least one of Volume Average Boiling Point (VABP), Universal Oil Characterization Factor (UOP-k), Mean Average Boiling Point (MEABP), molecular weight, kinematic viscosity, asphaltenes, pour point, and mercaptan content of the unknown oil. ### Claim 6 (depends on 1) The method as claimed in claim 1, wherein the plurality of physical properties includes at least one of Sulphur content, Carbon content, Hydrogen content, Nitrogen content, API gravity, Mercaptan value, Kinematic viscosity, Pour point, Saturates, Aromatics, Resins, and Asphaltenes. ### Claim 7 (depends on 1) The method as claimed in claim 1, wherein the coefficients of regression are calculated based on one of linear regression and non-linear regression. ### Claim 8 (depends on 1) The method as claimed in claim 1, wherein the determining the refining characteristic includes deriving a yield profile with respect to temperature ranges of the oil sample. ### Claim 9 (depends on 8) The method as claimed in claim 8, wherein the yield profile includes yield profile for distillates, and wherein the distillates include at least one of Naphtha, Kerosene, Gas oil, and Vacuum Gas oil. ### Claim 10 (depends on 8) The method as claimed in claim 8, wherein the yield profile includes yield profile for residue, and wherein the residue includes at least one of Atmospheric residue and Vacuum residue. ### Claim 11 (depends on 9) The method as claimed in claim 9, wherein an increase in Naphtha and Kerosene production is positively correlated to API gravity and Sulphur content and negatively correlated to Carbon Residue Content. ### Claim 12 (depends on 9) The method as claimed in claim 9, wherein an increase in Vacuum Gas Oil production is positively correlated to Carbon Residue content and is negatively correlated to API gravity and Sulphur content; and an increase in Gas Oil production is positively correlated to Sulphur content and negatively correlated to API gravity and Carbon Residue content. ### Claim 13 (depends on 10) The method as claimed in claim 10, wherein an increase in Atmospheric residue production is positively correlated to Carbon Residue content and is negatively correlated to API gravity and Sulphur content. ### Claim 14 (depends on 10) The method as claimed in claim 10, wherein an increase in Vacuum Residue production is positively correlated to Carbon Residue content, negatively correlated to Sulphur content, and is negligibly dependent on API gravity. ### Claim 15 A characteristics prediction system (100) for predicting the refining characteristic of an oil sample of an unknown oil to plan, control, and optimize refinery operation of the unknown oil, the characteristics prediction system (100) comprising: a processor (104); an interface (106); and a memory (102) coupled to the processor (104), the memory (102) comprising: a receiving module (110) configured to receive values of a plurality of physical properties of the oil sample, wherein the physical properties includes at least one of Conradson Carbon Residue (CCR) content, Ramsbottom Carbon Residue (RCR) and Micro Carbon Residue (MCR); a regression module (112) configured to compute coefficients of regression based on correlation regression of the plurality of physical properties and refining characteristics of known crude oils stored in a Crude oil database (124); and a prediction module (114) configured to predict the refining characteristic of the oil sample based on the received values of the plurality of physical properties and the coefficients of regression, wherein the refining characteristic is a characteristic of the unknown oil used for planning, controlling, and optimizing refinery operation of the unknown oil. ### Claim 16 (depends on 15) The system as claimed in claim 15, wherein the prediction module (114) is further configured to predict potential of production of at least one of bitumen, Fuel Oil and Low Sulphur Heavy Stock from the oil sample. ### Claim 17 (depends on 15) The system as claimed in claim 15, wherein the refining characteristic comprises at least one of distillate yield profile, residue yield profile, processability, product qualities, hydrogen consumption in hydro processing, refinery processing cost and ranking of the oil sample. ### Claim 18 (depends on 15) The system as claimed in claim 15, wherein the refining characteristic further comprises at least one of Volume Average Boiling Point (VABP), Universal Oil Characterization Factor (UOP-k), molecular weight, kinematic viscosity, asphaltenes, pour point, and mercaptan content of the oil sample. ### Claim 19 (depends on 15) The system as claimed in claim 15, wherein the unknown oil is at least one of a crude oil, synthetic crude oil, unknown hydrocarbon mixture, and a combination thereof. ### Claim 20 (depends on 15) The system as claimed in claim 15, wherein the plurality of physical properties includes at least one of Sulphur content, Carbon content, Hydrogen content, Nitrogen content, API gravity, Mercaptan value, Kinematic viscosity, Pour point, Saturates, Aromatics, Resins and Asphaltenes. ### Claim 21 (depends on 15) The system as claimed in claim 15, wherein the regression module (112) is further configured to generate a yield prediction model based on a positive correlation of increase in Vacuum Gas Oil production, Atmospheric Residue production, and Vacuum Residue production with Conradson Carbon Residue (CCR) content. ### Claim 22 (depends on 15) The system as claimed in claim 15, wherein the regression module (112) is further configured to generate the yield prediction model based on negative correlation of increase in Naphtha production, Kerosene production and Gas Oil production with Conradson Carbon Residue (CCR) content. ### Claim 23 An apparatus (1000) for testing an oil sample (1002) for predicting refining characteristics, the apparatus (1000) comprising: an API test cell (1004) configured to test the oil sample (1002) for determining API gravity of the oil sample (1002); a CCR test cell (1006) configured to test the oil sample (1002) for determining CCR content of the oil sample (1002); a Sulphur test cell (1008) configured to test the oil sample (1002) for determining Sulphur content of the oil sample (1002); wherein the apparatus (1000) is configured to provide the API gravity of the oil sample, the CCR content of the oil sample, and the Sulphur content of the oil sample to a characteristic prediction system (100) for predicting the refining characteristics of the oil sample; and wherein the refining characteristic is a characteristic of the unknown oil used for planning, controlling, and optimizing refinery operation of the unknown oil. ### Claim 24 (depends on 23) The apparatus (1000) as claimed in claim 23, wherein the apparatus (1000) further comprises other test cells (1010) configured to test the oil sample (1002) to determine at least one of Carbon content, Hydrogen content, Nitrogen content, Mercaptan value, Kinematic viscosity, Pour point, Ramsbottom Carbon Residue (RCR), Micro Carbon Residue (MCR), Saturates, Aromatics, Resins, and Asphaltenes of the oil sample (1002). ### Claim 25 (depends on 23) The apparatus (1000) as claimed in claim 23, wherein the characteristic prediction system (100) configured to predict the refining characteristics based on a prediction model by using the API gravity of the oil sample, the CCR content of the oil sample, and the Sulphur content of the oil sample as an input to the prediction model, wherein the prediction model is based on coefficients of regression obtained from correlating the refining characteristics with the plurality of physical properties for known crude oils, and wherein the plurality of physical properties includes at least one of CCR content, RCR, MCR, Sulphur content, Carbon content, Hydrogen content, Nitrogen content, API gravity, Mercaptan value, Kinematic viscosity, Pour point, Saturates, Aromatics, Resins, and Asphaltenes.