OA16971A - Prediction of refining characteristics of oil. - Google Patents

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).

Description

PREDICTION OF REFINING CHARACTERISTICS OF ÔIL

Field of Invention ’ [0001] The présent invention relates to a method of prédiction of refîning characteristics 5 - of oil and, in particular, relates to prédiction of refîning characteristics based on physical ' · properties of the oi(. v

Background · ¹ [0002] - Crude oil generally refers to a complex mixture of hydrocarbons which is 10 obtained from geological formations beneath the earth, and from which refîned petroleum products can be obtained through fractional distillation. Fractional distillation in a refinery is a multï-step process. Each step in the process yields different products in the form.of distillâtes and residues at different boiling ranges. Crude oîls vary considerably from each other in yields of » t these products and in properties of the yields obtained. A detailed analysis of crude oil * 15 characteristics, such as probable yields, blends, pricîng, processabilîty, hydrogen consumption in hydro processing, quality, resîdue-potential, and the like, is used for the purpose of making business decisions, and for planning, controlling and optimization of refinery operations.' Such characteristics of a crude oil will be herein referred to as refîning characteristics.

[0003] ' The refîning characteristics hclp not only in taking business decisions for a crude * * , oif sample, but are also a source for meeting refinery constraints, product demand and -spécifications, predicting distillâtes and residue yields, predicting processing costs, routing of ‘ intermediate distillate streams for maximum profits, and hydrogen management.

[0004] The conventional methods for evaluating the refîning characteristics of crude oîls either involvelaboratory distillation of an oil sample or detailed molecular and spectrôscopic 25 analysis based on, for example, Nuclear magnetic résonance (NMR) spectroscopy, Gas Chromatography-Mass Spectroscopy (GC-MS), Infrared (IR) spectroscopy and Ultraviolet (UV) spectroscopy. The spectroscopic methods exploit the magnetic properties and the spectra of light for certain atomic nuclei to détermine the chemical and physical properties of the sample in which they are contained.

• ! . . SUMMARY ' ‘ ^f · I * ______ J • j [0005] This summary is provided to introduce concepts related to’prediction of refining

.. characteristics ofa given oil sample, which is further described below in the detailed description.

· This summary is not intended to identity esscntial features of the claimed subject matter nor is it . - intended for use in determining or limiting the scope ofthe claimed subject matter.

’ [0006] · In one embodiment of the présent subject matter, method(s) and system(s) for ’ ’ i *

- predicting refiningcharacteristics of an oil sample are described. The method of predicting the refining characteristics may include development of a prédiction model based on régression. The 10 method may further include determining physical properties of the oil sample and predicting the refining characteristics based on the developed prédiction model. The détermination 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).

. BRtEF DESCRIPTION OF DRAWINGS [0007], . Thedetailed description is provided with reference to the accompanying figures.

In the figures, the left-most digit(s) of a reference number identifies thie figure in which the ’ · reference number first appears. The same numbers are used throughout the drawings to reference like features and components.

[0008] Fig.’l illustrâtes a refining characteristics prédiction system, in accordance with ' an implémentation of the présent subject matter. .

[0009] Fig. 2 illustrâtes a method to déterminé coefficients of régression of a prédiction model, in accordance with an implémentation of the présent subject matter.

[0010] Fig.' 3 illustrâtes a method to predict refining characteristics based on the 25 prédiction model, in accordance with an implémentation of the présent subject matter.

t [0011] Fig.'4(a) illustrâtes a graphical représentation of validation of the prédiction model for'refînery distillation profile, in accordance with an implémentation of the présent subject matter. · [0012] · Fig. 4(b) illustrâtes a plot of refinery distillation profile of the given oil sample, in 30 accordance with an implémentation ofthe présent subject matter. ' [0013] Fig- 5(a) illustrâtes an influence of Sulphur on the refinery distillation profile of the given oil sample, in accordance with an implémentation ofthe présent subject matter.

^: [0014] ' Fig. 5(b) illustrâtes an influence of API gravity on the refinery distillation profile of the given oil sample, in accordance with an implémentation of the présent subject matter.

* [0015] : Fig.,6(a) illustrâtes an influence of CCR on the refinery distillation profile of the * given oil sample, în accordance with an implémentation of the présent subject matter.

[0016] · Fig. 6(b) illustrâtes a graph depicting minimization of Vacuum Residue based on physical properties of the given oit sample, in accordance with an implémentation of the présent , subject màtter. ’ ^f » 1 ♦ .

‘ (0017] · Fig. 7(a) illustrâtes an effect of differentiat ÀPI on the refinery distillation profile . of the given oil sanipte, in accordance with an implémentation of the présent subject matter.

[0018] ¹ Fig. 7(b) illustrâtes an effect of diffcrential Sulphur on the refinery distillation profile of the given oil sample, in accordance with an implémentation of the présent subject matter. .

[0019] ’ Fig. 8 illustrâtes an effect of difierential CCR on the refinery distillation profile of the given oil sample, in accordance with an implémentation of the présent subject matter.

[0020] . Fig. 9 (a) illustrâtes a graph showing refinery processing cost as function of CCR content of the given oïl sample, in accordance with an implémentation of the présent subject ' matter. ^; ’· · [0021] Fig. 9 (b) illustrâtes a graph showing ranking of crude oils vis-à-vis Brent crude ‘ oil pricing, in accordance with an implémentation of the présent subject matter. [0022] ' Fig. -10 illustrâtes an apparatus for measurement of the physical properties of the . given oil sample, in accordance with an implémentation of the présent subject matter. .

Detailed Description [0023] The présent subject matter, relates to a method of predicting the refming characteristics of an oil sample. The oil sample may indude, for example, a crude oil, crude oil blends, synthetic oils and hydrocarbon mixtures. While the following description uses crude oil 30 ds an example, it will be understood that any of the aforementioned types of oil sample may also be used, as would be évident to a person skilled in the art. There are various varieties of crude oils that arc available in the petroleum market, of which Bombay High crude,, Arab Light, and Saharan Blend Crude oil are prominent exemples. Evcry oil variety differs from the other in terms of composition and properties. Thus, the amount and quality of distillâtes and residues also • l * , differ. Generally, a detailed estimate of refîning characteristics of the oil may be used for the ' 5 planning ând optimization of refinery operations and profîtability.

• [0024] ' Conventional methods available for determining refîning characteristics of an oil . sample mày include methods like laboratoiy distillation or spectroscopic examination, such as by ;· Nuclear Magnetic Résonance (NMR) spectroscopy, Infrared (IR) spectroscopy, Gas t· • * Chromatography-Mass Spectroscopy (GC-MS), and the like., ’ It [0025] : Labôratory experimental data of true boiling point (TBP) distillation is currently the closest représentation qf refinery distillation profile. TBP distillation in a laboratoiy is basically a batch distillation operation following ASTM D2892 and D5236 methods in combination. This is used for fractionation of crude oils and for generating samples for cut-wise analyses for detailed characterization of oil. Hence, the data obtained from such TBP distillation

· and cut-wise analvses is also called crudc assay data. The detailed crude assay data contains accurate estimâtes of distillation profile and product qualifies. While this is an accurate method , for representing the refinery dîstillate, residue profile and product qualifies data, it is a costly and time consuming process. It typically costs over USD 30,000 per batch and takes four to six weeks to complété for each batch. :

[0026] The spectroscopic methods, on the other hand, may give detailed information regarding the molecular composition and overall properties of the oil sample faster, but the accuracy may vary over a large range based on factors, such as sensitivity of the oil sample for different properties, molecular functional groups présent in the oil, and the like. Further, the • accuracy of the spectroscopic methods may also dépend upôn the différences in geological, 25 physical and chemical properties of the unknown or target oil sample and the reference crude oil used for prédictionof these properties. If the target oil sample is similar to the reference crude oil assay, in terms of geological, chemical or physical properties, then the predicted properties * « would also be similar to the actual properties of the oil sample. However, if the target oil sample • is very different from the reference crude oil assay, the predicted properties may hâve large 30 déviations from the actual properties. Further, the spectroscopic methods do not provide the details of the refining characteristics, such as yield profile, of the oil sample, but only provide .

estimâtes bf the properties of the oil sample. · ’ [0027] ’ j As mentioned above, a detailed analysis of crude oi! refining characteristics, such as probable yields, blends, processability, hydrogen consumption in hydro processing, quality, ¹¹ · ^f · · residue-potential, and the like, is used for the purposes of makîng fînancial and operational business decisions. For example, estimation of the distillate and residue yield profile can be used for planning and optimization of refinery operations and profîtability, and for determîning other refînîng characteristics. _; [0028] In accordance with the présent subject matter, a method for predicting the refining characteristics for any given oi! sample is described. The method is used lo predict the refining characteristics for the oil sample by measurement of physical properties of the oil sample. The method uses a prédiction mode! in order to predict the refining characteristics for the oil sample, ·

including residue and distillate profile, accurately and quickly. In one implémentation, the coefficients in the prédiction model are determined based on corrélation régression, and hence the prédiction model may also be referred to as corrélation model.

[0029] ‘ The’method described herein, is based on the measurement of one or. more < physical properties, including -at least one of Conradson Carbon Residue (CCR) content, ^: Ramsbottôm Carbon Residue (RCR) and Micro Carbon Residue (MCR), of the oil sample and * then predicting the refining characteristics with the hetp of a prédiction model. It has been found that the use of carbon content of the oil, as measured by at least one of CCR, RCR and MCR, along with other physical properties, such as API gravity and Suplhur content, helps in better prédiction of the refining characteristics than when the other physical parameters are used · . without considering the carbon content.

{0030] : The prédiction model for a refining characteristic is; generated based on coefficients obtained bÿ régression between measured physical properties and measurement of the refining characteristic for known crude oils. Measured properties of the unknown oil sample act as an input for the prédiction model. These inputs, when substituted in the régression équations with the previously obtained coefficients of the prédiction model, give an output which includes one or more refining characteristics, such as yield profite, product quality and spécifications, hydrogen consumption in hydro processing, routing of intermediate refinery distillate streams, secondary unît processing, capacity utilisation, pricing and the like for the oil sample. .

[0031] In one implémentation, the output can be plottcd against the measured physical properties on the basis of known température ranges of the distillate and residues for the oi!

· sample. Thus, detailed information regarding the refining characteristics of the oil sample may • be obtained.

[0032] The régression analysis used to détermine the coefficients of the prédiction model may be based on linear régression techniques or non-linear régression techniques. It will be i , noted that even while the coefficients of régression may be determined using linear régression,

J the complété yield profile or refining characteristic profile obtained thercfrom may not necessarily be linear in nature, and hence the plot obtained may not be linear. · [0033] f As would be known to a person skilled ln the art, and, for the sake of clarîty and better understanding, the distillâtes and residues as obtained from the distillation of any given oil ? * · sample, with respect to different température ranges are listed below ih table 1. ·

. Table 1. Different products as obtained for increasing température ranges. *
Initial Boiling Point(IBP)~ 140 degree Celsius	Naphtha
: 140 degrees to 240 degrees Celsius	Kerosene
' 240 degrees to 360 degrees Celsius	Gas Oil
. 360 degrees and above	Atmospheric Residue
:360 degree Celsius to 565 degree Celsius	. Vacuum Gas Oil
* 565 degree Celsius and above	Vacuum Residue

J0034] ’ In another embodiment of the présent subject matter, the prédiction model may be used for the prédiction of hydrogen consumption in hydro processing and intermediate refinery t distillate streams. The prédiction of hydrogen consumption may be based on spécifie refinery configuration and assumptions. Further, the processing costs may include cost of the hydrogen consumed for hydro processing ofthe gas oils and cost of evacuatîng residues, such as fuel oil by 20 means of cutter stocks. Cutter stocks are petroleum stocks which are used to reduce the viscosity of a heavicr crude oil by dilution.

_; [0035] In yct another embodiment of the présent subject matter,' the prédiction model may be used for the prédiction of the ranking of the given oil sample. The prédiction of the ranking of the oi! sample may be based on the crude oil price differential and refinery processing cost differential. .The information regarding the predicted ranking of the oi! sample is further processed to détermine refinery processing costs based on the actual configuration of refinery î * : and thus crude oils can be ranked. This is based on the net differential of discounts on crude oils due to quàlities of given crude oils with respect to a reference crude oil, such as, Brent crude oîl;

⁴ t * and additional refinery processing costs.

’ [0036] ΐ In yet another embodiment of the présent subject matter, the prédiction of refming 10 '· characteristics may also include prédiction of at least one charactcristic selected from Volume Average Boiling Point (VABP), Universal Oil Characterization factor (UOP-k), mean average boiling point (MeABP), kinematic viscosity, asphaltenes, pour point, mercaptan, and molecular î · ‘ .

weight of the oil sample. The Universal Oil Characterization factor détermines the amount of * * · aromatics and paraffîh in an oil sample. The Volume Average Boiling Point is indicative of the average boiling point of an oi! sample as a whole. In an implémentation, the VABP, UOP-k; MeABP and the -molecular weight of the oil sample may be inter related. In another implémentation, the VABP, UOP-k, MeABP and molecular weight may côllectively be used to predict the aromatic, naphthenic and parafïïnic nature of the oil sample, which may be further used to select the oil sample based on the refinery configurations.

[0037] ’ ‘ In another embodiment, the prédiction of refming characteristics may further include prédiction of production of at least one of bitumen, Fuel Oil (FO) or Low Sulphur Heavy Stock (LSHS) from the oil sample. The predicted production of bitumen, FO and LSHS is indicative of the quality and ease of processing of the oil sample, as will be understood by a person skilled in the art.

[0038] The détermination of the refîning characteristics based on the methods of the présent subject matter are easier, less time consuming and more accurate than the conventional methods. Moreover, use of at least one of Conradson Carbon Residue (CCR) content, RamsbOttom Carbon Residue (RCR) and Micro Carbon Residue (MCR) for prédiction of refîning characteristics helps in determining more accurately the yield profile, especially for vacuum gas oil and vacuum residue, which in tum helps in better pricing and in planning and t

optimization of refînery operations for greater profîtability, .

θ [0039] Fig. I illustrâtes various components of a characteristic prédiction system I00, according to an embodiment of the présent subject matter. The characteristic prédiction system ' t

100 includes one or more processor(s) 104, ônê or more interfaces 106 and a memory, such as a ¹ memory 102, coupled to the processor(s) 104. It will be understood that the characteristic ' prédiction System 100 may be implemented as any suitable computing system known in the art, such as a desktop, â laptop, a server, and the like. '

- [0040] ; The interfaces 106 may include a variety of software and hardware interfaces, for ‘ · example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, ' and a printer. The interfaces 106 can facilitate multiple communications within a wide variety of ; networks and protocol types, including-wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the interfaces 106 may include one or more ports for connecting a number of devices to each other or to another computing systerm · [0041] The processor 104' can be a single processing unit or a number of units, ait of which could include multiple computing units. The processor 104 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logîc circuitries, and/or any devices that manipulate signais ' based on operational instructions. Among other capabilities, the processor 104 is configured to fetch and execute computer-readable instructions and data stored in the memory 102.

[0042] The fonctions of the various éléments shown in the figures, including any functional blocks labeled as “processor(s) may be provided through,the use of dedicated hardware as well as hardware capable of cxecutïng software in association with appropriate software. When provided by a processor, the fonctions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of indîvidual- processors, some of which may be shared. Moreover, explicit use of the term “processor'* should not be construed to reftr exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application spécifie integrated circuit (ÀS1C), field programmable gâte array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and non volatile storage.- .

[0043] The memory 102 may include any computer-readable medium known in the art including! for example, volatile memory, such as static random access memory (SRAM) and ' dynamic random access memory (DRAM), and/or non-volatile memory, such as read only • mcmôry (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and ’ ► . · magnetic.tapes. The memory. 102 includes module(s) 108 and data 116. The modules 108, amongst other things, include routines, programs, objects, components, data structures, etc., * which perform particular tasks or implement particular abstract data types, i · ' [0044] ' The data 116 serves, amongst other things, as a repository for storing data processed, received and generated by one or more of the modules 108. The data 116 may include ' data relatcd to samples of known crude oils, i.e., crude oil data 118, régression data 120 and other data 122. The modules 108 further include, for example, a receiving module 110, a . régression module 112, and a prédiction module 114. The data 116 includes data generated as a ' resuit of the cxecu don of one ormore modules.· * * * I· [0045J In operation and to generate a prédiction model, according to an implémentationt ,» of the présent subject marier, the receiving module 110 of the characteristic prédiction System ï

' 100 receives physical properties, including at least at least one of Conradson Carbon Residue (CCR) content, Ramsbottom Carbon Residue (RCR) and Micro Carbon Residue (MCR), for a plurality of known oil samples. The measurement of physical properties for these known oil samples may be done by industry specified protocol methods such as shown in Table.2. ^;

‘ Table 2. Industrial Methods for measurement of Physical properties.
' Sample	Analyses Details * *	. Method
i Any given oil sample	Density, Spécifie Gravity and/or API Gravity	ASTM D4052
Sulphur	ASTM D2622, D4294, . D5453
Mercaptan	ASTMD3227
Kinematic Viscosity (KV)	ASTM D445
Pour Point	ASTM D97, D5853, D5930
Acidity ,	ASTM D664

	Fe, V, Ni, Na, Cu, Zn ·	ICP-AES .
Total Nitrogen	ASTM D4629
Basic Nitrogen	UOP269
Yieids (%wt & %vol) '	ASTM D2892 and D5236
ASTM Distillation	ASTMD86 .
Freezing Point	ASTM D2386
Conradson Carbon Residue (CCR)	ASTM Di 89 ·
Micro Carbon Residue (MCR) .	ASTM D4S30 ·
Ramsbottom Carbon Residue (RCR)	ASTM DS24
Asphaltenes	ASTM D656O
Sait	ASTM D323O
RVP	ASTM D323 '
Aniline point	ASTMD6II ·

[0046] As depicted in table 2, industrial protocol methods for measurement of physical properties for any oil sample are known. In accordance with the présent subject matter.the’ . physical properties measured include at least one of CCR content, RCR and MCR along with one 5 or more other physical properties, such as Sulphur content, Carbon content, Hydrogen content,

Nitrogen content, API gravity, Pour point, Viscosity, Saturâtes, Aromatics, Resins, Asphaltenes , T ’ and the like. . ♦ . : · [0047] This data may be stored in a crude oil database 124 for further use in thè prédiction of the refining characteristics for the oil sample. The régression module 112 processes 10 ' this data for calculation of régression coefficients for the prédiction model that can be used to predict one or more of the refining characteristics of the oil sample. The refining characteristics are also referred to as characteristics hereinafter. .

[0048] - In one implémentation of the présent subject matter, the régression module 112 uses characteristics data for multiple known oil samples to calculate the coefficients of 15 régression. In one implémentation, the calculation of the coefficients of régression is based on a method of linear régression. These coefficients are then used to generate prédiction models that ’· are used to predietthe characteristics ôf the given oit sample. It may be noted that, these

- measured physical properties inciude at least one of CCR content, RCR and MCR. Additîonally, one or rnôre physical properties selected from the group of Sulphur content, Carbon content,

Hydrogen content, Nitrogen content, API gra’vity, Mercaptan value, Kinematic viscosity i Pour point, Ramsbottm Carbon Residue (RCR), Micro Carbon Residue (MCR),¹ Saturâtes, Aromatics, Resins, and Asphaltenes can bc used. It will be understood that other methods of non-lînear régression, such as polynomial régression and logarithmic régression, may also be used for ' détermination of the régression coefficients. * [0049] Further, the prédiction module 114 can predict the characteristics of the oil 10 sample based on the coefficients of régression and prédiction model generated by the régression module 112. The characteristics may inciude one or more of distillate and residue yield profile, hydrogen .consumption in hydro processing and intermediate refinery distillate streams, pricing parameter and ranking of crude oils on the basis of net margins offered over crude cost differentials, VABP and UOPK factors, Mean Average Boiting Point (MeABP) and molecular 15 weight. Further, the prédiction of characteristics may also inciude prédiction of residue potential for determining at least one of bitumen, Fuel Oil (FO) or Low Sulphur Heavy Stock (LSHS) production. It will be understood that multiple prédiction models tnay.be generated for the prédiction of different characteristics. Also, based on the predicted characteristics, further' decisions · can be taken, for example, regarding optimum crude blends, efficient resource 20 utilizatiori in the refinery, and the like. * [0050] ' The crude oil database 124 may be used to store the physical properties as obtained by the receiving module 110. The crude oil database 124 may also be used to store the measured coefficients of régression for plurality of known samples and their measured physical properties. Thus, it will be understood that the crude oil database 124 mày be used for storing 25 any relevant data rclating to the characteristic prédiction System 100.

[0051] . Fig. 2 illustrâtes an exemplary method 200 for calculating coefficients of régression for known crude oil samples, in accordance with an implémentation of the présent subject matter. Fig. 3 illustrâtes an exemplary method 300 for prédiction of characteristics for any given oil sample, in accordance with an implémentation of the présent subject matter. For 30 ’ explanation, the concepts of calculation of coefficients · of régression and prédiction of characteristics are described with reference to the characteristic prédiction system 100.

[0052] . The exemplary methods may be described in the general context of computer exécutable instructions. Generally, computer exécutable instructions can include routines, programs, objects, components, data structures, procedures, modules,’ functions, etc., that perform particular functions or implement particular abstract data types. The methods may also be practiced in a distributed computing environment where functions are¹ performed by remôte • processing devices that are linked through a communications network. In a distributed : ’ ♦

'. computing environment, computer exécutable instructions may be located in both local and remote computer storage media, including memory storage devices. * * . [0053] The: order in which the method blocks are described is not intended to be : construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method, or an alternative method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described herein. Furthermore, the methods can be implemented in any suitable hardware, software, firmware, or combination thereof. The methods are explained with reference to a characteristic prédiction system 100, however, it will be understood that the methods 200 and 300 can be implemented for a plurality of characteristic prédiction Systems also.

[0054] At block 202, values of a plurality of physical parameters are received for known oil samples. For example, the receiving module 110 may receive measured values for a plurality of physical properties like Conradson Carbon Residue (CCR) content. Carbon content, Hydrogen content, Nitrogen content, API gravity, Sulphur content, and the like. It may also receive other physical properties, such as boiling points, pour points, viscosities, RCR, MCR, asphaltenes, . mercaptan and the like, for the known oil samples. . [0055] As block 204, one or more refîning characteristics are received for the known oil samples. The refîning characteristics are then saved in the crude oit database 124 and may be retrieved, for example, by the receiving module 110. The plurality of physical properties and the ' corresponding refîning characteristics are hence available for further processing.

[0056] At block 206, a set of coefficients of régression are determined in order to generate the prédiction model between the physical parameters and the one or more refîning characteristics. In one implémentation, the set of coefficients of régression are calculated based on linear régression, for example, by the régression module 112.

[0057] . The set of coefficients of régression are calculated from multiple linear régression équations, based on the refming characteristics and physical properties of multiple known oil samples. Régression analysis is a well known statistical technique for determining coefficients . that correlate the value of a variable with values of one or more known parameters. Using such , régression techniques, coefficients can be determined to correlate a refining variable, such as yield of a distiilaté fraction, with one or more physical parameters. These coefficients can be then used to predict characteristics of any unknown or given oil sample, which'may include • 1 ______ ' crude oils, crude oil blends, and hydrocarbon mixtures. In one implémentation, the coefficients t * ; of régression are also stored in the crude oil database 124 for further processing.

[0058] i Moving now to Fig. 3 and exemplary method 300 for prédiction of characteristics for any given oil sample, in accordance with an implémentation of the présent subject matter.

[0059] > At block 302, a plurality of physical parameters values including at least one of CCR, RCR and MCR is received for an oil sample for which one or more refining characteristics are to be predicted. The oil sample may be any given unknown crude oil, unknown blend or 15 synthetic crude oilà or any unknown hydrocarbon. The plurality of physical property values is calculated for the oil sample based on the industrial protocol methods, as mentioned above in . Table2. ’ ' · [0060] At block 304, the measured physical properties, are used as inputs in the ' prédiction model. The prédiction model is generated using coefficients of régression, wherein, irr 20 one implémentation, the calculation of the coefficients of régression is based on linear régression, as' described earlier.' In one example, the prédiction mode! may be generated by the régression module 112, It may be noted, that multiple prédiction modeis may be formed for different characteristics to be predicted for any given oil sample. Thus, any number of characteristics may be predicted for the oil sample, based on the generated coefficients of 25 régression. > . ^r [0061] <' At block 306, one or more refining characteristics are calculated from the various prédiction modeis.' The refining characteristics are obtained as outputs from the prédiction modeis. In one example, the prédiction module 114 détermines the refining characteristics based on the prédiction modeis. The outputs correspond to the different characteristics including, 30 distiilaté and residue profile, probable yields and quality, blends, pricing, processability, hydrogen consumption in hydro processing, residue-potential, secondary processing, and the like ofthe oil sample.

‘ 4 , . [0062] . Fi g.. 4 (a) and fig. 4(b) illustrate the validation plots for the prédiction model as generated using the coefficients of régression for known oil samples. This validation is done by 5 : prcdicting the distillate and residue yield profile for three known crude oil samples viz., Bombay High (source: India), Saharan Blend (source; Algeria) and Arab Light (source: Saudi Arabia).

: Values of the measured physical properties for these crude oil samples are depicted in Table 3.

Table 3. Values of physical properties of crude oils.

Properties	Units	BH crude	Arab Light	Saharan Blend
Origin .	- .	India	Saudi Arabia	Algeria
API Gravity	-	38.94	32.93	. 42.35
Total Sulphur	%wt	0.158	1.754	0.163
RVP at 38°C	kg/cm2	0.310	0.358	0.380
Kinematic Viscosity, 40°C	cSt	2.544	' 7.959	’ 2.18
Pour Point	°C	12.00	INA	-39
TAN ’	mg KOH/gm	0.06	0.09	0.Ï8
CCR	%wt	1.250	4.476	1.080
Asphaltenes	%wt	0.155	1.687	0.047
C	%wt	86.32	85.6	85.35
. H . .	%wt	13.52	12.64	13.82
N .	Ppm	252	887	357
’ Vanadium	Ppm	1.6	13.00	<0.5
Nickel	1.4	• 7.00	1.3
Copper	0.196	5.8	0.1
‘ Iron	4.08	• 17.00	19.5

[0063] : As depicted in table 3 values of the physical properties for the three crude oils, 10 . taken for validation, are determined. These properties include API gravity, Sulphur content (in weight %), CÇR content (in weight %) and Asphaltenes content (in weight %). More physical properties, such as RCR, MCR, Pour point, Viscosity, Freeze point, and the like may also be listed down.

[0064] As shown in Fig, 4(a), the validation of model is based on a graph 400(a) between 15 the cumulative yiejds in weight percent of the crude oils and different température ranges in degree Celsius. The predîcted relation between cumulative yields and température ranges for the Saharan Blend crude are as depicted by the curve 402 of the graph. The actual relation between the cumulative yields of the Saharan Blend crude with respect to température ranges is as depicted by curve ,404. As is clearly évident from the two curves, the predicted value of the ; cumulative yields is very close to the actual cumulative yield values. * [0065] Similarly, the curve 406 depicts the relation as predicted bètween the cumulative · yields and the température ranges for Bombay High (BH) crude and the curve 408 depicts the ^: actual relation between cumulative yields and température ranges for the BH crude. Further, . predicted relation between cumulative yields in weight percentage and température ranges for the « * \ Saharàn Blend crude oil is as depicted in curve 410, whereas the actual relation between the » L t . cumulative yield and température ranges is as depicted in curve 412. As is clearly évident from 10 ^: the graph 400(a), the predicted relation of the cumulative yields and the température ranges is very close to the actual relation. Hence, the prédiction model is validated with an accuracy , ranging between 92-97%. .

[0066] A plot 400(b) of the refinery distillation profile is depicted in the fig. 4(b). The plot ïs obtained between cumulative yields and the température for both the weight percentage 15 and volume percentage. The plot 400(b) clearly depicts that the prédiction model generated for the prédiction of distillate and residue yield profile can predîct the yields both in the form of volume percentage¹ (as depicted by curve 414) as well as that of weight percentage (as depicted . by curve 416). · [0067] ’ In an embodiment of the présent subject matter, plot 400(b) may also be used for’ the prédiction of other physical parameters or refining characteristics, including 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 mercàptan content of the oil sample. In an implémentation, the VABP, UOP-k, MeABP and molecular weight may collectively be used to predîct the aromatic, naphthenic and parafïïnic nature of the oil sample, which may be further used to select an oil sample based on the refinery configurations. I ’ , ' , [0068] ' Figs. 5(a), 5 (b), and 6(a) depict the impact of different physical properties on the residue and distillate yield profile. It will be understood by a person skilled in the art, that similar influences of the same or other physical properties on the same or other refining characteristics, as described above, may also be depicted.

[0069] Fig ‘5(a) depicts the impact of the Sulphur content on the distillate and residue profile, collectively referred to as the distillation profile, for any given oil sample via a graph ΐ 500(a). The slopes of curves 502-514 depict how the impact on yield varies with température in degree Celsius as the sulphur content changes for six different unknown oil samples. The . Sulphur content in the samples varies from 0.1% by weight to 3% by weight as depicted in the ' graph 500(a). As is évident from the graph 500(a), as the value of Sulphur content in weight percentage increases, the impact on yield has a definite increase for a température range of about ‘ 80 degree Celsius to about 360 degree Celsius. Also, after a température of about 530 degree : Celsius there is a considérable drop in the yield due to négative impact on the yield as denoted by . the négative slope ofthe curves. Using table 1 and the graph 500(a), influence ofSulphur content on the yield of the distillâtes and residues may easily be understood. For example, for the graph _500(a), it can be understood that the Sulphur content will cause an increase in the yield

I , percentage for products falling in the température range of Initial Boiling Point (IBP) (hère 80 degree Celsius) to a température of 360 degree Celsius. Hencc, there will be an increase in the yield of Naphtha, Kcroscnc and Gas Oil for an increase in Sulphur for the oil sample. However, yield of Vacuum Residue, which is obtained for a température range of 565 degrees and above, will decrease for an increase in the Sulphur content of the oil sample. . · [0070] i Fig. 5(b) depicts the impact of API gravîty on the distillation profile for the oil sample via a graph 500(b). As shown by the slope of the curves 516 to 530, as the value of API gravîty increases for the oil sample, the impact on the yield for the température range of IBP to a température range of about 240 degree Celsius increases by a noticeable amount. That is, as the API gravîty increases for the oil sample, there is an increase in the yield of Naphtha and Kcrosene for the oil sample. As is also évident from the graph 500(b); there is a noticeable decrease in yield in the température ranges of about 240 degree Celsius to about 560 degree Celsius. Hence, it may be easily understood that as the API gravity for the oil sample increases there is à’decrease in the yietds of Gas oil and Vacuum Gas Oil. Also, as seen from the graph 500(b) the amoun’ of Vacuum residue does not change significantly with a change in the API gravity foir the oil sample. ‘ .

[0071] ^: -Fig. 6(a) depicts the influence of CCR content on the distillation profile in the form of the curves 602-616 in the graph 600(a). As is évident from the slopes of the curves 602616, for a température range of IBP to about 240 degree Celsius there is a négative impact on ’

yield for an increase in CCR content, i.e., the. amount of Naphtha and Kerosene produced decreases slightly for an increase in the CCR content Further, for a température range of about . 240 degrée Celsius to about 360 degree Celsius, there is a greater négative impact on the yields,

i.e., the amount ci Gas Oil production would decrease substantially for an increase in CCR ' content. Furthermore, the impact on yield of Vacuum Gas Oi! is slightly positive for an increase . in the value of CCR content as depicted from the section of the graph 600(a) between température ranges of about 360 degree Celsius to about 560 degree Celsius. Similarly, it can be ; seen that there is a considérable positive impact on yield of Vacuum Residue for an increase in

'. the value of CCR content for the oil sample, as is évident from the graph 600(a).

; [0072] ' · Thus, while Sulphur content and API gravity show a positive impact on the yields of Hghter fractions and négative impact on yields of heavier fractions, carbon content shows a négative impact on the yields of the lighter fractions and positive impact on yields of heavier fractions, as can be seen from graphs 500(a), 500(b) and 600(a). Moreover, the effect of carbon content is more pronouneed on' the yields obtained above 240 degree Celsius. Hence, by including a measure of carbon content, such as CCR, the prédiction model becomes more accurate. ' [0073] . Fig.' 6(b) depicts the combined effect of Sulphur content ànd CCR on Vacuum Residue for a given oil sample, throughthe curves 618-632 in thegraph 600(b). Vacuum Residue is the end product obtained in the process of Vacuum Distillation and hence, it affects 20 the refïnery profitability. Therefore, the refineries may want to select an oil sample that produces minimum amount of Vacuum Residue in order to meet refïnery constraints and for better refïnery profitability. The graph 600(b) is piotted between the amount of Sulphur content in weight percentage and the CCR content in weight percentage. The graph 6Q0(b) can be used in order to select any oil sample for obtaining Vacuum Residue less than a maximum acceptable amount as 25 per the refïnery constraints. . . ^: [0074] . Figs. 7(a), 7(b) and 8 depict the influence of three physîcal properties i.e. API gravity, Sulphur content and CCR content on the yield of spécifie distillation products for the oil sample. It may be noted by a person skilled in the art that such graphs may be obtained for other physical properties* influence on the distillation products as well.

[0075] Fig. 7(a) depicts the influence of API gravity for different distillâtes and residues for the oil sample. As depicted, the graph 700(a) shows a relation between the differentîal yields in weight percentage and the API gravity for different distillation products. As depicted by 702 and 704 respectively, the yields of Naphtha and Kerosene increase with an increase in the API

I ¹ gravity ofthe oil sample. Similarly, 708 and 710 respectively show a decrease in the yield of , Vacuum Gas Oil and Gas Oil for an increase in the value of- API gravity. Also, the yield of

Vacuum Residue is almost constant for an increase in the API gravity, as is shown by 706, This ' is inline with the impact on yield of API gravity as dépicted in Fig. 5(b).

[0076] ' Fig. 7(b) depicts the influence of Sulphur content for different distillâtes and « * i • residues for the oil sample. The graph 700(b) shows a relation between the differential yields in · '· weight percentage and the Sulphur content for different distillation products. As depicted by 712, ^: 714 and 716 respectively, the yields of Kerosene, Naphtha and Gas Oil increase with an increase in the Sulphur content of the oil sample, Similarly, 718 and 720 respectively show a decrease in • the yield of Vacuum Residue and Vacuum Gas Oil for an increase in the value of Sulphur. This

I * · is inline with the impact on yield of Sulphur content as depicted in Fig. 5(a). e ► ’ · [0077] Fig.'8 depicts the influence of CCR content on yields of different distillâtes and residues through a graph 800. It may be shown that the yields of Vacuum Residue and Vacuum Gas oil increase with an increase in CCR content. This is shown by 802 and 804 respectively. On a similar note, it is shown by curves 806,808 and 810 respectively, that there is a decrease in the . yields of Naphtha, Kerosene and Gas Oil for an increase in the value of CCR content for any oil sample. This is inline with the impact on yield of CCR as depicted in Fig. 6(a).

[0078] Thus, from thé various graphs discussed above, it can be seen that the distillation profiles can be correlated with'the physical parameters. In one example, the predicted yields may be directly proportional to the physical parameters. However, it will be understood that the proportionality constants may be either positivç or négative, thereby indicating a positive corrélation or négative corrélation, respectively. ' * [0079] ' Further, from Fig. 7(a), 7(b) and 8, it can be inferred that the increase in Naphtha and Kerosene .production is positively correlated to API gravity and Sulphur content and negatively correlated to Carbon Residue content Also, the increase in. Vacuum Gas Oil and Atmospheric Residue‘production is positively correlated to Carbon Residue content and is negatively correlated to API gravity and Sulphur content. Furthermore, the increase in Gas Oil production is positively correlated to Sulphur content and negatively correlated to API gravity and Carbon Residue content. Also, the increase in Vacuum Residue production is positively correlated to Carbon Residue content, negatively correlated to Sulphur content, and is negligibly dépendent on API gravity.

• i ‘· [0080] ‘ As mentioned above, the characteristic prédiction system 100 and methods 200 t ·* and 300 can be used for predicting any refining characteristic. In an embodiment of the présent .

. subject matter, method(s) and system(s) for predicting refinery processing cost as the refining · - characteristic has also been described. The refinery processing costs may include cost of > resources such as hydrogen, which is consumed in the hydro processing of gas oils derived from ? the oil sample and the cost of evacuating residue by means of cutter stocks (based on spécifie refinery configurations and assumptions). The method of predicting the refinery processing costs ’ ! _φ i may include development of a prédiction model based on régression as described with reference to method 200. The method may further include determining the physical properties of the oil sample and predicting the refining characteristics based on the developed prédiction model as described with reference to method 300. The détermination of the physical properties of the oil . sample includes determining at least one of CCR content, RCR and MCR. - ’ 1 [0081] . Fig.9 (a) depicts the influence of CCR content on the refinery processing cost for low Sulphur (S < 1 wt %) oil samples and high Sulphur (S > 1 wt %) oil samples. As previously described, the refinery, processing costs include costs for hydro processing and residue évacuation as fuel oils at cheaper prices for refineries where upgfadation facilities are unavailable. While evacuating residues as fuel oil, .valuablc distillâtes arc also being used as 20 cutter stocks, and hence downgraded in the process. Thus, predicting refinery costs may be used • for selecting oil samples having lesser processing costs. As depicted in the graph 900(a), for the t t low Sulphur oil sample, the refinery processing cost (in dollan/barrel) increases for an increase in the CCR content. Similarly, as shown, for high Sulphur oil samples as the CCR content increases, the refinery processing cost increases. Further, initia] refinery. processing cost for a 25 low Sulphur oil sample is lesser than that for a high Sulphur oil sample. However, lf the CCR content of the low sulphur oil sample is high, i.e. of the order of 2 wt% or more, then the refinery processing cost of the high CCR and low sulphur oil becomes équivalent to the initial refinery processing cost of high sulphur oil. Thus, use of CCR content helps in more accuratcly predicting the refinery processing cost. ' [0082] . In yêt another embodiment of the présent subject matter, method(s) and system(s) for ranking of crude oils as the refining characteristic, based on scénarios of crude oil quality and pricing, yield profile, spécifie refinery constraints and configuration has also been described. In . an implémentation, the ranking of the cnide oils may be predicted on the basis of crude price . différentiel and refinery processing cost differential. The crude price differential may be ί ' estimated'by calculating the differential of the price of the crude oil sample with respect to the price of a standard crude oil, such as the Brent Crude oil, for a barrel.' Further, the refinery processing cost differential may bc estimated by calculating the refinery processing cost differential of the crude oil with respect to the refinery processing cost of the standard cnide oil for a spécifie refinery. The method may further include prcdicting the ranking of the cnide oil '' based on the crude oil price differential and the processing cost differential. Furthermore, the

». ¹ ’ method may include determining the physical properties of the oil sample and predicting the refining characteristics based on the developed prédiction model. The détermination of the · physical properties of the oil sample includes determining at least Conradson Carbon Residue

- (CCR) content. .

[0083] ^; Fig 9 (b) depicts the ranking of crude oils vis-à-vis Brent crude price variations for given lcrude oils, e.g., Arab Mix (source; Saüdi Arabia), Brega (source: Libya), BH crude (source: India), Saharan Blend (source: Algeria) and Kuwait (source: Kuwait). The cnide price variations are due’to variation in net differential discounts, i.e., discounts due to crude oil qualifies and refinery processing cost, for various Brent crude price scénarios. As shown in the plot 900(b) the cross over in net margin (ranking of crude oils) is évident duc to variation in * - - ^r

Brent crude oil price. Thus, measurement of physical properties sample, including determining at ’ least CCR content,:RCR and MCR, can be used for the ranking of crude oils for net margins at refïneries with varying Brent crude pricing. The ranking of crude oils can be then used for sélection of appropriate blend of crude oil.

[0084] * Fig.' 10 illustrâtes an apparatus 1000 for testing an oil sample 1002 for 25 détermination of different physical properties of the oil sample 1002. The apparatus 1000 may comprise of a plurality of test cells, for example, an API test cell 1004, a CCR test cell 1006, and a Sulphur test cell 1008. Further, the apparatus may also comprise other test cell(s) 1010. The apparatus 1000 may be connected to the characteristic prédiction system 100, either through a ' network or directly, in order that the characteristic prédiction system 100 may receive determined values of the physical properties of the oil sample 1002 for the prédiction of refining characteristics of the oil sample 1002. .

[0085] In operation, the oil sample 1002 is fed into the apparatus 1000. The oil sample 1002 may be chahnelized into the apparatus 1000 to ensure that the oi! sample 1002 is iridividually fed into each test cell or the spécifie test cells selected by a user.

·. [0086] ^; The API test cell 1004 is configured to test the oil sample for détermination of the 5 API gravity of the oil sample 1002. The API test cell may be further configured to store the , determined value of the API gravity of the oil sample 1002 in order to provide the value of API , I gravity to the characteristic prédiction system 100. Similarly, the CCR test cell 1006 may be configured to deteimine and store the value of CCR content of the oil sample 1002. Further, the Sulphur test cell 1008 may be configured to détermine and store the value of Sulphur content of 10. the oil sample 1002. - ‘ ‘ [0087] Thus, the apparatus 1000 may be used in a laboratory for performing the tests on the oil sample 1002 for measurement of physical properties in order to predict the refining characteristics.ofthe oil sample 1002. Furthermore, the other test cell(s) 1010 may be configured to détermine and store the value of other physical parameters of the oil sample 1002, such as 15 Carbon content, Hydrogen content, Nitrogen content, Mercaptan value, Kinematic viscosity,

Pour point, Ramsbottm Carbon Residue (RCR), Micro Carbon Residue (MCR), Saturâtes, Aromatics, Resins, and Asphaltenes. . : ' [0088] ‘ In yet another embodiment of the présent subject matter, method(s) and system for predicting hydrogen consumption in hydro processing and intermediate refinery distiilaté 20 streams as the refining characteristic has also been described. The method of predicting the hydrogen ’ consumption in hydro processing (based on refinery spécifie configuration and assumptions) and intermediate refinery distiilaté streams, which may be referred to as the secondary processing characteristics, may include development of a prédiction model based on régression. The method may further include determining the physical properties ofthe oil sample 25 and predicting thé refining characteristics based on the developed prédiction model. The détermination of the physical properties of the oil sample includes determining at least CCR content, RCR and MCR [0089] . In a further embodiment, the refining characteristic includes potential of production of at least one of bitumen, Fuel Oil (FO) or Low Sulphur Heavy Stock (LSHS) from 30 the oil sample. The bitumen, FO or LSHS may be predicted using the prédiction of the vacuum residue as described earlier. The prédiction of bitumen. FO or LSHS may then be used to estimate the best possible utilization of the vacuum residue for maximum refinery profitability, [0090] ; Details of distillation profiles and qualifies, obtained as a resuit of using the above subject matter, as an experiment in à laboratory, for five unknown oil samples are listed below in Tables 4-8. As can be seen, the yield profiles and quality of the different distillate yields can be predicted based on the properties of the crude oil. The présent subject matter can also be used for estimation of critïcal physical properties of crude oils from measurement of at least one of CCR content, RCR and MCR of the oil sample. The crifical properties of crude oils inciude kinematic ^: viscosity, pour point, asphaltenes, mercaptan, volume average boiling point (VABP), molecular . weight and UOPK. These properties are valuabte for refinery process abîlity of crude oit samples. ' .

[0091] The quality of distillâtes prédiction, e.g., API, Sulphur, Cetane, Flash, Freezing, Smoke, viscosity, pour point, nitrogen, acidity and aniline of the distillâtes, are also important information for refinery processing for meeting the product and pricing for refining business - decisions.’ The prédiction of quality of residue, e.g., asphaltenes, CCR, API, and Sulphur of the residue for best utilization of réside material can be possible. The decision for résidé utilization • for suitability of bitumen production and fuel oïl or low Sulphur heavy stock (LSHS) production is important for value addition at refineries. This can be predicted through measurement of crude oil properties including at least one of CCR content, RCR and MCR. .

Table 4. Prédiction of distillation profile and qualifies- Sample t

Analyses Details	Unit	Crude	Naphtha	KERO	CO	AR	vco	VR
Cuts	. -	-	IBP-I40	140- 240	240-360	360+'	360-565	363+
Yield ‘ !	%wt	. -	8.19	16.87	34.74	3725	29.35	7.9
%vol		10.17	18.02	34.57	3429	27.38 .	6.91
API Gravity	-	28.37	68.4	39.44		15.77 ‘	17.73	8.35
Sulphur '	%wt	0.273	0.0007	0.0682	0.26	0.479	0.4 i 60	0.65
i Mercaptan :	ppm	18	1.98	8.6	-	- '	-	-
KV@40C	cSt	6.05	-	-	5.305	33.4 @100	14.43 @ 100	-

Pour Point	C	-53	•	•	-40	40 ·	29.2	69
Acidity .	mg/KOH	0.67		0.275	0.586	i	1.06
Total Nitrogen	‘ ppm	1600	-	-	-	* 1	-
t · Basic Nitrogen	ppm	530	-	-	-		842.9 .
Freezing Point	C	- '	-	-77.33 '	-	• 1	-
Smoke Point'	mm	-	-	24	• .		-
Flash Point i	. C	-	0.2	48.4	127		-
1 Cetane Index	-	-	•	32.76	42.75		-
Aniline Point	C	‘ -	-	•	60.25		-
Conradson Carbon	%wt	1.339		-	0.0472	3.10	0.421
.Asphaltenes !	%wt		-	-	-	0.750	4.4

Table 5. Prédiction of distillation profile and qualifies-Sample 2
Analyses Details	Unit	Crude	Naphtha	Kerosene	GO	AR	VGO	VR
cuts	’ -	-	r 1BP-I40	140-240	240- 360	360+	' 360-565	565 +
Yield	%wt	-	0.54	9.26	47.00	42.42	36.04	6.38
%vol	-	0.64	10.05	47.48	41.07	35.06	6.01
API Gravity	-	21.94	49.90	32.65	23.47	16.97	17.88	12.99
Sulphur	%wt	0.12	0.01	0.02	0.06	0.19	0.17	0.26
Mercaptan	ppm	15.00	4.83 -		-	-	-	-
KV @ 40C	' cSt	18.21	-	-	6.85	18.35	11.62 @ I0OC	•
Pourpoint	C	' -48.00	-	-	<-50	-13.00	-25.00	17.00
Acidity ·	mg/KOH	0.43		0.04	0.31	-	059	•
Total Nitrogen	ppm		-	·	-	-	-	-
Basic Nitrogen *	Ppm	338.00	-			-	560.00	-
Freezing Point	C	-	-		-	-	-	-

Smoke Point	. mm	-	-	21.00 .	•	-	•	•
Flash Point	. C	-	0.10	57.00	130.00	•	-	-
Octane Index	‘ -	-	-	2722	56.32	-	-	-
Aniline Point	C	•	-	-	53.50	-	-	-
Conradson Carbon	%wt	0.69	-	-	0.01	1.20	0.30	10.03

- “ “ ' 1-	‘Table 6	Prédiction of distillation profile and qualifies- Sample 3
' Analyses Details	Unit	Crude	Naphtha	Kerosene	GO	AR	VGO	VR
Cuts t	-	-	IBP-140	140-240	240- 360	360+	360-565	565+
Yield '	%wt	- .	3.085	4.9	13.61	78.43	31.79	46.64
%vol	-	3.766	5.64	14.64	75.14	32	43.14
APIGravity .·	- ·	25.59	64.3	48.72	38.08	Ï92	27.07	13.83
Sulphur	%wt	0.117	0.0001	0.0217	0.0576	0.143	0.0959	0.174
Mercaptan	PPm	-	0.955	4	-	-	- ’	-
KV@40C	: cSt	26.01 @ I00C	-	•	5.2114	-	7.53@ IOOC	-
Pour Point	C	32	•	-	0	40	37	42
Acidity	mg/KOH	3.83	-	0.49	1.17	-	4.32
Total Nitrogen	PPm	2250	-	-	-	• .	-	-
' Smoke Point : ’	mm	-	-	33	•	-	-	•
Flash Point '	C	' -	-6.9	65.3		-	• .	-
Octane Index '	-	•	•	53.95	66.76	•	-	•
Aniline Point	C	-	-	•	5625	’ -	-	-
Conradson Carbon	%wt	10.13	-	•	0.0943	10.32	0.2609	16.95

Table 7. Prédiction of distillation profile and qualifies- Sample 4
Analyses Details	Unit	Crude	Naphtha	Kerosene	GO	AR	VGO	VR

cuti;	l	-	IBP-140	I40-24Ù '	240- 360	360+	360-565	1 565+
Yield . '	%wt	•	7.352	16.05 '	21.49	48.28	26.64	21.64
%vol	-	9.693	17.76	21.75	42.82	24.88	17.94
API Gravity ;	’ -	31.40	82.8	48.76	33.33	13.06	20.72 ’ .	3.55
Sulphur	; %Wt	2.75	0.017	0.163	1.9	4.603	3.4710	6.074
Mercaptan	PPm			3.4	' -	•	-	-
kv @4oc ;	: . cSt	7.61		-	4.622	-	8.979 @100C	. -
1 Pour Point	; c	-61.0		- '	-16.0	33	26.7	40
Acldity ;	mg/KOH	0.24		0.1328	0.1715	-	0.41	•
Total Nitrogen '	ppm	912		. -	-	-	. 1085	-
Basic Nitrogen	PPm	218		-	-	-	264	- .
Freezlng Point	C	-		-55.77	-	-	-
Smoke Point	mm	-		20	-	-	-	-
Flash Point	C	•	<0	46.60		-	-	- ’
Cetane Index*	-	-	-	35.59	49.20	-	-	•
Aniline Point	C		-	•	61.50	-	-	-
Conradson Carbon	%wt	5.69	-	•	0.0598	11.37	0.80 '	24.75
Asphaltenes '	%wt	-	’ -	•	-	7.170	- '	13.88

'Table8. Prédiction of distillation profile and qualltles- Sample S ·
Analyses Details	Unit	Crude	Naphtha	Kerosene	GO	ar :	VGO	VR
cuts.	-	-	IBP-140	140-240	240-360	360+	360-565	565+
. . - . . Yield ,	Wwt	-	8.27	15.15	21.96	52.47'	29.22	2325
Wvol	-	10.14	16.67	27.79	4820	27.67	20.53
API Gravity	-	29.66	80.8	45.35	32.39	15.22	2127	. 723
Sulphur .	Wwt	2.191	0.021	0.2947	12292	2.939	2259	3.759
Mercaptan	ppm	2350	46.8	233	-	- '	-	• -
KV@40C	cSt	9.736	•	-	4263	- '	4.1 @ I00C	-
PourPoint 1	C	-18.00	-	-	-18.0		39.00	51.00
Acidity ·	rng/KOH	1.956		0.636 -		2.73	3.82	-
Total Nitrogen	PPm			• -	-	-	-	• -
Basic Nitrogen	' PPm	270	-	-	-	5145	3550	•
Freezing Point	C	-	-	-66.1	-	. ·	-	-
Stnoke Point	mm	•	-	26.00	- -	•		•
Flash Point	C	-	<0	50.4	126.90	- :	-	-
Cetane Index '	-’	- ·	-	42.63	51.87	• ·	- .	-
Aniline Point	C	-	•	5625	67.15	- ‘	-	-
Conradson Carbon	%wt	432	-	-		-	028	' 18.8
Asphaitenes	; %wt	0.851	-	-	-	- ·	-	9.7

[0092] Although implémentations for prédiction of refîning characteristics of oil hâve been described in language spécifie to structural features and/or methods, it is to be understood 5 that the appended claims are not nccessarily limited to the spécifie features or methods described. Rather, the spécifie features and methods are disclosed as exemplary implémentations * i for prédiction of refîning characteristics of oil. ' .

Claims (25)

1. AMENDED CLAIMS received by the International Bureau on 23 July 2013 (23.07.13)

   1. A method for predicting a refining'characteristic of an oil sample .of an unknown oil for planning, controlling, and optimizing refïnery 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 ofthe oil sample based on a prédiction model by using the received values as an tnput to the prédiction model, wherein the prédiction . model is based on coefficients of régression obtained from cotTelating the refining characteristic with the plurality of physical properties for known crude oils and wherein the ' refining characteristic is a characteristic ofthe unknown oil used for planning, controlling, ' and optimizing refïnery operation ofthe unknown oil. . .
2. 2. The method as claimed in daim 1, wherein thé unknown oil is at least one of a crude oil, synthetic crude oil, an unknown hydrocarbon mixture, and a combination thereof.
3. 3. The method as claimed in daim 1» wherein the refining characteristic is at least one of , ' distillate yield profile, residue yield profite, processability, product qualifies, hydrogen consumption in hydro processing, refïnery processing cost, and ranking of the unknown oit.
4. 4. The method as claimed in daim 1, wherein the determining the refining characteristic further ¹ I * ' comprises determining potential of production of at least one of bitumen, Fuel Oil and Low Sulphur Heavy Stock from the oil sample. .
5. 5. The method as claimed in claim 1, wherein the refining characteristic comprises at least one of Volume Average Boiting Point (VABP), Universal Oil Characterizatioii Factor (UOP-k), Mean Average Boiting Point (MEABP), molecular weight, kinematic viscosity, asphaltenes, pour point, and mercaptan content of the unknown oil. .
6. 6. The method as claimed in daim 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, Saturâtes, Aromatics, Resins, and Asphaltenes.

   AMENDED SHEET (ARTICLE 19)
7. 7. The method as claimed in claim 1, wherein the coefficients of régression are calculated based on one of linear régression and non-linear régression.
8. 8. The method as claimed in claim 1, wherein the determining the refîning characteristic includes deriving a yield profile with respect to température ranges of the oil sample.
9. 9. The method as claimed In claim 8, wherein the yield profile includes yield profile-for f ' distillâtes, and wherein the distillâtes include at least one of Naphtha, Kerosene, Gas oîl, and Vacuum Gas oil. ' .
10. 10. 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 résidu^.
11. 11. 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 ncgatively correlated to Carbon Residue Content.
12. 12. 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. '*· ·
13. 13. 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 -·.
14. 14. 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 negligîbly dépendent on API gravity..
15. 15. A characteristics prédiction system (100) for predicting the refîning characteristic of an oil sample of an unknown oil to plan, control, and optîmize refînery operation of the unknown oil, the characteristics prédiction system (100) comprising:

    a proccssor (104);' an interface (106); anda memory (102) coupled to the processor (104), the memory (102) comprising:

    a receiving module (110) configured to receive values of a plurality of physical propertiès of the oil sample, wherein the physical properties includes at least one of

    AMENDED SHEET (ARTICLE 19)

    Conradson Carbon Residue (CCR) content, Ramsbottom Carbon Residue (RCR) and Micro Carbon Residue (MCR); .

    a régression module (112) configured to compute coefficients of régression based on corrélation régression of the plurality of physical properties and refining characteristics of ' known crude oils stored in a Crude oil database (124); and * l « a prédiction 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 régression, wherein the refining characteristic is a characteristic of the unknown oil used for' planning, controlling,'and optimizing refinery. operation of the unknown oil.
16. 16. The system as claimed in claim 15, wherein the prédiction 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. .
17. 17. The system as claimed in claim 15, wherein the refining characteristic comprises at least one of distillate yield profile, residue yield profile, processability, product qualifies, hydrogen , consumption in hydro processing, refinery processing cost and ranking of the oil sample.
18. 18. 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, kinematîc viscosity, asphaltenes, pour point, and mercaptan content of the oil sample.
19. 19. 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.
20. 20. 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, AP1 gravity, Mercaptan value, Kinematic viscosity, Pour point, Saturâtes, Aromatics, Resins and Asphaltenes.
21. 21. The system as claimed in claim 15, wherein the régression module (112) is further configured to generate a yield prédiction model based on a positive corrélation of increase in Vacuum Gas Oil production, Atmospheric Residue production, and Vacuum Residue production with Conradson Carbon Residue (CCR) content.

    AMENDED SHEET (ARTICLE 19)
22. 22. The system as claimed in claim 15, wherein the régression module (112) is further configured to generate the yield prédiction model based on négative corrélation of increase in Naphtha production, Kerosene production and Gas Oit production with Conradson Carbon Residue (CCR) content. .
23. 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 ofthe oil sample (1002); .’ a Sulphur test cell (1008) configured to test the oit sample (1002) for determining Sulphur content ofthe oil sample (1002); . ..

    . . wherein the apparatus (1000) is configured to provide the API gravity of the oit sample, the CCR content of the oit sample, and the Sulphur content of the oil sample to a characteristic prédiction 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.
24. 24. 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 détermine at leastoneof Carbon content,' Hydrogen content, Nitrogen content, Mercaptan value, Kinematic viscosity, ♦ ¹

    Pour point, Ramsbottom Carbon Residue (RCR), Micro Carbon Residue (MCR), Saturâtes, Aromatics, Resins, and Asphaltenes of the oit sample (1002).
25. 25. The apparatus (1000) as claimed in claim 23, wherein the characteristic prédiction System (100) configured tô predict the refining characteristics based on a prédiction model by using . the API gravity of the oit sample, the CCR content of the oil sample, and the Sulphur content of the oil sample as an input to the prédiction model, wherein the prédiction model Îs based on coefficients of régression 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

    AMENDED SHEET (ARTICLE 19)

    31 · • content, Hydrogen content, Nitrogen content, API gravity, Mercaptan value, Kinematic viscosity, Pour point, Saturâtes, Aromatics, Resins, and Asphaltenes.