DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is communication is responsive to application filed on 08/11/2023.
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/11/2023, 09/25/2024, and 01/29/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, and 9-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US Publication No. 2014/0200810 A1 issued to Zuo et al.
Claim 1. Zuo et al discloses a method comprising:
receiving sample information for reservoir fluid samples (See: [0012] Third is the comparison of geochemical fingerprints of fluid samples acquired from different locations in the reservoir. Petroleum is a complex chemical mixture, containing many different chemical compounds; the composition of that petroleum can therefore be treated as a fingerprint. If the composition of petroleum samples from two different places in the reservoir is the same, it is assumed that fluids can flow readily between those two places in the reservoir, and hence that the reservoir is connected. However, forces such as biodegradation and water washing can occur to different extents in different parts of the reservoir, causing two locations in the reservoir to have different fingerprints even if they are connected. Additionally, petroleum samples generated from the same source rock may have very similar fingerprints even if they come from locations in the reservoir that are presently disconnected; [0017] Fluid samples acquired from the wellbore are analyzed to measure concentration of the analyte(s) as a function of location within the wellbore. The simulated non-equilibrium concentration of the analyte(s) as a function of location within the wellbore is compared to the measured concentration of the analyte(s) as a function of location within the wellbore, and the architecture of the reservoir of interest is determined from such comparison; [0027] The fluid analysis module 25 includes means for measuring the temperature and pressure of the fluid in the flowline. The fluid analysis module 25 derives properties that characterize the formation fluid sample at the flowline pressure and temperature. In the preferred embodiment, the fluid analysis module 25 measures absorption spectra and translates such measurements into concentrations of several alkane components and groups (or lumps) in the fluid sample; [0030] The tool 10 of FIG. 1A is adapted to make in situ determinations regarding hydrocarbon-bearing geological formations by downhole sampling of reservoir fluid at one or more measurement stations within the borehole 12, and by conducting downhole fluid analysis of one or more reservoir fluid samples for each measurement station (including compositional analysis such as estimating concentrations of a plurality of compositional components of a given sample and other fluid properties));
automatically selecting one or more equations of state from a plurality of different equations of state (See: [0013] An alternative method to assess connectivity is to evaluate hydrocarbon fluid compositional grading. The chemical composition of petroleum must be different in different parts of a connected reservoir. This change in composition with position (typically depth) in the reservoir is referred to as compositional grading. The magnitude of this compositional grading (i.e., the difference in the composition of two fluids collected from different depths), in connected reservoirs at thermodynamic equilibrium, can be measured with downhole fluid analysis and predicted with a mathematical equation of state (EOS) model. The EOS model is based on assumptions that the reservoir is connected and at thermodynamic equilibrium. If the magnitude of compositional grading as measured matches the predicted composition grading, then the assumptions of the EOS model are confirmed. In the event that the magnitude of the measured compositional grading does not match the predictions of the EOS model, it can be assumed that there is reservoir compartmentalization or that the reservoir fluids are not in equilibrium; [0059] In step 205, the results of the delumping process of step 203 are used in conjunction with an equation of state (EOS) model (and possibly other predictive models) to predict compositional gradients and other property gradients (such as volumetric behavior of oil and gas mixtures) as a function of depth within the wellbore; [0063] The FHZ EOS model employs an equation of state together with flash calculations to predict compositions (including asphaltene) as a function of depth in the reservoir. The equation of state represents the phase behavior of the compositional components of the reservoir fluid);
automatically generating initial conditions based at least in part on the sample information (See: [0110] In the preferred embodiment, Eqs. (30)-(32) can be discretized into a set of numerical equations that are applied over computational cells with appropriate initial and boundary conditions. The unknowns of Eqs. (30)-(32) consist of the density of each component as a function of time and space (e.g., x, y, z coordinates in the event that a Cartesian grid is used). The numerical equations can be solved by numerical methods to obtain the non-equilibrium concentration (e.g., mole fractions) of the components of the reservoir fluids (including the concentration of the single phase components of the earlier charged hydrocarbons and the concentration of the later charged gas component) as a function of location in the reservoir over time. The initial conditions for the components of the earlier charged hydrocarbons can be derived from equilibrium conditions based upon the predicted compositional gradient of the reservoir fluid as calculated in steps 205 and 209 by solving Eqs. (17)-(19). The initial conditions for the components of the earlier charged hydrocarbons can also be derived from non-equilibrium conditions; [0112] As part of step 223, laboratory measurements and analysis can be performed on fluid samples (and/or core samples) acquired from the reservoir of interest in steps 201 and 207 in order to identify the appropriate hydrocarbon component(s) for the model and confirm non-equilibrium concentrations of such component(s) in the reservoir fluids);
simulating physical phenomena using at least a reservoir model to generate simulation results (See: [0113] In step 225, the concentration gradient of the hydrocarbon component(s) produced by the simulation results in step 223 is compared to the concentration gradient of such hydrocarbon component(s) as measured by downhole fluid analysis (and/or laboratory fluid analysis) in steps 201 and 207. In the preferred embodiment, the comparison is performed for simulation results of the time step whose concentration gradient is most similar to the concentration gradient of the single component hydrocarbon as measured by downhole fluid analysis (and/or laboratory fluid analysis) in steps 201 and 207), wherein the simulating utilizes the selected one or more equations of state and the initial conditions (See: [0110] [0110] In the preferred embodiment, Eqs. (30)-(32) can be discretized into a set of numerical equations that are applied over computational cells with appropriate initial and boundary conditions. The unknowns of Eqs. (30)-(32) consist of the density of each component as a function of time and space (e.g., x, y, z coordinates in the event that a Cartesian grid is used). The numerical equations can be solved by numerical methods to obtain the non-equilibrium concentration (e.g., mole fractions) of the components of the reservoir fluids (including the concentration of the single phase components of the earlier charged hydrocarbons and the concentration of the later charged gas component) as a function of location in the reservoir over time. The initial conditions for the components of the earlier charged hydrocarbons can be derived from equilibrium conditions based upon the predicted compositional gradient of the reservoir fluid as calculated in steps 205 and 209 by solving Eqs. (17)-(19). The initial conditions for the components of the earlier charged hydrocarbons can also be derived from non-equilibrium conditions); and
outputting at least a portion of the simulation results (See: [0113] In step 225, the concentration gradient of the hydrocarbon component(s) produced by the simulation results in step 223 is compared to the concentration gradient of such hydrocarbon component(s) as measured by downhole fluid analysis (and/or laboratory fluid analysis) in steps 201 and 207. In the preferred embodiment, the comparison is performed for simulation results of the time step whose concentration gradient is most similar to the concentration gradient of the single component hydrocarbon as measured by downhole fluid analysis (and/or laboratory fluid analysis) in steps 201 and 207).
Claim 2. Zuo et al discloses the method of claim 1, wherein the initial conditions comprise compositional variation with respect to depth of reservoir fluid for the reservoir model (See: [0110] The initial conditions for the components of the earlier charged hydrocarbons can be derived from equilibrium conditions based upon the predicted compositional gradient of the reservoir fluid as calculated in steps 205 and 209 by solving Eqs. (17)-(19)).
Claim 3. Zuo et al discloses the method of claim 1, wherein automatically generating initial conditions comprises detecting reservoir compartmentalization (See: [0114] In step 227, the differences between concentration gradient as derived from the comparison of step 225 is evaluated to determine if such differences are relatively small (e.g., satisfies a predetermined low threshold parameter) or are relatively large (e.g., satisfies a predetermined high threshold parameter). If the differences are relatively small, it is likely the reservoir is connected and in a state of non-equilibrium, and the operations continue to step 229. If the differences are relatively large, it is likely the reservoir is compartmentalized, and the operations continue to step 231; [0117] In step 231, the workflow has determined that it is likely that the reservoir is compartmentalized. Additional operations can be performed to verify that the reservoir is compartmentalized).
Claim 4. Zuo et al discloses the method of claim 3, wherein the initial conditions comprise a first set of initial conditions for a first reservoir compartment and a second set of initial conditions for a second reservoir compartment, wherein the initial conditions of the first set and the second set differ (See: [0100] In step 223, the evaluation of step 217 has indicated a likelihood that the reservoir is compartmentalized or in a state of non-equilibrium. In order to differentiate between these two possible reservoir architectures (compartmentalized versus connected but in state of non-equilibrium), the operations utilize a model to simulate diffusion of a single component gas charge as a function of depth in the wellbore over time. The model assumes that the reservoir was charged two times--an initial charge of hydrocarbons followed by a later charge of a single gaseous component (such as methane, carbon dioxide, or hydrogen sulfide) followed. The model describes the later charge process and simulates the diffusion and transport of the single gaseous component in the initial charge of hydrocarbons as a function of location in the wellbore over time. The simulation can be carried out over a number of time steps as desired by the user; [0110] In the preferred embodiment, Eqs. (30)-(32) can be discretized into a set of numerical equations that are applied over computational cells with appropriate initial and boundary conditions. The unknowns of Eqs. (30)-(32) consist of the density of each component as a function of time and space (e.g., x, y, z coordinates in the event that a Cartesian grid is used). The numerical equations can be solved by numerical methods to obtain the non-equilibrium concentration (e.g., mole fractions) of the components of the reservoir fluids (including the concentration of the single phase components of the earlier charged hydrocarbons and the concentration of the later charged gas component) as a function of location in the reservoir over time. The initial conditions for the components of the earlier charged hydrocarbons can be derived from equilibrium conditions based upon the predicted compositional gradient of the reservoir fluid as calculated in steps 205 and 209 by solving Eqs. (17)-(19). The initial conditions for the components of the earlier charged hydrocarbons can also be derived from non-equilibrium conditions).
Claim 5. Zuo et al discloses the method of claim 3, wherein detecting reservoir compartmentalization comprises comparing compositional variation with respect to depth in different areal regions (See: [0013] An alternative method to assess connectivity is to evaluate hydrocarbon fluid compositional grading. The chemical composition of petroleum must be different in different parts of a connected reservoir. This change in composition with position (typically depth) in the reservoir is referred to as compositional grading. The magnitude of this compositional grading (i.e., the difference in the composition of two fluids collected from different depths), in connected reservoirs at thermodynamic equilibrium, can be measured with downhole fluid analysis and predicted with a mathematical equation of state (EOS) model. The EOS model is based on assumptions that the reservoir is connected and at thermodynamic equilibrium. If the magnitude of compositional grading as measured matches the predicted composition grading, then the assumptions of the EOS model are confirmed; [0062] In the preferred embodiment, the modeling operations of step 205 employ a particular equation of state model, referred to herein as the Flory-Huggins-Zuo (FHZ) EOS model, that derives compositional gradients and other property gradients (e.g., pressure and temperature gradients) that describe the volumetric behavior of the oil and gas (and possibly water) mixture in reservoir fluids as a function of depth in the reservoir of interest).
Claim 9. Zuo et al discloses the method of claim 1, wherein automatically selecting one or more equations of state comprises selecting an equation of state for a reservoir location and selecting another, different equation of state for a surface location (See: [0060] The equation of state (EOS) model describes the thermodynamic behavior of the fluid and provides for characterization of the reservoir fluid at different locations within the reservoir. With the reservoir fluid characterized with respect to its thermodynamic behavior, fluid production parameters, transport properties, and other commercially useful indicators of the reservoir can be computed).
Claim 10. Zuo et al discloses the method of claim 9, wherein the surface location corresponds to a well mixing location where fluid from two or more wells mix (See: [0108] The model of step 223 also preferably treats the initial charge of hydrocarbons as a mixture of a number of components in a single phase (either gas or liquid) at one depth and ignores chemical reactions between components (mixing processes are physically mixing instead of chemically mixing). Gravity, diffusion, and chemical potential forces are also accounted for by the model; [0110] The numerical equations can be solved by numerical methods to obtain the non-equilibrium concentration (e.g., mole fractions) of the components of the reservoir fluids (including the concentration of the single phase components of the earlier charged hydrocarbons and the concentration of the later charged gas component) as a function of location in the reservoir over time. The initial conditions for the components of the earlier charged hydrocarbons can be derived from equilibrium conditions based upon the predicted compositional gradient of the reservoir fluid as calculated in steps 205 and 209 by solving Eqs. (17)-(19). The initial conditions for the components of the earlier charged hydrocarbons can also be derived from non-equilibrium conditions).
Claim 11. Zuo et al discloses the method of claim 10, wherein the simulating comprises simulating physical phenomena at the well mixing location (See: [0108] The model of step 223 also preferably treats the initial charge of hydrocarbons as a mixture of a number of components in a single phase (either gas or liquid) at one depth and ignores chemical reactions between components (mixing processes are physically mixing instead of chemically mixing). Gravity, diffusion, and chemical potential forces are also accounted for by the model; [0110] The numerical equations can be solved by numerical methods to obtain the non-equilibrium concentration (e.g., mole fractions) of the components of the reservoir fluids (including the concentration of the single phase components of the earlier charged hydrocarbons and the concentration of the later charged gas component) as a function of location in the reservoir over time. The initial conditions for the components of the earlier charged hydrocarbons can be derived from equilibrium conditions based upon the predicted compositional gradient of the reservoir fluid as calculated in steps 205 and 209 by solving Eqs. (17)-(19). The initial conditions for the components of the earlier charged hydrocarbons can also be derived from non-equilibrium conditions).
Claim 12. Zuo et al discloses the method of claim 11, wherein the well mixing location is in fluid communication with a processing facility and wherein the simulating comprises simulating physical phenomena at the processing facility (See: [0113] In step 225, the concentration gradient of the hydrocarbon component(s) produced by the simulation results in step 223 is compared to the concentration gradient of such hydrocarbon component(s) as measured by downhole fluid analysis (and/or laboratory fluid analysis) in steps 201 and 207. In the preferred embodiment, the comparison is performed for simulation results of the time step whose concentration gradient is most similar to the concentration gradient of the single component hydrocarbon as measured by downhole fluid analysis (and/or laboratory fluid analysis) in steps 201 and 207), wherein the simulating utilizes the selected one or more equations of state and the initial conditions (See: [0110] [0110] In the preferred embodiment, Eqs. (30)-(32) can be discretized into a set of numerical equations that are applied over computational cells with appropriate initial and boundary conditions. The unknowns of Eqs. (30)-(32) consist of the density of each component as a function of time and space (e.g., x, y, z coordinates in the event that a Cartesian grid is used). The numerical equations can be solved by numerical methods to obtain the non-equilibrium concentration (e.g., mole fractions) of the components of the reservoir fluids (including the concentration of the single phase components of the earlier charged hydrocarbons and the concentration of the later charged gas component) as a function of location in the reservoir over time. The initial conditions for the components of the earlier charged hydrocarbons can be derived from equilibrium conditions based upon the predicted compositional gradient of the reservoir fluid as calculated in steps 205 and 209 by solving Eqs. (17)-(19). The initial conditions for the components of the earlier charged hydrocarbons can also be derived from non-equilibrium conditions).
Claim 13. Zuo et al discloses the method of claim 1, wherein automatically selecting one or more equations of state comprises testing at least a portion of the plurality of different equations of state with respect to at least a portion of the sample information (See: [0096] In step 217, the measurements and predictions of the fluid properties of the reservoir fluid as derived in steps 201, 205, 207, and/or 209 are evaluated to assess reservoir properties and architecture. For example, the fluid property predictions of the EOS are based on the assumption that the reservoir fluids within the wellbore are connected and in a state of thermodynamic equilibrium… Moreover, a likelihood that the reservoir is connected and in a state of equilibrium can be indicated by consistencies (i.e., small differences) between the fluid property predictions of the EOS model (particularly GOR, asphaltene content, fluid density, and fluid viscosity) and the corresponding downhole fluid analysis measurements; [0112] As part of step 223, laboratory measurements and analysis can be performed on fluid samples (and/or core samples) acquired from the reservoir of interest in steps 201 and 207 in order to identify the appropriate hydrocarbon component(s) for the model and confirm non-equilibrium concentrations of such component(s) in the reservoir fluids. For example, the reservoir fluids can be subject to appropriate isotope analysis to measure the gradient of the suitable carbon isotopes (such as the C13-C12 isotope ratio) in order to ascertain whether there is a non-equilibrium concentration of methane in the reservoir fluids).
Claim 14. Zuo et al discloses the method of claim 1, wherein automatically selecting one or more equations of state comprises ranking at least a portion of the plurality of different equations of state (See: [0060] The equation of state (EOS) model describes the thermodynamic behavior of the fluid and provides for characterization of the reservoir fluid at different locations within the reservoir. With the reservoir fluid characterized with respect to its thermodynamic behavior, fluid production parameters, transport properties, and other commercially useful indicators of the reservoir can be computed; [0061] For example, the EOS model can provide the phase envelope that can be used to interactively vary the rate at which samples are collected in order to avoid entering the two phase region. In another example, the EOS can provide useful properties in assessing production methodologies for the reservoir. Such properties can include density, viscosity, and volume of gas formed from a liquid after expansion to a specified temperature and pressure. The characterization of the fluid sample with respect to its thermodynamic model can also be used as a benchmark to determine the validity of the obtained sample, whether to retain the sample, and/or whether to obtain another sample at the location of interest).
Claim 15. Zuo et al discloses the method of claim 1, wherein automatically generating initial conditions comprises subdividing a reservoir interval into depth windows (See: [0059] In step 205, the results of the delumping process of step 203 are used in conjunction with an equation of state (EOS) model (and possibly other predictive models) to predict compositional gradients and other property gradients (such as volumetric behavior of oil and gas mixtures) as a function of depth within the wellbore; [0110] In the preferred embodiment, Eqs. (30)-(32) can be discretized into a set of numerical equations that are applied over computational cells with appropriate initial and boundary conditions).
Claim 16. Zuo et al discloses the method of claim 15, comprising estimating a compositional variation with respect to depth for each of the depth windows (See: [0013] An alternative method to assess connectivity is to evaluate hydrocarbon fluid compositional grading. The chemical composition of petroleum must be different in different parts of a connected reservoir. This change in composition with position (typically depth) in the reservoir is referred to as compositional grading. The magnitude of this compositional grading (i.e., the difference in the composition of two fluids collected from different depths), in connected reservoirs at thermodynamic equilibrium, can be measured with downhole fluid analysis and predicted with a mathematical equation of state (EOS) model; [0110] In the preferred embodiment, Eqs. (30)-(32) can be discretized into a set of numerical equations that are applied over computational cells with appropriate initial and boundary conditions. The unknowns of Eqs. (30)-(32) consist of the density of each component as a function of time and space (e.g., x, y, z coordinates in the event that a Cartesian grid is used). The numerical equations can be solved by numerical methods to obtain the non-equilibrium concentration (e.g., mole fractions) of the components of the reservoir fluids (including the concentration of the single phase components of the earlier charged hydrocarbons and the concentration of the later charged gas component) as a function of location in the reservoir over time. The initial conditions for the components of the earlier charged hydrocarbons can be derived from equilibrium conditions based upon the predicted compositional gradient of the reservoir fluid as calculated in steps 205 and 209 by solving Eqs. (17)-(19)).
Claim 17. Zuo et al discloses the method of claim 16, comprising computing a composition variation with respect to depth for a depth span that encompasses more than two of the depth windows (See: [0110] The numerical equations can be solved by numerical methods to obtain the non-equilibrium concentration (e.g., mole fractions) of the components of the reservoir fluids (including the concentration of the single phase components of the earlier charged hydrocarbons and the concentration of the later charged gas component) as a function of location in the reservoir over time. The initial conditions for the components of the earlier charged hydrocarbons can be derived from equilibrium conditions based upon the predicted compositional gradient of the reservoir fluid as calculated in steps 205 and 209 by solving Eqs. (17)-(19). The initial conditions for the components of the earlier charged hydrocarbons can also be derived from non-equilibrium conditions).
Claim 18. Zuo et al discloses the method of claim 1, wherein automatically generating initial conditions comprises clustering the reservoir fluid samples based at least in part on the sample information to effectively reduce sample number of the reservoir fluid samples (See: [0115] An iterative scheme can be used that repeats steps 223 and 225 in order to minimize the difference between the concentration gradient derived by compositional simulation and the concentration gradient measured in steps 201 and 207 over successive simulation time steps. An iterative scheme that repeats steps 223, 225, and 227 for different hydrocarbon component sets can be used to minimize the differences between the concentration gradient derived by the compositional simulation and the concentration gradient measured in steps 201 and 207).
As per Claims 19-20: The instant claims recite substantially same limitation as the above rejected claim 1, and therefore rejected under the same rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2014/0200810 A1 issued to Zuo et al in view of US Publication No. 2016/0319640 A1 issued to Ratulowski et al.
Claim 6. Zuo et al discloses the method of claim 1, wherein automatically generating initial conditions comprises determining a location.
However, Zuo et al does not disclose fluid-fluid boundary. Ratulowski et al discloses fluid-fluid boundary (See: [0040] (i) Pressure/volume/temperature (PVT) behavior in a multiphase system, such as a two-phase compositional system (e.g., gas-oil system, oil-water-solvent system, etc.), a three-phase compositional system (e.g., gas-water-hydrate system, water-oil-microemulsion system, etc.), etc.; [0041] (ii) Fluid-fluid and fluid-solid surface phenomena, such as interfacial tension (IFT), surfactants, adsorption, wetting angles, etc.).
It would have been obvious before the effective filing date to combine construction of digital representation of complex compositional fluids as taught by Ratulowski et al to reservoir architecture of Zuo et al would be to construct a digital representation of complex compositional fluids (Ratulowski et al, [0073]).
Claim 7. Ratulowski et al discloses the fluid-fluid boundary corresponds to gas-oil contact (See: [0040] (i) Pressure/volume/temperature (PVT) behavior in a multiphase system, such as a two-phase compositional system (e.g., gas-oil system, oil-water-solvent system, etc.), a three-phase compositional system (e.g., gas-water-hydrate system, water-oil-microemulsion system, etc.), etc.; [0041] (ii) Fluid-fluid and fluid-solid surface phenomena, such as interfacial tension (IFT), surfactants, adsorption, wetting angles, etc.).
Claim 8. Ratulowski et al discloses the fluid-fluid boundary corresponds to oil-water contact (See: [0040] (i) Pressure/volume/temperature (PVT) behavior in a multiphase system, such as a two-phase compositional system (e.g., gas-oil system, oil-water-solvent system, etc.), a three-phase compositional system (e.g., gas-water-hydrate system, water-oil-microemulsion system, etc.), etc.; [0041] (ii) Fluid-fluid and fluid-solid surface phenomena, such as interfacial tension (IFT), surfactants, adsorption, wetting angles, etc.).
Conclusion
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 09/23/2026
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