DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The rejections from the Office Action of 12/22/2025 are hereby withdrawn. New grounds for rejection are presented below.
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 an abstract idea without significantly more. The claim(s) recite(s) the abstract idea of 1. a mental activity and/or mathematical algorithm for determining a target parameter for a subterranean sample based on its fluid chemistry parameters (i.e., the “comparing” and “determining” steps of Claim 1) and 2. a mental activity algorithm for identifying that a successful correlation indicates that the subterranean sample can be considered “identified.” A combination of abstract ideas is an abstract idea [See 2106.05(I) – "Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"].
This judicial exception is not integrated into a practical application because no further use for the “identified” sample status is performed nor is any further use of the “value of the target parameter” performed. The recitation that “recategorizing the unidentified subterranean sample alters a production allocation of a subterranean wellbore” amounts to an extension of the abstract idea of itself through the mathematical and/or mental step of making a further determination of a further number (this limitation is not the recitation of adjusting oil production itself, see Figs. 13 of the instant Specification and corresponding text).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use of the recited “sensor” or “sensor device” must necessarily be used in producing the data needed for performing the algorithm; the recitations of Claims 5-9 amount to mere field-of-use limitations in performing the sampling and data gathering. The recitations of Claim 4 with regards to the nature of the target parameter also amount to mere field-of-use limitations for the algorithm. The recited computer components in performing the algorithm amount to the recitation of the components of a general-purpose computer for performing the algorithm and do not serve to amount to significantly more than the recitation of the abstract idea itself (see Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014)).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-9 and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zulkipli et al., From Molecules to Barrels: A Case Study on Redefining Hydrocarbon Spectrum and DNA Tracing Through Gas-Chromatograph Fingerprinting, IPTC, 2019 [hereinafter “Zulkipli”], Zuo et al. (US 20130112406 A1)[hereinafter “Zuo 2”], and Torres-Verdin et al. (US 20230273180 A1)[hereinafter “Torres-Verdin”].
Regarding Claims 1 and 14, Zulkipli discloses a method (and corresponding analysis system/apparatus) for identifying an unidentified subterranean sample [Page 2 – “Gas chromatography works based on hydrocarbon components vaporization and separation principle. Liquid sample is injected into the equipment inlet, sample is vaporized and transported along the capillary column by mobile carrier gas till the sample is in contact with the heated detector where electrical signal will be generated. Each sample is characterized by a unique electrical response usually known as chromatogram or fingerprint.”Page 3 – “To ascertain hydrocarbon presence and fluid type, comparison of chromatograms from different liquid samples are conducted. The most critical requirement to trace hydrocarbon DNA is to have a baseline or reference sample which forms the basis of chromatogram comparison or overlay technique. In most cases, this reference sample refers to mud sample which typically shows C14 to C17 component peaks in the chromatogram signature. Compariosn of live hydrocarbon signature with the baseline sample will be the main focus of this paper and will be demonstrated in the following case studies.”], the method comprising:
comparing, using a comparison module of a controller [Fig. 1, chromatograph controller], chemical parameter values for a subset of fluid chemistry parameters associated with the unidentified subterranean sample to chemical parameter values of corresponding fluid chemistry parameters associated with a plurality of identified subterranean samples [Page 3 – “To ascertain hydrocarbon presence and fluid type, comparison of chromatograms from different liquid samples are conducted. The most critical requirement to trace hydrocarbon DNA is to have a baseline or reference sample which forms the basis of chromatogram comparison or overlay technique. In most cases, this reference sample refers to mud sample which typically shows C14 to C17 component peaks in the chromatogram signature. Compariosn of live hydrocarbon signature with the baseline sample will be the main focus of this paper and will be demonstrated in the following case studies.”See Fig. 6 and Page 5 – “When this signature is overlaid with mud sample composition, some hydrocarbon peaks are matching while other hydrocarbon components show independent peaks as portrayed in Figure 6.”].
Zulkipli fails to disclose generating a linear correlation between a source rock depth and one of the fluid chemistry parameters; determining, using the comparison module and based on the linear correlation between the source rock depth and the one of the fluid chemistry parameters, a depth within the wellbore from which the unidentified subterranean sample originates; and recategorizing the unidentified subterranean sample as originating from the depth within the wellbore, wherein recategorizing the unidentified subterranean sample alters a production allocation of a subterranean wellbore.
However, Zuo 2 discloses generating a linear correlation between a source depth and fluid chemistry parameters; determining based on the linear correlation between the source depth and the one of the fluid chemistry parameters, a depth within a wellbore [Paragraph [0143] – “A linear function of the form of Eq. (4) can be used to correlate a property of the oil mixture (such as density .rho..sub.m, molar volume .nu..sub.m, and the solubility parameter .delta..sub.m) as a function of depth.”], and wherein the depth is relative to source rock [See Fig. 3B, steps 327 and 329 and corresponding text].
It would have been obvious to recategorize the unidentified subterranean sample relative to such a function in order to ascertain sample depth. It would have been further obvious to alter a production allocation of a subterranean wellbore based on such an analysis because Zuo 2 teaches the downhole analysis is useful in doing so [Paragraph [0012] – “Furthermore, the EOS model can be extended with other reservoir evaluation techniques for compositional simulation of flow and production behavior of the petroleum fluid of the reservoir, as is well known in the art.”].
Zulkipli also fails to disclose identifying an unused fluid chemistry parameter from among the subset of fluid chemistry parameters that is unused in recategorizing the unidentified subterranean sample; and purging the unused fluid chemistry parameter by avoiding use of the unused fluid chemistry parameter to recategorize subsequent unidentified subterranean samples.
However, Torres-Verdin discloses the training of a numerical model relating sample properties from a well to the condition of the well where data corresponding to known conditions is used in performing the training [See Paragraphs [0074]-[0076].Paragraph [0074] – “Simulation data 212 may include data generated by simulations for formation sampling and pulse sequence outputs based on analytical methods, physics-based models, or numerical methods (e.g., based on neural network models trained on empirical data collected from previous formation tests). In some cases, the numerical model 238 may generate one or more outputs, including but not limited to the formation condition 240, time values corresponding to one or more industrially relevant parameters, target testing values, and the like. The numerical model may be trained using previously obtained data (e.g., simulation data and/or measurement data for other or related formations) and/or known outputs (e.g., for other or related formations) to predict output parameters for a formation under test.”Paragraph [0076] – “In some cases, the algorithm may be an unsupervised learning model, trained to cluster fluid parameters 236 with input data 210 from the same or other wells and/or simulation data 212 to determine formation properties. Use of a combination of input data 210, which may contain historical data, for example, and simulation data 212, which may contain physics-based simulation results may be advantageous as such a combination can be useful for filling in gaps of historical data to allow for sufficient coverage of the sample space to train an algorithm.”].
Torres-Verdin discloses identifying and purging unused parameters as part of the process [Paragraph [0075]– “In some cases, the updated sampling parameters 250 may replace the sampling parameters 202, such that the testing tool 130 implements formation sampling 232 or pulse sequence 234 operations only according to the updated sampling parameters 250.”].
It would have been obvious to take such an approach and to use data sets associated with a target parameter as training data when characterizing new samples because doing so would have provided a manner for more appropriately estimating the target parameter.
Regarding Claim 19, Zulkipli discloses a computer-implemented method for identifying an unidentified subterranean sample [See Fig. 1], the computer-implemented method comprising:
facilitate obtaining a first plurality of chemical parameter values for a subset of fluid chemistry parameters associated with an unidentified subterranean sample and a second plurality of chemical parameter values of corresponding fluid chemistry parameters associated with a plurality of identified subterranean samples [Page 2 – “Gas chromatography works based on hydrocarbon components vaporization and separation principle. Liquid sample is injected into the equipment inlet, sample is vaporized and transported along the capillary column by mobile carrier gas till the sample is in contact with the heated detector where electrical signal will be generated. Each sample is characterized by a unique electrical response usually known as chromatogram or fingerprint.”Page 3 – “To ascertain hydrocarbon presence and fluid type, comparison of chromatograms from different liquid samples are conducted. The most critical requirement to trace hydrocarbon DNA is to have a baseline or reference sample which forms the basis of chromatogram comparison or overlay technique. In most cases, this reference sample refers to mud sample which typically shows C14 to C17 component peaks in the chromatogram signature. Compariosn of live hydrocarbon signature with the baseline sample will be the main focus of this paper and will be demonstrated in the following case studies.”];
facilitate comparing the first plurality of chemical parameter values to the second plurality of chemical parameter values [Page 3 – “To ascertain hydrocarbon presence and fluid type, comparison of chromatograms from different liquid samples are conducted. The most critical requirement to trace hydrocarbon DNA is to have a baseline or reference sample which forms the basis of chromatogram comparison or overlay technique. In most cases, this reference sample refers to mud sample which typically shows C14 to C17 component peaks in the chromatogram signature. Compariosn of live hydrocarbon signature with the baseline sample will be the main focus of this paper and will be demonstrated in the following case studies.”See Fig. 6 and Page 5 – “When this signature is overlaid with mud sample composition, some hydrocarbon peaks are matching while other hydrocarbon components show independent peaks as portrayed in Figure 6.”].
Zulkipli fails to disclose generating a linear correlation between a source rock depth and one of the fluid chemistry parameters; facilitating determining, using the comparison module and based on the linear correlation between the source rock depth and the one of the fluid chemistry parameters, a depth within the wellbore from which the unidentified subterranean sample originates; and facilitating recategorizing the unidentified subterranean sample as originating from the depth within the wellbore, wherein recategorizing the unidentified subterranean sample alters a production allocation of a subterranean wellbore.
However, Zuo 2 discloses generating a linear correlation between a source depth and fluid chemistry parameters; determining based on the linear correlation between the source depth and the one of the fluid chemistry parameters, a depth within a wellbore [Paragraph [0143] – “A linear function of the form of Eq. (4) can be used to correlate a property of the oil mixture (such as density .rho..sub.m, molar volume .nu..sub.m, and the solubility parameter .delta..sub.m) as a function of depth.”], and wherein the depth is relative to source rock [See Fig. 3B, steps 327 and 329 and corresponding text].
It would have been obvious to recategorize the unidentified subterranean sample relative to such a function in order to ascertain sample depth. It would have been further obvious to alter a production allocation of a subterranean wellbore based on such an analysis because Zuo 2 teaches the downhole analysis is useful in doing so [Paragraph [0012] – “Furthermore, the EOS model can be extended with other reservoir evaluation techniques for compositional simulation of flow and production behavior of the petroleum fluid of the reservoir, as is well known in the art.”].
Zulkipli also fails to disclose facilitating identifying an unused fluid chemistry parameter from among the subset of fluid chemistry parameters that is unused in recategorizing the unidentified subterranean sample; and facilitating purging the unused fluid chemistry parameter by avoiding use of the unused fluid chemistry parameter to recategorize subsequent unidentified subterranean samples.
However, Torres-Verdin discloses the training of a numerical model relating sample properties from a well to the condition of the well where data corresponding to known conditions is used in performing the training [See Paragraphs [0074]-[0076].Paragraph [0074] – “Simulation data 212 may include data generated by simulations for formation sampling and pulse sequence outputs based on analytical methods, physics-based models, or numerical methods (e.g., based on neural network models trained on empirical data collected from previous formation tests). In some cases, the numerical model 238 may generate one or more outputs, including but not limited to the formation condition 240, time values corresponding to one or more industrially relevant parameters, target testing values, and the like. The numerical model may be trained using previously obtained data (e.g., simulation data and/or measurement data for other or related formations) and/or known outputs (e.g., for other or related formations) to predict output parameters for a formation under test.”Paragraph [0076] – “In some cases, the algorithm may be an unsupervised learning model, trained to cluster fluid parameters 236 with input data 210 from the same or other wells and/or simulation data 212 to determine formation properties. Use of a combination of input data 210, which may contain historical data, for example, and simulation data 212, which may contain physics-based simulation results may be advantageous as such a combination can be useful for filling in gaps of historical data to allow for sufficient coverage of the sample space to train an algorithm.”].
Torres-Verdin discloses identifying and purging unused parameters as part of the process [Paragraph [0075]– “In some cases, the updated sampling parameters 250 may replace the sampling parameters 202, such that the testing tool 130 implements formation sampling 232 or pulse sequence 234 operations only according to the updated sampling parameters 250.”].
It would have been obvious to take such an approach and to use data sets associated with a target parameter as training data when characterizing new samples because doing so would have provided a manner for more appropriately estimating the target parameter.
Regarding Claims 2 and 15, Zulkipli discloses measuring, using a sensor device, the chemical parameter values for the subset of fluid chemistry parameters associated with the unidentified subterranean sample before comparing the chemical parameter values for the subset of fluid chemistry parameters associated with the unidentified subterranean sample to the chemical parameter values of corresponding fluid chemistry parameters associated with a plurality of identified subterranean samples [Page 2 – “Gas chromatography works based on hydrocarbon components vaporization and separation principle. Liquid sample is injected into the equipment inlet, sample is vaporized and transported along the capillary column by mobile carrier gas till the sample is in contact with the heated detector where electrical signal will be generated. Each sample is characterized by a unique electrical response usually known as chromatogram or fingerprint.”Page 3 – “To ascertain hydrocarbon presence and fluid type, comparison of chromatograms from different liquid samples are conducted. The most critical requirement to trace hydrocarbon DNA is to have a baseline or reference sample which forms the basis of chromatogram comparison or overlay technique. In most cases, this reference sample refers to mud sample which typically shows C14 to C17 component peaks in the chromatogram signature. Compariosn of live hydrocarbon signature with the baseline sample will be the main focus of this paper and will be demonstrated in the following case studies.”].
Regarding Claims 3, 18, and 20, Zulkipli, as modified, would disclose identifying an unknown chemical parameter value associated with the unidentified subterranean sample [Page 11 – “Through the compositional analysis, a fit-for-purpose chemical recipe for stimulation design can be deployed to resolve wax and scaling issues. In terms of resolving well integrity issue and determining the root cause for major LOPC incidents, GC fingerprinting proves to add value in tracing possible leak path along the wellbore and downhole completion, identifying the source of fluid leakage and also ascertaining shallow gas presence which prompts the appropriate remedial solutions as well as quick monetizing efforts to bring the wells back on production.”Also, Page 6 – “It is deduced from this comparison that in addition to drilling mud, some reservoir fluid has also migrated into the wellbore through any possible leak path along the wellbore as well as behind the casing annulus. Leak and flow point survey is conducted to investigate further. Six wellbore conditions are created to investigate the wellbore dynamics as given in Table 2. Results show that packer integrity is possible leaking and liner integrity at deeper section is also affected as illustrated in Figure 7.”] before determining the depth within the wellbore from which the unidentified subterranean sample originates [per Zuo 2].
Regarding Claim 4, Zulkipli discloses that the corresponding fluid chemistry parameters associated with the identified subterranean samples are measured further comprise at least one of a group consisting of a produced fluid source, a water cut, original oil in place, hydrogen sulfide content, a recovery percentage of water flood, a gas-oil-ratio, drained rock volume, reservoir temperature, reservoir pressure, wax risk, asphaltene risk, and an allocation [Page 8 – “Well test conducted in the first exploration well indicates high condensate-to-gas (CGR) ratio and significant production of condensates from 500 to 953 barrels per day. … GC fingerprinting results show that the sample represents significant amount of C6+ components and heavier molecules (carbon number C21 and above) as illustrated in figure 11 below. The results have led to the decision to fast track the field development plan (FDP) to monetize the gas potential with estimated gas-in-place, GIIP of 448 BSCF and condensate-in-place (CIIP) of 25.98 MMSTB.”Page 11 – “Through the compositional analysis, a fit-for-purpose chemical recipe for stimulation design can be deployed to resolve wax and scaling issues. In terms of resolving well integrity issue and determining the root cause for major LOPC incidents, GC fingerprinting proves to add value in tracing possible leak path along the wellbore and downhole completion, identifying the source of fluid leakage and also ascertaining shallow gas presence which prompts the appropriate remedial solutions as well as quick monetizing efforts to bring the wells back on production.”].
Regarding Claim 5, Zulkipli discloses that the plurality of identified subterranean samples are taken from a common subterranean formation from which the unidentified subterranean sample is taken [Page 5 – “Figure 6—Comparison of ICP sample and mud sample GC composition for well D”].
Regarding Claim 6, Zulkipli discloses that the plurality of identified subterranean samples are taken from the common wellbore drilled through the subterranean formation [Page 5 – “Figure 6—Comparison of ICP sample and mud sample GC composition for well D”], and wherein each of the plurality of identified subterranean samples are taken from a horizontal component or a vertical component of the common wellbore [Page 5 – “During surface bleed-off it is observed that there is dark blackish liquid return from intermediate casing annulus, ICP. Liquid sample is obtained and sent to the laboratory for GC compositional analysis.”See Fig. 7, vertical wellbore.].
Regarding Claim 7, Zulkipli discloses that the plurality of identified subterranean samples are taken from multiple wellbores drilled through the subterranean formation, and wherein the multiple wellbores are from a common pad [Page 10 – “Surface gas sample from annulus C conductor casing, SCP is obtained, the composition is measured and compared with open hole reservoir sample from the shallow gas accumulation in one of the nearby appraisal wells. Results in Figure 14 show a reasonable match between the two samples, hence confirming the presence of shallow gas pool which is planned to be further monetized through work-over or add-perf job.”Page 10 – “Figure 14—Shallow gas indication from mud logs in nearby wells, possible gas migration into wellbore and C1-C3 compositional comparison between SCP sample (yellow) and reservoir gas samples (blue, brown and grey)”].
Regarding Claim 8, Zulkipli discloses that the plurality of identified subterranean samples are taken from a produced fluid collected at the surface [Page 5 – “During surface bleed-off it is observed that there is dark blackish liquid return from intermediate casing annulus, ICP. Liquid sample is obtained and sent to the laboratory for GC compositional analysis.”].
Regarding Claim 9, Zulkipli discloses that the fluid chemistry parameters are measured using at least one of a group consisting of an oil/gas chromatograph, a GC-MS, a stable carbon isotope analysis, a stable sulfur isotope analysis, SARA, a sulfur analysis, a Ni/V analysis, a DNA sequencing analysis, a water analysis, an alkylbenzene analysis, WOGC, a biomarker analysis, and a 2D/3D GC-MS [Page 2 – “Gas chromatography works based on hydrocarbon components vaporization and separation principle. Liquid sample is injected into the equipment inlet, sample is vaporized and transported along the capillary column by mobile carrier gas till the sample is in contact with the heated detector where electrical signal will be generated. Each sample is characterized by a unique electrical response usually known as chromatogram or fingerprint.”Page 3 – “To ascertain hydrocarbon presence and fluid type, comparison of chromatograms from different liquid samples are conducted. The most critical requirement to trace hydrocarbon DNA is to have a baseline or reference sample which forms the basis of chromatogram comparison or overlay technique. In most cases, this reference sample refers to mud sample which typically shows C14 to C17 component peaks in the chromatogram signature. Compariosn of live hydrocarbon signature with the baseline sample will be the main focus of this paper and will be demonstrated in the following case studies.”].
Regarding Claim 12, Zulkilpi, as modified, would disclose reorganizing, after purging the unused fluid chemistry parameter, a remainder of the parameter fluid chemistry parameters associated with the plurality of identified subterranean samples before recategorizing subsequent unidentified subterranean samples [Paragraph [0075] of Torres-Verdin – “In some cases, the updated sampling parameters 250 may replace the sampling parameters 202, such that the testing tool 130 implements formation sampling 232 or pulse sequence 234 operations only according to the updated sampling parameters 250.”].
Regarding Claim 13, Zulkipli discloses quantifying a production allocation of an additional unidentified subterranean sample using the values of the corresponding fluid chemistry parameters associated with the plurality of identified subterranean samples, wherein the plurality of identified subterranean samples is from a first formation, and wherein the additional unidentified subterranean sample is from a second formation [Page 11 – “Figure 15—Possible shallow gas migration into wellbore M indicated from diagnostic logs and comparison of surface sample well M with SCP sample from nearby field showing fair C1, C2 and C3 compositional match”].
Regarding Claim 16, Zulkipli discloses a controller communicably coupled to the sensor device and the analysis apparatus, wherein the controller is configured to control the sensor device and communicate measurements of the chemical parameter values for the subset of fluid chemistry parameters associated with the unidentified subterranean sample to the analysis apparatus [Use of the setup of Fig. 1 to produce the results of Fig. 6].
Regarding Claim 17, the combination would disclose a storage repository communicably coupled to the controller, wherein the storage repository is configured to store a plurality of algorithms that correlate the chemical parameter values of the corresponding fluid chemistry parameters associated with the plurality of identified subterranean samples [Use of the setup of Fig. 1 to produce the results of Fig. 6 of Zulkipli], and wherein the controller is further configured to modify the plurality of algorithms based on future chemical parameter values of the corresponding parameters associated with the plurality of identified subterranean samples [Paragraph [0075] of Torres-Verdin – “For example, the model may generate updated sampling parameters 250 to draw additional sampled fluid 220 when the formation condition 240 indicates occlusion or high contamination. … In some cases, the updated sampling parameters 250 may replace the sampling parameters 202, such that the testing tool 130 implements formation sampling 232 or pulse sequence 234 operations only according to the updated sampling parameters 250.”].
Claim(s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zulkipli et al., From Molecules to Barrels: A Case Study on Redefining Hydrocarbon Spectrum and DNA Tracing Through Gas-Chromatograph Fingerprinting, IPTC, 2019 [hereinafter “Zulkipli”]; Torres-Verdin et al. (US 20230273180 A1)[hereinafter “Torres-Verdin”]; Zuo et al. (US 20130112406 A1)[hereinafter “Zuo 2”]; and Zuo et al. (US 20160252454 A1)[hereinafter “Zuo”].
Regarding Claim 10, Zulkipli fails to disclose comparing, using the comparison module of the controller, the chemical parameter values for the subset of fluid chemistry parameters associated with the unidentified subterranean sample to the chemical parameter values of the corresponding fluid chemistry parameters associated with the plurality of identified subterranean samples, further generates a non-linear relationship between two of the fluid chemistry parameters.
However, Zuo discloses relating a wellbore sample parameter to a wellbore parameter through use of a non-linear approximation [See Figs. 6 and 7 and corresponding text]. It would have been obvious to take such an approach in order to appropriately fill in missing values from the target parameter determination from sample data values.
Regarding Claim 11, Zulkipli fails to disclose that one of the two fluid chemistry parameters is water cut.
However, Zuo 2 discloses the use of such a parameter [Paragraph [0047] – “The fluid analyzer 208 also includes dual spectrometers--a filter-array spectrometer and a grating-type spectrometer. The filter-array spectrometer of the analyzer 208 includes a broadband light source providing broadband light that passes along optical guides and through an optical chamber in the flowline to an array of optical density detectors that are designed to detect narrow frequency bands (commonly referred to as channels) in the visible and near-infrared spectra as described in U.S. Pat. No. 4,994,671, incorporated herein by reference in its entirety. Preferably, these channels include a subset of channels that detect water absorption peaks (which are used to characterize water content in the fluid) and a dedicated channel corresponding to the absorption peak of CO.sub.2 with dual channels above and below this dedicated channel that subtract out the overlapping spectrum of hydrocarbon and small amounts of water (which are used to characterize CO.sub.2 content in the fluid).”]. It would have been obvious to analyze such a parameter to better assess the contents of the reservoir/wellbore.
Response to Arguments
Applicant argues:
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780
787
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406
785
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Examiner’s Response:
The Examiner respectfully disagrees. This judicial exception is not integrated into a practical application because no further use for the “identified” sample status is performed nor is any further use of the “value of the target parameter” performed. The recitation that “recategorizing the unidentified subterranean sample alters a production allocation of a subterranean wellbore” amounts to an extension of the abstract idea of itself through the mathematical and/or mental step of making a further determination of a further number (this limitation is not the recitation of adjusting oil production itself, see Figs. 13 of the instant Specification and corresponding text).
Applicant argues:
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642
789
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Greyscale
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407
784
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Greyscale
Examiner’s Response:
The Examiner respectfully disagree. The claim(s) recite(s) the abstract idea of 1. a mental activity and/or mathematical algorithm for determining a target parameter for a subterranean sample based on its fluid chemistry parameters (i.e., the “comparing” and “determining” steps of Claim 1) and 2. a mental activity algorithm for identifying that a successful correlation indicates that the subterranean sample can be considered “identified.” A combination of abstract ideas is an abstract idea [See 2106.05(I) – "Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"].
The recited computer components in performing the algorithm amount to the recitation of the components of a general-purpose computer for performing the algorithm and do not serve to amount to significantly more than the recitation of the abstract idea itself (see Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014)).
Applicant argues:
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541
784
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Examiner’s Response:
The Examiner agrees. New grounds for rejection are presented above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20220091090 A1 – SYSTEM AND METHOD FOR DETERMINING NATURAL HYDROCARBON CONCENTRATION UTILIZING ISOTOPE DATA
US 20190106987 A1 – METHODS AND SYSTEMS FOR RESERVOIR CHARACTERIZATION AND OPTIMIZATION OF DOWNHOLE FLUID SAMPLING
US 20150330218 A1 – Methods And Apparatus For Planning And Dynamically Updating Sampling Operations While Drilling In A Subterranean Formation
US 20150134620 A1 – SYSTEM AND METHOD FOR ANALYZING AND VALIDATING OIL AND GAS WELL PRODUCTION DATA
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ROBERT QUIGLEY whose telephone number is (313)446-4879. The examiner can normally be reached 9AM-5PM EST.
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857