Method for estimating sludge precipitation amount

Abstract

To provide a method of accurately estimating the amount of sludge deposition when mixing crude oils, a device for implementing the same, system, computer, method of using the same, computer program for making the computer function as the device, and storage medium therefor.SOLUTION: A method of estimating the amount of sludge deposition in a crude oil mixture is provided, the method comprising: identifying a sludge deposition amount estimation model for a plurality of reference crude oil mixtures on the basis of mixture composition information and mixture physical property information including the sludge deposition amount; and estimating the sludge deposition amount based on the sludge deposition amount estimation model.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a method for estimating the amount of sludge precipitation during crude oil mixing, an apparatus, a system, a computer, and a method of using the same, as well as a computer program for causing a computer to execute the apparatus and a recording medium thereof.

Background Art

[0002] In a crude oil tank for storing crude oil, a paste-like substance called sludge accumulates. Components of the sludge accumulated in the crude oil tank include wax, asphaltene, resin, saturated components, aromatic components, etc. caused by "petroleum fractions", and water, iron (rust), earth and sand, etc. caused by "impurities". Such sludge is generally a concern for the following effects on facilities. That is, when sludge accumulates in the crude oil tank and is sent to downstream processes for refining crude oil, it may cause failures of crude oil supply pumps, clogging of heat exchangers and filters, and adverse effects on catalysts in reactors. In addition, the sludge accumulated on the tank bottom plate may also cause corrosion.

[0003] On the other hand, much of the unutilized crude oil in the country is classified as heavy or extra-heavy crude oil. When these unutilized crude oils are used at domestic refineries due to their high viscosity and high sulfur content, it is necessary to adjust their properties by mixing them with relatively light conventional crude oils. However, mixing unutilized crude oil and conventional crude oil may cause sludge to precipitate, raising concerns about deposition in storage tanks and blockage of heat exchangers and the like. Therefore, it has been previously considered to predict the amount of sludge precipitation during the mixing of feed oils in advance to avoid economic damage caused by sludge.

[0004] For example, Patent Document 1 discloses that when mixing two or more types of petroleum, in order for asphaltene to maintain a solute state, petroleum is combined using the insoluble value IN and the solubility blend number SBN as indices.

Prior Art Documents

Patent Documents

[0005] Patent Document 1 Japanese Patent Publication No. 2001-505953 Summary of the Invention Problems to be Solved by the Invention

[0006] However, as a result of investigations by the applicant, it has become clear that depending on the mixing ratio of crude oil, etc., it may be difficult to accurately estimate the amount of sludge precipitation using the insoluble value IN and the solubility blend value SBN as indices. Furthermore, as a result of intensive investigations by the applicant, it has been found that by using a sludge precipitation amount estimation model preset with reference to the mixing composition information and the mixture property information including the sludge precipitation amount for a plurality of reference crude oil mixtures, the amount of sludge precipitation during crude oil mixing can be estimated with high accuracy. The present invention is based on such findings.

[0007] Therefore, one object of the present invention is to provide a new technical means for accurately estimating the amount of sludge precipitation during crude oil mixing.

[0008] To achieve the above object, the present inventors have created the following present invention. That is, the gist of the present invention is as follows. In one embodiment of the present invention, there is provided a method for estimating the amount of sludge precipitation in a crude oil mixture, including the step of estimating the amount of sludge precipitation in the crude oil mixture based on a device that stores a sludge precipitation amount estimation model preset with reference to the mixing composition information and the mixture property information including the sludge precipitation amount for a plurality of reference crude oil mixtures.

[0009] Also, in another embodiment of the present invention, there are provided an apparatus and a system for estimating the amount of sludge precipitation in a crude oil mixture, their operation methods, a computer program for executing them, a recording medium thereof, and a computer storing the same. Advantages of the Invention

[0010] According to the present invention, it is possible to accurately estimate the amount of sludge precipitation during crude oil mixing.

Brief Description of the Drawings

[0011]

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Mode for Carrying Out the Invention

[0012] <Definition> In explaining the embodiments of the present invention, first, the terms or expressions used in this specification will be explained.

[0013] (1) "Petroleum" In this specification, "petroleum" is a general term concept including crude oil, various fractions obtained by distilling crude oil, and fractions obtained by subjecting the various fractions to treatments by secondary devices such as reforming and decomposition. Alternatively, it may refer to a fraction obtained by further fractionating a certain fraction obtained by distilling crude oil into components such as saturated hydrocarbons and aromatic hydrocarbons. (2) "Component" "Component" means "a lump grouped based on certain specific physical or chemical properties of a mixture", that is, "a fraction fractionated based on certain specific physical or chemical properties". Examples of methods for grouping based on specific physical or chemical properties include, for example, specifying the boiling point range in a distillation test and fractionating those within that temperature range as one component. In this case, the mixture would be "an aggregate of fractions". Alternatively, "component" may be regarded as each constituent member that makes up a multi-component mixture and is "an aggregate of molecules recognized as belonging to the same molecular species". Here, "the same" may be understood to mean "perfectly specifying the molecular structure and being the same thereon", or "isomers in terms of molecular structure (having the same molecular formula but different structures) are regarded as the same", and for example, it may be understood to mean "being the same in the structure specified by a method such as JACD described later". Furthermore, it may be broadly understood to mean "an aggregate of molecules grouped based on arbitrarily determined criteria".

[0014] (3) "Compose" To "compose" a multi-component mixture such as petroleum does not necessarily mean assuming all 100% of the components present in the multi-component mixture. Depending on how the molecular structures of the respective components specified by the present invention are utilized and according to the degree of detail required for specifying molecular species as components, the "components that compose" may be appropriately determined. For example, only molecular species having a certain abundance (presence ratio) or more in the multi-component mixture may be regarded as the "components that compose". The necessity of identifying the molecular structures of all the huge variety of molecular species such as in petroleum is not always high, and for molecular species that exist only in trace amounts, they may be ignored if necessary. For example, when targeting polycyclic aromatic resin components (PA) as the "multi-component mixture", the presence of paraffinic compounds and olefinic compounds may be ignored as components that compose PA.

[0015] (4) "Fraction" "Fraction" may be anything that indicates the proportion of existence, such as mass fraction, volume fraction, or molar fraction, and is a concept that includes all of them. When calculating the average Hansen solubility index value of the entire liquid phase, preferably volume fraction is used, and it is calculated as a weighted average value weighted by the volume fraction of each component in the liquid phase.

[0016] (5) "Specify the molecular structure", "molecule" "Specify the molecular structure" encompasses any act of specifying any information regarding the structure of a "molecule" in the above "component". Depending on the purpose and necessity, the degree and the way of representation may be appropriately selected. It is not only the act of specifying the structure of the entire molecule, but information regarding the structure of a part of the molecule may also be incorporated. For example, only the structure of the core part may be specified, and the side chain part and the cross-linked part may be left as the molecular formula without specifying the structure.

[0017] In this specification, preferably, the molecular structure is specified by "JACD" described later. A molecule whose structure is specified by "JACD" is a concept that includes all isomers due to differences in the bonding positions of the attributes described later. In this specification, "molecule" may be regarded as a concept that includes all isomers.

[0018] (6) "Specify the proportion of each component present" "Specify the proportion of each component present" encompasses any act of specifying the ratio in which each component constituting the mixture exists. Also, it does not mean that the proportion of all component species constituting the mixture must be specified. Only when the proportion of all components including those components that are present in such a small amount that detection is difficult by the analysis technique or components that do not need to be specified is specified, it is considered that "the proportion of each component present has been specified". Such trace components etc. may be collectively treated as "other components". Furthermore, these may be excluded from the scope of "each component constituting the mixture" and may not be included in the denominator when calculating the proportion of other components.

[0019] (7) "All" In this specification, "all" does not necessarily mean "100% all". For example, for a statement about "all peaks" in a mass spectrum, it not only literally means "100% all peaks", but also, for example, for peaks related to molecules that are not necessarily required for the purpose of consideration in that scenario, or peaks that are difficult to distinguish, etc., it may be construed as referring to the other peaks after appropriately excluding them.

[0020] (8) "Peak" The horizontal axis of the peak obtained in mass spectrometry is the m / z for the molecular ion or pseudomolecular ion of each component constituting the multi-component mixture. Since the numerical value indicated by this m / z corresponds to the mass of the molecular ion or pseudomolecular ion, it generally represents the molecular weight of the molecule attributed to that peak. In this specification, this "peak of m / z for the molecular ion or pseudomolecular ion obtained in mass spectrometry" may be referred to as "the peak obtained in mass spectrometry" or simply "peak". Also, the height of the peak indicates the relative abundance of the molecule attributed to that peak.

[0021] (9) "Molecular formula" The "molecular formula" refers to a formula that only indicates the types and numbers of elements constituting a molecule, and refers to something whose structure is not specified. Since the types and numbers of elements constituting the molecule are known, information such as the molecular weight and the DBE value described later can be obtained. In the mass spectrometry using the Fourier transform ion cyclotron resonance method (hereinafter also referred to as "FT-ICR MS") mainly used in the present invention, the value of m / z can be determined up to the fourth decimal place. Therefore, by performing precise mass alignment considering the existence of atomic isotopes, the molecular formula of the molecule attributed to the peak can be determined. Since the molecular formula only represents the types and numbers of elements constituting the molecule, there may be multiple isomers as the molecules corresponding to the determined molecular formula. That is, a single peak may be attributed to multiple isomers with the same molecular formula.

[0022] However, due to the characteristics of FT-ICR MS, even if the molecular formulas are the same, for example, if a hydrogen ion is added to the molecular ion, the mass will be different from the original molecular ion, and thus it may appear as a different peak. Therefore, even if it appears as a different peak in the measurement, those with the same types and numbers of elements constituting the molecular formula may be regarded as "the same molecular formula". In the phrase "the molecule corresponding to the molecular formula", "the molecular formula" may be understood in the sense of such "the same molecular formula". Also, in the case of "a certain peak", it may be considered as a concept that collectively captures all the various m / z peaks regarded as representing "the same molecular formula" in the above sense.

[0023] (10) "Core", "Single core", "Double core", "Hetero core" "Core" is a type of "attribute" described in the "JACD" section below. Specifically, it is a heterocyclic ring or naphthene ring itself, a structure in which a heterocyclic ring and a naphthene ring are directly bonded rather than crosslinked, or a structure in which an aromatic ring is directly bonded to a heterocyclic ring or naphthene ring rather than crosslinked. Since crosslinks or side chains are attributes different from the core, "core" means something that has no crosslinks or side chains at all.

[0024] On the one hand, "single core" refers to the concept of a molecule having only one core. Since it is a concept referring to a molecule, it includes those in which side chains are bonded to the core. A molecule formed by cross-linking two or more of the above cores is called a "multi-core". Since "multi-core" also means a molecule, it includes those in which side chains are bonded to the core. A molecule formed by cross-linking two cores is called a "double core". For example, the naphthalene molecule shown below consists of one aromatic ring, so it is a "single core" and not a double core consisting of two benzene rings.

[0025] [Chemical formula] In addition, a core containing a heteroatom is also referred to as a "hetero-core".

[0026] (11) "DBE value" The "DBE value" is the value calculated by the following formula (1) when the molecular formula is "C c H h N n O o S s ". DBE = c - h / 2 + n / 2 + 1 ···(1) (In the formula, c represents the number of carbon atoms, h represents the number of hydrogen atoms, n represents the number of nitrogen atoms, o represents the number of oxygen atoms, and s represents the number of sulfur atoms.) This value generally indicates the degree of unsaturation in the molecule, especially the presence of double bonds and rings.

[0027] (12) "JACD (Jack D)" "Juxtaposed Attributes for Chemical-structure Description)" "JACD" is a novel display method for molecular structures, which displays the structure of a molecule by the type and number of attributes. It does not display at which position of any other attribute an attribute is bonded.

[0028] In the above, "attribute" refers to the concept that indicates the parts on the chemical structure that constitute the molecule. In the case of aromatic compounds, specifically, it refers to the aforementioned "core", "bridge", and "side chain". According to this display method, regarding each of the huge number of molecules that make up petroleum, their structures can be specified to a necessary and sufficient extent. Taking the molecule represented by the following chemical formula as an example for explanation.

[0029] [Chemical formula]

[0030] When this compound is represented by JACD, it becomes as shown in Table 1 below.

[0031] [Table 1]

[0032] The molecule represented by JACD and with its structure specified is a concept that includes all isomers due to the differences in the bonding positions of the attributes.

[0033] (13) "Physical property value" "Physical property value" refers to anything that expresses the physical, chemical properties, characteristics, or traits of a substance, regardless of its name, and is included in "physical property value". In this specification, "physical property value" is not limited to these, but for example, melting point, Hansen solubility index value, Gibbs free energy of formation, ionization potential, polarizability, dielectric constant, vapor pressure, liquid density, API gravity, gas viscosity, liquid viscosity, surface tension, boiling point, critical temperature, critical pressure, critical volume, heat of formation, heat capacity, dipole moment, enthalpy, entropy, etc.

[0034] (14) "Equipment related to petroleum" In this specification, the "equipment related to petroleum" includes all equipment related to the treatment of petroleum, such as distillation equipment, extraction equipment, and equipment involving chemical reactions such as reforming equipment, hydrogenation reaction equipment, and desulfurization equipment. Collectively, the "equipment related to petroleum" is also referred to as the "petroleum refining equipment".

[0035] <Method for estimating sludge precipitation amount in crude oil mixture> According to one embodiment of the present invention, a method for estimating the sludge precipitation amount in a crude oil mixture includes a step of estimating the sludge precipitation amount in the above crude oil mixture based on a device that stores an estimation model of the sludge precipitation amount preset with reference to mixture property information including mixing composition information and sludge precipitation amount for a plurality of reference crude oil mixtures.

[0036] It is an unexpected fact for those skilled in the art that by specifying an estimation model with reference to mixture property information including mixing composition information and sludge precipitation amount for a plurality of reference crude oil mixtures, it is possible to achieve highly accurate estimation of the sludge precipitation amount in the crude oil mixture to be inspected.

[0037] According to one embodiment of the present invention, the crude oil constituting the crude oil mixture to be inspected is two or more types of crude oil including light oil and heavy oil. The above heavy oil is preferably a fraction having a boiling point higher than the boiling point (360°C) of light oil conforming to JIS K2254. Specific examples of the residual oil from atmospheric distillation include heavy oil (boiling point: about 400°C or higher) obtained from the bottom of the atmospheric distillation of crude oil, etc., and heavy oil is referred to as atmospheric residue.

[0038] According to one embodiment of the present invention, from the perspective of achieving highly accurate estimation, the types and numbers of crude oils in the plurality of reference crude oil mixtures used for setting the estimation model of the sludge precipitation amount are preferably the same as those of the crude oils constituting the crude oil mixture to be inspected. For example, when the crude oil constituting the crude oil mixture to be inspected consists of two types, light oil and heavy oil, the crude oils constituting the plurality of reference crude oil mixtures are preferably two types, light oil and heavy oil.

[0039] Further, according to a preferred embodiment of the present invention, at least one of the crude oils in the plurality of reference crude oil mixtures used for setting the sludge precipitation amount estimation model is preferably of the same type or the same as the crude oil constituting the crude oil mixture to be tested. For example, when the crude oil mixture to be tested is composed of two types, light oil α and heavy oil A, the plurality of reference crude oil mixtures are preferably composed of the light oil α contained in the object to be tested and heavy oils B, C, or D that are not contained in the object to be tested.

[0040] Further, according to a preferred embodiment of the present invention, the number of reference crude oil mixtures is not particularly limited, but from the viewpoint of ensuring estimation accuracy, it is preferably set within a range where a correlation is confirmed.

[0041] Also, the mixing composition information in the plurality of reference crude oil mixtures preferably includes, in addition to the types and numbers of the crude oils constituting the crude oil mixture as described above, the mixing ratio of the crude oils to be mixed. From the viewpoint of setting a highly accurate sludge precipitation amount estimation model, the plurality of reference crude oil mixtures preferably include not only mixtures with the same mixing ratio as the crude oil mixture but also mixtures with a plurality of different mixing ratios.

[0042] Therefore, according to a preferred embodiment of the present invention, the mixing composition information and the sludge precipitation amount mixing composition information include the types of each crude oil and the mixing ratio of the crude oils in the plurality of reference crude oil mixtures.

[0043] In addition, as suitable examples of the mixture property information in a plurality of reference crude oil mixtures, in addition to the mixing composition information and the sludge precipitation amount as described above, for example, the insolubility value (IN), the solubility blend value (SBN), the ratio of the insolubility value (IN) to the solubility blend value (SBN) (IN / SBN), the asphaltene amount, the Hansen solubility index value (hereinafter, also referred to as "HSP value"), the average degree of aggregation, etc. in the crude oil mixture can be mentioned. For example, as shown in FIGS. 8A and 8B described later, a relationship diagram plotting the ratio (IN / SBN) of the insolubility value (IN) to the solubility blend value (SBN) in the crude oil mixture, the asphaltene amount in the crude oil mixture, the crude oil mixing ratio, and the sludge precipitation amount, and whether the predicted value by the candidate estimation model correlates with the sludge precipitation amount (measured value) in the reference crude oil mixture is confirmed, and the sludge precipitation amount estimation model may be appropriately set. Further, for example, as shown in FIGS. 9 and 10 described later, the estimation model may be appropriately set using the Hansen solubility index value and the average degree of aggregation as indices.

[0044] In addition, in setting the sludge amount estimation model, a range or threshold value in which the predicted value by the candidate estimation model correlates with the sludge precipitation amount (measured value) in the reference crude oil mixture is specified, and the sludge amount estimation model may be set within the specific range. Therefore, according to a preferred embodiment of the present invention, the sludge precipitation amount estimation model is set based on the threshold value or range of the sludge precipitation amount in the crude oil mixture.

[0045] The method for estimating the sludge precipitation amount in the crude oil mixture of the present invention may be carried out in two steps of specifying an estimation model for the sludge precipitation amount and estimating the sludge precipitation amount using the estimation model, as shown in FIG. 1. Therefore, according to a preferred embodiment of the present invention, the method for estimating the sludge precipitation amount in the crude oil mixture is (A) a step of specifying an estimation model for the sludge precipitation amount based on the mixture property information including the mixing composition information and the sludge precipitation amount for a plurality of reference crude oil mixtures, and (B) a step of estimating the sludge precipitation amount based on the sludge precipitation amount estimation model.

[0046] The sludge precipitation amount estimation model may be selected from known models by the above method, or may be created by statistical analysis or the like based on the above mixture property information.

[0047] <Weihe method> According to an embodiment of the present invention, the sludge precipitation amount estimation model specified by the above method is a model for the sludge precipitation amount in a crude oil mixture based on the correlation between the ratio (IN / SBN) of the insolubility value (IN) to the solubility blend value (SBN) in the crude oil mixture and the sludge precipitation amount. Such a sludge precipitation amount estimation model is also called the Weihe method and can be implemented according to the description in Irwin A. Wiehe, Raymond J Kennedy, G. Dickakian, EnergyFuels, 2001, 15, 5, 1057 - 1058 or Patent Document 1. The disclosure contents of these documents are incorporated herein by reference.

[0048] According to an embodiment of the present invention, in the Weihe method, first, the asphaltene amount is determined. The asphaltene amount information in each crude oil can be obtained by a known measurement method. For example, n - heptane is added to the crude oil, an air - cooling tube is attached, the mixture is reflux - boiled with an n - heptane insolubles tester, allowed to cool, and the asphaltene fraction can be separated from the obtained mixed solution using filter paper. Also, the alpha - ltene amount may use information described in known documents or the like.

[0049] (Insolubility value (IN) and solubility blend value (SBN)) According to an embodiment of the present invention, in order to determine the insolubility value and the solubility blend value for a crude oil containing asphaltene, it is necessary to test the solubility of the crude oil in the test liquid mixture at at least two volume ratios of the crude oil to the test liquid mixture. The test liquid mixture is prepared by mixing two liquids in various ratios, but it is preferable to select the same n - heptane as the test non - solvent and toluene as the test solvent.

[0050] In the initial test, the volume ratio of crude oil to the test liquid mixture is selected for convenience. For example, it is 5 ml of the test liquid mixture per 1 ml of crude oil. Next, various mixtures of the test liquid are prepared by blending n-heptane and toluene in various known ratios. The crude oil and the test liquid mixture are mixed so as to have a predetermined volume ratio. Then, it is determined whether the asphaltene is soluble or insoluble. Usually, a light microscopy method is applied using an optical microscope with a magnification of 50 to 600 times, and one drop of the blend of the test liquid mixture and the crude oil is placed between a glass slide and a glass cover slip and observed by transmitted light.

[0051] Also, it is preferable to use a spot test method in which one drop of the blend of the test liquid mixture and the crude oil is dropped onto a piece of filter paper and then dried. Determine the minimum percentage of toluene that dissolves the asphaltene and the maximum percentage of toluene that precipitates the asphaltene, prepare more test liquid mixtures at toluene percentages between these limit values, blend with the oil at a predetermined volume ratio of the oil to the test liquid mixture, and determine whether the asphaltene is soluble or insoluble. This process is continued until the target value is determined within the desired accuracy range. Finally, the average value of the minimum percentage of toluene that dissolves the asphaltene and the maximum percentage of toluene that precipitates the asphaltene is taken as the target value. This is the first data point T1 at a predetermined volume ratio R1 of the crude oil to the test liquid mixture.

[0052] The second data point can be determined by selecting a different volume ratio of the crude oil to the test liquid mixture and performing the same process as in the case of the first data point. In addition, a toluene percentage smaller than the toluene percentage that determined the first data point can be selected, and n-heptane can be added to a known amount of crude oil until the asphaltene just starts to precipitate. The volume ratio R2 of the crude oil to the test liquid mixture at a predetermined toluene percentage T2 in the test liquid mixture at this point becomes the second data point.

[0053] Preferably, the insolubility number IN is given by the following formula:

Equation

[0054] (mixture of crude oils) Once the solubility blend value for each crude oil is determined, the solubility blend value for the mixture of crude oils is given by the following formula.

[0055] [Number] In the formula, V1 is the volume of crude oil 1 in the mixture.

[0056] The criterion for determining the compatibility of a mixture of crude oils is that the solubility blend value of the mixture of crude oils is greater than the insoluble value of any of the crude oils in the mixture. Therefore, if the solubility blend value of any of the component oils in the blend of crude oils is less than or equal to the insoluble value of any of the components in the blend, this blend may exhibit incompatibility.

[0057] The range to which the Weihe method is applied is not particularly limited, but from the viewpoint of highly accurate estimation of the sludge precipitation amount in a crude oil mixture, it is preferably in the range where the sludge precipitation amount (predicted amount) is at a relatively low level. The range of the sludge precipitation amount (predicted value) to which the Weihe method is applied is usually 1% or less, preferably 0.9% or less, more preferably 0.5% or less, even more preferably 0.25% or less, and still more preferably 0.1% or less.

[0058] <mcam> Also, according to another embodiment of the present invention, the sludge precipitation amount estimation model identified by the above method is a multi-component aggregation model that identifies the molecular structure and the proportion of each component constituting the crude oil mixture, and uses the structure information and physical property value database obtained therefrom. Such a sludge precipitation amount estimation model is also referred to as a Multi-Component Aggregation Model (MCAM), and can be implemented according to the description of JP-A-2019-13270. The disclosure content of such a document is incorporated herein by reference. Using MCAM is advantageous in estimating the sludge precipitation amount based on the correlation between the average aggregation degree (Dagg) of the crude oil mixture and the sludge precipitation amount, regardless of the combination of crude oils.

[0059] Hereinafter, with reference to the flowchart of FIG. 2, each step of an embodiment when applying MCAM will be described. (1) Step 1 (Mass spectrometry) (S1 in FIG. 2) Step 1 is a step of performing mass spectrometry on a multi-component mixture, and for each of the obtained peaks, identifying the molecular formula of the molecule attributed to the peak and further identifying the proportion of the molecule present. That is, it is a step of performing mass spectrometry on a multi-component mixture, and for all the peaks obtained thereby, identifying the molecular formula of the molecule attributed to each peak and further identifying the proportion of the molecule corresponding to the molecular formula.

[0060] For mass spectrometry, it is preferable to use a high-resolution mass spectrometer. Specifically, using an FT-ICR mass spectrometer, a known method, that is, by soft ionizing the sample to form molecular ions or pseudomolecular ions, high-precision measurement is performed.

[0061] (2) Step 2 (Collision-induced dissociation) (S2 in FIG. 2) Step 2 is a step of performing collision-induced dissociation on the multi-component mixture. "Collision Induced Dissociation (hereinafter also referred to as "CID")" refers to an operation of ionizing a molecule, colliding it with an inert gas such as argon, and cleaving crosslinks and side chains. Usually, it is preferable to apply collision energy so that the crosslinks and side chains in each component constituting the multi-component mixture are cleaved. By cleaving the crosslinks and side chains, fragment ions for each core are generated. This core may have an aliphatic group with about 0 to 4 carbon atoms that could not be cleaved by collision-induced dissociation as a side chain.

[0062] When performing FT-ICR mass spectrometry on the multi-component mixture, the molecular formula of the molecules constituting the multi-component mixture can be determined from the m / z of the obtained peaks, but information regarding the "core" of the molecules cannot be obtained. Therefore, by further performing collision-induced dissociation to cleave the crosslinks and side chains in each molecule constituting the multi-component mixture, the types of cores present in the entire multi-component mixture can be known. As conditions for performing collision-induced dissociation, a collision energy capable of effectively cleaving crosslinks and side chains in the molecule, for example, 10 to 50 kcal / mol is preferable, and 20 to 40 kcal / mol is more preferable. Note that 40 kcal / mol corresponds to 32 eV when the molecular weight is 700.

[0063] (3) Step 3 (Identification of the structure and abundance ratio of each core) (S3 in Figure 2) Step 3 is a step of performing mass spectrometry, preferably FT-ICR mass spectrometry, on each fragment ion generated by the collision-induced dissociation in Step 2 to identify the structure and abundance ratio of the core constituting each fragment ion.

[0064] (a) First, a method for identifying the structure of the core constituting each fragment ion will be described. Specifically, it is a method of collating the information on the core obtained in step 2 with the information on the core described in a previously prepared core structure list to identify the structure of each core. Specifically, it is as follows. i. Acquisition of information on the core after collision-induced dissociation In the FT-ICR-mass spectrometry of each fragment ion after collision-induced dissociation, even if the core part is the same, fragment ions having an aliphatic group with 0 to 4 carbon atoms as a side chain appear as separate peaks because their masses are different depending on the type of the side chain. Therefore, for those with an aliphatic group having 0 to 4 carbon atoms as a side chain in the core, if these various masses are calculated in advance and the separate peaks that appear are compared and collated in various ways, it becomes possible to determine the mass of the core itself. Using this method, in step 2, for each of the peaks obtained after collision-induced dissociation, information such as what the mass of the core attributed to the peak is, how many heteroatoms such as O, N, or S atoms are present, and how many aromatic rings are present from the DBE value can be obtained.

[0065] ii. Identification of the structure of the core after collision-induced dissociation As a method for identifying the structure of the core after collision-induced dissociation, a "core structure list" is created in advance by listing various cores that can be assumed to constitute each component molecule of a multi-component mixture as models, and the information such as the molecular weight of the core stored in the list, the type and number of heteroatoms, etc. is collated with the information on the core obtained above, and the most appropriate core model is selected from this list and the core is made to correspond to the relevant core. By this method, a core can be assigned to all the peaks obtained by FT-ICR-mass spectrometry after collision-induced dissociation, and its structure can be known.

[0066] iii. Core structure list The type of core to be stored in the above core structure list is not particularly limited and can be any type. However, the validity of selecting the core to be stored is directly related to the validity of specifying the structure of each core. It is preferable to create a "core structure list" in advance according to the content of the multi-component mixture itself as the sample. For example, when the multi-component mixture is petroleum, a "core structure list for specifying the molecular structure of petroleum" can be created in advance based on previous knowledge about petroleum and used. In creating the list, it is advisable to store an appropriate number of cores considering various conditions such as the number of rings in the basic aromatic ring, the type and number of naphthene rings directly bonded to the aromatic ring (including the difference between kata type and peri type), and the mode of direct bonding (i.e., the mode of how the naphthene ring is bonded to which position of the basic aromatic ring). For example, the list can be created considering computational convenience, such as setting the size of the aromatic ring to up to 6 rings, assuming heteroatoms N, O, S, and setting the number of types of heterocycles to about 10.

[0067] iv. Selection from the core structure list There may be cases where there are multiple entries in the core structure list that have "the same molecular weight, DBE value, and type and number of heteroatoms, but different structural formulas". In this case, rules can be determined as appropriate regarding which of these multiple entries to select as the first priority. For example, the following 1 to 3 can be cited as priorities. 1. Prefer those consisting only of aromatic rings. 2. Prefer those with more unsaturated bonds. 3. Prefer those with fewer rings.

[0068] (a) Next, a method for specifying the abundance ratio of each core will be described. As described above, from the height of each peak obtained after collision-induced dissociation in step 2, the abundance ratio of the core having that m / z, that is, that mass, can be determined. The structure of each core after collision-induced dissociation obtained in this step 3 will be used later in step 5, and the abundance ratio of each core after collision-induced dissociation will be used later in step 4.

[0069] (4) Step 4 (Estimation of the existence mode and abundance ratio of cores for each class) (S4 in Fig. 2) This is a step of classifying the molecules attributed to each peak in step 1 into "classes" based on the "type and number of heteroatoms (including zero) and DBE value", and estimating the existence mode and abundance ratio for all molecules belonging to each of these "classes". In other words, for the molecules attributed to all peaks in step 1, they are classified into "classes" based on the "type and number of heteroatoms (including zero) and DBE value" in each molecular formula specified in step 1, and the existence mode and abundance ratio are estimated for all molecules belonging to each of these "classes".

[0070] Hereinafter, step 4 will be described in detail. (a) In step 1, since the molecular formula has been specified for all peaks, the type and number of heteroatoms and the DBE value in the molecular formula are known. Therefore, in this step, based on this "type and number of heteroatoms and DBE value", each molecule attributed to all peaks is incorporated into the respective "class" grouped by the "type and number of heteroatoms and DBE value". The "type and number of heteroatoms" specifically refers to the "number of heteroatoms for each type of heteroatom". Since heteroatoms are preferably nitrogen atoms, sulfur atoms, and oxygen atoms, the "type and number of heteroatoms" can preferably be said to be the "number of nitrogen atoms, sulfur atoms, and oxygen atoms respectively". Therefore, regarding heteroatoms, those with "all the number of nitrogen atoms, sulfur atoms, and oxygen atoms being the same" will fall into the same "class".

[0071] (ii) Next, in each class enclosed by the "type and number of heteroatoms and DBE value" described in (i), estimate what kind of single-core or multi-core each molecule belonging to that class is. Also, estimate in what ratio these single-cores and multi-cores exist, respectively. In making these estimations, for practical computational convenience, it is preferable to make some assumptions. Here, "multi-core" can have various combinations depending on which cores are cross-linked and bonded. However, the sum of the DBE values of the multiple cores forming the multi-core and the sum of the numbers corresponding to the types of heteroatoms are the same for all those belonging to that class.

[0072] (iii) As described above, the molecules attributed to each of the peaks obtained by FT-ICR mass spectrometry were regrouped by class consisting of those with the same type and number of heteroatoms and DBE value. The molecules belonging to that class are single-core or multi-core. A preferable method for estimating what kind of core these single-cores or multi-cores are composed of will be described below.

[0073] When the molecule belonging to that class is a single-core, the single-core having the type and number of heteroatoms and DBE value corresponding to that class is applicable. When the molecule belonging to that class is a multi-core, the combination of cores such that the sum of the numbers for each type of heteroatom existing in the multiple cores constituting the multi-core and the sum of the DBE values of these multiple cores match the type and number of heteroatoms and DBE value of that class is applicable. Since it is only necessary that the sum of the numbers corresponding to the types of heteroatoms and the sum of the DBE values of the multiple cores match the type and number of heteroatoms and DBE value of that class, the combination of the multiple cores constituting the multi-core usually is not limited to one and there are several possibilities.

[0074] (e) Next, estimate "what is the ratio of each single-core and multi-core molecule belonging to that class?" Preferably, first, assume that the proportion of multi-cores is the product of the proportions of each of the multiple cores that make up the multi-core, and use this as the estimated value.

[0075] (5) Step 5 (Determination of core structure, side chains, and crosslinks) (S5 in Figure 2) Step 5 is a step of determining the structure of the cores that make up each molecule whose existence mode was estimated in Step 4, and further determining and assigning side chains and crosslinks.

[0076] (a) For "each molecule whose existence mode was estimated in Step 4", "determining the structure of the cores that form them" is performed by the following operations i to v. i. In the case of a multi-core whose existence mode was estimated in Step 4, separate (release) and capture each core that makes it up.

[0077] ii. For all those whose existence mode was estimated to be a single core in Step 4 and the cores generated by releasing the multi-core as in i above, regroup them into their respective "classes" for those with the same "type and number of heteroatoms and DBE value". Incidentally, the "class" mentioned here is a concept related to the original single-core and the cores obtained by releasing the multi-core, and is different from the "class" related to the molecules described in Step 4.

[0078] iii. For all "classes" of "type and number of heteroatoms and DBE value" grouped in ii above, assign a specific structure to all the cores existing in that "class".

[0079] (b) Determine side chains and crosslinks further by the following operations i to iii. i. As described above, the structure of the core part of a single-core or multi-core could be specified. However, merely assuming the existence of only the core part does not match the mass indicated by the m / z of the peak obtained by FT-ICR mass spectrometry for the target sample. That is, even when the masses based on the carbon, hydrogen, and heteroatoms involved in the core part are totaled, there is a difference from the mass indicated by the m / z of the peak obtained by FT-ICR mass spectrometry. Therefore, it is considered that the mass difference is due to the existence of side chains bonded to the core and crosslinks connecting the cores. The number of carbon atoms and the number of hydrogen atoms are determined so as to eliminate the difference, and they are assigned to the core as side chains and crosslinks. For example, for a peak with m / z = n, assume that a certain double-core formed by crosslinking core 1 and core 2 is assigned by the above procedure. At this time, The mass difference (d) = n - (the mass of core 1 + the mass of core 2) is due to the existence of side chains and crosslinks.

[0080] ii. In the above i, the number of carbon atoms and the number of hydrogen atoms to be assigned as side chains and crosslinks are determined, but the structure of the side chains and crosslinks has not yet been determined. Therefore, when estimating what structure of side chains and crosslinks is appropriate, by considering the existence probability of the assumed combinations of side chains and crosslinks, for example, the following rules can be determined and estimated according to them. As rules, conditions such as the upper limit of the number of carbon atoms constituting the side chains or crosslinks and the number of side chains can be determined in advance. iii. In the above i, when there is no side chain or crosslink corresponding to the mass difference, a structure in which core 1 and core 2 are simply bonded may be applied. (c) "Assigning to the core" the side chains and crosslinks determined above does not mean including determining to which position of which core the side chains and crosslinks are bonded.

[0081] (e) In this way, in step 5, for each single core or double core whose existence mode was estimated in step 4, the structure of the cores constituting them can be determined, and furthermore, the side chains and crosslinks can be determined.

[0082] By the above steps 1 to 5, for each component constituting the multi-component mixture, its molecular structure can be specified by JACD, and its abundance ratio can also be specified.

[0083] In the present invention, the multi-component mixture may be one fraction obtained by fractionating a certain multi-component mixture into two or more arbitrary parts. That is, when the "multi-component mixture" in the above is regarded as one fraction I obtained by fractionating the "multi-component mixture A" in a large bracket, the "multi-component mixture A" can be regarded as a mixture of fractions such as fraction I, fraction II, etc., as many as the number of fractions. For fraction II, the molecular structure of each component constituting fraction II can be specified by the same method as that used for fraction I.

[0084] (6) Step 6 (Obtaining melting point and Hansen solubility index value) (S6 in FIG. 2) From the molecular structures of the components of the multi-component mixture specified using JACD by steps (1) to (5), the melting point and Hansen solubility index value of each component are obtained. These physical property values are preferably specified using the entire petroleum molecular database (Comcat) for the molecular structures of the components of the multi-component mixture specified as described above.

[0085] Comcat refers to the "JACD-Physical Property Value Database" in which JACD and each physical property value are associated. The number of registered molecules in the database is approximately 25 million, and all components contained in petroleum are available in the model system analysis assuming that they are all composed of the molecules contained in Comcat.

[0086] The physical property values registered in the database are approximately 200 physical property values such as melting point, Hansen solubility index value, boiling point, critical humidity, critical pressure, critical volume, vapor pressure, liquid density, gas viscosity, liquid viscosity, surface tension, dipole moment, polarizability, ionization potential, heat of formation, enthalpy, entropy, free energy, heat capacity, etc.

[0087] These physical property values are usually calculated using the group contribution method or the molecular orbital method. The group contribution method is a method of calculating the physical property value of a substance by identifying the chemical structure of the substance and calculating the physical property value of the substance based on the specific parameter values of various atomic groups, that is, "groups" that exist. That is, it is premised that the "groups" possessed by the substance are identified. Also, in the molecular orbital method, first, the "groups" possessed by the substance are identified, and based on that, the structure is identified. In the present invention, as described above, for each component constituting the multi-component mixture, since various atomic groups existing are identified, the physical property value of the component can be calculated using the known specific parameter values of various atomic groups. Furthermore, since the proportion of each component is also specified, by considering this proportion, it is possible to appropriately estimate the physical property value of the entire multi-component mixture from the physical property values of each component.

[0088] (7) Step 7 (Separation into liquid phase component and non-liquid phase component) (S7 in FIG. 2) In the above steps (1) to (6), the fraction, melting point, and Hansen solubility index value of each component are obtained, and a desired temperature T is set. Among the components constituting the multi-component mixture, the components having a melting point lower than the desired temperature T are classified as liquid phase components, and the components having a melting point equal to or higher than the desired temperature T are classified as non-liquid phase components. Here, the desired temperature T is as defined above.

[0089] (8) Step 8 (Calculation of average HSP value of the entire liquid phase) (S8 in FIG. 2) For the HSP values of each component classified as a liquid-phase component in step (7), a weighted average value weighted by the volume fraction of each component in the liquid phase is calculated as the average HSP value of the entire liquid phase. For each component, the volume fraction can be calculated by obtaining various information regarding physical properties such as density and molecular weight in advance.

[0090] (9) Step 9 (Calculation of the difference in HSP values between the entire liquid phase and each non-liquid-phase component) (S9 in FIG. 2) Calculate the difference (Δδ) between the average HSP value of the entire liquid phase calculated in step (8) and the HSP value of each component in the non-liquid-phase component.

[0091] (10) Step 10 (Update of the classification of each component based on Δδ) (S10 in FIG. 2) Each component in the non-liquid-phase component is reclassified as a liquid-phase component or a non-liquid-phase component based on the difference (Δδ) calculated in step (9), and each component reclassified as a liquid-phase component is incorporated from the non-liquid-phase component to the liquid-phase component to update the liquid-phase component and the non-liquid-phase component. In this update during reclassification, for each component in the non-liquid-phase component, it may be performed one by one in order, or it may be performed for a plurality of components at a time.

[0092] (11) Step 11 (Calculation of the average HSP value of the entire liquid phase after update) (S11 in FIG. 2) For the HSP values of each component in the liquid-phase component after update in step (10), a weighted average value weighted by the volume fraction of each component in the updated liquid phase is calculated as the average HSP value of the entire updated liquid phase.

[0093] (12) Step 12 (Repeated execution of steps 9 to 11) (S12 in FIG. 2) Repeat steps (9) to (11) until the final stage where there are no non-liquid-phase components reclassified as liquid-phase components in step (10).

[0094] (13) Step 13 (Calculation of the degree of aggregation of non-liquid-phase components) (S13 in FIG. 2) Calculate the degree of aggregation D of the non-liquid-phase components after updating at the final stage at the desired temperature.

[0095] (14) Step 14 (Classification of non-liquid-phase components based on the degree of aggregation) (S14 in Fig. 2) Classify each component in the non-liquid-phase components after updating at the final stage into an aggregated-phase component and a solid-phase component based on the degree of aggregation D.

[0096] (15) Step 15 (Calculation of the average degree of aggregation of the aggregated-phase components) (S15 in Fig. 2) Calculate the average degree of condensation D of the aggregated-phase components classified in Step 14.

[0097] (16) Step 16 (Output of the properties of the multi-component mixture - Estimation of the sludge precipitation amount) (S16 in Fig. 2) Output the properties of the multi-component mixture based on the information obtained from the above steps, and estimate the sludge precipitation amount.

[0098] Also, according to an embodiment of the present invention, when applying MCAM, from the viewpoint of realizing a highly accurate estimation of the sludge precipitation amount, it is preferable to set the average Dagg value according to the light oil used in the crude oil mixture. It is an unexpected fact that the correlation of the sludge precipitation amount is improved according to the average Dagg value of the light oil used in the crude oil mixture. According to an embodiment of the present invention, the average Dagg value of the light oil is preferably 11 or more, more preferably 18 or more.

[0099] Also, the range to which MCAM is applied is not particularly limited, but from the viewpoint of highly accurate estimation of the sludge precipitation amount, it is preferable that the sludge precipitation amount (predicted amount) is in a relatively high level range. The range of the sludge precipitation amount (predicted value) to which MCAM is applied is usually 1% or more, preferably 1 to 10%, more preferably 1 to 5%.

[0100] <Sludge precipitation amount estimation device / system> Further, according to an embodiment of the present invention, there is provided an apparatus for estimating the amount of sludge precipitation in a crude oil mixture, the apparatus comprising a sludge precipitation amount estimation unit that estimates the amount of sludge precipitation in the crude oil mixture based on a sludge precipitation amount estimation model preset with reference to mixture property information including mixing composition information and sludge precipitation amount for a plurality of reference crude oil mixtures.

[0101] Hereinafter, with reference to FIG. 3, a specific embodiment of the apparatus for estimating the amount of sludge precipitation in the crude oil mixture of the present invention will be described. FIG. 3 is a functional block diagram of the sludge precipitation amount estimation apparatus 1 of the embodiment. By causing a computer to execute the program of the present invention, the computer functions as an apparatus for estimating the amount of sludge precipitation. In addition, in FIG. 3, illustration of an interface for inputting and outputting information is omitted.

[0102] In FIG. 3, the apparatus 1 for estimating the amount of sludge precipitation in the crude oil mixture includes (A) a sludge precipitation amount estimation model selection unit 10 that specifies a sludge precipitation amount estimation model based on mixture property information including mixing composition information and sludge precipitation amount for a plurality of reference crude oil mixtures, and (B) a sludge precipitation amount estimation unit 20 that estimates the sludge precipitation amount based on the sludge precipitation amount estimation model.

[0103] This apparatus 1 may be composed of one CPU or may be composed of a plurality of apparatuses connected to each other via a communication line.

[0104] The sludge precipitation amount estimation model selection unit 10 may be composed of a storage unit that stores mixing composition information, sludge precipitation amount and other mixture property information for a plurality of reference crude oil mixtures, and a calculation unit that specifies an appropriate sludge precipitation amount estimation model by statistical analysis or the like based on the information in the storage unit.

[0105] The sludge precipitation amount estimation unit 20 uses the sludge precipitation estimation model specified by the sludge precipitation estimation model selection unit 10 to estimate the sludge precipitation amount of the crude oil mixture to be inspected. For example, it may include an analytical device such as an FT-ICR mass spectrometer, a storage unit storing a database, a calculation unit, and the like.

[0106] Each part of the purification efficiency estimation device of the present invention may be integrally configured, but each part may also be configured separately as desired. When estimating the sludge precipitation amount in the crude oil mixture by such independent parts, the sludge precipitation amount estimation device can be provided as a system for estimating the sludge precipitation efficiency.

[0107] Therefore, according to another embodiment of the present invention, there is provided a system for estimating the sludge precipitation amount in a crude oil mixture, which includes a sludge precipitation amount estimation unit that estimates the sludge precipitation amount in the above crude oil mixture based on a sludge precipitation amount estimation model preset by referring to mixture property information including mixing composition information and sludge precipitation amount for a plurality of reference crude oil mixtures. According to a preferred embodiment of the present invention, the system for estimating the sludge precipitation amount in a crude oil mixture is (A) a sludge precipitation amount estimation model selection unit 10 that specifies a sludge precipitation amount estimation model based on mixture property information including mixing composition information and sludge precipitation amount for a plurality of reference crude oil mixtures, and (B) includes a sludge precipitation amount estimation unit 20 that estimates the sludge precipitation amount based on the above sludge precipitation amount estimation model.

[0108] <Sludge precipitation amount estimation computer program, etc.> In the present invention, a series of processes including estimation of a molecular structure using JACD, association of the estimated molecular structure information with physical property values, and estimation of properties of a multicomponent mixture using an aggregation model can be executed by hardware, software, or a combination thereof. When executing the process by software, a program recording the process sequence may be installed in a memory in a computer incorporated in dedicated hardware for execution, or the program may be installed in a general-purpose computer capable of executing various processes for execution.

[0109] For example, the program can be pre-recorded on a hard disk or ROM as a recording medium. Further, the program can be temporarily or permanently stored (recorded) on a removable recording medium such as a flexible disk, CD-ROM, MO disk, DVD, magnetic disk, or semiconductor memory.

[0110] In addition to installing the program from the removable recording medium as described above into the computer, the program can be wirelessly transferred to the computer from a download site, or transferred to the computer by wire via a network such as a LAN or the Internet. The computer can receive the program transferred in such a manner and install it in a recording medium such as a built-in hard disk.

[0111] The method of the present invention can be preferably implemented by a computer storing the above computer program in an internal storage device.

[0112] Also, the various processes described in this specification are not limited to being executed in time series according to the description, but may be executed in parallel or individually depending on the processing capabilities of the device executing the process and as necessary. Further, in this specification, a system is a logical set configuration of a plurality of devices, and is not limited to those in which the devices of each configuration are within the same housing.

Example

[0113] Hereinafter, the present invention will be described by way of examples, but the present invention is not limited to these examples.

[0114] Test Example 1 (1) Evaluation of various crude oils Using light crude oil and heavy crude oil as evaluation targets, the compatibility test shown in FIG. 4 was carried out.

[0115] Regarding the evaluation results of SBN and IN of each crude oil, the relationship with API is organized as shown in FIG. 5. It was found that the phase SBN has a high correlation with API, and the lighter the crude oil with a large API, the smaller the SBN tends to be.

[0116] From the Wiehe's Oil Compatibility Model, it is speculated that for all crude oils, the phenomenon that the solvent power of the crude oil for asphaltenes decreases because the resin component near the asphaltenes becomes thinner and the ratio of the saturated fraction increases as the crude oil becomes lighter.

[0117] On the other hand, regarding IN, the correlation with API is low, and it is considered to be caused by other factors such as the origin of the crude oil, the structure of asphaltenes, and their distribution.

[0118] Therefore, as a result of analyzing IN and the amount of asphaltenes by origin, it was found that IN of crude oils produced in the Middle East and South America shows a good correlation with the amount of asphaltenes, but crude oils produced in North America do not show a correlation. This is because most of the crude oils produced in North America contain a large amount of bitumen extracted from oil in oil sands with a solvent. Different from naturally produced crude oils, even in crude oils with a large amount of asphaltenes, the components near the asphaltenes that dissolve the asphaltenes also increase similarly. As a result, it is speculated that the ease of precipitation of asphaltenes, that is, IN hardly changes.

[0119] (2) Mixed evaluation of various crude oils Based on the evaluation results of various crude oils, the mixed evaluation results of conventional crude oil L (API: 33.0) and non-conventional and extra-heavy crude oils A, B, C, E, H, I, J, M, and P are shown in Table 2, the mixed evaluation results of conventional crude oil α (API: 40.1) and non-conventional and extra-heavy crude oils A, B, C, E, H, I, J, M, and P are shown in Table 3, and the mixed evaluation results of conventional crude oil β (API: 41.7) and non-conventional and extra-heavy crude oils A, B, C, E, H, I, J, M, and P are shown in Table 4.

[0120] The left column shows the volume ratios of crude oils L, α, or β, and the upper right of the frame shows the compatibility (INmax / SBNmix) of the mixed crude oil calculated from the formula of Irwin Wiehe et al.

[0121]

Number

[0122] For sample preparation, crude oils L, α, or β with low SBN were added to and mixed with crude oils A, B, C, E, H, I, J, M, and P with high SBN, and the mixture was left standing at 25 °C in a temperature-controlled sample storage for 1 day. For the evaluation, the state of the sample after standing was observed under a microscope, and the presence or absence of sludge precipitation was recorded as ○ or ×. The results of the dry sludge test (in accordance with ISO 10307-1) are shown at the lower left. Note that the test results also include those obtained using the crude oil mixing characteristics evaluation apparatus (Figure 6).

[0123]

Table 2

Table 3

Table 4

[0124] Based on the mixed evaluation results of various crude oils obtained, as a result of examining the creation of a prediction formula for the amount of sludge precipitation based on the compatibility test results (IN / SBN value) by the Wiehe method, as shown in Figure 7A, a correlation was confirmed within the range of a sludge precipitation amount of 1.0%. Furthermore, when the predicted value and the measured value were confirmed using the above prediction formula with a verification sample, as shown in Figure 7B, a correlation was confirmed. It is suggested that the Wiehe method can achieve high-precision estimation if the mixing ratio of crude oils and the range of sludge precipitation amount are selected for use.

[0125] (3) Examination of the mixing ratio of various crude oils and sludge precipitation Light crude oil α or β was mixed with heavy crude oil A or E, and the relationships between the Wiehe value (IN / SBN), the proportion of heavy crude oil in the mixed crude oil, the amount of asphaltenes (As) in the mixed crude oil (%), and the dry sludge amount × 5 (wt%) were analyzed.

[0126] The results were as shown in Figures 8A and 8B. In Figures 8A and 8B, an inverse relationship was observed between the amount of As in the mixed crude oil and the compatibility of As (IN / SBN), and the amount of sludge precipitation was maximized near the mixing ratio at which the two intersect. From this result, it is inferred that the difference in sludge precipitation behavior is due to the change in the relationship between the amount of As and the solubility of As depending on the combination of crude oils.

[0127] Also, from the correlation relationships shown in Figures 8A and 8B as described above, it is suggested that depending on the mixing ratio of the feedstock oil, it may be preferable to select a method other than the Wiehe method from the perspective of performing highly accurate sludge precipitation prediction.

[0128] (4) Construction of JACD for various crude oils We investigated a method to predict the presence or absence of sludge deposition and its amount using MCAM from detailed structure data of crude oils in order to predict the sludge deposition observed in the blending evaluation of various crude oils. MCAM can calculate the degree of aggregation (Dagg) of each component calculated from the Hansen Solubility Index (HSP), temperature, and mole fraction. In addition, the HSP of each component contained in crude oil can be calculated by the above-mentioned method of calculating HSP from the structure developed by the applicant using the atomic group contribution method. For the AR fraction of crude oil fractions, there is JACD data (structural data) necessary for HSP calculation measured by FT-ICR-MS, but for other light fractions, there is no JACD data because it is outside the scope of application of FT-ICR-MS. Therefore, a method is needed to convert the composition of light fractions into JACD from various analytical results.

[0129] Following the JACD construction method for light fractions, the JACD for the naphtha fraction of crude oil was created from the results of PONA analysis. The JACD for the kerosene fraction was created from the results of HPLC or GC×GC analysis. HPLC is a method for separating components according to their polarity, and can classify sample compositions into saturated fractions (Sa), cyclic aliphatic fractions (O), single-ring aromatics (1A), double-ring aromatics (2A), and triple-ring or higher aromatics (3A+). GC×GC is an analytical method that is becoming mainstream, and uses two columns with different polarities to separate components in a sample in detail according to their boiling point and polarity (structural attributes).

[0130] Regarding the composition of the kerosene fraction, crude oils α and β were distributed in cyclic aliphatic hydrocarbons and monocyclic aromatic hydrocarbons with a peak in linear aliphatic hydrocarbons at around carbon number 11. In addition, regarding the diesel fraction, crude oils α and β were distributed in the carbon number range of 12 to 26, with a focus on the carbon number range of 15 to 21, and it was inferred that the difference in carbon distribution between crude oil types was not very large.

[0131] On the other hand, for the kerosene and light diesel fraction of heavy crude oil, a method was used to construct the JACD from the HPLC analysis results, on the assumption that estimation error is acceptable due to the low blend ratio of crude oil.

[0132] (5) Verification of MCAM Results and Actual Dry Sludge Test Results for Various Crudes

[0133] For light crude oil α, JACD prepared by mixing crude oil E at volume ratios of 5%, 20%, and 40% was analyzed by MCAM. To obtain the Dagg value where the amount of sludge and the estimated value of MCAM match, a correlation analysis was performed between the amount of precipitated sludge and the actual amount of sludge at each Dagg value.

[0134] As a result, when the threshold was set at Dagg value of 18, it was found that the correlation coefficient (R 2 ) was closest to 1 and the correlation with the actual amount of sludge was high.

[0135] Also, the threshold obtained for crude oil α was applied to crude oil β and MCAM analysis was performed. MCAM analysis was carried out using JACD prepared by mixing crude oil A, B, C, E, I, and J at volume ratios of 5% and 20% with crude oil β. The results of using Dagg > 18 as the threshold, similar to crude oil α, and the amount of sludge are shown in Figure 9.

[0136] Also, as a result of performing a correlation analysis in the same way as when mixing with crude oil α, Dagg > 11 was derived. Figure 10 shows a comparison of the MCAM precipitation prediction and the amount of sludge at Dagg > 11. For crude oil A and E mixed with crude oil β, the order of magnitude of the precipitation amount and the precipitation behavior for each mixing ratio were also consistent.

[0137] In addition, to consider the result that the Dagg threshold is different for crude oils α and β, the results of the integrated Dagg value when mixing with crude oil A and E are shown in Figure 11. Regarding the result that the amount of sludge increases when mixing with crude oil β, which is lighter than crude oil α, looking at the integrated Dagg value, the Dagg value tends to decrease proportionally to each mixing ratio when mixing with crude oil β.

[0138] From the above results, it was found that by setting different Dagg thresholds for each light crude oil, the measured value of the sludge precipitation amount and the MCAM predicted value can be correlated.

Explanation of Symbols

[0139] 1 Sludge precipitation amount estimation device 10 Sludge precipitation amount estimation model selection unit 20 Sludge precipitation amount estimation unit< / mcam>

Claims

**Claim 1** A method for estimating the amount of sludge precipitation in a crude oil mixture using a computer, comprising: estimating the amount of sludge precipitation in the crude oil mixture using a computer that stores an estimation model for estimating the amount of sludge precipitation based on the mixture property information of the crude oil mixture; the mixture property information includes the ratio (IN / SBN) of the insoluble value (IN) to the solubility blend number (SBN), and the molecular structure and abundance ratio of each component constituting the crude oil mixture obtained by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS); (A) When the mixture property information is IN / SBN, the estimation model is an estimation model for estimating the amount of sludge precipitation in the crude oil mixture based on the correlation between IN / SBN and the amount of sludge precipitation; (B) When the mixture property information is the molecular structure and abundance ratio of each component constituting the crude oil mixture obtained by FT-ICR MS, the estimation model is an estimation model for estimating the amount of sludge precipitation in the crude oil mixture by a multi-component aggregation model (MCAAM) based on the molecular structure and abundance ratio of each component; When the estimated value of the sludge precipitation amount is less than 1% by mass, the estimated value of the sludge precipitation amount obtained by the estimation model of (A) is selected; When the estimated value of the sludge precipitation amount is 1% by mass or more, the estimated value of the precipitation amount obtained by the estimation model of (B) is selected. **Claim 2** The method according to claim 1, wherein the crude oil mixture is a mixture of two or more crude oils including light oil and heavy oil. **Claim 3** The method according to claim 1, wherein in the estimation model of (B), an average Dag value is set according to the light oil used in the crude oil mixture. **Claim 4** An operation method of an apparatus related to petroleum, wherein operation conditions are set based on the estimated value of the sludge precipitation amount obtained by the method according to any one of claims 1 to 3.

5. An apparatus for estimating the amount of sludge precipitation in a crude oil mixture, which executes the method according to any one of Claims 1 to 3.

6. A system for estimating the amount of sludge precipitation in a crude oil mixture, which executes the method according to any one of Claims 1 to 3.

7. A computer program for causing execution of the method according to any one of Claims 1 to 3, the apparatus according to Claim 5, or the system according to Claim 6.

8.

9. A recording medium having recorded thereon the computer program according to Claim 7.

10. A computer having stored in an internal storage device the computer program according to Claim 7.

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