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 .
DETAILED ACTION
The following NON-FINAL Office Action is in response to application 18/545,271 filed on 12/19/2023. This communication is the first action on the merits.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/19/2023 and 09/17/2024 has been considered by the examiner.
Drawings
The drawings were received on 12/19/2023. These drawings are acceptable.
Duplicate Claims Warning
Applicant is advised that should claim 13 be found allowable, claim 14 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 recites "obtaining an inversion model of a downhole formation: however, it is unclear whether the recited "an inversion model" refers to the inversion model recited in parent claim 1 or a different inversion model. If applicant intends to introduce a different inversion model, claim 13 should be amended to recite "obtaining a second inversion model of a downhole formation". Appropriate correction is required.
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 a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, representative Claim 1 recites:
A computer-implemented method to estimate a boundary of a downhole formation, comprising:
obtaining an inversion model of a downhole formation;
defining a boundary of the downhole formation;
determining the boundary based on values associated with the inversion model;
organizing the boundary into one or more clusters;
determining uncertainties associated with the one or more clusters; and
estimating the boundary based on the one or more clusters and the uncertainties.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Claim 16 and Claim 19 comprise the similar abstract idea limitations which perform the method of Claim 1.
Under Step 1 of the analysis, claim 1 belongs to a statutory category, namely it is a method claim. Likewise, claim 16 is a system claim and claim 19 is a non-transitory machine-readable medium claim.
Under Step 2A, prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, claim 1 is found to recite at least one judicial exception (i.e. abstract idea), that being a Mental Process and a Mathematical Concept. This can be seen in the claim limitations of “obtaining an inversion model of a downhole formation”, “defining a boundary of the downhole formation”, “determining the boundary based on values associated with the inversion model”, “organizing the boundary into one or more clusters”, “determining uncertainties associated with the one or more clusters”, and “estimating the boundary based on the one or more clusters and the uncertainties” which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements in order to evaluate and estimate a formation boundary based on clustered inversion-model data and is capable of being performed mentally and/or with the aid of pen and paper. Additionally, the aforementioned limitations recite mathematical calculations, e.g. see Spec. [0021]-[0023] describing the use of clustering techniques, including k-means, DBSCAN, Gaussian mixtures, and Ward hierarchical clustering, as well as determining statistical values such as means, standard deviations, and uncertainty values for clusters in order to estimate formation boundaries.
Similar limitations comprise the abstract ideas of Claim 16 and Claim 19.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in claim 1, the claimed method recites additional elements including “a computer-implemented method to estimate a boundary of a downhole formation” however these elements are found to be data gathering and output steps, which are recited at a high level of generality, and thus merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”. Furthermore, the claim recites that the steps, e.g. “estimating”, and “obtaining”, are performed by a computer however this is found to be equivalent to adding the words “apply it” and mere instructions to apply a judicial exception on a general purpose computer does not integrate the abstract idea into a practical application. See MPEP 2106.05(f).
The generic data gathering, processing, and output steps, are recited at such a high level of generality that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed system. For instance, nothing is done with the estimated formation boundary beyond organizing inversion-model data into clusters, determining uncertainties associated with the clusters, and estimating the boundary using statistical calculations. For example, although, the claim recites estimating a boundary of a downhole formation, the claim does not recite controlling drilling equipment, modifying a drilling trajectory, improving sensor operation or otherwise applying the estimated boundary in a meaningful technological manner beyond merely generating and analyzing the information.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activit(ies) (claims 1, 16 and 19). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1, as well as claim 16 and 19, amount to significantly more than the abstract idea.
With regards to the dependent claims, claims 2-15, 17-18 and 20, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for claims 1, 16, and 19. Specifically:
With respect to dependent claims 2-7, 10, 17-18, and 20 specifically, the claims further recite limitations directed to additional mathematical analysis and data evaluation of the abstract idea, including organizing boundary points into cluster, identifying subsets of boundary points using threshold distances or resistivity ranges, determining mean values and standard deviation, assigning uncertainty values based on standard deviation thresholds, and utilizing clustering techniques such as k-means, DBSCAN, Gaussian mixtures, and Ward hierarchical clustering. These limitations merely further refine how the inversion model data is mathematically analyzed and processed and therefore merely expand upon the mathematical concepts and mental processes underlying the abstract idea recited in claim 1. Such limitations do not improve computer functionality or another technology, but instead merely use mathematical relationships and statistical calculations to analyze data. Accordingly, these limitations fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g)(h).
With respect to dependent claims 8 and 9 specifically, the claims further recite limitations directed to generating and analyzing additional data representations, including utilizing a three-dimensional inversion model comprising a plurality of two-dimensional slices, determining boundary points of each slice, determining certainty values indicative of the certainty of boundary locations, and providing certainty values for display on an electronic device. These limitations merely relate to organize, modeling, and displaying information and therefore amount to insignificant extra solution activity and output of results. Additionally, limiting the abstract idea to a particular type of data representation or display environment does not integrate the abstract idea into a practical application. Accordingly, these limitations fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(f)(g).
With respect to dependent claims 11 and 12 specifically, the claims further recite limitations directed to generating geosteering recommendations and requesting a drilling system to autonomously follow the recommendation. However, these limitations merely use the results of the abstract data analysis to generate recommendations or instructions and do not recite any specific improvement to drill technology, drilling equipment, sensor operation, or computer functionality. For example, the claims do not recite any particular control algorithm, machine configuration, or technical implementation for autonomously controlling the drilling system, but instead merely limit the abstract idea to a particular technological environment and fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05 (f).
With respect to dependent claims 13-15 specifically, the claims further recite limitations directed to repeating the same abstract data analysis process for a second boundary and second inversion model, as well as defining boundaries using additional data selection criteria such as specific resistivity values, percentage changes, trends, and user specific values. These limitations merely expand the abstract idea by applying the same mathematical concepts and mental processes to additional data sets and by specifying particular types of information used in the analysis. Such limitations amount to insignificant extra solution activity and/or attempts to limit the abstract idea to a particular field of use or source of data. Accordingly, these limitations fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(g)(h).
Accordingly, for the reasons above and those discussed in relation to independent claim 1, 16 and 19, the dependent claims are insufficient to integrate the claimed abstract ideas into a practical application or significant more.
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.
Claims 1-3, 7, 9, 11, 15-17, and 19-20 are rejected under 35 U.S.C. 102(a)(1)\(a)(2) as being anticipated by US 20220404520 A1, Ma et al. (hereinafter Ma).
Regarding Claim 1, 16 and 19, Ma discloses a computer-implemented method to estimate a boundary of a downhole formation (Ma, [0028] A computer system 50 located at the surface receives a digital telemetry signal, demodulates the signal, and displays the tool data or well logs to a user [0038] The formation resistivity may be used to generate a resistivity model of the formation and determine the uncertainty of a parameter included in or determined from the formation data), comprising:
obtaining an inversion model of a downhole formation (Ma, [0019] An inversion algorithm may start with an initial set of conditions to describe the subterranean formation, such as, for example, a number of strata layers, and a randomly assigned resistivity value for each layer. This initial set of conditions may be successively modified, for example a strata's resistivity value, until the algorithm produces a solution. [0042] FIG. 3 shows a flow chart of a method 300 to generate a formation model using formation data measured from the resistivity logging tool 200 of FIG. 2, in accordance with one or more aspects);
defining a boundary of the downhole formation (Ma, [0048] Each set of difference values between pixels that have a large magnitude difference may represent a presumptive boundary location. In some non-limiting examples, the magnitude of sequential pixel resistivity differences greater than or equal to about 2% may be used to identify those pixels at or near strata boundaries (presumptive layer boundaries). It may be understood that some pixilated convergent solutions may not include sequential resistivity difference values meeting the criterion to be considered an indicator of a layer boundary. Based on this derivative method, sequential pixels in each pixilated convergent solution may be analyzed, and those that provide boundary locations may be identified);
determining the boundary based on values associated with the inversion model (Ma, [0047] a derivative or difference in resistivity values between successive pixels along a single pixilated convergent solution may be calculated along the measurement depth. It may be understood that sequential pixels close to or at a layer boundary may show larger difference values than sequential pixels located within a particular layer. Sequential pixels within a stratum may have nearly identical resistivity values (apart from noise) while pixels located at or near a boundary may possess resistivity values depending on which side of a boundary they are found);
organizing the boundary into one or more clusters (Ma, [0048] the pixels so identified may then be analyzed according to a cluster analysis, wherein groups of such boundary-defining pixels may be considered as a single cluster. Each of the boundary clusters may then be further analyzed to provide a mean or median value for the measurement depth of the cluster. The mean or median value of the resistivity at that boundary may also be determined [0055] Those pixels identified as having differences greater than a threshold may form clusters 432a,b);
determining uncertainties associated with the one or more clusters (Ma, [0038] The formation resistivity may be used to generate a resistivity model of the formation and determine the uncertainty of a parameter included in or determined from the formation data. A resistivity model may be used to identify boundary positions between formation strata and determine the wellbore trajectory to produce formation fluids... the uncertainty of a parameter refers to a range of suitable values for the parameter or a measure that is used to quantify a variation in the parameter (e.g., standard deviation). The parameter may include any one or any combination of a horizontal resistivity, vertical resistivity, conductivity, an anisotropy ratio, a boundary position of formation layers, and a formation dip); and
estimating the boundary based on the one or more clusters and the uncertainties (Ma, [0049] The mean or median measurement depth of the clusters may be used to determine the presumptive boundaries between sequential strata. In some aspects, the measurement of these presumptive boundaries may be chosen by an algorithm to improve the contrast of a model of the formation strata. Alternatively, the presumptive boundaries may be chosen by a user of the model calculations by hand [0050] The graphs of the individual ensemble statistics may be truncated at measurement depths indicated by the cluster analysis as locations of the presumptive layer boundaries).
Regarding Claim 2, 3, and 17, Ma discloses the downhole formation boundary estimation system of claim 16, wherein the one or more processors are further configured to:
determine, based on values associated with the inversion model (Ma, [0005] a boundary mapping algorithm such as a distance-to-bed-boundary (DTBB) inversion algorithm (hereafter inversion algorithm) may be used to interpret the tool responses qualitatively and evaluate the subterranean earth formation to identify formation zones that are suitable for producing formation fluids, such as hydrocarbons), at least one of a set of boundary points, a contour, and a body along the boundary (Ma, [0047] A derivative or difference in resistivity values between successive pixels along a single pixilated convergent solution may be calculated along the measurement depth. It may be understood that sequential pixels close to or at a layer boundary may show larger difference values than sequential pixels located within a particular layer. Sequential pixels within a stratum may have nearly identical resistivity values (apart from noise) while pixels located at or near a boundary may possess resistivity values depending on which side of a boundary they are found);
assign at least one of the set of boundary points, the contour, and the body into the one or more clusters (Ma, [0048] The pixels so identified may then be analyzed according to a cluster analysis, wherein groups of such boundary-defining pixels may be considered as a single cluster. Each of the boundary clusters may then be further analyzed to provide a mean or median value for the measurement depth of the cluster. The mean or median value of the resistivity at that boundary may also be determined);
identify a subset of the set of boundary points that are within a threshold distance of each other (Ma, [0048] Each set of difference values between pixels that have a large magnitude difference may represent a presumptive boundary location. In some non-limiting examples, the magnitude of sequential pixel resistivity differences greater than or equal to about 2% may be used to identify those pixels at or near strata boundaries (presumptive layer boundaries)); and
assign the subset as one cluster of the one or more clusters (Ma, [0048] The pixels so identified may then be analyzed according to a cluster analysis, wherein groups of such boundary-defining pixels may be considered as a single cluster. Each of the boundary clusters may then be further analyzed to provide a mean or median value for the measurement depth of the cluster. The mean or median value of the resistivity at that boundary may also be determined).
Regarding Claim 7, Ma discloses the computer-implemented method of claim 2, further comprising:
identifying a subset of the set of boundary points that are within a resistivity range of each other (Ma, [0047] It may be understood that sequential pixels close to or at a layer boundary may show larger difference values than sequential pixels located within a particular layer. Sequential pixels within a stratum may have nearly identical resistivity values (apart from noise) while pixels located at or near a boundary may possess resistivity values depending on which side of a boundary they are found),
wherein assigning the set of boundary points comprises assigning the subset as one cluster of the one or more clusters (Ma, [0048] The pixels so identified may then be analyzed according to a cluster analysis, wherein groups of such boundary-defining pixels may be considered as a single cluster. Each of the boundary clusters may then be further analyzed to provide a mean or median value for the measurement depth of the cluster. The mean or median value of the resistivity at that boundary may also be determined).
Regarding Claim 9, Ma discloses the computer-implemented method of claim 2, further comprising:
determining, based on the boundary and the uncertainties, a certainty value indicative a certainty of a location of the boundaries at the set of boundary points (Ma, [0038] The formation resistivity may be used to generate a resistivity model of the formation and determine the uncertainty of a parameter included in or determined from the formation data. A resistivity model may be used to identify boundary positions between formation strata and determine the wellbore trajectory to produce formation fluids. The uncertainty of a parameter indicates a range of suitable values for a particular parameter such as the uncertainty of resistivity values or boundary positions of formation layers); and
providing the certainty value for display on an electronic device of an operator (Ma, [0028] A computer system 50 located at the surface receives a digital telemetry signal, demodulates the signal, and displays the tool data or well logs to a user [0028] a user, for example a driller, may interact with the system 50 and the software 52 via one or more input devices 54 and 55 and one or more output devices 56. In some system aspects, the driller may employ the system 50 to make geosteering decisions (for example modifying the wellbore trajectory or steering the drill bit 14) and communicate appropriate commands to the bottom-hole assembly 24 to execute those decisions).
Regarding Claim 11, Ma discloses the computer-implemented method of claim 1, further comprising:
dynamically determining a geosteering recommendation based on the estimated boundary (Ma, [0028] user, for example a driller, may interact with the system 50 and the software 52 via one or more input devices 54 and 55 and one or more output devices 56. In some system aspects, the driller may employ the system 50 to make geosteering decisions (for example modifying the wellbore trajectory or steering the drill bit 14) and communicate appropriate commands to the bottom-hole assembly 24 to execute those decisions. On receipt of the geosteering instructions from the user, the bottom-hole assembly 24 may change its orientation or speed accordingly. The computer system 50 may be operable to perform calculations or operations to evaluate the formation, identify formation boundary positions, and/or steer the drill bit 14 as further described herein); and
providing the geosteering recommendation to an electronic device (Ma, [0042] the formation model may be used to evaluate the formation, develop a wellbore trajectory, or steer a drill bit to produce formation fluids. Alternatively, a driller may use the formation model to determine a direction and/or orientation of the drill to proceed. In this manner, the driller or model user may use the model to direct the drill into a portion of the formation most likely to produce extractable fluids).
Regarding Claim 15 and 20, Ma discloses the computer-implemented method of claim 1, wherein defining the boundary of the downhole formation comprises defining the boundary based on one or more of a specific resistivity value (Ma, [0047] Sequential pixels within a stratum may have nearly identical resistivity values (apart from noise) while pixels located at or near a boundary may possess resistivity values depending on which side of a boundary they are found), a magnitude of the resistivity value, a percentage change to the resistivity value (Ma, [0048] the magnitude of sequential pixel resistivity differences greater than or equal to about 2% may be used to identify those pixels at or near strata boundaries (presumptive layer boundaries)), a trend with respect to the resistivity value (Ma, [0047] A derivative or difference in resistivity values between successive pixels along a single pixilated convergent solution may be calculated along the measurement depth. It may be understood that sequential pixels close to or at a layer boundary may show larger difference values than sequential pixels located within a particular layer. Sequential pixels within a stratum may have nearly identical resistivity values (apart from noise) while pixels located at or near a boundary may possess resistivity values depending on which side of a boundary they are found), and a user specified value that is associated with the inversion model (Ma, [0049] The mean or median measurement depth of the clusters may be used to determine the presumptive boundaries between sequential strata. In some aspects, the measurement of these presumptive boundaries may be chosen by an algorithm to improve the contrast of a model of the formation strata. Alternatively, the presumptive boundaries may be chosen by a user of the model calculations by hand).
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 4-6, 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220404520 A1, Ma et al. (hereinafter Ma) in view of US 20230313616 A1, Wu et al. (hereinafter Wu).
Regarding Claim 4, 5, and 18, Ma discloses the downhole formation boundary estimation system of claim 17, wherein the one or more
processors are further configured to:
determine a corresponding value associated with a respective boundary point for each boundary point of the subset of boundary points (Ma, [0038] The parameter may include any one or any combination of a horizontal resistivity, vertical resistivity, conductivity, an anisotropy ratio, a boundary position of formation layers, and a formation dip), wherein the corresponding value is a true vertical depth of the respective boundary point (Ma, [0045] modeled resistivity values at a measurement distance as a function of true vertical depth);
determine one or a mean value of the subset of boundary points (Ma, [0048] Each of the boundary clusters may then be further analyzed to provide a mean or median value for the measurement depth of the cluster. The mean or median value of the resistivity at that boundary may also be determined), wherein the mean value is the mean vertical depth of the subset of boundary points (Ma, [0049] The mean or median measurement depth of the clusters may be used to determine the presumptive boundaries between sequential strata);
determine a standard deviation of the subset of boundary points (Ma, [0038] the uncertainty of a parameter refers to a range of suitable values for the parameter or a measure that is used to quantify a variation in the parameter (e.g., standard deviation)), wherein the standard deviation is the standard deviation of all values associated with the subset of boundary points (Ma, [0038] the uncertainty of a parameter refers to a range of suitable values for the parameter or a measure that is used to quantify a variation in the parameter (e.g., standard deviation). The parameter may include any one or any combination of a horizontal resistivity, vertical resistivity, conductivity, an anisotropy ratio, a boundary position of formation layers, and a formation dip);
Ma does not disclose assign the uncertainty of the cluster a first value if the standard deviation is greater than a first standard deviation threshold; and
assign the uncertainty of the cluster a second value that is less than the first value if the standard deviation is less than or equal to first standard deviation threshold.
However, Wu teaches assign the uncertainty of the cluster a first value if the standard deviation is greater than a first standard deviation threshold (Wu, [0076] At block 710, the weighted scores for the clusters are determined based upon a standard deviation, number of models and/or the defined misfit (e.g., a median or average misfit). The standard deviation score represents the models' consistency within each cluster. In this example, the cluster with a smaller standard deviation means that cluster has a better consistency, which receives a higher score); and
assign the uncertainty of the cluster a second value that is less than the first value if the standard deviation is less than or equal to first standard deviation threshold (Wu, [0077] if the standard deviation score of cluster i is the largest and the number of models of cluster i is less than 5% of the total number of models, then set: sd_score.sub.i=smallest sd_score).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Ma and Wu teaching because both references are directed to downhole formation analysis using inversion models and clustering techniques for geosteering operations. Ma teaches organizing formation boundary information into clusters and determining uncertainties associated with clusters, while Wu teaches evaluating clusters using standard deviation scoring and consistency measurements. One of ordinary skill in the art would have been motivated to integrate Wu’s standard deviated cluster evaluation techniques into Ma’s boundary estimation system in order to improve the reliability, consistency, and accuracy of the uncertainty determinations associated with the clusters and resulting boundary estimations.
Regarding Claim 6, Ma in view of Wu teaches the computer-implemented method of claim 4, wherein the uncertainty of the cluster increases as the standard deviation increases, and wherein the uncertainty of the cluster decreases as the standard deviation decreases (Wu, [0029] A weighted score for each cluster is then determined based upon a standard deviation between the average model of all inversions within one set of clusters and all the inversion models within the set of clusters, a number of inversion models in the set of clusters, and/or a defined misfit of the set of the clusters. Thereafter, a cluster is selected based upon the weighted scores. The cluster with the highest score, lowest score, or some other threshold score may then be selected and used for further processing [0076] At block 710, the weighted scores for the clusters are determined based upon a standard deviation, number of models and/or the defined misfit (e.g., a median or average misfit). The standard deviation score represents the models' consistency within each cluster. In this example, the cluster with a smaller standard deviation means that cluster has a better consistency, which receives a higher score).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Ma and Wu teaching because both references are directed to downhole geosteering operations utilizing inversion models and cluster analysis for evaluating formation characteristics. Ma teaches determining uncertainties associated with clustered boundary information, while Wu teaches evaluating cluster consistency using standard deviation weighted scoring techniques. One of ordinary skill in the art would have been motivated to integrate Wu’s standard deviation based cluster consistency evaluation into Ma’s boundary uncertainty determination system in order to improve the reliability, consistency, and accuracy of the cluster uncertainty estimations and resulting formation boundary determinations.
Regarding Claim 10, Ma in view of Wu teaches the computer-implemented method of claim 1, further comprising utilizing one or more of k-means, DBSCAN, Gaussian mixtures, Ward hierarchical clustering technique to assign the boundary into the one or more clusters (Wu, [0058] inversion modeler 214 may use K-means clustering to cluster inversion models 224 or a selected subset of inversion models 224 qualified for performing inversion based on the comparison between actual and predicted tool responses).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Ma and Wu teaching because both references are directed to downhole inversion modeling and cluster analysis for geosteering applications. Ma teaches organizing formation boundary information into clusters, while Wu teaches utilizing K-means clustering to cluster inversion models for evaluating formation characteristics. One of the ordinary skill in the art would have been motivated to integrate Wu’s K-means clustering techniques into Ma’s boundary clustering system in order to improve the consistency, automation, and accuracy of assigning formation boundary information into clusters during downhole formation analysis.
Claims 8, 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220404520 A1, Ma et al. (hereinafter Ma) in view of US 20250116177 A1, Li et al. (hereinafter Li).
Regarding Claim 8, Ma in view of Li teaches the computer-implemented method of claim 2, wherein the inversion model is a three dimensional model comprising a plurality of two dimensional slices (Li, [0002] The computer-implemented method further includes generating at least one of: one-dimensional, two-dimensional, and three-dimensional representations of a subsurface area which indicate geological features and properties of the wellbore subsurface reservoir and adjusting a drilling parameter of a downhole drill within the wellbore subsurface reservoir based on the augmented inversion image), and wherein determining the one or more boundary points along the boundary comprises determining, for each slice of the plurality of two dimensional slices (Li, [0049] the subsurface modeling system 208 creates 1D, 2D, or 3D representations of a subsurface area, which may indicate crucial geological features and rock properties), the one or more boundary points along the boundary based on corresponding values associated with the respective slice of the plurality of two dimensional slices (Li, [0091] the boundary identification system 206 generates and/or obtains an inversion result profile pair that includes the labeled inversion result profile and the corresponding original or unlabeled inversion result profile. In some implementations, the inversion result profile includes 1D, 2D, and/or 3D images. Indeed, the boundary identification system 206 may process images in different dimensions. For example, the labeled inversion result profiles 304 in FIG. 4 shows a 1D inversion result profile with reservoir boundary labels).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Ma and Li teaching because both references are directed to downhole formation analysis using inversion models for geosteering and reservoir boundary identification application. Ma teaches determining formation boundaries based on inversion model values, while Li teaches utilizing one-dimensional, two-dimensional, and three dimensional inversion representations and processing inversion result profiles in different dimensions. One of ordinary skill in the art would have been motivated to integrate Li’s multidimensional inversion modeling techniques into Ma’s boundary estimation system in order to improve the visualization, analysis, and accuracy of identifying formation boundaries across different dimensional representations of the subsurface formation.
Regarding Claim 12, Ma discloses The computer-implemented method of claim 1, further comprising:
dynamically determining a geosteering recommendation based on the estimated
boundary (Ma, [0028] user, for example a driller, may interact with the system 50 and the software 52 via one or more input devices 54 and 55 and one or more output devices 56. In some system aspects, the driller may employ the system 50 to make geosteering decisions (for example modifying the wellbore trajectory or steering the drill bit 14) and communicate appropriate commands to the bottom-hole assembly 24 to execute those decisions. On receipt of the geosteering instructions from the user, the bottom-hole assembly 24 may change its orientation or speed accordingly. The computer system 50 may be operable to perform calculations or operations to evaluate the formation, identify formation boundary positions, and/or steer the drill bit 14 as further described herein);
Ma does not disclose requesting a drilling system to autonomously follow the geosteering recommendation.
However, Li teaches requesting a drilling system to autonomously follow the geosteering recommendation (Li, [0108] In some implementations, the series of acts 600 includes using the augmented inversion image to adjust a drilling parameter of a downhole drill within a wellbore subsurface reservoir based on the augmented inversion image. In some cases, adjusting the drilling parameter of the downhole drill automatically adjusts a geosteering direction of the downhole drill).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Ma and Li teaching because both references are directed to downhole formation analysis and geosteering operations using inversion model formation evaluations. Ma teaches generating geosteering recommendation and steering drilling operations based on identified formation boundaries, while Li teaches automatically adjusting drilling parameters and geosteering directions based on inversion-image analysis. One of ordinary skill in the art would have been motivated to integrate Li’s autonomous drilling adjustment techniques into Ma’s geosteering recommendation system in order to improve automation, operational efficiency, and real-time responsiveness of downhole drilling operations during formation boundary estimation and geosteering.
Regarding Claim 13, Ma discloses the computer-implemented method of claim 1, further comprising:
obtaining an inversion model of a downhole formation (Ma, [0019] An inversion algorithm may start with an initial set of conditions to describe the subterranean formation, such as, for example, a number of strata layers, and a randomly assigned resistivity value for each layer. This initial set of conditions may be successively modified, for example a strata's resistivity value, until the algorithm produces a solution. [0042] FIG. 3 shows a flow chart of a method 300 to generate a formation model using formation data measured from the resistivity logging tool 200 of FIG. 2, in accordance with one or more aspects);
defining a second boundary of the downhole formation (Ma, [0048] Each set of difference values between pixels that have a large magnitude difference may represent a presumptive boundary location. In some non-limiting examples, the magnitude of sequential pixel resistivity differences greater than or equal to about 2% may be used to identify those pixels at or near strata boundaries (presumptive layer boundaries). It may be understood that some pixilated convergent solutions may not include sequential resistivity difference values meeting the criterion to be considered an indicator of a layer boundary. Based on this derivative method, sequential pixels in each pixilated convergent solution may be analyzed, and those that provide boundary locations may be identified);
determining the second boundary based on the values associated with the inversion model (Ma, [0047] a derivative or difference in resistivity values between successive pixels along a single pixilated convergent solution may be calculated along the measurement depth. It may be understood that sequential pixels close to or at a layer boundary may show larger difference values than sequential pixels located within a particular layer. Sequential pixels within a stratum may have nearly identical resistivity values (apart from noise) while pixels located at or near a boundary may possess resistivity values depending on which side of a boundary they are found);
organizing the second boundary into a second set of one or more clusters (Ma, [0048] the pixels so identified may then be analyzed according to a cluster analysis, wherein groups of such boundary-defining pixels may be considered as a single cluster. Each of the boundary clusters may then be further analyzed to provide a mean or median value for the measurement depth of the cluster. The mean or median value of the resistivity at that boundary may also be determined [0055] Those pixels identified as having differences greater than a threshold may form clusters 432a,b);
determining uncertainties associated with the second set of one or more clusters (Ma, [0038] The formation resistivity may be used to generate a resistivity model of the formation and determine the uncertainty of a parameter included in or determined from the formation data. A resistivity model may be used to identify boundary positions between formation strata and determine the wellbore trajectory to produce formation fluids... the uncertainty of a parameter refers to a range of suitable values for the parameter or a measure that is used to quantify a variation in the parameter (e.g., standard deviation). The parameter may include any one or any combination of a horizontal resistivity, vertical resistivity, conductivity, an anisotropy ratio, a boundary position of formation layers, and a formation dip); and
estimating the second boundary based on the second set one or more clusters and the uncertainties associated with the second set of one or more clusters (Ma, [0049] The mean or median measurement depth of the clusters may be used to determine the presumptive boundaries between sequential strata. In some aspects, the measurement of these presumptive boundaries may be chosen by an algorithm to improve the contrast of a model of the formation strata. Alternatively, the presumptive boundaries may be chosen by a user of the model calculations by hand [0050] The graphs of the individual ensemble statistics may be truncated at measurement depths indicated by the cluster analysis as locations of the presumptive layer boundaries).
Ma does not disclose a second boundary
However, Li teaches a second boundary (Li, [0025] The terms “inversion image” and “inversion result image” refer to a segment of an inversion result profile. For instance, multiple inversion images combine to create an inversion result profile [0026] Reservoir boundaries usually encompass a top or ceiling boundary and a bottom or base boundary. Reservoir boundaries may also include side, cap, or end boundaries [0106] As further shown, the series of acts 600 includes an act 640 of generating an augmented inversion result profile based on the augmented inversion image. For instance, in example implementations, the act 640 involves generating an augmented longitudinal electromagnetic inversion result profile based on the augmented inversion image)
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Ma and Li teaching because both references are directed to inversion model formation and reservoir boundary analysis for downhole geosteering applications. Ma teaches determining and estimating formation boundaries using clustered inversion model data, while Li teaches utilizing multiple inversion images and reservoir boundaries including top, bottom, side, cap, and end boundaries to generate augmented inversion result profiles. One of ordinary skill in the art would have been motivated to integrate Li’s multiple boundary and multiple inversion image techniques into Ma’s boundary estimation system in order to improve the analysis, identification, and estimation of multiple formation boundaries within subsurface formations and to improve the resulting inversion formation modeling.
Regarding Claim 14, Ma discloses The computer-implemented method of claim 1, further comprising:
obtaining a second inversion model of the downhole formation (Ma, [0019] An inversion algorithm may start with an initial set of conditions to describe the subterranean formation, such as, for example, a number of strata layers, and a randomly assigned resistivity value for each layer. This initial set of conditions may be successively modified, for example a strata's resistivity value, until the algorithm produces a solution [0042] FIG. 3 shows a flow chart of a method 300 to generate a formation model using formation data measured from the resistivity logging tool 200 of FIG. 2, in accordance with one or more aspects);
defining a second boundary of the downhole formation (Ma, [0048] Each set of difference values between pixels that have a large magnitude difference may represent a presumptive boundary location. In some non-limiting examples, the magnitude of sequential pixel resistivity differences greater than or equal to about 2% may be used to identify those pixels at or near strata boundaries (presumptive layer boundaries). It may be understood that some pixilated convergent solutions may not include sequential resistivity difference values meeting the criterion to be considered an indicator of a layer boundary. Based on this derivative method, sequential pixels in each pixilated convergent solution may be analyzed, and those that provide boundary locations may be identified);
determining the second boundary based on values associated with the second inversion model (Ma, [0047] a derivative or difference in resistivity values between successive pixels along a single pixilated convergent solution may be calculated along the measurement depth. It may be understood that sequential pixels close to or at a layer boundary may show larger difference values than sequential pixels located within a particular layer. Sequential pixels within a stratum may have nearly identical resistivity values (apart from noise) while pixels located at or near a boundary may possess resistivity values depending on which side of a boundary they are found);
organizing the second boundary into a second set of one or more clusters (Ma, [0048] the pixels so identified may then be analyzed according to a cluster analysis, wherein groups of such boundary-defining pixels may be considered as a single cluster. Each of the boundary clusters may then be further analyzed to provide a mean or median value for the measurement depth of the cluster. The mean or median value of the resistivity at that boundary may also be determined [0055] Those pixels identified as having differences greater than a threshold may form clusters 432a,b);
determining uncertainties associated with the second set of one or more clusters (Ma, [0038] The formation resistivity may be used to generate a resistivity model of the formation and determine the uncertainty of a parameter included in or determined from the formation data. A resistivity model may be used to identify boundary positions between formation strata and determine the wellbore trajectory to produce formation fluids... the uncertainty of a parameter refers to a range of suitable values for the parameter or a measure that is used to quantify a variation in the parameter (e.g., standard deviation). The parameter may include any one or any combination of a horizontal resistivity, vertical resistivity, conductivity, an anisotropy ratio, a boundary position of formation layers, and a formation dip); and
estimating the second boundary based on the second set one or more clusters and the uncertainties associated with the second set of one or more clusters (Ma, [0049] The mean or median measurement depth of the clusters may be used to determine the presumptive boundaries between sequential strata. In some aspects, the measurement of these presumptive boundaries may be chosen by an algorithm to improve the contrast of a model of the formation strata. Alternatively, the presumptive boundaries may be chosen by a user of the model calculations by hand [0050] The graphs of the individual ensemble statistics may be truncated at measurement depths indicated by the cluster analysis as locations of the presumptive layer boundaries).
Ma does not disclose a second inversion model
However, Li teaches a second inversion model (Li, [0052] the inversion image manager 210 obtains inversion result profiles 220 and inversion images 222 from the subsurface modeling system 208 [0052] the inversion image manager 210 may provide the inversion images 222 to the image modeling manager 212 to determine reservoir boundaries within the inversion result profiles 220 and/or inversion images 222 that are made up of the inversion images 222)
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Ma and Li teaching because both references are directed to inversion model analysis of subsurface formations and reservoir boundaries for downhole geosteering applications. Ma teaches obtaining inversion models, clustering boundary information, determining uncertainties associated with the clusters, and estimating formation boundaries, while Li teaches utilizing multiple inversion images and inversion result profiles for identifying and analyzing multiple reservoir boundaries. One of ordinary skill in the art would have bene motivated to integrated Li’s multiple inversion image and multiple boundary estimation system in order to improve the identification, analysis, and estimation of multiple subsurface boundaries and corresponding inversion models within a formation.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclose:
-WO 2017147217 A1, describing methods and systems for characterizing subterranean formations using electromagnetic logging measurements and two-dimensional inversion imaging models, wherein electromagnetic property data associated with a subterranean formation is analyzed to estimate orientations and properties of the formation relative to a borehole trajectory.
-US 20230267594 A1, describing methods and systems for interpreting borehole images for drilling operations, wherein contextual information and mathematical models are utilized to process borehole images and generating interpreted borehole images used to adjust drilling operations associated with a geological formation.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM NAGI SHOHATEE whose telephone number is (571)272-6612. The examiner can normally be reached 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby Turner can be reached at (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/IBRAHIM NAGI SHOHATEE/
Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights