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 .
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 1-9 and 16-20 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 16 recite, at the second “receiving” element, the term “a prior a parameter.” This term is unclear in scope because the instant Specification gives no explanation regarding the use of this term and, even if it were a typographical error and “a prior parameter” was intended, this would leave it further unclear as to how this term would differ from the previously recited “a prior parameter” (i.e., perhaps this term would need to be corrected to “the prior parameter).
Claims 1 and 16 recite, at the second “outputting” element, the phrase “outputting, from the collar identifier, a collar depth and a collar identifier.” This leaves the scope of the claim unclear because the same term is used twice to refer to two different things (i.e. a functional unit and a parameter). For example, the final claim elements recite “the collar identifier” which could conceivably be referring back to either of the two “collar identifiers” and causes an antecedent basis issue. The Examiner proposes changing the second “collar identifier” to “collar identification” (including for the final claim element as well) for the purposes of clarity.
The remaining claims are rejected based on their dependency from Claims 1 and 16.
Claims 17-20 are rejected because they depend from “the system” of Claim 15. The recited system lacks antecedent basis. The Examiner is treating these claims as meaning to depend from independent system claim 16.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the abstract idea of a mathematical and/or mental activity algorithm for determining collar depth in a wellbore using tool measurements.
This judicial exception is not integrated into a practical application because no improvement to the underlying wellbore or the operation of the measurement tool are realized through the performance of the algorithm. Although Claim 9, for example, recites to “automate depth control,” this recitation is generic and non-specific and amounts to the recitation to “use it” with regards to the recitation of the abstract idea. The “automation” of Claim 16 could amount to merely implementing the abstract idea through use of a general-purpose computer.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recited data receiving, data outputting, computer-readable medium, processor, and machine learning amounts to the recitation of general-purpose computer elements/steps in implementing the abstract idea through use of a general-purpose computer and does not serve to amount to significantly more than the recitation of the abstract idea itself (see Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014)). The recited winch, tool string, and casing collar locator amount to the recitation of well-understood, routine, and conventional data gathering elements [See Fig. 1 and associated text of US 20230184984 A1 and Fig. 1 and associated text of US 20200284141 A1] and do not serve to amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-8, 10-12, and 14-19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Fouda et al. (US 20230184984 A1)[hereinafter “Fouda”].
Regarding Claim 1, Fouda discloses a method for tool string depth estimation [Abstract – “A method and system may include disposing an electromagnetic (EM) logging tool in a wellbore. The EM logging tool may include a transmitter disposed on the EM logging tool and a receiver disposed on the EM logging tool. The method may further include transmitting an electromagnetic field from the transmitter into one or more, measuring the eddy current in the one or more tubulars with the receiver, and forming an EM log from the plurality of measurements. Additionally, the method may include identifying a representative signature of a collar from the EM log, locating a plurality of signatures that are similar to the representative signature, iteratively locating a collar signature based at least in part on the representative signature, the plurality of signatures that are similar to the representative signature, and a joint length; and displaying a depth for each of the one or more collars.”], comprising:
receiving, at a collar detector that executes on a processor [See Fig. 1 – information handling system 144Paragraph [0032] – “information handling system 144 may be a personal computer, a network storage device, or any other suitable device”], a casing collar locator ("CCL") signal, a depth stamp, and a prior parameter [See the operation of EM logging tool 100 in Fig. 1.Paragraph [0037] – “It should be understood that while casing string 108 is illustrated as a single casing string, there may be multiple layers of concentric pipes disposed in the section of wellbore 110 with casing string 108. EM log data may be obtained in two or more sections of wellbore 110 with multiple layers of concentric pipes. For example, EM logging tool 100 may make a first measurement of pipe string 138 comprising any suitable number of joints 130 connected by collars 132. Measurements may be taken in the time-domain and/or frequency range. EM logging tool 100 may make a second measurement in a casing string 108 of first casing 134, wherein first casing 134 comprises any suitable number of pipes connected by collars 132. Measurements may be taken in the time-domain and/or frequency domain. These measurements may be repeated any number of times for first casing 134, for second casing 136, and/or any additional layers of casing string 108. In this disclosure, as discussed further below, methods may be utilized to determine the location of any number of collars 132 in casing string 108 and/or pipe string 138.”See Fig. 3, logging positions Z1-Z5 being depth stamps. Gathered measurements over time being of a prior parameter.];
outputting, from the collar detector, a collar detection depth and a collar detection probability [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”];
receiving, at a collar identifier [Fig. 9, Steps 912-916 and following steps], at least one of the outputs of the collar detector and a prior parameter [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”Paragraph [0074] – “In block 912, other collar signatures are identified that are similar to the collar signature chosen by personnel in block 910. During this operation, the reference collar locations on the pseudo-thickness logs may be used to identify similar patterns, in collar signatures, which have more than a threshold of 95% similarity with the user input collar location (i.e., collar signature).”];
outputting, from the collar identifier, a collar depth and a collar identifier [Paragraph [0074] – “In block 914, joint length for pipe string 138 and one or more casings 134, 136, and/or 402 (e.g., referring to FIG. 4) may be found utilizing the identified locations (i.e., identified collar signatures) of collars. Based on the distance between the picks, the joint length for each pipe is determined. After locating collars in blocks 912 and 914, on each string (e.g., pipe string 138 and one or more casings 134, 136, and/or 402) identifying collars is initiated from the deepest collar location on each string.”];
correlating prior information when the detection probability meets a threshold [Paragraph [0074] – “In block 912, other collar signatures are identified that are similar to the collar signature chosen by personnel in block 910. During this operation, the reference collar locations on the pseudo-thickness logs may be used to identify similar patterns, in collar signatures, which have more than a threshold of 95% similarity with the user input collar location (i.e., collar signature).”]; and
outputting a depth and depth certainty for the collar identifier [See Figs. 12A-12C, which show measurement results relative to depth (per Figs. 1 and 3).Paragraph [0078] – “Solid lines 1208 illustrate likelihood curves identified by the methods in system in FIG. 11 to identify signatures, which are collars 132 (e.g., referring to FIG. 1).”].
Regarding Claim 2, Fouda discloses that the collar identifier receives a prior casing tally as an input [Paragraph [0075] – “In block 926, the user selection is validated to check if all the desired characteristics for a casing collar locations are satisfied or not (i.e., the number of user-selected collar signatures matches the number of suggested collar signatures prompted by the algorithm).”].
Regarding Claim 3, Fouda discloses receiving the outputs of the collar detector at a feature identifier [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”Paragraph [0074] – “In block 912, other collar signatures are identified that are similar to the collar signature chosen by personnel in block 910. During this operation, the reference collar locations on the pseudo-thickness logs may be used to identify similar patterns, in collar signatures, which have more than a threshold of 95% similarity with the user input collar location (i.e., collar signature).”], wherein the feature identifier outputs a feature depth and a feature uncertainty, both of which are correlated to prior information to output the depth and depth uncertainty [Paragraph [0074] – “In block 912, other collar signatures are identified that are similar to the collar signature chosen by personnel in block 910. During this operation, the reference collar locations on the pseudo-thickness logs may be used to identify similar patterns, in collar signatures, which have more than a threshold of 95% similarity with the user input collar location (i.e., collar signature).”See Figs. 12A-12C, which show measurement results relative to depth (per Figs. 1 and 3).Paragraph [0078] – “Solid lines 1208 illustrate likelihood curves identified by the methods in system in FIG. 11 to identify signatures, which are collars 132 (e.g., referring to FIG. 1).”].
Regarding Claim 4, Fouda discloses that the collar detector organizes time series data of the CCL signal into segments [Paragraph [0037] – “It should be understood that while casing string 108 is illustrated as a single casing string, there may be multiple layers of concentric pipes disposed in the section of wellbore 110 with casing string 108. EM log data may be obtained in two or more sections of wellbore 110 with multiple layers of concentric pipes. For example, EM logging tool 100 may make a first measurement of pipe string 138 comprising any suitable number of joints 130 connected by collars 132. Measurements may be taken in the time-domain and/or frequency range. EM logging tool 100 may make a second measurement in a casing string 108 of first casing 134, wherein first casing 134 comprises any suitable number of pipes connected by collars 132. Measurements may be taken in the time-domain and/or frequency domain. These measurements may be repeated any number of times for first casing 134, for second casing 136, and/or any additional layers of casing string 108. In this disclosure, as discussed further below, methods may be utilized to determine the location of any number of collars 132 in casing string 108 and/or pipe string 138.”] that are used as inputs to a machine learning model [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102.”], wherein the collar detection probability corresponds to respective segments [See Figs. 12A-12C, which show measurement results relative to depth (per Figs. 1 and 3).Paragraph [0078] – “Solid lines 1208 illustrate likelihood curves identified by the methods in system in FIG. 11 to identify signatures, which are collars 132 (e.g., referring to FIG. 1).” See the individual curves indicating probable collar locations.].
Regarding Claim 5, Fouda discloses that the collar detector further outputs a collar detection uncertainty that is used in determining the depth certainty [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”].
Regarding Claim 6, Fouda discloses that samples of the CCL signal are stamped against depth [Fig. 11, see depth as the y-axis] and the depth stamps are used to match and identify collars [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”].
Regarding Claim 7, Fouda discloses that the collar detector uses the inputs and prior parameters to create the collar detection outputs [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”] based on segmentations of the CCL signal [Paragraph [0037] – “It should be understood that while casing string 108 is illustrated as a single casing string, there may be multiple layers of concentric pipes disposed in the section of wellbore 110 with casing string 108. EM log data may be obtained in two or more sections of wellbore 110 with multiple layers of concentric pipes. For example, EM logging tool 100 may make a first measurement of pipe string 138 comprising any suitable number of joints 130 connected by collars 132. Measurements may be taken in the time-domain and/or frequency range. EM logging tool 100 may make a second measurement in a casing string 108 of first casing 134, wherein first casing 134 comprises any suitable number of pipes connected by collars 132. Measurements may be taken in the time-domain and/or frequency domain. These measurements may be repeated any number of times for first casing 134, for second casing 136, and/or any additional layers of casing string 108. In this disclosure, as discussed further below, methods may be utilized to determine the location of any number of collars 132 in casing string 108 and/or pipe string 138.”], and wherein the collar detector executes a function to perform at least one of: Bayesian statistical filter testing; Matched Filtering; and Long Short-Term Memory [Paragraph [0069] – “In a non-limiting example, algorithms utilized for supervised learning may include Neural Networks, K-Nearest Neighbors, Naïve Bayes, Decision Trees, Classification Trees, Regression Trees, Random Forests, Linear Regression, Support Vector Machines (SVM), Gradient Boosting Regression, and Perception Back-Propagation.”].
Regarding Claim 8, Fouda discloses that the collar detector uses the inputs and prior parameters to create the collar detection outputs based on classification of the CCL signal, and wherein the collar detector includes at least one of: Wavelet Decomposition and Shallow Neural Networks; One-dimensional Convolutional Neural Networks; and Long Short-Term Memory [Paragraph [0071] – “In examples to determine a relationship using machine learning, a neural network (NN) 800, as illustrated in FIG. 8, may be utilized to locate collars on one or more pipe strings and/or casings in a well plan 400 (e.g., referring to FIG. 4). A NN 800 is an artificial neural network with one or more hidden layers 802 between input layer 804 and output layer 806. As illustrated, input layer 804 may include all extracted electromagnetic responses from EM logging tool 100 (e.g., referring to FIG. 1), and output layers 806 may include pipe information from other sources. During operations, input data is taken by neurons 812 in first layer which then provide an output to the neurons 812 within next layer and so on which provides a final output in output layer 806. Each layer may have one or more neurons 812. The connection between two neurons 812 of successive layers may have an associated weight. The weight defines the influence of the input to the output for the next neuron 812 and eventually for the overall final output. The training process of NN 800 is to locate collars on one or more pipe strings and/or casings in a well plan 400.” The disclosure of a single hidden layer discloses that the neural network is “shallow.”].
Regarding Claim 10, Fouda discloses a non-transitory, computer-readable medium containing instructions [Paragraph [0056]] for a casing collar locator ("CCL") framework for detection and depth control [Abstract – “A method and system may include disposing an electromagnetic (EM) logging tool in a wellbore. The EM logging tool may include a transmitter disposed on the EM logging tool and a receiver disposed on the EM logging tool. The method may further include transmitting an electromagnetic field from the transmitter into one or more, measuring the eddy current in the one or more tubulars with the receiver, and forming an EM log from the plurality of measurements. Additionally, the method may include identifying a representative signature of a collar from the EM log, locating a plurality of signatures that are similar to the representative signature, iteratively locating a collar signature based at least in part on the representative signature, the plurality of signatures that are similar to the representative signature, and a joint length; and displaying a depth for each of the one or more collars.”], the instructions when executed by a processor causing the processor to perform stages comprising:
receiving as inputs at least three of a CCL signal, a CCL depth, a cable speed, a timestamp, a known feature, and prior information [See the operation of EM logging tool 100 in Fig. 1.Paragraph [0037] – “It should be understood that while casing string 108 is illustrated as a single casing string, there may be multiple layers of concentric pipes disposed in the section of wellbore 110 with casing string 108. EM log data may be obtained in two or more sections of wellbore 110 with multiple layers of concentric pipes. For example, EM logging tool 100 may make a first measurement of pipe string 138 comprising any suitable number of joints 130 connected by collars 132. Measurements may be taken in the time-domain and/or frequency range. EM logging tool 100 may make a second measurement in a casing string 108 of first casing 134, wherein first casing 134 comprises any suitable number of pipes connected by collars 132. Measurements may be taken in the time-domain and/or frequency domain. These measurements may be repeated any number of times for first casing 134, for second casing 136, and/or any additional layers of casing string 108. In this disclosure, as discussed further below, methods may be utilized to determine the location of any number of collars 132 in casing string 108 and/or pipe string 138.”See Fig. 3, logging positions Z1-Z5 being depth stamps. Gathered measurements over time being of a prior parameter.], wherein the prior information includes at least one of a casing tally, a reference log, and a known feature at an approximate depth [Paragraph [0075] – “In block 926, the user selection is validated to check if all the desired characteristics for a casing collar locations are satisfied or not (i.e., the number of user-selected collar signatures matches the number of suggested collar signatures prompted by the algorithm).”];
sending the CCL signal to a trained machine learning model, the machine learning model identifying a collar depth and an identification probability [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe.”]; and
when the identification probability is above a threshold [Paragraph [0074] – “In block 912, other collar signatures are identified that are similar to the collar signature chosen by personnel in block 910. During this operation, the reference collar locations on the pseudo-thickness logs may be used to identify similar patterns, in collar signatures, which have more than a threshold of 95% similarity with the user input collar location (i.e., collar signature).”], outputting a depth and a depth uncertainty [See Figs. 12A-12C, which show measurement results relative to depth (per Figs. 1 and 3).Paragraph [0078] – “Solid lines 1208 illustrate likelihood curves identified by the methods in system in FIG. 11 to identify signatures, which are collars 132 (e.g., referring to FIG. 1).”].
Regarding Claim 11, Fouda discloses the stages further comprising receiving the outputs of the collar detector at a feature identifier [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”Paragraph [0074] – “In block 912, other collar signatures are identified that are similar to the collar signature chosen by personnel in block 910. During this operation, the reference collar locations on the pseudo-thickness logs may be used to identify similar patterns, in collar signatures, which have more than a threshold of 95% similarity with the user input collar location (i.e., collar signature).”], wherein the feature identifier outputs a feature depth and a feature uncertainty, both of which are correlated to prior information to output the depth and depth uncertainty.
Regarding Claim 12, Fouda discloses that the collar detector further outputs a collar detection uncertainty that is used in determining the depth certainty [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”], and wherein samples of the CCL signal are stamped against depth [Fig. 11, see depth as the y-axis] and the depth stamps are used to match and identify collars [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”].
Regarding Claim 14, Fouda discloses that the collar detector uses the inputs and prior parameters to create the collar detection outputs [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”] based on segmentations of the CCL signal [Paragraph [0037] – “It should be understood that while casing string 108 is illustrated as a single casing string, there may be multiple layers of concentric pipes disposed in the section of wellbore 110 with casing string 108. EM log data may be obtained in two or more sections of wellbore 110 with multiple layers of concentric pipes. For example, EM logging tool 100 may make a first measurement of pipe string 138 comprising any suitable number of joints 130 connected by collars 132. Measurements may be taken in the time-domain and/or frequency range. EM logging tool 100 may make a second measurement in a casing string 108 of first casing 134, wherein first casing 134 comprises any suitable number of pipes connected by collars 132. Measurements may be taken in the time-domain and/or frequency domain. These measurements may be repeated any number of times for first casing 134, for second casing 136, and/or any additional layers of casing string 108. In this disclosure, as discussed further below, methods may be utilized to determine the location of any number of collars 132 in casing string 108 and/or pipe string 138.”], and wherein the collar detector executes a function to perform at least one of: Bayesian statistical filter testing; Matched Filtering; and Long Short-Term Memory [Paragraph [0069] – “In a non-limiting example, algorithms utilized for supervised learning may include Neural Networks, K-Nearest Neighbors, Naïve Bayes, Decision Trees, Classification Trees, Regression Trees, Random Forests, Linear Regression, Support Vector Machines (SVM), Gradient Boosting Regression, and Perception Back-Propagation.”].
Regarding Claim 15, Fouda discloses that the collar detector uses the inputs and prior parameters to create the collar detection outputs based on classification of the CCL signal, and wherein the collar detector includes at least one of: Wavelet Decomposition and Shallow Neural Networks; One-dimensional Convolutional Neural Networks; and Long Short-Term Memory [Paragraph [0071] – “In examples to determine a relationship using machine learning, a neural network (NN) 800, as illustrated in FIG. 8, may be utilized to locate collars on one or more pipe strings and/or casings in a well plan 400 (e.g., referring to FIG. 4). A NN 800 is an artificial neural network with one or more hidden layers 802 between input layer 804 and output layer 806. As illustrated, input layer 804 may include all extracted electromagnetic responses from EM logging tool 100 (e.g., referring to FIG. 1), and output layers 806 may include pipe information from other sources. During operations, input data is taken by neurons 812 in first layer which then provide an output to the neurons 812 within next layer and so on which provides a final output in output layer 806. Each layer may have one or more neurons 812. The connection between two neurons 812 of successive layers may have an associated weight. The weight defines the influence of the input to the output for the next neuron 812 and eventually for the overall final output. The training process of NN 800 is to locate collars on one or more pipe strings and/or casings in a well plan 400.” The disclosure of a single hidden layer discloses that the neural network is “shallow.”].
Regarding Claim 16, Fouda discloses a wireline system [Fig. 1], the wireline system comprising:
a winch [Fig. 1 – winch 118];
a tool string [Fig. 1 – wireline conveyance 106];
a casing collar locator ("CCL") connected to the tool string [Fig. 1 – EM logging tool 100]; and
a depth estimator framework [Abstract – “A method and system may include disposing an electromagnetic (EM) logging tool in a wellbore. The EM logging tool may include a transmitter disposed on the EM logging tool and a receiver disposed on the EM logging tool. The method may further include transmitting an electromagnetic field from the transmitter into one or more, measuring the eddy current in the one or more tubulars with the receiver, and forming an EM log from the plurality of measurements. Additionally, the method may include identifying a representative signature of a collar from the EM log, locating a plurality of signatures that are similar to the representative signature, iteratively locating a collar signature based at least in part on the representative signature, the plurality of signatures that are similar to the representative signature, and a joint length; and displaying a depth for each of the one or more collars.”] that executes on a processor [See Fig. 1 – information handling system 144] to perform stages comprising:
receiving, at a collar detector that executes on a processor [See Fig. 1 – information handling system 144Paragraph [0032] – “information handling system 144 may be a personal computer, a network storage device, or any other suitable device”], a signal from the CCL, a depth stamp, and a prior parameter See the operation of EM logging tool 100 in Fig. 1.Paragraph [0037] – “It should be understood that while casing string 108 is illustrated as a single casing string, there may be multiple layers of concentric pipes disposed in the section of wellbore 110 with casing string 108. EM log data may be obtained in two or more sections of wellbore 110 with multiple layers of concentric pipes. For example, EM logging tool 100 may make a first measurement of pipe string 138 comprising any suitable number of joints 130 connected by collars 132. Measurements may be taken in the time-domain and/or frequency range. EM logging tool 100 may make a second measurement in a casing string 108 of first casing 134, wherein first casing 134 comprises any suitable number of pipes connected by collars 132. Measurements may be taken in the time-domain and/or frequency domain. These measurements may be repeated any number of times for first casing 134, for second casing 136, and/or any additional layers of casing string 108. In this disclosure, as discussed further below, methods may be utilized to determine the location of any number of collars 132 in casing string 108 and/or pipe string 138.”See Fig. 3, logging positions Z1-Z5 being depth stamps. Gathered measurements over time being of a prior parameter.];
outputting, from the collar detector, a collar detection depth and a detection probability [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”];
receiving, at a collar identifier [Fig. 9, Steps 912-916 and following steps], at least one of the outputs of the collar detector and a prior parameter [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”Paragraph [0074] – “In block 912, other collar signatures are identified that are similar to the collar signature chosen by personnel in block 910. During this operation, the reference collar locations on the pseudo-thickness logs may be used to identify similar patterns, in collar signatures, which have more than a threshold of 95% similarity with the user input collar location (i.e., collar signature).”];
outputting, from the collar identifier, a collar depth and a collar identifier [Paragraph [0074] – “In block 914, joint length for pipe string 138 and one or more casings 134, 136, and/or 402 (e.g., referring to FIG. 4) may be found utilizing the identified locations (i.e., identified collar signatures) of collars. Based on the distance between the picks, the joint length for each pipe is determined. After locating collars in blocks 912 and 914, on each string (e.g., pipe string 138 and one or more casings 134, 136, and/or 402) identifying collars is initiated from the deepest collar location on each string.”];
correlating prior information when the detection probability meets a threshold [Paragraph [0074] – “In block 912, other collar signatures are identified that are similar to the collar signature chosen by personnel in block 910. During this operation, the reference collar locations on the pseudo-thickness logs may be used to identify similar patterns, in collar signatures, which have more than a threshold of 95% similarity with the user input collar location (i.e., collar signature).”]; and
outputting a depth and depth certainty for the collar identifier [See Figs. 12A-12C, which show measurement results relative to depth (per Figs. 1 and 3).Paragraph [0078] – “Solid lines 1208 illustrate likelihood curves identified by the methods in system in FIG. 11 to identify signatures, which are collars 132 (e.g., referring to FIG. 1).”], wherein the depth and depth certainty are used to automate an operation of the wireline system [Determination of collar depths per Figs. 12A-12C].
Regarding Claim 17, Fouda discloses the stages further comprising receiving the outputs of the collar detector at a feature identifier [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”Paragraph [0074] – “In block 912, other collar signatures are identified that are similar to the collar signature chosen by personnel in block 910. During this operation, the reference collar locations on the pseudo-thickness logs may be used to identify similar patterns, in collar signatures, which have more than a threshold of 95% similarity with the user input collar location (i.e., collar signature).”], wherein the feature identifier outputs a feature depth and a feature uncertainty, both of which are correlated to prior information to output the depth and depth uncertainty [Paragraph [0074] – “In block 912, other collar signatures are identified that are similar to the collar signature chosen by personnel in block 910. During this operation, the reference collar locations on the pseudo-thickness logs may be used to identify similar patterns, in collar signatures, which have more than a threshold of 95% similarity with the user input collar location (i.e., collar signature).”See Figs. 12A-12C, which show measurement results relative to depth (per Figs. 1 and 3).Paragraph [0078] – “Solid lines 1208 illustrate likelihood curves identified by the methods in system in FIG. 11 to identify signatures, which are collars 132 (e.g., referring to FIG. 1).”].
Regarding Claim 18, Fouda discloses that the collar detector organizes time series data of the CCL signal into segments [Paragraph [0037] – “It should be understood that while casing string 108 is illustrated as a single casing string, there may be multiple layers of concentric pipes disposed in the section of wellbore 110 with casing string 108. EM log data may be obtained in two or more sections of wellbore 110 with multiple layers of concentric pipes. For example, EM logging tool 100 may make a first measurement of pipe string 138 comprising any suitable number of joints 130 connected by collars 132. Measurements may be taken in the time-domain and/or frequency range. EM logging tool 100 may make a second measurement in a casing string 108 of first casing 134, wherein first casing 134 comprises any suitable number of pipes connected by collars 132. Measurements may be taken in the time-domain and/or frequency domain. These measurements may be repeated any number of times for first casing 134, for second casing 136, and/or any additional layers of casing string 108. In this disclosure, as discussed further below, methods may be utilized to determine the location of any number of collars 132 in casing string 108 and/or pipe string 138.”] that are used as inputs to the machine learning model [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102.”], wherein the collar detection probability corresponds to respective segments [See Figs. 12A-12C, which show measurement results relative to depth (per Figs. 1 and 3).Paragraph [0078] – “Solid lines 1208 illustrate likelihood curves identified by the methods in system in FIG. 11 to identify signatures, which are collars 132 (e.g., referring to FIG. 1).” See the individual curves indicating probable collar locations.].
Regarding Claim 19, Fouda discloses that the collar detector further outputs a collar detection uncertainty that is used in determining the depth certainty [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”], and wherein samples of the CCL signal are stamped against depth [Fig. 11, see depth as the y-axis] and the depth stamps are used to match and identify collars [Paragraph [0077] – “EM logging tool 100 is disposed in pipe string 138 with a collar 132. Graph 1100 illustrates a variable density log for measurements taken by each receiver 104 of collar 132. The measurements may be inputs for a convolutional NN 1102. Outputs from convolutional NN 1102 may be utilized as inputs for a fully-connected NN 1104 that may output the probability of a collar signature, which is a location of a collar, at a given pipe. A given collar signature on the raw data may be assigned to the pipe with maximum likelihood. This assignment may be used to automate the step of locating picks in blocks 912-916 for workflow 900 (e.g., referring to FIG. 9). Additionally, the likelihood curves may be displayed to guide user selection of ambiguous collar locations.”].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 9, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fouda et al. (US 20230184984 A1)[hereinafter “Fouda”] and Engels et al. (US 20050223790 A1)[hereinafter “Engels”].
Regarding Claims 9, 13, and 20, Fouda fails to disclose that the depth and depth certainty outputs are used to automate depth control in real-time of a wireline conveyance.
However, Engels discloses controlling the speed of a downhole measurement system based on the quality of measurements and slowing down the logging speed in order to increase measurement accuracy [See Fig. 5 and Paragraphs [0032]-[0037]]. It would have been obvious to implement such a type of control and to slow down the wireline tool based on the depth and depth certainty outputs in order to ensure higher quality measurements in the area of expected collar locations and to also speed up the overall measurement process.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20140202691 A1 – A System And Method For Correcting Downhole Speed
US 20030010495 A1 – System And Methods For Detecting Casing Collars
US 20150285069 A1 – High Resolution Continuous Depth Positioning In A Well Bore Using Persistent Casing Properties
US 20150285069 A1 – High Resolution Continuous Depth Positioning In A Well Bore Using Persistent Casing Properties
US 6151961 A – Downhole Depth Correlation
US 11125076 B1 – Accelerometer Based Casing Collar Locator
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857
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