DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 12/29/2023.
Claims 1 – 20 are presented for examination.
Priority
ADS dated 3/1/2023 does not claim any domestic or international priority.
Information Disclosure Statement
IDS dated 3/1/2023, 3/2/2023, 12/29/2023 have been reviewed. See attached.
Drawings
The drawings dated 3/1/2023 have been reviewed. They are accepted.
Specification
The abstract dated 3/1/2023 has 146 words, 10 lines, and no legal phraseology. The abstract is accepted.
Restriction
The Examiner notes that claim 1 – 12 and claims 13 – 18 are separate inventions. Claim 1 performs experiments to create a mathematical model. Claim 13 collects data from a wellbore and performs a calculation. Claim 19 – 20 combines these two distinct inventions. Accordingly, the claims are restrictable according to MPEP 809. While the Examiner has not yet restricted these claims, if amendments cause the claims to further diverge the Examiner reserves the right make a restriction.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception without significantly more.
Claim 1.
STEP 1. Yes. The claim recites “A method”
STEP 2A PRONG ONE: Yes. The claim recites:
“… comprising: preparing a plurality of cement slurries, wherein the plurality of cement slurries each comprise a cement and volume fraction of water; curing the plurality of cement slurries to form a plurality of set cement samples; exposing the plurality of set cement samples to a chemical species; allowing the chemical species to at least partially modify the plurality of set cement samples to form a plurality of composite cement samples; measuring a dynamic physical property of each of the plurality of composite cement samples to generate a dynamic physical property dataset; measuring a static physical property of each of the plurality of composite cement samples to generate a static physical property dataset; and correlating the static physical property dataset as a function of the dynamic physical property dataset and volume fraction of water to generate a composite cement property model” which is a mathematical correlation. Accordingly the claim is directed towards a mathematical abstract idea.
STEP 2A PRONG TWO: No.
While the claim recites: “… comprising: preparing a plurality of cement slurries, wherein the plurality of cement slurries each comprise a cement and volume fraction of water; curing the plurality of cement slurries to form a plurality of set cement samples; exposing the plurality of set cement samples to a chemical species; allowing the chemical species to at least partially modify the plurality of set cement samples to form a plurality of composite cement samples; measuring a dynamic physical property of each of the plurality of composite cement samples to generate a dynamic physical property dataset; measuring a static physical property of each of the plurality of composite cement samples to generate a static physical property dataset” these elements amount to pre-solution data gathering. MPEP 2106.05(g) states that mere data gathering is not a practical application and cites:
Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989);
Testing a system for a response, the response being used to determine system malfunction, In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982);
Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis).
The recited elements in the claim articulate a laboratory experiment. The MPEP’s cited example include clinical testing, system response testing, and biological testing. Accordingly, testing sample of cement is similarly found to be extra solution data gathering. Further, the testing recited in the claim is considered to be a well-known type of testing because such testing is outlined in industry standards such as the API Specification for Materials and Testing for Well Cements. This industry standard is cited by Jamenez_2019 (US 2019/0330513 A1) which is included in the ground of rejection of the claim. The API specification is also cited by Duguid_2010 which is also included in the ground of rejection of the claim. The API specification is also cited by Zhongcun_2019 which is also included in the ground of rejection of the claim.
STEP 2B: No.
While the claim recites insignificant extra-solution data gathering steps, such data gathering is found to be well-understood routine activities. Reciting well-understood and routine data gathering activities is not a recitation of elements other than the abstract idea which are significantly more than the abstract idea itself. The evidence in support of this finding is that the recited data gathering activities are experimentation outlined in industry standards such as the API Specification for Materials and Testing for Well Cements. This industry standard is cited by Jamenez_2019 (US 2019/0330513 A1) which is included in the ground of rejection of the claim. The API specification is also cited by Duguid_2010 which is also included in the ground of rejection of the claim. The API specification is also cited by Zhongcun_2019 which is also included in the ground of rejection of the claim.
Therefore, due to the above reasons, the claim is rejected under 35 USC 101.
Claim 2 recites: “further comprising: preparing a test cement slurry; measuring a dynamic physical property of the test cement slurry; calculating a static physical property of the test cement slurry using the composite cement property model wherein the dynamic physical property of the test cement slurry is an input to the composite cement property model; and calculating a well life integrity with a numerical simulator using the static physical property of the test cement slurry as and input to the numerical simulator”, however, these elements merely articulate further mathematical calculations performed using data gathered from data gathering tests. The numerical simulator is merely tool invoked to execute the mathematical calculation to calculate a numerical value (i.e., integrity). As outlined above, data gathering is not a practical application nor significantly more than the abstract idea.
Claim 3 recites “wherein the dynamic physical property comprises at least one property selected from the group consisting of dynamic Young's modulus, dynamic Poisson's ratio, dynamic unconfined compressive strength, dynamic tensile strength, dynamic flexural strength, dynamic modulus of elasticity, dynamic shear strength, and combinations thereof” which merely links the numerical values/parameters to a field of use. Such elements characterize the variables and parameters used in the mathematical calculation and merely contextualize the mathematical calculation. Such elements are not a practical application nor significantly more than the abstract idea itself. Indeed symbolic abstraction is when a human uses variables to mentally represent some physical attribute.
Claim 4 recites “wherein the static physical property comprises at least one property selected from the group consisting of static Young's modulus, static Poisson's ratio, static unconfined compressive strength, static tensile strength, static flexural strength, static modulus of elasticity, static shear strength, and combinations thereof” which merely links the numerical values/parameters to a field of use. Such elements characterize the variables and parameters used in the mathematical calculation and merely contextualize the mathematical calculation. Such elements are not a practical application nor significantly more than the abstract idea itself. Indeed symbolic abstraction is when a human uses variables to mentally represent some physical attribute.
Claim 5 recites “wherein the cement property model has at least one form selected from the group consisting of linear, multilinear, parabolic, exponential, derivative, integral, hyperbolic, trigonometric, and combinations thereof” which merely further recites the mathematical abstract idea.
Claim 6 recites “wherein the cement property model has at least one form selected from the group consisting of artificial neural network, convolutional neural network, recurrent neural network, decision tree, random forest, machine learning boosting, extreme gradient boosting, Gaussian process regression, spline regression, multi-variate adaptive regression spline, and combinations thereof” which are part of the mathematical abstract idea.
Claim 7 recites: “wherein the chemical species comprises at least one species selected from the group consisting of carbon dioxide, ammonia, hydrogen sulfide (H2S), acid, and combinations thereof” which is merely part of the extra-solution data gathering experimental activities. Data gathering is not a practical application of the recited mathematical abstract idea nor is such data gathering activities significantly more than the abstract idea.
Claim 9 recites: “wherein measuring the dynamic physical property of each of the plurality of composite cement samples comprises measuring the dynamic physical property using an ultrasonic pulse velocity method, acoustic method, a flat-jack test method, or a combination thereof” which are data gathering activities. these elements are not a practical application of the recited mathematical abstract idea nor are they significantly more than the recited mathematical abstract idea.
Claim 9 recites “wherein measuring the static physical property of each of the plurality of composite cement samples comprises measuring the static physical property using a crushing tests in the presence or absence of confined pressure, a pull out test, a hardness test, a scratch resistance tests, or any combination thereof” which is part of the laboratory data gathering activities which are extra-solution activities. The recited tests are part of the API standard and accordingly are further well-understood and routine activities. Accordingly, these elements are not a practical application of the recited mathematical abstract idea nor are they significantly more than the recited mathematical abstract idea.
Claim 10 recites “wherein exposing the plurality of set cement samples to the chemical species comprises exposing at least a portion of the set cement samples to differing concentrations of the chemical species” which is part of the laboratory experiments used for data gathering. Using different concentrations of reactants is a standard practice when doing data gathering experiments. Therefore, these are found to be standard testing elements recited as part of mere data gathering activities. Such elements are not a practical application of the abstract mathematical idea nor is this significantly more than the abstract mathematical idea.
Claim 11 recites “wherein exposing the plurality of set cement samples to the chemical species comprises exposing at least a portion of the set cement samples to the chemical species for differing amounts of time” which is part of pre-solution data gathering activities. As outlined previously, the API Specification for testing material properties teaches to cure cement for periods of time, temperature, and pressure. Therefore, these are found to be standard testing elements recited as part of mere data gathering activities. Such elements are not a practical application of the abstract mathematical idea nor is this significantly more than the abstract mathematical idea.
Claim 12 recites “wherein the set cement samples are cured at a pressure in a range of about 1 bar to about 1500 bar and wherein the set cement samples are cured at a temperature in a range of from about 20 0C to about 200 0C” which merely further defines the pre-solution data gathering activities. As outlined previously, the API Specification for testing material properties teaches to cure cement for periods of time, temperature, and pressure. Therefore, these are found to be standard testing elements recited as part of mere data gathering activities. Such elements are not a practical application of the abstract mathematical idea nor is this significantly more than the abstract mathematical idea.
Claim 13.
STEP 1. Yes. The claim recites: “A method”
STEP 2A PRONG ONE: Yes. The claim recites:
“…comprising: introducing an ultrasonic tool into a wellbore comprising a composite cement sheath wherein the composite cement sheath comprises an unmodified portion and a chemically modified portion; transmitting an ultrasonic wave into the composite cement sheath using the ultrasonic tool; measuring an ultrasonic response using the ultrasonic tool; determining, based at least in part on the ultrasonic response, at least one dynamic cement physical property; and inputting the dynamic cement physical property and a volume fraction of water utilized to prepare the composite cement sheath into a composite cement property model and calculating a dynamic cement physical property” which is a mathematical abstract idea.
STEP 2A PRONG TWO: No.
While the claim recites: “…comprising: introducing an ultrasonic tool into a wellbore comprising a composite cement sheath wherein the composite cement sheath comprises an unmodified portion and a chemically modified portion; transmitting an ultrasonic wave into the composite cement sheath using the ultrasonic tool; measuring an ultrasonic response using the ultrasonic tool; determining, based at least in part on the ultrasonic response, at least one dynamic cement physical property” these elements are data gathering activities used to gather data for use in a mathematical calculation. MPEP 2106.05(g) states that mere data gathering is not a practical application and cites:
Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989);
Testing a system for a response, the response being used to determine system malfunction, In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982);
Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis).
The recited elements in the claim articulate a laboratory experiment. The MPEP’s cited example include clinical testing, system response testing, and biological testing. Gathering data using downhole equipment is common in the oil and gas industry.
STEP 2B. No.
Gathering data for use in a mathematical equation is not significantly more than the abstract idea itself.
Claim 14 recites: “wherein the dynamic physical property comprises at least one property selected from the group consisting of dynamic Young's modulus, dynamic Poisson's ratio, dynamic unconfined compressive strength, dynamic tensile strength, dynamic flexural strength, dynamic modulus of elasticity, dynamic shear strength, and combinations thereof” which merely links the numerical values/parameters to a field of use. Such elements characterize the variables and parameters used in the mathematical calculation and merely contextualize the mathematical calculation. Such elements are not a practical application nor significantly more than the abstract idea itself. Indeed symbolic abstraction is when a human uses variables to mentally represent some physical attribute.
Claim 15 recites: “wherein the static physical property comprises at least one property selected from the group consisting of static Young's modulus, static Poisson's ratio, static unconfined compressive strength, static tensile strength, static flexural strength, static modulus of elasticity, static shear strength, and combinations thereof” which merely links the numerical values/parameters to a field of use. Such elements characterize the variables and parameters used in the mathematical calculation and merely contextualize the mathematical calculation. Such elements are not a practical application nor significantly more than the abstract idea itself. Indeed symbolic abstraction is when a human uses variables to mentally represent some physical attribute.
Claim 16 recites: “wherein the cement property model has at least one form selected from the group consisting of linear, multilinear, parabolic, exponential, derivative, integral, hyperbolic, trigonometric, and combinations thereof” which is part of the abstract idea itself.
Claim 17 recites: “wherein the cement property model has at least one form selected from the group consisting of artificial neural network, convolutional neural network, recurrent neural network, decision tree, random forest, machine learning boosting, extreme gradient boosting, Gaussian process regression, spline regression, multi-variate adaptive regression spline, and combinations thereof” which is part of the abstract idea itself.
Claim 18 recites: “wherein the chemically modified portion of the composite cement sheath is modified by at least one chemical species selected from the group consisting of carbon dioxide, ammonia, hydrogen sulfide (H2S), acid, and combinations thereof” which is merely descriptive of the environment in which the data gathering occurs. This, at best, links the data gathering to a field of use, however, linking pre-solution data gathering to a field of use is not a practical application of the mathematical abstract idea nor is it significantly more than the abstract idea. Indeed, “carbon dioxide, ammonia, hydrogen sulfide (H2S), acid, and combinations thereof” is a common environment of well bore cement. Indeed, the cited prior art used in the grounds of rejection under 35 USC 103 teach that carbon dioxide is a common species and that carbon dioxide in solution of water results in acid (carbonic acid) to which the cement is exposed. Therefore, gathering data from cement that is known to be exposed to carbon dioxide is not significantly more than the abstract idea.
Claim 19.
STEP 1. Yes. The claim recites: “A method”
STEP 2A PRONG ONE. YES. The claim recites:
“… comprising: preparing a plurality of cement slurries, wherein the plurality of cement slurries each comprise a cement and volume fraction of water; curing the plurality of cement slurries to form a plurality of set cement samples; exposing the plurality of set cement samples to a chemical species; allowing the chemical species to at least partially modify the plurality of set cement samples to form a plurality of composite cement samples; measuring a dynamic physical property of each of the plurality of composite cement samples to generate a dynamic physical property dataset; measuring a static physical property of each of the plurality of composite cement samples to generate a static physical property dataset; correlating the static physical property dataset as a function of the dynamic physical property dataset and volume fraction of water to generate a composite cement property model; introducing an ultrasonic tool into a wellbore comprising a composite cement sheath wherein the composite cement sheath comprises an unmodified portion and a chemically modified portion; transmitting an ultrasonic wave into the composite cement sheath using the ultrasonic tool; measuring an ultrasonic response using the ultrasonic tool; determining, based at least in part on the ultrasonic response, at least one dynamic cement physical property of the composite cement sheath; and inputting the at least one dynamic cement physical property of the composite cement sheath and a volume fraction of water utilized to prepare the composite cement sheath into the composite cement property model and calculating a dynamic cement physical property of the composite cement sheath. Which is a mathematical abstract idea.
Step 2A PRONG TWO. No.
While the claim recites:
“… comprising: preparing a plurality of cement slurries, wherein the plurality of cement slurries each comprise a cement and volume fraction of water; curing the plurality of cement slurries to form a plurality of set cement samples; exposing the plurality of set cement samples to a chemical species; allowing the chemical species to at least partially modify the plurality of set cement samples to form a plurality of composite cement samples; measuring a dynamic physical property of each of the plurality of composite cement samples to generate a dynamic physical property dataset; measuring a static physical property of each of the plurality of composite cement samples to generate a static physical property dataset…” and “introducing an ultrasonic tool into a wellbore comprising a composite cement sheath wherein the composite cement sheath comprises an unmodified portion and a chemically modified portion; transmitting an ultrasonic wave into the composite cement sheath using the ultrasonic tool; measuring an ultrasonic response using the ultrasonic tool; determining, based at least in part on the ultrasonic response, at least one dynamic cement physical property of the composite cement sheath” these are data gathering activities. these elements amount to pre-solution data gathering. MPEP 2106.05(g) states that mere data gathering is not a practical application and cites:
Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989);
Testing a system for a response, the response being used to determine system malfunction, In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982);
Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis).
STEP 2B. No.
Data gathering activities are not significantly more than the abstract idea itself. Indeed, the fundamental scientific activity is to gather data and to mathematically model (e.g., hypothesize about observed relationship/correlations) and then to collect more data and input the newly collected into the mathematical model. Accordingly, the data gathering activities are not significantly more than the abstract mathematical idea itself.
Claim 20 recites: “wherein the dynamic physical property comprises at least one property selected from the group consisting of dynamic Young's modulus, dynamic Poisson's ratio, dynamic unconfined compressive strength, dynamic tensile strength, dynamic flexural strength, dynamic modulus of elasticity, dynamic shear strength, and combinations thereof and wherein the static physical property comprises at least one property selected from the group consisting of static Young's modulus, static Poisson's ratio, static unconfined compressive strength, static tensile strength, static flexural strength, static modulus of elasticity, static shear strength, and combinations thereof” which merely links the numerical values/parameters to a field of use. Such elements characterize the variables and parameters used in the mathematical calculation and merely contextualize the mathematical calculation. Such elements are not a practical application nor significantly more than the abstract idea itself. Indeed symbolic abstraction is when a human uses variables to mentally represent some physical attribute.
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, 3, 4, 5, 7, 8, 9, 10, 11, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Jamenez_2019 (US 2019/0330513 A1) in view of Duguid_2010 (Degradation of oilwell cement due to exposure to carbonated brine, International Journal of Greenhouse Gas Control 4 (2010) 546 – 560) in view of Liu_2019 (US 2019/0265142 A1).
Claim 1. Jamenez_2019 A makes obvious “A method comprising: preparing a plurality of cement slurries, wherein the plurality of cement slurries each comprise a cement and volume fraction of water (Par 8: “… a cement composition is mixed with water… Portland cement and water…”; par 9: “suitable cement compositions generally may comprise water and a cementitious component, such as Portland cement… about 8 pounds per gallon…”; par 10: “… water used in the cement compositions may include, for example, freshwater, saltwater (e.g., water containing one or more salts dissolved therein), brine… the water may be included in an amount sufficient to form a pumpable slurry…”; par 36: “… may comprise first forming a baseline with pure Portland cement and water. A slurry… a second test may comprise replacing a portion of the Portland with fly ash and line… a third test may be performed that reduces the amount of Portland again. The process… may be carried out repeatedly… the method may then be repeated for another cementitious component…”);
Curing the plurality of cement slurries to form a plurality of set cement samples (par 33: “… measured at a specified time after the cementitious component has been mixed with water…”; Par 34: “… after the treatment fluid has been allowed to set for a period of time at specified temperature and pressure…”; par 35: “… a cement sample may be introduced… and allowed to set or hydrate…”; par 43: “… preparing the cement composition; and allowing the cement composition to set…”; par 51: “… preparing the cement composition; and allowing the cement composition to set…”);
Measuring a static physical property of each of the plurality of composite cement samples to generate a static physical property dataset (Par 32 – 35: “… cementitious component may be measured at a specified time after the cementitious component has been mixed with water and the resultant cement composition is maintained under specific temperature and pressure conditions… Young’s modulus… a number of different laboratory techniques may be used to measure the Young’s modulus…”; Par 65: “… a system 100 for analyzing the cementitious components… the system may comprise a cementitious component sample 105, analytical instrument 110, and computer system 115… the sample may be placed or fed into analytical instrument 110… analytical instrument 110 may be configured to analyze the physical and chemical properties of cementitious component sample 105… take the data from the analytical instrument 110 as input and store it in the storage…”); and correlating the static physical property dataset to generate a composite cement property model” (par 24: “reactivity mapping is the process of using laboratory techniques to analyze the physiochemical properties of a cementitious component and generate predictive maps and models of the behavior of a component in a cement composition. Reactivity mapping may comprise several steps. One step may comprise measuring the physical and chemical properties of different materials through standardized tests. Another step may comprise categorizing the materials through analysis of data collected and the predicted effects on cement slurry properties. Yet another step may comprise utilizing the data to estimate material reactivity, optimizing cement performance, predicting blend mechanical properties mathematically based on analytical results…”; Par 36: “… performing a multi-linear regression analysis…”; par 65: “… take the data from analytical instruments 110 as input and store it in the storage for later processing. Processing the data may comprise inputting the data into algorithms which compute results… from a sample and generate correlations, charts, and models…”).
Jamenez_2019 does not explicitly teach:
Exposing the plurality of set cement samples to a chemical species;
Allowing the chemical species to at least partially modify the plurality of set cement samples to form a plurality of composite cement samples;
Measuring a dynamic physical property of each of the plurality of composite cement samples to generate a dynamic physical property dataset;
Nor modeling the static physical properties “as a function of the dynamic physical property dataset and volume fraction of water”
Duguid_2010, however, makes obvious Exposing the plurality of set cement samples to a chemical species; Allowing the chemical species to at least partially modify the plurality of set cement samples to form a plurality of composite cement samples (abstract: “… growing interest in geologic carbon sequestration has highlighted the need for more data on how well cements react to CO2 exposure. This paper describes a series of experiments that was conducted to examine the effects of flowing carbonated brine on well cements; Fig. 1:
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Jamenez_2019 and Duguid_2010 are analogous art because they are from the same field of endeavor called testing cementitious samples. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Jamenez_2019 and Duguid_2010. The rationale for doing so would have been that Jamenez_2019 teaches a system and method that tests cementitious samples used in the construction of wells for their physical properties and teaches that cement compositions may include, for example, brine. Jamenez_2019 further teaches to use the predicative modeling to optimize cement performance. Duguid_2010 teaches that cement used in wells can be degraded by CO2 exposure due to carbonated brine.Therefore, it would have been obvious to combine Jamenez_2019 and Duguid_2010 for the benefit of modeling the degradation of cement to optimize cement performance and reduce or prevent degradation do to CO2 exposure to obtain the invention as specified in the claims.
Jamenez_2019 and Duguid_2010 do not explicitly teach:
Measuring a dynamic physical property of each of the plurality of composite cement samples to generate a dynamic physical property dataset;
Nor modeling the static physical properties “as a function of the dynamic physical property dataset and volume fraction of water”
Liu_2019; however, makes obvious: Measuring a dynamic physical property of each of the plurality of composite cement samples to generate a dynamic physical property dataset and modeling the static physical properties “as a function of the dynamic physical property dataset and volume fraction of water” (abstract: “a method for obtaining a conversion relationship between dynamic and static elastic parameters include:… measuring the dynamic elastic parameters… measuring the static elastic parameters… establishing a function relationship of the ratio between the dynamic and static elastic parameters…”; FIG. 1: S2, S2, S5; Par 3: “elastic parameters… such as Young’s modulus, Poisson’s ratio and the like… one can obtain dynamic elastic parameters… through laboratory ultrasonic… or through acoustics… obtain static elastic parameters… “)
Jamenez_2019 and Duguid_2010 and Liu_2019 are analogous art because they are from the same field of endeavor called characterizing physical properties of samples. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Jamenez_2019 and Duguid_2010 and Liu_2019. The rationale for doing so would have been Jamenez_2019 teaches to make a plurality of cement samples and to characterize cement samples by using API tests including the static mechanical property testing of Young’s Modulus (par 25) for the purpose of characterizing cement to create predictive models and correlations for cement used in wells. and Duguid_2010 teaches that the physical properties of cement used in wells can be degraded by exposure to Co2 in water/brine and teaches API testing procedures that create cement samples that have specific water/cement ratio (W/C) (page 546). Liu_2019 teaches that logging data for down hole dynamic elastic parameters such as Young’s modulus can be measured and converted to static parameters for the purpose of optimizing oil well completion solutions (par 2) and also teaches that the downhole environment can include a saturated brine condition. Therefore, it would have been obvious to combine Jamenez_2019 and Duguid_2010 and Liu_2019 for the benefit of measuring downhole dynamic conditions and converting them static conditions for the purpose of evaluating potential degradation of downhole wells and optimizing the oil well completion concrete to obtain the invention as specified in the claims.
Claim 3. Liu_2019 makes obvious “wherein the dynamic physical property comprises at least one property selected from the group consisting of dynamic Young’s modulus, dynamic Poisson’s ratio, dynamic unconfined compressive strength, dynamic tensile strength, dynamic flexural strength, dynamic modulus of elasticity, static shear strength, and combinations thereof” (par 3: “elastic parameters… such as Young’s modulus… dynamic elastic parameters…”; par 59: “… dynamic Young’s moduli…”)
Claim 4. Jamenez_2019 makes obvious “wherein the static physical properties comprises at least one property selected from the group consisting of static Young’s modulus, static Poisson’s ratio, static unconfined compressive strength, static tensile strength, static flexural strength, static modulus of elasticity, static shear strength, and combinations therefore” (par 34: “Young’s modulus…”).
Liu_2019 also makes obvious “wherein the static physical properties comprises at least one property selected from the group consisting of static Young’s modulus, static Poisson’s ratio, static unconfined compressive strength, static tensile strength, static flexural strength, static modulus of elasticity, static shear strength, and combinations therefore” (par 64: “… static Young’s moduli…”).
Claim 5. Jamenez_2019 makes obvious “wherein the cement property model has at least one form selected from the group consisting of linear, multilinear, parabolic, exponential, derivative, integral, hyperbolic, trigonometric, and combinations thereof” (Par 36: “… determined, such as by performing a multi-linear regression analysis
Liu_2019 also makes obvious “wherein the cement property model has at least one form selected from the group consisting of linear, multilinear, parabolic, exponential, derivative, integral, hyperbolic, trigonometric, and combinations thereof” (par 76: “… dynamic and static linear regression method…”)
Claim 7. Duguid_2010 makes obvious “wherein the chemical species comprises at least one species selected from the group consisting of carbon dioxide, ammonia, hydrogen sulfide (H2S), acid, and combination thereof” (abstract: “… CO2 exposure…”).
Claim 8. Liu_2019 makes obvious “wherein measuring the dynamic physical property of each of the plurality of composite cement samples comprises measuring the dynamic physical property using an ultrasonic pulse velocity method, acoustic method, a flat-jack test method, or a combination thereof” (par 3: “… elastic parameters… such as Young’s modulus… to obtain dynamic elastic parameters… through laboratory ultrasonic… or through acoustic… logging data…”).
Claim 9. Jamenez_2019 makes obvious “wherein measuring the static physical property of each of the plurality of composite cement samples comprises measuring the static physical property using a crushing tests in the presence or absence of confined pressure, a pull out test, a hardness test, a scratch resistance tests, or any combination thereof” (Par 32: “… the destructive method physically tests the strength of treatment fluid samples at various points in time by crushing the samples…”).
Claim 10. Duguid_2010 also makes obvious “wherein exposing the plurality of set cement samples to the chemical species comprises exposing at least a portion of the set cement samples to differing concentrations of the chemical species” (page 559: “… at higher pressure there would be more dissolved CO2 or carbonic acid in the system and thus more CO2, more than an order of magnitude more, available for reaction (fig. 17). This may allow for deeper penetration by the reaction front and thus more damage to the cement…”
Claim 11. Jamenez_2019 makes obvious “wherein exposing the plurality of set cement samples to the chemical species comprises exposing at least a portion of the set cement samples to the chemical species for differing amounts of time” (Par 32: “… can be measured at a time in the range of about 24 to about 48 hours (or longer)… physically tests the strength of treatment fluid samples at various points in time…”; par 34: “… treatment fluid has been allowed to set for a period of time…”).
Duguid_2010 also makes obvious “wherein exposing the plurality of set cement samples to the chemical species comprises exposing at least a portion of the set cement samples to the chemical species for differing amounts of time” (Fig. 4, Fig. 5).
Claim 12. Jamenez_2019 makes obvious “wherein the set cement samples are cured at a pressure in a range of about 1 bar to about 1500 bar and wherein the set cement samples are cured at a temperature in a range of from about 20c to about 200c (Par 32: “… measured at a time in the range of about 24 to about 48 hours (or longer) after the fluid is mixed and the fluid is maintained at a temperature of from 100 F to about 200 F and at atmospheric pressure…” EXAMINER NOTE: 20 C to about 200 C is the range of 68 f to 392 f and 1 bar is atmospheric pressure at sea level. Jamenez_2019 teaches a range that is inside the claimed range.).
Claims 6 are rejected under 35 U.S.C. 103 as being unpatentable over Jamenez_2019 in view of Duguid_2010 in view of Liu_2019 in view of Coveney_1999 (6.009,419)
Claim 6. Coveney_1999 makes obvious “wherein the cement property model has at least one form selected from the group consisting of artificial neural network, convolutional neural network, recurrent neural network, decision tree, random forest, machine learning boosting, extreme gradient boosting, Gaussian process regression, spline regression, multi-variate adaptive regression spline, and combinations thereof” (abstract: “a method of predicting a desired property… of a cement slurry comprising… correlating these with measured values of the desired property. In one embodiment the method comprises measuring and determining the other properties and inputting values corresponding to these properties to a neural network device configured to output a value representative of the desired property… trained with a dataset comprising series of values of said properties and a value corresponding to the desired property when measured for a slurry…”; COL 1 lines 5 – 15: “… method for predicting properties of cement… in oilwell drilling, cement is used to secure a lining or casing inside a drilled hole… cement slurry is pumped between the casing and the borehole wall and allowed to set…”; COL 2 lines 30 – 60: “… neural network device… trained with a dataset… neural network arrangement specified above or any suitable multivariate statistical method…”; COL 3 lines 10 – 15: “… using computer-based artificial neural network as the framework…”).
Jamenez_2019 and Coveney_1999 are analogous art because they are from the same field of endeavor called predicting cement properties for wells. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Jamenez_2019 and Coveney_1999.
The rationale for doing so would have been that Jamenez_2019 teaches to create a model using measured data so that the model can predict properties of cement used in a well (par 66: “… predictive model…” in order to generate a cement composition that meets the engineering requirements of the well.). Coveney_1999 teaches to use a predictive model known as a neural network to predict the properties of cement used in the casing of wells. Therefore, it would have been obvious to combine Jamenez_2019 and Coveney_1999 for the benefit of having a predictive model that can predict the properties of cement used in wells in order to make sure the cement meets engineering requirements to obtain the invention as specified in the claims.
Claims 2 are rejected under 35 U.S.C. 103 as being unpatentable over Jamenez_2019 in view of Duguid_2010 in view of Liu_2019 in view of Zhongcun_2019 (Simulation of the degradation of oilwell cement for the prediction of long-term performance, Construction and Building Materials 202 (2019) 669-680).
Claim 2. Zhongcun_2019 makes obvious “further comprising: Preparing a test cement slurry; measuring a dynamic physical property of the test cement slurry; calculating a static physical property of the test cement slurry using the composite cement property model wherein the dynamic physical property of the test cement slurry is an input to the composite cement property model; and calculating a well life integrity with a numerical simulator using the static physical property of the test cement slurry as an input to the numerical simulator” (abstract: “… the cement sheath surrounding a wellbore… the service life of a well… the degradation of the well cement, as a result of exposure to groundwater, is therefore necessary to determine if this barrier remains intact… a simulated time period of 1000 years…”; introduction: “… the chemical reactions that occur between the formation fluid and the cement are acid-based reactions… these reactions develop an altered zone of cement, the properties of which are generally poorer than those of the original cement. The chemical changes to the cement…”; page 670: “… various degradation experiments have been conducted… specimens were immersed in brine with high concentrations of aqueous species to cause significant change to the composition and properties of cement… cement samples with hydrochloric acid for a period of 50 h… cement degradation observed in these tests invariably accelerated by the strong acid and/or high concentration of the brine… the evaluation of the durability of cementitious materials has been considered as a relevant issue in a broad range of industry sectors…”; section 3.2: “determination of simulated degradation front…”; Fig. 2; Fig. 3: “corrosion rate… cement composition…” section 4 long term simulations).
Duguid_2010 and Zhongcun_2019 are analogous art because they are from the same field of endeavor called oil wells. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Duguid_2010 and Zhongcun_2019. The rationale for doing so would have been that Duguid_2010 teaches that geologic carbon sequestration requires more data on how the well cement reacts to CO2 exposure because the CO2 exposure causes degradation of the cement which could cause the cement to fail. Zhongcun_2019 teaches to use a simulation to determine if a cement barrier will remain intact by accelerating experimentation. Therefore, it would have been obvious to combine Duguid_2010 and Zhongcun_2019 for the benefit of accelerating experiments to determine if the cement barrier will remain intact over longer periods of time than can be done using laboratory experiments to obtain the invention as specified in the claims.
Claims 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jamenez_2019 in view of Duguid_2010 in view of Liu_2019 in view of Zeroug_2022 (US 2025/0084753 A1 provisional 7/21/2021).
Claim 19. Jamenez_2019 makes obvious “A method comprising:
Preparing a plurality of cement slurries, wherein the plurality of cement slurries each comprise a cement and volume fraction of water ((Par 8: “… a cement composition is mixed with water… Portland cement and water…”; par 9: “suitable cement compositions generally may comprise water and a cementitious component, such as Portland cement… about 8 pounds per gallon…”; par 10: “… water used in the cement compositions may include, for example, freshwater, saltwater (e.g., water containing one or more salts dissolved therein), brine… the water may be included in an amount sufficient to form a pumpable slurry…”; par 36: “… may comprise first forming a baseline with pure Portland cement and water. A slurry… a second test may comprise replacing a portion of the Portland with fly ash and line… a third test may be performed that reduces the amount of Portland again. The process… may be carried out repeatedly… the method may then be repeated for another cementitious component…”);
Curing the plurality of cement slurries to form a plurality of set cement samples (par 33: “… measured at a specified time after the cementitious component has been mixed with water…”; Par 34: “… after the treatment fluid has been allowed to set for a period of time at specified temperature and pressure…”; par 35: “… a cement sample may be introduced… and allowed to set or hydrate…”; par 43: “… preparing the cement composition; and allowing the cement composition to set…”; par 51: “… preparing the cement composition; and allowing the cement composition to set…”);
Measuring a static physical property of each of the plurality of composite cement samples to generate a static physical property dataset (Par 32 – 35: “… cementitious component may be measured at a specified time after the cementitious component has been mixed with water and the resultant cement composition is maintained under specific temperature and pressure conditions… Young’s modulus… a number of different laboratory techniques may be used to measure the Young’s modulus…”; Par 65: “… a system 100 for analyzing the cementitious components… the system may comprise a cementitious component sample 105, analytical instrument 110, and computer system 115… the sample may be placed or fed into analytical instrument 110… analytical instrument 110 may be configured to analyze the physical and chemical properties of cementitious component sample 105… take the data from the analytical instrument 110 as input and store it in the storage…”);
Correlating the static physical property dataset as to generate a composite cement property model (par 24: “reactivity mapping is the process of using laboratory techniques to analyze the physiochemical properties of a cementitious component and generate predictive maps and models of the behavior of a component in a cement composition. Reactivity mapping may comprise several steps. One step may comprise measuring the physical and chemical properties of different materials through standardized tests. Another step may comprise categorizing the materials through analysis of data collected and the predicted effects on cement slurry properties. Yet another step may comprise utilizing the data to estimate material reactivity, optimizing cement performance, predicting blend mechanical properties mathematically based on analytical results…”; Par 36: “… performing a multi-linear regression analysis…”; par 65: “… take the data from analytical instruments 110 as input and store it in the storage for later processing. Processing the data may comprise inputting the data into algorithms which compute results… from a sample and generate correlations, charts, and models…”);
Jamenez_2019 does not explicitly teach:
Exposing the plurality of set cement samples to a chemical species;
Allowing the chemical species to at least partially modify the plurality of set cement samples to form a plurality of composite cement samples;
Measuring a dynamic physical property of each of the plurality of composite cement samples to generate a dynamic physical property dataset;
Nor modeling the static physical properties “as a function of the dynamic physical property dataset and volume fraction of water”
Nor “Introducing an ultrasonic tool into a wellbore comprising a composite cement sheath wherein the composite cement sheath comprises an unmodified portion and a chemically modified portion;
Transmitting an ultrasonic wave into the composite cement sheath using the ultrasonic tool;
Measuring an ultrasonic response using the ultrasonic tool;
Determining, based at least in part on the ultrasonic response, at least one dynamic cement physical property of the composite cement sheath; and
Inputting the at least one dynamic cement physical property of the composite cement sheath and volume fraction of water utilized to prepare the composite cement sheath into the composite cement property model and calculating a dynamic cement physical property of the composite cement sheath.”
Duguid_2010, however, makes obvious Exposing the plurality of set cement samples to a chemical species; Allowing the chemical species to at least partially modify the plurality of set cement samples to form a plurality of composite cement samples (abstract: “… growing interest in geologic carbon sequestration has highlighted the need for more data on how well cements react to CO2 exposure. This paper describes a series of experiments that was conducted to examine the effects of flowing carbonated brine on well cements; Fig. 1:
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Jamenez_2019 and Duguid_2010 are analogous art because they are from the same field of endeavor called testing cementitious samples. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Jamenez_2019 and Duguid_2010. The rationale for doing so would have been that Jamenez_2019 teaches a system and method that tests cementitious samples used in the construction of wells for their physical properties and teaches that cement compositions may include, for example, brine. Jamenez_2019 further teaches to use the predicative modeling to optimize cement performance. Duguid_2010 teaches that cement used in wells can be degraded by CO2 exposure due to carbonated brine.Therefore, it would have been obvious to combine Jamenez_2019 and Duguid_2010 for the benefit of modeling the degradation of cement to optimize cement performance and reduce or prevent degradation do to CO2 exposure to obtain the invention as specified in the claims.
Jamenez_2019 and Duguid_2010 do not explicitly teach:
Measuring a dynamic physical property of each of the plurality of composite cement samples to generate a dynamic physical property dataset;
Nor modeling the static physical properties “as a function of the dynamic physical property dataset and volume fraction of water”
Nor “Introducing an ultrasonic tool into a wellbore comprising a composite cement sheath wherein the composite cement sheath comprises an unmodified portion and a chemically modified portion;
Transmitting an ultrasonic wave into the composite cement sheath using the ultrasonic tool;
Measuring an ultrasonic response using the ultrasonic tool;
Determining, based at least in part on the ultrasonic response, at least one dynamic cement physical property of the composite cement sheath; and
Inputting the at least one dynamic cement physical property of the composite cement sheath and volume fraction of water utilized to prepare the composite cement sheath into the composite cement property model and calculating a dynamic cement physical property of the composite cement sheath.”
Liu_2019; however, makes obvious: Measuring a dynamic physical property of each of the plurality of composite cement samples to generate a dynamic physical property dataset and modeling the static physical properties “as a function of the dynamic physical property dataset and volume fraction of water” (abstract: “a method for obtaining a conversion relationship between dynamic and static elastic parameters include:… measuring the dynamic elastic parameters… measuring the static elastic parameters… establishing a function relationship of the ratio between the dynamic and static elastic parameters…”; FIG. 1: S2, S2, S5; Par 3: “elastic parameters… such as Young’s modulus, Poisson’s ratio and the like… one can obtain dynamic elastic parameters… through laboratory ultrasonic… or through acoustics… obtain static elastic parameters… “) and “Introducing an ultrasonic tool into a wellbore comprising a composite cement sheath wherein the composite cement sheath comprises an unmodified portion and a chemically modified portion;
Transmitting an ultrasonic wave into the composite cement sheath using the ultrasonic tool;
Measuring an ultrasonic response using the ultrasonic tool;
Determining, based at least in part on the ultrasonic response, at least one dynamic cement physical property of the composite cement sheath” (par 3: Elastic parameters are parameters that are used to describe the magnitude of the stress between the rock and the resulting strain, such as Young's modulus, Poisson's ratio and the like. There are two approaches to obtain elastic parameters: one is to obtain dynamic elastic parameters of rock through laboratory ultrasonic and density measurements or through acoustic and density logging data at continuous downhole depths; the other is to obtain static elastic parameters of rock through stress and strain measurements in laboratories. Static elastic parameters are significant guidance for oil and gas formation fracturing transformation, but cannot be continuously applied downhole since they can only be obtained through measurements in laboratories. Therefore, in general, dynamic elastic parameters at continuous depths are firstly obtained by using well logging data, which are then converted into static elastic parameters by using the conversion rule between dynamic elastic parameters and static elastic parameters obtained through experimental measurements, and the static elastic parameters are eventually applied in the evaluation of rock mechanical parameters of downhole full profile formation.).
Jamenez_2019 and Duguid_2010 and Liu_2019 are analogous art because they are from the same field of endeavor called characterizing physical properties of samples. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Jamenez_2019 and Duguid_2010 and Liu_2019. The rationale for doing so would have been Jamenez_2019 teaches to make a plurality of cement samples and to characterize cement samples by using API tests including the static mechanical property testing of Young’s Modulus (par 25) for the purpose of characterizing cement to create predictive models and correlations for cement used in wells. and Duguid_2010 teaches that the physical properties of cement used in wells can be degraded by exposure to Co2 in water/brine and teaches API testing procedures that create cement samples that have specific water/cement ratio (W/C) (page 546). Liu_2019 teaches that logging data for down hole dynamic elastic parameters such as Young’s modulus can be measured and converted to static parameters for the purpose of optimizing oil well completion solutions (par 2) and also teaches that the downhole environment can include a saturated brine condition. Therefore, it would have been obvious to combine Jamenez_2019 and Duguid_2010 and Liu_2019 for the benefit of measuring downhole dynamic conditions and converting them static conditions for the purpose of evaluating potential degradation of downhole wells and optimizing the oil well completion concrete to obtain the invention as specified in the claims.
Zeroug_2022 makes obvious “Introducing an ultrasonic tool into a wellbore comprising a composite cement sheath wherein the composite cement sheath comprises an unmodified portion and a chemically modified portion;
Transmitting an ultrasonic wave into the composite cement sheath using the ultrasonic tool;
Measuring an ultrasonic response using the ultrasonic tool;
Determining, based at least in part on the ultrasonic response, at least one dynamic cement physical property of the composite cement sheath; and
Inputting the at least one dynamic cement physical property of the composite cement sheath and volume fraction of water utilized to prepare the composite cement sheath into the composite cement property model and calculating a dynamic cement physical property of the composite cement sheath” (FIG. 2 illustrates a measuring tool downhole. FIG. 3 EXAMINER NOTE: during training, dynamic estimates are provided to the nonlinear regression model to predict/calculate dynamic estimates. Also, FIG. 3 illustrates the acquisition of ultrasonic data downhole. Therefore, the image teaches both dynamic measurements from an ultrasonic tool in a composite cement sheath in a laboratory setting and also ultrasonic data measurements from an ultrasonic tool “downhole” in a wellbore. Par 57: “… there is interest in relating ultrasonic parameters to hydraulic properties of the interface in question and that is because the ultrasonic measurements are non-invasive and can be conducted downhole whereas the dynamic measurements cannot be performed downhole in a non-invasive way… the ultrasonic parameters can be though of as a proxy for the dynamic measurements… relationships or correlations are found that may be present between the two sets of data… data are acquired simultaneously using controlled experiments conducted in a laboratory setting on cemented samples covering the parametric operational envelope of interest. To derive these correslations, advanced deep learning techniques are used to build non-linear regressions between the two sets of data and use the developed neural network in inference mode to interpret ultrasonic estimates in terms of hydraulic estimates. As such, data acquired downhole with ultrasonic tools can then be interpreted in terms of hydraulic properties that would inform the decision taking on the sealing capacity of the annual fill…”).
Jamenez_2019 and Zeroug_2022 are analogous art because they are from the same field of endeavor called modeling cement properties. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Jamenez_2019 and Zeroug_2022. The rationale for doing so would have been that Jamenez_2019 teaches to create a model to predict cement properties and to use those model predictions to optimize the cement properties to meet engineering requirements for wells. Zeroug_2022 teaches that well integrity in the oil and gas industry is critical and that cements are often used to fill the annular space between steel casing and rock formation and a tight bond is essential (see par 3). Because a tight bond is essential it is necessary to evaluate the cement used in the well and that acoustic measurements have been widely used for this need (see par 4). Zeroug_2022 further teaches to use in situ ultrasonic measurements, to generate datasets and to train a neural network to correlate in situ measurements with physical properties that allow a determination of the cements physical properties and fitness for use in the well completion. Therefore, it would have been obvious to combine Jamenez_2019 and Zeroug_2022 for the benefit of having a trained model that is able to determine whether optimized cement formulations taught by Jamenez_2019 are indeed forming the required tight bonds downhole to obtain the invention as specified in the claims.
Claim 20. Jamenez_2019 “wherein the dynamic physical property comprises at least one property selected from the group consisting of dynamic Young’s modulus, dynamic Poisson’s ration, dynamic unconfined compressive strength, dynamic tensile strength, dynamic flexural strength, dynamic modulus of elasticity, dynamic shear strength, and combinations thereof and wherein the static physical property comprises at least one property selected from the group consisting of static Young’s modulus, statis Poisson’s ratio, static unconfined compressive strength, static tensile strength, static flexural strength, static modulus of elasticity, static shear strength, and combinations thereof” (par 34: Young’s modulus).
Also, Liu_2019 makes obvious “wherein the dynamic physical property comprises at least one property selected from the group consisting of dynamic Young’s modulus, dynamic Poisson’s ration, dynamic unconfined compressive strength, dynamic tensile strength, dynamic flexural strength, dynamic modulus of elasticity, dynamic shear strength, and combinations thereof and wherein the static physical property comprises at least one property selected from the group consisting of static Young’s modulus, statis Poisson’s ratio, static unconfined compressive strength, static tensile strength, static flexural strength, static modulus of elasticity, static shear strength, and combinations thereof” (par 59: “… dynamic Young’s moduli…”; par 64: “… static Young’s moduli…”).
Claims 13, 17, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zeroug_2022 in view of Duguid_2010
Claim 13 Zeroug_2022 makes obvious “A method comprising:
Introducing an ultrasonic tool into a wellbore comprising a composite cement sheath wherein the composite cement sheath comprises an unmodified portion and a modified portion;
Transmitting an ultrasonic wave into the composite sheath using the ultrasonic tool;
Measuring an ultrasonic response using the ultrasonic tool;
Determining based at least in part on the ultrasonic response, at least one dynamic cement physical property; and
Inputting the dynamic cement physical property and a volume fraction of water utilized to prepare the composite cement sheath into a composite cement property model and calculating a dynamic cement physical property” (FIG. 2 illustrates a measuring tool downhole. FIG. 3 EXAMINER NOTE: during training, dynamic estimates are provided to the nonlinear regression model to predict/calculate dynamic estimates. Also, FIG. 3 illustrates the acquisition of ultrasonic data downhole. Therefore, the image teaches both dynamic measurements from an ultrasonic tool in a composite cement sheath in a laboratory setting and also ultrasonic data measurements from an ultrasonic tool “downhole” in a wellbore. Par 57: “… there is interest in relating ultrasonic parameters to hydraulic properties of the interface in question and that is because the ultrasonic measurements are non-invasive and can be conducted downhole whereas the dynamic measurements cannot be performed downhole in a non-invasive way… the ultrasonic parameters can be though of as a proxy for the dynamic measurements… relationships or correlations are found that may be present between the two sets of data… data are acquired simultaneously using controlled experiments conducted in a laboratory setting on cemented samples covering the parametric operational envelope of interest. To derive these correslations, advanced deep learning techniques are used to build non-linear regressions between the two sets of data and use the developed neural network in inference mode to interpret ultrasonic estimates in terms of hydraulic estimates. As such, data acquired downhole with ultrasonic tools can then be interpreted in terms of hydraulic properties that would inform the decision taking on the sealing capacity of the annual fill…”).
Zeroug_2022 does not teach “chemically” modified.
Duguid_2010 makes obvious “chemically” modified (abstract: “… CO2 exposure… carbonated brine on well cements…”; introduction: “… carbonic acid created by sequestration…”).
Zeroug_2022 and Duguid_2010 are analogous art because they are from the same field of endeavor called oil wells. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zeroug_2022 and Duguid_2010. The rationale for doing so would have been that Zeroug_2022 teaches to quantify the bonding between cement and casings because good bonding is necessary to maintain well integrity. Duguid_2010 teaches that exposure to CO2 can cause significant degradation of well cement. Therefore, it would have been obvious to combine Zeroug_2022 and Duguid_2010 for the benefit of quantifying the integrity of sequestration wells which are exposed to CO2 to obtain the invention as specified in the claims.
Claim 17. Zeroug_2022 makes obvious “wherein the cement property model has at least one form selected from the group consisting of artificial neural network, convolutional neural network, recurrent neural network, decision tree, random forest, machine learning boosting, extreme gradient boosting, Gaussian process regression, spline regression, multi-variate adaptive regression spline, and combinations thereof” (par 57: “... deep learning… non-linear regressions between the two sets of data and use the developed neural network…”).
Claim 18. Duguid_2010 makes obvious “wherein the chemically modified portion of the composite cement sheath is modified by at least one chemical species selected from the group consisting of carbon dioxide, ammonia, hydrogen sulfied (H2S), and acid, and combinations thereof” (abstract: “… CO2 exposure… carbonated brine on well cements…”; introduction: “… carbonic acid created by sequestration…”).
Claims 14, 15, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zeroug_2022 in view of Duguid_2010 in view of Liu_2019
Claim 14. Liu_2019 makes obvious “wherein the dynamic physical property comprises at least one property selected from the group consisting of dynamic Young’s modulus, dynamic Poisson’s ratio, dynamic unconfined compressive strength, dynamic tensile strength, dynamic flexural strength, dynamic modulus of elasticity, static shear strength, and combinations thereof” (par 3: “elastic parameters… such as Young’s modulus… dynamic elastic parameters…”; par 59: “… dynamic Young’s moduli…”)
Zeroug_2022 and Liu_2019 are analogous art because they are from the same field of endeavor called quantifying properties. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zeroug_2022 and Liu_2019. The rationale for doing so would have been that Zeroug_2022 teaches a method for quantifying and correlate relationships between dynamic and static properties but does not explicitly identify any particular property. Liu_2019, however, teaches the need for a conversion between dynamic and static young’s modulus because access in a wellbore is limited to well logging equipment and laboratory techniques such as stress and strain testing is not available downhole. Therefore, it would have been obvious to combine Zeroug_2022 and Liu_2019 for the benefit of getting Young’s modulus from downhole measuring tools to obtain the invention as specified in the claims.
Claim 15. Liu_2019 makes obvious “wherein the static physical properties comprises at least one property selected from the group consisting of static Young’s modulus, static Poisson’s ratio, static unconfined compressive strength, static tensile strength, static flexural strength, static modulus of elasticity, static shear strength, and combinations therefore” (par 64: “… static Young’s moduli…”).
Zeroug_2022 and Liu_2019 are analogous art because they are from the same field of endeavor called quantifying properties. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zeroug_2022 and Liu_2019. The rationale for doing so would have been that Zeroug_2022 teaches a method for quantifying and correlate relationships between dynamic and static properties but does not explicitly identify any particular property. Liu_2019, however, teaches the need for a conversion between dynamic and static young’s modulus because access in a wellbore is limited to well logging equipment and laboratory techniques such as stress and strain testing is not available downhole. Therefore, it would have been obvious to combine Zeroug_2022 and Liu_2019 for the benefit of getting Young’s modulus from downhole measuring tools to obtain the invention as specified in the claims.
Claim 16. Liu_2019 makes obvious “wherein the cement property model has at least one form selected from the group consisting of linear, multilinear, parabolic, exponential, derivative, integral, hyperbolic, trigonometric, and combinations thereof” (par 4: “… conversion between dynamic and statice elastic parameters… a linear conversion relationship between the dynamic and static elastic parameters are then established…”).
Zeroug_2022 and Liu_2019 are analogous art because they are from the same field of endeavor called quantifying properties. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Zeroug_2022 and Liu_2019. The rationale for doing so would have been that Zeroug_2022 teaches a method for quantifying and correlate relationships between dynamic and static properties but does not explicitly identify any particular property. Liu_2019, however, teaches the need for a conversion between dynamic and static young’s modulus because access in a wellbore is limited to well logging equipment and laboratory techniques such as stress and strain testing is not available downhole. Therefore, it would have been obvious to combine Zeroug_2022 and Liu_2019 for the benefit of getting Young’s modulus from downhole measuring tools to obtain the invention as specified in the claims.
Conclusion
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/BRIAN S COOK/Primary Examiner, Art Unit 2187
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