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 .
Response to Amendment
Applicant' s amendment and response filed 4/28/2026 has been entered and made record. This application contains 20 pending claims.
Claims 1, 8, and 15 have been amended.
Response to Arguments
Applicant' s arguments filed 4/28/2026 regarding claims rejections under 35 U.S.C. 101 in claims 1-20 have been fully considered and are persuasive. Claims 1, 8, and 15 have been amended, and the amended claims integrated the judicial exception into a practical application and overcome the 101 rejections. Thus, the 101 claims rejections in claims 1-20 have been withdrawn.
Applicant' s arguments filed 4/28/2026 regarding claims rejections under 35 U.S.C. 102 and 103 in claim 1-20 have been fully considered but they are not persuasive. The claims 1, 8, and 15 have been amended, and the amended claims limitation necessitate a new ground of rejection. Thus, newly discovered prior arts, “Yarus US20160145991” and “Tawi US 20210319304”, will be used in combination with prior arts cited in the previous office action to reject the amended claims limitations.
The applicant argues on pages 7-8 of the remark filed on 4/28/2026 that “… The Examiner fails to show that Hong teaches or suggests streaming data as recited by the present claims. Paragraph [0128] of Hong simply describes that the data is managed through discrete, depth-defined segments rather than a continuous stream. Specifically, Hong describes that measurements are windowed by depth, meaning the system processes a specific distance or interval rather than continuous data points. Hong, para. [0127]. This windowed data is then used by a trained machine learning model to select an appropriate geological model specific to that individual window. Id., para. [0121]. Because the analysis is performed on these distinct regions, the results are "spliced together" or consolidated using statistical techniques to connect the individual segments. Id., para. [0093]. The Examiner fails to show that spliced data encompasses streaming data analyzed by a model. Accordingly, Hong does not teach or suggest "streaming, using at least one hardware processor, data comprising petrophysical data associated with at least one subsurface formation obtained in real time" or "analyzing, using the at least one hardware processor, the stream of data to determine at least one model configured to evaluate the at least one subsurface formation" as recited by the present claims. Because Hong fails to disclose at least these features, Hong fails to anticipate independent claims 1, 8, and 15. Applicant, therefore, respectfully requests withdrawal of the rejection to claims 1, 8, and 15.”
The Examiner respectfully disagrees applicant' s argument. Hong teaches that seismic data are acquired, data for geologic region are received, and information and electrical borehole images are acquired (Hong, [0027], [0055], [0118]). Hong also teaches that seismic attribute values are measured at one instant in time or over a time window, and may be measured on a single trace, on a set of traces (Hong, [0039]). When the data are measured over a time window and/or on a set of traces, the data would be continuously measured. Therefore, Hong teaches streaming data comprising petrophysical data associated with at least one subsurface formation obtained in real time using at least one hardware processor.
Dependent claims 2-7, 9-14, and 16-20 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application. Therefore, claims 2-7, 9-14, and 16-20 are also patent ineligible.
Hence, the Examiner submits that the rejections of Claims 1-20 are proper.
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, 5-6, 8, 10, 12-13, 15, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hong et al. (US 20230041525, hereinafter Hong) in view of
Yarus et al. (US20160145991, hereinafter Yarus), and further in view of Tawi et al. (US 20210319304, hereinafter Tawil).
As to claims 1, 8, and 15, Hong teaches streaming, using at least one hardware processor (FIG. 2 shows computer(s) 254 ad processor(s) 256), data comprising petrophysical data associated with at least one subsurface formation obtained in real time ([0039] and [0110] disclose real-time measurements of downhole from a bottom hole assembly (BHA); and seismic attribute values are measured at one instant in time or over a time window, and may be measured on a single trace, on a set of traces (i.e., when the data are measured over a time window and/or on a set of traces, the data would be continuously measured – emphasis added by Examiner); [0128]);
analyzing, using the at least one hardware processor (FIG. 2 shows computer(s) 254 ad processor(s) 256), the stream of data to determine at least one model configured to evaluate the at least one subsurface formation ([0122] discloses acquire data in real-time at various depths in the borehole, and the data may be fed to a trained machine learning model that can output a model; [0265] discloses “receiving data for a geologic region; based at least in part on the data, selecting a model from a plurality of models using a trained machine learning mode”);
executing, using the at least one hardware processor (FIG. 2 shows computer(s) 254 ad processor(s) 256), the at least one model to evaluate the at least one subsurface formation using the stream of data as input ([0055] and [0135] discloses a model that is selected is utilized for purposes of evaluation, drilling, placement, fracturing, perforation number, perforation spacing, perforation location, etc.; and borehole image data may be utilized to identify one or more types of features such as, for example, horizons, fractures, faults, etc.; [0136]).
Hong does not explicitly teach automatically adjusting, using the at least one hardware processor, a drill bit speed or drilling direction in real time to reach a target geological zone based on a representation of formation characteristics generated from evaluating the at least one subsurface formation.
Yarus teaches automatically adjusting, using the at least one hardware processor ([0035], [0072]), a drill bit speed or drilling direction in real time to reach a target geological zone based on a representation of formation characteristics generated from evaluating the at least one subsurface formation (FIG. 9, [0063] and [0064] disclose if the current determined path have switched to indicate that success is not likely to occur at these cells based on the geological property values of the encountered cell, a new drill path is determined based on the surrounding cells along the drill path, and
real time drilling uses data gathered during the drilling operation to generate a probability model for automatically making adjustments in a drill path in the direction of the closest cell that has a probability of success that exceeds a predetermined threshold (e.g., probability of success >90%) (i.e., a drill path in the direction of the closest cell that has a probability of success that exceeds a predetermined threshold will lead to reach a target geological zone - emphasis added by Examiner)).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Yarus into Hong for the purpose of improving a well drilling process by using data gathered during a real-time drilling operation to generate a probability model for automatically making
adjustments in a drill path. This combination would improve in automatically altering the drill path by proceeding in the in the direction that have the highest probability of success among the cells and recover subterranean deposits of oil and gas.
The combination of Hong and Yarus does not explicitly teach wherein changes in drilling parameters improves production from the at least one subsurface formation.
Tawil teaches wherein changes in drilling parameters improves production from the at least one subsurface formation ([0165] discloses applying an adjustment to drilling trajectories of a current drilling operation based on control parameters of a new drilling plan, and the reservoir Earth Model (REM) can use the geological drilling instructions to dynamically adjust directional controls of a wellbore to maximize production of hydrocarbons 608 in a particular zone of a reservoir).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Tawil into Hong in view of Yarus for the purpose of optimizing well planning and placement based on an integration of static and dynamic data in order to maximize reservoir contact during drilling operations and ensure optimal drainage of hydrocarbons. This combination would improve in planning new well trajectories, and keeping a wellbore in a particular section of a reservoir to maximize economic production from the well while minimizing gas or water breakthrough. By combining Yarus’s automatically making adjustments in a drill path in the direction of subterranean deposits of oil and gas using data gathered during the real time drilling operation, with Tawil’s adjustment to drilling trajectories of a current drilling operation based on control parameters of a new drilling plan and
maximize production of hydrocarbons in a particular zone of a reservoir into Hong, the drilling direction would be automatically adjusted in real time and reach a target geological zone based on a representation of formation characteristics generated from evaluating the at least one subsurface formation, and the adjustments in drilling parameters would improve production from the subsurface formation.
As to claims 3, 10, and 17, the combination of Hong, Yarus, and Tawil teaches the claimed limitations as discussed in claims 1, 8, and 15, respectively.
Hong teaches wherein outputting the formation characterization in real time comprises rendering the formation characterization for multiple instances of a visualization system ([0101] and [0116] disclose in FIG. 5, the method 500 is illustrated graphically where information for a subsurface region 510 (e.g., logs, etc.) is received and analyzed to identify three sub-regions 522, 524 and 526 and where results 532, 534 and 536 are generated for the three subregions 522, 524 and 526, individually. The results 532, 534 and 536 (i.e., rendering the formation characterization for multiple instances of a visualization system - emphasis added by Examiner) are then consolidated to output results 540 for the subsurface region 510. A user can visually analyze measurement of a subsurface region or logs; [0108] discloses various types of information that can be rendered to a display during one or more real-time field operations (i.e., the formation characterization would be outputted in real time - emphasis added by Examiner)).
As to claims 5, 12, and 19, the combination of Hong, Yarus, and Tawil teaches the claimed limitations as discussed in claims 1, 8, and 15, respectively.
Hong teaches analyzing the stream of data to determine at least one model configured to evaluate the at least one subsurface formation in view of a context extracted from the petrophysical data and drilling data ([0039]; [0055], [0135], and [0214] disclose a model that is selected is utilized for purposes of evaluation, drilling, placement, fracturing, perforation number, perforation spacing, perforation location, etc.; and borehole image data may be utilized to identify one or more types of features such as, for example, horizons, fractures, faults, etc. (i.e., evaluate the subsurface formation in view of a context extracted from the petrophysical data - emphasis added by Examiner). MWD equipment provides real time or near real time data of interest (e.g., inclination, direction, pressure, temperature, real weight on the drill bit, torque stress, etc. (i.e., evaluate the subsurface formation in view of a context extracted from the drilling data - emphasis added by Examiner)), and LWD equipment sends to the surface various types of data of interest, including for example, geological data ( e.g., gamma ray log, resistivity, density and sonic logs, etc. (i.e., evaluate the subsurface formation in view of a context extracted from the petrophysical data - emphasis added by Examiner)).
As to claims 6, 13, and 20, the combination of Hong, Yarus, and Tawil teaches the claimed limitations as discussed in claims 1, 8, and 15, respectively.
Hong teaches wherein the at least one model is a trained machine learning model deployed based on a type of inputs to the trained machine learning model being found in the petrophysical data ([0122] discloses as tool is moved in a borehole to acquire data at various depths in the borehole, the data may be fed to a trained machine learning model (i.e., the petrophysical data is used to train a machine leaning or model - emphasis added by Examiner); [0188] discloses generating a relatively large amount of training data by varying one or more model parameters such as resistivity, anisotropy, dip and invasion, etc. As an example, modeled measurements together with model type can be used as input to supervised machine learning workflow (i.e., the petrophysical data is used to train a machine learning or model - emphasis added by Examiner)).
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Hong, Yarus, and Tawil in view of Imhof et al. (US 20110048731, hereinafter Imhof).
As to claims 2, 9, and 16, the combination of Hong, Yarus, and Tawil teaches the claimed limitations as discussed in claims 1, 8, and 15, respectively.
Hong teaches analyzing the stream of data to determine models configured to evaluate data associated with multiple subsurface formations obtained in real time ([0039]; [0110] and [00240] disclose measurements are obtained in real-time from a bottom hole assembly (BHA); and formation evaluation is performed for interpreting data acquired from a drilled borehole to provide information about the geological formations and/or in-situ fluid(s) that can be used for assessing the producibility of reservoir rocks penetrated by the borehole).
Hong does not explicitly teach analyzing the data to determine models configured to simultaneously evaluate data associated with multiple subsurface formations obtained.
Imhof teaches analyzing the data to determine models configured to simultaneously evaluate data associated with multiple subsurface formations obtained ([0057] discloses “The ability to pick many surfaces simultaneously (i.e., the ability to skeletonize seismic data) enables a pattern recognition or machine learning method to search geological or geophysical data for direct indications of hydrocarbons or elements of the hydrocarbon system such as reservoir, seal, source, maturation and migration to determine and delineate potential accumulations of hydrocarbons (i.e., simultaneously evaluate geological or geophysical data associated with multiple subsurface formations obtained - emphasis added by Examiner)”; [0127]).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Imhof into Hong in view of Yarus and Tawil for the purpose of obtaining a seismic data volume representing the subsurface region and hydrocarbon potential of subterranean regions in order to simultaneously analyzing them for hydrocarbon indications. This combination would optimize in creation and analysis of many stratigraphically consistent surfaces from seismic data volumes so that objects such as surfaces and geobodies of regions with a potential to contain hydrocarbons can be created, and a potential for hydrocarbon accumulations in the subsurface region can be predicted.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hong, Yarus, and Tawil, in view of Moreau et al. (US 20220268392, hereinafter Moreau).
As to claims 4, 11, and 18, the combination of Hong, Yarus, and Tawil teaches the claimed limitations as discussed in claims 1, 8, and 15, respectively.
Hong teaches the stream of data ([0039] discloses seismic attribute values are measured at one instant in time or over a time window, and may be measured on a single trace, on a set of traces (i.e., when the data are measured over a time window and/or on a set of traces, the data would be continuously measured – emphasis added by Examiner)).
Hong teaches does not explicitly teach wherein the data is converted to a standardized format in real time.
Moreau teaches wherein the data is converted to a standardized format in real time ([0048] discloses data from the image processor 20 can be recorded for logging and/or review by an operator, and the data from the image processor 20 can be transmitted in a wide range of formats (e.g. DICONDE standard formats and/or TIFF) to an operator for real-time observation).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Moreau into Hong in view of
Yarus and Tawil for the purpose of recording data or image signal of pipe filled with static or dynamic fluids, such as oil, gas, and/or water in order to convert and transmit the data in standard formats data. This combination would improve in recording, converting, and transmitting the data in a wide range of formats including standard formats to ensure consistency, quality, and compatibility, which enables accurate analysis, seamless integration across systems, efficient operations.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hong, Yarus, and Tawil, in view of Song et al. (US 20230281544, hereinafter Song).
As to claims 7 and 14, the combination of Hong, Yarus, and Tawil teaches the claimed limitations as discussed in claims 1 and 8, respectively.
Hong teaches does not explicitly teach wherein the at least one model is deployed using a custom deployer configured for deployment in an environment where data is obtained in differing formats.
Song teaches wherein the at least one model is deployed using a custom deployer configured for deployment in an environment where data is obtained in differing formats ([0046] discloses oil-and-gas multi-source heterogeneous data volumes, thereby realizing integrated data management in an oil and gas field with various types of data (i.e., in an environment where data is obtained in differing formats - emphasis added by Examiner). The intelligence algorithm component library 103 integrates the multi-scenario production control and prediction technology based on big data and artificial intelligence (AI); and forms a knowledge map of oil and gas data and a customized pattern featuring end-to-end no-code development of models in different scenarios, and meanwhile provides basic algorithms and intelligence algorithms customized based on specific scenarios (i.e., model is deployed using a custom deployer configured for deployment in an environment - emphasis added by Examiner); [0113] discloses FIG. 9 shows a model building interface diagram of a custom algorithm editing module; and FIG. 10 shows a visual model result display interface of a custom algorithm editing module (i.e., FIGs. 9 and 10 show model that is deployed in an environment, and thus, would have been deployed by using a custom deployer - emphasis added by Examiner)).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Song into Hong
in view of Yarus and Tawil for the purpose of realizing integrated data management in an oil and gas field in order to increase correlation of business scenarios in the production of oil and gas industry. This combination would improve in realizing the digital and intelligent transformation of the oil and gas industry, and achieving the purpose of cost decreasing and benefit increasing oil and gas enterprises.
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 LAL CE MANG whose telephone number is (571)272-0370. The examiner can normally be reached Monday to Friday- 8:30-12:00, 1:00-5:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine T Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LAL CE MANG/Examiner, Art Unit 2857
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