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 .
DETAILED ACTION
The following NON-FINAL Office Action is in response to application 18/452,290 filed on 08/18/2023. This communication is the first action on the merits.
Status of Claims
Claims 1-20 are currently pending and have been rejected as follows.
Drawings
The drawings filed on 08/18/2023 are accepted.
IDS
The information disclosure statement filed on 07/16/2024 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered.
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 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 discloses:
“…calibrating a model to detect formation boundaries, the calibrating being based on the log data from the set of reference wells; receiving log data from a set of target wells in the subsurface formation, the set of target wells being different than the set of reference wells; reconstructing log data from the set of target wells based on a machine learning model, the machine learning model being trained on the log data from the set of reference wells; determining depths of formation boundaries based on the model…” In this case, it is unclear which model the applicant is referring to, and thus Claim 1 is rejected under 35 U.S.C. 112(b).
Claim 1 further discloses:
“…determining similarities between log data in intervals defined by the determined depths of formation boundaries from two or more wells of the set of reference wells and the set of target wells…” In this case, it is unclear how many wells and of what type(s) can be used in determining similarities between log data in intervals defined by the determined depths of formation boundaries, and thus Claim 1 is rejected under 35 U.S.C. 112(b).
Regarding claims 2-11, 13-16. and 18-20, claims 2-11, 13-16, and 18-20 are rejected under 35 U.S.C. 112(b), second paragraph, due to their dependency from a rejected base claims given that Claims 12 and 17 recite analogous limitations. Appropriate correction is required.
Additionally, Claim 9 recites the limitation, which includes the method of claim 1, “wherein the log data comprises formation tops data, cuttings-based lithology data, and well logging data.” There is insufficient antecedent basis for this limitation in the claim. This is because “the log data” can be referring to the reference well log data, the target well log data, or the reconstructed log data, and thus Claim 9 is rejected under 35 U.S.C. 112(b).
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 and 3-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Representative Claim 1 recites:
A method for determining formation boundaries in a subsurface formation, the method comprising:
receiving log data from a set of reference wells in a subsurface formation;
calibrating a model to detect formation boundaries, the calibrating being based on the log data from the set of reference wells;
receiving log data from a set of target wells in the subsurface formation, the set of target wells being different than the set of reference wells;
reconstructing log data from the set of target wells based on a machine learning model, the machine learning model being trained on the log data from the set of reference wells;
determining depths of formation boundaries based on the model;
determining similarities between log data in intervals defined by the determined depths of formation boundaries from two or more wells of the set of reference wells and the set of target wells;
correlating formation boundaries between the two or more wells in the subsurface formation based on the determined similarities;
and generating a visual representation of the depth of formation boundaries in the subsurface formation based on the correlated formation boundaries.
The claim limitations in the abstract idea have been highlighted in bold above. They constitute a mental process and/or mathematical calculation, thus qualifies those elements as abstract ideas. The remaining limitations are “additional elements.”
Under Step 1 of the analysis, claim 1 does belong to a statutory category, namely it is a process claim. Likewise, claim 12 is an apparatus claim, and claim 17 is a machine claim.
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim., Under Step 2A, Prong One, the broadest reasonable interpretation of the steps recited in Claim 1 include at least one judicial exception, that being a mathematical concept and/or mental process. This can be seen in the claimed process steps of “receiving log data from a set of reference wells in a subsurface formation” (FIG. 2, [0008] of the instant specification), “calibrating a model to detect formation boundaries, the calibrating being based on the log data from the set of reference wells” (FIG. 2, [0027] of the instant specification), “receiving log data from a set of target wells in the subsurface formation, the set of target wells being different than the set of reference wells” (FIG. 2 (204a-d, 206a-b)), [0033] of the instant specification), “reconstructing log data from the set of target wells based on a machine learning model, the machine learning model being trained on the log data from the set of reference wells” (FIG. 2 ( blocks 220, 221), [0037] of the instant specification), “determining depths of formation boundaries based on the model” (FIG. 3 (step 310), [0046] of the instant specification), “determining similarities between log data in intervals defined by the determined depths of formation boundaries from two or more wells of the set of reference wells and the set of target wells” (FIG. 3 (step 312), [0047] of the instant specification), “correlating formation boundaries between the two or more wells in the subsurface formation based on the determined similarities” (FIG. 3 (step 314), [0048] of the instant specification), each of which encompasses mathematical concepts requiring specific mathematical calculations and/or mental steps.
For example, (See paras. [0034-0035] of the instant specification) where “A change point detection model can determine break points in series data…The change point detection model can have several architectures depending on the how the architecture performs on a given set of data…a dynamic programming change point detection model, a change point detection model with linear computation cost, a multiple change-point detection model with a reproducing kernel, a binary segmentation change point detection model, a bottom-up segmentation change point detection model, or a sliding window change point detection algorithm. Calibration of the change point detection model can include determining an effective change point detection model architecture, cost function, and hyper parameters (e.g., number of break points, minimum distance between change points, etc.)…an experiment can include the data processing system executing a change point detection model having a specified cost function 215, architecture or search method 216, set of well log data 217, and values of hyper parameters 218 with the input to the change point detection model being the log data from the reference wells 204…a metric can be based on an average distance between predicted depths and measured depths of formation boundaries in the reference wells. Another example metric can be based on a difference between a number of known formation boundaries and a number of predicted formation boundaries. Another example metric is given by:”
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to perform the process of determining the depths of formation boundaries based on the collected log data, and therefore encompasses mathematical concepts and/or mental steps. For example, when given the broadest reasonable interpretation in light of the specification, the steps of “receiving,” “calibrating,” “reconstructing,” “determining,” and “correlating” are performed using one or more training algorithms (machine learning model(s)) and/or mental steps. Claims 12 and 17 recite analogous judicial exceptions.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in claims 1, 12 and 17, the claimed method recites additional elements including “receiving log data from a set of reference wells in a subsurface formation” and “receiving log data from a set of target wells in the subsurface formation” and
“generating a visual representation of the depth of formation boundaries in the subsurface formation based on the correlated formation boundaries,” however these elements are found to be data gathering and output steps, which are recited at a high level of generality, and thus merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity.” Machine claims 12 and 17 recite analogous additional elements.
The generic data gathering, processing, and output steps, are recited at such a high level of generality (e.g. using “at least one processor; and a memory storing instructions” and “one or more non-transitory machine-readable storage devices storing instructions”) that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activit(ies) (claims 1, 12 and 17). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, displaying an output, and storing and retrieving information from memory.)
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1, as well as claims 12 and 17, amount to significantly more than the abstract idea.
Regarding dependent Claims 3-5, 9, 11, 13, 15, and 18, these claims merely add more abstract idea limitations. Therefore, the recited judicial elements in the claim limitations do not have further additional elements that would integrate the recited subject matter into a particular practical application. Therefore, these claims are rejected under 35 U.S.C. 101.
Regarding dependent Claims 6-8, 10, 14, 16, 19 and 20, these claims merely recite further detailed use of the machine learning model. However, the details remain under the category of generic computer processing unit. Although the claims (e.g. claim 6) recite a specific type of generic ML model, e.g. ensemble-based regression or ANN, this merely attempts to limit the abstract idea to a particular field of use and/or is still found to be the use of generic computer technology as a tool to apply the abstract idea. The other mentions of “training” in the claims, e.g. claim 7, merely specify the type of data parameters used and does not specify any particular details on how the model itself is being trained. The claims merely characterize the data inputs and outputs, e.g. claim 10, without describing any details of the functioning of the ML model itself. Therefore, the recited judicial elements in the claim limitations do not have further additional elements that would integrate the recited subject matter into a particular practical application for the reasons mentioned in Step 2A/2B above. Therefore, these claims are rejected under 35 U.S.C. 101.
Regarding Claim 2, “drilling a well at the determined location” is an additional element which is sufficient to integrate the claim into a particular practical application since it reflects a real-world transformation. Therefore, Claim 2 is not rejected under 35 U.S.C. 101.
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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Akkurt International Publication WO 2018/208634 A1 (hereinafter “Akkurt”)
Regarding representative claim 1, Akkurt teaches a method for determining formation boundaries in a subsurface formation, the method comprising: [Akkurt: Abstract]; receiving log data from a set of reference wells in a subsurface formation; calibrating a model to detect formation boundaries, the calibrating being based on the log data from the set of reference wells; receiving log data from a set of target wells in the subsurface formation, the set of target wells being different than the set of reference wells; (Akkurt, paras. [0143], [0096], [0162], respectively; [0143]: [“Given an ML model, the user then uses it to predict the formation properties, for the test wells, as shown in the bottom row. Unlike the training-wells, test-wells do not have the ground truth. Given the ML model (bottom row, middle) 310, predictor data from any test- well (bottom row, left) 308 is fed into the model, and formation properties (Sw and PHIT in this case) are predicted (bottom row, right) 312.”]; [0096]: [“A candidate well is defined as one that has the appropriate input and ground data that can be used in building an ML model.”]; [0162]: [A model may be built using a given a number of candidate training wells. The candidate wells have both the input data to be used in the prediction (e.g., MGL+DD+GR) and the ground truth to be used in the training (Sw, PHIT). The candidate wells may also have the WL or LWD logs that are used in the determination of the ground truth.”]);reconstructing log data from the set of target wells based on a machine learning model, the machine learning model being trained on the log data from the set of reference wells; determining depths of formation boundaries based on the model; (Akkurt, paras. [0108], [0110], [0243], [0053], [0212, steps 1-2 and 6], respectively; [0108]: [“Consider a case where one of the logs on the WL run is bad, for example, the density log. Either the problem is discovered too late to repeat the measurement, or a rerun is not considered for operational reasons. A replacement "density" log can be created in a number of ways: (i) using from MGL+DD alone from adjacent wells, (ii) using WL or LWD logs from adjacent wells, (iii) using a combination of (i) and (ii). The caveat in the third case is that invasion physics may be taken into account when combining data acquired at different times during the drilling of a well.”]; [0110]: [“Another variation is a well where there is no log data, due to well collapse, stuck pipe, instability, etc. Replacement logs can be computed from MGL+DD, as they would be acquired as soon as the bit penetrated the formation.”]; [0243]: [“Jaccard similarity is not sensitive to situations where the footprint of well A is a subset of well B. In these situations, Jaccard similarity will be less than one, even if the footprint of well A is completely contained within the footprint of well B. A user may identify these situations because, in this case, well B would be a strong candidate to build a predictive model to reconstruct logs in well A. Overlap similarity provides a way to identify such overlaps.”]; [0053]: [“The systems and methods disclosed herein may predict formation properties that are normally interpreted or measured directly, using Machine Learning (ML) Algorithms. The systems and methods disclosed herein use of Mud Gas Logs (MGL) and Drilling Data (DD), rather than Wireline (WL) or Logging-While-Drilling (LWD) logs, in the prediction of formation properties such as water saturation or total porosity.”]; [0212, steps 1-2 and 6]: [“1. The user selects a well on which to predict the response variable of interest. 2. A pre-existing Quantile Regression Forest (QRF) model is selected from a library or a new QRF model is created from a training dataset. 6. For each measured depth sample in the well of interest”]; determining similarities between log data in intervals defined by the determined depths of formation boundaries from two or more wells of the set of reference wells and the set of target wells; correlating formation boundaries between the two or more wells in the subsurface formation based on the determined similarities; (Akkurt, Claims: 13-14; paras. [0232], [0219], respectively; [Claim 13]: [“The computing system of claim 9, wherein the wells are sorted into the groups using a petrophysical similarity analysis that includes determining a similarity matrix using the well log data, the flag, or both.”]; [Claim 14]: [“The computing system of claim 9, wherein the wells are sorted into the groups on a well- by-well basis using one or more similarity metrics selected from the group consisting of Jaccard similarity, overlap similarity, overlap indicator, overlap similarity by row, one-way similarity by row, and a symmetric similarity matrix.”]; [0232]: [“For each possible pair of wells, compute the sample-wise Jaccard distance (equal to one minus the Jaccard similarity) by testing the footprint of the first well with the sample data of the second and vice versa.”]; [0219]: [“The term footprint is used to describe the decision boundary computed from a one-class support vector machine. In practice, a well may have multiple footprints if partitioning the logs into related groups MDL, DD, WL etc. A well can also have multiple footprints formed by splitting its logs by zone, facies or fluids.”]) and generating a visual representation of the depth of formation boundaries in the subsurface formation based on the correlated formation boundaries; (Akkurt, para. [0270]; [“The method 200 may also include sorting the wells into groups, as at 1206. The wells may be sorted based on the well log data and/or the flag. The wells may be sorted into groups using a petrophysical similarity analysis. The petrophysical similarity analysis may include computing a similarity matrix using the well log data and/or the flag. The similarity metrics used in the similarity analysis may include Jaccard, overlap, etc., as described in greater detail above. The results from the similarity analysis may be visualized.”]).
Regarding Claim 2, Akkurt teaches the elements of parent claim 1 as discussed above as well as the method of claim 1, further comprising: determining a location in the subsurface formation comprising hydrocarbons based on the correlated formation boundaries; and drilling a well at the determined location; (Akkurt, paras. [0104], [0261], [0037], respectively; [0104]: [“MGL+DD, in combination with cuttings shows and other non-traditional data can be used to flag zones containing hydrocarbons.”]; [0261]: [“The similarity metrics described above represent the similarity of petrophysical responses between wells. There is also information in the relative spatial location of the wells that may be used to weight the similarity of their petrophysical responses. Two wells that are relatively close spatially should have their petrophysical similarity weighted more highly than two wells that are further apart. A spatial proximity matrix is defined as a measure of proximity for each pair of wells.”]; [0037]: [For example, the management components 1 10 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150. In turn, further information about the geologic environment 150.”]).
Regarding Claim 3, Akkurt teaches the method of claim 1, wherein calibrating the model comprises determining a set of log data for each well that minimizes an uncertainty of determined locations of formation boundaries; (Akkurt, para. [0179]; [“One criterion for matching the new well data against the library of stored models is to minimize the amount of extrapolation that occurs when applying a model to the new well data. It is known that predictive models tend towards poor performance in extrapolation situations: those where the predictor variables for the new well fall outside the range of the training set for which the model was designed.”]).
Regarding Claims 4-5, Akkurt teaches the method of claim 3, wherein the model comprises a change point detection model; The method of claim 4, wherein the change point detection model comprises a dynamic programming change point detection model, a change point detection model with linear computation cost, a multiple change-point detection model with a reproducing kernel, a binary segmentation change point detection model, a bottom-up segmentation change point detection model, or a sliding window change point detection algorithm; (Akkurt, paras. [0215], [0217], [0218], respectively; [0215]: [“One-class Support Vector Machine [0216] A classification algorithm called the Support Vector Machine (SVM) may be used where the idea of the algorithm is to choose a small number of the training data samples (these are the so-called support vectors) to define a decision boundary which governs the classification process. SVMs have proven to be popular due to their flexibility in capturing complex decision boundaries. An extension to SVMs may allow the user to trace the boundary of a training data set, a problem they call domain description.”];
Specifically, wherein the change point detection model comprises a dynamic programming change point detection model;
Examiner note: Although not specifically named a dynamic programming change point detection model, the disclosure of SVMs in an analogous art in Akkurt read on the claimed limitation mentioned above considering that, for defining purposes, dynamic programming changepoint detection models are used to identify the locations of changepoints within a sequence, which rely on a penalty parameter to regulate the number of changepoints. To estimate this penalty parameter, a variety of simple models may be used such as linear models or decision trees (NPL: Nguyen, 2024).
Given the broadest reasonable interpretation, “creating a decision boundary from logs from one or more input wells” reads on “identifying the locations of changepoints in a sequence” and “parameter being used to create a decision boundary that is tight around the remaining bulk of the training set” reads on a “relying on a penalty parameter to regulate the number of changepoints” where the “penalty parameter is estimated” in steps 1-9 para. [0218].
[0217]: [“The parameters that control the algorithm are:
1. The outlier fraction, a small percentage (e.g., 5%) of the training samples can be treated as outside the decision boundary. This parameter may be used to create a decision boundary that is tight around the remaining bulk of the training set.
2. A parameter that controls the number of support vectors and hence the amount of detail in the decision boundary.”];
[0218]: [“The outlier fraction is a parameter in training the one-class SVM model for outlier detection. An automated procedure is used to pick the value of this parameter, as follows:
1. Select logs from one or more input wells;
2. Train a one-class support vector machine using a value of zero the outlier fraction;
3. Output trained model which defines the normal data footprint at zero percent outliers;
4. Test the entire training set against the footprint, compute the SVM score at each data point;
5. Compute the empirical cumulative distribution function of the SVM score at each data point;
6. Define a regular sampling of cumulative probability between 0 and 100% (e.g., 1%);
7. Resample the empirical cumulative distribution function of SVM scores to the regular sampling of cumulative probability defined in 6;
8. Compute the 2.sup.nd derivative of the resampled empirical cumulative distribution defined in 7;
9. Identify largest peak in the 2.sup.nd derivative subject to user specified limits on cumulative probability; and 10. Output the cumulative probability of the picked peak and the associated SVM score. The output cumulative probability is the value for the outlier fraction parameter.”]).
Regarding Claim 6, Akkurt teaches the method of claim 1, wherein the machine learning model comprises an ensemble-based regression model or an artificial neural network model; (Akkurt, paras. [0053], [0158], [0193], respectively; [0053]: [“The systems and methods disclosed herein may predict formation properties that are normally interpreted or measured directly, using Machine Learning (ML) Algorithms…The systems and methods disclosed herein may also use of two classes of ML algorithms, called Random Forest (RF) and Support Vector Machines (SVM)… RF is an ensemble method because it utilizes the output of many decision trees,” where the [See para. 0158] “RF algorithm” is a “variation” of the “Quantile Regression Forest (QRF) Algorithm.”]; [0193]: [“The idea of random forests is to construct a large number of regression trees from bootstrap samples of the training data.”]).
Regarding Claims 7-9, Akkurt teaches The method of claim 1, further comprising: training the machine learning model based on well log data, cuttings-based lithology data, drilling data, and mud gas data from the set of reference wells; [0142]: [“The wells used in the training or Model Building are called the training wells. A well in the training-set has the corresponding data for both the predictor and the ground truth (MGL+DD+GR, and Sw or PHIT, respectively).”]; [0138]: [“The systems and methods disclosed herein may use non-traditional logs, such as Mud Gas Logs (MGL) and Drilling Data (DD), with the addition of a GR log obtained from cuttings or an MWD/LWD run, to predict interpreted formation properties, that may also be determined from WL or LWD logs.”]; [0104]: [“While the WL or LWD logs may not have the resolution to detect laminated pay, MGL+DD, in combination with cuttings shows and other non-traditional data can be used to flag zones containing hydrocarbons. Early knowledge of such a zone may then lead to the collection of additional petrophysical information (e.g., cores or MDT tests) to validate the predictions.”];
[0212]: [“For each measured depth sample in the well of interest:
1. predict the quantiles for the discretized CCDF using the QRF model.
2. interpolate the discretized CCDF to a regular sampling of the response variable.
3. compute the CPDF by differencing the CCDF derived above.
4. compute the Information Gain, a measure of the distance between the CPDF and a reference uniform prior distribution.
5. predict the quantiles for the prediction interval using the QRF model.
6. compute the prediction interval width.”]).
The method of claim 1, wherein the machine learning model is trained on well log data, cuttings-based lithology data, drilling data, and mud gas data from the set of reference wells;
[0142]: [“The wells used in the training or Model Building are called the training wells. A well in the training-set has the corresponding data for both the predictor and the ground truth (MGL+DD+GR, and Sw or PHIT, respectively).”]; [0138]: [“The systems and methods disclosed herein may use non-traditional logs, such as Mud Gas Logs (MGL) and Drilling Data (DD), with the addition of a GR log obtained from cuttings or an MWD/LWD run, to predict interpreted formation properties, that may also be determined from WL or LWD logs.”]; [0104]: [“While the WL or LWD logs may not have the resolution to detect laminated pay, MGL+DD, in combination with cuttings shows and other non-traditional data can be used to flag zones containing hydrocarbons. Early knowledge of such a zone may then lead to the collection of additional petrophysical information (e.g., cores or MDT tests) to validate the predictions.”];
[0212]: [“For each measured depth sample in the well of interest:
1. predict the quantiles for the discretized CCDF using the QRF model.
2. interpolate the discretized CCDF to a regular sampling of the response variable.
3. compute the CPDF by differencing the CCDF derived above.
4. compute the Information Gain, a measure of the distance between the CPDF and a reference uniform prior distribution.
5. predict the quantiles for the prediction interval using the QRF model.
6. compute the prediction interval width.”]).
The method of claim 1, wherein the log data comprises formation tops data, cuttings-based lithology data, and well logging data; [0138]: [“The systems and methods disclosed herein may use non-traditional logs, such as Mud Gas Logs (MGL) and Drilling Data (DD), with the addition of a GR log obtained from cuttings or an MWD/LWD run, to predict interpreted formation properties, that may also be determined from WL or LWD logs.”]; [0104]: [“While the WL or LWD logs may not have the resolution to detect laminated pay, MGL+DD, in combination with cuttings shows and other non-traditional data can be used to flag zones containing hydrocarbons. Early knowledge of such a zone may then lead to the collection of additional petrophysical information (e.g., cores or MDT tests) to validate the predictions.”];
Regarding Claim 10, Akkurt teaches The method of claim 1, wherein reconstructing log data comprises: providing cuttings-based lithology data, drilling data, and mud gas data from the set of target wells as input to the machine learning model; and receiving the reconstructed log data as output from the machine learning model; (Akkurt, paras. [0142], [0143], [0108], [0110], [0104], respectively; [0142]: [“The wells used in the training or Model Building are called the training wells. A well in the training-set has the corresponding data for both the predictor and the ground truth (MGL+DD+GR, and Sw or PHIT, respectively).”]; [0143]: [“Given an ML model, the user then uses it to predict the formation properties, for the test wells, as shown in the bottom row. Unlike the training-wells, test- wells do not have the ground truth. Given the ML model (bottom row, middle) 310, predictor data from any test- well (bottom row, left) 308 is fed into the model, and formation properties (Sw and PHIT in this case) are predicted (bottom row, right) 312.”]; [0108]: [“Consider a case where one of the logs on the WL run is bad, for example, the density log. Either the problem is discovered too late to repeat the measurement, or a rerun is not considered for operational reasons. A replacement "density" log can be created in a number of ways: (i) using from MGL+DD alone from adjacent wells, (ii) using WL or LWD logs from adjacent wells, (iii) using a combination of (i) and (ii). The caveat in the third case is that invasion physics may be taken into account when combining data acquired at different times during the drilling of a well.”]; [0110]: [“Another variation is a well where there is no log data, due to well collapse, stuck pipe, instability, etc. Replacement logs can be computed from MGL+DD, as they would be acquired as soon as the bit penetrated the formation.”]; [0104]: [“The systems and methods disclosed herein may use non-traditional logs, such as Mud Gas Logs (MGL) and Drilling Data (DD), with the addition of a GR log obtained from cuttings or an MWD/LWD run, to predict interpreted formation properties, that may also be determined from WL or LWD logs.”]).
Regarding Claim 11, Akkurt teaches The method of claim 1, wherein determining similarities between identified intervals comprises determining similarities based on an adjusted Rand index, an adjusted mutual information metric, an area under a receiver operating characteristic curve, or an area under a precision-recall curve;
Specifically, an adjusted mutual information metric;
Examiner note: Although not specifically named an adjusted mutual information metric,
the process described in paras. [0144] and [0156] in an analogous art in Akkurt read on the claimed limitation mentioned above considering that, given the broadest reasonable interpretation, the process below involves quantitative mutual information values from a set of wells, and this data is adjusted in the way that 2 out of the 6 wells are used to build the RF model and the corrected data is applied to the remaining 4 wells, decisions being based on expected accuracy/precision where in order to decide whether the predictions made by the ML model are reliable enough for decision-making, the end-user may use some metric (uncertainty) to assess the quality of the answers provided.
(Akkurt, paras. [0144 and 0156], respectively; [0144]: [“An example for Sw prediction is shown in Figure 3B, which shows three tracks 320, 322, 324. Given a set of 6 wells, with MGL, DD, WL and core data, a RF model for Sw may be built from two wells, and applied to the remaining four wells. The comparison of the ground truth vs. the prediction, from one of the wells is shown in the middle track 322 of Figure 3B. The agreement between the predicted Sw and ELAN based SW is strong, supporting the case that the predictions are quantitative and have the accuracy/precision expected from logs.”]; [0156]: [“To decide whether the predictions made by the ML model are reliable enough for decision-making, the end-user may use some metric to assess the quality of the answers provided. Such metrics are referred to as measures of "uncertainty," and there are many different approaches and algorithms to produce them.”];
Regarding representative claim 12, Akkurt teaches A system for determining formation boundaries in a subsurface formation, the system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
receiving log data from a set of reference wells in a subsurface formation; calibrating a model to detect formation boundaries, the calibrating being based on the log data from the set of reference wells; receiving log data from a set of target wells in the subsurface formation, the set of target wells being different than the set of reference wells; reconstructing log data from the set of target wells based on a machine learning model, the machine learning model being trained on the log data from the set of reference wells; determining depths of formation boundaries based on the calibrated model; determining similarities between log data in intervals defined by the determined depths of formation boundaries from two or more wells of the set of reference wells and the set of target wells; correlating formation boundaries between the two or more wells in the subsurface formation based on the determined similarities; and generating a visual representation of the depth of formation boundaries in the subsurface formation based on the correlated formation boundaries; (Akkurt, para. [0004]; [“A computing system includes a processor and a memory system. The memory system includes a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the computing system to perform operations. The operations include…”]; (Akkurt, paras. [0143], [0096], [0162], respectively; [0143]: [“Given an ML model, the user then uses it to predict the formation properties, for the test wells, as shown in the bottom row. Unlike the training-wells, test-wells do not have the ground truth. Given the ML model (bottom row, middle) 310, predictor data from any test- well (bottom row, left) 308 is fed into the model, and formation properties (Sw and PHIT in this case) are predicted (bottom row, right) 312.”]; [0096]: [“A candidate well is defined as one that has the appropriate input and ground data that can be used in building an ML model.”]; [0162]: [A model may be built using a given a number of candidate training wells. The candidate wells have both the input data to be used in the prediction (e.g., MGL+DD+GR) and the ground truth to be used in the training (Sw, PHIT). The candidate wells may also have the WL or LWD logs that are used in the determination of the ground truth.”]); (Akkurt, paras. [0108], [0110], [0243], [0053], [0212, steps 1-2 and 6], respectively; [0108]: [“Consider a case where one of the logs on the WL run is bad, for example, the density log. Either the problem is discovered too late to repeat the measurement, or a rerun is not considered for operational reasons. A replacement "density" log can be created in a number of ways: (i) using from MGL+DD alone from adjacent wells, (ii) using WL or LWD logs from adjacent wells, (iii) using a combination of (i) and (ii). The caveat in the third case is that invasion physics may be taken into account when combining data acquired at different times during the drilling of a well.”]; [0110]: [“Another variation is a well where there is no log data, due to well collapse, stuck pipe, instability, etc. Replacement logs can be computed from MGL+DD, as they would be acquired as soon as the bit penetrated the formation.”]; [0243]: [“Jaccard similarity is not sensitive to situations where the footprint of well A is a subset of well B. In these situations, Jaccard similarity will be less than one, even if the footprint of well A is completely contained within the footprint of well B. A user may identify these situations because, in this case, well B would be a strong candidate to build a predictive model to reconstruct logs in well A. Overlap similarity provides a way to identify such overlaps.”]; [0053]: [“The systems and methods disclosed herein may predict formation properties that are normally interpreted or measured directly, using Machine Learning (ML) Algorithms. The systems and methods disclosed herein use of Mud Gas Logs (MGL) and Drilling Data (DD), rather than Wireline (WL) or Logging-While-Drilling (LWD) logs, in the prediction of formation properties such as water saturation or total porosity.”]; [0212, steps 1-2 and 6]: [“1. The user selects a well on which to predict the response variable of interest. 2. A pre-existing Quantile Regression Forest (QRF) model is selected from a library or a new QRF model is created from a training dataset. 6. For each measured depth sample in the well of interest”]).
Regarding Claim 13, Akkurt teaches The system of claim 12, wherein the model comprises a dynamic programming change point detection model, a change point detection model with linear computation cost, a multiple change-point detection model with a reproducing kernel, a binary segmentation change point detection model, a bottom-up segmentation change point detection model, or a sliding window change point detection algorithm; (Akkurt, paras. [0215], [0217], [0218], respectively; [0215]: [“One-class Support Vector Machine [0216] A classification algorithm called the Support Vector Machine (SVM) may be used where the idea of the algorithm is to choose a small number of the training data samples (these are the so-called support vectors) to define a decision boundary which governs the classification process. SVMs have proven to be popular due to their flexibility in capturing complex decision boundaries. An extension to SVMs may allow the user to trace the boundary of a training data set, a problem they call domain description.”];
Specifically, wherein the change point detection model comprises a dynamic programming change point detection model, considering that dynamic programming changepoint detection models are used to identify the locations of changepoints within a sequence, which rely on a penalty parameter to regulate the number of changepoints. To estimate this penalty parameter, a variety of simple models may be used such as linear models or decision trees (NPL: Nguyen, 2024).
[0217]: [“The parameters that control the algorithm are:
1. The outlier fraction, a small percentage (e.g., 5%) of the training samples can be treated as outside the decision boundary. This parameter may be used to create a decision boundary that is tight around the remaining bulk of the training set.
2. A parameter that controls the number of support vectors and hence the amount of detail in the decision boundary.”];
[0218]: [“The outlier fraction is a parameter in training the one-class SVM model for outlier detection. An automated procedure is used to pick the value of this parameter, as follows:
1. Select logs from one or more input wells;
2. Train a one-class support vector machine using a value of zero the outlier fraction;
3. Output trained model which defines the normal data footprint at zero percent outliers;
4. Test the entire training set against the footprint, compute the SVM score at each data point;
5. Compute the empirical cumulative distribution function of the SVM score at each data point;
6. Define a regular sampling of cumulative probability between 0 and 100% (e.g., 1%);
7. Resample the empirical cumulative distribution function of SVM scores to the regular sampling of cumulative probability defined in 6;
8. Compute the 2.sup.nd derivative of the resampled empirical cumulative distribution defined in 7;
9. Identify largest peak in the 2.sup.nd derivative subject to user specified limits on cumulative probability; and 10. Output the cumulative probability of the picked peak and the associated SVM score. The output cumulative probability is the value for the outlier fraction parameter.”]).
Regarding Claim 14, Akkurt teaches The system of claim 12, wherein the machine learning model comprises an ensemble-based regression model or an artificial neural network model; (Akkurt, paras. [0053], [0158], [0193], respectively; [0053]: [“The systems and methods disclosed herein may predict formation properties that are normally interpreted or measured directly, using Machine Learning (ML) Algorithms…The systems and methods disclosed herein may also use of two classes of ML algorithms, called Random Forest (RF) and Support Vector Machines (SVM)… RF is an ensemble method because it utilizes the output of many decision trees,” where (See [para. 0158]) the “RF algorithm” is a “variation” of the “Quantile Regression Forest (QRF) Algorithm.”]; [0193]: [“The idea of random forests is to construct a large number of regression trees from bootstrap samples of the training data.”]).
Regarding Claim 15, Akkurt teaches The system of claim 12, wherein the log data comprises formation tops data, cuttings-based lithology data, and well logging data; (Akkurt, paras. [0104], [0138], respectively; [0138]: [“The systems and methods disclosed herein may use non-traditional logs, such as Mud Gas Logs (MGL) and Drilling Data (DD), with the addition of a GR log obtained from cuttings or an MWD/LWD run, to predict interpreted formation properties, that may also be determined from WL or LWD logs.”]; [0104]: [“While the WL or LWD logs may not have the resolution to detect laminated pay, MGL+DD, in combination with cuttings shows and other non-traditional data can be used to flag zones containing hydrocarbons. Early knowledge of such a zone may then lead to the collection of additional petrophysical information (e.g., cores or MDT tests) to validate the predictions.”];
Regarding Claim 16, Akkurt teaches The system of claim 12, wherein reconstructing log data comprises: providing cuttings-based lithology data, drilling data, and mud gas data from the set of target wells as input to the machine learning model; and receiving the reconstructed log data as output from the machine learning model; (Akkurt, paras. [0142], [0143], [0108], [0110], [0104], respectively; [0142]: [“The wells used in the training or Model Building are called the training wells. A well in the training-set has the corresponding data for both the predictor and the ground truth (MGL+DD+GR, and Sw or PHIT, respectively).”]; [0143]: [“Given an ML model, the user then uses it to predict the formation properties, for the test wells, as shown in the bottom row. Unlike the training-wells, test- wells do not have the ground truth. Given the ML model (bottom row, middle) 310, predictor data from any test- well (bottom row, left) 308 is fed into the model, and formation properties (Sw and PHIT in this case) are predicted (bottom row, right) 312.”]; [0108]: [“Consider a case where one of the logs on the WL run is bad, for example, the density log. Either the problem is discovered too late to repeat the measurement, or a rerun is not considered for operational reasons. A replacement "density" log can be created in a number of ways: (i) using from MGL+DD alone from adjacent wells, (ii) using WL or LWD logs from adjacent wells, (iii) using a combination of (i) and (ii). The caveat in the third case is that invasion physics may be taken into account when combining data acquired at different times during the drilling of a well.”]; [0110]: [“Another variation is a well where there is no log data, due to well collapse, stuck pipe, instability, etc. Replacement logs can be computed from MGL+DD, as they would be acquired as soon as the bit penetrated the formation.”]; [0104]: [“The systems and methods disclosed herein may use non-traditional logs, such as Mud Gas Logs (MGL) and Drilling Data (DD), with the addition of a GR log obtained from cuttings or an MWD/LWD run, to predict interpreted formation properties, that may also be determined from WL or LWD logs.”]).
Regarding representative claim 17, Akkurt teaches One or more non-transitory machine-readable storage devices storing instructions for determining formation boundaries in a subsurface formation, the instructions being executable by one or more processors, to cause performance of operations comprising:
receiving log data from a set of reference wells in a subsurface formation; calibrating a model to detect formation boundaries, the calibrating being based on the log data from the set of reference wells; receiving log data from a set of target wells in the subsurface formation, the set of target wells being different than the set of reference wells; reconstructing log data from the set of target wells based on a machine learning model, the machine learning model being trained on the log data from the set of reference wells; determining depths of formation boundaries based on the model; determining similarities between log data in intervals defined by the determined depths of formation boundaries from two or more wells of the set of reference wells and the set of target wells; correlating formation boundaries between the two or more wells in the subsurface formation based on the determined similarities; and generating a visual representation of the depth of formation boundaries in the subsurface formation based on the correlated formation boundaries; (Akkurt, para. [0005]; [“A non-transitory computer-readable medium is also disclosed. The medium stores instructions that, when executed by a processor of a computing system, cause the computing system to perform operations. The operations include…”]; (Akkurt, paras. [0143], [0096], [0162], respectively; [0143]: [“Given an ML model, the user then uses it to predict the formation properties, for the test wells, as shown in the bottom row. Unlike the training-wells, test-wells do not have the ground truth. Given the ML model (bottom row, middle) 310, predictor data from any test- well (bottom row, left) 308 is fed into the model, and formation properties (Sw and PHIT in this case) are predicted (bottom row, right) 312.”];
[0096]: [“A candidate well is defined as one that has the appropriate input and ground data that can be used in building an ML model.”]; [0162]: [A model may be built using a given a number of candidate training wells. The candidate wells have both the input data to be used in the prediction (e.g., MGL+DD+GR) and the ground truth to be used in the training (Sw, PHIT). The candidate wells may also have the WL or LWD logs that are used in the determination of the ground truth.”]); (Akkurt, paras. [0108], [0110], [0243], [0053], [0212, steps 1-2 and 6], respectively; [0108]: [“Consider a case where one of the logs on the WL run is bad, for example, the density log. Either the problem is discovered too late to repeat the measurement, or a rerun is not considered for operational reasons. A replacement "density" log can be created in a number of ways: (i) using from MGL+DD alone from adjacent wells, (ii) using WL or LWD logs from adjacent wells, (iii) using a combination of (i) and (ii). The caveat in the third case is that invasion physics may be taken into account when combining data acquired at different times during the drilling of a well.”]; [0110]: [“Another variation is a well where there is no log data, due to well collapse, stuck pipe, instability, etc. Replacement logs can be computed from MGL+DD, as they would be acquired as soon as the bit penetrated the formation.”]; [0243]: [“Jaccard similarity is not sensitive to situations where the footprint of well A is a subset of well B. In these situations, Jaccard similarity will be less than one, even if the footprint of well A is completely contained within the footprint of well B. A user may identify these situations because, in this case, well B would be a strong candidate to build a predictive model to reconstruct logs in well A. Overlap similarity provides a way to identify such overlaps.”]; [0053]: [“The systems and methods disclosed herein may predict formation properties that are normally interpreted or measured directly, using Machine Learning (ML) Algorithms. The systems and methods disclosed herein use of Mud Gas Logs (MGL) and Drilling Data (DD), rather than Wireline (WL) or Logging-While-Drilling (LWD) logs, in the prediction of formation properties such as water saturation or total porosity.”]; [0212, steps 1-2 and 6]: [“1. The user selects a well on which to predict the response variable of interest. 2. A pre-existing Quantile Regression Forest (QRF) model is selected from a library or a new QRF model is created from a training dataset. 6. For each measured depth sample in the well of interest”]).
Regarding Claim 18, Akkurt teaches The non-transitory, machine-readable storage devices of claim 17, wherein the model comprises a dynamic programming change point detection model, a change point detection model with linear computation cost, a multiple change-point detection model with a reproducing kernel, a binary segmentation change point detection model, a bottom-up segmentation change point detection model, or a sliding window change point detection algorithm; (Akkurt, para. [0005]; [“A non-transitory computer-readable medium is also disclosed. The medium stores instructions that, when executed by a processor of a computing system, cause the computing system to perform operations. The operations include…”]; (Akkurt, paras. [0215], [0217], [0218], respectively; [0215]: [“One-class Support Vector Machine [0216] A classification algorithm called the Support Vector Machine (SVM) may be used where the idea of the algorithm is to choose a small number of the training data samples (these are the so-called support vectors) to define a decision boundary which governs the classification process. SVMs have proven to be popular due to their flexibility in capturing complex decision boundaries. An extension to SVMs may allow the user to trace the boundary of a training data set, a problem they call domain description.”];
Specifically, wherein the change point detection model comprises a dynamic programming change point detection model, considering that dynamic programming changepoint detection models are used to identify the locations of changepoints within a sequence, which rely on a penalty parameter to regulate the number of changepoints. To estimate this penalty parameter, a variety of simple models may be used such as linear models or decision trees (NPL: Nguyen, 2024).
[0217]: [“The parameters that control the algorithm are:
1. The outlier fraction, a small percentage (e.g., 5%) of the training samples can be treated as outside the decision boundary. This parameter may be used to create a decision boundary that is tight around the remaining bulk of the training set.
2. A parameter that controls the number of support vectors and hence the amount of detail in the decision boundary.”];
[0218]: [“The outlier fraction is a parameter in training the one-class SVM model for outlier detection. An automated procedure is used to pick the value of this parameter, as follows:
1. Select logs from one or more input wells;
2. Train a one-class support vector machine using a value of zero the outlier fraction;
3. Output trained model which defines the normal data footprint at zero percent outliers;
4. Test the entire training set against the footprint, compute the SVM score at each data point;
5. Compute the empirical cumulative distribution function of the SVM score at each data point;
6. Define a regular sampling of cumulative probability between 0 and 100% (e.g., 1%);
7. Resample the empirical cumulative distribution function of SVM scores to the regular sampling of cumulative probability defined in 6;
8. Compute the 2.sup.nd derivative of the resampled empirical cumulative distribution defined in 7;
9. Identify largest peak in the 2.sup.nd derivative subject to user specified limits on cumulative probability; and 10. Output the cumulative probability of the picked peak and the associated SVM score. The output cumulative probability is the value for the outlier fraction parameter.”]).
Regarding Claim 19, Akkurt teaches The non-transitory, machine-readable storage devices of claim 17, wherein the machine learning model comprises an ensemble-based regression model or an artificial neural network model; (Akkurt, para. [0005]; [“A non-transitory computer-readable medium is also disclosed. The medium stores instructions that, when executed by a processor of a computing system, cause the computing system to perform operations. The operations include…”]; (Akkurt, paras. [0053], [0158], [0193], respectively;
[0053]: [“The systems and methods disclosed herein may predict formation properties that are normally interpreted or measured directly, using Machine Learning (ML) Algorithms…The systems and methods disclosed herein may also use of two classes of ML algorithms, called Random Forest (RF) and Support Vector Machines (SVM)… RF is an ensemble method because it utilizes the output of many decision trees,” where (See para. [0158]) the “RF algorithm” is a “variation” of the “Quantile Regression Forest (QRF) Algorithm.”]; [0193]: [“The idea of random forests is to construct a large number of regression trees from bootstrap samples of the training data.”]).
Regarding Claim 20, Akkurt teaches The non-transitory, machine-readable storage devices of claim 17, wherein reconstructing log data comprises: providing cuttings-based lithology data, drilling data, and mud gas data from the set of target wells as input to the machine learning model; and receiving the reconstructed log data as output from the machine learning model; (Akkurt, para. [0005]; [“A non-transitory computer-readable medium is also disclosed. The medium stores instructions that, when executed by a processor of a computing system, cause the computing system to perform operations. The operations include…”];
(Akkurt, paras. [0142], [0143], [0108], [0110], [0104], respectively; [0142]: [“The wells used in the training or Model Building are called the training wells. A well in the training-set has the corresponding data for both the predictor and the ground truth (MGL+DD+GR, and Sw or PHIT, respectively).”]; [0143]: [“Given an ML model, the user then uses it to predict the formation properties, for the test wells, as shown in the bottom row. Unlike the training-wells, test- wells do not have the ground truth. Given the ML model (bottom row, middle) 310, predictor data from any test- well (bottom row, left) 308 is fed into the model, and formation properties (Sw and PHIT in this case) are predicted (bottom row, right) 312.”]; [0108]: [“Consider a case where one of the logs on the WL run is bad, for example, the density log. Either the problem is discovered too late to repeat the measurement, or a rerun is not considered for operational reasons. A replacement "density" log can be created in a number of ways: (i) using from MGL+DD alone from adjacent wells, (ii) using WL or LWD logs from adjacent wells, (iii) using a combination of (i) and (ii). The caveat in the third case is that invasion physics may be taken into account when combining data acquired at different times during the drilling of a well.”];
[0110]: [“Another variation is a well where there is no log data, due to well collapse, stuck pipe, instability, etc. Replacement logs can be computed from MGL+DD, as they would be acquired as soon as the bit penetrated the formation.”]; [0104]: [“The systems and methods disclosed herein may use non-traditional logs, such as Mud Gas Logs (MGL) and Drilling Data (DD), with the addition of a GR log obtained from cuttings or an MWD/LWD run, to predict interpreted formation properties, that may also be determined from WL or LWD logs.”]).
Conclusion
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/LOGAN D COONS/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857