DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-5 and 7-20 are pending.
Claim 6 is cancelled.
Claims 1 and 18 are amended.
This office action is in response of the Applicant’s arguments and remarks filed 10/01/2026.
Response to Arguments
Applicant's arguments filed 10/01/2026 have been fully considered but they are not persuasive.
In response to Applicant’s arguments in page 5, Examiner withdraws the 101 rejection as being directed to a judicial exception without significantly more of claims 1 and 18. Therefore, claims 1 and 18 are directed towards eligible subject matter.
In response to Applicant’s arguments that Donderici in view of Eltaher does not teach the one or more pseudo attributes are computed based at least in part on one or more combinations of one or more pipe attributes. First of all, the Examiner is using the broadest reasonable interpretation regarding the rejection of the claims, wherein Donderici in view of Eltaher clearly teaches the same concept as presented by the Applicant in his claims. Donderici discloses equations (1), (2), (3), (4) and (5) with one or more combinations of one or more pipe attributes, where ω is the angular frequency of the excitation source, μ is the magnetic permeability of the pipe, σ is the electrical conductivity of the pipe, and t is the thickness of the pipe, Technically equivalent : pseudo attributes are computed based at least in part on one or more combinations of one or more pipe attributes (par[0042]). In RFEC, the estimated quantity may be the overall thickness of the metal. Thus, for multiple concentric pipes, the estimated parameter may be the overall or sum of the thicknesses of the pipes. The quasi-linear variation of the phase of mutual impedance with the overall metal thickness may be employed to perform fast estimation to estimate the overall thickness of multiple concentric pipes. For this purpose, for any given set of pipes dimensions, material properties, and tool configuration, such linear variation may be constructed quickly and may be used to estimate the overall thickness of concentric pipes (par[0043]).
Therefore, Examiner maintains his rejection.
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claim 1 recites the limitations “one or more model-based attributes” in line 10. The Examiner suggests to change the limitations “one or more model-based attributes” to the limitations “the one or more model-based attributes” in order to be consistent with the mentioned limitations in the claim.
Claims 2-17 are objected as stated above because due to their dependency from claim 1.
Claim 7 recites the limitations “one or more pseudo attributes” in line 1. The Examiner suggests to change the limitations “one or more pseudo attributes” to the limitations “the one or more pseudo attributes” in order to be consistent with the mentioned limitations in the claim.
Claims 8-9 are objected as stated above because due to their dependency from claim 7.
Claim 9 recites the limitations “one or more regularization parameters” in line 2. The Examiner suggests to change the limitations “one or more regularization parameters” to the limitations “the one or more regularization parameters” in order to be consistent with the mentioned limitations in the claim.
Claim 14 recites the limitations “one or more pseudo attributes” in line 2. The Examiner suggests to change the limitations “one or more pseudo attributes” to the limitations “the one or more pseudo attributes” in order to be consistent with the mentioned limitations in the claim.
Claims 15-17 are objected as stated above because due to their dependency from claim 14.
Claim 15 recites the limitations “model-based attributes and pseudo attributes” in line 3. The Examiner suggests to change the limitations “model-based attributes and pseudo attributes” to the limitations “the model-based attributes and the pseudo attributes” in order to be consistent with the mentioned limitations in the claim.
Claims 16-17 are objected as stated above because due to their dependency from claim 15.
Claim 18 recites the limitations “one or more model-based attributes” in lines 11-12. The Examiner suggests to change the limitations “one or more model-based attributes” to the limitations “the one or more model-based attributes” in order to be consistent with the mentioned limitations in the claim.
Claims 19-20 are objected as stated above because due to their dependency from claim 18.
Appropriate correction is required.
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.
Claims 12-13 are 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 12 recites the limitation "the quality" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 is rejected as stated above because due to their dependency from claim 12. Claim 13 is also indefinite.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
1. Claim(s) 1-5, 7-12, 14 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Donderici et al. (US2020/0309986A1) hereafter Donderici in view of Eltaher et al. (US2024/0412044A1).
Regarding claim 1, Donderici discloses a method comprising:
disposing an electromagnetic (EM) logging tool into a pipe string configured to perform measurements at one or more depths (par[0030]: Conveyance 106 and EM logging tool 100 may extend within casing string 108 to a desired depth within the wellbore 110. Signals recorded by EM logging tool 100 may be stored on memory and then processed by display and storage unit 120 after recovery of EM logging tool 100 from wellbore 110);
creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string (fig 6; par[0030], [0048]: Signals recorded by EM logging tool 100 may be stored on memory and then processed by display and storage unit 120 after recovery of EM logging tool 100 from wellbore 110. Alternatively, signals recorded by EM logging tool 100 may be conducted to display and storage unit 120 by way of conveyance 106. Display and storage unit 120 may process the signals, and the information contained therein may be displayed for an operator to observe and store for future processing and reference);
computing one or more pseudo attributes of one or more pipes with the log (par[0034], [0046]: Characterization of casing string 108 and pipe string 138, including determination of pipe attributes, may be performed by measuring and processing these electromagnetic fields. Pipe attributes may include, but are not limited to, pipe thickness, pipe conductivity, and/or pipe permeability. By analyzing the signal levels at these different channels with inversion methods, it is possible to relate a certain received signal to a certain metal loss or gain at each pipe. In addition to loss of metal, other pipe properties such as magnetic permeability and conductivity may also be estimated by inversion methods. However, there may be factors that complicate interpretation of losses.), wherein the one or more pseudo attributes are computed based at least in part on one or more combinations of one or more pipe attributes (par[0042]: Equation (1) and (2): where ω is the angular frequency of the excitation source, μ is the magnetic permeability of the pipe, σ is the electrical conductivity of the pipe, and t is the thickness of the pipe. By using the common definition of skin depth for the metals as: Technically : pseudo attributes are computed based at least in part on one or more combinations of one or more pipe attributes);
computing one or more model-based attributes with a model-based inversion based at least on the one or more pseudo attributes as an approximation for one or more model-based attributes (Donderici par[0046] and fig 17:1702; par[0082], [0083]; fig 18:Inversion; par[0084], [0085]: By analyzing the signal levels at these different channels with inversion methods, it is possible to relate a certain received signal to a certain metal loss or gain at each pipe. In addition to loss of metal, other pipe properties such as magnetic permeability and conductivity may also be estimated by inversion methods).
Donderici does not explicitly disclose the method comprising: determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes.
Eltaher discloses the method comprising:
determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes (fig 2:211-213; par[0028]: By applying corrections to the EM log 211, the apparently metal loss, as detected in EM log 201, has been reduced. Fig 4:411, par[0030]: The disruptive joints, as identified in EM log 401 and thickness map 402 can be corrected according to implementations of the present disclosure. EM log 411 and thickness map 412 in panel 410 show results for the same casing after spotting those unique joints and applying the proper corrections in post data processing. ).
One of ordinary skill in the art would be aware of the Donderici and Eltaher references since both pertain to the field of downhole inductive coupling systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Donderici to implement the correction feature as disclosed by Eltaher to gain the functionality of providing a system and technology to improve the detection of metal loss in casing pipes placed in, e.g., downhole completed well environments, and automatically differentiating features that are attributed to an actual casing pipe metal loss from those attributed to other downhole environmental factors (e.g., change in casing pipe properties or downhole completion effects), where both would induce a similar effect on the electromagnetic raw data used for automated metal loss calculation.
Regarding claim 2, Donderici in view of Eltaher discloses the method of claim 1, wherein the pseudo attributes of the one or more pipes are pipe thickness, metal loss, magnetic permeability, or electrical permeability of the one or more pipes (Donderici par[0034]: Characterization of casing string 108 and pipe string 138, including determination of pipe attributes, may be performed by measuring and processing these electromagnetic fields. Pipe attributes may include, but are not limited to, pipe thickness, pipe conductivity, and/or pipe permeability. By analyzing the signal levels at these different channels with inversion methods, it is possible to relate a certain received signal to a certain metal loss or gain at each pipe. In addition to loss of metal, other pipe properties such as magnetic permeability and conductivity may also be estimated by inversion methods. However, there may be factors that complicate interpretation of losses.).
Regarding claim 3, Donderici in view of Eltaher discloses the method of claim 1, wherein computing the one or more pseudo attributes comprises building a regression model to estimate at least one pseudo attribute from the one or more pseudo attributes (Dondirici par[0052]: Referring back to FIG. 5, collar locator method 500 may implement two inputs (input sample matrix and target value matrix) and performs a training operation 502. Machine learning techniques that may be used for the collar locator may be nearest neighbors, linear support vector machine, kernel vector machine, Gaussian process, decision tree, random forest, neural net, Ada Boost, Naive Bayes and quadratic classifier).
Regarding claim 4, Donderici in view of Eltaher discloses the method of claim 1, wherein computing the one or more pseudo attributes comprises training a machine learning model to estimate at least one pseudo attribute from the one or more pseudo attributes (Donderici fig 5:502; par[0049], [0050], [0053]: input samples consist of the training input data of the system, while target values consist of the training output data. When these input and output data are paired and/or concatenated into a concatenated matrix, they constitute a training set for the adaptive learning system. Input sample matrix may be comprised of depth, pipe configuration, and signal, while target value matrix may be comprised of an initial set of collar picks in collar proximity form. It should be noted that pipe configuration parameters may include a nominal thickness, a nominal inner diameter, a nominal outer diameter, a nominal magnetic permeability, or a nominal conductivity of pipe. Additionally, pipe configuration parameters may further include are a magnetic permeability of pipes, a conductivity of pipes, a diameter of pipes, an inner diameter of pipes, an outer diameter of pipes, an eccentricity of pipes, a thickness of pipes, a core properties of EM pipe inspection tool transmitters, a core properties of EM pipe inspection tool receivers, a logging depth, a location of an artifact, or an overlap of artifacts on different pipes. Any additional information, such as information from other tools (collar locator tools, neutron logging tools, acoustic logging tools etc.) may be included in the sample matrix input. After training operation 502 may be applied, box 504, herein referred to as predictor 504, a predictor function, may be used to predict output 506 given any new input that may not be included in training operation 502. The new input may be in the same format as the original (for example, FIG. 6). Output 506 may be a list of collar pick locations in depth dimension. [0053] In examples, the method for determining defects, discussed above, may be further refined. For example, a plurality of artifacts may be deviations corresponding to a plurality of defects on one or more concentric pipes.).
Regarding claim 5, Donderici in view of Eltaher discloses the method of claim 1, further comprising generating a first database with at least one or more pipe attributes (Donderici fig 6; par[0050]: Input sample matrix may be comprised of depth, pipe configuration, and signal, while target value matrix may be comprised of an initial set of collar picks in collar proximity form. It should be noted that pipe configuration parameters may include a nominal thickness, a nominal inner diameter, a nominal outer diameter, a nominal magnetic permeability, or a nominal conductivity of pipe. Additionally, pipe configuration parameters may further include are a magnetic permeability of pipes, a conductivity of pipes, a diameter of pipes, an inner diameter of pipes, an outer diameter of pipes, an eccentricity of pipes, a thickness of pipes, a core properties of EM pipe inspection tool transmitters, a core properties of EM pipe inspection tool receivers, a logging depth, a location of an artifact, or an overlap of artifacts on different pipes. Any additional information, such as information from other tools (collar locator tools, neutron logging tools, acoustic logging tools etc.) may be included in the sample matrix input. FIG. 6 shows an example input sample matrix. ).
Regarding claim 7, Donderici in view of Eltaher discloses the method wherein one or more pseudo attributes are used to extract prior knowledge on a direction of metal loss progression (Donderici par[0046]: Due to eddy current physics and electromagnetic attenuation, pipe string 138 and/or casing string 108 may generate an electrical signal that is in the opposite polarity to the incident signal and results in a reduction in the received signal. Typically, more metal volume translates to more lost signal. As a result, by inspecting the signal gains, it is possible to identify zones with metal loss (such as corrosion). By analyzing the signal levels at these different channels with inversion methods, it is possible to relate a certain received signal to a certain metal loss or gain at each pipe. In addition to loss of metal, other pipe properties such as magnetic permeability and conductivity may also be estimated by inversion methods.).
Regarding claim 8, Donderici in view of Eltaher discloses the method of claim 7, wherein at least the prior knowledge is used to determine one or more regularization parameters (Donderici fig 18:1804, par[0085]: Inversion parameters 1804 may be regularization parameter, channel weights, cost function threshold, form of cost function (for example polynomial coefficients), inversion zone start and end depths, inversion thickness constraints, inversion magnetic permeability and conductivity constraints, number of iterations, use of Fast Mode vs. Full Mode, and/or eccentricity constraints. Calibration parameters 1806 may be coefficient limits (maximum and minimum), magnetic permeability limits (maximum and minimum), assumed value, conductivity limits (maximum and minimum), and/or assumed value.).
Regarding claim 9, Donderici in view of Eltaher discloses the method of claim 8, wherein the model-based inversion comprises an optimization algorithm of one or more regularization parameters of the model-based inversion (Donderici par[0084], [0085]: FIG. 18 illustrates an adaptive optimization of inversion/calibration parameters. The optimum inversion and calibration parameters may be identified by an operator, and they may be provided to a machine learning algorithm along with the standard inputs as described in previous embodiments. As a result, a system may learn what inversion and calibration parameters may be ideal for which example and be able to predict the parameters given new data (depth, pipe configuration and signal). If an operator finds that the parameters may not be ideal, the operator may make further modifications which may also be fed back to training 1802 set to replace the previous inferior training pair. Inversion parameters 1804 may be regularization parameter, channel weights, cost function threshold, form of cost function (for example polynomial coefficients), inversion zone start and end depths, inversion thickness constraints, inversion magnetic permeability and conductivity constraints, number of iterations, use of Fast Mode vs. Full Mode, and/or eccentricity constraints. Calibration parameters 1806 may be coefficient limits (maximum and minimum), magnetic permeability limits (maximum and minimum), assumed value, conductivity limits (maximum and minimum), and/or assumed value.).
Regarding claim 10, Donderici in view of Eltaher discloses the method of claim 6, wherein the model-based inversion comprises a cost function using gradient descent methods or a brute-force search of the database (Donderici par[0083], [0085]: In examples, it may be possible to feed pipe parameters from predictor 1708 back in the inversion workflow 1702 to refine pipe parameters. For example, inversion workflow 1702 may use information from pipe properties to determine corrections to zone definitions or weights of the cost function in the inversion workflow 1027. Inversion workflow 1702 may also enforce the thicknesses, magnetic permeability, and conductivities to be close to the predictor results within a certain threshold. For example, the threshold may be between 5% and 75%. Inversion parameters 1804 may be regularization parameter, channel weights, cost function threshold, form of cost function (for example polynomial coefficients), inversion zone start and end depths, inversion thickness constraints, inversion magnetic permeability and conductivity constraints, number of iterations, use of Fast Mode vs. Full Mode, and/or eccentricity constraints. Calibration parameters 1806 may be coefficient limits (maximum and minimum), magnetic permeability limits (maximum and minimum), assumed value, conductivity limits (maximum and minimum), and/or assumed value.).
Regarding claim 11, Donderici in view of Eltaher discloses the method of claim 10, wherein the cost function is defined in terms of the one or more pseudo attributes (Donderici par[0083], [0085]: In examples, it may be possible to feed pipe parameters from predictor 1708 back in the inversion workflow 1702 to refine pipe parameters. For example, inversion workflow 1702 may use information from pipe properties to determine corrections to zone definitions or weights of the cost function in the inversion workflow 1027. Inversion workflow 1702 may also enforce the thicknesses, magnetic permeability, and conductivities to be close to the predictor results within a certain threshold. For example, the threshold may be between 5% and 75%. Inversion parameters 1804 may be regularization parameter, channel weights, cost function threshold, form of cost function (for example polynomial coefficients), inversion zone start and end depths, inversion thickness constraints, inversion magnetic permeability and conductivity constraints, number of iterations, use of Fast Mode vs. Full Mode, and/or eccentricity constraints. Calibration parameters 1806 may be coefficient limits (maximum and minimum), magnetic permeability limits (maximum and minimum), assumed value, conductivity limits (maximum and minimum), and/or assumed value.).
Regarding claim 12, Donderici in view of Eltaher discloses the method of claim 6, further comprising performing a quality control on the one or more model-based attributes, wherein the quality of the one or more model-based attributes at a measurement depth is determined based on the one or more pseudo attributes at the measurement depth (Donderici fig 10:1004&1006; par[0059], [0060], fig 17, par[0082]: FIG. 10 illustrates a box diagram 1000 of collar locator method 500 (e.g., Referring to FIG. 5). Predictor 504 is used to produce a set of collar picks as discussed above. An automated collar locator 1002 may employ conventional collar picking principles to produce a second set of collar picks. The second set of collar picks may be compared to the picks from predictor 504 in quality control 1004 to form a quality estimate of 1006. FIG. 17 illustrates an inversion workflow 1702 integrated with the adaptive learning system 1700. In examples, the depth, pipe configuration, and signal from well measurement 1704 may be passed through inversion workflow 1702 and resulting pipe properties may be used in the training 1706 of adaptive learning system 1700. It should be noted that pipe configuration parameters may include a nominal thickness, a nominal inner diameter, a nominal outer diameter, a nominal magnetic permeability, or a nominal conductivity of pipe. A selection based on quality may be applied to the pipe property results before they may be input into training 1706, where results with a quality above certain threshold may be input into training 1706. The quality of a certain results may be calculated based on inversion misfit, eccentricity indicator, and/or a manual flag that an operator may set. For example, quality may be high if a (normalized) inversion misfit is between 0 and 0.01, medium if it is between 0.01 and 0.1, or low if it is larger than 0.1. Following this same example, the threshold may further be selected as quality level of “high”, thus effectively selecting the results with an inversion misfit of 0.01 or below.).
Regarding claim 14, Donderici in view of Eltaher discloses the method of claim 6, further comprising comparing the model-based attributes to one or more pseudo attributes to form a comparison (Donderici par[0062]: Calibration 1108 may be calculated by comparing recorded responses from EM logging tool 100 with modeled tool responses in the same known environment and finding a mapping method that may match simulated responses to recorded responses. This mapping may be linear and/or non-linear, where each calibrated signal may be expressed as a linear combination of uncalibrated signals.).
Regarding claim 18, Donderici discloses the system comprising:
an electromagnetic (EM) logging tool into a pipe string configured to perform measurements at one or more depths (par[0030]: Conveyance 106 and EM logging tool 100 may extend within casing string 108 to a desired depth within the wellbore 110. Signals recorded by EM logging tool 100 may be stored on memory and then processed by display and storage unit 120 after recovery of EM logging tool 100 from wellbore 110); and
an information handling system configured to:
create a log from the measurements at one or more depths taken by the EM logging tool in the pipe string (fig 6; par[0030], [0048]: Signals recorded by EM logging tool 100 may be stored on memory and then processed by display and storage unit 120 after recovery of EM logging tool 100 from wellbore 110. Alternatively, signals recorded by EM logging tool 100 may be conducted to display and storage unit 120 by way of conveyance 106. Display and storage unit 120 may process the signals, and the information contained therein may be displayed for an operator to observe and store for future processing and reference);
compute one or more pseudo attributes of one or more pipes with the log (par[0034], [0046]: Characterization of casing string 108 and pipe string 138, including determination of pipe attributes, may be performed by measuring and processing these electromagnetic fields. Pipe attributes may include, but are not limited to, pipe thickness, pipe conductivity, and/or pipe permeability. By analyzing the signal levels at these different channels with inversion methods, it is possible to relate a certain received signal to a certain metal loss or gain at each pipe. In addition to loss of metal, other pipe properties such as magnetic permeability and conductivity may also be estimated by inversion methods. However, there may be factors that complicate interpretation of losses.), wherein the one or more pseudo attributes are computed based at least in part on one or more combinations of one or more pipe attributes (par[0042]: Equation (1) and (2): where ω is the angular frequency of the excitation source, μ is the magnetic permeability of the pipe, σ is the electrical conductivity of the pipe, and t is the thickness of the pipe. By using the common definition of skin depth for the metals as: Technically : pseudo attributes are computed based at least in part on one or more combinations of one or more pipe attributes);
computing one or more model-based attributes with a model-based inversion based at least on the one or more pseudo attributes as an approximation for one or more model-based attributes (Donderici par[0046] and fig 17:1702; par[0082], [0083]; fig 18:Inversion; par[0084], [0085]: By analyzing the signal levels at these different channels with inversion methods, it is possible to relate a certain received signal to a certain metal loss or gain at each pipe. In addition to loss of metal, other pipe properties such as magnetic permeability and conductivity may also be estimated by inversion methods).
Donderici does not explicitly disclose the system configured to: determine a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes.
Eltaher discloses the system configured to:
determine a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes (fig 2:211-213; par[0028]: By applying corrections to the EM log 211, the apparently metal loss, as detected in EM log 201, has been reduced. Fig 4:411, par[0030]: The disruptive joints, as identified in EM log 401 and thickness map 402 can be corrected according to implementations of the present disclosure. EM log 411 and thickness map 412 in panel 410 show results for the same casing after spotting those unique joints and applying the proper corrections in post data processing. ).
One of ordinary skill in the art would be aware of the Donderici and the Eltaher references since both pertain to the field of downhole inductive coupling systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Donderici to implement the correction feature as disclosed by Eltaher to gain the functionality of providing a system and technology to improve the detection of metal loss in casing pipes placed in, e.g., downhole completed well environments, and automatically differentiating features that are attributed to an actual casing pipe metal loss from those attributed to other downhole environmental factors (e.g., change in casing pipe properties or downhole completion effects), where both would induce a similar effect on the electromagnetic raw data used for automated metal loss calculation.
Regarding claim 19, Donderici in view of Eltaher discloses the system of claim 18, wherein the EM logging tool operates in time-domain or frequency domain (Donderici par[0027], [0038]: The EM logging tools may use pulse eddy current (time-domain) and may employ multiple (long, short, and transversal) coils to evaluate multiple types of defects in double pipes. It should be noted that the techniques utilized in time-domain may be utilized in frequency-domain measurements. EM logging tool 100 may use any suitable EM technique based on Eddy current (“EC”) for inspection of concentric pipes (e.g., casing string 108 and pipe string 138). EC techniques may be particularly suited for characterization of a multi-string arrangement in which concentric pipes are used. EC techniques may include, but are not limited to, frequency-domain EC techniques and time-domain EC techniques.).
Regarding claim 20, Donderici in view of Eltaher discloses the system of claim 18, wherein the EM logging tool comprises at least one transmitter coil (Donderici fig 1:102; par[0035]: transmitter 102 may be a coil implemented for transmission of magnetic field while also measuring EM fields ) and at least one receiver coil (Donderici fig 1:104; par[0030]: EM logging tool 100 may include a transmitter 102 and receiver 104 that are in the form of coils or solenoids coaxially positioned within a downhole tubular (e.g., casing string 108) and separated along the tool axis).
2. Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Donderici in view of Eltaher, and further in view of Hall-Thompson et al. (US2024/0220678A1) hereafter Hall-Thompson.
Regarding claim 13, Donderici in view of Eltaher does not explicitly disclose the method wherein a Monte Carlo algorithm is used to estimate the one or more pseudo attributes or the one or more model-based attributes for different regularization parameters of measurement weights or random noise.
Hall-Thompson discloses the method wherein a Monte Carlo algorithm is used to estimate the one or more pseudo attributes or the one or more model-based attributes for different regularization parameters of measurement weights or random noise (par[0032], [0033]: Table 1; the one or more stochastic models 106 can execute a Monte Carlo algorithm to quantify the uncertainty based on the Monte Carlo methodology, which involves the use of random sampling and statistical analysis to estimate the probability of different outcomes. For example, the system 100 can utilize the stochastic models 106 to analyze the uncertainty and/or variability of various parameters (e.g., geological, petrophysical, and/or fluid properties) of models characterizing the subsurface reservoir. By executing the Monte Carlo algorithm, the one or more stochastic models 106 can generate a large number of possible realizations of the reservoir via probability distributions of the input parameters, and then simulate the behavior of the reservoir under each realization; thereby enabling the one or more stochastic models 106 to estimate the range of possible outcomes and the likelihood of different outcomes occurring. Table 1, presented below, includes example Monte Carlo simulation (“MCS”) parameters that can be considered by the one or more stochastic models 106 in accordance with one or more embodiments described herein.).
One of ordinary skill in the art would be aware of the Donderici, Eltaher and Hall-Thompson references since all pertain to the field of downhole inductive coupling systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Donderici to implement the Monte Carlo algorithm feature as disclosed by Hall-Thompson to gain the functionality of providing the ability to handle complex problems with many variables, model uncertainty, and providing probabilistic results by excelling at scenario analysis, sensitivity analysis, and visualizing potential outcomes, making them valuable for decision-making and risk assessment.
3. Claim(s) 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Donderici in view of Eltaher, and further in view of Fouda et al. (US2021/0054731A1) hereafter Fouda.
Regarding claim 15, Donderici in view of Eltaher does not explicitly disclose the method wherein the comparison comprises a cross-covariance between individual pipe thicknesses or metal loss, misfit of a cost function, difference between model-based attributes and pseudo attributes.
Fouda discloses the method wherein the comparison comprises a cross-covariance between individual pipe thicknesses or metal loss, misfit of a cost function, difference between model-based attributes and pseudo attributes (par[0042] with Table 1, par[0043]: FIG. 4 illustrates a flow chart 400 for adaptive adjustment of a vector comprising regularization parameters. The choice of the optimum regularization weights (parameters), which may determine the weight of the regularization term in the cost function with respect to the misfit term, may be automated using flow chart 400 as disclosed in FIG. 4. As mentioned above, the last term in Equation (6) or Equation (8) is defined as the regularization term, and the weights W.sub.x are defined as regularization parameters. Regularization may be a common optimization technique for dealing with ill-posedness (or extreme sensitivity to errors in measurement) which may often be encountered in practical applications. In examples, the optimum regularization weights may be chosen by the minimization of the negative correlation (or mirroring) between curves of various pipe properties. Mirroring may point to ill-posedness in the problem, which may be remediated by regularization. The optimum regularization parameter may be the one that minimizes the mirroring between thickness curves).
One of ordinary skill in the art would be aware of the Donderici, Eltaher and Fouda references since all pertain to the field of downhole inductive coupling systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Donderici to implement the cross-covariance algorithm feature as disclosed by Fouda to gain the functionality of identifying relationships between variables of the model-based attributes and pseudo attributes, particularly in time series analysis, also useful for feature selection and dimensionality reduction in machine learning, and providing a comprehensive view of interactions within and between the model-based attributes, extending beyond basic covariance to analyze multiple pseudo attributes variables simultaneously.
Regarding claim 16, Donderici in view of Eltaher and Fouda discloses the method of claim 15, wherein acceptable ranges for the comparison are determined through statistical analysis (Fouda fig 4:416; par[0045]: Step 416 may determine whether or not the objective functional has fallen below a threshold (e.g., 0.01) or if the maximum number of iterations has been reached. If the objective functional has fallen below a threshold or the maximum number of iterations has been reached, a concluding step 418 that identifies the final regularization parameters vector and quality indicators may end the course of flow chart 400. If the objective functional has not fallen below a threshold or the maximum number of iterations has not been reached, step 420 may occur. Step 420 may update the cost functional parameters (e.g., the regularization parameters vector). The updated cost functional parameters from step 420 may be fed back to step 406 wherein another inversion cost functional may be constructed and inversion run again, forming a feedback loop. In this example, in addition to the inputs from step 408, step 410, and step 412, the updated cost functional parameters of step 420 may be implemented in the construction of another inversion cost functional. The process may repeat a plurality of times until the logical decision in step 416 is satisfied to lead to concluding step 418. In step 418 a final cost functional parameters and quality indicators may be determined. The cost functional parameters thus optimized may then be inserted into the cost functional of Equation (6) or Equation (8) for subsequent estimation of the unknown vector (x), which may represent an unknown set of pipe thicknesses, pipe permeabilities, pipe conductivities, pipe inner or outer diameters, or pipe eccentricities ).
Regarding claim 17, Donderici in view of Eltaher and Fouda discloses the method of claim 16, further comprising adjusting regularization parameters of the model-based inversion if the comparison is outside of an acceptable range (Fouda fig 4:420; par[0045]: Step 416 may determine whether or not the objective functional has fallen below a threshold (e.g., 0.01) or if the maximum number of iterations has been reached. If the objective functional has fallen below a threshold or the maximum number of iterations has been reached, a concluding step 418 that identifies the final regularization parameters vector and quality indicators may end the course of flow chart 400. If the objective functional has not fallen below a threshold or the maximum number of iterations has not been reached, step 420 may occur. Step 420 may update the cost functional parameters (e.g., the regularization parameters vector). The updated cost functional parameters from step 420 may be fed back to step 406 wherein another inversion cost functional may be constructed and inversion run again, forming a feedback loop. In this example, in addition to the inputs from step 408, step 410, and step 412, the updated cost functional parameters of step 420 may be implemented in the construction of another inversion cost functional. The process may repeat a plurality of times until the logical decision in step 416 is satisfied to lead to concluding step 418. In step 418 a final cost functional parameters and quality indicators may be determined. The cost functional parameters thus optimized may then be inserted into the cost functional of Equation (6) or Equation (8) for subsequent estimation of the unknown vector (x), which may represent an unknown set of pipe thicknesses, pipe permeabilities, pipe conductivities, pipe inner or outer diameters, or pipe eccentricities).
Conclusion
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/AMINE BENLAGSIR/Primary Examiner, Art Unit 2687
