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 .
1. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 112
2. 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 6 and 14 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.
The term “shorter interval than the first machine learning model.” in claim 6 and 14 is a relative term which renders the claim indefinite. The term “shorter interval than the first machine learning model.” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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.
3. 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.
3.1 Claim(s) 1, 8-9 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over KHAKWANI et al. (US 20190164073 A1) in view of Zha (US 20190277135 A1).
Regarding claims 1, 9 and 17, Khakwani discloses a method and a non-transitory computer readable medium comprising instruction ([0042], The computer, computer system, and/or server system that processes all of this data from the flow meters 104 and other sensors includes a computer readable medium and a processor where instructions can be executed through the use of a computer program), when executed by a computing system of detecting long-term trends across operations at a pumping system for a well ([0042], the computer program can be configured to use various algorithms that take in the sensor data and to predict future events such as well, the well is predicted to turn from a dry oil producing well to a wet oil producing well), comprising:
a storage (storge 206 and 212) configured to store instructions; a processor configured to execute the instructions ([0042], a processor where instructions can be executed through the use of a computer program) and cause the processor to:
receiving first measurement data associated with or from equipment submersed into a downhole environment during a portion of a current operation for extracting materials from the downhole environment at a pumping system for a well ([0041]-[0047], Fig. 3, data collection flow path for two distinct databases collecting well measurement information. Different pieces of information can be gathered including current hydrocarbon production rate, wellhead pressure, and other information regarding the status of each well. Each well can have one or more multiphase flowmeters 104 for measuring sensor data associated with the production of the well. A multiphase flowmeter 104 can measure the amount and volume of the flow through the pipeline by the well);
normalizing the first measurement data based on corresponding measurement data associated with other downhole environments ([0034],[0037],[0044], different pieces of information can be gathered including current hydrocarbon production rate, wellhead pressure, and other information regarding the status of each well and then combined and normalized);
combining the normalized data with previous data into complete well data, (Abstract, a first database is designed to store real time data about an active producing well and a second database can be a historical production information database, such as an Online Transactions Processing (OLTP) database. The data from both databases can be normalized, compared, and correlated to create a predictive model that accurately predicts when a particular well is expected to transition from a dry oil producing well to a wet oil producing well), wherein the previous data is associated with previous operations of the downhole environment ([0006], An OLTP database is configured to store information about the wells including their age, the production history, the type of completion, and the various geochemical tests conducted on the fluid samples taken from the wells);
providing the complete well data into a first machine learning model for identifying trends associated with the complete well data ([0010], [0036], comparing and correlating the historical online transaction processing data and the real-time data from each of the online transaction processing database and real-time database, respectively, using machine learning. The machine learning algorithms can be used to build predictive models that combine both the streaming data and the historical data); and
displaying information pertaining to trends and events associated with an entirety of the current operation ([0039], [0040], a display on the user's computer can display various sensor measurements including, but not limited to, water cut information, historical trends, and predictive information relating to future events or wellsite. the historical and real-time data that is gathered for all the wells at and around the well-site can be combined and displayed to the user to predict when each well will become wet or dry).
However, Khakwani fails to disclose receiving labels associated with operation of a first equipment from the first machine learning model, wherein the labels are associated with the portion of the current operation.
Zha discloses receiving labels associated with operation of a first equipment from the first machine learning model, wherein the labels are associated with the portion of the current operation (Abstract, [0009]-[0011],[0055],[0064], Each of the plurality of segments are processed by a pre-trained model such that one or more labels are determined. The one or more labels are relating to one or more downhole events corresponding to each of the plurality of segments).
Zha and Khakwani are analogous art. They relate to predicating well performance in real time.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify detect downhole events from the sensor, taught by Zha, incorporated with real-time sensor data, taught by Khakwani, in order to achieve maximum efficiency in the production of well system by receiving real time sensor data quickly and efficiently.
Regarding claims 8 and 16, Zha discloses the labels comprise information indicating changes from the previous operations ([0042], [0064], the processor 202 is configured to determine one or more labels relating to one or more downhole events using the drilling data).
3.2 Claim(s) 2-4, 6, 10-12, 14 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over KHAKWANI et al. (US 20190164073 A1) in view of Zha (US 20190277135 A1) further in view of Verma et al. (US 20210285321 A1).
Regarding claims 2-3, 6, 10-11, 14 and 18-19, the combination of Zha and Khakwani discloses the limitation of claim 1, 9 and 17, but fail to disclose the limitations of claims 2-3, 6, 10-11, 14 and 18-19. However, Verma discloses the limitations of claims 2-3, 6, 10-11, 14 and 18-19 as follow:
Regarding claims 2, 10 and 18, Verma discloses identifying an alarm condition associated with a trend regarding operation of the equipment based on a current trend experienced within the downhole environment ([0012], identifying a lost circulation event occurring and identifying the likelihood of a lost circulation event occurring in connection with a drilling operation by using a machine-learning process and monitoring real-time data in connection with the drilling operation).
Regarding claims 3, 11 and 19, Verma discloses in response to detecting termination of the trend ([0012], damages to the drilling equipment from the lost circulation event can be prevented or mitigated), selecting a portion of data corresponding to the trend and providing the portion of data to a supervision system for input into a training dataset (Abstract, [0018], [0014], A wellbore drilling system can generate a machine-learning model trained using historic drilling operation data for monitoring for a lost circulation event. Real-time data for a drilling operation can be received and the machine-learning model can be applied to the real-time data to identify a lost circulation event that is occurring. An alarm can then be outputted to indicate a lost circulation event is occurring for the drilling operation).
Regarding claims 6 and 14, Verma discloses detecting a second alarm condition issue based on the first measurement data using a second machine learning model, wherein the second machine learning model is configured to evaluate data over a shorter interval than the first machine learning model ([0037], The pre-processed data can be used in an automated mud loss detection process 608. The automated mud loss detection process 608 may include applying a first model 622 to make a judgment if the data suggests that a lost circulation event has begun. If the analysis on the data suggests a normal operation, the data can be passed to a second model 626 to identify early warning precursors in the data that may indicate that a lost circulation event is about to occur. The second model 626 may be more probabilistic than the first model 622 as it is predicting whether a lost circulation event is about to occur).
Verma, Zha and Khakwani are analogous art. They relate to predicating well performance in real time.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify a wellbore drilling system can generate a machine-learning model trained, taught by Verma, incorporated with the teaching of Zha and Khakwani, as stated above, in order to Identify a lost circulation event earlier can prevent the magnitude of lost drilling fluid.
Regarding claims 4, 12 and 20, Zha discloses an operator of the well facility provides input into a device to control the equipment and avert the trend ([0007], [0011], [0016], [0065], The notification can be displayed in real-time so that the operator can perform any necessary remediation to mitigate damage of the drilling equipment, such as the drill string 110, from the one or more downhole events. For example, if one of the one or more downhole events is categorized as severe, the drilling operation may be modified, e.g., an RPM of the drill string 110 decreased, and/or one or more parts of the drill string 110 may be inspected and/or replaced. Accordingly, to mitigate the damage, the drill string 110 can be controlled, via the controller 206, to mitigate the one or more downhole events).
3.3 Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over KHAKWANI et al. (US 20190164073 A1) in view of Zha (US 20190277135 A1) further in view of Verma et al. (US 20210285321 A1) furthermore in view of Hotelt (US 20040124009 A1).
Regarding claims 5 and 13, the combination of Verma, Zha and Khakwani discloses the limitation of claims 1, 2, 9, 10 and 17-18, but fail to disclose the limitations of claims 5 and 13. However, Hotelt discloses the limitations of claims 5 and 13 as follow:
Regarding claims 5 and 13, Hotelt discloses identifying a corrective action to take based on the alarm condition to resolve the trend ([0163], when an undesirable event is detected, the system determines 444 a corrective drilling rig action which will avert or mitigate the event. The system then issues a corrective action command 446 to the rig), and controlling the first equipment based on the corrective action ([0163], which has suitable computer-controlled equipment for implementing the command).
Hotelt, Verma, Zha and Khakwani are analogous art. They relate to predicating well performance in real time.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify averting undesirable drilling events during a drilling process, taught by Hotelt, incorporated with the teaching of Verma, Zha and Khakwani, as stated above, in order to automatically detecting the drilling rig activity or `rig state` in real-time, this rig state information can be fed into problem detection algorithms thereby greatly increasing the accuracy of such algorithm.
3.4 Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over KHAKWANI et al. (US 20190164073 A1) in view of Zha (US 20190277135 A1) further in view of Verma et al. (US 20210285321 A1) furthermore Salman et al. (US 20190162868 A1).
Regarding claims 7 and 15, the combination of Verma, Zha and Khakwani discloses the limitation of claims 1, 2, 9-10 and 17-18, but fail to disclose the limitations of claims 7 and 15. However, Salman discloses the limitations of claims 7 and 15 as follow:
Regarding claims 7 and 15, Salman discloses the first machine learning model is configured to detect the alarm condition ([0014], performing a field operation by at least detecting an unknown fault in a target seismic volume using a machine learning model) using a sliding window over a period of time using the complete well data ([0014],[0032], [0037], the machine learning model is applied to a sliding window at multiple sliding window positions throughout the target seismic volume to generate a predicted label for each sliding window position).
Salman, Verma, Zha and Khakwani are analogous art. They relate to predicating well performance in real time.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify, detecting an unknown fault in a target seismic volume, taught by Salman, incorporated with the teaching of Verma, Zha and Khakwani, as stated above, in order to adjust a drilling equipment or a flow control valve in a wellsite to perform a field operation.
Citation Pertinent prior art
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Paulk et al. (US 20040065477 A1) discloses monitoring and controlling the pressure in a wellbore characterized by a drilling system utilizing real-time bottom hole pressure measurements and a control system adapted to control parameters such as well shut-in, drilling fluid weight, pumping rate, and choke actuation.
MAX et al. (US20140116776A1) discloses drilling operations through the use of real-time drilling data to predict bit wear, lithology, pore pressure, a rotating friction coefficient, permeability, and cost in real-time and to adjust drilling parameters in real-time based on the predictions. The real-time lithology prediction is made by processing the real-time drilling data through a multilayer neural network.
Srivastav (US20220205351A1) describe training machine learning models specifically for high-importance data segments to correct drilling data and identify abnormal conditions
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1 009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. Biocraft Labs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1, 215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969).
Conclusion
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed Kidest Worku whose telephone number is 571-272-3737. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ali Mohammad can be reached on 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KIDEST WORKU/Primary Examiner, Art Unit 2119