DETAILED ACTIONS
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/06/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 07/06/2026.
Status of the Claims
Claims 1-20 are still pending.
Claims 1-2, 5,8-9, 12, and 15-18 are amended.
Rejections Under 35 U.S.C. 103
Applicant's arguments, see remarks page 9-13, filed 07/06/2026.
with respect to the rejection(s) of Claims under 35 U.S.C. §103 has been considered and are moot because the amendment has necessitated a new ground of rejections. The new rejections are set forth below.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Montgomery et al. (US 2023/0243803 A1, hereinafter Montgomery, IDS ref, previously mentioned) and in view of Sorgi et al. (US 2025/0103782 A1, hereinafter Sorgi).
Regarding Claim 1, Montgomery teaches,
A method for obtaining mechanical properties of a rock sample, (Montgomery, [0003] According to a first aspect, a method comprises determining a mechanical property of a rock sample”), the method comprising:
dividing the rock sample into a first portion, a second portion, and a third portion;
measuring a bulk density of the first portion of the rock sample; measuring a total porosity of the second portion of the rock sample; (Montgomery, [0001] “methods of determining values of anisotropy parameters from rock samples, and methods of determining parameters indicative of the porosity, bulk density or matrix density of rock samples. Montgomery, [0004] The method may comprise taking into account (a)a respective amount of each of two or more constituent
phases in the rock sample and (b) a corresponding mechanical property parameter associated with each of the two or more constituent phases. [0008] the two or more constituent phases determined and/or taken into account may be a subset of the total number of constituent phases in the rock sample” Note: “rock samples” reads as subset of total plurality of samples. Therefore, any sample set can be first, second and third portions of sample sets used to measure mechanical properties. This is a design choice)
crushing and grinding the third portion of the rock sample into a fine powder Montgomery, [0194], “The sample (which may be powdered)”; identifying mineral phases and measuring volume fractions of the mineral phases relative to a total volume of the third portion of the rock sample by performing X-ray diffraction (XRD) analysis of the fine powder; (Montgomery, [0160] the volume fraction of each phase present in a cuttings sample can be determined experimentally. This may be achieved using, for example, X-ray diffraction-based methods [0196], QXRD can be used to determine the amounts of different phases present in multi-phase samples, as well as the characteristics of single phases”. [0087] It may be that the amount of each constituent phase in the rock sample is a parameter indicative of a volume ( e.g.total volume) of the said constituent phase in the rock sample. The parameter indicative of a volume (e.g. total volume) of the said constituent phase in the rock sample may be a volume (e.g. total volume) of the said constituent phase in the rock samp”. NOTE: “a cuttings sample” powdered and XRD analysis reads on “ third portion”);
inputting measured data of the bulk density, the total porosity, and the volume fractions of the mineral phases (Montgomery, Figure 6 [0091] The method may further comprise determining (e.g. calculating) a parameter indicative of the porosity of the rock sample (e.g. calculating the porosity of the rock sample) based on the determined matrix density of the rock sample (e.g. and based on the bulk density of the rock Sample”) into a computational model executing on a computing system; ;(Montgomery,[0195] the present inventors have found that quantitative spectroscopic mineralogical analysis of samples taken from
a particular region is possible by fitting a spectroscopic model to a reference dataset compiled using more quantitatively accurate mineralogical analysis techniques, such as quantitative X-ray diffraction (QXRD), and a set of reference
rock samples taken from the same region [0196] The reference training data set is compiled by measuring the mineralogical and/or carbon content data for a plurality of different samples of rock taken from an area. [0197] The reference training data set includes, for example, the amount (e.g. volume fraction) of each mineralogical phase and/or organic phase (e.g. TOC content) identified in each sample using each method” NOTE: a Model is trained validated [0198] and processed the measured data to obtain characteristics values see [0199] Once the calibration model has been built, it can be used to determine the mineral and/or organic content of an unknown rock sample (…) particularly suitable for the compositional analysis of large volumes of cuttings samples extracted from
hydrocarbon wells.)
processing the data input into the computational model;
displaying a main user interface on the computing system which comprises an input pane, a mineral composition pie chart, and a predicted properties pane; (Montgomery, Figure 6, and Figure 8-15, [0212] “ the methods and calculations described hereinabove are suited to automation and implementation in computer software (for example, computer software 102 stored on a computer readable medium 101, for execution by a computer processor 100, as shown in FIG. 26)” NOTE: the results are displayed as a graphical representation on the computer display of the computing system and alerted in case the calibration did not match see [0200] “an alert is triggered” and . See FIGS. 8 to 19 displays graphical representation of the measurement and processed data and composition result see [0201] “illustrate how accurately FTIR measurements can be fit to QXRD and Rock-Eva! Reference measurements when training the calibration model for the new spectroscopic method” obtained different results such as “ In particular, the weight percentage amount of quartz, illite-smectite (I-S) group minerals, feldspar, ankerite, total carbonate and TOC,”, “ measure of the volume of free hydrocarbons (i.e. gas and oil) present in a sample)” .
determining static (Montgomery, [ 0208]” FIGS. 20 and 21 illustrate the level of accuracy achieved for static property calculations using such methods”), and dynamic mechanical properties of the rock sample based on the processed data (Montgomery, Figure 18-19, [0206]-[0207] “[0207] “ with accurate compositional input, it is possible to calculate the dynamic properties (i.e., DTC and DTS) for a given sample, and therefore it is also possible to calculate static mechanical properties (such as Young's modulus and Poisson's ratio)”).
Montgomery is silent on
performing an uncertainty analysis on the static and dynamic mechanical properties;
identifying an oil and gas reservoir based on the dynamic mechanical properties of the rock sample and the uncertainty analysis;
estimating a reserve of the oil and gas reservoir based on the dynamic mechanical properties of the rock sample and the uncertainty analysis;
designing effective production strategies for the oil and gas reservoir based on the static mechanical properties of the rock sample and the uncertainty analysis; and
selecting appropriate drilling tools and hydraulic fracturing operations for the oil and gas reservoir based on the static mechanical properties of the rock sample and the uncertainty analysis.
However, Sorgi teaches performing an uncertainty analysis on the static and dynamic mechanical properties; (Sorgi, Figure 2, Uncertainty Analysis 212,Input condition 210B, [0097] In the example workflow 200, an uncertainty analysis 212 may be performed. To account for this uncertainty, an ensemble generator 216 of the uncertainty analysis 212 may generate an ensemble 218 of models 218A-218N by varying uncertain parameters ( e.g., key parameters 214) within expected ranges. In some aspects, each model 218A-218N realization represents a different plausible scenario based on the same or similar input conditions 210. [0095] Inputs 210B to the geomechanical model may include, but are not limited to, detailed rock mechanical properties such as Young's modulus, Poisson's ratio, and/or compressive/tensile strengths.”).
identifying an oil and gas reservoir based on the dynamic mechanical properties of the rock sample and the uncertainty analysis; estimating a reserve of the oil and gas reservoir based on the dynamic mechanical properties of the rock sample and the uncertainty analysis; (Sorgi, Figure 2, step 212,risk Map 226 [0095] In some aspects, a geo-mechanical model may simulates how rocks mechanically respond to changes in stress, pressure, and/or loading over time. Inputs 210B to the geo-mechanical model may include, but are not limited to, detailed rock mechanical properties such as Young's modulus, Poisson's ratio, and/or compressive/tensile strengths.”). [0097] In some aspects, an uncertainty analysis may enhance reliability by capturing the range of possible behaviors rather than just a single base case model. In examples, each model (e.g., 218A) may be a simulated CO2 injection. Sources of uncertainty (e.g., key parameters 214) may include, but are not limited to, reservoir properties, fault properties, injection parameters, etc”)
designing effective production strategies for the oil and gas reservoir based on the static mechanical properties of the rock sample and the uncertainty analysis; (Sorgi, Figure 2, Figure 5, [0121] Certain aspects of the present disclosure apply
modeling to objectively and quantitatively assess risks, advancing the MMV into a more operational process encompassing (M)MMV. Aspects of the present disclosure may include a coupled (flow and geomechanics) numerical modeling with uncertainty, allowing for leakage quantitative risk assessment. Aspects of the present disclosure may link modeling with MMV by means of sensitivity and uncertainty analysis of a numerical models' ensemble. [0122] FIG. 5 depicts a comprehensive system 500 for modeling, measuring, monitoring, and verifying the performance of a subsurface operation, such as a carbon dioxide
storage project or an oil and gas reservoir. [0124] the conformance MM module 522, which may ensure that the operation is conforming to the planned design”) ;and
selecting appropriate drilling tools and hydraulic fracturing operations for the oil and gas reservoir based on the static mechanical properties of the rock sample and the uncertainty analysis. (Sorgi, Figure 2, the uncertainty analysis 212 an ensemble 218 of models 218A-218N, Inputs 210B to the geo-mechanical model, and Figure 5, [0125] The verification component 506 may compare the data collected by the measurements and monitoring component 504 to predetermined thresholds at 527 and take appropriate actions if necessary. If the data indicates that the subsurface operation is not performing as expected, the verification component 506 may determine if contingency actions are needed. the contingency actions module 528 may initiate field interventions 530 to address the issue, perform one or more contingency monitoring and anagement operations 534 to monitor the issue, and the reporting module 532 may generate a report detailing the actions taken and the results achieved. The report may then be used to update the models ensemble and the risk analysis and assessment module in the modeling component 502, allowing for continuous improvement of the subsurface operation.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Montgomery’s model of estimating mechanical properties of rock sample to incorporate Sorgi’s model of uncertainty analysis determining uncertainty of rock mechanical properties and risk assessment with the benefit of predicting reservoir properties and measuring, monitoring, and verifying the performance of a subsurface operation to ensure the safe and effective operation of the subsurface project and to predict hydrocarbon production rates, volumes from hydrocarbon reservoirs over time as taught by Sorgi (Sorgi,[0121]- [0122],[0127], [0128]). It would have been obvious to a person of ordinary skill to include the well-known Uncertainty Analysis and geo-mechanical modeling, and a method for measuring rock mechanical properties uncertainty and predicting reservoir properties and risk in order to yield the predicted results of generating accurate prediction, yet with higher accuracy (KSR).
Regarding Claim 2, combination of Montgomery and Sorgi teaches the method according to claim 1,
Montgomery further teaches wherein the dynamic mechanical properties of the rock sample include acoustic velocities to infer lithology fluid content, and mechanical properties of rock formations of a subsurface of the oil and gas reservoir. (Montgomery, [0193], “Compositions determined using this method can serve as input to the equations defined in the preceding sections for the calculation of dynamic properties such as acoustic wave velocities and travel times, as well as
the rock matrix density, and therefore also the static elastic constants of rocks. [0206] The calculated values were based on rock compositions determined precisely using QXRD, porosities determined based on WIP, and reference values for the dynamic and static mechanical properties of the individual constituent phases identified in the rock”).
Regarding Claim 3, combination of Montgomery and Sorgi teaches the method according to claim 1,
Montgomery further teaches wherein performing the XRD analysis (Montgomery, [0206] “The calculated values were based on rock compositions determined precisely using QXRD”) includes: calculating the volume fractions (Montgomery, [0204] “In particular, spectroscopy in combination with the calibration model can be used to determine the volume fractions of the various mineral and organic phases present in a sample, and then Equation (39) can be applied”)of three mineral groups including inclusions, clay minerals and kerogen (Montgomery, Figure 7, [0013], “Each solid constituent phase may be a mineralogical phase (for example, a mineral phase or a mineraloid phase) or an organic phase (i.e. a solid or substantially solid (i.e. semi-solid) organic phase such as kerogen, bitumen or pyrobitumen). Each solid constituent phase may be a single material ( e.g. a single mineral or mineraloid) or a composite comprising two or more materials (e.g. a composite comprising two or more minerals and/or mineraloids). Example minerals include quartz, feldspar, calcite, dolomite, pyrite and clay minerals (such as kaolinite, illite and montmorillonite”); and
calculating a clay packing density and a volume fraction of kerogen relative to a total volume of clay and kerogen. (Montgomery, [0019] “It may be that the amount of each constituent phase in the rock sample is a parameter indicative of a volume ( e.g. total volume) of the said constituent phase in the rock sample. The parameter indicative of a volume (e.g. total volume) of the said constituent phase in the rock sample may be a volume (e.g. total volume) of the said constituent phase in the rock sample. Alternatively, the parameter indicative of a volume of the said constituent phase in the rock sample may be a volume fraction of the said constituent phase in the rock sample (e.g. the fraction of the total volume of the rock sample constituted by the said constituent phase”).
Regarding Claim 4, combination of Montgomery and Sorgi teaches the method according to claim 3,
Montgomery further teaches further comprises: calculating an average stiffness tensor of a homogenized solid clay and kerogen fabric; calculating a level I stiffness tensor of a homogenized porous clay and kerogen composite; calculating dynamic mechanical properties of the rock sample from a level II undrained stiffness tensor; and calculating static mechanical properties of the rock sample from a level II drained stiffness tensor. (Montgomery, [0133] “The principal mechanical properties of concern in the present application are the elastic properties of rocks, that is to say, the properties (or parameters) of rocks which govern their elastic behavior. The elastic properties of rocks, as with other materials more generally, can be quantified in terms of elastic constants. [0134] for a continuous elastic material as equation 1 where σ is the second-order stress tensor and E 1s the second-order strain tensor, defined by equation 2-3, and c is the fourth-order stiffness or elasticity tensor”. [0140] and G is the engineering shear modulus. Young's modulus is a measure of the resistance of a material to elastic deformation under uniaxial stretching or compression (i.e. the stiffness of the material). Poisson's ratio is the ratio of the lateral and longitudinal strains induced in a material subjected to uniaxial tensile stress”).
Regarding Claim 5, combination of Montgomery and Sorgi teaches the method according to claim 1,
Montgomery further teaches, wherein the input pane includes: mineralogy data of inclusions, clay minerals, and organic matters; and laboratory rock properties including the bulk density of the rock sample and the total porosity of the rock sample (Montgomery, Figures 2-15, [0001] The present disclosure concerns methods of determining mechanical properties of rock samples, methods of determining values of anisotropy parameters from rock samples, and methods of determining parameters indicative of the porosity, bulk density or matrix density of rock
samples, as well as associated computer programs, computer-readable media, data carrier signals and data sets, and methods of compiling associated data sets for use in such methods”).
Regarding Claim 6, combination of Montgomery and Sorgi teaches the method according to claim 1,
Montgomery further teaches, wherein the mineral composition pie chart indicates mineral abundance by volume. (Montgomery, [0212] “the methods and calculations described hereinabove are suited to automation and implementation in computer software (for example, computer software 102 stored on a computer readable medium 101, for execution by a computer processor 100, as shown in FIG. 26)” NOTE: the results are displayed as a graphical representation on the computer display of the computing system as in Figures 2-15. Presenting the data in the form of a Pie chart is an operating/ design choice, presenting the data as a pie chart is not an inventive concept. Same data can be presented in other graphical means representing same information).
Regarding Claim 7, combination of Montgomery and Sorgi teaches the method according to claim 1,
Montgomery further teaches the method according to claim 1, wherein the predicted properties pane is a quartz, feldspar, and pyrite (QFP) prediction of rock properties pane which displays the static and dynamic mechanical properties of the rock sample. (Montgomery, Figure 7, [0013] Example minerals include quartz, feldspar,
calcite, dolomite, pyrite and clay minerals (such as kaolinite, illite and montmorillonite”)
Regarding Claim 8, Montgomery teaches,
A non-transitory computer readable medium (CRM) storing instructions executable by a computer processor, the instructions comprising functionality fo r(Montgomery, Figure 6, and Figure 8-15,figure 26 [0212] “ the methods and calculations described hereinabove are suited to automation and implementation in computer software (for example, computer software 102 stored on a computer readable medium 101, for execution by a computer processor 100, as shown in FIG. 26)” NOTE: the results are displayed as a graphical representation on the computer display of the computing system:
identifying mineral phases and measuring volume fractions of the mineral phases relative to total volume of a rock sample by performing X-ray diffraction (XRD) analysis
of fine powder of the rock sample (Montgomery, [0160] the volume fraction of each phase present in a cuttings sample can be determined experimentally. This may be achieved using, for example, X-ray diffraction-based methods [0196], QXRD can be used to determine the amounts of different phases present in multi-phase samples, as well as the characteristics of single phases”);
inputting measured data of bulk density, total porosity, and volume fractions of the mineral phases (Montgomery, Figure 6 [0091] The method may further comprise determining (e.g. calculating) a parameter indicative of the porosity of the rock sample (e.g. calculating the porosity of the rock sample) based on the determined matrix density of the rock sample (e.g. and based on the bulk density of the rock Sample”)into a computational model executing on a computing system;(Montgomery,[0195] the present inventors have found that quantitative spectroscopic mineralogical analysis of samples taken from
a particular region is possible by fitting a spectroscopic model to a reference dataset compiled using more quantitatively accurate mineralogical analysis techniques, such as quantitative X-ray diffraction (QXRD), and a set of reference
rock samples taken from the same region [0196] The reference training data set is compiled by measuring the mineralogical and/or carbon content data for a plurality of different samples of rock taken from an area. [0197] The reference training data set includes, for example, the amount (e.g. volume fraction) of each mineralogical phase and/or organic phase (e.g. TOC content) identified in each sample using each method” NOTE: a Model is trained validated [0198] and processed the measured data to obtain characteristics values see [0199] Once the calibration model has been built, it can be used to determine the mineral and/or organic content of an unknown rock sample (…) particularly suitable for the compositional analysis of large volumes of cuttings samples extracted from
hydrocarbon wells.);
processing the data input into the computational model; displaying a main user interface on the computing system which includes an input pane, a mineral composition pie chart, and a predicted properties pane (Montgomery, Figure 6, and Figure 8-15, [0212] “ the methods and calculations described hereinabove are suited to automation and implementation in computer software (for example, computer software 102 stored on a computer readable medium 101, for execution by a computer processor 100, as shown in FIG. 26)” NOTE: the results are displayed as a graphical representation on the computer display of the computing system and alerted in case the calibration did not match see [0200] “an alert is triggered” and . See FIGS. 8 to 19 displays graphical representation of the measurement and processed data and composition result see [0201] “illustrate how accurately FTIR measurements can be fit to QXRD and Rock-Eva! Reference measurements when training the calibration model for the new spectroscopic method” obtained different results such as “ In particular, the weight percentage amount of quartz, illite-smectite (I-S) group minerals, feldspar, ankerite, total carbonate and TOC,”, “ measure of the volume of free hydrocarbons (i.e. gas and oil) present in a sample)” )determining static (Montgomery, [ 0208]” FIGS. 20 and 21 illustrate the level of accuracy achieved for static property calculations using such methods”), and dynamic mechanical properties of the rock sample based on the processed data (Montgomery, Figure 18-19, [0206]-[0207] “[0207] “ with accurate compositional input, it is possible to calculate the dynamic properties (i.e., DTC and DTS) for a given sample, and therefore it is also possible to calculate static mechanical properties (such as Young's modulus and Poisson's ratio)”).
Montgomery is silent on
performing an uncertainty analysis on the static and dynamic mechanical properties;
identifying an oil and gas reservoir based on the dynamic mechanical properties of the rock sample and the uncertainty analysis;
estimating a reserve of the oil and gas reservoir based on the dynamic mechanical properties of the rock sample and the uncertainty analysis;
designing effective production strategies for the oil and gas reservoir based on the static mechanical properties of the rock sample and the uncertainty analysis; and
selecting appropriate drilling tools and hydraulic fracturing operations for the oil and gas reservoir based on the static mechanical properties of the rock sample and the uncertainty analysis.
However, Sorgi teaches performing an uncertainty analysis on the static and dynamic mechanical properties; (Sorgi, Figure 2, Uncertainty Analysis 212,Input condition 210B, [0097] In the example workflow 200, an uncertainty analysis 212 may be performed. To account for this uncertainty, an ensemble generator 216 of the uncertainty analysis 212 may generate an ensemble 218 of models 218A-218N by varying uncertain parameters ( e.g., key parameters 214) within expected ranges. In some aspects, each model 218A-218N realization represents a different plausible scenario based on the same or similar input conditions 210. [0095] Inputs 210B to the geomechanical model may include, but are not limited to, detailed rock mechanical properties such as Young's modulus, Poisson's ratio, and/or compressive/tensile strengths.”).
identifying an oil and gas reservoir based on the dynamic mechanical properties of the rock sample and the uncertainty analysis; estimating a reserve of the oil and gas reservoir based on the dynamic mechanical properties of the rock sample and the uncertainty analysis; (Sorgi, Figure 2, step 212,risk Map 226 [0095] In some aspects, a geo-mechanical model may simulates how rocks mechanically respond to changes in stress, pressure, and/or loading over time. Inputs 210B to the geo-mechanical model may include, but are not limited to, detailed rock mechanical properties such as Young's modulus, Poisson's ratio, and/or compressive/tensile strengths.”). [0097] In some aspects, an uncertainty analysis may enhance reliability by capturing the range of possible behaviors rather than just a single base case model. In examples, each model (e.g., 218A) may be a simulated CO2 injection. Sources of uncertainty (e.g., key parameters 214) may include, but are not limited to, reservoir properties, fault properties, injection parameters, etc”)
designing effective production strategies for the oil and gas reservoir based on the static mechanical properties of the rock sample and the uncertainty analysis; (Sorgi, Figure 2, Figure 5, [0121] Certain aspects of the present disclosure apply
modeling to objectively and quantitatively assess risks, advancing the MMV into a more operational process encompassing (M)MMV. Aspects of the present disclosure may include a coupled (flow and geomechanics) numerical modeling with uncertainty, allowing for leakage quantitative risk assessment. Aspects of the present disclosure may link modeling with MMV by means of sensitivity and uncertainty analysis of a numerical models' ensemble. [0122] FIG. 5 depicts a comprehensive system 500 for modeling, measuring, monitoring, and verifying the performance of a subsurface operation, such as a carbon dioxide
storage project or an oil and gas reservoir. [0124] the conformance MM module 522, which may ensure that the operation is conforming to the planned design”) ;and
selecting appropriate drilling tools and hydraulic fracturing operations for the oil and gas reservoir based on the static mechanical properties of the rock sample and the uncertainty analysis. (Sorgi, Figure 2, the uncertainty analysis 212 an ensemble 218 of models 218A-218N, Inputs 210B to the geo-mechanical model, and Figure 5, [0125] The verification component 506 may compare the data collected by the measurements and monitoring component 504 to predetermined thresholds at 527 and take appropriate actions if necessary. If the data indicates that the subsurface operation is not performing as expected, the verification component 506 may determine if contingency actions are needed. the contingency actions module 528 may initiate field interventions 530 to address the issue, perform one or more contingency monitoring and anagement operations 534 to monitor the issue, and the reporting module 532 may generate a report detailing the actions taken and the results achieved. The report may then be used to update the models ensemble and the risk analysis and assessment module in the modeling component 502, allowing for continuous improvement of the subsurface operation.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Montgomery’s model of estimating mechanical properties of rock sample to incorporate Sorgi’s model of uncertainty analysis determining uncertainty of rock mechanical properties and risk assessment with the benefit of predicting reservoir properties and measuring, monitoring, and verifying the performance of a subsurface operation to ensure the safe and effective operation of the subsurface project and to predict hydrocarbon production rates, volumes from hydrocarbon reservoirs over time as taught by Sorgi (Sorgi,[0121]- [0122],[0127], [0128]). It would have been obvious to a person of ordinary skill to include the well-known Uncertainty Analysis and geo-mechanical modeling, and a method for measuring rock mechanical properties uncertainty and predicting reservoir properties and risk in order to yield the predicted results of generating accurate prediction, yet with higher accuracy (KSR).
Regarding Claim 9, combination of Montgomery and Sorgi teaches the non-transitory CRM according to claim 8,
Montgomery further teaches wherein the dynamic mechanical properties of the rock sample include acoustic velocities to infer lithology fluid content, and mechanical properties of rock formations of a subsurface of the oil and gas reservoir. (Montgomery, [0193], “Compositions determined using this method can serve as input to the equations defined in the preceding sections for the calculation of dynamic properties such as acoustic wave velocities and travel times, as well as
the rock matrix density, and therefore also the static elastic constants of rocks. [0206] The calculated values were based on rock compositions determined precisely using QXRD, porosities determined based on WIP, and reference values for the dynamic and static mechanical properties of the individual constituent phases identified in the rock”).
Regarding Claim 10, combination of Montgomery and Sorgi teaches the non-transitory CRM according to claim 8,
Montgomery further teaches wherein performing the XRD analysis (Montgomery, [0206] “The calculated values were based on rock compositions determined precisely using QXRD”) includes: calculating the volume fractions (Montgomery, [0204] “In particular, spectroscopy in combination with the calibration model can be used to determine the volume fractions of the various mineral and organic phases present in a sample, and then Equation (39) can be applied”)of three mineral groups including inclusions, clay minerals and kerogen (Montgomery, Figure 7, [0013], “Each solid constituent phase may be a mineralogical phase (for example, a mineral phase or a mineraloid phase) or an organic phase (i.e. a solid or substantially solid (i.e. semi-solid) organic phase such as kerogen, bitumen or pyrobitumen). Each solid constituent phase may be a single material ( e.g. a single mineral or mineraloid) or a composite comprising two or more materials (e.g. a composite comprising two or more minerals and/or mineraloids). Example minerals include quartz, feldspar, calcite, dolomite, pyrite and clay minerals (such as kaolinite, illite and montmorillonite”); and
calculating a clay packing density and a volume fraction of kerogen relative to a total volume of clay and kerogen. (Montgomery, [0019] “It may be that the amount of each constituent phase in the rock sample is a parameter indicative of a volume ( e.g. total volume) of the said constituent phase in the rock sample. The parameter indicative of a volume (e.g. total volume) of the said constituent phase in the rock sample may be a volume (e.g. total volume) of the said constituent phase in the rock sample. Alternatively, the parameter indicative of a volume of the said constituent phase in the rock sample may be a volume fraction of the said constituent phase in the rock sample (e.g. the fraction of the total volume of the rock sample constituted by the said constituent phase”).
Regarding Claim 11, combination of Montgomery and Sorgi teaches the non-transitory CRM according to claim 10,
Montgomery further teaches further comprises: calculating an average stiffness tensor of a homogenized solid clay and kerogen fabric; calculating a level I stiffness tensor of a homogenized porous clay and kerogen composite; calculating dynamic mechanical properties of the rock sample from a level II undrained stiffness tensor; and calculating static mechanical properties of the rock sample from a level II drained stiffness tensor. (Montgomery, [0133] “The principal mechanical properties of concern in the present application are the elastic properties of rocks, that is to say, the properties (or parameters) of rocks which govern their elastic behavior. The elastic properties of rocks, as with other materials more generally, can be quantified in terms of elastic constants. [0134] for a continuous elastic material as equation 1 where σ is the second-order stress tensor and E 1s the second-order strain tensor, defined by equation 2-3, and c is the fourth-order stiffness or elasticity tensor”. [0140] and G is the engineering shear modulus. Young's modulus is a measure of the resistance of a material to elastic deformation under uniaxial stretching or compression (i.e. the stiffness of the material). Poisson's ratio is the ratio of the lateral and longitudinal strains induced in a material subjected to uniaxial tensile stress”).
Regarding Claim 12, combination of Montgomery and Sorgi teaches the non-transitory CRM according to claim 8,
Montgomery further teaches, wherein the input pane includes: mineralogy data of inclusions, clay minerals, and organic matters; and laboratory rock properties including the bulk density of the rock sample and the total porosity of the rock sample (Montgomery, Figures 2-15, [0001] The present disclosure concerns methods of determining mechanical properties of rock samples, methods of determining values of anisotropy parameters from rock samples, and methods of determining parameters indicative of the porosity, bulk density or matrix density of rock
samples, as well as associated computer programs, computer-readable media, data carrier signals and data sets, and methods of compiling associated data sets for use in such methods”).
Regarding Claim 13, combination of Montgomery and Sorgi teaches the non-transitory CRM according to claim 8,
Montgomery further teaches, wherein the mineral composition pie chart indicates mineral abundance by volume. (Montgomery, [0212] “the methods and calculations described hereinabove are suited to automation and implementation in computer software (for example, computer software 102 stored on a computer readable medium 101, for execution by a computer processor 100, as shown in FIG. 26)” NOTE: the results are displayed as a graphical representation on the computer display of the computing system as in Figures 2-15. Presenting the data in the form of a Pie chart is an operating/ design choice, presenting the data as a pie chart is not an inventive concept. Same data can be presented in other graphical means representing same information).
Regarding Claim 14, combination of Montgomery and Sorgi teaches the non-transitory CRM according to claim 8
Montgomery further teaches wherein the predicted properties pane is a quartz, feldspar, and pyrite (QFP) prediction of rock properties pane which displays the static and dynamic mechanical properties of the rock sample. (Montgomery, Figure 7, [0013] Example minerals include quartz, feldspar, calcite, dolomite, pyrite and clay minerals (such as kaolinite, illite and montmorillonite”).
Regarding Claim 15, Montgomery teaches,
A system for obtaining mechanical properties of a rock sample Montgomery, [0003] According to a first aspect, a method comprises determining a mechanical property of a rock sample”), the system comprising:
a total porosity measuring device configured to measure a total porosity of the rock sample (Montgomery, [ 0075] “The method may be a method of determining the parameter indicative of the matrix density of the rock sample by immersion porosimetry, for example by water immersion porosimetry (WIP)”. Figure 6 [0091] “determining (e.g. calculating) a parameter indicative of the porosity of the rock sample (e.g. calculating the porosity of the rock sample) based on the determined matrix density of the rock sample (e.g. and based on the bulk density of the rock Sample”);
a powder X-ray diffractometer configured to identify mineral phases of a fine powder (Montgomery, [0194], “The sample (which may be powdered)”;
of the rock sample and to measure volume fractions of the mineral phases relative to total volume of the rock sample (Montgomery, [0160] the volume fraction of each phase present in a cuttings sample can be determined experimentally. This may be achieved using, for example, X-ray diffraction-based methods [0196], QXRD can be used to determine the amounts of different phases present in multi-phase samples, as well as the characteristics of single phases”);
a user input device configured to input measured data comprising bulk density, total porosity, and volume fractions of the mineral (Montgomery, Figure 6 [0091] The method may further comprise determining (e.g. calculating) a parameter indicative of the porosity of the rock sample (e.g. calculating the porosity of the rock sample) based on the determined matrix density of the rock sample (e.g. and based on the bulk density of the rock Sample”) into a computational model executing on a computing system;(Montgomery,[0195] the present inventors have found that quantitative spectroscopic mineralogical analysis of samples taken from a particular region is possible by fitting a spectroscopic model to a reference dataset compiled using more quantitatively accurate mineralogical analysis techniques, such as quantitative X-ray diffraction (QXRD), and a set of reference rock samples taken from the same region [0196] The reference training data set is compiled by measuring the mineralogical and/or carbon content data for a plurality of different samples of rock taken from an area. [0197] The reference training data set includes, for example, the amount (e.g. volume fraction) of each mineralogical phase and/or organic phase (e.g. TOC content) identified in each sample using each method” NOTE: a Model is trained validated [0198] and processed the measured data to obtain characteristics values see [0199] Once the calibration model has been built, it can be used to determine the mineral and/or organic content of an unknown rock sample (…) particularly suitable for the compositional analysis of large volumes of cuttings samples extracted from
hydrocarbon wells.)
the computing system including a processor (Montgomery, Figure 26, processor 100) configured to:
processing the data input into the computational model; display a main user interface on the computing system which comprises an input pane, a mineral composition pie chart, and a predicted properties pane (Montgomery, Figure 6, and Figure 8-15, [0212] “ the methods and calculations described hereinabove are suited to automation and implementation in computer software (for example, computer software 102 stored on a computer readable medium 101, for execution by a computer processor 100, as shown in FIG. 26)” NOTE: the results are displayed as a graphical representation on the computer display of the computing system and alerted in case the calibration did not match see [0200] “an alert is triggered” and . See FIGS. 8 to 19 displays graphical representation of the measurement and processed data and composition result see [0201] “illustrate how accurately FTIR measurements can be fit to QXRD and Rock-Eva! Reference measurements when training the calibration model for the new spectroscopic method” obtained different results such as “ In particular, the weight percentage amount of quartz, illite-smectite (I-S) group minerals, feldspar, ankerite, total carbonate and TOC,”, “ measure of the volume of free hydrocarbons (i.e. gas and oil) present in a sample)” )determining static (Montgomery, [ 0208]” FIGS. 20 and 21 illustrate the level of accuracy achieved for static property calculations using such methods”), and dynamic mechanical properties of the rock sample based on the processed data (Montgomery, Figure 18-19, [0206]-[0207] “[0207] “ with accurate compositional input, it is possible to calculate the dynamic properties (i.e., DTC and DTS) for a given sample, and therefore it is also possible to calculate static mechanical properties (such as Young's modulus and Poisson's ratio)”).
Montgomery is silent on
performing an uncertainty analysis on the static and dynamic mechanical properties;
identifying an oil and gas reservoir based on the dynamic mechanical properties of the rock sample and the uncertainty analysis;
estimating a reserve of the oil and gas reservoir based on the dynamic mechanical properties of the rock sample and the uncertainty analysis;
designing effective production strategies for the oil and gas reservoir based on the static mechanical properties of the rock sample and the uncertainty analysis; and
selecting appropriate drilling tools and hydraulic fracturing operations for the oil and gas reservoir based on the static mechanical properties of the rock sample and the uncertainty analysis.
However, Sorgi teaches performing an uncertainty analysis on the static and dynamic mechanical properties; (Sorgi, Figure 2, Uncertainty Analysis 212,Input condition 210B, [0097] In the example workflow 200, an uncertainty analysis 212 may be performed. To account for this uncertainty, an ensemble generator 216 of the uncertainty analysis 212 may generate an ensemble 218 of models 218A-218N by varying uncertain parameters ( e.g., key parameters 214) within expected ranges. In some aspects, each model 218A-218N realization represents a different plausible scenario based on the same or similar input conditions 210. [0095] Inputs 210B to the geomechanical model may include, but are not limited to, detailed rock mechanical properties such as Young's modulus, Poisson's ratio, and/or compressive/tensile strengths.”).
identifying an oil and gas reservoir based on the dynamic mechanical properties of the rock sample and the uncertainty analysis; estimating a reserve of the oil and gas reservoir based on the dynamic mechanical properties of the rock sample and the uncertainty analysis; (Sorgi, Figure 2, step 212,risk Map 226 [0095] In some aspects, a geo-mechanical model may simulates how rocks mechanically respond to changes in stress, pressure, and/or loading over time. Inputs 210B to the geo-mechanical model may include, but are not limited to, detailed rock mechanical properties such as Young's modulus, Poisson's ratio, and/or compressive/tensile strengths.”). [0097] In some aspects, an uncertainty analysis may enhance reliability by capturing the range of possible behaviors rather than just a single base case model. In examples, each model (e.g., 218A) may be a simulated CO2 injection. Sources of uncertainty (e.g., key parameters 214) may include, but are not limited to, reservoir properties, fault properties, injection parameters, etc”)
designing effective production strategies for the oil and gas reservoir based on the static mechanical properties of the rock sample and the uncertainty analysis; (Sorgi, Figure 2, Figure 5, [0121] Certain aspects of the present disclosure apply
modeling to objectively and quantitatively assess risks, advancing the MMV into a more operational process encompassing (M)MMV. Aspects of the present disclosure may include a coupled (flow and geomechanics) numerical modeling with uncertainty, allowing for leakage quantitative risk assessment. Aspects of the present disclosure may link modeling with MMV by means of sensitivity and uncertainty analysis of a numerical models' ensemble. [0122] FIG. 5 depicts a comprehensive system 500 for modeling, measuring, monitoring, and verifying the performance of a subsurface operation, such as a carbon dioxide
storage project or an oil and gas reservoir. [0124] the conformance MM module 522, which may ensure that the operation is conforming to the planned design”) ;and
selecting appropriate drilling tools and hydraulic fracturing operations for the oil and gas reservoir based on the static mechanical properties of the rock sample and the uncertainty analysis. (Sorgi, Figure 2, the uncertainty analysis 212 an ensemble 218 of models 218A-218N, Inputs 210B to the geo-mechanical model, and Figure 5, [0125] The verification component 506 may compare the data collected by the measurements and monitoring component 504 to predetermined thresholds at 527 and take appropriate actions if necessary. If the data indicates that the subsurface operation is not performing as expected, the verification component 506 may determine if contingency actions are needed. the contingency actions module 528 may initiate field interventions 530 to address the issue, perform one or more contingency monitoring and anagement operations 534 to monitor the issue, and the reporting module 532 may generate a report detailing the actions taken and the results achieved. The report may then be used to update the models ensemble and the risk analysis and assessment module in the modeling component 502, allowing for continuous improvement of the subsurface operation.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Montgomery’s model of estimating mechanical properties of rock sample to incorporate Sorgi’s model of uncertainty analysis determining uncertainty of rock mechanical properties and risk assessment with the benefit of predicting reservoir properties and measuring, monitoring, and verifying the performance of a subsurface operation to ensure the safe and effective operation of the subsurface project and to predict hydrocarbon production rates, volumes from hydrocarbon reservoirs over time as taught by Sorgi (Sorgi,[0121]- [0122],[0127], [0128]). It would have been obvious to a person of ordinary skill to include the well-known Uncertainty Analysis and geo-mechanical modeling, and a method for measuring rock mechanical properties uncertainty and predicting reservoir properties and risk in order to yield the predicted results of generating accurate prediction, yet with higher accuracy (KSR).
Regarding Claim 16, combination of Montgomery and Sorgi teaches the system according to claim 15,
Montgomery further teaches wherein the dynamic mechanical properties of the rock sample include acoustic velocities to infer lithology fluid content, and mechanical properties of rock formations of a subsurface of the oil and gas reservoir. (Montgomery, [0193], “Compositions determined using this method can serve as input to the equations defined in the preceding sections for the calculation of dynamic properties such as acoustic wave velocities and travel times, as well as
the rock matrix density, and therefore also the static elastic constants of rocks. [0206] The calculated values were based on rock compositions determined precisely using QXRD, porosities determined based on WIP, and reference values for the dynamic and static mechanical properties of the individual constituent phases identified in the rock”).
Regarding Claim 17, combination of Montgomery and Sorgi teaches the system according to claim 15,
Montgomery taches wherein the powder X-ray diffractometer is further configured (Montgomery, [0206] “The calculated values were based on rock compositions determined precisely using QXRD”) to: calculating the volume fractions (Montgomery, [0204] “In particular, spectroscopy in combination with the calibration model can be used to determine the volume fractions of the various mineral and organic phases present in a sample, and then Equation (39) can be applied”)of three mineral groups including inclusions, clay minerals and kerogen (Montgomery, Figure 7, [0013], “Each solid constituent phase may be a mineralogical phase (for example, a mineral phase or a mineraloid phase) or an organic phase (i.e. a solid or substantially solid (i.e. semi-solid) organic phase such as kerogen, bitumen or pyrobitumen). Each solid constituent phase may be a single material ( e.g. a single mineral or mineraloid) or a composite comprising two or more materials (e.g. a composite comprising two or more minerals and/or mineraloids). Example minerals include quartz, feldspar, calcite, dolomite, pyrite and clay minerals (such as kaolinite, illite and montmorillonite”); and
calculating a clay packing density and a volume fraction of kerogen relative to a total volume of clay and kerogen. (Montgomery, [0019] “It may be that the amount of each constituent phase in the rock sample is a parameter indicative of a volume ( e.g. total volume) of the said constituent phase in the rock sample. The parameter indicative of a volume (e.g. total volume) of the said constituent phase in the rock sample may be a volume (e.g. total volume) of the said constituent phase in the rock sample. Alternatively, the parameter indicative of a volume of the said constituent phase in the rock sample may be a volume fraction of the said constituent phase in the rock sample (e.g. the fraction of the total volume of the rock sample constituted by the said constituent phase”).
Regarding Claim 18, combination of Montgomery and Sorgi teaches the system according to claim 15,
Montgomery further teaches, wherein the input pane includes: mineralogy data of inclusions, clay minerals, and organic matter; and laboratory rock properties including the bulk density of the rock sample and the total porosity of the rock sample (Montgomery, Figures 2-15, [0001] The present disclosure concerns methods of determining mechanical properties of rock samples, methods of determining values of anisotropy parameters from rock samples, and methods of determining parameters indicative of the porosity, bulk density or matrix density of rock
samples, as well as associated computer programs, computer-readable media, data carrier signals and data sets, and methods of compiling associated data sets for use in such methods”).
Regarding Claim 19, combination of Montgomery and Sorgi teaches the system according to claim 15,
Montgomery further teaches, wherein the mineral composition pie chart indicates mineral abundance by volume. (Montgomery, [0212] “the methods and calculations described hereinabove are suited to automation and implementation in computer software (for example, computer software 102 stored on a computer readable medium 101, for execution by a computer processor 100, as shown in FIG. 26)” NOTE: the results are displayed as a graphical representation on the computer display of the computing system as in Figures 2-15. Presenting the data in the form of a Pie chart is a operating/ design choice, how the data is presented, not an inventive concept).
Regarding Claim 20, combination of Montgomery and Sorgi teaches the system according to claim 15,
Montgomery further teaches the method according to claim 1, wherein the predicted properties pane is a quartz, feldspar, and pyrite (QFP) prediction of rock properties pane which displays the static and dynamic mechanical properties of the rock sample. (Montgomery, Figure 7, [0013] Example minerals include quartz, feldspar,
calcite, dolomite, pyrite and clay minerals (such as kaolinite, illite and montmorillonite”).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xia et al. (US 20220259960 A1) recites “Techniques for hydraulic fracturing a subsurface formation can include using a deterministic model to simulate a deviated well comprising a casing and at least one perforation tunnel. The techniques can include determining a different statistical distribution for each of one or more parameters to the deterministic model. The statistical distributions can be determined from the log data along the measured depth within the wellbore. The techniques can include probabilistically solving the deterministic model to determine a mean and a standard deviation of breakdown pressures along the measured depth within the wellbore. The techniques can include processing the mean and the standard deviation of breakdown pressures to determine an upper bound breakdown pressure based on a level of confidence. The techniques can include drilling and completing a deviated well based on the determined upper bound breakdown pressure and injecting hydraulic fluid to cause hydraulic fracturing of the subsurface formation” (abstract).
Walters et al. (US 20180329113 A1) discloses “Systems and methods for generating and storing measurements in point and vector format for a plurality of formations of reservoirs. In one embodiment, the methods comprise generating a set of measurements corresponding to a plurality of formations, reservoirs, or wellbores; determining physical locations for the set of measurements, wherein the physical locations are represented in a point and vector representation; associating the vector representations with the determined physical locations, wherein the vector representations comprise at least a magnitude and a direction derived from the measurement; wherein the magnitude and direction tracks the physical location in space and time; manipulating the set of measurements such that a change in physical location is updated in the vector representation; generating a repository of vector representations accessible to determine an optimal completion design for a set of parameters for a subterranean formation.” (abstract),
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DILARA SULTANA/Examiner, Art Unit 2858 09/11/2026
/SON T LE/Primary Examiner, Art Unit 2858
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