DETAILED ACTION
Claims 1-20 are presented for examination.
Rejection of claims 2-4 and 11 under 35 U.S.C. 112(b) for lacking antecedent basis are withdrawn.
Rejection of claims 1-20 under 35 U.S.C. 101 for being directed to unpatentable subject matter are maintained are withdrawn.
Rejection of claims 1-20 under 35 U.S.C. 103 as being obvious over Keller in view of Groover are maintained.
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 .
Response to the Arguments
In response to the rejection of the claims under 35 U.S.C. 112(b), Examiner agrees that the claims, as amended, overcome the rejection. Accordingly, the rejection under 12 U.S.C. 112(b) is withdrawn.
Regarding the rejection of the claims under 35 U.S.C. 101, while the amendments sufficiently address the rejection, the claims appear to be reversed in the causation of the bottom hole assembly. Instead of “controlling…by drilling,” the logical causation appears to be to perform the step of “drilling…by controlling.” Thus, the claims are now objected to for this issue, which does not rise to the level of a rejection under 35 U.S.C. 112(b).
Thus, while the rejection of the claims under 35 U.S.C. 112(b) are withdrawn, new objections to the claims are raised in this Office Action.
Regrading the rejection of the claims under 35 U.S.C. 101, Examiner agrees that the claims, as now presented, overcome the rejection by integrating the judicial exception into a practical application. Accordingly, the rejection is withdrawn.
Regarding rejection of the claims under 35 U.S.C. 103:
Applicant asserts that “Keller's MCMC simulation in Keller's block 306 of Keller's block 204 only is used to approximate a posterior distribution of parameters which describes a likelihood of parameter values that describe a steering behavior of Keller's bottom hole assembly 130 used to calibrate Keller's steering model. There is no teaching or suggestion in Keller that an output of its MCMC simulation performed in Keller's block 306 of Keller's block 204 is used to stochastically project at least one trajectory of a bottom hole assembly. As such, Keller does not disclose ‘stochastically projecting a first one or more trajectories of the bottom hole assembly’ as recited in pending Claims 1, 13, and 19 as the Office Action alleges.” Response at pg. 15.
Though not cited in the Response, at [0019], Keller discloses that “Block 204 performs steering model calibrations. In examples, steering model calibrations may operate and function to control drilling system 100 during drilling operations. For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations, which may be transmitted to block 206…The calibrated model parameters and drill-bit trajectory estimations may be utilized as inputs to control the function and operation of control logic in block 206. Control logic in block 206 may operate and function to control the trajectory, speed, revolutions-per-minute, and other parameters of drill bit 122 during drilling operations.” Keller at [0019].
In the above citation, and as apparent in FIG. 2 (see below), the MCMC, which is part of block 204, can include, as output, “drill-bit trajectory estimation,” which is analogous to a “stochastically projected trajectory of a bottom hole assembly.” Along with the “calibrated model parameters,” analogous to “one or more system model parameters,” the trajectory estimation is utilized, in conjunction with measurements from the drill-bit, to estimate one or more trajectories. Control logic 206 provides a steering command, which is utilized to control the drilling performed by the drill-bit. This is analogous to “using the stochastically projected first one or more trajectories of the bottom hole assembly to guide borehole placement.”
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Accordingly, Examiner disagrees that Keller does not teach the limitation “stochastically projecting a first one or more trajectories of the bottom hole assembly,” as asserted in the Response.
Further, Applicant asserts that “As established above [referring to the Graham factor of combining prior art references according to known methods to yield predictable results], the Office Action has not articulated a finding that the applied combination of the cited portions of Keller and Groover includes "each element claimed" in pending Claims 1, 13, and 19. Therefore, per the above-cited portions of the MPEP, the applied combination of the cited portions of Keller and Groover does not establish a conclusion of obviousness for pending Claims 1, 13, and 19.” Response at pg. 16.
First, Examiner is not relying on the same factor as cited by the Applicant. Examples of rationales that may support a conclusion of obviousness include:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(C) Use of known technique to improve similar devices (methods, or products) in the same way;
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
(E) “Obvious to try” – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
MPEP 2143(I), emphasis added.
Thus, while Applicant argues that the combination does not satisfy Graham rationale (A), Examiner is relying on rationale (G), which is satisfies when the references provide motivation to combine. As indicated in the Office Action, “Motivation to combine includes reducing computing time and resources required to make estimations by limiting the number of estimations that are performed (i.e., by not estimating trajectories based on continuously received measurements).” Office Action at pg. 32. As correctly stated by the Applicant, “Keller's use of its MCMC simulation in block 306 is beneficial in that it is computationally cheap but produces estimates for parameters which are accurate and stable.” Response at pg. 15. See also Keller at [0029]. A person having ordinary skill in the art would be motivated to combine Keller and Groover to improve steering commands provided to a drilling assembly with increased accuracy and reduced computational resources, thus producing better and less expensive results.
Accordingly, rejection of the claims under 35 U.S.C. 103 is maintained.
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.
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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Keller et al., (U.S. Patent Pub. No. 2021/0103843) in view of Groover (U.S. Patent No. 11,408,228).
Claim 1
Keller discloses:
A method for stochastically projecting a well trajectory of a bottom hole assembly in a subsurface formation, the method comprising:
Block 204 performs steering model calibrations. In examples, steering model calibrations may operate and function to control drilling system 100 during drilling operations. For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations…Keller at [0019].
receiving a first one or more system model parameters
The calibrated parameters are identified as τ is a depth constant, Kact is the magnitude of the bottom hole assembly 130 turning capability, Kbias represents both the inherent steering tendency of bottom hole assembly 130 as well as any external forces on bottom hole assembly 130. Keller at [0024].
The “calibrated parameters” are analogous to the “system model parameters.”
See also Specification at pg. 10, lines 22-24, indicating the same parameters as Keller discloses as “calibrated parameters.”
from a system model parameter probability distribution;
See FIG. 3, illustrating blocks 307 and 310, which include the system model parameters as determined by block 204, each having a mean and distribution value for τ, Kact, Kbias. Thus, after the system is executed once, the system model parameters are selected from a “probability distribution.”
receiving a first one or more steering inputs;
Block 306 is a Markov Chain Monte Carlo simulation (MCMC). In examples, the MCMC may be utilized to calibrate a steering model. The steering model may be calibrated and used to estimate a position of drill bit 122 (e.g., referring to FIG. 1) and attitude is the following depth-based second order differential equation, seen below as:
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which describes the dynamics of bottom hole assembly 130 (e.g., referring to FIG. 1) in the inclination and azimuth planes. Keller at [0024].
receiving a first one or more values corresponding to bottom hole assembly initial conditions at a first position within the subsurface formation;
Variables defined within block 302 are the initial conditions θ0 and
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. The initial conditions are output 303. Keller at [0021].
stochastically projecting a first one or more trajectories of the bottom hole assembly
Block 204 performs steering model calibrations. In examples, steering model calibrations may operate and function to control drilling system 100 during drilling operations. For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations…Keller at [0019].
based at least in part on one or more of the first one or more system model parameters, the first one or more steering inputs, and the first one or more values corresponding to the bottom hole assembly initial conditions; and
As illustrated in FIG.2 and FIG. 3, block 204 includes system model parameters (block 310), steering inputs (calculated by block 306 using Equation (2)), and initial conditions (block 303).
and controlling the bottom hole assembly and a drill bit using the stochastically projected first one or more trajectories of the bottom hole assembly to guide borehole placement from the first position to the second position
Control logic in block 206 may be a model-based control logic, where the calibrated steering model is used to determine a corrective steering command such that at least one objective is achieved…This may allow drilling system 100 to drill into formation 106 (e.g., referring to FIG. 1) at any suitable angle, horizontally, and/or the like.” Keller at [0019].
See also FIG. 2, wherein the “steering command” is generated by the control logic 206, which receives, as input, “drill-bit trajectory estimation” (analogous to a “stochastically projected well trajectory”).
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by drilling along a selected one of the stochastically projected first one or more trajectories.
Control logic in block 206 may operate and function to control the trajectory, speed, revolutions-per-minute, and other parameters of drill bit 122 during drilling operations. Keller at [0019].
Keller does not explicitly disclose:
Groover, which is analogous art, discloses:
from the first position within the subsurface formation to a second position within the subsurface formation
The actual position of the drill bit is shown at 715 with respect to the well plan at 705 and target line 710. Projection 720 illustrates the predicted future position of the drill bit at a second stationary survey station… Groover at col. 26, lines 18-23.
At the “first position” (i.e., location 715), the estimated trajectory is determined. At the “second position” (i.e., the “second station survey station”) the borehole is shown where it should be after advancing based on the projected trajectory.
Groover is analogous art to the claimed invention because both are related to estimating the trajectory of a borehole drill. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the clamed invention, to combine the stochastic trajectory projection process of Keller with the method disclosed in Groover to result in a system that estimates a trajectory at intervals. Motivation to combine includes reducing computing time and resources required to make estimations by limiting the number of estimations that are performed (i.e., by not estimating trajectories based on continuously received measurements).
Claim 2
Keller discloses:
stochastically projecting
The estimation process is represented graphically in FIG. 4. As illustrated, FIG. 4 uses simulated data as a visual representation of the statistical bagging process used to estimate the prior distributions of the initial conditions. In FIG. 4, Xb=[80,110] ft (24,417 meters). The group of lines is each best fit line to a different random sample from the measurements in window Xb. Keller at [0022].
Additionally
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is a vector of inclination estimates calculated using a model with parameters θ. Keller at [0027].
For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations… Keller at [0019].
Each projection includes selecting one of the vectors in θ and calculating the trajectory.
See also [0026]-[0028].
Keller does not appear to explicitly disclose:
projecting a first confidence region between the first position and the second position based at least in part one or more of the first one or more
Groover discloses:
In some embodiments, a probability that the drill bit will be in a certain position (or a range of certain positions) is also provided or displayed. For example, standard methods of computing standard deviations, which produce a confidence interval, can be used to define a confidence range for the motor yield and rotary tendency (e.g., there is a 95% probability that the motor yield is in between X and Y). Groover at col. 26, lines 24-30.
These ranges for motor yield and rotary tendency, in turn, can provide a confidence range for future positions of the drill bit (e.g., there is a 95% probability that the drill bit will be in a specific position or a range of positions). Groover at col. 26, lines 34-37.
The likelihood that the drill bit will be between two locations and/or the likelihood that the drill bit will be in a specific location is analogous to a “confidence region.”
based at least in part one or more of the first one or more
At step 608, the toolface calculation engine 404 generates a predicted future position of a drill bit on the BHA for each of a plurality of stationary survey stations subsequent to the first stationary survey station, based on implementation of the created steering instructions. Groover at col. 25, line 65-col. 26, line 2.
the first one or more system model parameters,
At step 602, the toolface calculation engine 404 receives a user-input control or a planned drilling path (e.g., a well plan). The control or planned drilling path is the desired path that may be based on multiple factors, but frequently is intended to provide a most efficient or effective path from the drilling rig to the target location. Groover at col. 25, lines 8-13.
“Planned drilling path” is analogous to “system model parameters.”
the received one or more steering inputs, and
At step 606, the toolface calculation engine 404 creates forward steering instructions based on the well plan, historical drilling data, and the locational and directional data of the BHA. Groover at col. 25, lines 43-46.
the received one or more values corresponding to the bottom hole assembly initial conditions.
At step 604, the toolface calculation engine 404 receives locational and directional data of the BHA from a plurality of sensors (e.g., ROP sensor 130 a, toolface sensor 170 c, inclination sensor 170 e, and/or azimuth sensor 170 f) at a first stationary survey station. Groover at col. 25, lines 14-18.
“Locational and directional data” are analogous to “initial conditions.”
It would have been obvious to a person of ordinary skill in the art, before the effective date of the clamed invention, to project one or more variables using a stochastic model as opposed to the probability-based model of Groover. Motivation to combine would be to generate more accurate estimations by taking into account various paths and not merely estimating based on the mean distribution of values.
Claim 3
Keller does not appear to disclose:
providing one or more of the first one or more stochastically projected trajectories and the first confidence region to one or more of a display and a trajectory controller.
Groover discloses:
providing one or more of the first one or more stochastically projected trajectories and
Projection 720 illustrates the predicted future position of the drill bit at a second stationary survey station, and projection 725 illustrates the predicted future position of the drill bit at a third stationary survey station. Groover at col. 26, lines 11-13.
the first confidence region to
In some embodiments, a probability that the drill bit will be in a certain position (or a range of certain positions) is also provided or displayed. For example, standard methods of computing standard deviations, which produce a confidence interval, can be used to define a confidence range for the motor yield and rotary tendency (e.g., there is a 95% probability that the motor yield is in between X and Y). Groover at col. 26, lines 24-30.
one or more of a display and
At step 610, the toolface calculation engine 404 displays the predicted future position of the drill bit for each of the plurality of stationary survey stations on a HMI or a GUI. Groover at col. 26, lines 11-13. (i.e., displayed trajectory)
For example, a survey is conducted at a measured depth of 10,000 feet (P0). The toolface calculation engine 404 recommends a 10 foot slide at a gravity toolface of 0 degrees. Based on historically-derived motor yield and rotary tendency, the toolface calculation engine 404 can project 90 feet ahead to the next survey station, P1, assuming that the provided instructions are followed. This future position is assessed against the drilling windows, tolerances, and rules in effect for the wellbore, considering the statistic uncertainty present at this station. Groover at col. 26, lines 42-51.
wherein the controller is further configured to display the uncertainty of the predicted future position of the drill bit for each of the plurality of stationary survey stations on the graphical use interface. Groover at claim 2. (i.e., displayed confidence interval)
a trajectory controller.
The apparatus 100 also includes the controller 190 configured to control or assist in the control of one or more components of the apparatus 100. (i.e., trajectory provided to controller).
wherein the controller is further configured to assess an uncertainty of the predicted future position of the drill bit for each of the plurality of stationary survey stations... Groover at claim 8. (i.e., confidence interval provided to controller).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the display of Groover with the process of Keller to result in providing an operator with a visual representation of the trajectory and confidence interval. Motivation to combine improves ease of assessment by the operator of the potential outcomes of the suggested drilling, thus avoiding improper and wasted drilling.
Further, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine providing a confidence interval to a controller of the drill bit to result in a system whereby drilling decisions can be automatically decided without operator input. Motivation to combine would be that drilling could be performed quicker and objectively, thus reducing expense in compensating the operator to monitor the drilling progress and reducing expense in prolonging the drilling process.
Claim 4
Keller discloses:
discarding one or more outliers in the first one or more stochastically projected trajectories of the bottom hole assembly before stochastically projecting the first confidence region.
The data is then organized and filtered to prevent outliers or corrupted data, due to inherent variability or measurement error, which may affect estimates and inferences. For example, a statistical method, adjusted boxplot, may be used to detect outliers. Outlier are defined as data points that fall out of the lower bound and upper bound of the data distribution after taking skewness of data into consideration. Keller at [0018].
By using the model for measurement uncertainty estimation, corrupted data and unmodeled dynamics may be detected and the calibration may be prevented from producing a poor model estimate. This serves as a form of quality control for the calibration and attitude estimation process. The identified measurement uncertainty variable σx is then used in block 306. Keller at [0023].
Filtering data “to prevent outliers or corrupted data, due to inherent variability…which may affect estimates and inferences” is analogous to “discarding one or more outliers,” which is performed “before stochastically projecting the first confidence region” (i.e., projecting the first confidence region is performed on the data without the outliers).
Claim 5
Keller does not explicitly disclose:
further comprising advancing the bottom hole assembly from the first position to the second position.
Groover discloses:
further comprising advancing the bottom hole assembly from the first position to the second position.
At step 614, the toolface calculation engine 404 executed the received directions, and drilling commences. Groover at col. 26, lines 66-67.
The actual position of the drill bit is shown at 715 with respect to the well plan at 705 and target line 710. Projection 720 illustrates the predicted future position of the drill bit at a second stationary survey station… Groover at col. 26, lines 18-23.
At the “first position” (i.e., location 715), the estimated trajectory is determined. At the “second position” (i.e., the “second station survey station”) the borehole is shown where it should be after advancing based on the projected trajectory.
It would have been obvious to a person of ordinary skill in the art, before the effective date of the claimed invention, to perform the method of Keller at intervals, as disclosed in Groover, instead of continuously, as disclosed in Keller. Motivation to combine would be that the measurements are taken only when the drill is non-operational, thus resulting in more accurate measurements than would be detected when the assembly (and sensors) are moving. Further, trajectories could be estimated at intervals without changes in assembly location, thus resulting in more accurate predictions.
Claim 6
Keller discloses:
further comprising stochastically projecting a second one or more trajectories of the bottom hole assembly
Block 204 performs steering model calibrations. In examples, steering model calibrations may operate and function to control drilling system 100 during drilling operations. For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations…Keller at [0019].
based at least in part on one or more of the first one or more system model parameters, the received one or more steering inputs, and the received one or more values corresponding to the bottom hole assembly initial conditions.
As illustrated in FIG.2 and FIG. 3, block 204 includes system model parameters (block 310), steering inputs (calculated by block 306 using Equation (2)), and initial conditions (block 303).
Keller does not explicitly disclose:
from the second position in the subsurface formation to a third position in the subsurface formation
Groover discloses:
[projecting a second one or more trajectories of the bottom hole assembly] from the second position to a third position
The actual position of the drill bit is shown at 715 with respect to the well plan at 705 and target line 710. Projection 720 illustrates the predicted future position of the drill bit at a second stationary survey station… Groover at col. 26, lines 18-23.
At the “second position” (i.e., location 720), the estimated trajectory is determined. At the “third position” (i.e., location 725) the borehole is shown where it should be after advancing based on the projected trajectory.
“Predicted future position” is determined based on “projected trajectory.”
Claim 7
Keller discloses:
receiving a second one or more system model parameters from the system model parameter probability distribution and
The parameters from block 306 that form posteriors in block 307 are sent to block 308 where posteriors in block 307 may motivate priors in block 310, for next calibration. Keller at [0028].
After the initial trajectory estimation (i.e., from “first position” to “second position”), priors (analogous to system model “parameters”) are recalculated (to a “second one or more system model parameters”) and provided to MCMC 306 to perform trajectory estimation. The “priors” include a “mean” and “distribution” (i.e., a “probability distribution”).
stochastically projecting a second one or more trajectories of the bottom hole assembly from the second position to a third position based at least in part on one or more of the second one or more system model parameters, the received one or more steering inputs, and the received one or more values corresponding to the bottom hole assembly initial conditions.
The estimation process is represented graphically in FIG. 4. As illustrated, FIG. 4 uses simulated data as a visual representation of the statistical bagging process used to estimate the prior distributions of the initial conditions. In FIG. 4, Xb=[80,110] ft (24,417 meters). The group of lines is each best fit line to a different random sample from the measurements in window Xb. Keller at [0022].
Additionally
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is a vector of inclination estimates calculated using a model with parameters θ. Keller at [0027].
For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations… Keller at [0019].
Each projection includes selecting one of the vectors in θ and calculating the trajectory.
See also [0026]-[0028].
Claim 8
Keller discloses:
wherein the first one or more system model parameters are randomly selected from the system model parameter probability distribution.
Without limitation, θ is also used to represent a vector of the calibrated parameters… Keller at [0024].
Second, a random sample of the measurements is created by randomly selecting a percentage, identified as P %, of the measurements without replacement. Keller at [0021].
The parameters from block 306 that form posteriors in block 307 are sent to block 308 where posteriors in block 307 may motivate priors in block 310, for next calibration. Keller at [0028].
The initial system model parameters are determined randomly from θ. When the system executes, new system model parameters are determined by the MCMC at block 306, resulting in new mean and distribution, which is processed at block 308 into new system model parameters (i.e., the “first one or more system model parameters.”).
Claim 9
Keller discloses:
generating a second one or more one or more steering inputs and
Block 306 is a Markov Chain Monte Carlo simulation (MCMC). In examples, the MCMC may be utilized to calibrate a steering model. The steering model may be calibrated and used to estimate a position of drill bit 122 (e.g., referring to FIG. 1) and attitude is the following depth-based second order differential equation, seen below as:
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Keller at [0024].
The Monte Carlo simulation generates new posteriors, including new steering inputs at block 307, which are used for the next iteration of the system.
stochastically projecting a second one or more trajectories of the bottom hole assembly
For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations… Keller at [0019].
Additionally, the objective function used in the MCMC simulation separates the survey measurements and continuous measurements from input 301 into different reference frames. Keller at [0029].
See also [0026]-[0028].
Block 204 utilizes an MCMC (which is stochastic) and outputs a trajectory estimation (see FIG. 2).
Block 204 takes, as inputs to the MCMC, either directly or indirectly, system model parameters (block 310), steering inputs (calculated by block 306 using Equation (2)), and initial conditions (block 303).
Claim 10
Keller discloses:
wherein the stochastically projecting a first one or more trajectories of the bottom hole assembly occurs in real-time.
This disclosure details a methods and systems for calibrating a steering model and estimating drill-bit position and orientation both in real-time and after operations. Keller at [0009].
“Projecting a first one or more trajectories” is a step in “calibrating a steering mode” (i.e., at block 206).
Claim 11
Keller discloses:
selecting a second one or more steering inputs based at least in part on one or more of the first one or more stochastically projected trajectories;
Block 306 is a Markov Chain Monte Carlo simulation (MCMC). In examples, the MCMC may be utilized to calibrate a steering model. The steering model may be calibrated and used to estimate a position of drill bit 122 (e.g., referring to FIG. 1) and attitude is the following depth-based second order differential equation, seen below as:
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which describes the dynamics of bottom hole assembly 130 (e.g., referring to FIG. 1) in the inclination and azimuth planes. Keller at [0024].
The Monte Carlo simulation generates new posteriors, which includes new steering inputs, at block 307, which are used for the next iteration of the system.
Block 204 performs steering model calibrations. In examples, steering model calibrations may operate and function to control drilling system 100 during drilling operations. For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations… Keller at [0019].
Steering inputs and trajectory estimations are both output of block 204.
Keller does not disclose:
providing the second one or more steering inputs and the first confidence region to the one or more of the display and the trajectory controller.
Groover discloses:
providing the second one or more steering inputs and
In several embodiments, the steering instructions and the predicted future positions of the drill bit are displayed on the HMI or GUI for approval of the operator or user. Groover at col. 26, lines 13-16.
the first confidence region to
In some embodiments, a probability that the drill bit will be in a certain position (or a range of certain positions) is also provided or displayed. For example, standard methods of computing standard deviations, which produce a confidence interval, can be used to define a confidence range for the motor yield and rotary tendency (e.g., there is a 95% probability that the motor yield is in between X and Y). Groover at col. 26, lines 24-30.
“A probability that the drill bit will be in a certain position” is analogous to “a confidence region.”
one or more of the display and
At step 610, the toolface calculation engine 404 displays the predicted future position of the drill bit for each of the plurality of stationary survey stations on a HMI or a GUI. In several embodiments, the steering instructions and the predicted future positions of the drill bit are displayed on the HMI or GUI for approval of the operator or user. Groover at col. 26, lines 11-16.
For example, a survey is conducted at a measured depth of 10,000 feet (P0). The toolface calculation engine 404 recommends a 10 foot slide at a gravity toolface of 0 degrees. Based on historically-derived motor yield and rotary tendency, the toolface calculation engine 404 can project 90 feet ahead to the next survey station, P1, assuming that the provided instructions are followed. This future position is assessed against the drilling windows, tolerances, and rules in effect for the wellbore, considering the statistic uncertainty present at this station. Groover at col. 26, lines 42-51.
wherein the controller is further configured to display the uncertainty of the predicted future position of the drill bit for each of the plurality of stationary survey stations on the graphical use interface. Groover at claim 2.
the trajectory controller.
The apparatus 100 also includes the controller 190 configured to control or assist in the control of one or more components of the apparatus 100.
wherein the controller is further configured to assess an uncertainty of the predicted future position of the drill bit for each of the plurality of stationary survey stations... Groover at claim 8.
Claim 12
Keller discloses:
receiving a second one or more system model parameters
The calibrated parameters are identified as τ is a depth constant, Kact is the magnitude of the bottom hole assembly 130 turning capability, Kbias represents both the inherent steering tendency of bottom hole assembly 130 as well as any external forces on bottom hole assembly 130. Keller at [0024].
The “calibrated parameters” are analogous to the “system model parameters.”
from the system model parameter probability distribution;
See FIG. 3, illustrating blocks 307 and 310, which include the system model parameters as determined by block 204, each having a mean and distribution value for τ, Kact, Kbias. Thus, once the system is executed once, the system model parameters are selected from a “probability distribution.”
receiving a second one or more steering inputs;
Block 306 is a Markov Chain Monte Carlo simulation (MCMC). In examples, the MCMC may be utilized to calibrate a steering model. The steering model may be calibrated and used to estimate a position of drill bit 122 (e.g., referring to FIG. 1) and attitude is the following depth-based second order differential equation, seen below as:
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which describes the dynamics of bottom hole assembly 130 (e.g., referring to FIG. 1) in the inclination and azimuth planes. Keller at [0024].
receiving a second one or more values corresponding to the bottom hole assembly initial conditions at a second position within the subsurface formation; and
Variables defined within block 302 are the initial conditions θ0 and
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. The initial conditions are output 303. Keller at [0021].
stochastically projecting a second one or more trajectories of the bottom hole assembly
Block 204 performs steering model calibrations. In examples, steering model calibrations may operate and function to control drilling system 100 during drilling operations. For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations…Keller at [0019].
based at least in part on one or more of the second one or more system model parameters, the second one or more steering inputs, and the second one or more values corresponding to the bottom hole assembly initial conditions.
As illustrated in FIG.2 and FIG. 3, block 204 includes system model parameters (block 310), steering inputs (calculated by block 306 using Equation (2)), and initial conditions (block 303).
Keller does not explicitly disclose:
from the second position in the subsurface formation to a third position in the subsurface formation
Groover discloses:
from the second position in the subsurface formation to a third position in the subsurface formation
The actual position of the drill bit is shown at 715 with respect to the well plan at 705 and target line 710. Projection 720 illustrates the predicted future position of the drill bit at a second stationary survey station… Groover at col. 26, lines 18-23.
At the “second position” (i.e., location 720), the estimated trajectory is determined. At the “third position” (i.e., location 725) the borehole is shown where it should be after advancing based on the projected trajectory.
“Predicted future position” is determined based on “projected trajectory.”
Claim 13
Keller discloses:
A system for stochastically projecting a well trajectory of a bottom hole assembly, the system comprising:
Block 204 performs steering model calibrations. In examples, steering model calibrations may operate and function to control drilling system 100 during drilling operations. For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations…Keller at [0019].
a bottom hole assembly comprising one or more transducers;
For example, as illustrated in FIG. 1, bottom hole assembly 130 may include a measurement assembly 134… In examples, measurement assembly 134 may comprise at least one transducer 136a, which may be disposed at the surface of measurement assembly 134. Keller at [0014].
an information handling system coupled to the transducers, the information system comprising: a processor, and a non-transitory computer readable medium for storing one or more instructions
Without limitation, bottom hole assembly 130 may be connected to and/or controlled by information handling system 138, which may be disposed on surface 108. Keller at [0015].
As illustrated, communication link 140 (which may be wired or wireless, for example) may be provided that may transmit data from bottom hole assembly 130 to an information handling system 138 at surface 108. Information handling system 138 may include a personal computer 141…and/or non-transitory computer-readable media 146 (e.g., optical disks, magnetic disks) that can store code representative of the methods described herein. Keller at [0017].
Keller does not disclose:
a trajectory controller coupled to the bottom hole assembly.
Groover discloses:
a trajectory controller coupled to the bottom hole assembly.
The drill string 155 includes interconnected sections of drill pipe 165, a bottom hole assembly (“BHA”) 170, and a drill bit 175. Groover at col. 3, lines 43-45.
The apparatus 100 also includes the controller 190 configured to control or assist in the control of one or more components of the apparatus 100. For example, the controller 190 may be configured to transmit operational control signals to…the BHA 170… Groover at col. 6, lines 1-6.
Keller, in view of Groover, discloses:
that, when executed, causes the processor to: perform the method recited in claim 1…
Because the remainder of claim 13 is substantially similar to claim 1, the same citations and reasoning as asserted in rejection of claim 1 are asserted in rejection of the remainder of claim 13.
Claim 14
Keller discloses:
wherein the one or more instructions that, when executed, further causes the processor to stochastically project
The estimation process is represented graphically in FIG. 4. As illustrated, FIG. 4 uses simulated data as a visual representation of the statistical bagging process used to estimate the prior distributions of the initial conditions. In FIG. 4, Xb=[80,110] ft (24,417 meters). The group of lines is each best fit line to a different random sample from the measurements in window Xb. Keller at [0022].
Additionally
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is a vector of inclination estimates calculated using a model with parameters θ. Keller at [0027].
For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations… Keller at [0019].
Each projection includes selecting one of the vectors in θ and calculating the trajectory.
See also [0026]-[0028].
Kelly does not disclose:
a confidence region for the projected trajectory of the bottom hole assembly between the first position within the subsurface formation to the second position within the subsurface formation.
Groover discloses:
a confidence region for the projected trajectory of the bottom hole assembly between the first position within the subsurface formation to the second position within the subsurface formation.
In some embodiments, a probability that the drill bit will be in a certain position (or a range of certain positions) is also provided or displayed. For example, standard methods of computing standard deviations, which produce a confidence interval, can be used to define a confidence range for the motor yield and rotary tendency (e.g., there is a 95% probability that the motor yield is in between X and Y). Groover at col. 26, lines 24-30.
These ranges for motor yield and rotary tendency, in turn, can provide a confidence range for future positions of the drill bit (e.g., there is a 95% probability that the drill bit will be in a specific position or a range of positions). Groover at col. 26, lines 34-37.
The likelihood that the drill bit will be between two locations and/or the likelihood that the drill bit will be in a specific location is analogous to a “confidence region.”
It would have been obvious to a person of ordinary skill in the art, before the effective date of the clamed invention, to project one or more variables using a stochastic model as opposed to the probability-based model of Groover. Motivation to combine would be to generate more accurate estimations by taking into account various paths and not merely estimating based on the mean distribution of values.
Claim 15
Keller discloses:
a display (element 142 of FIG. 1)
Keller does not disclose:
wherein the one or more instructions that, when executed, further causes the processor to provide one or more of the first one or more stochastically projected trajectories and the first confidence region to one or more of the display and the trajectory controller.
Groover discloses:
wherein the one or more instructions that, when executed, further causes the processor to provide one or more of the first one or more stochastically projected trajectories and
Projection 720 illustrates the predicted future position of the drill bit at a second stationary survey station, and projection 725 illustrates the predicted future position of the drill bit at a third stationary survey station. Groover at col. 26, lines 11-13.
the first confidence region to
In some embodiments, a probability that the drill bit will be in a certain position (or a range of certain positions) is also provided or displayed. For example, standard methods of computing standard deviations, which produce a confidence interval, can be used to define a confidence range for the motor yield and rotary tendency (e.g., there is a 95% probability that the motor yield is in between X and Y). Groover at col. 26, lines 24-30.
one or more of the display and
At step 610, the toolface calculation engine 404 displays the predicted future position of the drill bit for each of the plurality of stationary survey stations on a HMI or a GUI. Groover at col. 26, lines 11-13. (i.e., displayed trajectory)
For example, a survey is conducted at a measured depth of 10,000 feet (P0). The toolface calculation engine 404 recommends a 10 foot slide at a gravity toolface of 0 degrees. Based on historically-derived motor yield and rotary tendency, the toolface calculation engine 404 can project 90 feet ahead to the next survey station, P1, assuming that the provided instructions are followed. This future position is assessed against the drilling windows, tolerances, and rules in effect for the wellbore, considering the statistic uncertainty present at this station. Groover at col. 26, lines 42-51.
wherein the controller is further configured to display the uncertainty of the predicted future position of the drill bit for each of the plurality of stationary survey stations on the graphical use interface. Groover at claim 2. (i.e., displayed confidence interval)
the trajectory controller.
The apparatus 100 also includes the controller 190 configured to control or assist in the control of one or more components of the apparatus 100. Groover at col. 6, lines 1-6. (i.e., trajectory provided to controller).
wherein the controller is further configured to assess an uncertainty of the predicted future position of the drill bit for each of the plurality of stationary survey stations... Groover at claim 8. (i.e., confidence interval provided to controller).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the display of Groover with the process of Keller to result in providing an operator with a visual representation of the trajectory and confidence interval. Motivation to combine improves ease of assessment by the operator of the potential outcomes of the suggested drilling, thus avoiding improper and wasted drilling.
Further, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine providing a confidence interval to a controller of the drill bit to result in a system whereby drilling decisions can be automatically decided without operator input. Motivation to combine would be that drilling could be performed quicker and objectively, thus reducing expense in compensating the operator to monitor the drilling progress and reducing expense in prolonging the drilling process.
Claim 16
Keller discloses:
wherein the one or more instructions that, when executed, further causes the processor to randomly select the first one or more system model parameters from the system model parameter probability distribution.
Without limitation, θ is also used to represent a vector of the calibrated parameters… Keller at [0024].
Second, a random sample of the measurements is created by randomly selecting a percentage, identified as P %, of the measurements without replacement. Keller at [0021].
The parameters from block 306 that form posteriors in block 307 are sent to block 308 where posteriors in block 307 may motivate priors in block 310, for next calibration. Keller at [0028].
The initial system model parameters are determined randomly from θ. When the system executes, new system model parameters are determined by the MCMC at block 306, resulting in new mean and distribution, which is processed at block 308 into new system model parameters (i.e., the “first one or more system model parameters.”).
Claim 17
Keller discloses:
stochastically project
The estimation process is represented graphically in FIG. 4. As illustrated, FIG. 4 uses simulated data as a visual representation of the statistical bagging process used to estimate the prior distributions of the initial conditions. In FIG. 4, Xb=[80,110] ft (24,417 meters). The group of lines is each best fit line to a different random sample from the measurements in window Xb. Keller at [0022].
Additionally
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is a vector of inclination estimates calculated using a model with parameters θ. Keller at [0027].
For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations… Keller at [0019].
Each projection includes selecting one of the vectors in θ and calculating the trajectory.
See also [0026]-[0028].
the trajectory of the bottom hole assembly [in real time] or
This disclosure details a methods and systems for calibrating a steering model and estimating drill-bit position and orientation both in real-time and after operations. Keller at [0009].
“Projecting a first one or more trajectories” is a step in “calibrating a steering mode” (i.e., at block 206).
Keller does not disclose:
Groover discloses:
In various embodiments, the statistical certainty of the future positions is also provided or displayed. For example, a confidence interval can be used to define a “confidence range” for the future positions. In several embodiments, real-time inclination and real-time azimuth measurements are used to improve the steering instructions and the estimates of the future positions. Groover at col. 26, lines 24-30.
These ranges for motor yield and rotary tendency, in turn, can provide a confidence range for future positions of the drill bit (e.g., there is a 95% probability that the drill bit will be in a specific position or a range of positions). Groover at col. 26, lines 34-37.
The likelihood that the drill bit will be between two locations and/or the likelihood that the drill bit will be in a specific location is analogous to a “confidence region.”
It would have been obvious to a person of ordinary skill in the art, before the effective date of the clamed invention, to project one or more variables using a stochastic model as opposed to the probability-based model of Groover. Motivation to combine would be to generate more accurate estimations by taking into account various paths and not merely estimating based on the mean distribution of values.
Claim 18
Keller discloses:
receive a second one or more system model parameters
The calibrated parameters are identified as τ is a depth constant, Kact is the magnitude of the bottom hole assembly 130 turning capability, Kbias represents both the inherent steering tendency of bottom hole assembly 130 as well as any external forces on bottom hole assembly 130. Keller at [0024].
The “calibrated parameters” are analogous to the “system model parameters.”
from the system model parameter probability distribution;
See FIG. 3, illustrating blocks 307 and 310, which include the system model parameters as determined by block 204, each having a mean and distribution value for τ, Kact, Kbias. Thus, once the system is executed once, the system model parameters are selected from a “probability distribution.”
receive a second one or more steering inputs;
Block 306 is a Markov Chain Monte Carlo simulation (MCMC). In examples, the MCMC may be utilized to calibrate a steering model. The steering model may be calibrated and used to estimate a position of drill bit 122 (e.g., referring to FIG. 1) and attitude is the following depth-based second order differential equation, seen below as:
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which describes the dynamics of bottom hole assembly 130 (e.g., referring to FIG. 1) in the inclination and azimuth planes. Keller at [0024].
receive a second one or more values corresponding to the bottom hole assembly initial conditions at the second position within the subsurface formation; and
Variables defined within block 302 are the initial conditions θ0 and
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. The initial conditions are output 303. Keller at [0021].
stochastically project a second one or more trajectories of the bottom hole assembly
Block 204 performs steering model calibrations. In examples, steering model calibrations may operate and function to control drilling system 100 during drilling operations. For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations…Keller at [0019].
based at least in part on one or more of the second one or more system model parameters, the second one or more steering inputs, and the second one or more values corresponding to the bottom hole assembly initial conditions.
As illustrated in FIG.2 and FIG. 3, block 204 includes system model parameters (block 310), steering inputs (calculated by block 306 using Equation (2)), and initial conditions (block 303).
Keller does not explicitly disclose:
from the second position in the subsurface formation to a third position in the subsurface formation
Groover discloses:
[project a second one or more trajectories of the bottom hole assembly] from the second position in the subsurface formation to a third position in the subsurface formation
Projection 720 illustrates the predicted future position of the drill bit at a second stationary survey station, and projection 725 illustrates the predicted future position of the drill bit at a third stationary survey station.
“Predicted future position” is determined based on “estimated trajectory.”
Claim 19
Keller discloses:
A method for stochastically projecting a well trajectory of a bottom hole assembly in a subsurface formation in real time, the method comprising:
Block 204 performs steering model calibrations. In examples, steering model calibrations may operate and function to control drilling system 100 during drilling operations. For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations…Keller at [0019].
receiving a first one or more system model parameters
The calibrated parameters are identified as τ is a depth constant, Kact is the magnitude of the bottom hole assembly 130 turning capability, Kbias represents both the inherent steering tendency of bottom hole assembly 130 as well as any external forces on bottom hole assembly 130. Keller at [0024].
The “calibrated parameters” are analogous to the “system model parameters.”
from a system model parameter probability distribution;
See FIG. 3, illustrating blocks 307 and 310, which include the system model parameters as determined by block 204, each having a mean and distribution value for τ, Kact, Kbias. Thus, once the system is executed once, the system model parameters are selected from a “probability distribution.”
receiving a first one or more steering inputs;
Block 306 is a Markov Chain Monte Carlo simulation (MCMC). In examples, the MCMC may be utilized to calibrate a steering model. The steering model may be calibrated and used to estimate a position of drill bit 122 (e.g., referring to FIG. 1) and attitude is the following depth-based second order differential equation, seen below as:
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which describes the dynamics of bottom hole assembly 130 (e.g., referring to FIG. 1) in the inclination and azimuth planes. Keller at [0024].
receiving a first one or more values corresponding to bottom hole assembly initial conditions at a first position within the subsurface formation;
Variables defined within block 302 are the initial conditions θ0 and
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. The initial conditions are output 303. Keller at [0021].
stochastically projecting a first one or more trajectories of the bottom hole assembly
Block 204 performs steering model calibrations. In examples, steering model calibrations may operate and function to control drilling system 100 during drilling operations. For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations…Keller at [0019].
receiving a second one or more system model parameters from the system model parameter probability distribution;
The calibrated parameters are identified as τ is a depth constant, Kact is the magnitude of the bottom hole assembly 130 turning capability, Kbias represents both the inherent steering tendency of bottom hole assembly 130 as well as any external forces on bottom hole assembly 130. Keller at [0024].
The “calibrated parameters” are analogous to the “system model parameters.”
See FIG. 3, illustrating blocks 307 and 310, which include the system model parameters as determined by block 204, each having a mean and distribution value for τ, Kact, Kbias. Thus, once the system is executed once, the system model parameters are selected from a “probability distribution.”
receiving a second one or more steering inputs;
Block 306 is a Markov Chain Monte Carlo simulation (MCMC). In examples, the MCMC may be utilized to calibrate a steering model. The steering model may be calibrated and used to estimate a position of drill bit 122 (e.g., referring to FIG. 1) and attitude is the following depth-based second order differential equation, seen below as:
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which describes the dynamics of bottom hole assembly 130 (e.g., referring to FIG. 1) in the inclination and azimuth planes. Keller at [0024].
receiving a second one or more values corresponding to the bottom hole assembly initial conditions at a second position within the subsurface formation;
Continuous measurements are found if drilling system 100 is performing continuous drilling operations. Stationary measurements are found if drilling operations have stopped for drilling system 100. Input 301, populated by the measurements discussed above, is fed into block 302. Block 302 is an initial condition estimation. Variables defined within block 302 are the initial conditions θ0 and
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. Keller at [0020]-[0021].
New initial conditions are calculated based on new measurements (input 301) corresponding to a new position of the drill.
stochastically projecting a second one or more trajectories of the bottom hole assembly
Block 204 performs steering model calibrations. In examples, steering model calibrations may operate and function to control drilling system 100 during drilling operations. For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations…Keller at [0019].
controlling the bottom hole assembly and a drill bit using the stochastically projected first one or more trajectories of the bottom hole assembly to guide borehole placement from the first position to the second position
Control logic in block 206 may be a model-based control logic, where the calibrated steering model is used to determine a corrective steering command such that at least one objective is achieved…This may allow drilling system 100 to drill into formation 106 (e.g., referring to FIG. 1) at any suitable angle, horizontally, and/or the like.” Keller at [0019].
See also FIG. 2, wherein the “steering command” is generated by the control logic 206, which receives, as input, “drill-bit trajectory estimation” (analogous to a “stochastically projected well trajectory”).
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by drilling along a selected one of the stochastically projected first one or more trajectories.
Control logic in block 206 may operate and function to control the trajectory, speed, revolutions-per-minute, and other parameters of drill bit 122 during drilling operations. Keller at [0019].
Keller does not explicitly disclose:
from the second position within the subsurface formation to a third position within the subsurface formation
advancing the bottom hole assembly from the first position to the second position;
Groover discloses:
from the second position within the subsurface formation to a third position within the subsurface formation
The actual position of the drill bit is shown at 715 with respect to the well plan at 705 and target line 710. Projection 720 illustrates the predicted future position of the drill bit at a second stationary survey station… Groover at col. 26, lines 18-23.
At the “second position” (i.e., location 720), the estimated trajectory is determined. At the “third position” (i.e., location 725) the borehole is shown where it should be after advancing based on the projected trajectory.
“Predicted future position” is determined based on “estimated trajectory.”
advancing the bottom hole assembly from the first position to the second position;
At step 614, the toolface calculation engine 404 executed the received directions, and drilling commences. Groover at col. 26, lines 66-67.
The actual position of the drill bit is shown at 715 with respect to the well plan at 705 and target line 710. Projection 720 illustrates the predicted future position of the drill bit at a second stationary survey station… Groover at col. 26, lines 18-23.
At the “first position” (i.e., location 715), the estimated trajectory is determined. At the “second position” (i.e., the “second station survey station”) the borehole is shown where it should be after advancing based on the projected trajectory.
Claim 20
Keller discloses:
stochastically projecting
The estimation process is represented graphically in FIG. 4. As illustrated, FIG. 4 uses simulated data as a visual representation of the statistical bagging process used to estimate the prior distributions of the initial conditions. In FIG. 4, Xb=[80,110] ft (24,417 meters). The group of lines is each best fit line to a different random sample from the measurements in window Xb. Keller at [0022].
Additionally
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is a vector of inclination estimates calculated using a model with parameters θ. Keller at [0027].
For example, outputs from block 204 may be calibrated model parameters and drill-bit trajectory estimations… Keller at [0019].
Each projection includes selecting one of the vectors in θ and calculating the trajectory.
See also [0026]-[0028].
Keller does not disclose:
Groover discloses:
In some embodiments, a probability that the drill bit will be in a certain position (or a range of certain positions) is also provided or displayed. For example, standard methods of computing standard deviations, which produce a confidence interval, can be used to define a confidence range for the motor yield and rotary tendency (e.g., there is a 95% probability that the motor yield is in between X and Y). Groover at col. 26, lines 24-30.
These ranges for motor yield and rotary tendency, in turn, can provide a confidence range for future positions of the drill bit (e.g., there is a 95% probability that the drill bit will be in a specific position or a range of positions). Groover at col. 26, lines 34-37.
The likelihood that the drill bit will be between two locations and/or the likelihood that the drill bit will be in a specific location is analogous to a “confidence region.”
These ranges for motor yield and rotary tendency, in turn, can provide a confidence range for future positions of the drill bit (e.g., there is a 95% probability that the drill bit will be in a specific position or a range of positions). Groover at col. 26, lines 34-37.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Pat. Pub. No. 2019/0284908: “Directional drilling with automatic uncertainty mitigation”
U.S. Pat. Pub. No. 2012/0118637: “Drilling Advisory Systems And Methods Utilizing Objective Functions”
U.S. Pat. Pub. No. 2016/0281489: “Managing wellbore operations using uncertainty calculations”
U.S. Pat. No. 12,129,751: “Adaptive trajectory control for automated directional drilling”
U.S. Pat. No. 8,892,407: “Robust well trajectory planning”
U.S. Pat. No. 7,957,946: “Method of automatically controlling the trajectory of a drilled well”
WIPO Pub. No. 2018/118020: “Real-time Trajectory Control During Drilling Operations”
Li, et al., “Stochastic optimal control and algorithm of the trajectory of horizontal wells”
Communication
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH MORRIS whose telephone number is (703)756-5735. The examiner can normally be reached M-F 8:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached at (571) 272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOSEPH MORRIS
Examiner
Art Unit 2188
/JOSEPH P MORRIS/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188