DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 4, and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bisht et al. (US 20240062119 A1) in view of Souza et al. (“CNN Prediction Enhancement by Post-Processing for Hydrocarbon Detection in Seismic Images”).
Regarding claim 1, Bisht et al. disclose a method for assessing the quality of LWD (Logging While Drilling) image logs (borehole images, [0092], processing the field equipment data using a trained machine learning model to generate a quality score for the field equipment data; and outputting the quality score, abstract, ingest various types of data files e.g., CSV, LAS, DLIS, raster, document, logs etc. where the data engine can output quality metrics, [0154], ML model service that can provide for data agnostic implementation for generation of one or more types of quality metrics for various types of data e.g., log data, seismic survey data, [0196], wireline logging, directional drilling, etc., [0267]), characterized in that it comprises: performing the processing of a plurality of LWD image logs (processing the field equipment data using a trained machine learning model to generate a quality score for the field equipment data; and outputting the quality score, abstract, a framework such as the TECHLOG framework can dynamically incorporate data as they are streamed directly from a wellsite for real-time processing and instantaneous analysis as a well is drilled, [0093], automatically processing the field equipment data using a trained machine learning model to generate a quality score for the field equipment data, [0133]); performing the normalization of each pseudo-image of the LWD image logs (As to a data pre-processing component, as explained, one or more ML models may be utilized to automatically classify data, uncover data type, etc. As an example, pre-processing can include analysis of the outliers, null treatment, standardization, normalization of data, etc., [0181], As an example, a data pre-processing engine can be implemented that provides for data treatment, which can include analysis of outliers, null treatment, standardization, normalization of data, skewness treatment, etc. As an example, a base data pre-processing pipeline can be implemented to perform actions to convert raw data into processed data before feeding to a trained ANN classifier, [0261]); and classifying the LWD image log according to its quality, which comprises classifying a plurality of sections of the LWD image log into three quality categories, including: good, medium or poor, using a trained neural network model for quality assessment of the LWD image log (processing the field equipment data using a trained machine learning model to generate a quality score for the field equipment data, [0133], a control action may be taken in response to a data metric being above, below or at a certain value (e.g., a threshold, etc.). For example, consider taking a sensor off-line where the sensor is generating data of poor quality, [0139], As an example, one or more trained ML models can be suitable for implementation in real-time workflows where streaming data from one or more sources can be assessed to output metrics (e.g., quality score, statues, etc.), [0151], a customizable color coding scheme can be implemented for highlighting data quality scores (e.g., on a scale such as 0 to 100), [0203], deep learning classifier to generate quality metrics (e.g., completeness, fairness, validity, accuracy, etc.)., [0248]) [as scores can be 0-100 this implies at a minimum low/medium/high quality, where low can be 0 high 100 and medium anything between].
Souza et al. disclose a method for assessing the quality of LWD (Logging While Drilling) image logs, characterized in that it comprises: performing the processing of a plurality of LWD image logs (Each image generated for the training set was post-processed through reconstruction, thresholding - binarization and deblurring -, and outlier removal, abstract, The pre-processing stage can be organized into three major steps: cleaning, patch generation, and data augmentation, with their technicalities, part IIB); subdividing the LWD image logs into smaller pseudo-images of the same size (
PNG
media_image1.png
786
474
media_image1.png
Greyscale
, part IIB); and classifying the LWD image log according to its quality, which comprises classifying a plurality of sections of the LWD image log into quality categories, using a trained neural network model for quality assessment of the LWD image log (a simple binarization was applied to the images to leave them with two labels only, thus identifying a given pixel as ``lead'' or ``no-lead''. This way, the original annotations of the geological bounds were eliminated to purge all the no-interest areas of the image and maintain the hydrocarbon region as interpreted. Lead pixels were colored
in white, whereas no-lead pixels were colored in black, resulting in a binary mask as depicted in Fig. 3,
PNG
media_image2.png
46
462
media_image2.png
Greyscale
, collectible vs non collectible designation, see Fig. 4, part IIB) [collectible interpreted as high quality and non-collectible interpreted as low quality].
Bisht et al. and Souza et al. are in the same art of wellbore/seismic/petroleum images (Bisht et al., [0038], [0059], [0092], [0267]; Souza et al., part I). The combination of Souza et al. with Bisht et al. enables subdividing the LWD image logs into smaller pseudo-images of the same size and performing the normalization of each pseudo-image of the LWD image logs. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the division of Souza et al. with the invention of Bisht et al. as this was known at the time of filing, the combination would have predictable results, and as Souza et al. state “To put another way, a proper classification of leads was hindered due to partial comprehensions of the network while parsing the image patches. Here, we resort to U-net to improve the process of lead identification through patch-based semantic segmentation and postprocessing (image reconstruction, thresholding and outlier removal). With a pixel-wise scrolling, a much more accurate prediction of the potential hydrocarbon zones is delivered. Moreover, since U-net is tailored to label whole images, its architecture becomes a suitable choice to deal with this particular problem. As it will be seen, our outcomes overperform those previously obtained with other methods” (part I), indicating a large commercial benefit of the combination in the petrochemical field.
Regarding claim 2, Bisht et al. and Souza et al. disclose the method according to claim 1. Bisht et al. and Souza et al. further indicate processing a plurality of LWD image logs comprises removing spurious points from the LWD image logs (Bisht et al., pre-processing, outlier treatment, [0185], “normal” and “abnormal” data, where those data points located far away from the normal data point space can be considered outliers and referred to as anomalies, [0260], data treatment service that can perform data adjustments, filtering, etc. Such an approach can include analysis of outliers, null treatment, completeness, standardization, normalization of data, etc., which may provide for conversion of raw data into a standard type of processed data with an improved quality score, [0281]; Souza et al., outlier removal, abstract, part I, part IIG3).
Regarding claim 4, Bisht et al. and Souza et al. disclose the method according to claim 1. Souza et al. further indicate performing data augmentation (The pre-processing stage
can be organized into three major steps: cleaning, patch generation, and data augmentation; to expand the image bank, data augmentation operations were applied to the patches, such as rotations and shearing part IIB).
Regarding claim 6, Bisht et al. and Souza et al. disclose the method according to claim 1. Souza et al. further indicate the neural network model for assessing the quality of the LWD image log comprises a convolutional neural network architecture followed by a direct network (
PNG
media_image3.png
274
674
media_image3.png
Greyscale
).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bisht et al. (US 20240062119 A1) and Souza et al. (“CNN Prediction Enhancement by Post-Processing for Hydrocarbon Detection in Seismic Images”) as applied to claim 1 above, further in view of Fu et al. (“Deep learning based lithology classification of drill core images”).
Regarding claim 3, Bisht et al. and Souza et al. disclose the method according to claim 1. Bisht et al. and Souza et al. do not disclose performing the processing a plurality of LWD image logs additionally comprises defining a single classification window of 120 rows and 120 columns.
Fu et al. teach defining a single classification window of 120 rows and 120 columns (
PNG
media_image4.png
432
604
media_image4.png
Greyscale
, These structures usually contain impurities other than their own lithology, such as sediment particles, tree roots, etc. An example of the image is shown in Fig 3. In addition to the lithology that needs to be classified, it is one way to add an extra ‘garbage’ class to all lithology categories to absorb these non-core parts, part 2.4).
Bisht et al. and Souza et al. and Fu et al. are in the same art of wellbore/seismic/petroleum images (Bisht et al., [0038], [0059], [0092], [0267]; Souza et al., part I; Fu et al., part 2.3). The combination of Fu et al. with Bisht et al. and Souza et al. enables subdividing the LWD image logs into defining a single classification window of 120 rows and 120 columns. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the 120x120 division of Fu et al. with the invention of Bisht et al. and Souza et al. as this was known at the time of filing, the combination would have predictable results, and as Fu et al. state “Drill core lithology is an important indicator reflecting the geological conditions of the drilling area. Traditional lithology identification usually relies on manual visual inspection, which is time-consuming and professionally demanding. In recent years, the rapid development of convolutional neural networks has provided an innovative way for the automatic prediction of drill core images. In this work, a core dataset containing a total of 10 common lithology categories in underground engineering was constructed… The test results show that the proposed method is optimal and effective for automatic lithology classification of borehole cores” and “The test results showed that larger crop sizes are generally better, include more lithology information, and have lower losses in training and validation” (part 2.2) indicating a large commercial benefit to the combination in the petrochemical field, and as it has been held that discovering an optimum value of a result effective variable involves only routine skill in the art In re Boesch, 617 F.2d 272, 205 USPQ 215 (CCPA 1980).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bisht et al. (US 20240062119 A1) and Souza et al. (“CNN Prediction Enhancement by Post-Processing for Hydrocarbon Detection in Seismic Images”) as applied to claim 1 above, further in view of Yang et al. (“An Automatic Method for Discontinuity Recognition in Coal-Measure Strata Borehole Images”).
Regarding claim 5, Bisht et al. and Souza et al. disclose the method according to claim 1. Bisht et al. and Souza et al. do not disclose the good quality category comprises a section where it is possible to identify boundaries between rock layers, as well as textural elements internal to the layers; the medium quality category comprises a section where it is possible to identify boundaries between layers of rocks, but it is not possible to identify elements internal to them; and the poor quality category comprises a missing section of boundaries between layers or their internal characteristics.
Yang et al. teach a good quality category comprises a section where it is possible to identify boundaries between rock layers, as well as textural elements internal to the layers; the medium quality category comprises a section where it is possible to identify boundaries between layers of rocks, but it is not possible to identify elements internal to them; and the poor quality category comprises a missing section of boundaries between layers or their internal characteristics (This paper presents a novel approach to automatically convert coal-measure strata borehole images into identified discontinuity maps, abstract, “In this study, we initially analyzed the underlying mechanisms of the poor quality of coal measure strata borehole images” “Second, discontinuities can be easily hidden in a noisy background for their recognition and identification. The coal-measure rocks are commonly rich in dark/dull bands due to the organic-bearing strata and therefore the borehole wall of coal measure strata is expected to be a dark hue”, p105074, discontinuity recognition, a meaningful textural feature is of primary importance for the clustering of image regions, extract textural features from the
coal-measure strata borehole images, rock discontinuity is characterized by the variation of size and geometry, discontinuity-induced textural variation, p105075, FCM enables us to evaluate the belonging of data points having maximum membership values below 0.6, because these points possess a high degree of uncertainty in their cluster membership, p105076, Instead of
making use of a gradient-based edge detector like the methods listed in Table 1 did (reasons are discussed in Section IV), we developed a method to pick out discontinuities through
recognizing typical patterns in the intensity transection of regions, p105077, The model in Figure 7d is commonly seen at discontinuities filled with reflective minerals or other interlayers, p105078, This indicates that the proposed method overcame the adverse conditions described in Section II and yielded quite satisfactory results for various types of discontinuities including
filled and unfilled fractures, cracks, beddings, interlayers, etc. More notably, the discontinuities identified in dashed boxes of Figure 9b-11b can be one-to-one corresponding to discontinuities traced in Figure 9a-11a. These dashed boxes covered the areas of rough cuts, uneven illumination, and mud contamination shown in Figure 1a b c, therefore, these results of recognition demonstrate that the adaptability of the proposed method to the coal-measure environment is as good as it of the manual tracing, p105079) [does not use terms “good quality” “medium quality” “poor quality” but does describe each of these conditions]
Bisht et al. and Souza et al. and Yang et al. are in the same art of wellbore/seismic/petroleum images (Bisht et al., [0038], [0059], [0092], [0267]; Souza et al., part I; Yang et al., abstract, part IV). The combination of Yang et al. with Bisht et al. and Souza et al. enables identifying good and bad images by their features. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the features of Yang et al. with the invention of Bisht et al. and Souza et al. as this was known at the time of filing, the combination would have predictable results, and as Yang et al. state “An automatic recognition of discontinuities in borehole images is a desirable way to overcome the inefficiency and inconsistency inherent in the conventional method of manual annotation” “The proposed method is proven to be superior in the respects of noise suppression, discontinuity positioning, and recognition completeness” (abstract) suggesting a large commercial benefit to the combination in the petrochemical field.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bisht et al. (US 20240062119 A1) and Souza et al. (“CNN Prediction Enhancement by Post-Processing for Hydrocarbon Detection in Seismic Images”) as applied to claim 6 above, further in view of Shebl et al. (US 20220207079 A1).
Regarding claim 7, Bisht et al. and Souza et al. disclose the method according to claim 6. Bisht et al. and Souza et al. do not disclose the convolutional neural network is a VGG16 network.
Shebl et al. teach the convolutional neural network is a VGG16 network (
PNG
media_image5.png
494
452
media_image5.png
Greyscale
, At a step s12, the one or more rock samples are classified, using the classifying module 104, by applying convolutional neural network (CNN) processing to the input rock image data to identify one or more attributes of the input rock samples. For example, the image data may be classified according to one or more of mineralogy (such as dolomite vs calcite based on presence or absence of dolomite crystals), texture, and rock quality, [0057], At a step s34, trained convolution neural network (CNN) processing may be used to classify the rock samples, for example by using the classifying module 104. For example, at a step s36, the thin section rock samples may be classified according to carbonate texture and reservoir rock quality. In some examples, the thin section rock samples may be classified at the step s36 according to dolomite, and rock quality classes, [0071], In examples, the second attribute 204 relates to Limestone Modified Dunham (1971) texture such as whether the texture is classified as Grainstone, Packestone, Wackestone or other texture. In examples, the third attribute 206 relates to the rock quality, such as whether the thin section is classified as high, medium, or low rock quality, [0073], FIG. 20 shows an example of a convolution neural network VGG 16—Open Source VGG16 Deep Convolution Neural Net architecture for implementation by the classifying module 104, [0140]).
Bisht et al. and Souza et al. and Shebl et al. are in the same art of analyzing rocks (Bisht et al., [0070]; Souza et al., part I; Shebl et al., abstract). The combination of Shebl et al. with Bisht et al. and Souza et al. enables using VGG16. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the VGG16 of Shebl et al. with the invention of Bisht et al. and Souza et al. as this was known at the time of filing, the combination would have predictable results, and as Shebl et al. state “The examples described herein may help improve rock description efficiency and accuracy, as well as helping accelerate Geomodel development” ([0028]) “the labelled image training database may be used to train convolution (CNN) and encoder-decoder convolution neural nets of varying architectures to a satisfactory accuracy” ([0129]) suggesting an efficiency and accuracy benefit when the inventions are combined.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US 20170199297 A1 (A method and a resistivity image logging tool connected or connectable to one or more processing devices process geological log data to construct missing information from destroyed or occluded parts using cues from observed data. The geological log data signals can be generated through use of the logging tool having one or more electrodes interacting with a formation intersected by a borehole. The processing involves the steps of: in respect of one or more data dimensions associated with missing values in a log data set, decomposing the signal into a plurality of morphological components; and morphologically reconstructing the signal such that missing values are estimated. The invention relates to geological log data processing methods and apparatuses. In particular the method and apparatus of the invention are useful for reconstructing missing or incomplete data in a region of a subterranean borehole that has undergone a logging process); US 10295691 B2 (In addition to the foregoing the existing image logging techniques produce an image of the rock at the interface defining the outer extremity of the borehole. Prior art image logging methods are not capable of interpolating to produce data on the approximately cylindrical region of rock that is removed during the process of drilling or otherwise forming the borehole. Moreover the prior art has not provided any good way of synthesizing images of cores, i.e. discrete cylindrical or essentially cylindrical sections of rock used e.g. for assessing various qualities of the rock in which a borehole is to be drilled or has been drilled. The data array for various reasons may include unknown values for f.sub.ij. Such reasons may include characteristics of imaging tools such as tool 10 which lead to corrupted, occluded, or incomplete data points. It is possible to define a mask m.sub.ij that has a value of 1 if a given f.sub.ij is known and 0 if f.sub.ij is unknown. As is evident from study of FIGS. 5 and 6 the geological features apparent at the boundary of the borehole as logged by the logging tool may in the reconstructed view be continued through the region of removed rock).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHELLE M ENTEZARI HAUSMANN whose telephone number is (571)270-5084. The examiner can normally be reached 10-7 M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vincent M Rudolph can be reached at (571) 272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHELLE M ENTEZARI HAUSMANN/Primary Examiner, Art Unit 2671