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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. PRO 63/504,954, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
Claims 1-7 and 18-20 are entitled to the benefit of the priority date of Provisional Application No. PRO 63/504,954, filed May 30, 2023.
Claims 8 -17 are entitled to the effective filling date of May 30, 2024, for the following reasons:
Regarding claim 8, “controlling one or more of the rig operations based at least on the level of sticking”, claim 11, “generating at least one control instruction associated with the level of sticking”, claim 12, “at least one control instruction comprises a control instruction to add an additive to drilling fluid to reduce risk of sticking”, and claim 13, “at least one control instruction comprises a control instruction to adjust speed of moving the drillstring in the borehole”, the provisional application doesn’t disclose or imply to a PHOSITA controlling the rig operation based on the level of sticking or generating control instruction associated with the level of sticking. The provisional application doesn’t disclose any control instructions to add an additive to drilling fluid to reduce risk of sticking neither disclose a control instruction to adjust speed of moving the drillstring in the borehole.
Regarding claim 14, provisional application is silent on generating the estimated hook load value comprises “using a filter” that comprises an input for the measured hook load value and an input for a predicted hook load value.
Regarding claims 15-16, provisional application doesn’t teach using any types of filters, more specifically doesn’t have any disclosure regarding Bayesian type of filter, claim 15, neither has any disclosure or implication for the “Bayesian type of filter comprises a Bayesian Kalman filter”, claim 16.
Regarding claim 17, provisional application doesn’t teach or imply to a PHOSITA “estimating uncertainty of the estimated hook load value”.
Regarding claim 9, provisional application lacks written description for “less than micro sticking” and because Claim 10 depends from claim 9 it is also entitled to the effective filling date of claim 9.
Claim Rejections - 35 USC § 101
Claims 1-11, and 14- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) abstract idea as discussed below. This judicial exception is not integrated into a practical application because of the reasons discussed below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the reasons discussed below.
Step 1 - Statutory Category: Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, claims 1-19 are directed to a process (method) and a machine (device/system). Accordingly, claims 1-19 fall within at least one of the four statutory categories of invention (process, machine, manufacture, or composition of matter) under 35 U.S.C. 101.
Claim 20 recites a computer-readable storage media which under BRI encompasses both physical storage media and transitory propagating signals which do not fall within any of four statutory categories. Because the claim encompasses non-statutory propagation signals (per se), the claim fails Step 1 of the § 101 framework. The applicant is encouraged to amend claim 20 to add “non-transitory” before “storage media”.
Claim 1 is reproduced below with the abstract idea underlined.
Claim 1: A method comprising: acquiring data for rig operations that move a drillstring in a borehole in a subsurface geologic region, wherein the drillstring comprises connected stands of drill pipe and a drill bit for drilling into the subsurface geologic region, and wherein the data comprise measured depth data, inclination data, mud density data, and measured hook load data; generating an estimated hook load value for a measured depth in the borehole using at least a trained model that receives a portion of the data as associated with the measured depth; performing a comparison between the estimated hook load value and a measured hook load value of the measured hook load data as associated with the measured depth; and based at least in part on the comparison, determining a level of sticking of the drillstring in the borehole.
Under Step 2A, Prong 1, Claim 1’s underlined limitations recite determining a numerical estimated hook-load value and comparing that value to another numerical value and the subsequent determination of sticking level is an evaluation based on the comparison which involve mathematical concepts and mental process. Accordingly, claim 1 recites a judicial exception in the form of mathematical concepts and mental processes.
Step 2A, Prong 2: examiner needs to determine if the claim(s) recite additional elements that integrate the exception into a practical application of the exception. The additional elements in the claim have been left in normal font. Claim 1 does not integrate the judicial exception into a practical application because of the following reasons:
Claim 1 additional elements recite acquiring the rig-operation data, that is merely obtaining the information used in the subsequent analysis and it is considered insignificant extra-solution activity. Reciting drillstring, borehole, surface geologic region, etc., is characterized as field of use limitations, because they limit the mathematical analysis to the field of drilling operations. Further, use of the trained model does not recite an improvement to the trained model or to the computer functionality. Accordingly, the additional elements, individually and in combination, do not integrate the abstract idea into a practical application.
Claim 19: the analysis with respect to claim 1 applies analogously to claim 19. Claim 19 merely recasts the limitations of claim 1 as a system comprising processor-executable instructions. The recited processor, memory, and instructions merely perform the same acquisition, mathematical analysis, and determination recited in claim 1 using generic computer components.
Claim 20: the analysis with respect to claim 1 applies analogously to claim 20.
Step 2A, Prong 1 for dependent claims: claims 2-18 depends from claim 1 and share the same abstract idea. More specifically, claims 2-3 and 5 recite techniques for generating the estimated hook load value, including using a friction factor, comparing estimated and measured hook load values for different friction factors, using inclination and mud density value. Claims 9- 10 distinguish the level of sticking and claims 14-18 recite using a filter, using Bayesian or Bayesian Kalman filter, estimating uncertainty, and using a Gaussian Process Regression model. These limitations further define how the estimated hook is calculated and amount to mathematical calculations and mental process.
Step 2A, Prong 2 for dependent claims: claim 4 additional element recites collecting data which is considered insignificant extra-solution activity. Claims 6 and 7 specify types of drilling operations, which amount to field of use limitations and do not impose meaningful technological implementation beyond the abstract idea of claim 1. Claim 8 recites controlling one or more rig operations based on the determined level of sticking; however, the claim does not recite a specific technical mechanism or particularized control of the drilling system, and therefore merely applies the abstract determination in a generic manner. Claim 11 recites generating control instructions based on the determined level of sticking. This limitation merely produces information for use in downstream decision-making and does not require any actual implementation of a physical change to the drilling system.
Accordingly, claims 2-11 and 14-18 do not integrate the abstract idea into a practical application.
Claims 12 and 13 recite specific physical actions performed in the drilling operation based on the determined level of sticking. In particular, claim 12 requires adding an additive to drilling fluid, and claim 13 requires adjusting the speed of moving the drillstring in the borehole. These are concrete operational changes to the drilling process itself.
Accordingly, claims 12 and 13 integrate the abstract idea into a practical application under Step 2A, Prong 2.
Step 2B: Claims 1-11 and 14-20: the additional elements, considered individually and in combination, do not amount to significantly more than the abstract idea for the same reasons set forth with respect to Step 2A, Prong 2.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-8, 11-12, 14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alzahrani et al. (US 20170306726 A1) hereinafter Alzahrani, and further in view of Gutarov et al. (US 20220120176 A1) hereinafter Gutarov 176, and Revheim et al. (US 20210293130 A1) hereinafter Revheim.
Regarding claim 1, Alzahrani teaches a method ( a method for monitoring and predicting a stuck pipe, ¶[7]) comprising: acquiring data (a device for receiving a plurality of hook load readings each corresponding to a respective one of a plurality of bit depths, ¶ [8]) for rig operations that move a drillstring in a borehole ( the hook load samples can be taken while moving the drill string out of the hole, ¶ [25]) in a subsurface geologic region (the samples are for a given well, ¶ [25]), wherein the drillstring comprises a drill bit for drilling into the subsurface geologic region (the prediction model incorporates an application that can monitor drilling conditions in real time, ¶ [5]. The sample data (that is collected from moving drill string in hole for a given well, ¶ [25]) is used to predict a stuck pipe, ¶ [5]. The disclosure of a drilling operation indexed by bit depth is analogous to teaching of a drill bit penetrating the subsurface geologic formation.).
Alzahrani teaches a drillstring having drill bit by its teaching of measuring bit depth, but Alzahrani doesn’t teach that drillstring comprises connected stands of drill pipe.
Gutarov teaches drillstring 225, fig. 2 that may be composed of a series of pipes threadably connected together to form a long tube with the drill bit 226 at the lower end thereof, ¶ [55], (definition of connection, ¶ [180], definition of stand, ¶ [179]) (the drillstring comprises connected stands of drill pipe).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to structure Alzahrani’s drillstring in connected stands as taught by Gutarov 176, because the physical addition of each stand of pipe provides a systematic, repeatable operational boundary to trigger segment, and update Alzahrani’s regression model thereby preventing noisy false-positive alerts.
Alzahrani in view of Gutarov 176 further teaches the data comprise measured depth data (bit depth, ¶ [8]), and measured hook load data (hook load reading, ¶ [8]).
Alzahrani doesn’t teach that the acquired data comprises inclination data, and mud density data.
Gutarov 176 teaches inclination data (inclination data) acquired via MWD sensors, ¶ [74].
Revheim teaches that the operation of drilling rig 100 is monitored by a sensor(s) 120, the output 124 of sensor(s) 120 is stored in computer 154 and it can be used to verify the quality of operations and to identify deviations or early warnings for undesired events, ¶ [31-32]. The sensor 120 is a hook load sensor, … a mud density input sensor (mud density data acquired via mud density sensor) (mud density data), a drill depth sensor, or any other suitable sensor, ¶ [31].
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify method of Alzahrani to utilize the inclination data as taught by Gutarov 176 and the mud density data as taught by Revheim to allow the model to subtract normal hydrostatic buoyancy and trajectory-based normal forces. This integration yields the predictable result of preventing false positive sticking alarms caused by routine mud weight adjustments or geometric friction changes in deviated wells, thereby improving the accuracy of the predictive diagnostic system.
Alzahrani in view of Gutarov 176 and Revheim further teaches generating an estimated hook load value (Alzahrani , a second hook load reading obtained from the linear regression model, ¶ [7]) for a measured depth in the borehole (Alzahrani ,the hook readings associated with a specified bit depth, ¶ [7]) using at least a trained model that receives a portion of the data as associated with the measured depth (Alzahrani, the linear regression model has been fitted on previous hook load data associated with bit depth (y is predicted hook load and inputs x are the bit depths), ¶ [24]).
Alzahrani in view of Gutarov 176 and Revheim teaches performing a comparison between the estimated hook load value and a measured hook load value of the measured hook load data as associated with the measured depth (Alzahrani, the prediction application compares the current hook load value with a normal hook load value generated from the linear regression model at the current bit depth, ¶ [27], also see step c in ¶ [7]).
Alzahrani in view of Gutarov 176 and Revheim further teaches based at least in part on the comparison, determining a level of sticking of the drillstring in the borehole (Alzahrani, generating an indication of a risk of stuck pipe in response to the first hook load reading being greater than the second hook load reading, ¶ [7]. A binary alert (risk of sticking) vs. no alert (normal operation) represents two distinct levels of sticking (e.g., a high risk/sticking level vs a normal/no-sticking level)).
Claim 19 is rejected for the same reasons set forth with respect to rejection of claim 1 as claim 19 is a system that performs the method steps limitations of claim 1.
Alzahrani also teaches a system for stuck pipe prediction that can comprise at least one device for receiving a plurality of hook load readings each corresponding to a respective one of a plurality of bit depths; at least one computer processing device, ¶ [8]. Alzahrani discloses that the computing device has a memory, ¶ [36].
Claim 20 is rejected for the same reasons set forth with respect to rejection of claim 1 as claim 20 is non-transitory computer-readable medium that executes the method steps limitations of claim 1.
Alzahrani teaches storing its diagnostic methodology on a non-transitory computer-readable medium for use by or in connection with an instruction execution system, ¶ [45].
Regarding claim 2, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 1 as set forth with respect to rejection of claim 1.
While Alzahrani in view of Gutarov 176 and Revheim teaches generating the estimated hook load value (Alzahrani, a second hook load reading obtained from the linear regression model, ¶ [7]), Alzahrani in view of Revheim does not use friction factor to estimate hook load value.
Alzahrani in view of Revheim does not teach the generating generates the estimated hook load value using a friction factor value.
Gutarov 176 teaches generating modeled hook load values (such as pickup and slackoff hook loads HKLD-PU and HKLD-SO), fig. 16. Gutarov 176 teaches a friction factor (a friction factor value) based on a drillstring off bottom condition value (like hook load value) and utilizes a broomstick model, ¶ [327 & 353 -354]. Gutarov 176 further teaches that computed off-bottom values are used for display of a broomstick model against actual measurement values.
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to integrate friction factor taught by Gutarov 176 with Alzahrani in view of Revheim statistical baseline to create a predictive model that subtract background mechanical drag from the raw sensor stream. This combination yields the predictive result of dynamically calibrating the predicated baseline hook load to match the specific physical friction regime of the wellbore, preventing false-positive stuck pipe alert.
Regarding claim 3, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 2 as set forth with respect to rejection of claim 2.
Alzahrani in view of Gutarov 176 and Revheim further teaches determining the friction factor value (determining a friction factor based at least in part on a drillstring off-bottom condition value (hook-load free rotate, (Gutarov 176, ¶ [353])) utilizing a broomstick model, Gutarov 176 ¶ [354])
Alzahrani in view Revheim does not teach comparing a number of estimated hook load values for different friction factors for a span of measured depths to a number of measured hook load values of the measured hook load data for the span of measured depths.
Gutarov 176 teaches utilizing a broomstick model. Gutarov 176 teaches that the system executes the comparison by generating and rendering to one or more displays a visualization of a broomstick model against actual measurement, ¶ [314], and that comparisons may be made for the pick-up and slack-off weights taken during connections using a broomstick model, ¶ [306] (comparing a number of estimated hook load values for different friction factors to a number of measured hook load values of the measured hook load data). for a span of measured depths.
Gutarov 176 teaches that the actual hook load points are plotted and compared stand by stand over measured depth by disclosing a trained machine model that can operate as one or more filters that can be applied to time series data, for example, on a drill stand by drill stand basis, ¶ [249]( for a span of measured depths)
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Alzahrani’s statistical model (which is prone to false alarms) by incorporating Gutarov 176e’s technique of broomstick-based friction factor determination. The combined system can dynamically identify the actual friction factor of the wellbore. This enables the sticking diagnostic system to dynamically account for trajectory-specific sliding drag and prevents triggering false sticking alerts.
Regarding claim 4, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 1.
Alzahrani in view of Gutarov 176 and Revheim teaches a trained model (Alzahrani, the linear regression model has been fitted on previous hook load data associated with bit depth (y is predicted hook load and inputs x are the bit depths), ¶ [24]). Alzahrani in view of Gutarov 176 further teaches the data comprise measured depth data (bit depth, ¶ [8]), and measured hook load data (hook load reading, ¶ [8]).
Alzahrani doesn’t teach that the acquired data comprises inclination data, and mud density data.
Gutarov 176 teaches inclination data (an inclination value of the inclination data) acquired via MWD sensors, ¶ [74].
Revheim teaches that the operation of drilling rig 100 is monitored by a sensor(s) 120, the output 124 of sensor(s) 120 is stored in computer 154 and it can be used to verify the quality of operations and to identify deviations or early warnings for undesired events, ¶ [31-32]. The sensor 120 is a hook load sensor, … a mud density input sensor (mud density data acquired via mud density sensor) (a mud density value of the mud density data), a drill depth sensor, or any other suitable sensor, ¶ [31].
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify method of Alzahrani to utilize the inclination data as taught by Gutarov 176 and the mud density data as taught by Revheim to allow the model to subtract normal hydrostatic buoyancy and trajectory-based normal forces. This integration yields the predictable result of preventing false positive sticking alarms caused by routine mud weight adjustments or geometric friction changes in deviated wells, thereby improving the accuracy of the predictive diagnostic system.
Regarding claim 5, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Alzahrani in view of Gutarov 176 and Revheim teaches the estimated hook load value (Alzahrani, a second hook load reading obtained from the linear regression model, ¶ [7]).
Alzahrani in view of Gutarov 176 further teaches the data comprise measured depth data (bit depth, ¶ [8]), and measured hook load data (hook load reading, ¶ [8]).
Alzahrani doesn’t teach that the acquired data comprises inclination data, and mud density data.
Gutarov 176 teaches inclination data (an inclination value) acquired via MWD sensors, ¶ [74].
Revheim teaches that the operation of drilling rig 100 is monitored by a sensor(s) 120, the output 124 of sensor(s) 120 is stored in computer 154 and it can be used to verify the quality of operations and to identify deviations or early warnings for undesired events, ¶ [31-32]. The sensor 120 is a hook load sensor, … a mud density input sensor (mud density data acquired via mud density sensor) (a mud density value), a drill depth sensor, or any other suitable sensor, ¶ [31].
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify method of Alzahrani to utilize the inclination data as taught by Gutarov 176 and the mud density data as taught by Revheim to allow the model to subtract normal hydrostatic buoyancy and trajectory-based normal forces. This integration yields the predictable result of preventing false positive sticking alarms caused by routine mud weight adjustments or geometric friction changes in deviated wells, thereby improving the accuracy of the predictive diagnostic system. Because the combined model receives measured depth, inclination and mud density values as active input parameters, the resulting estimated hook load output value generated by the model mathematically depends on the input data (depends on a measured depth value, an inclination value, and a mud density value).
Regarding claim 6, Alzahrani, in view of Gutarov 176 and Revheim teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Alzahrani in view of Gutarov 176 and Revheim further teaches the acquiring acquires real-time data (the system can monitor drilling condition in real-time and apply machine learning techniques and predict hook-load values in real-time and near real-time, Alzahrani, ¶ [5]) during one or more types of the rig operations (acquiring these hook load measurements is during the Pull-out-of-Hole POOH operation, Alzahrani,¶ [5])
Regarding claim 7, Alzahrani, in view of Gutarov 176 and Revheim teaches the method of claim 6 as set forth with respect to rejection of claim 6.
Alzahrani in view of Gutarov 176 and Revheim further teaches acquiring data in real-time data (the system can monitor drilling condition in real-time and apply machine learning techniques and predict hook-load values in real-time and near real-time, Alzahrani, ¶ [5]) during the rig operations comprise a pulling out type of rig operation (acquiring these hook load measurements is during the Pull-out-of-Hole POOH operation, Alzahrani,¶ [5])
Alzahrani in view of Revheim does not teach acquiring data in running in type of operation.
Gutarov 176 framework utilizes a partition block 1510 for partitioning time series data into RIH (run-in-hole), Drilling and POOH partitions, ¶ [262] (a running in type of rig operation).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Alzahrani in view of Revheim’s stuck-pipe framework to acquire real-time data during running-in-hole operations as taught by Gutarov 176. Because Alzahrani in view of Revheim’s model leaves the drilling assembly vulnerable to sticking during running-in phases. The combination provides continuous wellbore safety monitoring throughout all tripping phases and reduces the Non-Productive Time (NPT).
Regarding claim 8, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 6 as set forth with respect to rejection of claim 6.
Alzahrani in view of Gutarov 176 and Revheim teaches when a sticking risk is identified, the application generates an indication of a risk of stuck pipe, (Alzahrani ¶ [7]) allowing remedial action to be preemptively taken (Alzahrani ¶ [6]). Alzahrani in view of Gutarov 176 and Revheim further teaches that the prediction model can provide an environment for Autonomous Drilling such as Auto-Driller technologies, Remote execution and Controlling of Drilling and Geo-steering operations (Alzahrani, ¶ [5]).
However, Alzahrani in view of Revheim does not teach controlling one or more of the rig operations based at least on the level of sticking.
Gutarov 176 teaches controlling one or more of the rig operations (an optional issuance block 1450 for issuing at least one control instruction for at least one operation, ¶ [246]. predicates to making one or more drilling decisions, which may include one or more control decisions (e.g., of a controller that is operatively coupled to one or more pieces of field equipment, etc.), ¶ [151]) based at least on the level of sticking (a method can be implemented as part of a control system that can operate to reduce risk of stuck pipe and/or reduce incidents of pipe sticking, ¶ [177]).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Alzahrani in view of Ravheim’s predictive alarm engine to incorporate the active rig control system taught by Gutarov 176. Because human drillers have slow response times to passive warnings. The combined method would control active rig parameters in real-time in response to sticking alarm and reduce costly Non-Productive Time (NPT).
Regarding claim 11, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Alzahrani in view of Gutarov 176 and Revheim teaches when a sticking risk is identified, the application generates an indication of a risk of stuck pipe, (Alzahrani ¶ [7]) allowing remedial action to be preemptively taken (Alzahrani ¶ [6]). Alzahrani in view of Gutarov 176 and Revheim further teaches that the prediction model can provide an environment for Autonomous Drilling such as Auto-Driller technologies, Remote execution and Controlling of Drilling and Geo-steering operations (Alzahrani, ¶ [5]).
However, Alzahrani in view of Revheim does not teach controlling one or more of the rig operations based at least on the level of sticking.
Gutarov 176 teaches generating at least one control instruction (an optional issuance block 1450 for issuing at least one control instruction for at least one operation, ¶ [246]. predicates to making one or more drilling decisions, which may include one or more control decisions (e.g., of a controller that is operatively coupled to one or more pieces of field equipment, etc.), ¶ [151]) associated with the level of sticking (a method can be implemented as part of a control system that can operate to reduce risk of stuck pipe and/or reduce incidents of pipe sticking, ¶ [177]).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Alzahrani in view of Ravheim’s predictive alarm engine to incorporate the active rig control system taught by Gutarov 176. Because human drillers have slow response times to passive warnings. The combined method would control active rig parameters in real-time in response to sticking alarm and reduce costly Non-Productive Time (NPT).
Regarding claim 12, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 11 as set forth with respect to rejection of claim 11.
Alzahrani in view of Gutarov 176 and Revheim further teaches the at least one control instruction (Gutarov 176, an optional issuance block 1450 for issuing at least one control instruction for at least one operation, ¶ [246]. predicates to making one or more drilling decisions, which may include one or more control decisions (e.g., of a controller that is operatively coupled to one or more pieces of field equipment, etc.), ¶ [151]) to reduce risk of sticking (Gutarov 176, a method can be implemented as part of a control system that can operate to reduce risk of stuck pipe and/or reduce incidents of pipe sticking, ¶ [177])) .
Alzahrani in view of Revheim does not teach that the instruction comprises a control instruction to add an additive to drilling fluid.
Gutarov 176 teaches a control instruction to add an additive (the controlled remedial operations include adjusting one or more properties of mud ¶ [330]) to drilling fluid (mud ¶ [330])
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to combine Alzahrani in view of Revheim’s real-time predictive diagnostic predictive system with Gutarov 176’s automated mud property adjusting loop. The combination represents a routine engineering integration that yields the expected result of dynamically lowering the downhole friction coefficient, allowing the drillstring to slide freely.
Regarding claim 14, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Alzahrani in view of Revheim teaches comparing a measured hook load value to a baseline normal hook load value derived from a linear regression model to predict stuck pipe (Alzahrani, Abstract). Alzahrani in view of Revheim performs a direct algebraic comparison rather than combining measured and predicted values within an active filter.
Alzahrani in view of Revheim does not teach the generating the estimated hook load value comprises using a filter that comprises an input for the measured hook load value and an input for a predicted hook load value.
Gutarov 176 teaches the generating the estimated hook load value (Free hook load value (HKLD-FR), Pickup hook load (HKLD-FR) and Slack off hook load values (HKLD-FR) ¶ [193]) comprises using a filter (discloses an automated data-processing framework that implements a filter model to process time-series surface data, ¶ [185& 193].) that comprises an input for the measured hook load value ( hook load time series data (HKLD) ¶ [194]) and an input for a predicted hook load value (DrHkldMed or ConHkldMed representing the dynamic threshold baselines¶ [195-196]).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to integrate the baseline stuck-pipe comparison engine of Alzahrani in view of Revheim with dynamic threshold filtering architecture of Gutarov 176 to reduce impact of noise in data and isolate clean consistent off-bottom states and thereby reducing false positive alarms.
Claims 9-10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Alzahrani, and further in view of Gutarov 176, Revheim, and Ringer et al. (US 20180171774 A1) hereinafter Ringer.
Regarding claim 9, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Alzahrani in view of Gutarov 176 and Revheim teaches generating indication of risk for stuck pipe (Alzahrani, generating an indication of a risk of stuck pipe in response to the first hook load reading being greater than the second hook load reading, ¶ [7]).
However, Alzahrani in view of Gutarov 176 and Revheim does not teach the level of sticking comprises a less than micro sticking level and a micro sticking level.
Ringer teaches the level of sticking (Ringer teaches a Bayesian network that takes downhole and surface indicators to compute a risk probability of the drilling string sticking, ¶ [5]. Risk of sticking information may be classified into different categories (e.g., high, medium, low, etc.), ¶ [225]) comprises a less than micro sticking level (low risk, ¶ [225]) and a micro sticking level (medium risk, ¶ [225]).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to utilize Ringer’s three-tier classification scheme on Alzahrani in view of Gutarov 176 and Revheim’s monitoring system. The combination would result in predictable result of a structured, multi-stage warning system.
Regarding claim 10, Alzahrani in view of Gutarov 176, Revheim, and Ringer teaches the method of claim 9 as set forth with respect to rejection of claim 9.
Alzahrani in view of Gutarov 176, Revheim and Ringer further teaches the micro sticking level (Ringer teaches a Bayesian network that takes downhole and surface indicators to compute a risk probability of the drilling string sticking, ¶ [5]. Risk of sticking information may be classified into different categories (e.g., high, medium, low, etc.), ¶ [225], (medium risk represents the micro sticking level))
Alzahrani in view of Gutarov 176, Revheim doesn’t teach the micro sticking level is associated with an increased risk of a higher level of sticking.
Ringer teaches a framework that tracks the progression of the risk states in real time showing that for early times, the probability distribution is largely low level and then transitions to a region where it is largely a combination of medium level and high level, ¶ [226] and fig. 16. This tiered probability tracking is designed specifically to capture pre-cursors to one or more sticking events and evaluate the risk that a pipe and/or tool is about to become stuck, ¶ [114].
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to configure the real-time sticking alert system of Alzahrani 726 to mathematically associate its calculated hook load data deviation levels with an increased risk of transitioning to a higher level of sticking as taught by Ringer. The combined system provides a highly accurate, predictive early -warning system. This integration allows a driller or an automated rig controller to identify when minor downhole restrictions are trending towards a failure point, enabling timely, proactive adjustments to secure the wellbore and prevent costly NPT.
Regarding claim 17, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Alzahrani in view of Gutarov 176 and Revheim teaches generating an expected hook load baseline using a trained model (a linear regression model (Alzahrani, the linear regression model has been fitted on previous hook load data associated with bit depth (y is predicted hook load and inputs x are the bit depths), ¶ [24]).) to diagnose stuck up pipe.
However, Alzahrani in view of Gutarov 176 and Revheim regression model doesn’t estimate uncertainty of estimated hook load value. Alzahrani in view of Gutarov 176 and Revheim doesn’t teach the generating comprises estimating uncertainty of the estimated hook load value.
Ringer teaches estimating uncertainty (estimating uncertainty associated with the information ¶ [5]) of the estimated hook load value (uncertainty information such as in the form of one or more uncertainty metrics may accompany a value. For example, a standard deviation may accompany a hook load value, which may be estimated. ¶ [228]).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Alzahrani in view of Gutarov 176 and Revheim’s predictive stuck-pipe framework to incorporate the uncertainty estimation taught by Ringer. Comparing raw measured hook load values directly to a single regression line is highly prone to false positive alerts. Estimating a standard deviation (uncertainty metric) alongside the predicted hook load value allows the diagnostic system to differentiate between minor sensor noise and an actual physical sticking event.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Alzahrani, and further in view of Gutarov 176, Revheim, and Ahmed (US 20180266233 A1).
Regarding claim 13, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 11 as set forth with respect to rejection of claim 11.
Alzahrani in view of Gutarov 176 and Revheim further teaches the at least one control instruction (Gutarov 176, an optional issuance block 1450 for issuing at least one control instruction for at least one operation, ¶ [246]. predicates to making one or more drilling decisions, which may include one or more control decisions (e.g., of a controller that is operatively coupled to one or more pieces of field equipment, etc.), ¶ [151]).
However, Alzahrani in view of Gutarov 176 and Revheim doesn’t teach a control instruction to adjust speed of moving the drillstring in the borehole.
Ahmed teaches a control instruction (in response to identifying pressure risk, the controller outputs an adjusted running speed for the drill string ¶ [8]) to adjust speed of moving the drillstring in the borehole (outputting the adjusted running speed comprises transmitting to a drill rig a recommended adjusted running speed. Claim 7 of Ahmed).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify the predictive stuck-pipe framework of Alzahrani in view of Gutarov 176 and Revheim to incorporate the automated speed adjustment control taught by Ahmed. The physical process of drilling sticking often escalates catastrophically during continuous, high-speed movement. Relying on a human operator to manually slow down the hosing winch in response to Alzahrani in view of Gutarov 176 and Revheim’s passive warnings introduces critical reaction delays. The combined system though prevents downhole sticking hazards and optimizes NPT.
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Alzahrani, and further in view of Gutarov 176, Revheim, and Raman et al. (WO 2023177767 A1) hereinafter Raman.
Regarding claim 15, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 14 as set forth with respect to rejection of claim 14.
Alzahrani in view of Gutarov 176 and Revheim teaches a baseline hook-load filtering framework that inputs raw measured hook load and predicted hook loads to generate a clean, estimated hook load, (Gotarov, ¶ [193-196])
Alzahrani in view of Gutarov 176 and Revheim doesn’t teach the filter comprises a Bayesian type of filter.
Raman teaches the filter comprises a Bayesian type of filter (teaches a Bayesian sensor fusion model which is a statistical model used to combine data from multiple sensors to improve the accuracy and reliability of a measurement. The model is based on Bayesian probability theory, which provides a way to update the probability of a hypothesis based on new evidence, ¶ [38] and a bayes filter that calculates the joint probability distribution between the parameter and the measured variables. It then uses the received measurement to update the conditional probability distribution of the parameters, ¶ [69])
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify the logic-based filtering of Alzahrani in view of Gutarov 176 and Revheim to be mathematically executed by the Bayesian filter taught by Ramen. Because This filter recursively updates the state probability distribution based on incoming evidence, minimizing noise while simultaneously quantifying the uncertainty of the final hook-load estimate.
Regarding claim 16, Alzahrani in view of Gutarov 176, Revheim and Raman teaches the method of claim 15 as set forth with respect to rejection of claim 15.
Alzahrani in view of Gutarov 176, Revheim and Raman teaches a hook-load filtering framework that inputs raw measured hook load and predicted hook loads to generate a clean, estimated hook load, (Gotarov, ¶ [193-196]) using Bayesian filter (Raman, ¶ [38 &69]).
Alzahrani in view of Gutarov 176 and Revheim doesn’t teach the Bayesian type of filter comprises a Bayesian Kalman filter.
Ramen teaches the Bayesian type of filter comprises a Bayesian Kalman filter (teaches using a simple Bayesian Kalman filter to fuse real-time sensor measurements with predicted model states to output optimal physical parameters, ¶ [69])
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to implement the specific Bayesian Kalman Filter architecture of Raman to mathematically execute the hook load filtering process of Alzahrani in view of Gutarov 176 and Revheim. Because this filter continuously calculates a joint probability distribution, minimizing the mean squared error of the estimates to isolate a highly stable, noise -filtered estimated hook load.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Alzahrani, and further in view of Gutarov 176, Revheim, and Xu et al. (US 20200011158 A1) hereinafter Xu.
Regarding claim 18, Alzahrani in view of Gutarov 176 and Revheim teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Alzahrani in view of Gutarov 176 and Revheim teaches generating an expected hook load baseline using a trained model (a linear regression model (Alzahrani, the linear regression model has been fitted on previous hook load data associated with bit depth (y is predicted hook load and inputs x are the bit depths), ¶ [24]).) to diagnose stuck up pipe.
However, Alzahrani in view of Gutarov 176 and Revheim doesn’t teach that the trained model comprises a Gaussian Process Regression model.
Xu teaches that the trained model comprises a Gaussian Process Regression model (Xu teaches a real-time subterranean data monitoring and predictive framework that utilizes trained, non-linear regression machine-learning models, fig. 4. Xu further teaches that the trained model can be Gaussian Process Regressions (GPR), ¶ [121]. Xu identifies that GPR obtained the least standard deviation of error distribution among the different models tested, ¶ [122]).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Alzahrani in view of Gutarov 176 and Revheim’s framework by replacing linear regression model with Gaussian Process Regression model taught by Xu. Because GPR yields the highest accuracy and the lowest standard deviation of error compared to other models. The combined method would reduce false alerts.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shahri et al. (Implementation of a Fully Automated Real-Time Torque and Drag Model for Improving Drilling Performance: Case Study, Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, USA, September 2018.) outlines the development, testing, and deployment of a rig-based torque and drag advisory system designed to overcome traditional limitations in real-time modeling.
Conclusion
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/SAEEDE NAFOOSHE/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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