Detailed Action
Status of Claims
Claims 1-14 are currently pending and addressed below.
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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Molla (US Pub No 20210062650)
Molla discloses in claim 1. A method of generating a model for predicting at least one property of a fluid (Molla abstract) at a sample location within a hydrocarbon reservoir, comprising:
providing a training data set comprising input data (Molla [0039]-[0040] sample database) and target data (Molla [0039]-[0040] sample database comprising different fluid properties), the input data comprising mud-gas data and petrophysical data for each of a plurality of sample locations (Molla [0039]-[0040] sample database filled with 1000+ samples [0071] and includes advanced mud-gas data for providing a continuous fluid property log during drilling), and the target data comprising the at least one property of the fluid for each of the plurality of sample locations (Molla [0039]-[0040] sample database comprising 1000+ samples comprising different fluid properties such as gas composition [0066]);
and generating the model (Molla Fig 11; 445 [0067] training data used to train and optimize cluster classification model) using the training data set such that the model can be used to predict the at least one property of the fluid at the sample location based on measured mud-gas data and measured petrophysical data for the sample location (Molla [0068] Fig 11; 455 & 456 are statistical prediction models used to validate prediction models 465 & 466 to predict C6+),
wherein a drilling fluid recycling correction has not been applied to the mud-gas data (Molla Fig 11 does not show a drilling fluid recycling correction has been applied to collected data).
Molla discloses in claim 2. The method according to claim 1, wherein generating the model comprises instructing a machine learning algorithm to generate the model using the training data set (Molla Fig 3 & Fig 7 [0040] & [0059] model is developed utilizing sample data base to build and train a machine learning model).
Molla discloses in claim 3. The method according to claim 1, wherein the at least one property comprises a property influenced by oil-related components of the fluid (Molla [005] [0038] properties influenced are black oil, gas condensate, dry gas, molar gas composition [0058] % GOR, STO density).
Molla discloses in claim 4. The method according to claim1, wherein the at least one property comprises one or more of:
a density of the fluid at the sample location (Molla [005] [0038] properties influenced are black oil, gas condensate, dry gas, molar gas composition [0058] % GOR, STO density);
a gas-oil ratio of the fluid at the sample location(Molla [005] [0038] properties influenced are black oil, gas condensate, dry gas, molar gas composition [0058] % GOR, STO density);
and a concentration of C7+ hydrocarbons within the fluid at the sample location (Molla [005] [0038] properties influenced are black oil, gas condensate, dry gas, molar gas composition [0058] % GOR, STO density).
Molla discloses in claim 5. The method according to claim1, wherein the mud-gas data of the training data set comprises measured standard mud-gas data for the sample location (Molla [0058] data includes standard mud-gas data C1-C5 Mol % ).
Molla discloses in claim 6. The method according to claim 5, wherein an extraction efficiency correction has been applied to the mud-gas data of the training data set (Molla [0058] data includes standard mud-gas data C1-C5 Mol % [0071] advanced mud gas data is applied as it is considered optional).
Molla discloses in claim 7. The method according to claim 5, wherein an extraction efficiency correction has not been applied to the mud-gas data of the training data set (Molla [0058] data includes standard mud-gas data C1-C5 Mol % [0071] advanced mud gas data is not applied as it is considered optional), and wherein the training data comprise drilling mud compositional data (Molla [0022] [0037] drilling mud composition used in developing the training data).
Molla discloses in claim 8. The method according to claim 5, wherein the measured mud-gas data was collected without the use of heating (Molla [0029] mud-gas data can be collected without the use of heating as Molla describes an optional ability to heat drilling mud).
Molla discloses in claim 9. The method according to claim1, wherein the petrophysical data comprise one or more of:
bulk density (Molla [0041] density);
neutron porosity (Molla [0071] neutron density-porosity);
resistivity data (Molla [0041] resistivity data);
acoustic data (Molla [0071] sonic);
natural gamma ray (Molla [0041] gamma ray);
nuclear magnetic resonance data (Molla [0041] NMR);
Molla discloses in claim 10. A computer-based model for predicting at least one property of a fluid at a sample location within a hydrocarbon reservoir based on measured mud-gas data and measured petrophysical data for that sample location (Molla Fig 13 processing system [0072] [0068] Fig 11; 455 & 456 are statistical prediction models used to validate prediction models 465 & 466 to predict C6+), the computer-based model having been generated by the method according to claim 1 (See claim 1 above and Molla [0072] processing system utilizing computing devices).
Molla discloses in claim 11. A tangible computer-readable medium storing the computer-based model according to claim 10 (Molla Fig 13; 930).
Molla discloses in claim 12. The method according to claim 1, further comprising:
receiving measured mud-gas data and measured petrophysical data for the sample location (Molla [0039]-[0040] sample database comprising 1000+ samples comprising different fluid properties such as gas composition [0066] disclosing the work flow in Fig 11 utilizing various measured data samples and measured petrophysical data points further emphasized in [0071] disclosing various properties to be collected);
supplying the measured mud-gas data and the measured petrophysical data to the model (Molla [0039]-[0040] sample database filled with 1000+ samples [0071] and includes advanced mud-gas data for providing a continuous fluid property log during drilling); and
predicting a value of a property of a fluid at a sample location by using the model. (Molla [0068] Fig 11; 455 & 456 are statistical prediction models used to validate prediction models 465 & 466 to predict C6+).
Molla discloses in claim 13. The method according to claim 12, further comprising:
predicting a value of a fluid property of a fluid at a plurality of sample locations along a length of a well (Molla [0039]-[0040] sample database filled with 1000+ samples [0071] and includes advanced mud-gas data for providing a continuous fluid property log during drilling [0068] Fig 11; 455 & 456 are statistical prediction models used to validate prediction models 465 & 466 to predict C6+).
Molla discloses in claim 14. The method according to claim 13, further comprising: displaying, using an electronic display screen (Molla [0077] display device), a graph plotting the predicted values of the fluid property against a location of the respective sample location for each of the plurality of sample locations along the length of the well (Molla [0023] inter and intra well fluid facies mapping, reservoir complexities, [0071] selection for well landing points, geo-steering using fluid information).
Response to Arguments
Applicant’s amendments and arguments, filed 06/11/2026, with respect to claims 1-14 have been fully considered and are persuasive. The rejections of claims 1-14 under 35 USC 112b has been withdrawn.
Applicant’s amendments and arguments, filed 06/11/2026, with respect to claims 1-14 have been fully considered and are persuasive. The rejections of claims 12-14 under 35 USC 101 has been withdrawn.
Applicant's arguments filed 06/11/2026 have been fully considered but they are not persuasive.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., standard vs advanced mud logging) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Furthermore, Applicants arguments regarding Molla not teaching the use of a combination of mud-gas data and petrophysical data is unpersuasive. Examiner’s opinion under broadest reasonable interpretation is that Molla teaches that Mud-gas data ([0037]) from the drilling operations is used in combination with reservoir fluid samples and historical data ([0037]) to predict the fluid composition along the wellbore ([0038]) these teachings in the Examiners opinion teach the limitations of the claim 1 and therefore, Examiner does not find the arguments presented as persuasive.
Conclusion
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 Nicholas D Wlodarski whose telephone number is (571)272-3970. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicole Coy can be reached at (571) 272-5405. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICHOLAS D WLODARSKI/Examiner, Art Unit 3672
/Nicole Coy/Supervisory Patent Examiner, Art Unit 3672
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