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 . Claims 1-3, 6-7, 9-14, 17-23, and 25 are pending.
Drawings
The drawings are objected to because FIGS. 6, 9, 10A-10D feature informal screenshots, white font, and low resolution that’s not suitable for reproduction.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 3, 7, 10-14, 21, and 23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claims 3 and 21, applicant recites “one or more of: shape of the geological boundaries and sedimentological environments.” Applicant’s specification and previous manner of claiming would suggest that this is to be interpreted as the conjunctive, i.e., both shape of the geological boundaries and sedimentological environments are required. Further, it’s also unclear if “shape of” precedes both the “geological boundaries and “sedimentological environments” or only the former. The disclosure doesn’t provide much clarity, as seen on page 12 of applicant’s specification as filed. Given the ambiguity, the metes and bounds are unclear.
In claims 7 and 23, applicant recites “the DNN is a fully trained GeoNet,” where the term “fully” is a relative term that renders the claim indefinite. Applicant’s specification does not articulate what defines a “fully” trained model, whether that’s number of epochs or other objective criteria. Given the ambiguity, the metes and bounds are unclear.
In claim 10, applicant recites “compute a set of NN weights for each of the reconstruction of the PCA vector and the reconstruction of the perturbed PCA vector,” where it’s unclear if the “set of NN weights” represents aggregate weights that apply to both the “reconstruction of the PCA vector” and the “reconstruction of the perturbed PCA vector,” or if the computed weights represent respective weights associated with each of the “reconstruction of the PCA vector” and the “reconstruction of the perturbed PCA vector.” Given the ambiguity, the metes and bounds are unclear.
In claim 14, applicant recites “updating the DNN weights,” where the underlined lacks antecedent basis. Applicant had previously introduced a NN, not a DNN. Given the ambiguity, the metes and bounds are unclear.
Any claims dependent on the above are rejected by virtue of their dependency. Appropriate correction is required.
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 1-3, 6-7, 9-14, 17-23, and 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claims 1-3, 6-7, and 9 are to an apparatus (i.e., a machine), claims 10-14 to a method (i.e., a process), and claims 17-23 and 25 to a non-transitory computer-readable medium (i.e., a manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1 and 17 is (claim 1 being representative):
receive a geological model, the geological model including a 3D array of cells representative of a geological volume;
generate a probabilistic geological model, the probabilistic geological model including, for each cell, and for a set of J facies, a probability of the cell being each of the J facies, given the geological model and other predetermined conditions; and
output the probabilistic model.
The abstract idea of claim 10 is:
obtain a reconstruction mpca of a PCA vector obtained from a geological model of a geological volume;
obtain a reconstruction m̃pca of a perturbed PCA vector created from the geological model;
compute a set of NN weights for each of the reconstruction of the PCA vector and the reconstruction of the perturbed PCA vector;
compute a total loss, including a style loss, based on the respective NN weights; and
compute a backpropagation of the NN based upon the total loss.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. Examiner notes that the claims are broadly written and encompass an individual: receiving, generating, and outputting a probability model (claims 1 and 17); or generating a vector and a slightly altered (i.e., perturbed) version, wherein the vectors comprise geological data; defining a set of weights; calculating the output discrepancy via a loss function; and differentiating the loss function with respect to each weight using the chain rule (claim 10).
These are steps an administrator could accomplish with pen and paper and involve observing and evaluation data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe mathematical calculations that include: receiving, generating, and outputting a probability model (claims 1 and 17); or generating a vector and slightly altered (i.e., perturbed) version, wherein the vectors comprise geological data; defining a set of weights; calculating the output discrepancy via a loss function; and differentiating the loss function with respect to each weight using the chain rule (claim 10).
If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts, including mathematical relationships, mathematical formulas or equations, mathematical calculations, then it falls within the Mathematical Concepts grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites the following additional elements: a processor implementing a deep neural network ("DNN"); an input interface; an output interface.
Claim 10 recites no additional elements.
Claim 17 recites the following additional elements: one or more non-transitory computer-readable storage media comprising a set of instructions, which, when executed on a processor including a DNN module, cause the DNN module.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen on pages 5 and 25 of applicant’s specification as filed.
Examiner interprets the “a processor implementing a deep neural network ("DNN")” and the “DNN module,” described on page 5 of applicant’s specification as filed, as additional elements. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
[…]
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, “a processor implementing a deep neural network ("DNN")” and the “DNN module” are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 2-3, 6-7, 9, 11-14, 18-23, and 25 further narrow the abstract idea with additional steps and/or information and would fall into the same groupings highlighted above. This narrowing of the abstract idea does not integrate it into practical application or add significantly more.
Some of the dependent claims recite further additional elements: the DNN is a fully trained GeoNet (claims 7 and 23). Similar to above, these are generic computing elements, claimed in a results-oriented manner, that merely facilitate the tasks of the abstract idea. Whether viewed alone or in combination, these additional elements do not integrate the abstract idea into practical or add significantly more. See MPEP 2106.05(f).
Accordingly, claims 1-3, 6-7, 9-14, 17-23, and 25 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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.
Claims 10-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “3D CNN-PCA: A deep-learning-based parameterization for complex geomodels” by Liu et al. (NPL attached; hereinafter Liu).
Claim 10
Liu discloses:
A method of training a neural network ("NN") {See 2.1. PCA Representation: Besides generating new models, PCA can also be applied to approximately reconstruct realizations of the original models. We will see later that this is required for the supervised-learning-based loss function used to train 3D CNN-PCA.}, comprising:
obtain a reconstruction mpca of a PCA vector obtained from a geological model of a geological volume {See 2.3. 3D CNN-PCA formulation: As discussed in Section 2.1, we can approximately reconstruct realizations of the original model with PCA using Eq. (4) to obtain the corresponding reconstructed PCA model.};
obtain a reconstruction m̃pca of a perturbed PCA vector created from the geological model {See 2.3. 3D CNN-PCA formulation: To address this issue, we have found that it is beneficial to perturb the reconstructed PCA models used in training. We proceed by adding random noise to the in Eq. (3) […] The reconstructed PCA model after perturbation is shown in Fig. 1c.};
compute a set of NN weights for each of the reconstruction of the PCA vector and the reconstruction of the perturbed PCA vector {See 2.3. 3D CNN-PCA formulation: Each training sample consists of a pair of corresponding models (non-perturbed, perturbed) and an unrelated new PCA model. The new PCA model and the reconstructed PCA model are fed through the model transform net. The reconstruction loss is evaluated using and the original model and Eq. (10) […] The final loss entails a weighted combination of reconstruction loss, style loss and hard data loss. The trainable parameters in are updated based on the gradient of the loss computed with back-propagation.};
compute a total loss, including a style loss, based on the respective NN weights {See 2.3. 3D CNN-PCA formulation: The final loss entails a weighted combination of reconstruction loss, style loss and hard data loss.}; and
compute a backpropagation of the NN based upon the total loss {See 2.3. 3D CNN-PCA formulation: The trainable parameters in are updated based on the gradient of the loss computed with back-propagation.}.
Claim 11
Liu further discloses: further comprising at least one of: first receiving a style image and a hard data array for the geological volume {See 2.3 2.3. 3D CNN-PCA formulation: We experimented with several pretrained 3D CNNs for extracting features from 3D geomodels, which are required to compute style loss in 3D CNN-PCA […] A hard data loss term is also included to assure hard data (e.g., facies type at well locations) are honored. Hard data loss is given by […] indicating the presence of hard data at cell j and the absence of hard data. Examiner notes that “at least one of A, B, or C” style claiming requires only one of the alternatives.}; repeating the method for each of Nr times, where Nr is a number of geological model realizations from which the mpca was generated; or repeating the method for each of Nr times, where Nr is a number of geological model realizations from which the mpca was generated, in an inner loop, and wherein the inner loop for each of the Nr realizations is performed a number of times Nepochs, in an outer loop.
Claim 12
Liu further discloses: wherein compute the total loss further includes to compute both a reconstruction loss and a hard-data loss {See previous citation to 2.3. 3D CNN-PCA formulation}.
Claim 13
Liu further discloses: wherein each of the reconstruction loss, style loss and hard-data loss are weighted using user defined weights, in the total loss {See previous citation to 2.3. 3D CNN-PCA formulation}.
Claim 14
Liu further discloses: further comprising, following computation of the back propagation, updating the DNN weights {See previous citation to 2.3. 3D CNN-PCA formulation}.
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.
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.
Claims 1, 6, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Pandey (US 20200160173) in view of Camargo (US 20210254458).
Claims 1 and 17
Pandey discloses:
[Claim 1: An apparatus for computing {[0102] FIG. 9 illustrates a schematic diagram of a set of general components of an example computing device 900. In this example, the computing device 900 includes a processor 902 for executing instructions that can be stored in a memory device or element 904. The computing device 900 can include many types of memory, data storage, or non-transitory computer-readable storage media, such as a first data storage for program instructions for execution by the processor 902, a separate storage for images or data, a removable memory for sharing information with other devices, etc.}, comprising:]
[Claim 17: One or more non-transitory computer-readable storage media comprising a set of instructions, which, when executed on a processor including a DNN module {See previous citation to [0102]. Also see [0029]: In an embodiment as illustrated, the deep learning process 100 uses well logs as input data 103. In an example, the well logs provide one or more petrophysical properties, facies, and other related attributes along the trajectory of the wells. These properties available in the well logs are used for training a deep neural network (DNN) 110, such as a deep feedforward network, for predicting the petrophysical properties at the random locations in a region of interest away from the location of wells.}, cause the DNN module to:]
[an input interface, configured to] receive: a geological model {[0103] The computing device 900 typically may include some type of display element 906, such as a touch screen or liquid crystal display (LCD). As discussed, the computing device 900 in many embodiments will include at least one input element 910 able to receive conventional input from a user. [0054] FIG. 2B illustrates a flowchart of an example process 250 for displaying a 3D reservoir model using data from well logs.};
[a processor implementing a deep neural network ("DNN"), the processor configured to] generate a probabilistic geological model, the probabilistic geological model including, for each cell, and for a set of J facies, a probability of the cell being each of the J facies, given the geological model and other predetermined conditions {[0029] In an embodiment as illustrated, the deep learning process 100 uses well logs as input data 103. In an example, the well logs provide one or more petrophysical properties, facies, and other related attributes along the trajectory of the wells. These properties available in the well logs are used for training a deep neural network (DNN) 110, such as a deep feedforward network, for predicting the petrophysical properties at the random locations in a region of interest away from the location of wells.
[0097] In an embodiment, the softmax function provides a probability that a given sample point belongs to a particular facies. The observed facies values are converted to one-hot encoded values following a “winner-take-all” principle (e.g., where nodes in a layer compete with each other for activation, and only the node with the highest activation stays active while all other nodes are shut down). The facies labels in the one-hot encoded format contain binary indicators, which are 1 for indicating specific facies presence at a location and 0 otherwise. As referred to herein, one-hot encoding can refer to a group of bits among which the valid combinations of values are only those with a single high (1) bit and all the others low (0). As an example, for a dataset containing 3 facies, the one-hot encoded values for the 3 facies may be facies 1≡(1, 0, 0), facies 2≡(0, 1, 0), and facies 3≡(0, 0, 1). }; and
[an output interface, configured to] output the probabilistic model {[0099] FIG. 7 illustrates a perspective view of an example point cloud representation 700 of an output of a model for a petrophysical property (e.g., porosity) (e.g., corresponding to the first DNN in FIG. 2A) in accordance with some embodiments. As illustrated, the example point cloud representation 700 is a graphical representation of a point-cloud with 500,000 points. The different colors in FIG. 7 correspond to different respective values for the petrophysical property. It is appreciated that the number of points in the point cloud is for purposes of illustration only, and developing a model with a significantly larger number of points may be possible in a distributed memory architecture implementation of the subject technology.}.
Pandey doesn’t explicitly disclose, however, Camargo, in a similar field of endeavor directed to modeling subsurface reservoirs, teaches:
the geological model including a 3D array of cells representative of a geological volume {[0029] In the drawings, a computerized 3-dimensional geomechanical grid model M is shown in FIG. 1. The geomechanical grid model M is a computerized 3-dimensional grid cell model of subsurface rock formations in a region of interest for hydrocarbon production. The computerized geomechanical grid model M includes input data indicating relevant mechanical properties and boundary conditions of the subsurface region of interest. The subsurface region of interest is one being analyzed for further production from a hydrocarbon reservoir, or for other well operations such as fracturing. The geomechanical grid model M and its data contents are one component of a workflow W (FIG. 2) according to the present invention.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Pandey to include the features of Camargo. Given that Pandey is directed to predicting petrophysical properties, one of ordinary skill in the art would have been motivated to look to Camargo, in order to facilitate more accurate and reliable estimates of rock mechanical properties and stress responses in a subsurface formation {See [0003] of Camargo}.
Claim 6
Pandey further discloses: wherein the processor is further configured to apply an argmax function to each cell of the probabilistic model to obtain a facies map, with the same number of cells as the probabilistic model, with a facies value for each cell {[0097] As an example, the output softmax probabilities may then be evaluated against the one-hot encoded values of the facies labels at the sample point to calculate cross-entropy loss C given by the following equation (6): […] where K is the number of facies in the input data, and L are one-hot encoded values for the observed facies and y represents the probability of output belonging to a particular facies computed using the softmax function.}.
Claims 2-3, 9, 18-22, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Pandey and Camargo, further in view of Liu.
Claims 2 and 18
The combination of Pandey and Camargo, while teaching the features above, doesn’t explicitly teach, however, Liu, in a similar field of endeavor directed to deep-learning-based parameterization for complex geomodels, teaches: wherein the other predetermined conditions include: a style image, and hard data for predefined regions of the geological volume {See Abstract: Specifically, we introduce a new supervised-learning-based reconstruction loss, which is used in combination with style loss and hard data loss.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Pandey and Camargo to include the features of Liu. Given that Pandey is directed to predicting petrophysical properties, one of ordinary skill in the art would have been motivated to look to Liu, in order to improve the quality of parameterized models, thereby enhancing geological realism of said models {See Introduction of Liu}.
Claim 3
Pandey further discloses: wherein at least one of:
the probabilistic model is expressed as:
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the style image is an image that contains geological features to be reproduced in a geological model of the geological volume {[0043] At block 208, using at least the vertical and the horizontal variograms, an input feature is determined for providing to a first deep neural network (DNN), such as a deep feedforward network, for predicting a petrophysical property (e.g., porosity, lithology, water saturation, permeability, density, oil/water ratio, geochemical information, paleo data, etc.). In an example, for determining the input feature, the region of interest may be divided into layers using the range of a given vertical variogram. Further details of this approach are described in FIG. 4 below. Other types of (advanced) input features are also described further below. Examiner notes that “at least one of A, B, or C” style claiming requires only one of the alternatives.}; or
the style image is an image that contains geological features to be reproduced in a geological model of the geological volume, and wherein the geological features include one or more of: shape of the geological boundaries and sedimentological environments.
Claims 9 and 25
The combination of Pandey and Camargo, while teaching the features above, doesn’t explicitly teach, however, Liu, in a similar field of endeavor directed to deep-learning-based parameterization for complex geomodels, teaches: wherein the geological model is a reverse PCA reconstruction [of a geological model of the geological volume] {As represented by reconstructed PCA model, described in equations 1-4 in 2.1. PCA representation: Besides generating new models, PCA can also be applied to approximately reconstruct realizations of the original models.}
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Pandey and Camargo to include the features of Liu. Given that Pandey is directed to predicting petrophysical properties, one of ordinary skill in the art would have been motivated to look to Liu, in order to improve the quality of parameterized models, thereby enhancing geological realism of said models {See Introduction of Liu}.
Claim 19
Liu further teaches: wherein the probabilistic model is expressed as:
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{See 2.1. PCA representation, which describes PCA reconstruction of a geological model, including number of facies: We let the vector denote the set of geological variables (e.g., facies type in every cell or NF) that characterize the geomodel […] Besides generating new models, PCA can also be applied to approximately reconstruct realizations of the original models. style and hard-data described in 2.2. 2D CNN-PCA procedure and 2.3. 3D CNN-PCA formulation.}.
The motivation and rationale to include the additional features of Liu is the same as set forth previously.
Claim 20
Pandey further discloses: wherein the style image is an image that contains geological features to be reproduced in a geological model of the geological volume {[0043] At block 208, using at least the vertical and the horizontal variograms, an input feature is determined for providing to a first deep neural network (DNN), such as a deep feedforward network, for predicting a petrophysical property (e.g., porosity, lithology, water saturation, permeability, density, oil/water ratio, geochemical information, paleo data, etc.). In an example, for determining the input feature, the region of interest may be divided into layers using the range of a given vertical variogram. Further details of this approach are described in FIG. 4 below. Other types of (advanced) input features are also described further below.}.
Claim 21
Pandey further discloses: wherein the geological features include one or more of: shape of the geological boundaries and sedimentological environments {[0043] At block 208, using at least the vertical and the horizontal variograms, an input feature is determined for providing to a first deep neural network (DNN), such as a deep feedforward network, for predicting a petrophysical property (e.g., porosity, lithology, water saturation, permeability, density, oil/water ratio, geochemical information, paleo data, etc.). In an example, for determining the input feature, the region of interest may be divided into layers using the range of a given vertical variogram. Further details of this approach are described in FIG. 4 below. Other types of (advanced) input features are also described further below.}.
Claim 22
Pandey further discloses: wherein the processor is further configured to apply an argmax function to each cell of the probabilistic model to obtain a facies map, with the same number of cells as the probabilistic model, with a facies value for each cell {[0097] As an example, the output softmax probabilities may then be evaluated against the one-hot encoded values of the facies labels at the sample point to calculate cross-entropy loss C given by the following equation (6): […] where K is the number of facies in the input data, and L are one-hot encoded values for the observed facies and y represents the probability of output belonging to a particular facies computed using the softmax function.}.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Pandey and Camargo, further in view of Dana (US 20090161950).
Claim 7
The combination of Pandey and Camargo, while teaching the features above (including a facies map and geological aspect), doesn’t explicitly teach, however, Dana, in a similar field of endeavor directed to automated image processing, teaches: wherein at least one of:
the processor is further configured to take the input and at least one of:
apply a median filter to obtain an output model {[0037] 2) Execute a median filter on the region map R (e.g. each pixel P_ij is replaced by the median token ID of a 7.times.7 box around P_ij). Store the result in R-median. Examiner notes that “at least one of A, B, or C” style claiming requires only one of the alternatives.}; or
apply a median filter to the facies map, then impose hard data on each cell of the facies map for which there is hard data, to obtain an output geological model; or
the DNN is a fully trained GeoNet.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Pandey and Camargo to include the features of Dana. Given that Pandey is directed to predicting petrophysical properties, one of ordinary skill in the art would have been motivated to look to Dana, in order to facilitate the development of accurate and correct techniques that can be utilized in the operation of computers relating to images, to, for example, identify material and illumination characteristics of the image {See [0002] of Dana}.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Pandey, Camargo, and Liu, further in view of Dana.
Claim 23
The combination of Pandey, Camargo, and Liu, while teaching the features above (including a facies map and geological aspect), doesn’t explicitly teach, however, Dana, in a similar field of endeavor directed to automated image processing, teaches: wherein at least one of:
the processor is further configured to take the input and at least one of:
apply a median filter to obtain an output model {[0037] 2) Execute a median filter on the region map R (e.g. each pixel P_ij is replaced by the median token ID of a 7.times.7 box around P_ij). Store the result in R-median. Examiner notes that “at least one of A, B, or C” style claiming requires only one of the alternatives.}; or
apply a median filter to the facies map, then impose hard data on each cell of the facies map for which there is hard data, to obtain an output geological model; or
the DNN is a fully trained GeoNet.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Pandey, Camargo, and Liu to include the features of Dana. Given that Pandey is directed to predicting petrophysical properties, one of ordinary skill in the art would have been motivated to look to Dana, in order to facilitate the development of accurate and correct techniques that can be utilized in the operation of computers relating to images, to, for example, identify material and illumination characteristics of the image {See [0002] of Dana}.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Regeneration of channelized reservoirs using history-matched facies-probability map without inverse scheme” by Lee et al. (NPL attached), which teaches: In this study, a novel approach for re-static modeling scheme is proposed by history-matched facies-probability map without inverse modeling.
US 20210097390, which teaches: A method is described for predicting permeability including receiving a 3-D earth model including a volume of interest; generating 2-D property images; receiving 2-D fracture images; training a physics-guided neural network using the 2-D fracture images; and predicting permeability using the physics-guided neural network applied to the 2-D property images. The method is executed by a computer system.
US 20210165938, which teaches: A method, computer program product, and computing system are provided for defining one or more injector completions and one or more producer completions in one or more reservoir models. One or more edges between the one or more injector completions and the one or more producer completions in the one or more reservoir models may be defined. The one or more edges between the one or more injector completions and the one or more producer completions may define a graph network representative of the one or more reservoir models. The one or more reservoir models may be simulated along the one or more edges between the one or more injector completions and the one or more producer completions.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629
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