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 .
The present application was filed on 05/17/2023. Claims 1-20 are pending and have been examined. Claims 1, 6 and 16 are the independent claims.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The present application is a continuation application of international application no. PCT/MY2021/050101, filed on 11/16/2021, which claims foreign priority to Malaysia application no. MYPI2020006118, filed on 11/20/2020.
The examiner acknowledges that a certified copy of Malaysia application No. MYPI2020006118 has been retrieved (on 05/17/2023), as required by 37 CFR 1.55. The examiner notes that a translation of Malaysia application No. MYPI2020006118 does not appear to have been furnished to-date.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/17/2023, 09/17/2024, 11/11/2025 and 11/11/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
The numbering of claims is not in accordance with 37 CFR 1.126 which requires the original numbering of the claims to be preserved throughout the prosecution. When claims are canceled, the remaining claims must not be renumbered. When new claims are presented, they must be numbered consecutively beginning with the number next following the highest numbered claims previously presented (whether entered or not).
There are duplicate claims numbered as claim 7, and that for examination purposes, the second claim “7” is considered to be claim 8, and current claims 8-20 are considered to be claims 9-21, something like this: There are two different claims designated as "Claim 7". For examination purposes, the second claim 7 is considered to be claim 8, and the claims numbered as claims 8-20 are considered to be claims 9-21. Appropriate correction is required.
Claim 18 objected to under 37 CFR 1.75 as being a substantial duplicate of claim 2. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim 19 objected to under 37 CFR 1.75 as being a substantial duplicate of claim 3. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim 20 objected to under 37 CFR 1.75 as being a substantial duplicate of claim 3. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim 21 objected to under 37 CFR 1.75 as being a substantial duplicate of claim 5. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are:
Claim 1:
an input portion configured to receive at least one output signal communicated from at least one apparatus coupled to the device.
the processing portion is configured to process the output signal by manner of machine learning-based processing to produce at least one prediction signal corresponding to at least one visually perceivable graphics-based signal.
Clam 2:
the output portion is configured to transmit the prediction signal to the apparatus.
Claim 3:
wherein the output portion is configured to display the visually perceivable graphics-based signal
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recites sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over Dusterhoft (“US 10,621,500 B2”) in view of Qian (“Intelligent prediction and integral analysis of shale oil and gas sweet spots”).
Claim 1.
Dusterhoft teaches an input portion configured to receive at least one output signal communicated from at least one apparatus coupled to the device, wherein the output signal is based on at least one production data normalized based on geology and geophysics (G&G)-based data (Column 8 “FIG. 3 is a diagram illustrating an example point and vector representation of a geological property data point in a volume of interest, such as a formation, according to aspects of the present disclosure. In the embodiment shown, the vector 300 comprises a location, a magnitude, a direction, and a length. The location corresponds to point 301, which may correspond to the physical location to which the data point represented by the vector 300 is associated” and Column 6 “Once well performance can be captured based on the implementation of the completion solution 124, the data resulting from the well performance can be used to validate and enable the modelled results to be compared to the actual output. This will enable self-validation of the generated models to verify the quality of the predictions, which can then be updated in the central repository for further use and analysis” teaches updating the data based on completed result, which corresponds to normalization, Column 7 “the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation” teaches actual data (production data) of geological property data); and
a processing portion coupled to the input portion (Column 6 “The design tool may receive the input 202 and output suggested design and formation/reservoir parameters 204” teaches design tool receive the input).
Dusterhoft does not explicitly teach a device suitable for sweet spot-based machine learning (SSML), the device comprising… wherein: the processing portion is configured to process the output signal by manner of machine learning-based processing to produce at least one prediction signal corresponding to at least one visually perceivable graphics-based signal, further wherein the visually perceivable graphics-based signal is displayable as a three-dimensional (3D) productivity volume, the three dimensional (3D) productivity volume is configured to be used in identifying at least one sweet spot location, and the at least one sweet spot location corresponds with a placement of a structure.
However, in the same field, analogous art Qian teaches a device suitable for sweet spot-based machine learning (SSML), the device comprising (2.2 Model training & Page 747 “The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes” teaches sweet spot detection for a machine learning model):
wherein: the processing portion is configured to process the output signal by manner of machine learning-based processing to produce at least one prediction signal corresponding to at least one visually perceivable graphics-based signal (2.2 Model training & Page 747 “selected from the attributes fed into the fuzzy mathematics work f low can be used as inputs for the training of an intelligent prediction model that uses machine learning methods…The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes. The equation is listed below:
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where w is weight vector, b is the constant deviation, and <w; X> indicates a generated function which shows the linear or nonlinear relationship between them. R means real number set, and Rd indicates a d-dimensional set. Machine learning is a process of multivariate nonlinear regression” teaches machine learning based on processing prediction, prediction produce in a multi-dimensional set),
further wherein the visually perceivable graphics-based signal is displayable as a three-dimensional (3D) productivity volume, the three dimensional (3D) productivity volume is configured to be used in identifying at least one sweet spot location, and the at least one sweet spot location corresponds with a placement of a structure (2.2 Model training & Page 747 “The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes. The equation is listed below:
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where w is weight vector, b is the constant deviation, and <w; X> indicates a generated function which shows the linear or nonlinear relationship between them. R means real number set, and Rd indicates a d-dimensional set. Machine learning is a process of multivariate nonlinear regression” teaches d-dimensional (3D dimension) set for the visual representation of identifying sweet spot location of structure).
Dusterhoft and Qian are analogous art because they are both directed to a data-driven modeling approach for predicting and optimizing subsurface resource extraction outcomes.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Qian into the disclosed invention of Dusterhoft.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “high accuracy of sweet spot prediction” can be achieve through a workflow that can “effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data”, as suggested by Qian (Qian Abstract, Page 746).
Claim 2.
As discussed above, Dusterhoft in view of Qian teaches the device according to claim 1,
Dusterhoft further teaches the device further comprising an output portion coupled to the processing portion, wherein the output portion is configured to transmit the prediction signal to the apparatus, and the apparatus is capable of being configured to display the visually perceivable graphics-based signal (Column 3 “Additional components of the information handling system may include one or more disk drives, one or more network ports for communication with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display” and Column 6 “Once well performance can be captured based on the implementation of the completion solution 124, the data resulting from the well performance can be used to validate and enable the modelled results to be compared to the actual output. This will enable self-validation of the generated models to verify the quality of the predictions, which can then be updated in the central repository for further use and analysis” teaches transmit the predictions and the display the visual representation).
Claim 3.
As discussed above, Dusterhoft in view of Qian teaches the device according to claim 1,
Dusterhoft further teaches wherein the output portion is configured to display the visually perceivable graphics-based signal (Column 3 “Additional components of the information handling system may include one or more disk drives, one or more network ports for communication with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display” and Column 9 “the single CRS may comprise a geographic coordinate system that identifies a location based on its latitude and longitude on the surface of the Earth, as well as its depth under the surface at that latitude and longitude. The coordinates of the point 301 within the CRS may be measured directly, or calculated and extrapolated from indirect sources. For example, the latitude and longitude of the drilling rig may be known based on a global positioning system at the rig site, and the orientation of the point with respect to the drilling rig may be known from downhole sensors (e.g., accelerometers, magnetometers, etc.)” teaches display the geographic data that is graphic based signal).
Claim 4.
As discussed above, Dusterhoft in view of Qian teaches the device according to claim 1, wherein the structure corresponds with a completed structure (Column 12 “including advanced earth models predicting the performance of a particular oil well or the results of a completion operation, as well as the actual performance of the well or completion operation and the design information used to plan the oilwell and completion operation” teaches the structure corresponds to a completion operation).
Claim 5.
As discussed above, Dusterhoft in view of Qian teaches the device according to claim 4,
Dusterhoft further teaches wherein the structure corresponds with a completed oil well (Column 12 “including advanced earth models predicting the performance of a particular oil well or the results of a completion operation, as well as the actual performance of the well or completion operation and the design information used to plan the oilwell and completion operation” teaches the structure corresponds to completion operation and oil well).
Claim 6.
As discussed above, Dusterhoft teaches a processing method in association with completion-based machine learning (COMML), the processing method comprising (Column 7 “the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation” teaches a machine learning with completion):
receiving data associated with a completed structure, the data associated with the completed structure corresponding with completion (COM) data (Column 7 “the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation” teaches receiving completion result (completion data));
collecting production data (Column 7 “the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation” teaches actual data (production data));
normalizing production data based on completion (COM) data to produce at least one output signal (Column 6 “Once well performance can be captured based on the implementation of the completion solution 124, the data resulting from the well performance can be used to validate and enable the modelled results to be compared to the actual output. This will enable self-validation of the generated models to verify the quality of the predictions, which can then be updated in the central repository for further use and analysis” teaches update the data based on completed result corresponds to normalization and compare the result with actual data (production data)).
Dusterhoft does not explicitly teach and processing the output signal by manner of machine learning-based processing to generate at least one prediction signal, wherein at least one predictive machine learning model is derivable based on the prediction signal.
Qian teaches and processing the output signal by manner of machine learning-based processing to generate at least one prediction signal, wherein at least one predictive machine learning model is derivable based on the prediction signal (2.2 Model training & Page 747 “selected from the attributes fed into the fuzzy mathematics work f low can be used as inputs for the training of an intelligent prediction model that uses machine learning methods…The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes” teaches machine-learning comprising prediction).
Dusterhoft and Qian are analogous art because they are both directed to a data-driven modeling approach for predicting and optimizing subsurface resource extraction outcomes.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Qian into the disclosed invention of Dusterhoft.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “high accuracy of sweet spot prediction” can be achieve through a workflow that can “effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data”, as suggested by Qian (Qian Abstract, Page 746).
Claim 7.
Dusterhoft in view of Qian teaches the processing method according to claim 6,
Dusterhoft further teaches further comprising pre-processing the completion (COM) data to generate pre-processed data, wherein the production data is normalized based on the pre-processed data to produce at least one output signal (Column 6-7 “the central data repository can be further leveraged to at least partially automate the design process. Specifically, one or more machine learning algorithms may use the design and simulation data as well as the actual measurements and model predictions within the repository to provide a starting point for design operations…Analytic analysis 212 may be run to improve the suggested parameter values and reduce the uncertainty in the models, with the process being iteratively repeated until an optimized design solution 214 is output, similar to the process described above with respect to FIG. 1… the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation” teaches processing completion data and actual data, actual data updated based on the completion data).
Claim 81.
Dusterhoft in view of Qian teaches the processing method according to claim 6,
Dusterhoft further teaches wherein the completed structure corresponds with a completed oil-well (Column 12 “including advanced earth models predicting the performance of a particular oil well or the results of a completion operation, as well as the actual performance of the well or completion operation and the design information used to plan the oilwell and completion operation” teaches the structure corresponds to completion operation and oil well).
Claim 92
Dusterhoft in view of Qian teaches the processing method according to claim 6,
Dusterhoft further teaches wherein the prediction signal is further processable for the purpose of one or both of: model validation, and model evaluation and optimization (Column 6 “In order to improve the resulting completion operation, sensitivity analyses 114 may be run to evaluate uncertainties in the estimated variables, such as formation permeability and drainage area, formation stress, fracture propagation, etc” and Column 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is for the purpose of model validation, evaluation, and optimization).
Claim 103.
Dusterhoft in view of Qian teaches the processing method according to claim 6,
Dusterhoft further teaches wherein the prediction signal is further processable for the purpose of model validation (Column 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is provide model validation).
Claim 114.
Dusterhoft in view of Qian teaches the processing method according to claim 6,
Dusterhoft further teaches wherein the prediction signal is further processable for the purpose of model evaluation and optimization (Column 6 “In order to improve the resulting completion operation, sensitivity analyses 114 may be run to evaluate uncertainties in the estimated variables, such as formation permeability and drainage area, formation stress, fracture propagation, etc” and Column 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is provide model evaluation and optimization).
Claim 125.
Dusterhoft in view of Qian teaches the processing method according to claim 6,
Dusterhoft further teaches wherein the prediction signal is further processable for the purpose of model validation, model evaluation and model optimization (Column 6 “In order to improve the resulting completion operation, sensitivity analyses 114 may be run to evaluate uncertainties in the estimated variables, such as formation permeability and drainage area, formation stress, fracture propagation, etc” and Column 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is for the purpose of model validation, evaluation, and optimization).
Claim 136.
Dusterhoft in view of Qian teaches the processing method according to claim 6,
Dusterhoft further teaches wherein the prediction signal is further processed for the purpose of model validation (Column 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is for the purpose of model validation).
Claim 147.
Dusterhoft in view of Qian teaches the processing method according to claim 6,
Dusterhoft further teaches wherein the prediction signal is further processed for the purpose of model evaluation and optimization (Column 6 “In order to improve the resulting completion operation, sensitivity analyses 114 may be run to evaluate uncertainties in the estimated variables, such as formation permeability and drainage area, formation stress, fracture propagation, etc” and Column 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is for the purpose of model evaluation, and optimization).
Claim 158.
Dusterhoft in view of Qian teaches the processing method according to claim 6,
Dusterhoft further teaches wherein the prediction signal is further processed for the purpose of model validation, model evaluation and model optimization (Column 6 “In order to improve the resulting completion operation, sensitivity analyses 114 may be run to evaluate uncertainties in the estimated variables, such as formation permeability and drainage area, formation stress, fracture propagation, etc” and Column 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is for the purpose of model evaluation, and optimization).
Claim 169.
Dusterhoft in view of Qian teaches the processing method according to claim 6,
Dusterhoft further teaches wherein at least one predictive machine learning model is derived based on the prediction signal (Column 5 “including advanced earth models predicting the performance of a particular oil well or the results of a completion operation” teaches the processing method is based on prediction).
Claim 1710.
Dusterhoft teaches a device comprising: an input portion receiving at least one output signal communicated from at least one apparatus coupled to the device, wherein: the at least one output signal is based on at least one production data, and the at least one production data is normalized based on geology and geophysics (G&G)-based data (Column 8 “FIG. 3 is a diagram illustrating an example point and vector representation of a geological property data point in a volume of interest, such as a formation, according to aspects of the present disclosure. In the embodiment shown, the vector 300 comprises a location, a magnitude, a direction, and a length. The location corresponds to point 301, which may correspond to the physical location to which the data point represented by the vector 300 is associated” and Column 6 “Once well performance can be captured based on the implementation of the completion solution 124, the data resulting from the well performance can be used to validate and enable the modelled results to be compared to the actual output. This will enable self-validation of the generated models to verify the quality of the predictions, which can then be updated in the central repository for further use and analysis” teaches update the data based on completed result corresponds to normalization, Column 7 “the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation” teaches actual data (production data));
and a processing portion coupled to the input portion (Column 6 “The design tool may receive the input 202 and output suggested design and formation/reservoir parameters 204” teaches design tool receive the input),
Dusterhoft does not explicitly teach wherein: the processing portion processes the at least one output signal by manner of machine learning-based processing to produce at least one prediction signal, the at least one prediction signal corresponds with at least one visually perceivable graphics-based signal, the at least one visually perceivable graphics-based signal is displayed as a three-dimensional (3D) productivity volume, the three dimensional (3D) productivity volume is used in identifying at least one sweet spot location, and the at least one sweet spot location corresponds with a placement of a structure.
However, in the same field, analogous art, Qian teaches wherein: the processing portion processes the at least one output signal by manner of machine learning-based processing to produce at least one prediction signal, the at least one prediction signal corresponds with at least one visually perceivable graphics-based signal (2.2 Model training & Page 747 “selected from the attributes fed into the fuzzy mathematics work f low can be used as inputs for the training of an intelligent prediction model that uses machine learning methods…The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes. The equation is listed below:
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where w is weight vector, b is the constant deviation, and <w; X> indicates a generated function which shows the linear or nonlinear relationship between them. R means real number set, and Rd indicates a d-dimensional set. Machine learning is a process of multivariate nonlinear regression” teaches machine learning based to produce prediction, prediction produce in multi-dimensional set),
the at least one visually perceivable graphics-based signal is displayed as a three-dimensional (3D) productivity volume, the three dimensional (3D) productivity volume is used in identifying at least one sweet spot location, and the at least one sweet spot location corresponds with a placement of a structure (2.2 Model training & Page 747 “The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes. The equation is listed below:
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where w is weight vector, b is the constant deviation, and <w; X> indicates a generated function which shows the linear or nonlinear relationship between them. R means real number set, and Rd indicates a d-dimensional set. Machine learning is a process of multivariate nonlinear regression” teaches d-dimensional set for the visual representation, identifying sweet spot location of structure).
Dusterhoft and Qian are analogous art because they are both directed to a data-driven modeling approach for predicting and optimizing subsurface resource extraction outcomes.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Qian into the disclosed invention of Dusterhoft.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “high accuracy of sweet spot prediction” achieve through a workflow that can “effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data”, as suggested by Qian (Qian Abstract, Page 746).
Claim 1811.
Dusterhoft in view of Qian teaches the device according to claim 1,
Dusterhoft further teaches the device further comprising an output portion coupled to the processing portion, wherein the output portion transmits the at least one prediction signal to the apparatus, and the apparatus displays the at least one visually perceivable graphics-based signal (Column 3 “Additional components of the information handling system may include one or more disk drives, one or more network ports for communication with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display” and Column 6 “Once well performance can be captured based on the implementation of the completion solution 124, the data resulting from the well performance can be used to validate and enable the modelled results to be compared to the actual output. This will enable self-validation of the generated models to verify the quality of the predictions, which can then be updated in the central repository for further use and analysis” teaches transmit the predictions and the display the visual representation).
Claim 1912.
Dusterhoft in view of Qian teaches the device according to claim 1,
Dusterhoft further teaches wherein the output portion displays the at least one visually perceivable graphics-based signal (Column 3 “Additional components of the information handling system may include one or more disk drives, one or more network ports for communication with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display” and Column 9 “the single CRS may comprise a geographic coordinate system that identifies a location based on its latitude and longitude on the surface of the Earth, as well as its depth under the surface at that latitude and longitude. The coordinates of the point 301 within the CRS may be measured directly, or calculated and extrapolated from indirect sources. For example, the latitude and longitude of the drilling rig may be known based on a global positioning system at the rig site, and the orientation of the point with respect to the drilling rig may be known from downhole sensors (e.g., accelerometers, magnetometers, etc.)” teaches display the geographic data that is graphic based signal).
Claim 2013.
Dusterhoft in view of Qian teaches the device according to claim 1, wherein the structure corresponds with a completed structure (Column 12 “including advanced earth models predicting the performance of a particular oil well or the results of a completion operation, as well as the actual performance of the well or completion operation and the design information used to plan the oilwell and completion operation” teaches the structure corresponds to a completion operation).
Claim 2114.
Dusterhoft in view of Qian teaches the device according to claim 4, wherein the structure corresponds with a completed oil well (Column 12 “including advanced earth models predicting the performance of a particular oil well or the results of a completion operation, as well as the actual performance of the well or completion operation and the design information used to plan the oilwell and completion operation” teaches the structure corresponds to completion operation and oil well).
Conclusion
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/LOKESHA PATEL/ Examiner, Art Unit 2125
/KAMRAN AFSHAR/ Supervisory Patent Examiner, Art Unit 2125
1 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, the second claim 7 is considered to be claim 8.
2 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 8 is considered to be claim 9 as claims 8-20 are considered to be claims 9-21
3 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 9 is considered to be claim 10 as claims 8-20 are considered to be claims 9-21
4 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 10 is considered to be claim 11 as claims 8-20 are considered to be claims 9-21
5 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 11 is considered to be claim 12 as claims 8-20 are considered to be claims 9-21
6 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 12 is considered to be claim 13 as claims 8-20 are considered to be claims 9-21
7 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 13 is considered to be claim 14 as claims 8-20 are considered to be claims 9-21
8 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 14 is considered to be claim 15 as claims 8-20 are considered to be claims 9-21
9 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 15 is considered to be claim 16 as claims 8-20 are considered to be claims 9-21
10 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 16 is considered to be claim 17 as claims 8-20 are considered to be claims 9-21
11 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 17 is considered to be claim 18 as claims 8-20 are considered to be claims 9-21
12 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 18 is considered to be claim 19 as claims 8-20 are considered to be claims 9-21
13 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 19 is considered to be claim 20 as claims 8-20 are considered to be claims 9-21
14 As indicated in the claim objection above, there are two different claims designated as "Claim 7" and for examination purposes, claim 20 is considered to be claim 21 as claims 8-20 are considered to be claims 9-21
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