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 .
Information Disclosure Statement
The Information Disclosure Statements filed on 08/22/2025 and 10/28/2025 have been acknowledged and considered by examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 11 are rejected for the following reasons:
Claims 1 and 11 recite the limitation “producing an eddy current in the one or more nested pipes”. The claims initially introduce the limitation of transmitting an EM field into “two or more nested pipes”. However, the subsequent limitation recites “producing an eddy current in the one or more nested pipes”. Because the numerical scope changes from “two or more” to “one or more”, it is unclear whether “the one or more nested pipes” refers to the same previously introduced “two or more nested pipes”, a subset of those pipes, or a different set of pipes. There is insufficient antecedent basis for this limitation in the claims. For the purposes of examination and the prior art rejections applied below, the Examiner interprets the phrase “the one or more nested pipes” refers back to the initially introduced “two or more nested pipes”.
Claims 1 and 11 recite the limitation “different types of structural irregular zone”, the subsequent noun must be pluralized to provide a clear antecedent basis for the subsequent lists and determinations. A possible correction would be to amend the claims to recite “different types of structural irregular zone[s]”.
Claims 6 and 16 recite the limitation “the signal”. The independent claims and parent claims recite “a plurality of measurements” and “an EM log” but never define a “signal”, rendering the scope of the data being mapped ambiguous. There is insufficient antecedent basis for this limitation in the claims.
Claims 2-5, 7-15, and 17-20 are rejected for being dependent on a rejected claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Amineh et al. (US 2019/0064117) in view of Nair et al. (US 2024/0393210).
With regards to claim 1, Amineh et al. teaches a method comprising: disposing an electromagnetic (EM) logging tool (“EM logging tool 100”) in a wellbore, wherein the EM logging tool comprises: one or more transmitters (“transmitter 102”) disposed on the EM logging tool; and one or more receivers (“receivers 104”) disposed on the EM logging tool (“EM logging tool 100 may comprise transmitter 102 and receivers 104… EM logging tool 100 may extend within inner pipe 108 and outer pipe 110 to a desired depth within the wellbore 112” [0015]);
transmitting a first EM field from the one or more transmitters into two or more nested pipes to energize the two or more nested pipes (“Transmitter 102 may transmit magnetic fields into subterranean formation 124…” [0018]) with the first EM field thereby producing an eddy current in the one or more nested pipes (“The primary magnetic fields may produce Eddy currents in the inner pipe 108 and the outer pipe 110” [0018]);
measuring a second EM field generated by the eddy current in the two or more nested pipes with the one or more receivers to form a plurality of measurements (“secondary magnetic fields that may be sensed along with the primary magnetic fields by the receivers 104” [0018]);
forming an EM log from the plurality of measurements (“EM log data corresponding to different concentric pipes is collected” [0013]-[0014]);
While Amineh et al. teaches utilizing spectral analysis on the EM log to identify structural anomalies ([0030]), Amineh et al. does not teach differentiating an EM log distortion into different types of structural irregular zone comprising eccentricity, deformation, localized corrosion, and/or a combination thereof by utilizing a spectral analysis on the EM log; and determining the structural irregular zone type and severity qualitatively with magnitude and orientation of the EM log distortion.
However, Nair et al. teaches differentiating an EM log distortion into different types of structural irregular zone comprising eccentricity, deformation, localized corrosion, and/or a combination thereof (“analyzing the operational data using a plurality of signal processing pipelines… the one or more features from the response are classified into one or more failure modes using a classification model.” [0009]) by utilizing a spectral analysis on the EM log (“signal processing functions may include… Fast Fourier Transform (FFT)…” ([0055]) to differentiate “failure modes such as corrosion, spalling, pitting, electrical erosion, plastic deformation and so on” ([0003]) and “defects may be due to misalignment, eccentricity, loose foot, contamination, lubrication etc.” ([0046])) [0003], [0009], [0046], and [0055]); and
determining the structural irregular zone type and severity qualitatively with magnitude and orientation of the EM log distortion (“Each of the faults is associated with one or more characteristic frequencies… For example, misalignments… may be indicated by peak amplitudes at 3× frequencies in a Fast Fourier Transform” [0046]-[0047]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the EM log processing of Amineh et al. to include the spectral classification as taught by Nair et al. to differentiate an EM log distortion into different types of structural irregular zone comprising eccentricity, deformation, localized corrosion, and/or a combination thereof by utilizing a spectral analysis on the EM log; and determine the structural irregular zone type and severity qualitatively with magnitude and orientation of the EM log distortion to enable “determination of multiple features associated with the operational data” to make “accurate predictions of... failures” and “prevent unexpected downtimes” (Nair et al. [0003] and [0010]).
With regards to claim 2, Amineh et al. as modified by Nair et al. teaches the method of claim 1. Amineh et al. further teaches wherein the EM log comprises at least one receiver's data at multiple frequencies, multiple receivers' data at a single frequency, or multiple receivers' data at multiple frequencies (“EM logging tool 100 may include more than one transmitter 102 and more or less than six of the receivers 104… a single transmitter 102 may transmit, for example, a multi-frequency signal” [0020]).
With regards to claim 3, Amineh et al. as modified by Nair et al. teaches the method of claim 2. Amineh et al. further teaches further comprising applying depth alignment for multiple receivers (“the receivers 104 are aligned at any certain depths…” [0027]).
With regards to claim 4, Amineh et al. as modified by Nair et al. teaches the method of claim 3. Amineh et al. further teaches further comprising applying baseline removal for multiple receivers (“a high pass filter may be used to remove the thickness baseline of raw responses” [0043]).
With regards to claim 5, Amineh et al. as modified by Nair et al. teaches the method of claim 1. Nair et al. further teaches further comprising performing a radial one dimensional inversion, data mapping to the EM log, or a machine learning based method (“the classification model is a convolutional neural network” [0065]) to acquire the structural irregular zone type and severity quantitatively (“the one or more features are classified into one or more failure modes using a classification model” [0065]).
With regards to claim 6, Amineh et al. as modified by Nair et al. teaches the method of claim 5. Nair et al. further teaches wherein the data mapping comprises: building a database for different severity of different types of deformation for different signals (“a trained classification model that classifies the defect size into one of a plurality of severity levels…” [0075]); and mapping the signal in the database to find the best severity of the deformation (“lookup table may comprise values of contamination factor corresponding to each of the severity levels” [0075]).
With regards to claim 7, Amineh et al. as modified by Nair et al. teaches the method of claim 1. Amineh et al. further teaches wherein the one or more receivers are placed at different spacings away from the one or more transmitters to achieve different depths of penetration (“the receivers 104 may be positioned on the corrosion detection tool 100 at selected distances (axial spacing) away from the transmitter 102” [0020]).
With regards to claim 8, Amineh et al. as modified by Nair et al. teaches the method of claim 1. Nair et al. further teaches wherein the structural irregular zone type is caused by a collapse, dent, burst, or ridges (“failure modes such as corrosion, spalling, pitting, electrical erosion, plastic deformation and so on” [0003]).
With regards to claim 9, Amineh et al. as modified by Nair et al. teaches the method of claim 1. Amineh et al. further teaches wherein the EM log distortion is an increase or decrease in the second EM field generated by the eddy current in the two or more nested pipes (“the cost function in the inversion algorithm can be modified to operate based on the differential responses (responses of the defected sections minus responses of the non-defected sections)” [0034]. The calculation of a differential response inherently teaches detecting an increase or decrease because subtracting the baseline (non-defected) signal from the anomaly (defected) signal necessarily results in a positive or negative mathematical delta.).
With regards to claim 10, Amineh et al. as modified by Nair et al. teaches the method of claim 1. Nair et al. further teaches wherein the spectral analysis on the EM log is applied on the whole log or in a sliding window (“The signal processing functions may include… Short-time Fourier transform (STFT)…” [0055]. The use of an STFT inherently teaches applying spectral analysis in a sliding window because an STFT is computed by dividing a longer signal into shorter segments of equal length and computing the Fourier transform separately on each segment, which requires applying a sliding window across the data.).
With regards to claim 11, Amineh et al. teaches a system comprising: an electromagnetic (EM) logging tool (“EM logging tool 100”) disposed in a wellbore, wherein the EM logging tool comprises: one or more transmitters (“transmitter 102”) disposed on the EM logging tool (“EM logging tool 100 may comprise transmitter 102… EM logging tool 100 may extend within inner pipe 108 and outer pipe 110 to a desired depth within the wellbore 112…” [0015])
configured for transmitting a first EM field from the one or more transmitters into two or more nested pipes to energize the two or more nested pipes (“Transmitter 102 may transmit magnetic fields into subterranean formation 124…” [0018]) with the first EM field thereby producing an eddy current in the one or more nested pipes (“The primary magnetic fields may produce Eddy currents in the inner pipe 108 and the outer pipe 110” [0018]);
and one or more receivers disposed on the EM logging tool configured for measuring a second EM field generated by the eddy current in the two or more nested pipes with the one or more receivers to form a plurality of measurements (“These Eddy currents, in turn, produce secondary magnetic fields that may be sensed along with the primary magnetic fields by the receivers 104” [0018]);
and an information handling system (“The information handling system 126”) configured for: forming an EM log from the plurality of measurements (“The processing of EM log data… to derive attributes… for a pipe as a function of depth… The information handling system 126… may process EM log data by executing software or instructions…” [0013] and [0015]);
While Amineh et al. teaches utilizing spectral analysis on the EM log to identify structural anomalies ([0030]), Amineh et al. does not teach differentiating an EM log distortion into different types of structural irregular zone comprising eccentricity, deformation, localized corrosion, and/or a combination thereof by utilizing a spectral analysis on the EM log; and determining the structural irregular zone type and severity qualitatively with magnitude and orientation of the EM log distortion.
However, Nair et al. teaches differentiating an EM log distortion into different types of structural irregular zone comprising eccentricity, deformation, localized corrosion, and/or a combination thereof (“analyzing the operational data using a plurality of signal processing pipelines… the one or more features from the response are classified into one or more failure modes using a classification model.” [0009]) by utilizing a spectral analysis on the EM log (“signal processing functions may include… Fast Fourier Transform (FFT)…” ([0055]) to differentiate “failure modes such as corrosion, spalling, pitting, electrical erosion, plastic deformation and so on” ([0003]) and “defects may be due to misalignment, eccentricity, loose foot, contamination, lubrication etc.” [0003], [0009], [0046], and [0055]); and
determining the structural irregular zone type and severity qualitatively with magnitude and orientation of the EM log distortion (“Each of the faults is associated with one or more characteristic frequencies… For example, misalignments… may be indicated by peak amplitudes at 3× frequencies in a Fast Fourier Transform” [0046]-[0047]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the EM log processing of Amineh et al. to include the spectral classification as taught by Nair et al. to differentiate an EM log distortion into different types of structural irregular zone comprising eccentricity, deformation, localized corrosion, and/or a combination thereof by utilizing a spectral analysis on the EM log; and determine the structural irregular zone type and severity qualitatively with magnitude and orientation of the EM log distortion to enable “determination of multiple features associated with the operational data” to make “accurate predictions of... failures” and “prevent unexpected downtimes” (Nair et al. [0003] and [0010]).
With regards to claim 12, Amineh et al. as modified by Nair et al. teaches the method of claim 11. Amineh et al. further teaches wherein the EM log comprises at least one receiver's data at multiple frequencies, multiple receivers' data at a single frequency, or multiple receivers' data at multiple frequencies (“EM logging tool 100 may include more than one transmitter 102 and more or less than six of the receivers 104… a single transmitter 102 may transmit, for example, a multi-frequency signal” [0020]).
With regards to claim 13, Amineh et al. as modified by Nair et al. teaches the method of claim 12. Amineh et al. further teaches wherein the information handling system is further configured for applying depth alignment for multiple receivers (“the receivers 104 are aligned at any certain depths…” [0027]).
With regards to claim 14, Amineh et al. as modified by Nair et al. teaches the method of claim 13. Amineh et al. further teaches wherein the information handling system is further configured for applying baseline removal for multiple receivers (“a high pass filter may be used to remove the thickness baseline of raw responses” [0043]).
With regards to claim 15, Amineh et al. as modified by Nair et al. teaches the method of claim 11. Nair et al. further teaches wherein the information handling system is further configured for performing a radial one dimensional inversion, data mapping to the EM log, or a machine learning based method (“the classification model is a convolutional neural network” [0065]) to acquire the structural irregular zone type and severity quantitatively (“the one or more features are classified into one or more failure modes using a classification model” [0065]).
With regards to claim 16, Amineh et al. as modified by Nair et al. teaches the method of claim 15. Nair et al. further teaches wherein the data mapping comprises: building a database for different severity of different types of deformation for different signals (“a trained classification model that classifies the defect size into one of a plurality of severity levels…” [0075]); and mapping the signal in the database to find the best severity of the deformation (“…lookup table may comprise values of contamination factor corresponding to each of the severity levels” [0075]).
With regards to claim 17, Amineh et al. as modified by Nair et al. teaches the method of claim 11. Amineh et al. further teaches wherein the one or more receivers are placed at different spacings away from the one or more transmitters to achieve different depths of penetration (“the receivers 104 may be positioned on the corrosion detection tool 100 at selected distances (axial spacing) away from the transmitter 102” [0020]).
With regards to claim 18, Amineh et al. as modified by Nair et al. teaches the method of claim 11. Nair et al. further teaches wherein the structural irregular zone type is caused by a collapse, dent, burst, or ridges (“failure modes such as corrosion, spalling, pitting, electrical erosion, plastic deformation and so on” [0003]).
With regards to claim 19, Amineh et al. as modified by Nair et al. teaches the method of claim 11. Amineh et al. further teaches wherein the EM log distortion is an increase or decrease in the second EM field generated by the eddy current in the two or more nested pipes (“the cost function in the inversion algorithm can be modified to operate based on the differential responses (responses of the defected sections minus responses of the non-defected sections)” [0034]. The calculation of a differential response inherently teaches detecting an increase or decrease because subtracting the baseline (non-defected) signal from the anomaly (defected) signal necessarily results in a positive or negative mathematical delta.).
With regards to claim 20, Amineh et al. as modified by Nair et al. teaches the method of claim 11. Nair et al. further teaches wherein the spectral analysis on the EM log is applied on the whole log or in a sliding window (“The signal processing functions may include… Short-time Fourier transform (STFT)…” [0055]. The use of an STFT inherently teaches applying spectral analysis in a sliding window because an STFT is computed by dividing a longer signal into shorter segments of equal length and computing the Fourier transform separately on each segment, which requires applying a sliding window across the data.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OSAMAH MURSHED whose telephone number is (571)272-9534. The examiner can normally be reached Monday - Friday, 11 a.m. 8 p.m. ET..
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/OSAMAH MURSHED/ Examiner, Art Unit 2858
/JUDY NGUYEN/ Supervisory Patent Examiner, Art Unit 2858
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