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 § 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his 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. Specifically, exemplary claim 1 recites, wherein the dysfunction prediction is automatically associated with the input dataset that resulted in the dysfunction prediction. However, the element of the input dataset lacks clear antecedent basis. For this reason, the above listed claims are rejected for containing this language or being dependent on a claim that contains this language.
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) 1-4, 6-11, 13-14, 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ben et al. (hereinafter Ben), Development and Application of a Real-Time Drilling State Classification Algorithm with Machine Learning, in view of Ambrus et al. (hereinafter Ambrus), U.S. Patent Application Publication 2022/0284330, further in view of Zha et al. (hereinafter Zha), Monitoring downhole drilling vibrations using surface data through deep learning, further in view of Maximo, U.S. Patent Application Publication 2022/0327713.
Regarding Claim 1, Ben discloses a method for converting time series real-time drilling data into a dysfunction prediction of a downhole characteristic in a wellbore environment, the method comprising:
receiving or retrieving a first stream comprising the time series real-time drilling data [“data stream coming from various rig sensors each second” pg. 2 ¶3];
performing a machine-learning model on the first stream to obtain rig states [“a model that can detect the drilling rig state automatically, in real-time” pg. 2 ¶3] and to output a second stream comprising the time series real-time drilling data and the rig states [“system uses low-level time-series data labeled with a rig state for multiple higher-level models” pg. 10 ¶2];
determining trends of at least one drilling parameter in the third stream to obtain a fourth stream of trend analysis data comprising trend statuses [“patterns in the rig state” pg. 10 ¶3; “statistics and trends” pg. 10, ¶3; “trends during operations” pg. 12 ¶1], …, and the rig states [“patterns in the rig state” pg. 10 ¶3].
However, Ben fails to explicitly disclose preprocessing the second stream to obtain a third stream comprising cleaned rig data and the rig states;
the cleaned rig data.
Ambrus discloses preprocessing the second stream to obtain a third stream comprising cleaned rig data and the rig states [“A preprocessing step 120 may be performed upon the collected sensor readings. The preprocessing step may identify missing data and/or identify data outliers. Missing data identified in step 120 may indicate that a sensor is off-line and/or the sensor reading could not be collected for whatever reason. Rather than erroneously attributing a value, such as zero, to missing sensor readings, preprocessing step 120 may identify those sensor reading gaps and eliminate those gaps from the data set. Preprocessing step 120 may further identify outliers in the sensor readings collected in step 110. Outliers identified in step 120 may comprise, for example, physically impossible sensor readings and/or readings that are clearly impossible based upon historical trends of that or other sensors and/or contemporaneous readings of related sensors.” ¶31];
the cleaned rig data [“A preprocessing step 120 may be performed upon the collected sensor readings. The preprocessing step may identify missing data and/or identify data outliers. Missing data identified in step 120 may indicate that a sensor is off-line and/or the sensor reading could not be collected for whatever reason. Rather than erroneously attributing a value, such as zero, to missing sensor readings, preprocessing step 120 may identify those sensor reading gaps and eliminate those gaps from the data set. Preprocessing step 120 may further identify outliers in the sensor readings collected in step 110. Outliers identified in step 120 may comprise, for example, physically impossible sensor readings and/or readings that are clearly impossible based upon historical trends of that or other sensors and/or contemporaneous readings of related sensors.” ¶31].
It would have been obvious to one having ordinary skill in the art, having the teachings of Ben and Ambrus before him before the effective filing date of the claimed invention, to modify the method of Ben to incorporate the data preprocessing of Ambrus.
Given the advantage of using clean data which provides more accurate results, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Ben fails to explicitly disclose generating a time segmented drilling data batch comprising the trend analysis data received over a window of time; and
performing a deep learning model on the time segmented drilling data batch to obtain the dysfunction prediction and to output a fifth stream comprising the dysfunction prediction of the downhole characteristic associated with the wellbore environment; and
retraining the deep learning model based on the dysfunction prediction to produce an updated deep learning model, wherein the dysfunction prediction is automatically associated with the input dataset that resulted in the dysfunction prediction.
Zha discloses generating a time segmented drilling data batch comprising the trend analysis data received over a window of time [“dataset is then cut into short windows (e.g. 60 seconds). Each short window of multichannel surface data is treated as an input sample (𝑋𝑖) for the classification model” pg. 2102, col. 2, ¶2]; and
performing a deep learning model on the time segmented drilling data batch to obtain the dysfunction prediction and to output a fifth stream comprising the dysfunction prediction of the downhole characteristic associated with the wellbore environment [“The model consists of a parallel combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs are one type of deep neural networks that is well suited for image processing and pattern recognition” pg. 2103, col. 1, ¶2; “final output is a label corresponding to the type of vibration” pg. 2103, col. 2 ¶1; Fig. 2]; and
retraining the deep learning model based on the dysfunction prediction to produce an updated deep learning model, wherein the dysfunction prediction is automatically associated with the input dataset that resulted in the dysfunction prediction [Fig. 2; Examiner Note: This figure shows that input data is associated with the output automatically since the input data produces the output data].
It would have been obvious to one having ordinary skill in the art, having the teachings of Ben, Ambrus, and Zha before him before the effective filing date of the claimed invention, to modify the combination to incorporate further modeling of data to predict anomalies of Zha.
Given the advantage of further processing the data to obtain predictions to avoid equipment damage or delays, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Ben fails to explicitly disclose retraining the deep learning model based on the dysfunction prediction to produce an updated deep learning model, wherein the dysfunction prediction is automatically associated with the input dataset that resulted in the dysfunction prediction.
Maximo discloses retraining the deep learning model based on the dysfunction prediction to produce an updated deep learning model, wherein the dysfunction prediction is automatically associated with the input dataset that resulted in the dysfunction prediction [“the training of the deep learning model may be validated by a user (e.g., via a user input) and/or based on a set of validation data, and the deep learning model may be retrained and/or the training of the deep learning model may be adjusted based on the validation.” ¶21].
It would have been obvious to one having ordinary skill in the art, having the teachings of Ben, Ambrus, Zha, and Maximo before him before the effective filing date of the claimed invention, to modify the combination to incorporate the retraining based on validation of Maximo.
Given the advantage of ensuring high accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 2, Ben, Ambrus, Zha, and Maximo disclose the method of claim 1. Ben further discloses sending a first indicator and a second indicator for the dysfunction prediction to a user device [“the web-based user interface” pg. 10 ¶2; Figs. 9-11].
However, Ben fails to explicitly disclose further comprising:
determining a severity of the dysfunction prediction of the downhole characteristic; and
wherein the first indicator is indicative of a value of the dysfunction prediction associated with the window of time and the second indicator is indicative of the severity of the dysfunction prediction associated with the window of time.
Zha discloses further comprising:
determining a severity of the dysfunction prediction of the downhole characteristic [“the severity of downhole vibrations” pg. 1, col. 1, ¶2; “recognize patterns in the surface data relating to severe downhole lateral and torsional vibrations” pg. 1, col. 2, ¶1; “In the severe form of stick-slip, torsional vibration is coupled with lateral and axial vibrations” pg. 1, col. 2 ¶2]; and
wherein the first indicator is indicative of a value of the dysfunction prediction associated with the window of time and the second indicator is indicative of the severity of the dysfunction prediction associated with the window of time [Fig. 1; Note: the figure discloses values and severity of the vibrations].
It would have been obvious to one having ordinary skill in the art, having the teachings of Ben, Ambrus, Zha, and Maximo before him before the effective filing date of the claimed invention, to modify the combination to incorporate further modeling of data to predict anomalies of Zha.
Given the advantage of further processing the data to obtain predictions to avoid equipment damage or delays, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 3, Ben, Ambrus, Zha, and Maximo disclose the method of claim 2. Ben further discloses wherein the first indicator and the second indicator are sent to the user device via an application programming interface (API) [“API – Application Programming Interface” pg. 13 ¶4; Note: it is well-known by one having skill in the art that API’s are used to integrate several systems including data gathering and GUIs.].
Regarding Claim 4, Ben, Ambrus, Zha, and Maximo disclose the method of claim 2. Ben further discloses wherein the first indicator and the second indicator are visually depicted on a graph that is displayed on the user device, wherein the first indicator is a data point on the graph, and the second indicator is a color or pattern of the data point [Fig. 9-11; Note: these figures disclose visually displaying data using both data points and colors].
Regarding Claim 6, Ben, Ambrus, and Zha disclose the method of claim 1. Ben further discloses wherein the at least one drilling parameter comprises of a standpipe pressure, a hook load [“rig-specific hook load” pg. 3 ¶1], a flow rate of a wellbore fluid, or a combination thereof.
Regarding Claim 7, Ben, Ambrus, Zha, and Maximo disclose the method of claim 6.
However, Ben fails to explicitly disclose wherein the downhole characteristic is a stuck pipe or washout.
Ambrus discloses wherein the downhole characteristic is a stuck pipe or washout [unplanned events may refer to influxes or losses of drilling mud to the formation, drillstring washouts, etc.” ¶41].
It would have been obvious to one having ordinary skill in the art, having the teachings of Ben, Ambrus, Zha, and Maximo before him before the effective filing date of the claimed invention, to modify the combination to incorporate a well-known downhole characteristic of washouts.
Given the advantage of predicting potential problems in order to avoid or reduce the damage, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 8, Ben, Ambrus, Zha, and Maximo disclose the method of claim 1. Ben further discloses wherein each of the trend statuses is normal or abnormal [“detect abnormal drilling events” Abstract].
Regarding Claim 9, Ben, Ambrus, Zha, and Maximo disclose the method of claim 1, wherein each of the rig states is rotary drilling, slide drilling, tripping in, tripping out, circulating, connection, washing, pulling out of the hole (POOH), running in the hole (RIH), reaming up, or reaming down [“drilling states including: slide drilling, rotate drilling, pick up, in slips, and others” pg. 1 ¶1; “classify the data into seventeen rig states” pg. 1 ¶2].
Regarding Claim 10, Ben, Ambrus, Zha, and Maximo disclose the method of claim 1. Ben further discloses wherein the window of time is from about 1 minute to about 1 hour [“20 seconds of drilling in a given window” pg. 5, ¶1; “Moving Window” Fig. 3; Note: 20 seconds is about 1 minute].
Regarding Claim 11, Ben, Ambrus, Zha, and Maximo disclose the method of claim 1. Ben further discloses wherein determining trends comprises: …, hook loads [“rig-specific hook load” pg. 3 ¶1], torques [“torque were chosen as the main two features” pg. 5 ¶1].
However, Ben fails to explicitly disclose calculating standpipe pressures… and wellbore fluid flow rates for the cleaned rig data to obtain the stream of trend analysis data containing trend statuses.
Ambrus discloses calculating standpipe pressures [“standpipe pressure sensors” ¶43] …and wellbore fluid flow rates [flow out sensors, total pump output/flow in sensors ¶43] for the cleaned rig data to obtain the stream of trend analysis data containing trend statuses [“useful for cleansing data” ¶43]
It would have been obvious to one having ordinary skill in the art, having the teachings of Ben, Ambrus, Zha, and Maximo before him before the effective filing date of the claimed invention, to modify the combination to incorporate the specific values of Ambrus.
Given the advantage of utilizing known sensor to acquire necessary data for well predictions, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 13, Ben, Ambrus, Zha, and Maximo disclose the method of claim 1. Ben further discloses wherein the machine-learning model comprises a decision tree-based machine learning algorithm [“Random Forest (RF), Convolutional Neural Network (CNN), and hybrid Convolutional Neural Network / Recurrent Neural Network (CNN/RNN) models were trained and evaluated.” pg. 6 ¶1; Fig 4].
Regarding Claim 14, Ben, Ambrus, Zha, and Maximo disclose the method of claim 1. Ben further discloses wherein preprocessing the stream of real-time drilling data comprises capping minimum value of a drilling parameter, capping a maximum value of a drilling parameter, filling a gap in the time series values for a drilling parameter [“fill in any missing data” pg. 1 ¶2; “if there is a missing value, interpolation is used to fill the gap” pg. 2, last ¶], removing an impossible value for a drilling parameter, ignoring any data carried over from a previous well based on hole depth, normalizing data values between 0 and 1, or combinations thereof.
Regarding Claim 16, Ben, Ambrus, Zha, and Maximo disclose the method of claim 1. Ben further discloses wherein the time series real-time drilling data is generated in an unconventional onshore wellbore [“machine learning was applied to increase the detection accuracy of two of the most important drilling states for onshore unconventional wells (rotate drilling and slide drilling)” pg. 2 ¶5].
Regarding Claim 17, Ben, Ambrus, Zha, and Maximo disclose the method of claim 1. Ben further discloses further comprising:
generating the time series real-time drilling data [“data stream coming from various rig sensors each second” pg. 2 ¶3];
storing the time series real-time drilling data in a database [“static data sources such as an Enterprise Data Management (EDM) database” pg. 7 last ¶]; and
sending the time series real-time data from the database to the machine learning model, a data processing module for preprocessing, and a time segmented drilling data batch generator module for generating the time segmented drilling data batch [Fig. 7; Note: this figure discloses storing and transmitting data for processing by the system].
Claim 18 is rejected on the same grounds as claim 1. Ben further discloses a first computer device [“the web-based user interface” pg. 10 ¶2].
Regarding Claim 19, Ben, Ambrus, Zha, and Maximo disclose the computer system of claim 18. Ben further discloses further comprising: a data store networked with the first computer device and configured to store the time series real-time data and send the time series real-time data to the first computer device [“Enterprise Data Management (EDM) database” pg. 7 last ¶; Fig. 7].
Regarding Claim 20, Ben, Ambrus, Zha, and Maximo disclose the computer system of claim 19. Ben further discloses further comprising: a second computer device networked with the database and configured to generate the time series real-time drilling data [“time series data (typically one data point per second) from multiple sensors on a drilling rig” Abstract; “Enterprise Data Management (EDM) database” pg. 7 last ¶; Fig. 7].
Claim(s) 5, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ben, Ambrus, Zha, and Maximo, further in view of Payette et al. (hereinafter Payette), Mitigating Drilling Dysfunction Using a Drilling Advisory System: Results from Recent Field Applications.
Regarding Claim 5, Ben, Ambrus, Zha, and Maximo disclose the method of claim 4. Ben further discloses further comprising: sending a third indicator indicative of the rig state to the user device [“Using patterns in the rig state, the system aggregates KPIs such as rotary and slide ROP, footage, and time, allowing users to rapidly visualize statistics and trends.” pg. 10 ¶3; Fig. 9-11].
However, Ben fails to explicitly disclose wherein the third indicator is visually depicted on the graph as an outline of the data point.
Payette discloses wherein the third indicator is visually depicted on the graph as an outline of the data point [Fig. 1, 7,17, 20, 25, 26; Note: these figures disclose displaying data visually as an outline of a data point].
It would have been obvious to one having ordinary skill in the art, having the teachings of Ben, Ambrus, Zha, Maximo, and Payette before him before the effective filing date of the claimed invention, to modify the combination to incorporate the visual display of Payette.
Given the advantage of making visual identification on an interface easy for the user, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 15, Ben, Ambrus, Zha, and Maximo disclose the method of claim 1. Ben further discloses further comprising:
sending a first indicator, a second indicator, and a third indicator for the at least one drilling parameter or the wellbore characteristic to a user device [“the web-based user interface” pg. 10 ¶2; Fig. 9-11], wherein the first indicator is indicative of a value of the at least one drilling parameter or wellbore characteristic [“Rotate” “Slide” Fig. 9; “Hole Depth” Fig. 10; “directional drilling performance” Fig. 11], …and wherein the third indicator indicative of the rig state [“Rotate” “Slide” Fig. 9; “Hole Depth” Fig. 10; “directional drilling performance” Fig. 11];
wherein the first indicator, the second indicator, and the third indicator are visually depicted on a graph that is displayed on the user device [Fig. 9-11], wherein the first indicator is a data point on the graph [Fig. 9-11], and the second indicator is a color or pattern of the data point [Fig. 9-11].
However, Ben fails to explicitly disclose wherein the second indicator is indicative of the severity of the value of the at least one drilling parameter or wellbore characteristic.
Zha discloses wherein the second indicator is indicative of the severity of the value of the at least one drilling parameter or wellbore characteristic [“the severity of downhole vibrations” pg. 1, col. 1, ¶2; “recognize patterns in the surface data relating to severe downhole lateral and torsional vibrations” pg. 1, col. 2, ¶1; “In the severe form of stick-slip, torsional vibration is coupled with lateral and axial vibrations” pg. 1, col. 2 ¶2; Fig. 1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Ben, Ambrus, Zha, and Maximo before him before the effective filing date of the claimed invention, to modify the combination to incorporate displaying the severity of the issue of Zha.
Given the advantage of permitting a user to visually see the information for quick assessment, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Ben fails to explicitly disclose and the third indicator is an outline of the data point.
Payette discloses and the third indicator is an outline of the data point [Fig. 1, 7,17, 20, 25, 26; Note: these figures disclose displaying data visually as an outline of a data point].
It would have been obvious to one having ordinary skill in the art, having the teachings of Ben, Ambrus, Zha, Maximo, and Payette before him before the effective filing date of the claimed invention, to modify the combination to incorporate the visual display of Payette.
Given the advantage of making visual identification on an interface easy for the user, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ben, Ambrus, Zha, and Maximo, further in view of Del Pino Ruiz et al. (hereinafter Ruiz), U.S. Patent 11,423,725.
Regarding Claim 12, Ben, Ambrus, Zha, and Maximo disclose the method of claim 1.
However, Ben fails to explicitly disclose wherein the deep learning model is a trained deep learning model, the method further comprising: retraining the trained deep learning model at least once per year with a collection of the time series real-time drilling data collected over a past time period.
Ruiz discloses wherein the deep learning model is a trained deep learning model, the method further comprising: retraining the trained deep learning model at least once per year with a collection of the time series real-time drilling data collected over a past time period [“machine learning models may be periodically retrained on a regular basis, such as weekly, monthly, or yearly, or in response to certain triggers” col. 32, lines 32-35].
It would have been obvious to one having ordinary skill in the art, having the teachings of Ben, Ambrus, Zha, Maximo, and Ruiz before him before the effective filing date of the claimed invention, to modify the combination to incorporate the periodic retraining of Ruiz.
Given the advantage of periodically updating the model to ensure continued accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Response to Arguments
Regarding the §101 rejections, these rejections are overcome. Citing specification paragraph 27, Applicant explained in Remarks on page 11 that, “it has been found that, since the signatures of streaming data are different for different rig states (e.g., tripping signature is different than drilling signature), rig states are key to calculating real-time key performance indicators (KPIs) for drilling operations, and dysfunction predictions are more accurate when using a machine learning model to determine rig state before performing a deep learning model on the streaming data along with the rig states to determine the predicted dysfunction.” This is a valid improvement to the technology. Furthermore, this improvement is realized in the claims in, for example, claim 1 which recites, “performing a machine-learning model on the first stream to obtain rig states and to output a second stream comprising the time series real-time drilling data and the rig states; preprocessing the second stream to obtain a third stream comprising cleaned rig data and the rig states; determining trends of at least one drilling parameter in the third stream to obtain a fourth stream of trend analysis data comprising trend statuses, the cleaned rig data, and the rig states; generating a time segmented drilling data batch comprising the trend analysis data received over a window of time; [[and]] performing a deep learning model on the time segmented drilling data batch to obtain the dysfunction prediction and to output a fifth stream comprising the dysfunction prediction of the downhole characteristic associated with the wellbore environment.”
Regarding the prior art rejections, Applicant's arguments with respect to the claims have been considered but are moot because the arguments do not apply to the references being used in the current rejection of the limitations.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.B./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148