DETAILED ACTIONS
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 Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 05/19/2026.
Status of the Claims
Claims 1-2, 4-6, 8-18, 20, and 22-24 are pending.
Claims 1,5, 8,11, 15, and 20 are amended.
Claim 24 is new.
Response to Arguments
Rejections Under 35 U.S.C. §103
Applicant’s Argument
Applicant argues in the remarks pages 10-12, filed 05/19/2026, with respect to the rejection(s) of Claim 1 under 35 U.S.C. 103 that
“Claim 1 has been amended to recite, inter alia, "the sectional limit comprises a
changeable preferred maximum rate of penetration," "relaxing the sectional limit to increase the preferred maximum rate of penetration value to define a relaxed sectional limit by increasing an upper value of a rate of penetration window to the new rate of penetration value without changing the hard limit," "in response to the differential pressure stabilizing within the predefined differential pressure window subsequent to the relaxing the sectional limit, resetting the preferred maximum rate of penetration to define the sectional limit for the rate of penetration without changing the hard limit." Neither Belaskie, Chang, nor Wang, whether considered alone or in combination with one another, discloses, teaches, or fairly suggest at least these limitations”
of claim 1.”
Examiner’s response:
Applicant’s arguments see remarks pages 10-12, filed 05/19/2026, with respect to the rejection(s) of Claim 1 under 35 U.S.C.103 Has been considered, and are not persuasive.
The primary prior art Belaskie teaches the optimization of ROP to a desired value. In (Belaskie, [0035] ) it is disclosed that a controlled drilling is conducted to obtain desired ROP value using a set of algorithm/ model and inputting real time drilling parameter for a particular sections of a borehole.(i.e., a set of specifications for drilling and ancillary operations to construct the wellbore) indicates one or more sections of the well bore are to undergo controlled drilling, the desired bit rate of penetration may be converted to a surface rate of penetration value by a drill string response model as shown in FIG. 12 at 218. The calculated value of bit rate of penetration may then be sent to the controller (186 in FIG. 4) which operates the automatic driller (e.g., as in FIG. 2) to release the drill string at the surface ROP which will result in the desired ROP at the drill bit. A drilling plan with specific drilling parameter for each section is provided to the controller and a desired ROP is obtained. “the desired ROP” is a preferred maximum penetration rate of penetration at which a safe drilling operation is conducted. For each section of the pipe 32 the ROP is optimized to the “desired ROP” is the sectional limit. “Optimizing the ROP value” by the control unit is changing or adjusting the ROP value based on drilling parameters reads on the “changeable” value, See (Belaskie, [0043] The drilling models and relationships may adjust in real time in different subsurface formations and drilling conditions, thereby maintaining smooth and safe drilling without the need for manual control of parameters for the auto driller”. Belaskie, Figure 5, also teaches Off bottom pressure calibration limit [0029], and stand pipe pressure and mud flow rate while drilling and the off-bottom pressure and flow rate from the calibration of FIG. 5 may be used to compute the differential pressure as shown in FIG. 8 at 206. The differential Pressure limit/threshold value is set by calibration.
The secondary prior art Chang teaches detail steps of optimization method of performance drilling parameter (increasing/ decreasing/ relaxing) by defining a response score and objective score based on change in drill parameter values. The algorithm is disclosed in figure 12-13. The drill parameters (Chang, [0035]), “drilling parameters may include rotary speed (RPM), WOB, characteristics of the drill bit and drill string, mud weight, mud flow rate, lithology of the formation, pore pressure of the formation, torque, pressure, temperature, ROP, MSE, vibration measurements, etc.” are adjusted based on threshold/ optimum response score and objective score of each parameter. Where the maximum response score is 100% which reads on “hard limit”, the maximum objective score is also 100%. Objective score, and threshold value is 40% which reads on “sectional limit/threshold. The drill parameter ROP is adjusted based on the response score and objective score. The method of parameter adjustment based on new data is automatic. See example method utilizing response score and objective score of parameter adjustment steps in, (Chang, [0052] “the response-point based decision tree recommendations may provide qualitative recommendations, such as increase, decrease, or maintain a given drilling parameter (e.g., weight on bit, rotation rate,etc.), or the recommendation might be to pick up off bottom see [0100]. Adjusting drill parameters in real time with the benefit of the response-point based decision tree recommendations providing a qualitative recommendation, such as increase, decrease, or maintain a given drilling parameter (Chang, [0051]- [0052) and choosing a set of algorithms to adjust ROP is a design choice not an inventive step. Both Belaskie, and Change teaches. Prior art Al-Rubaii also teaches adjusting ROP rate based on drilling parameters and optimizing the ROP value for efficient drilling.
Therefore, applicant argument is not persuasive, the rejections are maintained.
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.
Claims 1-2, 4-6, 8-18, 20, and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Belaskie et al. (US 2018/0328160 A1, hereinafter Belaskie, previously cited) and in view of Chang et al. (US 2014/0277752 A1, hereinafter Chang, previously cited), and further in view of Wang et al. (US 2019/0032467 A1, hereinafter Wang).
Regarding Claim 1, Belaskie teaches,
A method for dynamically adjusting drilling parameters during a drilling operation (Belaskie, Figure 17, - [0041] “Real time relationships (dynamic relationship) based on drilling models according to the present disclosure may be used to control an auto driller at specific set points of rate of penetration). [0043] The drilling models and relationships may adjust in real time in different subsurface formations and drilling conditions) comprising:
measuring, in real time, a differential pressure (Belaskie, Figure 8, 206, [0029], compute the differential pressure as shown in FIG. 8 at 206) across a motor of a bottom hole assembly during a directional drilling operation (Belaskie, Figure 9-10, [0030] If a mud motor is used, the parameter model receives the bit torque, differential pressure and flow rate as inputs, as shown at 208 in FIG. 9);
measuring, in real time, a rate of penetration of the bottom hole assembly during the directional drilling operation (Belaskie, Figure 11, [0032] “The surface rate of penetration and the weight on bit may be input into a drill string response model at 218 in FIG. 11, which computes an estimate of the downhole rate of penetration”);
determining whether the differential pressure is within a predefined differential pressure window specifying a lower limit for the differential pressure and an upper limit for the differential pressure (Belaskie, Figure 5, Off bottom pressure calibration limit [0029] “The stand pipe pressure and mud flow rate while drilling and the off-bottom pressure and flow rate from the calibration of FIG. 5 may be used to compute the differential pressure as shown in FIG. 8 at 206”. Differential Pressure limit/threshold value is set by calibration);
define a sectional limit for the rate of penetration, wherein the sectional limit comprises a changeable preferred maximum rate of penetration value based on an average performance associated with the preferred maximum rate of penetration value (“Belaskie, [0035] When the drilling plan (i.e., a set of specifications for drilling and ancillary operations to construct the wellbore) indicates one or more sections of the well bore are to undergo controlled drilling, the desired bit rate of penetration may be converted to a surface rate of penetration value by a drill string response model as shown in FIG. 12 at 218. The calculated value of bit rate of penetration may then be sent to the controller (186 in FIG. 4) which operates the automatic driller (e.g., as in FIG. 2) to release the drill string at the surface ROP which will result in the desired ROP at the drill bit. The foregoing is shown in FIG. 12.”. NOTE: A drilling plan with specific drilling parameter for each section is provided to the controller and a desired ROP is obtained. “the desired ROP” reads on sectional limit with a preferred maximum penetration rate of penetration at which a safe drilling operation is conducted. For each section of the pipe 32 the ROP is optimized to the “desired ROP” is the sectional limit. “Optimizing the ROP value” by the control unit is changing or adjusting the ROP value based on drilling parameters reads on the “changeable” value, See (Belaskie, [0043] The drilling models and relationships may adjust in real time in different subsurface formations and drilling conditions, thereby maintaining smooth and safe drilling without the need for manual control of parameters for the auto driller”); and
in response to determining that the differential pressure is below the lower limit of the predefined differential pressure window or trending downwards towards the lower limit of the predefined differential pressure window (Belaskie, Figures 5, 15-16, [0039] “When the limiting parameter is differential pressure (i.e., the increase in standpipe pressure above the off bottom pressure measured as explained with reference to FIG. 5), the determined relationship between differential pressure and bit torque at 204 in FIG. 15 may be used with the bit drilling response model 214 to determine a desired bit torque as previously explained. Using desired bit: torque, at 212 in FIG. 16, the process shown in FIG. 15 may then be used to compute the set point for surface rate of penetration as explained with reference to FIG. 14. As previously explained, the foregoing setpoint may be communicated from the optimizer (194 in FIG. 4) to the controller (186 in FIG. 4) to operate the rig automatically to maintain the set point surface ROP.” NOTE: Optimizer use model to update new rate of penetration automatically in real time to obtain “set points” of ROP as pressure limits change. The calibration pressure limit is used as threshold i.e an upper limit and a lower limit. Any differential pressure change will optimize ROP set point.);
comparing the new rate of penetration value with the sectional limit;
comparing the new rate of penetration value with a hard limit for the rate of penetration, wherein the hard limit comprises an unchanging maximum rate of penetration value based on at least one of a risk of damage to equipment, a safety risk, or an environmental risk; (Belaskie, [0034] The relationships are dynamic, that is, they are continuously updated by input of real time data and thus may adapt to changing conditions in the wellbore. The relationships thus determine may be used to directly control the drilling operation by sending set points of RPM and rate of penetration (ROP) from the optimizer (194 in FIG. 4) to the controller (186 in FIG. 4).[0035] The calculated value of bit rate of penetration may then be sent to the controller (186 in FIG. 4) which operates the automatic driller (e.g., as in FIG. 2) to release the drill string at the surface ROP which will result in the desired ROP at the drill bit. The foregoing is shown in FIG. 12”. NOTE: “the desired ROP” reads on maximum penetration rate at which a safe drilling operation is conducted. The “desired ROP” is the safe value of ROP for the section without risking the well. It is implied that any value exceeding the desired value would break the system. In other word, unchanging maximum rate of penetration value (when it breaks) having a lower values ROP. This is all about optimization of ROP value based on drilling parameters and done by model. This is not an inventive step. See [0043] “The drilling models and relationships may adjust in real time in different subsurface formations and drilling conditions, thereby maintaining smooth and safe drilling without the need for manual control of parameters for the auto driller”).
in response to the new rate of penetration value being at or above the hard limit, determining a different drilling parameter than the rate of penetration to increase the differential pressure (Belaskie, Figure 8 [0029] The stand pipe pressure and mud flow rate while drilling and the off bottom pressure and flow rate from the calibration of FIG. 5 may be used to compute the differential pressure as shown in FIG. 8 at 206 without changing the hard limit ; and in response to the differential pressure stabilizing within the predefined differential pressure window subsequent to the relaxing the sectional limit, resetting the preferred maximum rate of penetration to define the sectional limit for the rate of penetration without changing the hard limit(Belaskie, Figure 11 0033] “The foregoing models may be used in the optimizer (194 in FIG. 4) in real-time to compute the weight on bit and
rotary speed of the bit (RPM) needed to optimize the rate of penetration (ROP) while maintaining the equipment inside limits for torque, WOB, RPM, rate of penetration and differential pressure.” Note: optimize the rate of penetration (ROP) when differential pressure value is maintained within limit),
Belaskie teaches real time measured data (see Figure 4, MWD 37, 194 optimizer) optimization of ROP. Bit Torque and Differential pressure within operational limit using a model algorithm see figure 15 and Figure 16. However, Belaskie is silent on detail steps of optimizations.
Belaskie is silent on in response to the new rate of penetration value being above the sectional limit:
in response to the new rate of penetration value being below the hard limit,
relaxing the sectional limit to increase the preferred maximum rate of penetration value to define a relaxed sectional limit by increasing an upper value of a rate of penetration window to the new rate of penetration value without changing the hard limit; and
However, Chang teaches in response to the new rate of penetration value being above the sectional limit: in response to the new rate of penetration value being below the hard limit, relaxing the sectional limit to increase the preferred maximum rate of penetration value to define a relaxed sectional limit by increasing an upper value of a rate of penetration window to the new rate of penetration value without changing the hard limit; (Chang, Figure 12, 13, see below Table 1-2, Chang discloses optimization method of performance drilling parameter (increasing/ decreasing/ relaxing) by defining a response score and objective score based on change in drill parameter values. The algorithm is disclosed in figure 12-13. The drill parameters (Chang, [0035], “drilling parameters may include rotary speed (RPM), WOB, characteristics of the drill bit and drill string, mud weight, mud flow rate, lithology of the formation, pore pressure of the formation, torque, pressure, temperature, ROP, MSE, vibration measurements, etc.”) are adjusted based on threshold/ optimum response score and objective score of each parameter. Where the maximum response score is 100% which reads on “hard limit”, the maximum objective score is also 100%. Objective score, and threshold value is 40% which reads on “sectional limit/threshold. The drill parameter ROP is adjusted based on the response score and objective score. The method of parameter adjustment based on new data is automatic. See example method utilizing response score and objective score of parameter adjustment steps in, (Chang, [0052] “the response-point based decision tree recommendations may provide qualitative recommendations, such as increase, decrease, or maintain a given drilling parameter (e.g., weight on bit, rotation rate,etc.), or the recommendation might be to pick up off bottom see [0100].The driller increases WOB as recommended, and this results in the generation of a new response point and an increase in the combined objective function value, which is calculated from a time-averaged ROP, time-averaged TSE, and ROP-weighted average of MSE.(…) The driller increases WOB as recommended, generating a third response point. The objective function value of this third response point decreases relative to that of the second response
point,(…)
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. This learning mode process continues until 20 response points are obtained, resulting in a response score of 100%, which triggers an application mode that recommends the averages of the parameters of the best response point and the results of a local search engine. [0110] A rate of penetration (ROP) for each response point can be based on the change in block position over a duration of time. Since there may be oscillations in the block position, the change in block position can be determined using the mean values of block position and time for subsets of data points within the subinterval of data”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Belaskie’s method of optimization to incorporate Chang’s optimization methods with a response point and objective points and adjusting drill parameters in real time with the benefit of the response-point based decision tree recommendations providing a qualitative recommendation, such as increase, decrease, or maintain a given drilling parameter (Chang, [0051]- [0052]). It would have been obvious to a person of ordinary skill to include response point-based decision tree (framework) is used to select an application mode or a learning mode, based on whether specified criteria are met for the response score and objective score. When a learning mode is activated, the recommendations can be based on principles such as increasing WOB, RPM, and/or flow rate until an objective function no longer improves along with the other machine learning network, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Both Belaskie and Chang are silent on applying a cost function to determine a new rate of penetration value that will increase the differential pressure and minimize a deviation of the new rate of penetration value from the sectional limit;
However, Wang teaches applying a cost function to determine a new rate of penetration value that will increase the differential pressure and minimize a deviation of the new rate of penetration value from the sectional limit (Wang, Figure 3-4, and Figure 6, [0089] By way of background, an objective function is a mathematical operation that seeks to either minimize or maximize a value, or a set of values, over a set of feasible alternatives. Where the function seeks to minimize the set of values, it may alternatively be referred to as a cost function. In the present disclosure, the objective function seeks to quantify the process of moving the drilling parameters from initial conditions (WOB0, RPM0 ), towards (WOB1, RPM0 ), and then to optimal conditions (WOB*, RPM*). [0103] Note that this equation, (See [0093]-[0102]) formulated as an objective (or "maximization") function, may be similarly expressed as a cost ( or "minimization") function. Additionally, and alternatively, the control variables (ROP, RPM) may be used instead of (WOB, RPM) for ROP-controlled operations. Either way, the objective function depends on two controllable variables plus time”NOTE: minimum cost function is estimated with higher ROP rate see [0156] The computer-based system seeks to provide an optimal path with minimum MSE and TSE values in order to provide higher ROP and to reduce the risk of unnecessary trips due to premature bit and tool wear).;
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Belaskie and Chang’s method of optimization and adjusting drill parameters and incorporating Wangs method of estimating Cost function with maximum ROP rate as taught by Wang in real time with the benefit of the seek to decrease the amount of time it takes to form the wellbore by increasing rate of penetration, or "ROP and optimize the drilling ramp-up procedure.(Wang, [0085]). It would have been obvious to a person of ordinary skill to include algorithm to estimate cost function with optimized drilling parameter. Known in the art that using equations formulated as an objective (or "maximization") function, may be similarly expressed as a cost ( or "minimization") function. Additionally, and alternatively, the control variables (ROP, RPM) may be used instead of (WOB, RPM) for ROP-controlled operations. Either way, the objective function depends on two controllable variables plus time. (Wang, [0103] in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Regarding Claim 2, Combination of Belaskie, Chang and Wang teach the method of claim 1,
Belaskie further teaches further comprising automatically increasing the rate of penetration to the new rate of penetration value that will increase differential pressure. (Belaskie, Figure 16, [0039] When the limiting parameter is differential pressure
(i.e., the increase in standpipe pressure above the off bottom pressure measured as explained with reference to FIG. 5), the determined relationship between differential pressure and bit torque at 204 in FIG. 15 may be used with the bit drilling response model 214 to determine a desired bit torque as previously explained. Using desired bit torque, at 212 in FIG. 16, the process shown in FIG. 15 may then be used to compute the set point for surface rate of penetration
as explained with reference to FIG. 14. As previously explained, the foregoing setpoint may be communicated from the optimizer (194 in FIG. 4) to the controller (186 in FIG. 4) to operate the rig automatically to maintain the set point surface ROP”).
Regarding Claim 4, Combination of Belaskie, Chang and Wang teach the method of claim 1,
Belaskie further teaches, further comprising, in response to determining that the differential pressure is below the lower limit of the predefined differential pressure window or trending downwards towards the lower limit of the predefined differential pressure window : identifying new values for one or more additional drilling parameters that will increase the differential pressure;(Belaskie, Figure 5, optimizer 194, Figure 8, 206, [0029] “The stand pipe pressure and mud flow rate while drilling and the off bottom pressure and flow rate from the calibration of FIG. 5 may be used to compute the differential pressure as shown in FIG. 8 at 206.” Optimizer optimize pressure values based on real time parameters [0025] “The optimizer 194 may be programmed using a drilling model that is data driven and is updated in real-time for the state condition of the surface and downhole equipment and for the formation being drilled”)
comparing the new values for the one or more additional drilling parameters with a sectional limit for the one or more additional drilling parameters;(Belaskie, Figure 9, adjusting other drilling parameter to determine differential pressure optimum value. [0030] If a mud motor is used, the parameter model receives the bit torque, differential pressure and flow rate as inputs, as shown at 208 in FIG. 9. The mud motor parameter model may compute the motor rotation speed (RPM) and may determine a relationship between the differential pressure (i.e., increase in pressure from the off-bottom calibration shown in FIG. 5) and the motor torque as shown at 212 in FIG. 9. The motor RPM and surface RPM may be input into an RPM relationship to compute the current bit RPM while drilling as shown at 210 in FIG9”).
Belaskie teaches real time measured data (see Figure 4, MWD 37, 194 optimizer) optimization of ROP. Bit Torque and Differential pressure within operational limit using a model algorithm see figure 15 and Figure 16. However, Belaskie is silent on detail steps of optimizations.
Belaskie is silent comparing the new values for the one or more additional drilling parameters with a hard limit for the one or more additional drilling parameters; and in response to the new values for the one or more additional drilling parameters being above the sectional limit for the one or more additional drilling parameters and below the hard limit for the one or more additional drilling parameters, increasing the upper value of a window one or more additional drilling parameters to the new values for the one or more additional drilling parameters.
However, Chang teaches comparing the new values for the one or more additional drilling parameters with a hard limit for the one or more additional drilling parameters; and in response to the new values for the one or more additional drilling parameters being above the sectional limit for the one or more additional drilling parameters and below the hard limit for the one or more additional drilling parameters, increasing the upper value of a window one or more additional drilling parameters to the new values for the one or more additional drilling parameters. (Chang, Figure 12, 13,see below Table 1-2, Chang discloses optimization method of performance drilling parameter by defining a response score and objective score based on change in drill parameter values. The algorithm is disclosed in figure 12-13. The drill parameters (Chang, [0035], “drilling parameters may include rotary speed (RPM), WOB, characteristics of the drill bit and drill string, mud weight, mud flow rate, lithology of the formation, pore pressure of the formation, torque, pressure, temperature, ROP, MSE, vibration measurements, etc.”) are adjusted based on threshold/ optimum response score and objective score of each parameter. Where the maximum response score is 100% which reads on “hard limit”, the maximum objective score is also 100%. Objective score, and threshold value is 40% which reads on “sectional limit/threshold. The drill parameter ROP is adjusted based on the response score and objective score. The method of parameter adjustment based on new data is automatic. See example method utilizing response score and objective score of parameter adjustment steps in, (Chang, [0052] “the response-point based decision tree recommendations may provide qualitative recommendations, such as increase, decrease, or maintain a given drilling parameter (e.g., weight on bit, rotation rate,etc.), or the recommendation might be to pick up off bottom see [0100].The driller increases WOB as recommended, and this results in the generation of a new response point and an increase in the combined objective function value, which is calculated from a time-averaged ROP, time-averaged TSE, and ROP-weighted average of MSE.(…) The driller increases WOB as recommended, generating a third response point. The objective function value of this third response point decreases relative to that of the second response point. This learning mode process continues until 20 response points are obtained, resulting in a response score of 100%, which triggers an application mode that recommends the averages of the parameters of the best response point and the results of a local search engine. [0110] A rate of penetration (ROP) for each response point can be based on the change in block position over a duration of time. Since there may be oscillations in the block position, the change in block position can be determined using the mean values of block position and time for subsets of data points within the subinterval of data”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Belaskie’s method of optimization to incorporate Chang’s optimization methods with a response point and objective points and adjusting drill parameters in real time with the benefit of the response-point based decision tree recommendations providing a qualitative recommendation, such as increase, decrease, or maintain a given drilling parameter (Chang, [0051]- [0052]). It would have been obvious to a person of ordinary skill to include response point-based decision tree (framework) is used to select an application mode or a learning mode, based on whether specified criteria are met for the response score and objective score. When a learning mode is activated, the recommendations can be based on principles such as increasing WOB, RPM, and/or flow rate until an objective function no longer improves along with the other machine learning network, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Regarding Claim 5, Belaskie teaches
A non-transitory, tangible computer-readable storage medium comprising instructions (Belaskie, Figure 18, storage media 106, computer system 101A [ 0047] The storage media 106 may be implemented as one or more computer-readable or machine-readable storage media). for dynamically adjusting drilling parameters during a drilling, wherein the dynamically adjusting the drilling parameters (Belaskie, Figure 17, - [0041] “Real time relationships (dynamic relationship) based on drilling models according to the present disclosure may be used to control an auto driller at specific set points of rate of penetration). [0043] The drilling models and relationships may adjust in real time in different subsurface formations and drilling conditions) comprises:
receiving, in real time, a measurement of a drilling parameter during the drilling operation; receiving, in real time, a response measurement during the drilling operation ([0025] “The optimizer 194 may be programmed using a drilling model that is data driven and is updated in real-time for the state condition of the surface and downhole equipment and for the formation being drilled”);
determining whether the response measurement is within a response window that defines a desired lower limit and a desired upper limit for the response measurement (Belaskie, Figure 5, [0026] As drilling progresses, off bottom calibrations may be performed at selected times, [0034], The relationships are dynamic, that is, they are continuously updated by input of real time data
and thus may adapt to changing conditions in the wellbore. The relationships thus determine may be used to directly control the drilling operation by sending set points of RPM and rate of penetration (ROP) from the optimizer (194 in
FIG. 4) to the controller (186 in FIG. 4).)” calibration done by unit 200 determine set points sets, the upper limit lower limit values of parameters during a safe operation);
defining a sectional limit for the drilling parameter, wherein the sectional limit comprises a changeable preferred maximum value based on an average performance associated with the preferred maximum value (“Belaskie, [0035] When the drilling plan (i.e., a set of specifications for drilling and ancillary operations to construct the wellbore) indicates one or more sections of the well bore are to undergo controlled drilling, the desired bit rate of penetration may be converted to a surface rate of penetration value by a drill string response model as shown in FIG. 12 at 218. The calculated value of bit rate of penetration may then be sent to the controller (186 in FIG. 4) which operates the automatic driller (e.g., as in FIG. 2) to release the drill string at the surface ROP which will result in the desired ROP at the drill bit. The foregoing is shown in FIG. 12.”. NOTE: A drilling plan with specific drilling parameter for each section is provided to the controller and a desired ROP is obtained. “the desired ROP” reads on sectional limit with a preferred maximum penetration rate of penetration at which a safe drilling operation is conducted. For each section of the pipe 32 the ROP is optimized to the “desired ROP” is the sectional limit. “Optimizing the ROP value” by the control unit is changing or adjusting the ROP value based on drilling parameters reads on the “changeable” value, See (Belaskie, [0043] The drilling models and relationships may adjust in real time in different subsurface formations and drilling conditions, thereby maintaining smooth and safe drilling without the need for manual control of parameters for the auto driller”); and
in response to determining that the response measurement is below the desired lower limit of the response window or trending downwards towards the desired lower limit of the response window (Belaskie, Figures 5, 15-16, [0039] “When the limiting parameter is differential pressure (i.e., the increase in standpipe pressure above the off bottom pressure measured as explained with reference to FIG. 5), the determined relationship between differential pressure and bit torque at 204 in FIG. 15 may be used with the bit drilling response model 214 to determine a desired bit torque as previously explained. Using desired bit: torque, at 212 in FIG. 16, the process shown in FIG. 15 may then be used to compute the set point for surface rate of penetration as explained with reference to FIG. 14. As previously explained, the foregoing setpoint may be communicated from the optimizer (194 in FIG. 4) to the controller (186 in FIG. 4) to operate the rig automatically to maintain the set point surface ROP.” NOTE: Optimizer use model to update new rate of penetration automatically in real time to obtain “set points” of ROP as pressure limits change. The calibration pressure limit is used as threshold i.e an upper limit and a lower limit. Any differential pressure change will optimize ROP set point.);
comparing the new drilling parameter value with the sectional limit; comparing the new drilling parameter value with a hard limit for the drilling parameter value, wherein the hard limit comprises an unchanging maximum drilling parameter value based on at least one of a risk of damage to equipment, a safety risk, or an environmental risk (Belaskie, [0034] The relationships are dynamic, that is, they are continuously updated by input of real time data and thus may adapt to changing conditions in the wellbore. The relationships thus determine may be used to directly control the drilling operation by sending set points of RPM and rate of penetration (ROP) from the optimizer (194 in FIG. 4) to the controller (186 in FIG. 4).[0035] The calculated value of bit rate of penetration may then be sent to the controller (186 in FIG. 4) which operates the automatic driller (e.g., as in FIG. 2) to release the drill string at the surface ROP which will result in the desired ROP at the drill bit. The foregoing is shown in FIG. 12”. NOTE: “the desired ROP” reads on maximum penetration rate at which a safe drilling operation is conducted. The “desired ROP” is the safe value of ROP for the section without risking the well. It is implied that any value exceeding the desired value would break the system. In other word, unchanging maximum rate of penetration value (when it breaks) having a lower values ROP. This is all about optimization of ROP value based on drilling parameters and done by model. This is not an inventive step. See [0043] “The drilling models and relationships may adjust in real time in different subsurface formations and drilling conditions, thereby maintaining smooth and safe drilling without the need for manual control of parameters for the auto driller”);
Belaskie teaches real time measured data (see Figure 4, MWD 37, 194 optimizer) optimization of ROP. Bit Torque and Differential pressure within operational limit using a model algorithm see figure 15 and Figure 16. However, Belaskie is silent on detail steps of optimizations.
Belaskie is silent on in response to the new drilling parameter value being above the sectional limit:in response the drilling parameter value being below the hard limit, relaxing the sectional limit to increase a preferred maximum drilling parameter value to define a relaxed sectional limit by increasing an upper value of a drilling parameter window for the drilling parameter to the new drilling parameter value without changing the hard limit, wherein the drilling parameter comprises a rate of penetration and the relaxing the sectional limit to increase the preferred maximum drilling parameter value comprises increasing the rate of penetration by at least fifty feet per hour; and
in response to the new drilling parameter value being at or above the hard limit, determining a different drilling parameter to increase the response measurement without changing the hard limit; and in response to the response measurement stabilizing within the response window subsequent to the relaxing the sectional limit, resetting the preferred maximum drilling parameter value to define the sectional limit for the drilling parameter value without changing the hard limit.
However, Change teaches in response to the new drilling parameter value being above the sectional limit: in response the drilling parameter value being below the hard limit, relaxing the sectional limit to increase a preferred maximum drilling parameter value to define a relaxed sectional limit by increasing an upper value of a drilling parameter window for the drilling parameter to the new drilling parameter value without changing the hard limit, wherein the drilling parameter comprises a rate of penetration and the relaxing the sectional limit to increase the preferred maximum drilling parameter value comprises increasing the rate of penetration by at least fifty feet per hour; (Chang, Figure 12, 13,see below Table 1-2, Chang discloses optimization method of performance drilling parameter by defining a response score and objective score based on change in drill parameter values. The algorithm is disclosed in figure 12-13. The drill parameters (Chang, [0035], “drilling parameters may include rotary speed (RPM), WOB, characteristics of the drill bit and drill string, mud weight, mud flow rate, lithology of the formation, pore pressure of the formation, torque, pressure, temperature, ROP, MSE, vibration measurements(etc.”) are adjusted based on threshold/ optimum response score and objective score of each parameter. Where the maximum response score is 100% which reads on “hard limit”, the maximum objective score is also 100%. Objective score, and threshold value is 40% which reads on “sectional limit/threshold. The drill parameter ROP is adjusted based on the response score and objective score. The method of parameter adjustment based on new data is automatic. See example method utilizing response score and objective score of parameter adjustment steps), and
in response to the new drilling parameter value being at or above the hard limit, determining a different drilling parameter to increase the response measurement without changing the hard limit; and in response to the response measurement stabilizing within the response window subsequent to the relaxing the sectional limit, resetting the preferred maximum drilling parameter value to define the sectional limit for the drilling parameter value without changing the hard limit., (Chang, [0052] “the response-point based decision tree recommendations may provide qualitative recommendations, such as increase, decrease, or maintain a given drilling parameter (e.g., weight on bit, rotation rate,etc.), or the recommendation might be to pick up off bottom see [0100].The driller increases WOB as recommended, and this results in the generation of a new response point and an increase in the combined objective function value, which is calculated from a time-averaged ROP, time-averaged TSE, and ROP-weighted average of MSE.(…) The driller increases WOB as recommended, generating a third response point. The objective function value of this third response point decreases relative to that of the second response point,(…). . This learning mode process continues until 20 response points are obtained, resulting in a response score of 100%, which triggers an application mode that recommends the averages of the parameters of the best response point and the results of a local search engine. [0110] A rate of penetration (ROP) for each response point can be based on the change in block position over a duration of time. Since there may be oscillations in the block position, the change in block position can be determined using the mean values of block position and time for subsets of data points within the subinterval of data”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Belaskie’s method of optimization to incorporate Chang’s optimization methods with a response point and objective points and adjusting drill parameters in real time with the benefit of the response-point based decision tree recommendations providing a qualitative recommendation, such as increase, decrease, or maintain a given drilling parameter (Chang, [0051]- [0052]). It would have been obvious to a person of ordinary skill to include response point-based decision tree (framework) is used to select an application mode or a learning mode, based on whether specified criteria are met for the response score and objective score. When a learning mode is activated, the recommendations can be based on principles such as increasing WOB, RPM, and/or flow rate until an objective function no longer improves along with the other machine learning network, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Both Belaskie and Chang are silent on applying a cost function to determine a new rate of penetration value that will increase the differential pressure and minimize a deviation of the new rate of penetration value from the sectional limit;
However, Wang teaches applying a cost function to determine a new rate of penetration value that will increase the differential pressure and minimize a deviation of the new rate of penetration value from the sectional limit (Wang, Figure 3-4, and Figure 6, [0089] By way of background, an objective function is a mathematical operation that seeks to either minimize or maximize a value, or a set of values, over a set of feasible alternatives. Where the function seeks to minimize the set of values, it may alternatively be referred to as a cost function. In the present disclosure, the objective function seeks to quantify the process of moving the drilling parameters from initial conditions (WOB0, RPM0 ), towards (WOB1, RPM0 ), and then to optimal conditions (WOB*, RPM*). [0103] Note that this equation, (See [0093]-[0102]) formulated as an objective (or "maximization") function, may be similarly expressed as a cost ( or "minimization") function. Additionally, and alternatively, the control variables (ROP, RPM) may be used instead of (WOB, RPM) for ROP-controlled operations. Either way, the objective function depends on two controllable variables plus time”NOTE: minimum cost function is estimated with higher ROP rate see [0156] The computer-based system seeks to provide an optimal path with minimum MSE and TSE values in order to provide higher ROP and to reduce the risk of unnecessary trips due to premature bit and tool wear).;
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Belaskie and Chang’s method of optimization and adjusting drill parameters and incorporating Wangs method of estimating Cost function with maximum ROP rate as taught by Wang in real time with the benefit of the seek to decrease the amount of time it takes to form the wellbore by increasing rate of penetration, or "ROP and optimize the drilling ramp-up procedure.(Wang, [0085]). It would have been obvious to a person of ordinary skill to include algorithm to estimate cost function with optimized drilling parameter. Known in the art that using equations formulated as an objective (or "maximization") function, may be similarly expressed as a cost (or "minimization") function. Additionally, and alternatively, the control variables (ROP, RPM) may be used instead of (WOB, RPM) for ROP-controlled operations. Either way, the objective function depends on two controllable variables plus time. (Wang, [0103] in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Regarding Claim 6, Combination of Belaskie, Chang and Wang teach the non-transitory, tangible computer-readable storage medium of claim 5,
Belaskie further teaches the dynamically adjusting the drilling parameters further comprising automatically increasing the drilling parameter to the new drilling parameter value that will increase the response measurement. (Belaskie, Figure 16, [0039] “When the limiting parameter is differential pressure (i.e., the increase in standpipe pressure above the off bottom pressure measured as explained with reference to FIG. 5), the determined relationship between differential pressure and bit torque at 204 in FIG. 15 may be used with the bit drilling response model 214 to determine a desired bit torque as previously explained. Using desired bit torque, at 212 in FIG. 16, the process shown in FIG. 15 may then be used to compute the set point for surface rate of penetration as explained with reference to FIG. 14. As previously explained, the foregoing setpoint may be communicated from the optimizer (194 in FIG. 4) to the controller (186 in FIG. 4) to operate the rig automatically to maintain the set point surface ROP” ROP is one of the examples of drilling parameter.).
Regarding Claim 8, Combination of Belaskie, Chang and Wang teach the non-transitory, tangible computer-readable storage medium of claim 5,
Belaskie further teaches resetting the preferred maximum drilling parameter value comprising generating one or more transition values for the drilling parameter window to gradually transition the drilling parameter window back to the sectional limit for the drilling parameter. ;(Belaskie, Figures 4- 5, [0025] “The optimizer 194 may be programmed using a drilling model that is data driven and is updated in real-time for the state condition of the surface and downhole equipment and for the formation being drilled”. Figure 8, 206, [0029] “The stand pipe pressure and mud flow rate while drilling and the off bottom pressure and flow rate from the calibration of FIG. 5 may be used to compute the differential pressure as shown in FIG. 8 at 206.” Optimizer optimize pressure values based on real time parameters).
Regarding Claim 9, Combination of Belaskie, Chang and Wang teach the non-transitory, tangible computer-readable storage medium of claim 5,
Belaskie further teaches the dynamically adjusting the drilling parameters further comprising, in response to determining that the response measurement is below the desired lower limit of the response window or trending downwards towards the desired lower limit of the response window: (Belaskie, Figure 10-16, Bit drill response model 214, Drilling response model 218, etc) are used to adjust / optimize drilling parameters in real time. [0033] The foregoing models may be used in the optimizer (194 in FIG. 4) in real-time to compute the weight on bit and rotary speed of the bit (RPM) needed to optimize the rate of penetration (ROP) while maintaining the equipment inside limits for torque, WOB, RPM, rate of penetration and differential pressure”);
Optimizer optimize pressure values based on real time parameters determining a plurality of new drilling parameter values for a plurality of drilling parameters that will increase the response measurement (Belaskie, [0025] “The optimizer 194 may be programmed using a drilling model that is data driven and is updated in real-time for the state condition of the surface and downhole equipment and for the formation being drilled”. Figure 5, [0026] As drilling progresses, off bottom calibrations may be performed at selected times,” calibration done by unit 200 sets the upper limit lower limit values of parameters during a safe operation.);
for one or more of the plurality of drilling parameters, comparing the plurality of new drilling parameter values with sectional limits for the plurality of drilling parameters; for one or more of the plurality of drilling parameters (Belaskie, Figure 1, sections 32, [0026] As drilling progresses, off bottom calibrations may be performed at selected times, including at every connection (i.e., when a section of pipe 32 in FIG. 1 is added to the drill string”. Each drilling section 32 will have a calibrated ROP set points and optimizer 194 use algorithm to compare with set point limit and optimize desired ROP in Figure 4, 5, 11, and 16),
Belaskie teaches real time measured data (see Figure 4, MWD 37, 194 optimizer) optimization of ROP. Bit Torque and Differential pressure within operational limit using a model algorithm see figure 15 and Figure 16. However, Belaskie is silent on detail steps of optimizations.
Belaskie is silent on comparing the plurality of new drilling parameter values with hard limits for the plurality of drilling parameters; and in response to the plurality of drilling parameter values being above the sectional limits for the plurality of drilling parameters and below the hard limits for the plurality of drilling parameters, increasing upper values for drilling parameter windows for the plurality of drilling parameters to the new drilling parameter values.
However, Chang teaches comparing the plurality of new drilling parameter values with hard limits for the plurality of drilling parameters; and in response to the plurality of drilling parameter values being above the sectional limits for the plurality of drilling parameters and below the hard limits for the plurality of drilling parameters, increasing upper values for drilling parameter windows for the plurality of drilling parameters to the new drilling parameter values. Chang, Figure 12, 13, see below Table 1-2, Chang discloses optimization method of performance drilling parameter by defining a response score and objective score based on change in drill parameter values. The algorithm is disclosed in figure 12-13. The drill parameters (Chang, [0035], “drilling parameters may include rotary speed (RPM), WOB, characteristics of the drill bit and drill string, mud weight, mud flow rate, lithology of the formation, pore pressure of the formation, torque, pressure, temperature, ROP, MSE, vibration measurements, etc.”) are adjusted based on threshold/ optimum response score and objective score of each parameter. Where the maximum response score is 100% which reads on “hard limit”, the maximum objective score is also 100%. Objective score, and threshold value is 40% which reads on “sectional limit/threshold. The drill parameter ROP is adjusted based on the response score and objective score. The method of parameter adjustment based on new data is automatic. See example method utilizing response score and objective score of parameter adjustment steps in, (Chang, [0052] “the response-point based decision tree recommendations may provide qualitative recommendations, such as increase, decrease, or maintain a given drilling parameter (e.g., weight on bit, rotation rate,etc.), or the recommendation might be to pick up off bottom see [0100].The driller increases WOB as recommended, and this results in the generation of a new response point and an increase in the combined objective function value, which is calculated from a time-averaged ROP, time-averaged TSE, and ROP-weighted average of MSE.(…) The driller increases WOB as recommended, generating a third response point. The objective function value of this third response point decreases relative to that of the second response point,(…). This learning mode process continues until 20 response points are obtained, resulting in a response score of 100%, which triggers an application mode that recommends the averages of the parameters of the best response point and the results of a local search engine. [0110] A rate of penetration (ROP) for each response point can be based on the change in block position over a duration of time. Since there may be oscillations in the block position, the change in block position can be determined using the mean values of block position and time for subsets of data points within the subinterval of data”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Belaskie’s method of optimization to incorporate Chang’s optimization methods with a response point and objective points and adjusting drill parameters in real time with the benefit of the response-point based decision tree recommendations providing a qualitative recommendation, such as increase, decrease, or maintain a given drilling parameter (Chang, [0051]- [0052]). It would have been obvious to a person of ordinary skill to include response point-based decision tree (framework) is used to select an application mode or a learning mode, based on whether specified criteria are met for the response score and objective score. When a learning mode is activated, the recommendations can be based on principles such as increasing WOB, RPM, and/or flow rate until an objective function no longer improves along with the other machine learning network, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Regarding Claim 10, Combination of Belaskie, Chang and Wang teach the non-transitory, tangible computer-readable storage medium of claim 9,
Belaskie further teaches wherein the response measurement comprises one or more of drillstring torque, hookload, weight on bit, or differential pressure. (Belaskie, Figure 1, [0014] A drilling unit or "rig" 10 includes a draw works 11 or similar lifting device known in the art to raise, suspend and lower a drill string. The drill string may include a number of threadedly coupled sections of drill pipe, shown generally at32.Figure 5-6, Hookload”).
Regarding Claim 11, Combination of Belaskie, Chang and Wang teach the non-transitory, tangible computer-readable storage medium of claim 9,
Belaskie further teaches wherein the drilling parameters at least one of surface drill string rotation speed, block speed, or pump stroke rate. (Belaskie, Figure 4, “ROP, Torque, RPM” reads on drilling parameters).
Regarding Claim 12, Combination of Belaskie, Chang and Wang teach the non-transitory, tangible computer-readable storage medium of claim 9,
wherein determining the plurality of the new drilling parameter values comprises selecting values for the plurality of the new drilling parameter values that minimize a difference between the plurality of the new drilling parameter values and the sectional limits for the plurality of drilling parameters. (Belaskie, Figure 17, [0040],” at least one relationship between at least one measured drilling operating parameter and corresponding values of a drilling response parameter at the surface and at the bottom of the drill string is established. At 236 a value of a rate of penetration parameter is selected at surface to operate the automatic drilling system so as to optimize a rate of penetration parameter at the bottom of the drill string”).
Regarding Claim 13, Combination of Belaskie, Chang and Wang teach the non-transitory, tangible computer-readable storage medium of claim 9,
Belaskie further teaches wherein a control system displays for a driller on a display (Belaskie, Figure 18, [0044], “A display device 105) the drilling parameter window created using the plurality of the new drilling parameter values and allows the driller to adjust the plurality of drilling parameters within the drilling parameter window. (Belaskie, Figure 17, “[0043] “The drilling models and relationships may adjust in real time in different subsurface formations and drilling conditions, thereby maintaining smooth and safe drilling without the need for manual control of parameters for the auto driller.”).
Regarding Claim 14, Combination of Belaskie, Chang and Wang teach the non-transitory, tangible computer-readable storage medium of claim 5,
Belaskie further teaches wherein a control system (Belaskie, Figures 3-4, controller 186) in autonomous mode adjusts a drilling rig operation to execute the drilling operation within the drilling parameter window created using the new drilling parameter values. (Belaskie, Figure 17, [0043] The drilling models and relationships may adjust in real time in different subsurface formations and drilling conditions, thereby maintaining smooth and safe drilling without the need for manual control of parameters for the auto driller”).
Regarding Claim 15, Belaskie teaches,
A system for dynamically adjusting drilling parameters during a drilling operation, the Belaskie, Figure 17, - [0041] “Real time relationships (dynamic relationship) based on drilling models according to the present disclosure may be used to control an auto driller at specific set points of rate of penetration). [0043] The drilling models and relationships may adjust in real time in different subsurface formations and drilling conditions), the system comprising:
a bottom hole assembly;(Belaskie, Figure 1, BHA 42)
a rig control system; (Belaskie, Figure 3, control system);
a computer system comprising one or more processors and memory devices (Belaskie, Figure 18, Processor 104, Storage memory 106); the computer system comprising instructions for:
receiving, in real time, a measurement of a drilling parameter value during the drilling operation; receiving, in real time, a response measurement during the drilling operation ([0025] “The optimizer 194 may be programmed using a drilling model that is data driven and is updated in real-time for the state condition of the surface and downhole equipment and for the formation being drilled”);
determining whether the response measurement is within a response window that defines a desired lower limit and a desired upper limit for the response measurement (Belaskie, Figure 5, [0026] As drilling progresses, off bottom calibrations may be performed at selected times, [0034], The relationships are dynamic, that is, they are continuously updated by input of real time data
and thus may adapt to changing conditions in the wellbore. The relationships thus determine may be used to directly control the drilling operation by sending set points of RPM and rate of penetration (ROP) from the optimizer (194 in
FIG. 4) to the controller (186 in FIG. 4).)” calibration done by unit 200 determine set points sets, the upper limit lower limit values of parameters during a safe operation);
defining a sectional limit for the drilling parameter value, wherein the sectional limit comprises a changeable preferred maximum value based on an average performance associated with the preferred maximum value(“Belaskie, [0035] When the drilling plan (i.e., a set of specifications for drilling and ancillary operations to construct the wellbore) indicates one or more sections of the well bore are to undergo controlled drilling, the desired bit rate of penetration may be converted to a surface rate of penetration value by a drill string response model as shown in FIG. 12 at 218. The calculated value of bit rate of penetration may then be sent to the controller (186 in FIG. 4) which operates the automatic driller (e.g., as in FIG. 2) to release the drill string at the surface ROP which will result in the desired ROP at the drill bit. The foregoing is shown in FIG. 12.”. NOTE: A drilling plan with specific drilling parameter for each section is provided to the controller and a desired ROP is obtained. “the desired ROP” reads on sectional limit with a preferred maximum penetration rate of penetration at which a safe drilling operation is conducted. For each section of the pipe 32 the ROP is optimized to the “desired ROP” is the sectional limit. “Optimizing the ROP value” by the control unit is changing or adjusting the ROP value based on drilling parameters reads on the “changeable” value, See (Belaskie, [0043] The drilling models and relationships may adjust in real time in different subsurface formations and drilling conditions, thereby maintaining smooth and safe drilling without the need for manual control of parameters for the auto driller”); and
in response to determining that the response measurement is below the desired lower limit of the response window or trending downwards towards the desired lower limit of the response window: applying a cost function to determine a new drilling parameter value that will increase the response measurement and minimize a deviation of the new drilling parameter value from the sectional limit(Belaskie, Figures 5, 15-16, [0039] “When the limiting parameter is differential pressure (i.e., the increase in standpipe pressure above the off bottom pressure measured as explained with reference to FIG. 5), the determined relationship between differential pressure and bit torque at 204 in FIG. 15 may be used with the bit drilling response model 214 to determine a desired bit torque as previously explained. Using desired bit: torque, at 212 in FIG. 16, the process shown in FIG. 15 may then be used to compute the set point for surface rate of penetration as explained with reference to FIG. 14. As previously explained, the foregoing setpoint may be communicated from the optimizer (194 in FIG. 4) to the controller (186 in FIG. 4) to operate the rig automatically to maintain the set point surface ROP.” Optimizer use model to update new rate of penetration automatically in real time to obtain “set points” of ROP as pressure limits change. The calibration pressure limit is used as threshold i.e an upper limit and a lower limit. Any differential pressure change will optimize ROP set point.);
; (Belaskie, [0034] The relationships are dynamic, that is, they are continuously updated by input of real time data and thus may adapt to changing conditions in the wellbore. The relationships thus determine may be used to directly control the drilling operation by sending set points of RPM and rate of penetration (ROP) from the optimizer (194 in FIG. 4) to the controller (186 in FIG. 4).[0035] The calculated value of bit rate of penetration may then be sent to the controller (186 in FIG. 4) which operates the automatic driller (e.g., as in FIG. 2) to release the drill string at the surface ROP which will result in the desired ROP at the drill bit. The foregoing is shown in FIG. 12”. NOTE: “the desired ROP” reads on maximum penetration rate at which a safe drilling operation is conducted. The “desired ROP” is the safe value of ROP for the section without risking the well. It is implied that any value exceeding the desired value would break the system. In other word, unchanging maximum rate of penetration value (when it breaks) having a lower values ROP. This is all about optimization of ROP value based on drilling parameters and done by model. This is not an inventive step. See [0043] “The drilling models and relationships may adjust in real time in different subsurface formations and drilling conditions, thereby maintaining smooth and safe drilling without the need for manual control of parameters for the auto driller”).
in response to the new rate of penetration value being at or above the hard limit, determining a different drilling parameter than the rate of penetration to increase the differential pressure (Belaskie, Figure 8 [0029] The stand pipe pressure and mud flow rate while drilling and the off bottom pressure and flow rate from the calibration of FIG. 5 may be used to compute the differential pressure as shown in FIG. 8 at 206 without changing the hard limit ; and in response to the differential pressure stabilizing within the predefined differential pressure window subsequent to the relaxing the sectional limit, resetting the preferred maximum rate of penetration to define the sectional limit for the rate of penetration without changing the hard limit(Belaskie, Figure 11 0033] “The foregoing models may be used in the optimizer (194 in FIG. 4) in real-time to compute the weight on bit and
rotary speed of the bit (RPM) needed to optimize the rate of penetration (ROP) while maintaining the equipment inside limits for torque, WOB, RPM, rate of penetration and differential pressure.” Note: optimize the rate of penetration (ROP) when differential pressure value is maintained within limit),
Belaskie teaches real time measured data (see Figure 4, MWD 37, 194 optimizer) optimization of ROP. Bit Torque and Differential pressure within operational limit using a model algorithm see figure 15 and Figure 16. However, Belaskie is silent on detail steps of optimizations.
Belaskie is silent on in response to the new rate of penetration value being above the sectional limit:
in response to the new rate of penetration value being below the hard limit,
relaxing the sectional limit to increase the preferred maximum rate of penetration value to define a relaxed sectional limit by increasing an upper value of a rate of penetration window to the new rate of penetration value without changing the hard limit; and
However, Chang teaches in response to the new rate of penetration value being above the sectional limit: in response to the new rate of penetration value being below the hard limit, relaxing the sectional limit to increase the preferred maximum rate of penetration value to define a relaxed sectional limit by increasing an upper value of a rate of penetration window to the new rate of penetration value without changing the hard limit;. (Chang, Figure 12, 13, see below Table 1-2, Chang discloses optimization method of performance drilling parameter (increasing/ decreasing/ relaxing) by defining a response score and objective score based on change in drill parameter values. The algorithm is disclosed in figure 12-13. The drill parameters (Chang, [0035], “drilling parameters may include rotary speed (RPM), WOB, characteristics of the drill bit and drill string, mud weight, mud flow rate, lithology of the formation, pore pressure of the formation, torque, pressure, temperature, ROP, MSE, vibration measurements, etc.”) are adjusted based on threshold/ optimum response score and objective score of each parameter. Where the maximum response score is 100% which reads on “hard limit”, the maximum objective score is also 100%. Objective score, and threshold value is 40% which reads on “sectional limit/threshold. The drill parameter ROP is adjusted based on the response score and objective score. The method of parameter adjustment based on new data is automatic. See example method utilizing response score and objective score of parameter adjustment steps in, (Chang, [0052] “the response-point based decision tree recommendations may provide qualitative recommendations, such as increase, decrease, or maintain a given drilling parameter (e.g., weight on bit, rotation rate,etc.), or the recommendation might be to pick up off bottom see [0100].The driller increases WOB as recommended, and this results in the generation of a new response point and an increase in the combined objective function value, which is calculated from a time-averaged ROP, time-averaged TSE, and ROP-weighted average of MSE.(…) The driller increases WOB as recommended, generating a third response point. The objective function value of this third response point decreases relative to that of the second response
This learning mode process continues until 20 response points are obtained, resulting in a response score of 100%, which triggers an application mode that recommends the averages of the parameters of the best response point and the results of a local search engine. [0110] A rate of penetration (ROP) for each response point can be based on the change in block position over a duration of time. Since there may be oscillations in the block position, the change in block position can be determined using the mean values of block position and time for subsets of data points within the subinterval of data”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Belaskie’s method of optimization to incorporate Chang’s optimization methods with a response point and objective points and adjusting drill parameters in real time with the benefit of the response-point based decision tree recommendations providing a qualitative recommendation, such as increase, decrease, or maintain a given drilling parameter (Chang, [0051]- [0052]). It would have been obvious to a person of ordinary skill to include response point-based decision tree (framework) is used to select an application mode or a learning mode, based on whether specified criteria are met for the response score and objective score. When a learning mode is activated, the recommendations can be based on principles such as increasing WOB, RPM, and/or flow rate until an objective function no longer improves along with the other machine learning network, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Both Belaskie and Chang are silent on applying a cost function to determine a new rate of penetration value that will increase the differential pressure and minimize a deviation of the new rate of penetration value from the sectional limit;
However, Wang teaches applying a cost function to determine a new rate of penetration value that will increase the differential pressure and minimize a deviation of the new rate of penetration value from the sectional limit (Wang, Figure 3-4, and Figure 6, [0089] By way of background, an objective function is a mathematical operation that seeks to either minimize or maximize a value, or a set of values, over a set of feasible alternatives. Where the function seeks to minimize the set of values, it may alternatively be referred to as a cost function. In the present disclosure, the objective function seeks to quantify the process of moving the drilling parameters from initial conditions (WOB0, RPM0 ), towards (WOB1, RPM0 ), and then to optimal conditions (WOB*, RPM*). [0103] Note that this equation, (See [0093]-[0102]) formulated as an objective (or "maximization") function, may be similarly expressed as a cost ( or "minimization") function. Additionally, and alternatively, the control variables (ROP, RPM) may be used instead of (WOB, RPM) for ROP-controlled operations. Either way, the objective function depends on two controllable variables plus time”NOTE: minimum cost function is estimated with higher ROP rate see [0156] The computer-based system seeks to provide an optimal path with minimum MSE and TSE values in order to provide higher ROP and to reduce the risk of unnecessary trips due to premature bit and tool wear).;
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Belaskie and Chang’s method of optimization and adjusting drill parameters and incorporating Wangs method of estimating Cost function with maximum ROP rate as taught by Wang in real time with the benefit of the seek to decrease the amount of time it takes to form the wellbore by increasing rate of penetration, or "ROP and optimize the drilling ramp-up procedure.(Wang, [0085]). It would have been obvious to a person of ordinary skill to include algorithm to estimate cost function with optimized drilling parameter. Known in the art that using equations formulated as an objective ( or "maximization") function, may be similarly expressed as a cost ( or "minimization") function. Additionally, and alternatively, the control variables (ROP, RPM) may be used instead of (WOB, RPM) for ROP-controlled operations. Either way, the objective function depends on two controllable variables plus time. (Wang, [0103] in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Regarding Claim 16, Combination of Belaskie, Chang and Wang teach the system of claim 15
Belaskie further teaches, wherein the computer system is a component of the rig control system. (Belaskie, Figure 1, drilling unit or "rig" 10).
Regarding Claim 17, Combination of Belaskie, Chang and Wang teach the system of claim 15
Belaskie further teaches, wherein the computer system is separate from and communicatively connected to the rig control system through an interface. Belaskie, Figure 18, Computer system 100)
Regarding Claim 18, Combination of Belaskie, Chang and Wang teach the system of claim 15
Belaskie further teaches, further comprising instructions for automatically increasing the drilling parameter value to the new drilling parameter value that will increase the response measurement. (Belaskie, Figure 16, [0039] When the limiting parameter is differential pressure (i.e., the increase in standpipe pressure above the off bottom pressure measured as explained with reference to FIG. 5), the determined relationship between differential pressure and bit torque at 204 in FIG. 15 may be used with the bit drilling response model 214 to determine a desired bit torque as previously explained. Using desired bit torque, at 212 in FIG. 16, the process shown in FIG. 15 may then be used to compute the set point for surface rate of penetration as explained with reference to FIG. 14. As previously explained, the foregoing setpoint may be communicated from the optimizer (194 in FIG. 4) to the controller (186 in FIG. 4) to operate the rig automatically to maintain the set point surface ROP” ROP is one of the examples of drilling parameter).
Regarding Claim 20, Combination of Belaskie, Chang and Wang teach the system of claim 15
Belaskie further teaches the resetting the preferred maximum drilling parameter value comprising instructions for generating one or more transition values for the drilling parameter window to gradually transition the drilling parameter window back to the sectional limit for the drilling parameter value. (Belaskie, Figures 4- 5, [0025] “The optimizer 194 may be programmed using a drilling model that is data driven and is updated in real-time for the state condition of the surface and downhole equipment and for the formation being drilled”. Figure 8, 206, [0029] “The stand pipe pressure and mud flow rate while drilling and the off-bottom pressure and flow rate from the calibration of FIG. 5 may be used to compute the differential pressure as shown in FIG. 8 at 206.” Optimizer optimize pressure values based on real time parameters).
Regarding Claim 22, Combination of Belaskie, Chang and Wang teach the method of claim 1,
Belaskie further teaches further comprising, in response to determining the differential pressure is trending downwards towards the lower limit of the predefined differential pressure window: determining a rate of change of the differential pressure (Belaskie, Figure 8, [0029] “The stand pipe pressure and mud flow rate while drilling and the off -bottom pressure and flow rate from the calibration of FIG. 5 may be used to compute the differential pressure as shown in FIG. 8 at 206. [0033] The foregoing models may be used in the optimizer (194 in FIG. 4) in real-time to compute the weight on bit and rotary speed of the bit (RPM) needed to optimize the rate of penetration (ROP) while maintaining the equipment inside limits for torque, WOB, RPM, rate of penetration and differential pressure”); estimating an amount of time for the rate of penetration to impact the differential pressure; and adjusting, based on the rate of change, the rate of penetration to maintain the differential pressure within the predefined differential pressure window. .(Belaski, Figure 16, .[ 0039] “When the limiting parameter is differential pressure (i.e., the increase in standpipe pressure above the off bottom pressure measured as explained with reference to FIG. 5), the determined relationship between differential pressure and bit torque at 204 in FIG. 15 may be used with the bit drilling response model 214 to determine a desired bit torque. Using desired bit torque, at 212 in FIG. 16, the process shown in FIG. 15 may then be used to compute the set point for surface rate of penetration as explained with reference to FIG. 14. the foregoing setpoint may be communicated from the optimizer (194 in FIG. 4) to the controller (186 in
FIG. 4) to operate the rig automatically to maintain the set point surface ROP”).
Regarding Claim 23, Combination of Belaskie, Chang and Wang teach the method of claim 1,
Both Belaskie and Chang are silent on wherein the applying the cost function adheres the new rate of penetration value to the sectional limit. [0103] Note that this equation (See [0093]-[0102]) formulated as an objective (or "maximization") function, may be similarly expressed as a cost ( or "minimization") function. Additionally, and alternatively, the control variables (ROP, RPM) may be used instead of (WOB, RPM) for ROP-controlled operations. Either way, the objective function depends on two controllable variables plus time”NOTE: minimum cost function is estimated with higher ROP rate see [0156] The computer-based system seeks to provide an optimal path with minimum MSE and TSE values in order to provide higher ROP and to reduce the risk of unnecessary trips due to premature bit and tool wear).;
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Belaskie and Chang’s method of optimization and adjusting drill parameters and incorporating Wangs method of estimating Cost function with maximum ROP rate as taught by Wang in real time with the benefit of the seek to decrease the amount of time it takes to form the wellbore by increasing rate of penetration, or "ROP and optimize the drilling ramp-up procedure.(Wang, [0085]). It would have been obvious to a person of ordinary skill to include algorithm to estimate cost function with optimized drilling parameter. Known in the art that using equations formulated as an objective (or "maximization") function, may be similarly expressed as a cost ( or "minimization") function. Additionally, and alternatively, the control variables (ROP, RPM) may be used instead of (WOB, RPM) for ROP-controlled operations. Either way, the objective function depends on two controllable variables plus time. (Wang, [0103] in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Belaskie and in view of Chang, and further in view of Wang and in further view of Al-Rubaii et al. (US 2019/0316457 A1, hereinafter Rubaii).
Regarding Claim 24, Combination of Belaskie, Chang and Wang teach the method of claim 1,
Combination of Belaskie, Chang and Wang are silent on wherein the relaxing the sectional limit comprises increasing the upper value of the rate of penetration window by at least fifty feet per hour.
However, Rubaii teaches wherein the relaxing the sectional limit comprises increasing the upper value of the rate of penetration window by at least fifty feet per hour. (Rubaii, figure 6, model optimize ROP max 168 ft/hour, also see figure 7 Optimized avg ROP (ft/hr) 75.35).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify the combine modified Belaskie method of optimization and adjusting drill parameters and incorporating Rubaii method of optimizing ROP rate in steps of higher ROP ft/hour rate with the benefit of improve performance of drilling based on drilling parameter changes. (Rubaii, fig. 6-8, and [[0011, [0065] [0073]-[0078]). It would have been obvious to a person of ordinary skill to include algorithm to adjust ROP increasing state automatically based on real time drilling parameters. with optimized drilling parameter. It is Known in the art that using model/ algorithm the control variables (WOB, RPM) can be changed to obtain desired ROP-controlled operations in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Alali et al. (US 2021/0372263 A1) recites “A method of drilling includes obtaining historical data for historical wells in a field, determining a set of drilling parameters, and determining a set of hole section sizes defining a wellbore geometry. For each combination of a parameter in the set of parameters and a hole section size in the set of hole section sizes, historical wells having average values of the parameter exceeding a threshold for the hole section size from the historical surface drilling data are selected. An expected output for each of a model to be trained by each of the selected historical wells is derived based on a rate of penetration while drilling. A final model is trained with the selected historical wells and expected outputs. An operating envelope is determined for each of the parameters in the set of parameters from the trained model. The operating envelopes may be used to guide drilling of a well in the field”(Abstract).
Salminen et al (US 2017/0044896 A1) discloses “Drilling a borehole involves a drilling system that uses drilling mud to transport cuttings of a formation to surface. During the operation, current parameters are obtained of the drilling operation conducted with the drilling system. The current parameters at least include a cuttings parameter related to the cuttings produced in the drilling operation. A current concentration of the cuttings is determined in the drilling operation based on the obtained parameters, and a desired rate of penetration for the drilling operation is determined based on the determined concentration. Based on the determined rate, a current rate of penetration is altered in an effort, for example, to mitigate issues with stuck pipe, damage to drilling components, reduced drilling efficiency, etc.” (abstract).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DILARA SULTANA/Examiner, Art Unit 2858
July 28th, 2026
/SON T LE/Primary Examiner, Art Unit 2858
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