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 (IDS) submitted on 06/17/2024 and 12/08/2025 were considered by the examiner.
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 in each of these claims is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“A method, comprising:
receiving historical well data comprising trajectories, performance data, and one or more drilling parameters for a plurality of wells;
clustering at least a portion of the plurality of wells into a plurality of clusters based on the trajectories, using a machine learning model;
receiving trajectory data for a subject well;
identifying one of the clusters based on the trajectory data of the subject well, using the machine learning model;
selecting one or more of the plurality of wells, or one or more sections thereof, in the cluster that was identified based on the performance data associated with the one or more of the similar wells or the portion thereof; and
visualizing the selected one or more of the similar wells or one or more sections thereof.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above; the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 1 recites a process and is therefore falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portion of claim 1 comprises process steps that fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category.
Individually and collectively, the steps:
“receiving historical well data comprising trajectories, performance data, and one or more drilling parameters for a plurality of wells”;
“clustering at least a portion of the plurality of wells into a plurality of clusters based on the trajectories”;
“receiving trajectory data for a subject well”;
“identifying one of the clusters based on the trajectory data of the subject well”;
“selecting one or more of the plurality of wells, or one or more sections thereof, in the cluster that was identified based on the performance data associated with the one or more of the similar wells or the portion thereof”; and
“visualizing the selected one or more of the similar wells or one or more sections thereof”
may be performed as mental processes. Receiving historical well data and trajectory data is collecting information, which may be performed as mental processes. Clustering at least a portion of the plurality of wells into a plurality of clusters, identifying one of the clusters, and selecting one or more of the plurality of the wells are each analysis, which may be performed as mental processes. Visualizing the selected one or more of the similar wells is the displaying of certain results of the analysis, which may be performed as mental processes. The type of high-level information collecting and analyzing data recited in these elements has been found by the Federal Circuit to constitute patent ineligible matter (see Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), a claim to "collecting information, analyzing it, and displaying 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).
Similar limitations comprise the mental processes type abstract idea recited by independent claims 10 and 17.
Step 2A, Prong Two of the analysis entails determining whether a claim includes additional elements that integrate the recited judicial exception (e.g., abstract idea) into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 1 does not include additional elements that integrate the recited abstract idea into a practical application. Based on the individual and collective limitations of claim 1, applying a broadest reasonable interpretation, the most significant of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” in any combination appear to integrate the abstract idea to technologically improve any aspect of a system that may be used to implement the highlighted steps such a generic computer. The additional element “using a machine learning model” amounts to mere instructions to implement the process steps in a computer (MPEP 2106.05(f)).
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements such as the “a machine learning model” are not utilized as a particularized manner of implementing the abstract idea process steps.
Regarding effectuation of a transformation or reduction of a particular article to a different state or thing, the claim includes no such transformation or reduction. Instead, the claim as a whole entails gathering or otherwise obtaining information, analyzing said information, and displaying certain results of the analysis. The visualizing the selected wells is not a transformation of a particular article.
Similar analysis applies to independent claim 10. Independent claim 10 recites additional elements including “a non-transitory computer-readable medium storing instructions”, “at least one processor of a computing system”, which are not utilized in a particular manner and amount to use of a generic machine, and therefore do not integrate the judicial exception into a practical application.
Similar analysis applies to independent claim 17. Independent claim 17 recites additional elements including “a computer system”, “one or more processors”, “a memory system”, and “one or more non-transitory computer-readable media storing instructions”, which are not utilized in a particular manner and amount to use of a generic machine, and therefore do not integrate the judicial exception into a practical application.
The above additional elements, considered individually and in combination with the claim elements reciting an abstract idea do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under Step 2B.
Regarding Step 2B, independent claims 1, 10 and 17, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in the relevant art as evidenced by the prior art of record as indicated in the rejections under 35 U.S.C. §103.
Independent claims 1, 10 and 17 are therefore not patent eligible.
Dependent claims 2-9, 11-16, and 18-20 provide additional features/steps which are part of an expanded algorithm that includes the abstract idea of the independent claims (Step 2A, Prong One). None of dependent claims 2-9, 11-16, and 18-20 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and all fail the “significantly more” test under the step 2B for the same reasons as discussed with regards to the independent claims. Claims 2, 11, and 18 recite adjusting a parameter for drilling the subject well, which may be performed as mental processes. Claims 3, 12, and 19 further detail the clustering, comprising normalizing the trajectories. Claims 4 and 13 further detail the normalizing. Claims 5 and 14 further detail the clustering. Claims 6 and 15 further detail the one or more trajectory parameters. Claim 7 further details the performance data. Claims 8, 16, and 20 recite steps of portioning each of the plurality of wells into sections, which are analysis and may be performed as mental processes. Claim 9 further details the sections.
The dependent claims 2-9, 11-16, and 18-20 therefore are also ineligible subject matter.
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.
Claim(s) 1-2, 7-8, 10-11, 16-18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rawlinson et al. (US 20210180439 A1, provided by applicant) in view of Zhernakov et al. (WO2021029996A1, provided by applicant)
Regarding claim 1, Rawlinson teaches A method, comprising:
receiving historical well data comprising trajectories ([0076] lines 11-18, “logging information may be provided to the seismic-to-simulation framework 302 and/or to the drilling framework 304. Such information may be utilized for model building (e.g., constructing a multidimensional model of a geologic environment), generating a trajectory for a well (e.g., or an extension thereof), generating a stimulation plan (e.g., fracturing, chemical treatment, etc.), controlling one or more drilling operations, etc.”), performance data ([0106] lines 1-4, “The OPTILOG™ technology can help to evaluate drilling system performance with single- or multiple-location measurements of drilling dynamics and internal temperature from a recorder.”), and one or more drilling parameters ([0156] lines 4-9, “the database 790 can include data organized for wells 791, equipment 792 and/or services 793. Such data may be organized or organizable by region (e.g., basin) and/or one or more other criteria (e.g., type of well, equipment, service, etc.).”) for a plurality of wells ([0116] lines 15-17, “FIG. 6 also shows an example of a table 670 as a point spreadsheet that specifies information for a plurality of wells.”);
clustering at least a portion of the plurality of wells into a plurality of clusters ([0143] As an example, an approach can consider wells with similar properties, which may be considered offset or not. For example, consider using a clustering technique to determine at a set of wells with similar properties to a well to be planned.) ([0205] As an example, a machine learning model can be built on data from previous wells. Once trained, the model can be used in an application to predict the performance of a job with inputs that have been selected for that job and, for example, to optimize the success rate by modifying the input parameters.);
receiving trajectory data for a subject well ([0030] lines 5-16, “Such a path may be referred to as a trajectory, which can include coordinates in a three-dimensional coordinate system where a measure along the trajectory may be a measured depth, a total vertical depth or another type of measure. During drilling, wireline investigations, etc., equipment may be moved into and/or out of a well or borehole. Such operations can occur over time and may differ with respect to time. As an example, drilling can include using one or more logging tools that can perform one or more logging operations while drilling or otherwise with a drillstring (e.g., while stationary, while tripping in, tripping out, etc.).”; [0146] lines 1-7, “The foregoing group of inputs includes eleven inputs where latitude and longitude may be considered to be a single input. As an example, latitude, longitude and TVD may be utilized as a proxy for a particular formation. For example, latitude and longitude can provide a surface location where TVD provides a depth that can be utilized to determine what formation exists at that depth.”);
identifying one of the clusters based on ([0143] “As an example, an approach can consider wells with similar properties, which may be considered offset or not. For example, consider using a clustering technique to determine at a set of wells with similar properties to a well to be planned.”), using the machine learning model ([0205] As an example, a machine learning model can be built on data from previous wells. Once trained, the model can be used in an application to predict the performance of a job with inputs that have been selected for that job and, for example, to optimize the success rate by modifying the input parameters.);
selecting one or more of the plurality of wells, or one or more sections thereof, in the cluster that was identified based on the performance data associated with the one or more of the similar wells or the portion thereof ([0213] lines 12-19, “a ML model-based approach can replace such a lengthy process by making clusters of similar wells and identifying which the planned well is most similar to and interpolates the chance of success of this well based on these. As an addition, an app can include confidence limits around one or more input parameters that can indicate how close current inputs are to the past data that have been used in the training model.”); and
visualizing the selected one or more of the similar wells or one or more sections thereof (Fig. 6).
Rawlinson does not teach the method, comprising:
clustering at least a portion of the plurality of wells into a plurality of clusters based on the trajectories; and
identifying one of the clusters based on the trajectory data of the subject well.
Zhernakov teaches an analogous method, comprising:
clustering at least a portion of the plurality of wells into a plurality of clusters based on the trajectories (Fig. 2, step 206); and
identifying one of the clusters based on the trajectory data of the subject well (Fig. 2, step 208).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rawlinson to include the trajectories in the clustering of Zhernakov because it would yield predictable and advantageous results, such as clustering wells with similar trajectories, thereby increasing the accuracy of the clustering.
Regarding claim 2, Rawlinson in view of Zhernakov teaches The method of claim 1, further comprising adjusting a parameter for drilling the subject well based on the one or more drilling parameters associated with the selected one or more of the plurality of wells or the portion thereof (Zhernakov: [0044] “drilling parameters (e.g., weight on bit, rotation speed, mud weight, etc.), or geometric parameters (e.g., dog leg severity) may be adjusted based on risks identified in the offset wells, among various other changes that may be made.”).
Regarding claim 7, Rawlinson in view of Zhernakov teaches The method of claim 1, wherein the performance data comprises one or more of: rate of penetration, non-productive time, or drilling cost (Rawlinson: [0138] lines 13-17, “In the data set for training the model, run objective met or desired objective met could be related to one or more of the inputs (e.g., TVD or ROP), time to completion of the well, a cost metric, or could be a combination of criteria.”; inputs of [0144]). The cost metric is the drilling cost.
Regarding claim 8, Rawlinson in view of Zhernakov teaches The method of claim 1, further comprising:
partitioning each of the plurality of wells into sections, each section comprising a finite, non-zero depth interval along one of the wells (Zhernakov: Fig. 3, step 304), wherein clustering the plurality of wells comprises clustering the individual sections of the plurality of wells based on the trajectories of the individual sections (Zhernakov: Fig. 3, step 312; [0048] “Referring again to Figure 3, the process 206 may proceed to selecting a segment of the offset well and a corresponding segment of the subject well, as at 308. For example, in Figure 4, the segments 412 and 414, which are “corresponding” as defined above may be selected. The process 206 may then proceed to calculating one or more distances between the corresponding segments of the subject well and the offset well, as at 310.”; [0051] “In addition, for calculating the shape similarity, the trajectory (or segment) is applied to evaluate the direction and calculate a distance measure to find a minimum value. Thus, shape similarity provides a search of offset wells with directional similarity, without taking into account exact values of azimuth.”); and
partitioning the subject well into a plurality of sections, each section comprising a finite, non-zero depth interval long the subject well, wherein identifying the cluster comprises identifying a cluster for an individual section of the plurality of sections (Zhernakov: Fig. 3, step 312; [0048] “Referring again to Figure 3, the process 206 may proceed to selecting a segment of the offset well and a corresponding segment of the subject well, as at 308. For example, in Figure 4, the segments 412 and 414, which are “corresponding” as defined above may be selected. The process 206 may then proceed to calculating one or more distances between the corresponding segments of the subject well and the offset well, as at 310.”).
Regarding claim 10, Rawlinson teaches A non-transitory computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations (Fig. 4; see [0090]), the operations comprising:
receiving historical well data comprising trajectories ([0076] lines 11-18, “logging information may be provided to the seismic-to-simulation framework 302 and/or to the drilling framework 304. Such information may be utilized for model building (e.g., constructing a multidimensional model of a geologic environment), generating a trajectory for a well (e.g., or an extension thereof), generating a stimulation plan (e.g., fracturing, chemical treatment, etc.), controlling one or more drilling operations, etc.”), performance data ([0106] lines 1-4, “The OPTILOG™ technology can help to evaluate drilling system performance with single- or multiple-location measurements of drilling dynamics and internal temperature from a recorder.”), and one or more drilling parameters ([0156] lines 4-9, “the database 790 can include data organized for wells 791, equipment 792 and/or services 793. Such data may be organized or organizable by region (e.g., basin) and/or one or more other criteria (e.g., type of well, equipment, service, etc.).”) for a plurality of wells ([0116] lines 15-17, “FIG. 6 also shows an example of a table 670 as a point spreadsheet that specifies information for a plurality of wells.”);
clustering at least a portion of the plurality of wells into a plurality of clusters ([0143] As an example, an approach can consider wells with similar properties, which may be considered offset or not. For example, consider using a clustering technique to determine at a set of wells with similar properties to a well to be planned.) ([0205] As an example, a machine learning model can be built on data from previous wells. Once trained, the model can be used in an application to predict the performance of a job with inputs that have been selected for that job and, for example, to optimize the success rate by modifying the input parameters.);
receiving trajectory data for a subject well ([0030] lines 5-16, “Such a path may be referred to as a trajectory, which can include coordinates in a three-dimensional coordinate system where a measure along the trajectory may be a measured depth, a total vertical depth or another type of measure. During drilling, wireline investigations, etc., equipment may be moved into and/or out of a well or borehole. Such operations can occur over time and may differ with respect to time. As an example, drilling can include using one or more logging tools that can perform one or more logging operations while drilling or otherwise with a drillstring (e.g., while stationary, while tripping in, tripping out, etc.).”; [0146] lines 1-7, “The foregoing group of inputs includes eleven inputs where latitude and longitude may be considered to be a single input. As an example, latitude, longitude and TVD may be utilized as a proxy for a particular formation. For example, latitude and longitude can provide a surface location where TVD provides a depth that can be utilized to determine what formation exists at that depth.”);
identifying one of the clusters based on ([0143] “As an example, an approach can consider wells with similar properties, which may be considered offset or not. For example, consider using a clustering technique to determine at a set of wells with similar properties to a well to be planned.”), using the machine learning model ([0205] As an example, a machine learning model can be built on data from previous wells. Once trained, the model can be used in an application to predict the performance of a job with inputs that have been selected for that job and, for example, to optimize the success rate by modifying the input parameters.);
selecting one or more of the plurality of wells, or one or more sections thereof, in the cluster that was identified based on the performance data associated with the one or more of the plurality of wells or the portion thereof ([0213] lines 12-19, “a ML model-based approach can replace such a lengthy process by making clusters of similar wells and identifying which the planned well is most similar to and interpolates the chance of success of this well based on these. As an addition, an app can include confidence limits around one or more input parameters that can indicate how close current inputs are to the past data that have been used in the training model.”); and
visualizing the selected one or more of the plurality of wells or one or more sections thereof (Fig. 6).
Rawlinson does not teach the instructions, comprising:
clustering at least a portion of the plurality of wells into a plurality of clusters based on the trajectories; and
identifying one of the clusters based on the trajectory data of the subject well.
Zhernakov teaches analogous instructions, comprising:
clustering at least a portion of the plurality of wells into a plurality of clusters based on the trajectories (Fig. 2, step 206); and
identifying one of the clusters based on the trajectory data of the subject well (Fig. 2, step 208).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the instructions of Rawlinson to include the trajectories in the clustering of Zhernakov because it would yield predictable and advantageous results, such as clustering wells with similar trajectories, thereby increasing the accuracy of the clustering.
Regarding claim 11, Rawlinson in view of Zhernakov teaches The medium of claim 10, wherein the operations further comprise adjusting a parameter for drilling the subject well based on the one or more drilling parameters associated with the selected one or more of the plurality of wells or the portion thereof (Zhernakov: [0044] “drilling parameters (e.g., weight on bit, rotation speed, mud weight, etc.), or geometric parameters (e.g., dog leg severity) may be adjusted based on risks identified in the offset wells, among various other changes that may be made.”).
Regarding claim 16, Rawlinson in view of Zhernakov teaches The medium of claim 10, wherein the operations further comprise:
partitioning each of the plurality of wells into sections, each section comprising a finite, non-zero depth interval along one of the wells (Zhernakov: Fig. 3, step 304), wherein clustering the plurality of wells comprises clustering the individual sections of the plurality of wells based on the trajectories of the individual sections (Zhernakov: Fig. 3, step 312; [0048] “Referring again to Figure 3, the process 206 may proceed to selecting a segment of the offset well and a corresponding segment of the subject well, as at 308. For example, in Figure 4, the segments 412 and 414, which are “corresponding” as defined above may be selected. The process 206 may then proceed to calculating one or more distances between the corresponding segments of the subject well and the offset well, as at 310.”; [0051] “In addition, for calculating the shape similarity, the trajectory (or segment) is applied to evaluate the direction and calculate a distance measure to find a minimum value. Thus, shape similarity provides a search of offset wells with directional similarity, without taking into account exact values of azimuth.”); and
partitioning the subject well into a plurality of sections, each section comprising a finite, non-zero depth interval long the subject well, wherein identifying the cluster comprises identifying a cluster for an individual section of the plurality of sections (Zhernakov: Fig. 3, step 312; [0048] “Referring again to Figure 3, the process 206 may proceed to selecting a segment of the offset well and a corresponding segment of the subject well, as at 308. For example, in Figure 4, the segments 412 and 414, which are “corresponding” as defined above may be selected. The process 206 may then proceed to calculating one or more distances between the corresponding segments of the subject well and the offset well, as at 310.”).
Regarding claim 17, Rawlinson teaches A computing system (Fig. 4), comprising:
one or more processors (one or more processors 472); and
a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations (Fig. 4; see [0090]), the operations comprising:
receiving historical well data comprising trajectories ([0076] lines 11-18, “logging information may be provided to the seismic-to-simulation framework 302 and/or to the drilling framework 304. Such information may be utilized for model building (e.g., constructing a multidimensional model of a geologic environment), generating a trajectory for a well (e.g., or an extension thereof), generating a stimulation plan (e.g., fracturing, chemical treatment, etc.), controlling one or more drilling operations, etc.”), performance data ([0106] lines 1-4, “The OPTILOG™ technology can help to evaluate drilling system performance with single- or multiple-location measurements of drilling dynamics and internal temperature from a recorder.”), and one or more drilling parameters ([0156] lines 4-9, “the database 790 can include data organized for wells 791, equipment 792 and/or services 793. Such data may be organized or organizable by region (e.g., basin) and/or one or more other criteria (e.g., type of well, equipment, service, etc.).”) for a plurality of wells ([0116] lines 15-17, “FIG. 6 also shows an example of a table 670 as a point spreadsheet that specifies information for a plurality of wells.”);
clustering at least a portion of the plurality of wells into a plurality of clusters ([0143] As an example, an approach can consider wells with similar properties, which may be considered offset or not. For example, consider using a clustering technique to determine at a set of wells with similar properties to a well to be planned.) ([0205] As an example, a machine learning model can be built on data from previous wells. Once trained, the model can be used in an application to predict the performance of a job with inputs that have been selected for that job and, for example, to optimize the success rate by modifying the input parameters.);
receiving trajectory data for a subject well ([0030] lines 5-16, “Such a path may be referred to as a trajectory, which can include coordinates in a three-dimensional coordinate system where a measure along the trajectory may be a measured depth, a total vertical depth or another type of measure. During drilling, wireline investigations, etc., equipment may be moved into and/or out of a well or borehole. Such operations can occur over time and may differ with respect to time. As an example, drilling can include using one or more logging tools that can perform one or more logging operations while drilling or otherwise with a drillstring (e.g., while stationary, while tripping in, tripping out, etc.).”; [0146] lines 1-7, “The foregoing group of inputs includes eleven inputs where latitude and longitude may be considered to be a single input. As an example, latitude, longitude and TVD may be utilized as a proxy for a particular formation. For example, latitude and longitude can provide a surface location where TVD provides a depth that can be utilized to determine what formation exists at that depth.”);
identifying one of the clusters based on ([0143] “As an example, an approach can consider wells with similar properties, which may be considered offset or not. For example, consider using a clustering technique to determine at a set of wells with similar properties to a well to be planned.”), using the machine learning model ([0205] As an example, a machine learning model can be built on data from previous wells. Once trained, the model can be used in an application to predict the performance of a job with inputs that have been selected for that job and, for example, to optimize the success rate by modifying the input parameters.);
selecting one or more of the plurality of wells, or one or more sections thereof, in the cluster that was identified based on the performance data associated with the one or more of the plurality of wells or the portion thereof ([0213] lines 12-19, “a ML model-based approach can replace such a lengthy process by making clusters of similar wells and identifying which the planned well is most similar to and interpolates the chance of success of this well based on these. As an addition, an app can include confidence limits around one or more input parameters that can indicate how close current inputs are to the past data that have been used in the training model.”); and
visualizing the selected one or more of the plurality of wells or one or more sections thereof (Fig. 6).
Rawlinson does not teach the system, comprising:
clustering at least a portion of the plurality of wells into a plurality of clusters based on the trajectories; and
identifying one of the clusters based on
Zhernakov teaches an analogous system, comprising:
clustering at least a portion of the plurality of wells into a plurality of clusters based on the trajectories (Fig. 2, step 206); and
identifying one of the clusters based on the trajectory data of the subject well (Fig. 2, step 208).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Rawlinson to include the trajectories in the clustering of Zhernakov because it would yield predictable and advantageous results, such as clustering wells with similar trajectories, thereby increasing the accuracy of the clustering.
Regarding claim 18, Rawlinson in view of Zhernakov teaches The computing system of claim 17, wherein the operations further comprise adjusting a parameter for drilling the subject well based on the one or more drilling parameters associated with the selected one or more of the plurality of wells or the portion thereof (Zhernakov: [0044] “drilling parameters (e.g., weight on bit, rotation speed, mud weight, etc.), or geometric parameters (e.g., dog leg severity) may be adjusted based on risks identified in the offset wells, among various other changes that may be made.”).
Regarding claim 20, Rawlinson in view of Zhernakov teaches The computing system of claim 17, wherein the operations further comprise:
partitioning each of the plurality of wells into sections, each section comprising a finite, non-zero depth interval along one of the wells (Zhernakov: Fig. 3, step 304), wherein clustering the plurality of wells comprises clustering the individual sections of the plurality of wells based on the trajectories of the individual sections (Zhernakov: Fig. 3, step 312; [0048] “Referring again to Figure 3, the process 206 may proceed to selecting a segment of the offset well and a corresponding segment of the subject well, as at 308. For example, in Figure 4, the segments 412 and 414, which are “corresponding” as defined above may be selected. The process 206 may then proceed to calculating one or more distances between the corresponding segments of the subject well and the offset well, as at 310.”; [0051] “In addition, for calculating the shape similarity, the trajectory (or segment) is applied to evaluate the direction and calculate a distance measure to find a minimum value. Thus, shape similarity provides a search of offset wells with directional similarity, without taking into account exact values of azimuth.”); and
partitioning the subject well into a plurality of sections, each section comprising a finite, non-zero depth interval long the subject well, wherein identifying the cluster comprises identifying a cluster for an individual section of the plurality of sections (Zhernakov: Fig. 3, step 312; [0048] “Referring again to Figure 3, the process 206 may proceed to selecting a segment of the offset well and a corresponding segment of the subject well, as at 308. For example, in Figure 4, the segments 412 and 414, which are “corresponding” as defined above may be selected. The process 206 may then proceed to calculating one or more distances between the corresponding segments of the subject well and the offset well, as at 310.”).
Claim(s) 3-6, 9, 12-15, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rawlinson in view of Zhernakov as applied to claims 1, 10, and 17, respectfully, above, and further in view of Wesley et al. (NO20220083A1, provided by applicant).
Regarding claim 3, Rawlinson in view of Zhernakov teaches The method of claim 1.
Rawlinson in view of Zhernakov does not teach the method, wherein clustering the plurality of wells comprises normalizing the trajectories of the plurality of wells.
Wesley teaches an analogous method, wherein clustering the plurality of wells (lithology similarity matching model 205, trajectory similarity classification model 203, similarity metrics of [0026-0027]) comprises normalizing ([0020] “Since the offset well data from data sources 101-104 can come in different formats, the data filter 113 can comprise a data normalizer for data corresponding to each type of feature.”) the trajectories of the plurality of wells ([0019] “The data sources 101-104 comprise disparate data sources that track data corresponding to each computed feature for a prospect well and candidate offset wells. The data sources can be lithology databases, well trajectory databases, offset well event logs, oil field blueprints, oil field specification databases, etc. and the raw data can be measured by sensors in logging-while-drilling operations or other equivalent drilling system measurement sensors, satellite global positioning systems, etc..”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rawlinson in view of Zhernakov to include the normalizing of trajectories of Wesley because it would yield predictable results, such as producing consistent and comparable values for analysis.
Regarding claim 4, Rawlinson in view of Zhernakov and Wesley teaches The method of claim 3, wherein normalizing comprises at least one of: aligning north-south or east-west axes of the plurality of wells; or aligning drilling trajectory along a vertical section azimuth (Wesley: [0027] “Inclination measures the angle with respect to the horizontal plane in two-dimensional degrees. Azimuth measures the angle of the well trajectory in the horizontal plane with respect to a reference compass direction, typically true or magnetic north. The trajectory similarity classification model 203 comprises a classification component that is trained on well trajectory data for each of the above three parameters to classify a well trajectory shape. The classes of well trajectory shapes include “J”, “L”, “S”, “Vertical,” and “Horizontal.””).
Regarding claim 5, Rawlinson in view of Zhernakov and Wesley teaches The method of claim 3, wherein clustering the plurality of wells comprises extracting one or more trajectory parameters associated with each of the plurality of wells, after normalizing the trajectories (Wesley: Fig. 1; [0027] “The trajectory data 219 comprises depth trajectory plots (i.e. plots of depth versus horizontal displacement) for the prospect well and the candidate offset wells. The trajectory plots can be evaluated based on three parameters - dogleg severity, inclination, and azimuth.”).
Regarding claim 6, Rawlinson in view of Zhernakov and Wesley teaches The method of claim 5, wherein the one or more trajectory parameters comprise one or more of: XYZ coordinates, azimuth, elevation, depth, delta change in depth, or delta distance along an axis (Rawlinson: [0030] lines 5-9, “Such a path may be referred to as a trajectory, which can include coordinates in a three-dimensional coordinate system where a measure along the trajectory may be a measured depth, a total vertical depth or another type of measure.”; [0116] lines 17-21, “As shown in the example table 670, coordinates such as “x” and “y” and “depth” can be specified for various features of the wells, which can include pad parameters, spacings, toe heights, step outs, initial inclinations, kick offs, etc.”). The three-dimensional coordinates, such as “x” “y” and “depth”, are the XYZ coordinates.
Regarding claim 9, Rawlinson in view of Zhernakov teaches The method of claim 8, comprising a different diameter of casing (Rawlinson: “last casing diameter (LastCasingDiameter)” of inputs of [0144]).
Rawlinson in view of Zhernakov does not teach the method, wherein the sections each define a different diameter of casing.
Wesley teaches an analogous method, wherein the sections each define a different diameter of casing ([0035] “At block 301, the data filter aggregates prospect well data and candidate offset well data. Depending on the stage of design for the casing schema of a prospect well, the data can correspond to all sections of the offset wells or specific well sections. The data filter sorts offset well data by feature as described variously above. The feature generator computes offset well features using filtered well data. Similarity features can be generated by data preprocessing and a combination of models and straightforward computations as described previously.”; [0042] “These casing design elements inform design choices including depth of particular casings in the prospect well and depth of various components of a drilling system, as well as a hole size for each casing section at the corresponding depths. The chosen casing schema is verified against pore pressure and fracture pressure envelopes for the prospect well.”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rawlinson in view of Zhernakov to include the sections of different casing diameter of Wesley because it would yield predictable results, such as defining section by similar diameter.
Regarding claim 12, Rawlinson in view of Zhernakov teaches The medium of claim 10.
Rawlinson in view of Zhernakov does not teach the medium, wherein clustering the plurality of wells comprises normalizing the trajectories of the plurality of wells.
Wesley teaches an analogous method, wherein clustering the plurality of wells (lithology similarity matching model 205, trajectory similarity classification model 203, similarity metrics of [0026-0027]) comprises normalizing ([0020] “Since the offset well data from data sources 101-104 can come in different formats, the data filter 113 can comprise a data normalizer for data corresponding to each type of feature.”) the trajectories of the plurality of wells ([0019] “The data sources 101-104 comprise disparate data sources that track data corresponding to each computed feature for a prospect well and candidate offset wells. The data sources can be lithology databases, well trajectory databases, offset well event logs, oil field blueprints, oil field specification databases, etc. and the raw data can be measured by sensors in logging-while-drilling operations or other equivalent drilling system measurement sensors, satellite global positioning systems, etc..”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the instructions of Rawlinson in view of Zhernakov to include the normalizing of trajectories of Wesley because it would yield predictable results, such as producing consistent and comparable values for analysis.
Regarding claim 13, Rawlinson in view of Zhernakov and Wesley teaches The medium of claim 12, wherein normalizing comprises at least one of: aligning north-south or east-west axes of the plurality of wells; or aligning drilling trajectory along a vertical section azimuth (Wesley: [0027] “Inclination measures the angle with respect to the horizontal plane in two-dimensional degrees. Azimuth measures the angle of the well trajectory in the horizontal plane with respect to a reference compass direction, typically true or magnetic north. The trajectory similarity classification model 203 comprises a classification component that is trained on well trajectory data for each of the above three parameters to classify a well trajectory shape. The classes of well trajectory shapes include “J”, “L”, “S”, “Vertical,” and “Horizontal.””).
Regarding claim 14, Rawlinson in view of Zhernakov and Wesley teaches The medium of claim 12, wherein clustering the plurality of wells comprises extracting one or more trajectory parameters associated with each of the plurality of wells, after normalizing the trajectories (Wesley: Fig. 1; [0027] “The trajectory data 219 comprises depth trajectory plots (i.e. plots of depth versus horizontal displacement) for the prospect well and the candidate offset wells. The trajectory plots can be evaluated based on three parameters - dogleg severity, inclination, and azimuth.”).
Regarding claim 15, Rawlinson in view of Zhernakov and Wesley teaches The medium of claim 14, wherein the one or more trajectory parameters comprise one or more of: XYZ coordinates, azimuth, elevation, depth, delta change in depth, or delta distance along an axis, and wherein the performance data comprises one or more of: rate of penetration, non- productive time, or drilling cost (Rawlinson: [0030] lines 5-9, “Such a path may be referred to as a trajectory, which can include coordinates in a three-dimensional coordinate system where a measure along the trajectory may be a measured depth, a total vertical depth or another type of measure.”; [0116] lines 17-21, “As shown in the example table 670, coordinates such as “x” and “y” and “depth” can be specified for various features of the wells, which can include pad parameters, spacings, toe heights, step outs, initial inclinations, kick offs, etc.”). The three-dimensional coordinates, such as “x” “y” and “depth”, are the XYZ coordinates.
Regarding claim 19, Rawlinson in view of Zhernakov teaches The computing system of claim 17.
Rawlinson in view of Zhernakov does not teach the system, wherein clustering the plurality of wells comprises normalizing the trajectories of the plurality of wells, wherein normalizing comprises at least one of aligning north-south or east-west axes of the plurality of wells, or aligning drilling trajectory along a vertical section azimuth.
Wesley teaches an analogous system, wherein clustering the plurality of wells (lithology similarity matching model 205, trajectory similarity classification model 203, similarity metrics of [0026-0027]) comprises normalizing ([0020] “Since the offset well data from data sources 101-104 can come in different formats, the data filter 113 can comprise a data normalizer for data corresponding to each type of feature.”) the trajectories of the plurality of wells ([0019] “The data sources 101-104 comprise disparate data sources that track data corresponding to each computed feature for a prospect well and candidate offset wells. The data sources can be lithology databases, well trajectory databases, offset well event logs, oil field blueprints, oil field specification databases, etc. and the raw data can be measured by sensors in logging-while-drilling operations or other equivalent drilling system measurement sensors, satellite global positioning systems, etc..”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Rawlinson in view of Zhernakov to include the normalizing of trajectories of Wesley because it would yield predictable results, such as producing consistent and comparable values for analysis.
Conclusion
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/B.B.G./Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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