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    • 专利摘要:
    Disclosed is a system and method of evaluating the suitability of a target component for use in a target chemical system. In use, the system calculates model charge density probability distribution information relating to a chemical component; the model charge density probability distribution information corresponding to a set of model values of physical properties of the target chemical system; obtains target charge density probability distribution information relating to the target component; and determins the similarity of the model charge density probability distribution information and the target charge density probability distribution information. This methodology provides for an objective evaluation of the suitability of a target chemical component, and may, be used to investigate the predicted properties of alternative chemical systems, for example by screening a chemical library, without the need to prepare and test multiple chemical systems.
    公开号:DK201570250A1
    申请号:DK201570250
    申请日:2015-04-29
    公开日:2015-05-11
    发明作者:Kristian Mogensen；Martin Bennetzen
    申请人:Mærsk Olie Og Gas As；
    IPC主号:
    专利说明:

    Evaluation of Chemical Components
    Field of the Invention
    The invention relates to methods and apparatus for reverse engineering and / or characterizing chemical systems, and in particular to the evaluation of chemical components suitable for use in complex chemical systems, for example in the field of enhanced oil recovery.
    Background to the Invention
    Characterization of chemical systems for industrial and research applications is most often based on a number of laboratory screening tests, to evaluate parameters such as compatibility of chemical components, thermal stability, rheology, phase behavior etc. For a given application it may be necessary to consider a number of properties of each component and this can be time consuming, or limit the number of properties or components that are considered,
    In some applications, experimental work to identify whether a chemical or a formulation can be used in an application is prohibitively expensive or may not be technically feasible. One such application is the field of enhanced oil recovery (EOR). In the primary stage of oil recovery, the natural pressure in the reservoir may be sufficient to drive the oil to the surface. However, as the reservoir is depleted, oil recovery enters a secondary stage in which wafer is injected into the reservoir in order to increase the pressure in the producing well. If the reservoir is further depleted, water flooding alone may not be sufficient to recover further oil. Various methods exist for recovering additional oil beyond this secondary stage, including chemical flooding, in which a fluid, such as an aqueous solution to which surfactants or polymers have been added, is injected into the reservoir to displace the oil,
    Chemical additives change the balance of viscous and interfacial forces between the displaced fluid (oil) and the displacing fluid (water containing the chemicals) during a flood. This may be expressed in terms of the capillary number, where Ν! ΆΡ »β //% and where ^ is the viscosity of the displacing fluid, v the superficial velocity of the displacing fluid and; displaced and the displacing fluid.
    The higher the capillary number obtained during a chemical flood the more oil is recovered, since ihierfaelal forces are reduced on the microscopic scale. .A ^ can be increased by Increasing µ (e, g. By adding polymers to the displacing water) or by decreasing y (e.g., by adding surfactants to the displacing water).
    Microscopic sweep efficiency depends not only on the capillary number but also on reservoir rock wettability, which reflects the thermodynamic preference of a solid to be in contact with one fluid rather than another, where both fluids are present. The displacing fluid may be capable of altering wettability from oil-wet to water-wet, hence releasing oil from the rock surface.
    Selection of surfactants and polymers for a particular oil field application requires that a number of screening tests be performed for a number of physical properties, which can be time consuming and costly. Hence there is a significant need for methods of characterizing complex chemical systems which reduce the requirement to conduct experiments.
    Turning now to theoretical approaches to describing chemical systems In general, one method to predict fluid properties based on molecular structure is the UNIGUAC (UNIversai QUasiChemieal) method (DS Abrams at, "Statistical Thermodynamics of Liquid Mixtures: A New Expression for the Excess Gibbs Energy of Partly or Completely-Miscible Systems ", AlChE X, 21, 1975}, UNIGUAC Is an activity coefficient model used for phase equilibrium calculations. The semi-empirical UNIFAG (UNIQUAQ Functional-group Activity Coefficients) method is a development of UNIGUAC (E.g., Fredenslund et al., "Group-Contribution Estimation -of Activity Coefficients in Nonideal Liquid Mixtures" AlChE X 21, 1975), in which chemical structure of molecules is taken into account and the activity coefficients of molecules are subdivided into functional groups contributions.
    More recently, COSMO (the Conductor Screening MOdel) theory has been developed, as described for example by; A, Klamt et al * COSMO-RS: a novel and efficient method for the a priori prediction of ihermophysicai data of liquids * Fluid
    Phase Equilibria, 172, 2000; A, Klamt, "The COSMO and COSMO-RS solvation models",, Wiley Interdisciplinary Reviews: Computational Molecular Science, VVIREs Cam put Mol, Sel, 2011; and A, Klamt, "CQ8MQ ~ RS: From Quantum Chemistry to Fluid Phase Thermodynamics and Drug Design", Elsevier Science Ltd ,, Amsterdam, The Netherlands, 2005. The main focus in COSMO theory is to characterize the screening charge density distribution on a molecular surface. For each kind of molecule X, a density functional calculation is performed In order to obtain the total energy £ x and the polarization (or screening) charge density σ of its molecular surface ·. o Is the surface charge density of a molecule a chemical component and the local value of ο vanes across a molecule. Hence, a surface charge landscape for a molecule X can fee represented by a charge density probability distribution px (n), In COSMO, this distribution is called a 'sigma profile'. Thermodynamic properties may be derived from knowledge of the sigma profiles of interacting molecules X,
    For example, for an ensemble (e.g. a solvent) B of i ~ f., N different molecules, the sigma profile is calculated as a weighted average of mole fractions Xi to give the sigma potential
    The so-called "sigma-potential profile" of the ensemble - le, the energy associated with the preference for the solvent to interact with a surface element represented by charge density or - can be derived from mathematical integration of all pair-wise interacting surface elements .: The chemical potential of a molecule X within the ensemble may be characterized by its own sigma profile, and the sigma profiles together used to calculate a variety of thermodynamic properties, a graphical representation of o-profiles and o-pcfential profiles is shown in Figure 1.
    Statistical moments (called "sigma moments1") of the sigma profiles can be used to predict properties of some solvent-solute systems to a good approximation. However, application of theoretical approaches to complex chemical systems (such as those encountered in EOR) where a range of characteristics such as the values of several physical properties are sought, it may not be clear how a given set of characteristics of a component of that chemical system would suit a particular application, for example in comparison to some alternative component. Consequently, there remains a need for improved methods of characterizing chemical systems, for example to estimate their physical properties.
    Summary of the invention
    According to a first aspect of the invention, there is provided a method of evaluating the suitability of a target component for use in a target chemical system, comprising: calculating model charge density probability distribution information relating to a chemical component; the model charge density probability distribution information corresponding to a set of model values of physical properties of the target chemical system; obtaining target charge density probability distribution information related to the target component; and determining the similarity of the model, charge density probability distribution information and the target charge density probability distribution information.
    Determining the similarity of respective model and target charge density probability distribution information, in accordance with the invention, provides for an objective evaluation of the suitability of a target chemical component, for use in the target chemical system. The value of the similarity of the model and charge density probability distribution information may be used for the evaluation of the suitability of the target chemical component.
    The model charge density probability distribution information calculated in accordance with the methods described herein may be predictive of the corresponding information of a chemical component which would be expected to give rise to a chemical system having suitable values of the said physical properties. The model values may correspond to a set of desired values, or may represent a set of values offering the closest predicted combination of those values to desired values. A chemical system may comprise or consist of a chemical component Accordingly, the target chemical system may comprise or consist of a target component.
    That is to say, a chemical system may comprise a single component (e, g, a compound) or may comprise more than one component (e.g. a mixture of compounds. Or a solution or interacting components of the same or a different phase). Thus, each physical property may be a property of a chemical component within a chemical system comprising multiple components (for example, the solubility of a compound in a solvent or solution, or a partition coefficient). Each physical property may be a property of a chemical system (for example, a viscosity or surface tension of a mixture or solution). Each physical property may be a property of a chemical component. That is, of a chemical system comprising a single chemical component, it will be understood that in the oasis of a target chemical system comprising a single component, the evaluation of the suitability of the target component for use in the target chemical system comprises evaluating the suitability of the target component as the chemical system.
    The charge density of a chemical component (e.g., a compound), or a chemical component within a chemical system, is dependent on the chemical structure of the chemical component and may vary spatially in relation to the chemical component. The charge density may be a molecular surface parameter. Accordingly, a charge density probability distribution (also known as a sigma profile} may reflect the probability of any point on a molecular surface (or, more generally, in the coordinate space of a chemical component) having a given charge density value,
    The model and the target charge density probability distribution information may comprise or consist of a charge density probability distribution.
    The model and the target charge density probability distribution information may comprise parameters or properties of a respective charge density probability distribution, such as one or more ..statistic moments of the probability distribution, one or mote average or peak values, integral or derivative values of the probability distribution.
    The similarity of the model and target charge density probability distribution information may be determined, for example, by any suitable method. For example, the
    Similarity may be a measure of the distance between the model and target charge density probability distributions (e.g., an integral or average of the magnitude of distances between corresponding points of each probability distribution).
    The similarity of the model and target charge density probability distribution information may be based on parameters of the probability distributions, such as a mean value or one more statistical moment of each of the probability distributions. The similarity may be a measure of the Euclidean or statistical distance between one or more parameters of the respective probability distributions, for example the Euclidian or statistical distances between one or more corresponding statistical moments of each of the charge density probability distributions (or other suitable parameters of the probability distributions).
    The target cnarge density probability distribution may correspond to target values of a set of physical properties.
    The target charge density probability distribution information may be obtained from a library or database, or may be calculated from Information obtained from a library or database, for example stored on a data store. The target charge density probability distribution information may be, or may be calculated from, a charge density probability distribution obtained from a library or database. The target .charge density probability distribution Information may be obtained by calculating, eg, from target values of a set of physical properties, for example using methods analogous to those used to calculate the model charge density probability information, or based on chemical structure information relating to the target component, eg, using COSfVIO theory.
    The method may comprise calculating the model and / or the target charge density probability distribution information from the corresponding charge density probability distribution.
    The method may, for example, comprise obtaining a target charge density probability distribution and / or calculating a model charge density, probability distribution, and calculating one or more parameters of the model / target charge density probability distribution therefrom.
    The method may comprise calculating a modal and / or a target charge density probability distribution from one or more parameters of a respective probability distribution.
    The method may for example comprise calculating one or more parameters of a model charge density probability distribution and / or obtaining one or more parameters of a target charge density probability distribution, and calculating a respective probability distribution therefrom, e.g. A probability distribution may be calculated based on one or more statistical moments, using a Generalized Lambda Distribution model (as described for example In Z, A, Karlam "Fitting Statistical Distributions ~ The Genaraiizéd Lambda. Distribution and Generalized Bootstrap Methods", Chapman and Hail / CRC, 2000).
    Calculating model charge density probability distribution information (such as a model charge density probability distribution, or a set of model values of parameters of a model charge density probability distribution) may comprise predicting a set of values of the physical properties from proposed charge density probability distribution information (such as a proposed charge density probability distribution, or a set of proposed values of parameters of a proposed probability distribution).
    Proposed charge density probability distribution; information may be calculated based on chemical structure information related to a chemical component For example, a proposed charge density probability distribution may be calculated using COSMO theory.
    The method may comprise proposing a chemical structure, calculating proposed charge density probability distribution, information (such as a proposed charge density probability distribution) based on the proposed chemical structure, predicting a set of values from the proposed charge density probability distribution, and determining whether the predicted values are an acceptable set of model values of the physical properties.
    The method may comprise amending the chemical structure information and calculating, a further proposed charge density probability distribution. The method may be Iterative, it is not required, for the chemical structure information from which a model or a proposed probability distribution is calculated to relate to the chemical structure of a known chemical component, or to a chemical structure which is theoretically possible. Thus, the method may comprise proposing a hypothetical chemical structure. The method may comprise designing a chemical component (or a chemical system), by determining that a hypothetical chemical structure gives rise to an acceptable set of model values (by calculating a proposed probability distribution- from the hypothetical chemical structure, and predicting a set of values therefrom), and by providing a chemical component having the hypothetical chemical structure.
    The method may comprise comparing the set of predicted values to a set of desired values (eg, a set of desired values of the physical properties of the target chemical system, or a set of desired values of parameters of a charge density probability distribution which corresponding to desired values of the physical properties), to determine whether the predicted values are an acceptable set of model values, and thus whether the predicted probability distribution information is suitable model charge density probability distribution information, A predicted value may be a suitable model value if if is equal to or within a predetermined amount of (e.g. within a range of) a corresponding desired value. The predetermined amount may, for example, be calculated as a percentage or may be an absolute value. The predetermined amount may be the same for each set of values, or the predetermined amount for one parameter or physical property may be different from the predetermined amount for another.
    Charge density probability · 'distribution, information which corresponds to a model value of one physical property may not correspond to a model value of another. Accordingly, the method may comprise solving interdependent simultaneous equations so as to calculate the model charge density probability distribution information.
    The method may comprise solving N simultaneous equations, based on N desired values of a set of N physical properties, or N corresponding parameters of a charge density probability distribution, for values of P variables. The variables may be descriptors in COSMO theory. The variables may have parameters related to probability distribution, such as statistical moments.
    In order to avoid overdetermination, Al a R Preferably, N '> P
    The method may comprise applying one or more exclusion conditions, such as to exclude solutions of the simultaneous equations which correspond to a value of a physical property or parameter which differs by more than a predetermined amount from the:, desired value.
    The model or proposed charge density probability distribution information may be calculated from a value of a physical property, based on a relationship between one or more statistical moments of a probability distribution and the physical property. The relationship may be a mathematical and / or empirical relationship.
    References herein to "empirical" and "empirical" include a posteriori, rather than solely a priori methods; i, e, methods based on observation rather than based solely on theory. For example, a relationship may be based on an observed "best fit" between parameters (such as the physical property and the statistical moment or moments), rather than being calculated or predicted based solely on a theoretical model.
    As such, the relationship may be regarded as an empirical relationship between the physical property and the one or more statistical moments. This is not to exclude that an empirical relationship may provide for some theoretical interpretation or insight, however.
    Similarly, an empirical value of a physical property may be based at least in part, on an experimental observation, moreover, the values of the parameters (e.g. the physical properties) upon which an empirical relationship is based may themselves be empirical values based. upon .experimental measurements. An empirical relationship may be based (or additionally) on values calculated non-empirically. For example, an empirical relationship may be determined (for example, based on an observation of a correlation, trend, and / or fit) between two sets of values that are themselves determined theoretically, for example from a theoretical calculation.
    An empirical value of a property (e> g, one or more statistical moments or a charge density probability distribution) need not be based on a direct experimental measurement of the property Itself, but may be calculated based on one or more data obtained from experimental observation. A charge density probability distribution may be calculated from one or more statistical moments by any salable method, such as a generalized Lambda distribution method.
    Accordingly, the invention extends in a second aspect to a method of evaluating the suitability of a target component for use in a target chemical system, comprising: calculating model charge density probability distribution information related to a chemical component, from a desired value of a physical property of the target chemical system, using a relationship between one or more statistical moments of the charge density probability distribution and the physical property; obtaining target charge density probability distribution information related to the target component; and determining the similarity of the model charge density probability distribution information and target charge density probability distribution information.
    The method may determine an empirical relationship between a physical property of a chemical system and one or more statistical moments of a charge density probability distribution and a physical property, for example as described in the applicant's co-pending patent application no. EP13183418.6, which is incorporated herein by reference.
    The empirical relationship may be a linear relationship (for example, a linear relationship between sigma moments and Abraham parameters, by Zissimos et al., J "Chem. Inf., Comp. Sol ,, 2002).
    The empirical relationship may be a non-linear relationship. The method may comprise; obtaining values (for example values obtained or calculated from empirical measurements) of a physical property of a plurality of known chemical systems; determining one or .more statistical moments Of a charge density probability distribution of a known component of each of the known chemical systems; and fitting the obtained values to a non-linear mathematical function of the one or more statistical moments; to determine an empirical relationship between the physical property and the one or more stoices! moments, A non-linear mathematical function may be considered to be a mathematical function one or more with non-linear terms. A non-linear mathematical function may include both linear and non-linear terms. Non-linear terms may include, for example, log transformations, exponential functions, power functions, A linear mathematical function may be considered to be a zeroth or first order polynomial
    The "non-linear mathematical function of the statistical moment" may be a non-linear mathematical function having at least one non-linear term which includes the statistical moment not raised to the power 1.
    An order statistical moment of a probability distribution p of a property x may be defined as:
    The method may comprise determining one or more statistical moments of order, where / is 0 or an integer greater than zero, if it is understood that the method may be applied to any number or any selection of statistical moments of probability distribution, In practice, / is typically in the range of 0 to 10, or from 0 to 8.
    The method may comprise determining values of one or more further descriptors of the known component. The method may comprise determining values one or more energy descriptors, for example values related to dielectric properties and aromatic rings of the known component. The method may comprise determining a molecular weight and / or volume of the known component.
    The non-linear mathematical function, and thus also the empirical relationship, may comprise temperature and pressure dependent terms.
    The method may comprise fitting the measured values Qv of physical property v to nonlinear mathematical function given by equation (1):
    where: coefficients a,% c ;, dfdt h ft h & r% 4 Φ sin grifs are real numbers; f, g, ft, u, r, s and w are functions;
    Mi Is the / border sigma moment of the target component, given by;
    where o is charge density; V is molecular volume of the target component; W Is molecular weight of the target component;
    Emg is the energy associated with aromatic ring systems of the target component;
    Emi Is the dielectric energy of the target component; £ MOi £ cuLAnte the molecular energy of the target component; / Is 0 or an integer greater than 0; and v {P, 7) is a function that depends on pressure P and temperature T.
    Bag may be zero for target components lacking an aromatic ring. Alternatively & may be set to zero for target components lacking an aromatic ring so that the term is not included in calculations. a may be related to electrostatic energy and optionally also hydrogen donor / acceptor energy.
    EmtmuLAX may be calculated using a density functional algorithm, or a quantum mechanical algorithm (which may be a density functional algorithm), such as COSMO theory, Emx £ cu (ar may be the van der Waal energy.
    The empirical relationship may be a non-linear relationship, or the empirical relationship may be a linear relationship.
    That is to say, in some embodiments, the coefficients of non-linear terms of the best fit of the measured values to the non-linear mathematical function may be zero.
    Fitting may comprise removing outlying values from the measured values. A set of measured values may include outlying values of certain known components which are inconsistent with the measured values of the other known components.
    Removing outlying values may comprise identifying the Influence of each measured value on the quality of fit between the measured data and the empirical relationship (for example on an R2 value), and / or on coefficients of the empirical relationship.
    Removing outlying values may comprise omitting a measured value having an influence on the quality of fit or on the coefficients, as the case may be, above a threshold value.
    Fitting may comprise applying one or more selection conditions, for example to identify and / or remove outlying values. A selection condition may influence an measured value above a predetermined threshold.
    The method may comprise calculating a mode! probability distribution from a set of desired values of a set of physical properties, using a set of empirical relationships between one or more statistical moments of the probability distribution and each of the physical properties.
    The method may comprise solving N simultaneous .empirical relationships between one or more statistical moments and N desired physical properties, based on N desired values, for values of P statistical moments (where N is preferred & P). Thus, the method may comprise calculating a set of optimized statistical moments, and calculating a model charge density probability distribution from the optimized statistical moments.
    The target probability distribution may be obtained from stored data, for example from a literature resource (e.g., a library of sigma-prof lies).
    Obtaining target charge density probability distribution information may comprise calculating a target charge density probability distribution based on chemical structure information relating to the target component; e, g, using GOSMG theory.
    The target charge density probability distribution information may be calculated from a known value of a: physical property (typically a measured value, for example obtained from literature data), based on an empirical relationship between one or more statistical moments of a charge density probability distribution and the physical property. The target charge density probability distribution information may be calculated from a set of known values of physical properties of the target component, using a set of one or more empirical relationships between one or more statistical moments of a charge density probability distribution and each of the physical properties (for example, by calculating a set of optimized statistical moments from N simultaneous empirical relationships, as described above).
    Accordingly, known values of a first set of one or more physical properties may be used to calculate the target charge density probability distribution information, for the evaluation of the suitability of the target component in relation to a set of model values of a different, second set of physical properties (eg, having no or only some physical properties in common with the first set, and / or relation: to another chemical system). The suitability of a target component may therefore be evaluated without knowledge of chemical structure information related to the component.
    The method may comprise screening (for example in silico) a library (i, e. Of two or more, or a plurality of) target components, by evaluating the suitability of each of the target components in the library,
    Each of the screened target components may be ranked in order of their suitability. Screening may comprise ranking the target components in order of suitability.
    Screening may determine which of the target components is most suitable (i.e., by Identifying the target charge density probability distribution having the highest value of suitability),
    Screening may comprise accepting target components with, a suitability above a predetermined threshold and rejecting target components with a suitability below the predetermi ned threshoid,
    The library may comprise information relating to known chemical components. The library may comprise information relating to proposed chemical components. The library may comprise or be stored in a database and may comprise data concerning various chemical components. The data may be obtained using any suitable method, for example experimentally, theoretically and / or by extrapolation or interpolation from other experimental or theoretical data.
    The information relating to a chemical component may comprise one or more of: values of one or more physical properties, chemical structure information relating to each of the chemical components; a charge density probability distribution, or one or more properties of a said probability distribution (e g one or more statistical moments).
    The library may comprise information relating to a group of two or more (or a plurality of) chemical components independently selected from the following groups of chemical components: wettability modifiers; Polymers; alcohols; basic (alkali) agents; acidic agents; gels, Including water swafiabSa gels; cross-linker molecules; surfactants; material / oompounds for fracking; salts; gases, including dissolved gases, ionic liquids.
    The library may comprise information relating to a group of two or more polymers, proteins, enzymes, polysaccharides, amino acids and / or ionic liquids.
    The one or more physical property of properties may each be selected from the group comprising; viscosity,, interfacial tension, surface tension, contact angle (between a fluid component of the chemical system and a solid component of the Chemical system), adsorption coefficient (eg, to a particular rock or rock type present in a well), adsorption enthalpy , partition coefficient, diffusion coefficient, solubility (eg solubility in brine), partition coefficient, dielectric constant, rheological properties (such as a rate of change or other parameters. descriptive -of changes in viscosity with shear rate, temperature and / or concentration) , thermal stability. The one or more physical property or properties may comprise at least one viscosity, interface! tension, surface tension, contact angle (between a fluid component of the chemical system and a solid component of the chemical system), adsorption coefficient (eg, to a particular rock: or rock type present in a well), adsorption enthalpy, partition coefficient , diffusion coefficient, solubility (eg solubility In brine), partition coefficient, dielectric constant, rheological properties (such as a rate of change or other parameter descriptive of changes in viscosity with shear rate, temperature and / or concentration), thermal stability.
    The one or more physical property or properties may be selected from the group; viscosity, solubility and interfacial tension. The one or more physical property or properties may comprise viscosity and / or solubility and / or interfacial tension.
    The method may be used to design a chert! Leaf system, by identifying a suitable (known or proposed) target component. The method may be used to design a target component, by identifying a proposed chemical component which would be a suitable target component.
    The invention extends in a third aspect to a method of providing a Chemical system (or one or more chemical components thereof), comprising identifying a suitable target component for use in the chemical system, by the method of other aspects of the invention, and providing the target component and, optionally, one or further components of the chemical system.
    The method may comprise identifying a target component which, as mentioned above, may be an existing / known chemical component. The chemical system may be provided by obtaining an amount of the target component (e, g, by synthesizing the target component. In accordance with established synthetic methods as known, to those skilled in the art, or by purchasing an amount of the chemical from a chemical supplier) and mixing, blending or the like the target component with other components of the chemical system.
    The invention is not concerned with any specific method for mixing or blending a chemical system comprising a formulation of various chemicals, If it is well understood that such formulations may be provided in a variety of ways according to specific requirements. For example, provision of a polymeric species within a solvent (e, g, aqueous) formulation may require pre-mixing with a co-solvent (eg, an alcohol) or addition of a co-solvent in order to facilitate dissolution of the polymer . Dissolution may be aided by mechanical agitation, such as high-shear mixing, sonication and the like, and / or modification of the physical form of the polymer prior to dissolution e.g. by grinding so as to increase surface area.
    Dissolution of components which are gaseous under, for example, ambient conditions may, for example, require modification of the temperature or pressure of a formulation, or use of apparatus in which the contact time of a gas with a liquid (e.g., an aqueous solvent or co-solvent formulation) Is increased, for example using a cyclone pump or the like.
    The target component, or the chemical system consisting of a target component, may have a new chemical For example, the method may comprise determining a chemical structure ..which would give rise to one or more desired physical properties (either alone or within a chemical system). Accordingly, the chemical component / system may be synthesized alternatively or additionally, in accordance with established synthetic methods. For example, the target component may have a structure similar to a known chemical and the method may comprise making one or more substitutions or additions of functional chemical groups of the known chemical.
    The method may comprise identifying a suitable target component of a chemical formulation, or providing a chemical formulation, for use in enhanced oil recovery. The chemical formulation may comprise one or more chemical compounds for injection into a well, or one or more chemical compounds which may be added to a solution (for example brine) for Injection Into a well, so as to facilitate recovery of oil from the well . The method may comprise providing the chemical, formulation and injecting the chemical formulation Into a well. The method may then comprise recovering oil from a well.
    The chemical formulation may increase the capillary number between oil in the well and displacing fluid (compared to brine). The physical property or properties may comprise one or more of, viscosity, interfacial tension ..
    According to a fourth aspect of the invention there is provided a system for evaluating: the suitability of a target component for use in a target chemical system, the system comprising a processing resource, operable to; calculate model charge density probability distribution: information related to a chemical component; the model charge density probability distribution information corresponding to a set of mode! values of physical properties of the target chemical system; to obtain target charge density probability distribution information relating to the target component; and to determine the similarity of the model charge density probability distribution information and target charge density probability distribution information.
    The system may comprise a processing module (operable to calculate the model charge density probability distribution information), and a comparator module (operable to determine the similarity of the model and target charge density probability distribution information).
    The system may comprise a data store. The data store may be operable to store or receive any or all of; chemical structure information, a value of one or more physical properties of one or more target components, one or more empirical relationships between statistical moment (s) and a physical property, charge density probability Information related to one or more target components. The chemical structure information may relate to one or more target components, and / or may comprise proposed chemical structure information.
    The processing resource may be operable to calculate a target charge density probability distribution of the charge density (of the target component), for example, from chemical structure information or values of one or more physical properties, received from the data store.
    The processing resource may be operable to calculate proposed charge density probability distribution information, based on proposed chemical structure information (for example, as received from a data store). The processing resource may be operable to revise the proposed chemical structure information and calculate further proposed charge density probability distribution information. The processing resource may be operable to execute an iterative algorithm, to predict a set of values of physical properties from proposed charge density probability distribution information, compare the predicted values to a set of desired values (and optionally one or more exclusion conditions), as amended. the proposed Chemical structure information and calculate revised charge density probability distribution information and to predict a revised set of values.
    The system may comprise an input module, e, g, a user interface. The input module may be operable to receive desired values of one or more physical properties, or one or more parameters related to a charge density probability distribution consistent with-the desired values, and / or one or more exclusion conditions,
    While particular modules and processing resources have been described as heroin, the functionality of one or more of those modules can be provided by a single module, processing resource or other component Conversely, the functionality of a given module can be provided by two or more modules, processing resources or other components in combination. Reference to a single module encompasses multiple components providing the functionality of that module®, whether or not such components are remote from one another, and reference to multiple modules encompasses a single component providing the functionality of those modules.
    According to a fifth aspect of the Invention, a computer readable medium comprising program code executable on a computing device (such as a system of the fourth aspect) is provided to perform methods in accordance with the invention.
    Further preferred and optional features of each aspect of the invention correspond to preferred and optional features of any other aspect of the invention,
    Description of the Drawings
    Non-limiting example embodiments of the invention will now be described with reference to the following drawings in which:
    Figure 1 is a graphical representation of (a) example sigma profiles and (b) example sigma potential profiles.
    Figure 2 is a schematic representation of a system for evaluating a target component. Figure 3 is a flowchart showing the method of using the system of Figure 2.
    Figure 4 is a flowchart showing a method of calculating a model sigma profile.
    Figure 5 is a flowchart showing an alternative method of calculating a sigma profile. Figure 8 is a flowchart showing a method of 'screening a library of target components. Detailed Description of Example Embodiments
    Figure 2 shows a system 1 for evaluating the suitability of a target component for use in a target chemical system, in accordance with the Invention. The system includes a processing resource 2, which includes a processing module 3, a comparator module 6 and a data store 7. The system 1 further comprises an optional user interface module 9. A library of chemical components is stored on the data store 7, in the form of sigma profiles for each component In the library. In other embodiments, the library contains chemical structure information, values of parameters of a sigma profile, or values of physical properties, of each component of the library,
    The processing module 3 is operable to calculate a model sigma profile (model charge density probability distribution information) which corresponds to a set of mode! values of physical properties of the target chemical system, as described in further detail below.
    The comparator module S is operable to obtain a target probability distribution of a target component from the data store 7, and determine the similarity of the mode! sigma profile and the target sigma profile, so as to generate a value of the suitability of the target component. By evaluating the suitability of each of the target components in the library (i, e, by screening the library) the most suitable target component or components can be identified.
    The system may comprise a computing device (such as a personal computer or a workstation), having a user interface (eg a keyboard and / or other user input device, one or more screens), a data store in the form of one or more volatile and / or nonvolatile data storage devices (eg a bard drive, RAM), functioning as a data store, and a processor, functioning as the processing resource.
    In the present embodiment, the processing module 3, comparator module 5 are implemented in computing apparatus (the system 1), by means of a computer program having computer-readable instructions that are executable by a central processing unit (CPU) of the computing apparatus to perform the method of the embodiment. The system may also include a hard drive and other components of a computing device including RAM, ROM, a data bus, an operating system including various device drivers, and hardware devices including a graphics card. Such components are not shown in the figures for clarity.
    Other embodiments of each module may be implemented in software, hardware or any suitable combination of hardware and software. For example, the various modules may be implemented as one or more ASICs (application specific integrated circuits) or FPGAs (field programmable gate arrays).
    In use, the system 1 functions according to the generalized work How set out in Figure 3. It will be appreciated by the skilled addressee that the sequence of steps may vary from the sequence set out in Figure 3 and described herein.
    At stage 10, a model sigma profile 11 was calculated using COSMO theory, for example using the program COSMOthermX (published by COSMO logic GmbH & Co. KG), Stage 10 is described in further detail below, with reference to Figures 4 and 5 .
    At stage 12, a target slgma-preflie 13 is obtained by the comparator module. 5, from a library of profiles stored on the data store 7 ,. In alternative embodiments (not shown) the target sigma profile, or parameters thereof, is / are calculated from chemical structure Information related to the corresponding target component (eg using COSMO theory), or from values of physical properties of a target component or a target chemsoal system comprising the target component, using one or more empirical relationships between sigma moments and the physical properties, The values may be measured values, may be calculated from measured values arid / pr may be predicted values. In some embodiments, a target sigma profile is then reconstructed using a generalized Lambda distribution model
    At stage 14, the comparator module determines the similarity of the sigma profiles 11, 13 by 'determining an integral of the distances between the sigma profiles (Le, proportional to the areas 15 (only two of which are shown in Figure 3, For illustrative purposes, the resulting value S of the target component's suitability corresponding to the sigma profile 13 is calculated. The value S and, optionally, other information related to the target component may be output to the user interface module 9 .
    Figure 4 shows an embodiment of stage 10., at stage 101, chemical structure information 17 Is input from the user interface module 9, the chemical structure information need not relate..to a real chemical component. For example, a starting point could be the structure of a hypothetical pseudo-moteoute; perhaps a variation of a component of an existing formulation.
    At stage 103, a proposed sigma profile 17 is calculated by the processing module 3 using COSMO theory. At stage 105, a set of predicted values | ij, k,., X} of physical properties [A, B, C .., M] are calculated from the proposed sigma profile 17, These properties could be, for example, low adsorption, a particular range of intertacial tensions, low viscosity, etc., or any other range of physical properties of interest. At stage © 107, the values {I j, k ,,, x} of these properties are compared to a corresponding set of desired values if the values {Μ, Κ., Χ} are acceptable as a set of model values ( eg, If each value x is within predetermined limits of the corresponding desired value x, j), then the proposed sigma profile 17 is used as the model profile 11.
    If the values {i, j, k ,,, x} are not acceptable, stages 101, 103 and 105 are repeated, based on modified chemical structure Information 15a until acceptable model values are achieved
    The modified chemical structure information 15a may be input by a user, or may be determined using an iterative algorithm.
    Whenever a model sigma profile gives rise to a set of model values, a chemical component having a similar "close-enough" sigma profile can be expected to possess a similar set of values of physical properties (or form part of a chemical system with these properties), Measuring the similarity, or quality of fit between sigma profiles provides an objective measure of how suitable a target component can be expected to be. For example, if values of several physical properties are sought, say viscosity and interfacial tension, it may be that one candidate chemical has a viscosity within 10% of the desired value, and an interfacial tension within, say, 5%. Another candidate component from the library may have a viscosity within 6% but an interfacial tension within 10%. In such circumstances, it may not be otherwise which component could be expected to be the most suitable, whereas evaluating the similarity of their respective sigma profiles to the model sigma profile may provide a value of similarity so as to distinguish between them .
    The library of target components can therefore be screened to find the dosast match or matches to the model sigma profile, and so effect to "reverse engineer * one, or a comparatively small number of target chemical systems which can then be prepared and further Thus, application of the method can significantly reduce the amount of experimental chemistry that needs to be performed before arriving at a chemical system suitable for a particular application.
    In an alternative embodiment (not shown), a set of proposed values of sigma moments (parameters of a proposed sigma profile) are calculated at stage 103 and, at stage 106, a set of desired values of corresponding sigma moments are calculated from a set of desired values of physical properties (e, g, from empirical relationships between the physical properties and the sigma moments). At stage 107, the proposed and desired values are compared. If the values are not acceptable as model values, the chemical structure Information is modified, as described above.
    Another embodiment of stage 10 is shown in Figure 5. At stage 102 * a set of desired values fMd, of physical properties {Α, Β, ϋ ,, Μ} are defined, typically Input by a user at the interface module 9. These properties might be the properties required for, say, a surfactant: or a polymer for a chemical flooding application. For example, the properties might have a desired vapor pressure, viscosity, Interfadal tension with water, constant adsorption, etc.
    At stage 104, empirical relationships ft g. ,, w between properties {A, B, C. "N} and Sigma moments M> are received (optionally, together with other descriptors of the target component, for example, the energy descriptors, molecular weight or volume, set out in Equation (1)}, The empirics! relationships may for example be stored by and received from the data store 7, received from the interface module 9, or may be determined by the processing module 5 by fitting measured values {/ (/ ', of a set of physical properties (which may be the same, or different to the set of properties {Α, Β, Ο ,, ,, Ν}), to Equation 1,
    Each of the empirical relationships may predict differing values for the sigma moments / descriptors, or the collection of sigma moments / descrlptors may be built up to a sigma profile which would not be associated with a suitable set of model parameters, in order to optimize the values and obtain a set of self-consistent descriptors, at stage 106, an algorithm is executed, to solve the N simultaneous equations using, as starting values, the set of desired values {qsjd, kd ",, xd} and (optionally) one or more exclusion conditions The solution of the N simultaneous equations yields a set of optimized sigma moments M) and, optionally, other descriptors. The optimized sigma moments / descriptors are then used, at stage 108, to calculate a model sigma profile 11, using the generalized lambda distribution model. As described above, this model sigma profile can be used to screen for suitable target components in the library '.
    Figure 6 shows an alternative embodiment of stage 14, in which a library 19 of sigma profiles of target components is screened, at stage 141. Values 5¾ S2 ,,, S * of the target components 1, 2 .. ,, n in the library is calculated in the lesser described above and, at stage 143, the highest value of suitability $ m8X Is determined. The corresponding sigma profile 21 of the most suitable target component, together with any additional information 23 regarding the target component stored on the data store 7, may then be output to the Interface module 9,
    The methods in accordance with the invention can be considered as a form of "reverse engineering" of a target chemical system or a target component, since model values of physical properties of the target chemical system are used to identify a target component, in contrast to more conventional methods of designing a chemical system and then testing its properties.
    For example, the method may be used to Investigate the predicted properties of chemical systems comprising one or more alternative chemical components, to determine which would be the most suitable, or which of the alternatives would be suitable, without the need to prepare and test multiple chemical systems. For example, the system and methods described above may be used to determine whether related chemical components {e.g. a polymer with a different average molecular weight) are predicted to improve a given physical property. A single chemical system, or a comparatively small number of systems, can then be provided for experimental evaluation.
    Accordingly, the method may comprise identifying a suitable target chemical component and obtaining or synthesizing (e.g. by conventional methods) an amount of that Chemical component, together with an amount of any other components of the target chemical system whose suitability the target component has been evaluated. in. relation to. The various components may then be contacted (e.g. by blending, mixing and / or beating and the like) and the Chemical system so provided may then be used as Intended. For example, the Chemical system may be used for enhanced oil recovery and its use may involve injecting the chemical system into a well. Oil, or an increased rate of production thereof, may then be recovered from the well.
    Thus, a potentially suitable chemical component can be identified, and chemical systems comprising that component provided, without the requirement to conduct experiments to investigate each of the physical properties of interest, or to obtain and potentially conduct multiple searches of data libraries.
    While certain embodiments have been described, these embodiments have been presented by way of example only and are not intended to limit the scope of the invention. Indeed, the novel apparatus and methods described herein may be embodied in a variety of other forms. Furthermore, various omissions, substitutions and changes to the embodiments described herein may be made without departing from the scope and spirit of the invention.
    权利要求:
    Claims (25)
    [1]
    A method for assessing the suitability of a target component for use in a chemical target system, comprising: calculating information about model probability distribution of charge density for a chemical component; wherein information on model probability distribution of charge density corresponds to a set of model values of physical properties of the chemical target system; obtaining information about target probability distribution of charge density regarding the target component; and determining the similarity between information on model density distribution of charge density and information on target probability distribution of charge density.
    [2]
    The method of claim 1, comprising assessing the suitability of a target component for use in enhanced oil recovery.
    [3]
    A method according to claim 1 or 2, comprising: - obtaining target probability distribution of charge density concerning the target component from a data store; or - using a processing resource to calculate information about target density distribution of charge density from a probability distribution of charge density or from target values of a set of physical properties obtained from a data store; and using a processing resource to: - calculate information on model probability distribution of charge density for a chemical component; the information on model probability distribution of charge density corresponding to a set of model values of physical properties of the chemical target system; and - determining the similarity between information on model density distribution of charge density and information on target probability distribution of charge density.
    [4]
    A method according to any one of the preceding claims, wherein the information on model and target probability distribution of charge density each comprises a probability density distribution.
    [5]
    A method according to any one of claims 1 to 3, wherein the information on the model and target probability distribution of charge density comprises one or more statistical moments of a respective probability distribution of charge density.
    [6]
    The method of claim 4, wherein the similarity of the information about model and target probability distribution of charge density is determined by a target number for the distance between the model and target probability distributions of charge density.
    [7]
    The method of claim 5, wherein the similarity of information about model and target probability distribution of charge density is determined by a target number for a distance between one or more corresponding statistical moments for each of the probability density distributions.
    [8]
    A method according to any one of the preceding claims, comprising calculating a model and / or target probability distribution of charge density from one or more parameters of a respective probability distribution.
    [9]
    A method according to any one of the preceding claims, wherein calculating information about model probability distribution of charge density comprises: predicting a set of values of the physical properties from proposed information about probability distribution of charge density; and comparing the set of the predicted values with a set of desired values of the physical properties of the chemical target system, or a set of desired parameter values of a probability distribution of charge density corresponding to desired values of the physical properties; so as to determine whether the predicted values are an acceptable set of model values, and thus whether the predicted probability distribution information is suitable information about model probability distribution of charge density.
    [10]
    A method according to claim 9, comprising solving N mutually dependent simultaneous equations based on N desired values of a set of N physical properties or N corresponding parameters of a probability distribution of charge density for values of P variable simultaneous equations so as to calculate the model probability distribution information. of charge density wherein N > P.
    [11]
    The method of claim 10, comprising using one or more exclusion conditions to exclude solutions of the simultaneous equations corresponding to a value of a physical property or parameter differing by more than a predetermined amount from the desired value.
    [12]
    A method according to any of the preceding claims, comprising calculating a proposed probability density distribution of charge based on a proposed chemical structure, predicting a set of values from the proposed probability density distribution, and determining whether the predicted values are a acceptable set of model values of the physical properties.
    [13]
    A method according to any one of the preceding claims, comprising calculating a model probability distribution from a set of desired values of a set of physical properties using a set of empirical relationships between one or more statistical moments of the probability distribution and each of the physical properties.
    [14]
    A method according to any one of the preceding claims, wherein obtaining information about target density distribution of charge density comprises calculating a target probability distribution of charge density based on chemical structure information regarding the target component.
    [15]
    The method of any one of claims 1-13, wherein obtaining information about target density distribution of charge density comprises calculating information about target probability distribution of charge density from one or more known values of a physical property, based on one or more empirical relationships between a or multiple statistical moments of a probability distribution of charge density and each or every physical property.
    [16]
    A method according to any one of the preceding claims, comprising screening a library of target components by assessing the suitability of each of the target components in the library.
    [17]
    The method of claim 16, wherein the library comprises a group of two or more chemical components independently selected from the following groups of chemical components: humidity modifiers; polymers; alcohols; basic (alkali) agents; acidic agents; gels, including water-swellable gels; crosslinking molecules; surfactants; materials / compounds for fracking; salts; gases including dissolved gases and ionic liquids.
    [18]
    The method of claim 16 or 17, wherein the target components are ranked sequentially by suitability.
    [19]
    A method according to any one of the preceding claims, wherein the physical properties are selected from the group comprising: viscosity, interface voltage, surface tension, contact angle, adsorption coefficient, adsorption enthalpy, partition coefficient, diffusion coefficient, solubility, partition coefficient, dielectric constant, rheological properties, stability.
    [20]
    A method of providing a chemical system or chemical components thereof, including identifying a suitable target component for use in the chemical system by the method of any of the preceding claims and providing the target component.
    [21]
    The method of claim 20, comprising providing one or more additional components of the chemical system.
    [22]
    The method of claim 20 or 21, wherein the chemical system is for use in enhanced oil recovery.
    [23]
    A system for assessing the suitability of a target component for use in or as a chemical target system, the system comprising a processing resource capable of operating to: calculate information on model probability distribution of charge density for a chemical component; the information on model probability distribution of charge density corresponding to a set of model values of physical properties of the chemical target system; for obtaining target probability distribution of charge density regarding a target component; and to determine the similarity between the information on the model probability distribution of charge density and the information on the target probability distribution of charge density.
    [24]
    The system of claim 23, comprising a data store, the data store comprising: target probability distribution information discharge density relating to the target component.
    [25]
    The system of claim 23, comprising a data store, the data store comprising target probability distribution of charge density or target values of a set of physical properties; wherein the processing resource can operate to calculate the information about target probability distribution of charge density from the probability distribution of charge density or target values.
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    同族专利:
    公开号 | 公开日
    WO2015032505A1|2015-03-12|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    DE10065444A1|2000-12-27|2002-07-04|Haarmann & Reimer Gmbh|Selection process for flavorings|CN105975772B|2016-05-04|2019-02-05|浙江大学|Tracking before multi-target detection based on probability hypothesis density filtering|
    EP3764252A4|2018-03-09|2021-12-01|Showa Denko K.K.|Polymer physical property prediction device, storage medium, and polymer physical property prediction method|
    法律状态:
    2016-06-06| PHB| Application deemed withdrawn due to non-payment or other reasons|Effective date: 20160510 |
    优先权:
    申请号 | 申请日 | 专利标题
    US201361874389P| true| 2013-09-06|2013-09-06|
    US201361874389|2013-09-06|
    EP13183429|2013-09-06|
    EP13183429|2013-09-06|
    PCT/EP2014/002410|WO2015032505A1|2013-09-06|2014-09-05|Evaluation of chemical components|
    EP2014002410|2014-09-05|
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