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 06/22/2026.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-5 and 7-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Examiner clarifies that the term “approximation” that is used in the new amendments to claims 1 and 18 is a broad term, wherein the Examiner is using the broadest reasonable interpretation regarding the rejection of the claims.
In response to Applicant’s arguments that claims 1 and 18 are eligible subject matter. Examiner respectfully disagrees. Claims 1 and 18 recite the steps of collecting measurements at one or more depths; creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log; wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes, are directed to abstract idea for the reason that these steps are an act of evaluating information that can be practically performed in the human mind, and found by the courts to be abstract ideas related to a mental process grouping, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). Further, claims 1 and 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites the step of collecting data and implementing these data to a generic computer that create a log using a machine learning model with the regression technique, wherein the steps of computing one or more pseudo attributes, computing one or more model-based attributes with a model-based inversion and determining a remedial operation based at least on the one or more pseudo attributes and/or the model based attributes, that can be practically performed by inputting data such as in the case of the values of the parameters to an algorithm or a machine learning model in a computer that is merely being used as a tool to perform the recited abstract idea (i.e., “apply it”), and generating the data by the generic computer such as a calculator. Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking) component cannot provide an inventive concept. The claim is not patent eligible. Therefore, Examiner maintains his 101 rejection.
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.
1. Claims 1-5 and 7-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1:
Claim 1 is directed to idea of itself (abstract idea) without significantly more for the following reason(s):
Step 1: Claim 1 recites series of steps for creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log, wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes. Thus, the claim is directed to a method, which is one of the statutory categories of the invention.
Step 2A prong 1, the claimed collecting measurements at one or more depths; creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log, wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes, are directed to abstract idea for the reason that these steps are processes found by the courts to be abstract ideas in that related to a mental process grouping “collecting information, analyzing it, and determining certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); That is, nothing in the claim element precludes the steps from practically being performed in the mind. The claim recites the step of collecting measurements at one or more depths; creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log, wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes, which is an act of evaluating information that can be practically performed in the human mind. Thus, this step is an abstract idea in the “mental process” grouping. Accordingly, the claim recites an abstract idea.
Step 2A prong 2, The Judicial exception is not integrated into a practical application. Treating claim 1 as a whole, the claim limitations do not show inventive concept in applying the judicial exception (e.g., The collection, processing and determination of data may be accurately identified without relying on a system for collecting measurements at one or more depths; creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log, wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes. From the claim scope, the claim fail to address this improvement because merely acquiring the features of collecting measurements at one or more depths; creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log, wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes is not enough to tie the claim towards the technical improvement. Thus, claim 1 as a whole is not significantly more than the abstract idea itself and is ineligible.
Step 2B, The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites the step of creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log, wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes, that can be practically performed by inputting data such as in the case of the values of the parameters to an algorithm or a machine learning model in a computer that is merely being used as a tool to perform the recited abstract idea (i.e., “apply it”), and generating the data by the generic computer such as a calculator. Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking) component cannot provide an inventive concept. The claim is not patent eligible.
Regarding dependent claims 2-5 and 7-17.
Dependent claims 2-17, The Judicial exception is not integrated into a practical application and said claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the claims are not patent eligible.
Regarding claim 18:
Claim 18 is directed to idea of itself (abstract idea) without significantly more for the following reason(s):
Step 1: Claim 1 recites series of steps for creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log, wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes. Thus, the claim is directed to a method, which is one of the statutory categories of the invention.
Step 2A prong 1, the claimed collecting measurements at one or more depths; creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log, wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes, are directed to abstract idea for the reason that these steps are processes found by the courts to be abstract ideas in that related to a mental process grouping “collecting information, analyzing it, and determining certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); That is, nothing in the claim element precludes the steps from practically being performed in the mind. The claim recites the step of collecting measurements at one or more depths; creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log, wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes, which is an act of evaluating information that can be practically performed in the human mind. Thus, this step is an abstract idea in the “mental process” grouping. Accordingly, the claim recites an abstract idea.
Step 2A prong 2, The Judicial exception is not integrated into a practical application. Treating claim 1 as a whole, the claim limitations do not show inventive concept in applying the judicial exception (e.g., The collection, processing and determination of data may be accurately identified without relying on a system for collecting measurements at one or more depths; creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log, wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes. From the claim scope, the claim fail to address this improvement because merely acquiring the features of collecting measurements at one or more depths; creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log, wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes is not enough to tie the claim towards the technical improvement. Thus, claim 1 as a whole is not significantly more than the abstract idea itself and is ineligible.
Step 2B, The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites the step of creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log, wherein the one or more pseudo attributes are computed through regression; 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; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes, that can be practically performed by inputting data such as in the case of the values of the parameters to an algorithm or a machine learning model in a computer that is merely being used as a tool to perform the recited abstract idea (i.e., “apply it”), and generating the data by the generic computer such as a calculator. Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking) component cannot provide an inventive concept. The claim is not patent eligible.
Regarding dependent claims 19-20.
Dependent claims 19-20, The Judicial exception is not integrated into a practical application and said claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the claims are not patent eligible.
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 Ji et al. (CN102061911A) hereafter Ji, and further in view 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 stored 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.);
computing one or more model-based attributes with a model-based inversion based at least on the one or more pseudo 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: the one or more pseudo attributes are computed through regression; computing as an approximation for one or more model-based attributes; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes.
Ji discloses the method wherein the one or more pseudo attributes are computed through regression (page 1 ln 39-42; page 2 ln 1-2; page 2 ln 9-15: A downhole casing wall thickness measurement method, which is characterized by eliminating the influence of formation density and cement ring density on the calculation of casing wall thickness according to the count rate curve of the near and far detectors, and using regression method to establish casing wall thickness calculation The formula calculates the thickness of the casing wall. The method for establishing the casing wall thickness calculation formula is to establish a calculation formula using a regression method based on the near detector count rate TL and the far detector count rate PL); and computing as an approximation for one or more model-based attributes (page 1 ln 39-42; page 2 ln 1-2; page 2 ln 1-19: In the above formula, TH is the calculated wall thickness value, THST is the standard casing wall thickness value, K is the scale factor, TL is the count rate of the near detector, TLST is the average count rate value of the standard interval, and PL is the far detection PLST is the standard value of the average interval of the well interval, and △ TH is the correction amount of formation density and cement ring density when calculating the casing wall thickness. The difference △ TH between the formation density and the standard value of the casing and the calculated wall thickness is a one-time quadratic relationship, and the difference △ TH between the density of the cement slurry and the standard value of the casing and the calculated wall thickness is a one-time primary relationship, Using the regression method, the expression of △ TH is obtained: △ TH = (-0.75ρ 2 Stratum + 5.24ρ Stratum -1.54ρ Grout )-8 Among them, THST is the standard casing wall thickness value, ρ Stratum Is the formation density, ρ Grout It is the density of cement slurry. When the formation density is different, the relationship between the formation density and the difference between the standard value of the casing and the calculated value of the casing wall thickness is ΔTH, and the difference between the formation density and the standard value of the casing and the calculated value of the casing wall thickness is ΔTH The relationship is a quadratic relationship of one yuan.).
One of ordinary skill in the art would be aware of the Donderici and Ji 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 regression feature as disclosed by Ji to gain the functionality of providing the ability to quantify relationships and predict continuous outcomes, to estimate the exact mathematical impact, direction and strength, that one or more independent variables have on a dependent variable, making it highly effective for forecasting and decision-making.
Donderici in view of Ji 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, Ji 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 2, Donderici in view of Ji and 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 (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 Ji and 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) and (Ji page 1 ln 39-42; page 2 ln 1-2; page 2 ln 9-15: A downhole casing wall thickness measurement method, which is characterized by eliminating the influence of formation density and cement ring density on the calculation of casing wall thickness according to the count rate curve of the near and far detectors, and using regression method to establish casing wall thickness calculation The formula calculates the thickness of the casing wall. The method for establishing the casing wall thickness calculation formula is to establish a calculation formula using a regression method based on the near detector count rate TL and the far detector count rate PL).
Regarding claim 4, Donderici in view of Ji and 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, a 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 input sample matrix. 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 Ji and 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, a 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 input sample matrix. FIG. 6 shows an example input sample matrix. ).
Regarding claim 7, Donderici in view of Ji and Eltaher discloses the method of claim 6, 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 Ji and 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 mini mum), assumed value, conductivity limits (maximum and minimum), and/or assumed value.).
Regarding claim 9, Donderici in view of Ji and 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 mini mum), assumed value, conductivity limits (maximum and minimum), and/or assumed value.).
Regarding claim 10, Donderici in view of Ji and 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 mini mum), assumed value, conductivity limits (maximum and minimum), and/or assumed value.).
Regarding claim 11, Donderici in view of Ji and 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 mini mum), assumed value, conductivity limits (maximum and minimum), and/or assumed value.).
Regarding claim 12, Donderici in view of Ji and 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 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 Ji and 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 stored 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.); and
computing one or more model-based attributes with a model-based inversion based at least on the one or more pseudo 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: the one or more pseudo attributes are computed through regression; compute as an approximation for one or more model-based attributes; and determine a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes.
Ji discloses the system wherein the one or more pseudo attributes are computed through regression (page 1 ln 39-42; page 2 ln 1-2; page 2 ln 9-15: A downhole casing wall thickness measurement method, which is characterized by eliminating the influence of formation density and cement ring density on the calculation of casing wall thickness according to the count rate curve of the near and far detectors, and using regression method to establish casing wall thickness calculation The formula calculates the thickness of the casing wall. The method for establishing the casing wall thickness calculation formula is to establish a calculation formula using a regression method based on the near detector count rate TL and the far detector count rate PL); and computing as an approximation for one or more model-based attributes (page 1 ln 39-42; page 2 ln 1-2; page 2 ln 1-19: In the above formula, TH is the calculated wall thickness value, THST is the standard casing wall thickness value, K is the scale factor, TL is the count rate of the near detector, TLST is the average count rate value of the standard interval, and PL is the far detection PLST is the standard value of the average interval of the well interval, and △ TH is the correction amount of formation density and cement ring density when calculating the casing wall thickness. The difference △ TH between the formation density and the standard value of the casing and the calculated wall thickness is a one-time quadratic relationship, and the difference △ TH between the density of the cement slurry and the standard value of the casing and the calculated wall thickness is a one-time primary relationship, Using the regression method, the expression of △ TH is obtained: △ TH = (-0.75ρ 2 Stratum + 5.24ρ Stratum -1.54ρ Grout )-8 Among them, THST is the standard casing wall thickness value, ρ Stratum Is the formation density, ρ Grout It is the density of cement slurry. When the formation density is different, the relationship between the formation density and the difference between the standard value of the casing and the calculated value of the casing wall thickness is ΔTH, and the difference between the formation density and the standard value of the casing and the calculated value of the casing wall thickness is ΔTH The relationship is a quadratic relationship of one yuan.).
One of ordinary skill in the art would be aware of the Donderici and Ji 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 regression feature as disclosed by Ji to gain the functionality of providing the ability to quantify relationships and predict continuous outcomes, to estimate the exact mathematical impact, direction and strength, that one or more independent variables have on a dependent variable, making it highly effective for forecasting and decision-making.
Donderici in view of Ji does not explicitly disclose the system 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, Ji 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 Ji and 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 Ji and 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 Ji and Eltaher, and further in view of Hall-Thompson et al. (US2024/0220678A1) hereafter Hall-Thompson.
Regarding claim 13, Donderici in view of Ji and 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, Ji 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 Ji and Eltaher, and further in view of Fouda et al. (US2021/0054731A1) hereafter Fouda.
Regarding claim 15, Donderici in view of Ji and 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, Ji 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 Ji, 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 Ji, 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).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
1. Claims 1-5, 7-12, 14 and 18-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over copending Application No. 18740920 herein Co-1, in view of Donderici et al. (US2020/0309986A1) hereafter Donderici, in view of Ji et al. (CN102061911A) hereafter Ji, and further in view Eltaher et al. (US2024/0412044A1).
This is a provisional nonstatutory double patenting rejection.
Instant Application # 18524491
Pending Application # Co-1
1. (Currently Amended) A method comprising: disposing an electromagnetic (EM) logging tool into a pipe string configured to perform measurements at one or more depths; creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string; computing one or more pseudo attributes of one or more pipes with the log; computing one or more model-based attributes with a model-based inversion based at least on the one or more pseudo attributes; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes.
1. A method for estimating metal loss of a casing wall, the method comprising: acquiring at least two electromagnetic measurement data sets that are taken at two or more different times; aligning one or more depths across the at least two electromagnetic measurement data sets; computing a thickness of a plurality of downhole pipes, which at least partially overlap, based upon an applied inversion algorithm to each of the at least two electromagnetic measurement data sets; determining a location of one or more metal loss locations based upon a change in thickness for a given one of the plurality of downhole pipes; and estimating one or more parameters of metal loss based upon the applied inversion algorithm.
14. The method of claim 1, wherein the applied inversion algorithm is a model-based inversion algorithm.
15. The method of claim 14, wherein the model-based inversion algorithm calculates at least one unknown material property at a given de
2. (Original) 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.
10. The method of claim 9, further comprising generating a pseudo-thickness of each pipe of the multiple pipes using at least one algorithm.
4. (Original) 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.
6. The method of claim 5, wherein the depth aligning includes performing a comparison of the at least two electromagnetic measurement data sets using a machine learning model to estimate a shift between the at least two electromagnetic measurement data sets.
18. (Currently Amended) A system comprising: an electromagnetic (EM) logging tool into a pipe string configured to perform measurements at one or more depths; 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; compute one or more pseudo attributes of one or more pipes with the log; compute one or more model-based attributes with a model-based inversion based at least on the one or more pseudo attributes; and determine a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes.
17. A metal loss calculation system comprising: a tool having a plurality of receivers and at least one transmitter; a calculation unit including at least one processor and at least one storage device that stores instructions to cause the processor to: acquire at least two electromagnetic measurement data sets that are taken at two or more different times; align one or more depths across the at least two electromagnetic measurement data sets; compute a thickness of a plurality of downhole pipes, which at least partially overlap, based upon an applied inversion algorithm to each of the at least two electromagnetic measurement data sets; determine a location of one or more metal loss locations based upon a change in thickness for a given one of the plurality of downhole pipes; and estimate one or more parameters of metal loss based upon the applied inversion algorithm.
19. (Original) The system of claim 18, wherein the EM logging tool operates in time-domain or frequency domain.
17. A metal loss calculation system comprising: a tool having a plurality of receivers and at least one transmitter; a calculation unit including at least one processor and at least one storage device that stores instructions to cause the processor to: acquire at least two electromagnetic measurement data sets that are taken at two or more different times…..
20. (Original) The system of claim 18, wherein the EM logging tool comprises at least one transmitter coil and at least one receiver coil.
17. A metal loss calculation system comprising: a tool having a plurality of receivers and at least one transmitter; a calculation unit including at least one processor and at least one storage device that stores instructions to cause the processor to: acquire at least two electromagnetic measurement data sets that are taken at two or more different times…
Regarding claims 1 and 18, Co-1 does not explicitly disclose a method wherein the one or more pseudo attributes are computed through regression; and computing as an approximation for one or more model-based attributes; and the method comprising: computing one or more model-based attributes with a model-based inversion based at least on the one or more pseudo attributes; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes.
Donderici discloses a method comprising:
computing one or more model-based attributes with a model-based inversion based at least on the one or more pseudo 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).
One of ordinary skill in the art would be aware of both the Co-1 and Donderici 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 model-based attributes feature as disclosed by Donderici to gain the functionality of identifying artifacts with an electromagnetic logging tool in an eccentric pipe configuration comprising a plurality of pipes.
Co-1 in view of Donderici does not explicitly disclose the method comprising: wherein the one or more pseudo attributes are computed through regression; and computing as an approximation for one or more model-based attributes; and determining a remedial operation based at least on the one or more pseudo attributes and/or the model-based attributes.
Ji discloses the method wherein the one or more pseudo attributes are computed through regression (page 1 ln 39-42; page 2 ln 1-2; page 2 ln 9-15: A downhole casing wall thickness measurement method, which is characterized by eliminating the influence of formation density and cement ring density on the calculation of casing wall thickness according to the count rate curve of the near and far detectors, and using regression method to establish casing wall thickness calculation The formula calculates the thickness of the casing wall. The method for establishing the casing wall thickness calculation formula is to establish a calculation formula using a regression method based on the near detector count rate TL and the far detector count rate PL); and computing as an approximation for one or more model-based attributes (page 1 ln 39-42; page 2 ln 1-2; page 2 ln 1-19: In the above formula, TH is the calculated wall thickness value, THST is the standard casing wall thickness value, K is the scale factor, TL is the count rate of the near detector, TLST is the average count rate value of the standard interval, and PL is the far detection PLST is the standard value of the average interval of the well interval, and △ TH is the correction amount of formation density and cement ring density when calculating the casing wall thickness. The difference △ TH between the formation density and the standard value of the casing and the calculated wall thickness is a one-time quadratic relationship, and the difference △ TH between the density of the cement slurry and the standard value of the casing and the calculated wall thickness is a one-time primary relationship, Using the regression method, the expression of △ TH is obtained: △ TH = (-0.75ρ 2 Stratum + 5.24ρ Stratum -1.54ρ Grout )-8 Among them, THST is the standard casing wall thickness value, ρ Stratum Is the formation density, ρ Grout It is the density of cement slurry. When the formation density is different, the relationship between the formation density and the difference between the standard value of the casing and the calculated value of the casing wall thickness is ΔTH, and the difference between the formation density and the standard value of the casing and the calculated value of the casing wall thickness is ΔTH The relationship is a quadratic relationship of one yuan.).
One of ordinary skill in the art would be aware of the Co-1, Donderici and Ji 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 Co-1 to implement the regression feature as disclosed by Ji to gain the functionality of providing the ability to quantify relationships and predict continuous outcomes, to estimate the exact mathematical impact, direction and strength, that one or more independent variables have on a dependent variable, making it highly effective for forecasting and decision-making.
Co-1 in view of Donderici and Ji 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 Co-1, Donderici, Ji 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 2, Co-1 in view of Donderici, Ji and 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 (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, Co-1 in view of Donderici, Ji and 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, Co-1 in view of Donderici, Ji and 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 (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, a 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 input sample matrix. 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, Co-1 in view of Donderici, Ji and 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, a 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 input sample matrix. FIG. 6 shows an example input sample matrix. ).
Regarding claim 7, Co-1 in view of Donderici, Ji and 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, Co-1 in view of Donderici, Ji and 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 mini mum), assumed value, conductivity limits (maximum and minimum), and/or assumed value.).
Regarding claim 9, Co-1 in view of Donderici, Ji and 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 mini mum), assumed value, conductivity limits (maximum and minimum), and/or assumed value.).
Regarding claim 10, Co-1 in view of Donderici, Ji and Eltaher discloses the method 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 mini mum), assumed value, conductivity limits (maximum and minimum), and/or assumed value.).
Regarding claim 11, Co-1 in view of Donderici, Ji and 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 mini mum), assumed value, conductivity limits (maximum and minimum), and/or assumed value.).
Regarding claim 12, Co-1 in view of Donderici, Ji and Eltaher discloses the method 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 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, Co-1 in view of Donderici, Ji and Eltaher discloses the method 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 19, Co-1 in view of Donderici, Ji and 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, Co-1 in view of Donderici, Ji and 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 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Co-1 in view of Donderici, Ji and Eltaher, and further in view of Hall-Thompson et al. (US2024/0220678A1) hereafter Hall-Thompson.
Regarding claim 13, Co-1 in view of Donderici, Ji and 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 Co-1, Donderici, Eltaher, Ji 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 Co-1 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 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Co-1 in view of Donderici, Ji and Eltaher, and further in view of Fouda et al. (US2021/0054731A1) hereafter Fouda.
Regarding claim 15, Co-1 in view of Donderici, Ji and 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 Co-1, Donderici, Eltaher, Ji 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 Co-1 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, Co-1 in view of Donderici, Ji, 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, Co-1 in view of Donderici, Ji, 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMINE BENLAGSIR whose telephone number is (571)270-5165. The examiner can normally be reached (571)270-5165.
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/AMINE BENLAGSIR/Primary Examiner, Art Unit 2687
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