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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/16/2026 has been entered.
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 03/04/2026.
Status of the Claims
Claims 1-23 are pending.
Claims 1-9, 12-13, 15, and 18-20 are amended.
Claim 23 are new.
Response to Arguments
Rejections Under 35 U.S.C. §102 and 35 U.S.C. §103
Applicant’s arguments see remarks pages 13-15, filed 03/04/2026, with respect to the rejection(s) of Claim 1 under 35 U.S.C. 103 have been fully considered but are moot because the amendment has necessitated a new ground of rejections. Please see the rejection set forth below.
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 1-23 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 preferred maximum rate of penetration value based on an average performance associated with the preferred maximum rate of penetration value; and (“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. 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 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.” 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 (Belaskie, Figure 4, optimizer 194, [0024] The optimizer 194 may operate a rate of penetration optimizing algorithm [0039]” 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);
comparing the new rate of penetration value with a hard limit for the rate of penetration, wherein the hard limit comprises a maximum rate of penetration value based on a risk of damage to at least one of 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. 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 rate of penetration value being above the sectional limit: in response to the new rate of penetration value being below the hard limit, increasing an upper value of a rate of penetration window to the new rate of penetration value; and in response to the new rate of penetration value being at or above the hard limit, determining a different drilling parameter than rate of penetration to increase the differential pressure.
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, increasing an upper value of a rate of penetration window to the new rate of penetration value. 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. (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, [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 [0100]. The response score is 5% (5% for each response point), and since this is less than a threshold of 100%, a learning mode is activated. A recommendation is displayed to increase WOB a specified step size of 2,000 pounds from the current parameters of 5,000 pounds WOB, 80 RPM, and a flow rate of 500 gallons per minute. 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. Since the objective function value increased, the next recommendation is to increase the WOB an additional step si7.e of2,000 pound~ from the current parameters of 7,000 pounds WOB, 80 RPM, and a flow rate of 500 gallons per minute. 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, so the next recommendation is to increase RPM by a specified step size of 5 RPM
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from the current parameters of 9,000 pounds WO B, 80 RPM, and a flow rate of 500 gallons per minute. The driller increases RPM as recommended, generating a fourth response point, which has an objective function value greater than the third 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 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 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 3, Combination of Belaskie, Chang and Wang teach the method of claim 1,
Belaskie further teaches further comprising:
monitoring 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). after increasing the rate of penetration to the new rate of penetration value; determining whether the differential pressure is stabilizing within the predefined differential pressure window; and in response to the differential pressure stabilizing within the predefined differential pressure window, resetting the upper value of the rate of penetration window to the sectional limit for the rate of penetration. (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),
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, [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 [0100] In the third example, drilling has just begun and there is only one response point generated by holding the current parameters. The response score is 5% (5% for each response point), and since this is less than a threshold of 100%, a learning mode is activated. A recommendation is displayed to increase WOB a specified step size of 2,000 pounds from the current parameters of 5,000 pounds WOB, 80 RPM, and a flow rate of 500 gallons per minute. 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. Since the objective function value increased, the next recommendation is to increase the WOB an additional step si7.e of2,000 pound~ from the current parameters of 7,000 pounds WOB, 80 RPM, and a flow rate of 500 gallons per minute. 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, so the next recommendation is to increase RPM by a specified step size of 5 RPM”)from the current parameters of 9,000 pounds WO B, 80 RPM, and a flow rate of 500 gallons per minute. The driller increases RPM as recommended, generating a fourth response point, which has an objective function value greater than the third 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 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 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. 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”). 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.” 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 parameter (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); comparing the new drilling parameter value with a hard limit for the drilling parameter value, wherein the hard limit comprises a maximum drilling parameter value based on a risk of damage to at least one of 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. 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”).
; and
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, increasing an upper value of a rate of penetration window to the new rate of penetration value; and
in response to the new rate of penetration value being at or above the hard limit, determining a different drilling parameter than rate of penetration to increase the differential pressure.
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, increasing an upper value of a rate of penetration window to the new rate of penetration value. in response to the new rate of penetration value being at or above the hard limit, determining a different drilling parameter than rate of penetration to increase the differential pressure. (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, [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 [0100] In the third example, drilling has just begun and there is only one response point generated by holding the current parameters. The response score is 5% (5% for each response point), and since this is less than a threshold of 100%, a learning mode is activated. A recommendation is displayed to increase WOB a specified step size of 2,000 pounds from the current parameters of 5,000 pounds WOB, 80 RPM, and a flow rate of 500 gallons per minute. 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. Since the objective function value increased, the next recommendation is to increase the WOB an additional step si7.e of2,000 pound~ from the current parameters of 7,000 pounds WOB, 80 RPM, and a flow rate of 500 gallons per minute. 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, so the next recommendation is to increase RPM by a specified step size of 5 RPM. from the current parameters of 9,000 pounds WO B, 80 RPM, and a flow rate of 500 gallons per minute. The driller increases RPM as recommended, generating a fourth response point, which has an objective function value greater than the third 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 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 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 7, Combination of Belaskie, Chang and Wang teach the non-transitory, tangible computer-readable storage medium of claim 5,
the dynamically adjusting the drilling parameters further comprising monitoring (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). after increasing the rate of penetration to the new rate of penetration value; determining whether the differential pressure is stabilizing within the predefined differential pressure window; and in response to the differential pressure stabilizing within the predefined pressure window, resetting the upper value of the rate of penetration window to the sectional limit for rate of penetration. (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).
Regarding Claim 8, Combination of Belaskie, Chang and Wang teach the non-transitory, tangible computer-readable storage medium of claim 7,
the dynamically adjusting the drilling parameters further 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,
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, [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 [0100] In the third example, drilling has just begun and there is only one response point generated by holding the current parameters. The response score is 5% (5% for each response point), and since this is less than a threshold of 100%, a learning mode is activated. A recommendation is displayed to increase WOB a specified step size of 2,000 pounds from the current parameters of 5,000 pounds WOB, 80 RPM, and a flow rate of 500 gallons per minute. 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. Since the objective function value increased, the next recommendation is to increase the WOB an additional step si7.e of2,000 pound~ from the current parameters of 7,000 pounds WOB, 80 RPM, and a flow rate of 500 gallons per minute. 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, so the next recommendation is to increase RPM by a specified step size of 5 RPM. from the current parameters of 9,000 pounds WO B, 80 RPM, and a flow rate of 500 gallons per minute. The driller increases RPM as recommended, generating a fourth response point, which has an objective function value greater than the third 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 are one or more of rate of penetration, 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 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. 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”).; 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.” 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;
comparing the new drilling parameter value with a hard limit for the drilling parameter value, wherein the hard limit comprises a maximum rate of penetration value based on a risk of damage to at least one of equipment, a safety risk, or an environmental risk; and(Belaskie, Figure 4, optimizer 194, [0024] The optimizer 194 may operate a rate of penetration optimizing algorithm [0039]” 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);
comparing the new rate of penetration value with a hard limit for the rate of penetration, wherein the hard limit comprises a maximum rate of penetration value based on a risk of damage to at least one of 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. 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 rate of penetration value being above the sectional limit: in response to the new rate of penetration value being below the hard limit, increasing an upper value of a rate of penetration window to the new rate of penetration value; and in response to the new rate of penetration value being at or above the hard limit, determining a different drilling parameter than rate of penetration to increase the differential pressure.
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, increasing an upper value of a rate of penetration window to the new rate of penetration value. 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. (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, [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 [0100]. The response score is 5% (5% for each response point), and since this is less than a threshold of 100%, a learning mode is activated. A recommendation is displayed to increase WOB a specified step size of 2,000 pounds from the current parameters of 5,000 pounds WOB, 80 RPM, and a flow rate of 500 gallons per minute. 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. Since the objective function value increased, the next recommendation is to increase the WOB an additional step si7.e of2,000 pound~ from the current parameters of 7,000 pounds WOB, 80 RPM, and a flow rate of 500 gallons per minute. 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, so the next recommendation is to increase RPM by a specified step size of 5 RPM
from the current parameters of 9,000 pounds WO B, 80 RPM, and a flow rate of 500 gallons per minute. The driller increases RPM as recommended, generating a fourth response point, which has an objective function value greater than the third 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 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 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 19, Combination of Belaskie, Chang and Wang teach the system of claim 15
Belaskie further teaches, further comprising instructions for: monitoring the response measurement (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) after increasing the drilling parameter value to the new drilling parameter value; determining whether the response measurement is stabilizing within the response window; and in response to the response measurement stabilizing within the response window, resetting the upper value of the drilling parameter window to the sectional limit for the drilling parameter value. (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).
.
Regarding Claim 20, Combination of Belaskie, Chang and Wang teach the system of claim 19
Belaskie further teaches further 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 21, Combination of Belaskie, Chang and Wang teach the method of claim 1,
Belaskie further teaches wherein increasing the upper value of the rate of penetration window to the new rate of penetration value comprises: generating one or more transition values for the rate of penetration window; and gradually transitioning the upper value of the rate of penetration window to the new rate of penetration value via the one or more transition values (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”);
.
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
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yang at al. (US 2015/0369031 A1) recites “Techniques for optimizing automated drilling processes are disclosed. Such techniques include modeling a formation and selecting a drilling trajectory in the formation. Measurements of rate of penetration (ROP), revolutions per minute (RPM), weight-on-bit (WOB) and torque-on-bit (TOB) of a drilling string at a position on the drilling trajectory in the formation are received. A functional relationship between depth of cut (DOC), WOB, and TOB for the modeled formation is determined. Operating constraints defining a safe operating envelope as a function of RPM and WOB along the selected drilling trajectory are determined, and an optimal RPM and WOB is determined based on operating constraints. A cost function of RPM and WOB is determined, and a path from current RPM and WOB to optimal RPM and WOB is determined based on the cost function” (Abstract).
Dykstra et al. (US 2016/0230530 A1) discloses “An example method for drilling automation may comprise generating a model of a drilling system based, at least in part, on a first set of downhole measurements. The model may accept drilling parameters of the drilling system as inputs. A rate of penetration for the drilling system may be determined based, at least in part on the model. The model may be simulated using a first set of values for the drilling parameters, and a control policy for the drilling system may be calculated based, at least in part, on the rate of penetration and the results of the simulation. A control signal to the drilling system may be generated based, at least in part, on the control policy”(Abstract).
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/DILARA SULTANA/Examiner, Art Unit 2858
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 4/24/2026
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