DETAILED ACTION
Claims 1-20 are presented for examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings received on 28 July 2022 are accepted.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because
The abstract includes phrases which can be implied. Examiner suggests amending the abstract as follows:
A data clustering process for interpreting formation data, such as delineating reservoirs in well placement models. The data clustering process can be used with correlating offset well data and high angle or horizontal (HAHZ) target well data. Facies distribution and thus stratigraphy and the position of a borehole within the stratigraphic setting can also be assessed using the data clustering process via unsupervised computer learning techniques. A method of performing a well operation associated with a wellbore and an automated directional drilling system are provided herein. In one example, the method includes: (1) obtaining target well data from a wellbore in a subterranean formation, (2) generating a facies cluster model for the subterranean formation using a clustering process on the target well data, and (3) performing a well operation associated with the wellbore using the facies cluster model.
A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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)(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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US patent 12,248,111 B2 Kulkarni, et al. [herein “Kulkarni”].
Claim 1 recites “1. A method of performing a well operation associated with a wellbore.” Kulkarni column 8 lines 51-53 disclose “such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time.” Geosteering a horizontal and deviated well with the tools corresponds to performing at least one well operation associated with the wellbore using the facie cluster model.
Claim 1 further recites “comprising: obtaining target well data from a wellbore in a subterranean formation.” Kulkarni column 30 lines 39-41 disclose “a reception block 1514 for receiving well logs, as series data, for wells, where the well logs represent stratigraphic characteristics of a field.” Receiving well logs corresponds with obtaining target well data. See further Kulkarni figure 15.
Claim 1 further recites “generating a facies cluster model for the subterranean formation using a clustering process on the target well data.” Kulkarni column 21 lines 63-67 disclose:
one or more machine learning models can provide for assessing well logs such as, for example, assessing log similarity (e.g., and/or dissimilarity). Understanding the similarity/dissimilarity of sub-sections of logs can be useful for numerous applications.
Kulkarni column 28 lines 20-26 and 54-58 disclose:
As to types of machine learning models, consider one or more of … a k-nearest neighbors (KNN) model …. As an example, a machine learning model can be … an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.
Assessing well logs for similarity and correlation using a k-nearest neighbor (KNN) or self-organizing map algorithm corresponds to generating a model of the subterranean formation using a clustering process for the respective facie cluster on respective target well data.
Claim 1 further recites “and performing a well operation associated with the wellbore using the facies cluster model.” Kulkarni column 8 lines 51-53 disclose “such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time.” Geosteering a horizontal and deviated well with the tools corresponds to performing at least one well operation associated with the wellbore using the facie cluster model.
Claim 2 further recites “2. The method as recited in Claim 1, wherein the wellbore is a high angle or horizontal (HAHZ) wellbore.” Kulkarni column 8 lines 51-53 disclose “such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time.” A horizontal and highly deviated well corresponds with a high angle or horizontal (HAHZ) wellbore.
Claim 3 further recites “3. The method as recited in Claim 1, wherein the clustering process is a machine learning process that uses an unsupervised clustering algorithm on the target well data.” Kulkarni column 28 lines 20-26 and 54-58 disclose:
As to types of machine learning models, consider one or more of … a k-nearest neighbors (KNN) model …. As an example, a machine learning model can be … an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.
k-nearest neighbors (KNN) and self-organizing map are unsupervised clustering algorithms.
Claim 4 further recites “4. The method as recited in Claim 3, wherein the unsupervised clustering algorithm is selected from the group consisting of Self-Organizing Maps, Generative adversarial networks, and K-nearest neighbors.” From the above list of alternatives the Examiner is selecting “K-nearest neighbors.”
Kulkarni column 28 lines 20-26 and 54-58 disclose:
As to types of machine learning models, consider one or more of … a k-nearest neighbors (KNN) model …. As an example, a machine learning model can be … an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.
Claim 5 further recites “5. The method as recited in Claim 1, wherein the well operation is drilling the wellbore and includes steering a drill bit using the facies cluster model.” Kulkarni column 8 lines 51-53 disclose “such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time.” Geosteering a horizontal and deviated well with the tools corresponds to steering a drill bit using the facie cluster model.
Claim 6 further recites “6. The method as recited in Claim 5, wherein steering the drill bit is performed automatically.” Kulkarni column 8 lines 51-53 disclose “such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time.” Kulkarni column 9 lines 57-63 disclose:
a computational framework can include features that facilitate interpretation of subsurface well logs by automatically and/or semi-automatically correlating points from one log to another. For example, given a log that has been interpreted by a human (e.g., in the sense of having identified different points of interest in the log), a framework may automatically interpret a set of neighboring logs.
Automatically interpreting the well logs used in real-time geosteering corresponds with automatically steering the drill bit.
Claim 7 further recites “7. The method as recited in Claim 1, further comprising providing a visual representation of the facies cluster model and manually performing the well operation using the visual representation.” Kulkarni column 9 lines 57-63 disclose:
a computational framework can include features that facilitate interpretation of subsurface well logs by automatically and/or semi-automatically correlating points from one log to another. For example, given a log that has been interpreted by a human (e.g., in the sense of having identified different points of interest in the log), a framework may automatically interpret a set of neighboring logs.
Semi-automatically interpreting the well logs corresponds with performing at least some processes manually.
Kulkarni column 8 lines 51-55 disclose:
such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time. As an example, deviated wells may be displayed overlain on seismic or 3D grid properties.
Displaying the deviated well overlain on the grid as a part of the geosteering corresponds with performing the well operation using the visual representation.
Claim 8 further recites “8. The method as recited in Claim 1, wherein generating the facies cluster model includes correlating the target well data with offset well data.” Kulkarni column 21 lines 63-67 disclose:
one or more machine learning models can provide for assessing well logs such as, for example, assessing log similarity (e.g., and/or dissimilarity). Understanding the similarity/dissimilarity of sub-sections of logs can be useful for numerous applications.
Assessing similarity/dissimilarity corresponds with assessing a correlation.
Kulkarni column 15 lines 39-41 disclose “sensor or sensor may be at an offset wellsite where the wellsite system 300 and the offset wellsite are in a common field (e.g., oil and/or gas field).” Sensor data from an offset wellsite corresponds with offset well data.
Claim 9 further recites “9. The method as recited in Claim 1, wherein the obtaining, the generating, and the performing are carried out in real-time.” Kulkarni column 8 lines 51-53 disclose “such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time.”
Claim 10 further recites “10. The method as recited in Claim 1, wherein the generating includes categorizing the target well data using a machine learning model that is trained using the clustering process.” Kulkarni column 23 lines 27-30 disclose “implement a self-supervised learning strategy. In such an example, self-learning can synthetically generate labels through data transformations to enable subsequent supervised training.” Generating labels for the well log data corresponds with categorizing well data using the machine learning model. Using that data in subsequent supervised training corresponds with a training using the clustering process.
Claim 11 further recites “11. The method as recited in in Claim 1, wherein performing the well operation includes modifying a well plan for the wellbore using the facies cluster model.” Kulkarni column 4 lines 8-12 disclose “The DRILLPLAN framework provides for digital well construction planning and includes features for automation of repetitive tasks and validation workflows, enabling improved quality drilling programs (e.g., digital drilling plans, etc.) to be produced quickly with assured coherency.” Producing improved digital drilling plans corresponds with modifying a well plan using the respective planning tool facies cluster model. Kulkarni column 5 lines 38-42 “the visualization features 123 may be implemented via the workspace framework 110, for example, to perform tasks as …, planning operations, constructing wells.”
Claim 12 recites “12. An automated directional drilling system, comprising: one or more processors.” Kulkarni column 8 lines 51-53 disclose “such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time.” Geosteering is a directional drilling system. Kulkarni column 15 lines 21-22 disclose “geosteering can include intentional directional control of a wellbore.” Directional control is a directional drilling.
Kulkarni column 16 lines 59-62 disclose “the system 460 can include one or more processors 462, memory 464 operatively coupled to at least one of the one or more processors 462, instructions 466 that can be, for example, stored in the memory 464.”
Claim 12 further recites “to perform operations including: generating a facies cluster model for a subterranean formation using a clustering process on target well data.” Kulkarni column 21 lines 63-67 disclose:
one or more machine learning models can provide for assessing well logs such as, for example, assessing log similarity (e.g., and/or dissimilarity). Understanding the similarity/dissimilarity of sub-sections of logs can be useful for numerous applications.
Kulkarni column 28 lines 20-26 and 54-58 disclose:
As to types of machine learning models, consider one or more of … a k-nearest neighbors (KNN) model …. As an example, a machine learning model can be … an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.
Assessing well logs for similarity and correlation using a k-nearest neighbor (KNN) or self-organizing map algorithm corresponds to generating a model of the subterranean formation using a clustering process for the respective facie cluster on respective target well data.
Claim 12 further recites “from a high angle or horizontal (HAHZ) wellbore in the subterranean formation.” Kulkarni column 8 lines 51-53 disclose “such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time.” A horizontal and highly deviated well corresponds with a high angle or horizontal (HAHZ) wellbore.
Claim 12 further recites “and drilling the HAHZ wellbore by steering a drill bit in the subterranean formation using the facies cluster model.” Kulkarni column 8 lines 51-53 disclose “such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time.” Geosteering a horizontal and deviated well with the tools corresponds to steering a drill bit using the facie cluster model.
Dependent claim 13 is substantially similar to claim 3 above and is rejected for the same reasons.
Claim 14 further recites “14. The automated directional drilling system as recited in Claim 13, wherein the unsupervised clustering algorithm is Self-Organizing Maps.” Kulkarni column 28 lines 20-26 and 54-58 disclose:
As to types of machine learning models, consider one or more of … a k-nearest neighbors (KNN) model …. As an example, a machine learning model can be … an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.
Claim 15 further recites “15. The automated directional drilling system as recited in Claim 12, the operations further include providing a visual representation of the facies cluster model.” Kulkarni column 8 lines 51-55 disclose:
such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time. As an example, deviated wells may be displayed overlain on seismic or 3D grid properties.
Displaying the deviated well overlain on the grid as a part of the geosteering corresponds with performing the well operation using the visual representation.
Dependent claims 16-18 are substantially similar to claims 8-10 above and are rejected for the same reasons.
Claim 19 recites “19. A computer program product having a series of operating instructions stored on a non-transitory computer readable medium that direct operation of one or more processors when initiated thereby.” Kulkarni column 16 lines 59-62 disclose “the system 460 can include one or more processors 462, memory 464 operatively coupled to at least one of the one or more processors 462, instructions 466 that can be, for example, stored in the memory 464.” Memory corresponds with a computer readable medium.
Claim 19 further recites “to perform operations in real-time.” Kulkarni column 8 lines 51-53 disclose “such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time.”
Claim 19 further recites “comprising: automatically generating a facies cluster model for a subterranean formation using a clustering process on target well data from a wellbore in the subterranean formation.” Kulkarni column 21 lines 63-67 disclose:
one or more machine learning models can provide for assessing well logs such as, for example, assessing log similarity (e.g., and/or dissimilarity). Understanding the similarity/dissimilarity of sub-sections of logs can be useful for numerous applications.
Kulkarni column 28 lines 20-26 and 54-58 disclose:
As to types of machine learning models, consider one or more of … a k-nearest neighbors (KNN) model …. As an example, a machine learning model can be … an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.
Assessing well logs for similarity and correlation using a k-nearest neighbor (KNN) or self-organizing map algorithm corresponds to generating a model of the subterranean formation using a clustering process for the respective facie cluster on respective target well data.
Claim 19 further recites “and directing a well operation associated with the wellbore using the facies cluster model.” Kulkarni column 8 lines 51-53 disclose “such a framework may be utilized to geosteer horizontal and highly deviated wells with one or more logging while drilling (LWD) tools, optionally in real time.” Geosteering a horizontal and deviated well with the tools corresponds to performing at least one well operation associated with the wellbore using the facie cluster model.
Dependent claim 20 is substantially similar to claims 2 and 5 above and are rejected for the same reasons.
Conclusion
Prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11401798 B2 Panchal; Neilkunal et al.
teaches
Real time geological localization with stochastic clustering and pattern matching
Wu, M., et al. “Stochastic clustering and pattern matching for real-time geosteering” Geophysics, vol. 84, no. 5 (2019)
A Bayesian statistical framework for quantitative geosteering in real time.
Gupta, I., et al. “Looking Ahead of the Bit Using Surface Drilling and Petrophysical Data: Machine-Learning-Based Real-Time Geosteering in Volve Field” Society of Petroleum Engineers, SPE, pp. 990-1006 (2020)
Machine-learning MWD Geosteering;
Identifying cluster properties using core data;
K-means, SOM, hierarchical clustering;
PCA dimensionality reduction.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jay B Hann whose telephone number is (571)272-3330. The examiner can normally be reached M-F 10am-7pm EDT.
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/Jay Hann/Primary Examiner, Art Unit 2186 19 November 2025