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 Objections
2. Claims 12 and 22 are objected to because of the following informalities: a) In claim 12 lines 1-2, please change "further comprising steering the wellbore responsive to the triple combo log" to:
--further comprising steering a wellbore responsive to the triple combo log--. b) In claim 22 lines 3-4, please change "receiving one or more measurements of drilling parameters from the one or more sensors;" to:
--receiving one or more measurements of drilling parameters from one or more sensors;--.
c) In claim 22 line 12, please change "drilling the borehole using the adjustment to the one or more drilling parameters" to: --drilling a borehole using the adjustment to the one or more drilling parameters--. Appropriate correction is required.
Claim Rejections - 35 USC § 101
3. 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 1-9 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In view of the new 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register Vol. 84, No. 4, January 7, 2019), the Examiner has considered the claims and has determined that under step 1, claims 1-12 are to a process, claims 13-21 are to a machine, and claims 22-30 are to an article of manufacture. Next under the new step 2A prong 1 analysis, the claims are considered to determine if they recite an abstract idea (judicial exception) under the following groupings: (a) mathematical concepts, (b) certain methods of organizing human activity, or (c) mental processes. The independent claims contain at least the following bolded limitations (see representative independent claims) that fall into the grouping of mathematical concepts and/or mental processes:
1. A method for drilling a borehole, comprising:
receiving one or more measurements of drilling parameters;
accessing historical drilling logs for one or more wells in a geographic region;
training, using one or more processors, a machine learning model to determine predicted values for a triple combo log for a new well in the geographic region;
determining, using the one or more processors, one or more formation properties from the triple combo log; and
determining, using the one or more processors, an adjustment to one or more drilling parameters based at least on the one or more formation properties.
13. A system for drilling a borehole, comprising:
one or more sensors;
a drilling rig;
one or more processors; and
a memory storing instructions when executed by the one or more processors perform operations, comprising: receiving one or more measurements of drilling parameters from the one or more sensors;
accessing historical drilling logs for one or more wells in a geographic region;
training, using one or more processors, a machine learning model to determine predicted values for a triple combo log for a new well in the geographic region;
determining, using the one or more processors, one or more formation properties from the triple combo log; determining, using the one or more processors, an adjustment to one or more drilling parameters; and
drilling the borehole using the adjustment to the one or more drilling parameters at the drilling rig.
22. A non-transitory computer readable medium storing instructions that when executed by one or more processors perform operations comprising:
receiving one or more measurements of drilling parameters from the one or more sensors;
accessing historical drilling logs for one or more wells in a geographic region;
training, using one or more processors, a machine learning model to determine predicted values for a triple combo log for a new well in the geographic region;
determining, using the one or more processors, one or more formation properties from the triple combo log; determining, using the one or more processors, an adjustment to one or more drilling parameters; and
drilling the borehole using the adjustment to the one or more drilling parameters at the drilling rig.
It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula."(see MPEP 2106.04(a)(2) I.). Thus the “training…a machine learning model to determine predicted values for a triple combo log for a new well in the geographic region" amounts to a description in words of carrying out calculations using an improved mathematical equation to solve for predicted values. This is evidenced by the description of mathematical algorithms used for training a mathematical model such as an extreme gradient boosting algorithm, involving calculations for gradient descent on the objective function and using a root mean square error as a cost function (see paragraph [0156] in the published specification). Patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101 (see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025)). The limitations of "determining…one or more formation properties from the triple combo log" amounts to a mental process to recognize formation properties or characteristics from a triple combo log, or a mathematical concept when the determination of the formation properties involves a more complex mathematical analysis to solve for formation properties. The limitations of "determining…an adjustment to one or more drilling parameters" amounts to a mental process to form a judgment of a correction or change needed to values of drilling parameters, or a mathematical concept to mathematically solve for correction adjustments to be applied to the values of the drilling parameters. It is clear that each of the independent claims recite some limitations that amount to mental process or mathematical concepts for calculating numerical predicted values, formation properties, and drilling parameter adjustment values.
Next in step 2A prong 2, the independent claims are analyzed to determine whether there are additional elements or combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception such that it is more than a drafting effort designed to monopolize the exception, in order to integrate the judicial exception into a practical application. These limitations have been identified and underlined above, and are not indicative of integration into a practical application because: (1) the limitations of "for drilling a borehole" and "a drilling rig" amount to generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), without any applied change to the technical environment or actual drilling described; (2) the limitations of "receiving one or more measurements of drilling parameters", "accessing historical drilling logs for one or more wells in a geographic region", "one or more sensors", and "receiving one or more measurements of drilling parameters from the one or more sensors," amount to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)); and (3) the limitations of "one or more processors", a system", "a memory storing instructions when executed by the one or more processors perform operations comprising:", and "a non-transitory computer readable medium storing instructions that when executed by one or more processors perform operations comprising:" amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Independent claims 13 and 22 do recite "drilling the borehole using the adjustment to the one or more drilling parameters at the drilling rig", which amounts to an integration of the judicial exception into a practical application to more effectively drill a borehole for an improvement to the technology of borehole drilling. Claims 13 and 22 (and dependent claims 14-21 and 23-30) are thus directed to patent eligible subject matter, and the analysis concludes for claims 13-30.
Next in step 2B, remaining independent claim 1 is considered to determine if it recites additional elements that amount to an inventive concept (“significantly more”) than the recited judicial exception. The limitation of "for drilling a borehole" does not add significantly more because such a limitation amounts to generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as the preamble describes the general environment without any applied change to the technical environment or any actual drilling described. The limitations of "receiving one or more measurements of drilling parameters" and "accessing historical drilling logs for one or more wells in a geographic region," do not add significantly more because such limitations amount to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)). The limitations of "one or more processors" does not add significantly more because such limitations amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Dependent claims 2-9 contain additional limitations that fall under the abstract idea grouping of a mental process or mathematical concepts to describe data definitions and details of the algorithms/calculations involved. Dependent claim 11 does not provide an integration into a practical application or significantly more because such limitations amount to generally linking the use of the judicial exception to a particular technological environment or field of use, as the method is performed in real-time during drilling (or drilling environment) but without affecting any applied change to the drilling. Dependent claims 10 and 12 amount to an integration into a practical application to describe real-world physical actions carried out in response to the determined drilling parameters and triple combo log, and are thus directed to patent eligible subject matter.
4. An invention is not rendered ineligible for patent simply because it involves an abstract concept. Applications of such concepts "to a new and useful end" remain eligible for patent protection (see Alice Corp., 134 S. Ct. at 2354 (quoting Benson, 409 U.S. at 67)). However, "a claim for a new abstract idea is still an abstract idea" (see Synopsys v. Mentor Graphics Corp. _F.3d_, 120 U.S.P.Q. 2d1473 (Fed. Cir. 2016)). There needs to be additional elements or combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception or render the claim as a whole to be significantly more than the exception itself in order to demonstrate “integration into a practical application” or an “inventive concept.” For instance, particular physical arrangements (or particular machine) for actively obtaining the sensor data, or further physical applications using the calculated values (formation properties, predicted values for a triple combo log, or determined adjustment to drilling parameters) to drive a transformation, change in physical operation, or repair/maintenance of a technology or technical process, could provide integration into a practical application.
Claim Rejections - 35 USC § 102
5. 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
6. Claim(s) 1-7, 9-19, 21-28, and 30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Boualleg et al. (US Pat. Pub. 2022/0170359, hereinafter "Boualleg").
In regards to claim 1, Boualleg teaches a method for drilling a borehole (Boualleg abstract and paragraph [0005] teach a method for drilling a borehole by predicting downhole tool behavior), comprising:
receiving one or more measurements of drilling parameters (Boualleg paragraph [0056] and [0121] teaches receiving real-time data measurements of drilling parameters from one or more sensors);
accessing historical drilling logs for one or more wells in a geographic region (Boualleg Fig. 27 and paragraph [0124], [0244], [0319], and [0338] teach accessing drilling parameter log data from previous (historical) runs for one or more offset wells in a geographic region);
training, using one or more processors, a machine learning model to determine predicted values for a triple combo log for a new well in the geographic region (Boualleg paragraphs [0005], [0312], and [0352] teach training a machine learning model using processor-executable instructions executed by a processor of a computer to determine predicted and forward modeling results for a behavior of a downhole tool, such as for planning the drilling of a future (new) well in the geographic region; Boualleg Fig. 27 and paragraph [0320]-[0321] teach determining predicted values for multiple feature indexes (including at least a triple combo of indexes) to generate logs of predicted values with respect to depth);
determining, using the one or more processors, one or more formation properties from the triple combo log (Boualleg paragraph [0124] and [0174] teach determining formation properties (such as formation top changes or inclination) from the one or more logging measurements, and using the one or more logs to characterize the formations from a geological viewpoint); and
determining, using the one or more processors, an adjustment to one or more drilling parameters based at least on the one or more formation properties (Boualleg paragraph [0124] and [0256] teach determining one or more drilling parameter changes using an acceptable understanding of the formation properties).
In regards to claim 2, Boualleg teaches the method wherein the drilling parameters comprise one or more of weight-on-bit, rate of penetration, torque, rotations per minute, or differential pressure (Boualleg paragraph [0122] teaches where the drilling parameters comprise one or more of weight-on-bit (WOB), rate of penetration (ROP), rotations per minute (RPM), etc.).
In regards to claim 3, Boualleg teaches the method wherein the historical drilling logs comprise one or more of values for gamma ray, resistivity, neutron porosity, or bulk density (Boualleg paragraphs [0152] and [0162] teach where historical drilling logs are obtained from logging-while-drilling tools and stored, and include measurements for gamma-ray, resistivity, porosity, and density).
In regards to claim 4, Boualleg teaches the method wherein the training the machine learning model uses one or more of depth shifting, outlier detection, and feature selection to tune one or more parameters of the machine learning model (Boualleg paragraph [0284] teaches where training the machine learning model uses depth shifting to adjust the model to the current depth; Boualleg paragraph [0383] teaches where training the model includes a threshold phase for outlier detection to select models that perform acceptably for downhole tool behavior prediction; Boualleg paragraph [0319] teaches where training the model involves a feature space selection to iteratively vary the predictions of a plot generated by the model).
In regards to claim 5, Boualleg teaches the method further comprising applying one or more gradient boosting algorithms to train predictions sequentially for predicted values for the triple combo log (Boualleg paragraphs [0378] and [0387] teach applying gradient boosted decision trees to train predictions sequentially (along branches of the trees)).
In regards to claim 6, Boualleg teaches the method wherein the one or more formation properties are predicted by applying a physics-based joint inversion model to the predicted values for the triple combo log (Boualleg paragraphs [0120] and [0239] teach applying a physics-based inversion model to the predicted values).
In regards to claim 7, Boualleg teaches the method further comprising determining one or more of reservoir properties comprising total porosity, clay volume, water saturation, volumetric concentrations of lithology, permeability, rock type, or geomechanical parameters (Boualleg paragraph [0194] teaches determining one or more modeled reservoir properties using a PETROMOD framework to predict how a reservoir has been charged with hydrocarbons, including at least geomechanical parameters for quantities, pore pressure, and hydrocarbon type in the reservoir).
In regards to claim 9, Boualleg teaches the method further comprising identifying rock type of a formation using the triple combo log (Boualleg paragraph [0375] teaches identifying a formation rock type or changes of types using the one or more variables of the predicted triple combo log).
In regards to claim 10, Boualleg teaches the method further comprising drilling a portion of a wellbore in accordance with the one or more formation properties and/or the drilling parameters (Boualleg paragraphs [0151] and [0241] teach drilling a portion of the wellbore within a zone of interest or a desirable productive portion of a reservoir based on the one or more formation properties and drilling parameters).
In regards to claim 11, Boualleg teaches the method wherein the method is performed in real-time during drilling of a wellbore (Boualleg paragraph [0125] teach where the method is performed in real-time during drilling of a wellbore).
In regards to claim 12, Boualleg teaches the method further comprising steering the wellbore responsive to the triple combo log (Boualleg paragraph [0233] and [0290] teach comprising automated predictive trajectory (steering) control in response to the predicted triple combo log).
In regards to claim 13, Boualleg teaches a system for drilling a borehole (Boualleg paragraph [0005] teaches a system for drilling a wellbore with a downhole tool), comprising:
one or more sensors (Boualleg paragraph [0121] teaches one or more sensors);
a drilling rig (Boualleg Fig. 1 and paragraphs [0003] and [0121] teach a drilling rig);
one or more processors (Boualleg paragraph [0005] teaches a processor); and
a memory storing instructions when executed by the one or more processors perform operations (Boualleg paragraph [0005] teaches a memory storing processor-executable instructions for execution by the processor to perform operations), comprising:
receiving one or more measurements of drilling parameters from the one or more sensors (Boualleg paragraph [0056] and [0121] teaches receiving real-time data measurements of drilling parameters from one or more sensors);
accessing historical drilling logs for one or more wells in a geographic region (Boualleg Fig. 27 and paragraph [0124], [0244], [0319], and [0338] teach accessing drilling parameter log data from previous (historical) runs for one or more offset wells in a geographic region);
training, using one or more processors, a machine learning model to determine predicted values for a triple combo log for a new well in the geographic region (Boualleg paragraphs [0005], [0312], and [0352] teach training a machine learning model using processor-executable instructions executed by a processor of a computer to determine predicted and forward modeling results for a behavior of a downhole tool, such as for planning the drilling of a future (new) well in the geographic region; Boualleg Fig. 27 and paragraph [0320]-[0321] teach determining predicted values for multiple feature indexes (including at least a triple combo of indexes) to generate logs of predicted values with respect to depth);
determining, using the one or more processors, one or more formation properties from the triple combo log (Boualleg paragraph [0124] and [0174] teach determining formation properties (such as formation top changes or inclination) from the one or more logging measurements, and using the one or more logs to characterize the formations from a geological viewpoint);
determining, using the one or more processors, an adjustment to one or more drilling parameters (Boualleg paragraph [0124] and [0256] teach determining one or more drilling parameter changes using an acceptable understanding of the formation properties); and
drilling the borehole using the adjustment to the one or more drilling parameters at the drilling rig (Boualleg paragraphs [0125] and [0241] teach optimizing drilling of borehole using adjustments to the drilling parameters).
In regards to claim 14, Boualleg teaches the system wherein the drilling parameters comprise one or more of weight-on-bit, rate of penetration, torque, rotations per minute, or differential pressure (Boualleg paragraph [0122] teaches where the drilling parameters comprise one or more of weight-on-bit (WOB), rate of penetration (ROP), rotations per minute (RPM), etc.).
In regards to claim 15, Boualleg teaches the system wherein the historical drilling logs comprise one or more of values for gamma ray, resistivity, neutron porosity, or bulk density (Boualleg paragraphs [0152] and [0162] teach where historical drilling logs are obtained from logging-while-drilling tools and stored, and include measurements for gamma-ray, resistivity, porosity, and density).
In regards to claim 16, Boualleg teaches the system wherein the training the machine learning model uses one or more of depth shifting, outlier detection, and feature selection to tune one or more parameters of the machine learning model (Boualleg paragraph [0284] teaches where training the machine learning model uses depth shifting to adjust the model to the current depth; Boualleg paragraph [0383] teaches where training the model includes a threshold phase for outlier detection to select models that perform acceptably for downhole tool behavior prediction; Boualleg paragraph [0319] teaches where training the model involves a feature space selection to iteratively vary the predictions of a plot generated by the model).
In regards to claim 17, Boualleg teaches the system wherein the operations further comprise applying one or more gradient boosting algorithms to train predictions sequentially for predicted values for the triple combo log (Boualleg paragraphs [0378] and [0387] teach applying gradient boosted decision trees to train predictions sequentially (along branches of the trees)).
In regards to claim 18, Boualleg teaches the system wherein the one or more formation properties are predicted by applying a physics-based joint inversion model to the predicted values for the triple combo log (Boualleg paragraphs [0120] and [0239] teach applying a physics-based inversion model to the predicted values).
In regards to claim 19, Boualleg teaches the system wherein the operations further comprise determining one or more of reservoir properties comprising total porosity, clay volume, water saturation, volumetric concentrations of lithology, permeability, rock type, or geomechanical parameters (Boualleg paragraph [0194] teaches determining one or more modeled reservoir properties using a PETROMOD framework to predict how a reservoir has been charged with hydrocarbons, including at least geomechanical parameters for quantities, pore pressure, and hydrocarbon type in the reservoir).
In regards to claim 21, Boualleg teaches the system wherein the operations further comprise identifying rock type of a formation using the triple combo log (Boualleg paragraph [0375] teaches identifying a formation rock type or changes of types using the one or more variables of the predicted triple combo log).
In regards to claim 22, Boualleg teaches a non-transitory computer readable medium storing instructions that when executed by one or more processors perform operations (Boualleg paragraphs [0005] and [0371] teach a non-transitory computer readable medium storing instructions that are executed by a processor to perform operations) comprising:
receiving one or more measurements of drilling parameters from the one or more sensors (Boualleg paragraph [0056] and [0121] teaches receiving real-time data measurements of drilling parameters from one or more sensors);
accessing historical drilling logs for one or more wells in a geographic region (Boualleg Fig. 27 and paragraph [0124], [0244], [0319], and [0338] teach accessing drilling parameter log data from previous (historical) runs for one or more offset wells in a geographic region);
training, using one or more processors, a machine learning model to determine predicted values for a triple combo log for a new well in the geographic region (Boualleg paragraphs [0005], [0312], and [0352] teach training a machine learning model using processor-executable instructions executed by a processor of a computer to determine predicted and forward modeling results for a behavior of a downhole tool, such as for planning the drilling of a future (new) well in the geographic region; Boualleg Fig. 27 and paragraph [0320]-[0321] teach determining predicted values for multiple feature indexes (including at least a triple combo of indexes) to generate logs of predicted values with respect to depth);
determining, using the one or more processors, one or more formation properties from the triple combo log (Boualleg paragraph [0124] and [0174] teach determining formation properties (such as formation top changes or inclination) from the one or more logging measurements, and using the one or more logs to characterize the formations from a geological viewpoint);
determining, using the one or more processors, an adjustment to one or more drilling parameters (Boualleg paragraph [0124] and [0256] teach determining one or more drilling parameter changes using an acceptable understanding of the formation properties); and
drilling the borehole using the adjustment to the one or more drilling parameters at the drilling rig (Boualleg paragraphs [0125] and [0241] teach optimizing drilling of borehole using adjustments to the drilling parameters).
In regards to claim 23, Boualleg teaches the non-transitory computer readable medium wherein the drilling parameters comprise one or more of weight-on-bit, rate of penetration, torque, rotations per minute, or differential pressure (Boualleg paragraph [0122] teaches where the drilling parameters comprise one or more of weight-on-bit (WOB), rate of penetration (ROP), rotations per minute (RPM), etc.).
In regards to claim 24, Boualleg teaches the non-transitory computer readable medium wherein the historical drilling logs comprise one or more of values for gamma ray, resistivity, neutron porosity, or bulk density (Boualleg paragraphs [0152] and [0162] teach where historical drilling logs are obtained from logging-while-drilling tools and stored, and include measurements for gamma-ray, resistivity, porosity, and density).
In regards to claim 25, Boualleg teaches the non-transitory computer readable medium wherein the training the machine learning model uses one or more of depth shifting, outlier detection, and feature selection to tune one or more parameters of the machine learning model (Boualleg paragraph [0284] teaches where training the machine learning model uses depth shifting to adjust the model to the current depth; Boualleg paragraph [0383] teaches where training the model includes a threshold phase for outlier detection to select models that perform acceptably for downhole tool behavior prediction; Boualleg paragraph [0319] teaches where training the model involves a feature space selection to iteratively vary the predictions of a plot generated by the model).
In regards to claim 26, Boualleg teaches the non-transitory computer readable medium wherein the operations further comprise applying one or more gradient boosting algorithms to train predictions sequentially for predicted values for the triple combo log (Boualleg paragraphs [0378] and [0387] teach applying gradient boosted decision trees to train predictions sequentially (along branches of the trees)).
In regards to claim 27, Boualleg teaches the non-transitory computer readable medium wherein the one or more formation properties are predicted by applying a physics-based joint inversion model to the predicted values for the triple combo log (Boualleg paragraphs [0120] and [0239] teach applying a physics-based inversion model to the predicted values).
In regards to claim 28, Boualleg teaches the non-transitory computer readable medium wherein the operations further comprise determining one or more of reservoir properties comprising total porosity, clay volume, water saturation, volumetric concentrations of lithology, permeability, rock type, or geomechanical parameters (Boualleg paragraph [0194] teaches determining one or more modeled reservoir properties using a PETROMOD framework to predict how a reservoir has been charged with hydrocarbons, including at least geomechanical parameters for quantities, pore pressure, and hydrocarbon type in the reservoir).
In regards to claim 30, Boualleg teaches the non-transitory computer readable medium wherein the operations further comprise identifying rock type of a formation using the triple combo log (Boualleg paragraph [0375] teaches identifying a formation rock type or changes of types using the one or more variables of the predicted triple combo log).
Claim Rejections - 35 USC § 103
7. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
8. Claims 8, 20, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Boualleg et al. (US Pat. Pub. 2022/0170359, hereinafter "Boualleg") as applied to claim 1, 8 or 22 respectively, and further in view of Benson (US Pat. Pub. 2019/0309614).
In regards to claim 8, Boualleg teaches the method further comprising:
determining a geologic model using the predicted values for a triple combo log (Boualleg paragraph [0233] teaches using the estimations (predicted values) of the LWD (including triple combo) log data to determine a geologic model of steering drilling performance to predict ahead of a bit in a formation, where the steering can be geometrical or geological). Boualleg fails to expressly teach validating the geologic model using one or more of mud logs and Elemental Capture Spectroscopy measurements.
Benson paragraph [0299] teaches confirming or early detection of drilling into or out of a geological formation, and enabling early detection and thus potential mitigation of drilling through undesirable geological formations. Benson paragraph [0299] teaches providing digital mud logs that can be correlated with gamma ray logs and drilling parameter logs, where the various correlated logs including the digital mud logs enable improved accuracy in determining an actual (validated) drilling location, such as a location of drill bit relative to a given formation as well as improved accuracy of other drilling information. Benson paragraph [0299] teaches that the analysis results from the mud analysis are integrated as feedback into a drilling and geosteering control loop implemented by a GCL (guidance control loop), where paragraph [0179] defines the GCL as having a drilling model class based on a borehole (geologic) model.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further combine the teachings of Benson because the logging data identifying undesirable geological formations (geological model) can be further correlated and validated with additional mud log data to improve accuracy in determining an actual drilling location. Therefore, it is well known to use at least additional mud log data as feedback into a drilling and geosteering control loop having a geological model to provide improved accuracy of drilling information in avoiding undesirable geological formations.
In regards to claim 20, Boualleg teaches the system wherein the operations further comprise: determining a geologic model using the predicted values for a triple combo log (Boualleg paragraph [0233] teaches using the estimations (predicted values) of the LWD (including triple combo) log data to determine a geologic model of steering drilling performance to predict ahead of a bit in a formation, where the steering can be geometrical or geological).
Boualleg fails to expressly teach validating the geologic model using one or more of mud logs and Elemental Capture Spectroscopy measurements.
Benson paragraph [0299] teaches confirming or early detection of drilling into or out of a geological formation, and enabling early detection and thus potential mitigation of drilling through undesirable geological formations. Benson paragraph [0299] teaches providing digital mud logs that can be correlated with gamma ray logs and drilling parameter logs, where the various correlated logs including the digital mud logs enable improved accuracy in determining an actual (validated) drilling location, such as a location of drill bit relative to a given formation as well as improved accuracy of other drilling information. Benson paragraph [0299] teaches that the analysis results from the mud analysis are integrated as feedback into a drilling and geosteering control loop implemented by a GCL (guidance control loop), where paragraph [0179] defines the GCL as having a drilling model class based on a borehole (geologic) model.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further combine the teachings of Benson because the logging data identifying undesirable geological formations (geological model) can be further correlated and validated with additional mud log data to improve accuracy in determining an actual drilling location. Therefore, it is well known to use at least additional mud log data as feedback into a drilling and geosteering control loop having a geological model to provide improved accuracy of drilling information in avoiding undesirable geological formations.
In regards to claim 29, Boualleg teaches the non-transitory computer readable medium wherein the operations further comprise:
determining a geologic model using the predicted values for a triple combo log (Boualleg paragraph [0233] teaches using the estimations (predicted values) of the LWD (including triple combo) log data to determine a geologic model of steering drilling performance to predict ahead of a bit in a formation, where the steering can be geometrical or geological). Boualleg fails to expressly teach validating the geologic model using one or more of mud logs and Elemental Capture Spectroscopy measurements.
Benson paragraph [0299] teaches confirming or early detection of drilling into or out of a geological formation, and enabling early detection and thus potential mitigation of drilling through undesirable geological formations. Benson paragraph [0299] teaches providing digital mud logs that can be correlated with gamma ray logs and drilling parameter logs, where the various correlated logs including the digital mud logs enable improved accuracy in determining an actual (validated) drilling location, such as a location of drill bit relative to a given formation as well as improved accuracy of other drilling information. Benson paragraph [0299] teaches that the analysis results from the mud analysis are integrated as feedback into a drilling and geosteering control loop implemented by a GCL (guidance control loop), where paragraph [0179] defines the GCL as having a drilling model class based on a borehole (geologic) model.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further combine the teachings of Benson because the logging data identifying undesirable geological formations (geological model) can be further correlated and validated with additional mud log data to improve accuracy in determining an actual drilling location. Therefore, it is well known to use at least additional mud log data as feedback into a drilling and geosteering control loop having a geological model to provide improved accuracy of drilling information in avoiding undesirable geological formations.
Pertinent Art
9. Applicants are directed to consider additional pertinent prior art included on the Notice of References Cited (PTOL 892) attached herewith. The Examiner has pointed out particular references contained in the prior art of record within the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the of the passage as taught by the prior art or disclosed by the Examiner. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
C. Chen et al. (US Pat. Pub. 2004/0133531) discloses Neural Network Training Data Selection Using Memory Reduced Cluster Analysis for Field Model Development. D. Fulton et al. (US Pat. Pub. 2009/0182693) discloses Determining Stimulation Design Parameters Using Artificial Neural Networks Optimized with a Genetic Algorithm.
E. Akkurt et al. (US Pat. Pub. 2021/0110280) discloses Integration Geoscience Data to Predict Formation Properties.
F. Xu et al. (US Pat. Pub. 2022/0325613) discloses System and Method for Petrophysical Modeling Automation Based on Machine Learning.
G. Mahiout et al. (US Pat. Pub. 2023/0084947) discloses Method for Identifying Environmental Effects in Downhole Data for Hydrocarbon Saturation Estimation.
H. Katterbauer et al. (US Pat. Pub. 2025/0230740) discloses Methods and Systems for Logging While Drilling and Optimized Telemetry Using Artificial Intelligence.
I. Li et al. (US Pat. Pub. 2025/0270915) discloses Geosteering Control Framework.
Conclusion
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PAUL D. LEE
Examiner
Art Unit 2857
/PAUL D LEE/Primary Examiner, Art Unit 2857 6/12/2026
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