DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-4 have been presented for examination based on the application filed on 11/10/2025.
Claims 1-4 are rejected under 35 U.S.C. 101.
Rejection for claims 1-4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph is WITHDRAWN in view amendment.
This action is made Final.
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Response to Arguments
(Argument ) Applicant has argued in Remarks Pg.:
First, in claim 1 Step S1, establishing a wellbore-formation transient heat transfer model, based on the principle of energy conservation combined with the heat transfer mechanism of each control area of a wellbore-formation, in consideration of the influence of heat generated by fluid circulation frictional resistance and a complex heat source term on the wellbore temperature; and in Step S6 calculating the wellbore temperature by using the wellbore-formation transient heat transfer model combined with the optimized relevant parameters. That is, the optimized relevant parameters are optimized from the initial data set composed of relevant parameters by combining actually-measured data of an on-site well group. Furthermore, the optimized relevant parameters are originated from the "actual" on-site well group. In claim 1 Step S4, training a wellbore temperature prediction model by using a random forest algorithm, and then optimizing hyperparameters of the random forest algorithm by using a genetic algorithm. That is, the training of the wellbore temperature prediction model requires the use of the normalized initial dataset in Step S3. Hence, claim 1 recite additional claim elements other than the abstract idea.
(Response 1) The preamble in the claim is directed to "...A wellbore temperature optimization and predication method...". While there may be prediction happening as argued above (underlined) using the actually-measured data, such exercise does nothing in the improvement in the field of temperature optimization of the wellbore. The steps as argued are merely data gathering and then use of gathered data for tuning the relevant parameters to match the actual data (See specification [0139]-[0155]) using mathematical concept (use of random forest algorithm and sampling for tuning the hyperparameters ([0152] Table 3) & coefficients ([0155]). These steps are considered as judicial exception that merely confirms the model represents actual captured data1. Merely training the model is not an improvement in the field of wellbore temperature control. This is not an improvement in the field of temperature optimization of the wellbore as none of the steps lead to changing any variables of actual wellbore operation that would affect the actual temperature, thereby improving the technical field of temperature optimization of wellbore.
(Argument 2) Applicant has argued in Remarks Pg.6:
Second, the method further includes the basic data set of drilled well data is used to train and optimize each data set, and then the optimal cooling construction parameters may be recommended [1]; and the recommended parameters are applied to the mathematical model to quickly obtain the lowest wellbore temperature of the undrilled well or the surrounding to-be-drilled new well. That is, the subject matter of the present disclosure is integrated into a practical application [2]. According to "Performance prediction and optimization of engine cooling system based on machine learning", published in Petrochemical Applications, the influence of formation temperature on wellbore temperature is relatively small, and the research results can provide data support for the mechanical analysis of pipe columns and theoretical basis for optimizing operational parameters. Therefore, the optimizing operational parameters is in-depth study for the wellbore temperature optimization and predication.
(Response 2) The normalization of the relevant parameters (of actual data – shown in Table 1, normalized – Table 2) is an exercise in mathematical concept of data averaging and can be considered as mathematical concept (MPEP 2106.04(a)(2)(I)(C)). The only discussion of the “optimal cooling construction parameters” is in specification [0005]. Examiner could not find discussion in disclosure what the optimal cooling construction parameters are and how the temperature prediction is related to the recommended optimal cooling construction parameters. If this is a mental step then this still remains a judicial exception (under MPEP 2106.04(a)(2)(III) – as argued above in [1]) and/or an idea of solution (under MPEP 2106.05(f)(1) – as argued above in [2]). These steps are considered as judicial exception that merely confirms the model represents actual captured data2. Merely training the model is not an improvement in the field of wellbore temperature control. This is not an improvement in the field of temperature optimization of the wellbore as none of the steps lead to changing any variables of actual wellbore operation that would affect the actual temperature, thereby improving the technical field of temperature optimization of wellbore.
(Argument 2) Applicant has argued in Remarks Pg.6:
Second, the method further includes the basic data set of drilled well data is used to train and optimize each data set, and then the optimal cooling construction parameters may be recommended [1]; and the recommended parameters are applied to the mathematical model to quickly obtain the lowest wellbore temperature of the undrilled well or the surrounding to-be-drilled new well. That is, the subject matter of the present disclosure is integrated into a practical application [2]. According to "Performance prediction and optimization of engine cooling system based on machine learning", published in Petrochemical Applications, the influence of formation temperature on wellbore temperature is relatively small, and the research results can provide data support for the mechanical analysis of pipe columns and theoretical basis for optimizing operational parameters. Therefore, the optimizing operational parameters is in-depth study for the wellbore temperature optimization and predication.
(Response 2) The normalization of the relevant parameters (of actual data – shown in Table 1, normalized – Table 2) is an exercise in mathematical concept of data averaging and can be considered as mathematical concept (MPEP 2106.04(a)(2)(I)(C)). The only discussion of the “optimal cooling construction parameters” is in specification [0005]. Examiner could not find discussion in disclosure what the optimal cooling construction parameters are and how the temperature prediction is related to the recommended optimal cooling construction parameters. If this is a mental step then this still remains a judicial exception (under MPEP 2106.04(a)(2)(III) – as argued above in [1]) and/or an idea of solution (under MPEP 2106.05(f)(1) – as argued above in [2]).
These steps are considered as judicial exception that merely confirms the model represents actual captured data3. Merely training the model is not an improvement in the field of wellbore temperature control. This is not an improvement in the field of temperature optimization of the wellbore as none of the steps lead to changing any variables of actual wellbore operation that would affect the actual temperature, thereby improving the technical field of temperature optimization of wellbore.
(Argument 2) Applicant has argued in Remarks Pg.6:
Second, the method further includes the basic data set of drilled well data is used to train and optimize each data set, and then the optimal cooling construction parameters may be recommended [1]; and the recommended parameters are applied to the mathematical model to quickly obtain the lowest wellbore temperature of the undrilled well or the surrounding to-be-drilled new well. That is, the subject matter of the present disclosure is integrated into a practical application [2]. According to "Performance prediction and optimization of engine cooling system based on machine learning", published in Petrochemical Applications, the influence of formation temperature on wellbore temperature is relatively small, and the research results can provide data support for the mechanical analysis of pipe columns and theoretical basis for optimizing operational parameters. Therefore, the optimizing operational parameters is in-depth study for the wellbore temperature optimization and predication.
(Response 2) The normalization of the relevant parameters (of actual data – shown in Table 1, normalized – Table 2) is an exercise in mathematical concept of data averaging and can be considered as mathematical concept (MPEP 2106.04(a)(2)(I)(C)). The only discussion of the “optimal cooling construction parameters” is in specification [0005]. Examiner could not find discussion in disclosure what the optimal cooling construction parameters are and how the temperature prediction is related to the recommended optimal cooling construction parameters. If this is a mental step then this still remains a judicial exception (under MPEP 2106.04(a)(2)(III) – as argued above in [1]) and/or an idea of solution (under MPEP 2106.05(f)(1) – as argued above in [2]).
(Argument 3) Applicant has argued in Remarks Pg.7:
Third, the subject matter of the present disclosure involves an inventive step. As stated in the background section of the present disclosure, At present, the single factor analysis method only obtains the degree of influence of each factor on the wellbore temperature, while the wellbore temperature is affected by multiple factors at the same time. When the values of individual parameters are optimal, the calculation result of the temperature model may be minimized. The application of these optimized parameters may eliminate the high temperature problem that undrilled wells or surrounding planned new wells face during the drilling process, and then the downhole temperature may be controlled to achieve the purpose of reducing the downhole temperature.
In order to overcome the fact that existing numerical models are mostly used to optimize a single parameter to guide cooling, the present application proposes a wellbore temperature optimization and prediction method integrating numerical models and machine learning, which trains a wellbore temperature prediction model by processing the actually-measured data of multiple wells on site, recommends multiple optimal parameters by using an optimization algorithm based on the prediction model, aiming to achieve reduction in the wellbore temperature, and then verifying the parameters optimized and recommended by machine learning by combining the established numerical model to ensure the accuracy and reliability of the optimized results, providing a new method for wellbore cooling.
(Response 3) Applicant has not shown how the parameters are optimized. The optimizing the of the relevant parameter, even if positively recited in step S5, is an idea of solution as there is no methodology recited to optimize the parameters. The use of random forest and GA only generates possible option based on the seed and does not teach how the relevant parameters are optimized. The process of optimization when there are multiple variables involved is not described. E.g. if the relevant variables viz. - well depth, drilling time, circulation time, inlet temperature, mechanical drilling speed, drilling pressure, rotation speed, flow rate, and wellbore temperature, are to be optimized, how the associated weights are determined is not disclosed. See specification [0158]-[0166], which (See [0164]) appears to test randomly generated solutions () for termination condition, but does not show how results (as shown in Table 4 with multiple optimized relevant variables) are achieved. Therefore this remains an idea of solution.
Examiner respectfully maintains the rejection.
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Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process without any additional elements that provide a practical application or amount to significantly more than the abstract idea.
Claim 1:
Step 1: the claims are drawn to a method, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: The claim 1 limitations recite (bolded for abstract idea identification):
Claim 1
Mapping Under Step 2A Prong 1
1. A wellbore temperature optimization and predication method integrating numerical models and machine learning, comprising following steps:
Step S1: establishing a wellbore-formation transient heat transfer model, based on a principle of energy conservation combined with a heat transfer mechanism of each of control areas of a wellbore-formation,
in consideration of influence of heat, generated by fluid circulation frictional resistance, and a complex heat source term on a wellbore temperature;
Step S2: obtaining an initial data set composed of relevant parameters by combining actually-measured data of an on-site well group;
Step S3: normalizing the initial data set;
Step S4: training a wellbore temperature prediction model by using a random forest algorithm, and then optimizing hyperparameters of the random forest algorithm by using a genetic algorithm;
Step S5: globally optimizing the wellbore temperature prediction model using an annealing algorithm to obtain an optimized wellbore temperature;
Step S6: calculating the wellbore temperature by using the wellbore-formation transient heat transfer model combined with the optimized relevant parameters;
Step S7: performing comparative verification on the wellbore temperature optimized in step S5 and the wellbore temperature calculated in step S6, wherein if the verification is passed, the optimized wellbore temperature is retained to guide control of the wellbore temperature, and if the verification is not passed, a pressure drop caused by a wellbore fluid circulation frictional resistance in the wellbore-formation transient heat transfer model is adjusted and the verification is re-performed until the verification is passed,
wherein in the wellbore-formation transient heat transfer model, a formula for a flow rate of a drilling fluid is as follows:
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D2 represents an annulus outer diameter and D1; represents an annulus inner diameter, mm; K represents a consistency coefficient; m represents a flow index; w represents a width of a fluid domain, m; and 1,, represents a wall shear stress, Pa, and T, represents a yield stress, Pa;
a formula for heat generated by rotation speed and drilling pressure is as shown below:
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in the formula: Sd represents heat generated by a drill bit breaking a rock, J; .eta. = 2.5939, represents a correction factor; f represents a friction coefficient between the drill bit and the formation; w represents the drilling pressure, N; R represents the rotation speed, rpm; D represents an outer diameter of the drill bit, m; and d represents an inner diameter of the drill bit, m;
a formula of a wellbore-formation temperature heat transfer model is as shown below:
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in the formula: T represents a temperature, *C; p represents a fluid density, kg/m3; C represents fluid specific heat capacity, J/(kg- C); k represents fluid thermal conductivity, W/(m-°C); t represents time, s;S represents the complex heat source term; r and z represent a radial direction and an axial direction respectively; and v represents a flow velocity, m/s;
for an annular heat transfer model, a linear equation after discretization using a fully implicit finite difference method is expressed as follows:
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in the formula, rpo represents an inner radius of a drill string, m; hpo represents a convective heat transfer coefficient of an outer wall of the drill string, W/(m2.°C); pm represents a density of the drilling fluid, kg/m3; q represents a flow rate, m3/s; Cm represents specific heat capacity of the drilling fluid, J/(kg.°C); hw represents a convective heat transfer coefficient of a well wall, W/(m2-°C); rw represents a radius of the well wall, m; j represents number of well depth nodes; n represents number of time nodes; Tn+1pj; represents a temperature of a wall of the drill string at a well depth j and time n+1, °C; Tn+1aj; represents an annular temperature at the well depth j and the time n+1, °C; Tn+1wj represents a temperature of the well wall at the well depth j and the time n+1, °C; Tnaj, represents an annular temperature at the well depth j and time n, °C; delta.z; represents a depth step; delta.t represents a time step; and Qsa represents heat generated by the complex heat source term, J.
Abstract Idea/Mathematical Concept/Mental Process: The transient heat transfer model recites mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)), and/or mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)).
Further in establishing transient heat transfer model recites mental process (as in MPEP 2106.04(a)(2)(III)(A)) based on observation of (influence of heat, generated by fluid circulation frictional resistance, and a complex heat source term on a wellbore temperature;) to generate a evaluation (transient heat transfer model).
See Step 2A Prong 2 & 2B.
Abstract Idea/Mathematical Concept: This is mathematical concept as disclosed in specification [0136]-[0137] to proportionally transform the original data into the [0,1] interval to ensure that the data have uniform magnitude.
Abstract Idea/Mathematical Concept/Mental Process: The wellbore temperature prediction model by using a random forest & optimizing hyperparameters recites mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)). See specification [0139]-[0157]. When given their broadest reasonable interpretation in light of the disclosure, training using the random forest algorithm and generically recited genetic algorithm are mathematical calculations. The plain meaning of these terms are optimization algorithms which compute neural network parameters (hyperparameters) using a series of mathematical calculations.
The above steps may also be considered as mental process as they disclose an algorithm to train the wellbore temperature prediction model by using a random forest algorithm (as in MPEP 2106.04(a)(2)(III)(A)).
Abstract Idea/Mathematical Concept/Mental Process: The globally optimizing the algorithm using an annealing algorithm is considered a mathematical concept (as in MPEP 2106.04(a)(2)(I)(C)) in view specification [0158]-[0165].
Abstract Idea/Mathematical Concept/Mental Process: The step evaluates (calculates) the wellbore temperature based on observations (wellbore-formation transient heat transfer model combined with the optimized relevant parameters). This may also involve mathematical Concept as detailed in the steps involved above.
Abstract Idea/Mathematical Concept/Mental Process: The step involves mental step of comparative verification on the well bore temperature based on observations of steps S5 and S6 and based on verification, forming a judgement what to do next (retain temperature or adjust pressure in the wellbore-formation transient heat transfer model iteratively). This may involve mathematical process detailed above.
Abstract Idea/Mathematical Concept: The wellbore temperature prediction model recites mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)), and mathematical formula/equations (as in MPEP 2106.04(a)(2)(I)(B)), specifically and is considered as abstract idea.
Abstract Idea/Mathematical Concept: The formula for heat generated by rotation speed and drilling pressure recites mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)), and mathematical formula/equations (as in MPEP 2106.04(a)(2)(I)(B)), specifically and is considered as abstract idea.
Abstract Idea/Mathematical Concept: The a formula of a wellbore-formation temperature heat transfer model recites mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)), and mathematical formula/equations (as in MPEP 2106.04(a)(2)(I)(B)), specifically and is considered as abstract idea.
Abstract Idea/Mathematical Concept: The formula for annular heat transfer model recites mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)), and mathematical formula/equations (as in MPEP 2106.04(a)(2)(I)(B)), specifically and is considered as abstract idea.
Under its broadest reasonable interpretation, these covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. That is, nothing in the claim element precludes the step from practically being performed in the mind or with the aid of pencil and paper (there is no recitation of computer or any hardware components executing this method). Also the mathematical concepts disclosed may also be performed in the mind or with the aid of pencil and paper.
Step 2A, Prong 2: In accordance with this step, the judicial exception is not integrated into a practical application.
Claim 1
Mapping Under Step 2A Prong 2
1. A wellbore temperature optimization and predication method integrating numerical models and machine learning, comprising following steps:
Step S1: establishing a wellbore-formation transient heat transfer model, based on a principle of energy conservation combined with a heat transfer mechanism of each of control areas of a wellbore-formation,
in consideration of influence of heat, generated by fluid circulation frictional resistance, and a complex heat source term on a wellbore temperature;
Step S2: obtaining an initial data set composed of relevant parameters by combining actually-measured data of an on-site well group;
Step S3: normalizing the initial data set;
Step S4: training a wellbore temperature prediction model by using a random forest algorithm, and then optimizing hyperparameters of the random forest algorithm by using a genetic algorithm;
Step S5: globally optimizing the wellbore temperature prediction model using an annealing algorithm to obtain an optimized wellbore temperature;
Step S6: calculating the wellbore temperature by using the wellbore-formation transient heat transfer model combined with the optimized relevant parameters;
Step S7: performing comparative verification on the wellbore temperature optimized in step S5 and the wellbore temperature calculated in step S6, wherein if the verification is passed, the optimized wellbore temperature is retained to guide control of the wellbore temperature, and if the verification is not passed, a pressure drop caused by a wellbore fluid circulation frictional resistance in the wellbore-formation transient heat transfer model is adjusted and the verification is re-performed until the verification is passed,
wherein in the wellbore-formation transient heat transfer model, a formula for a flow rate of a drilling fluid is as follows:
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D2 represents an annulus outer diameter and D1; represents an annulus inner diameter, mm; K represents a consistency coefficient; m represents a flow index; w represents a width of a fluid domain, m; and 1,, represents a wall shear stress, Pa, and T, represents a yield stress, Pa;
a formula for heat generated by rotation speed and drilling pressure is as shown below:
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in the formula: Sd represents heat generated by a drill bit breaking a rock, J; .eta. = 2.5939, represents a correction factor; f represents a friction coefficient between the drill bit and the formation; w represents the drilling pressure, N; R represents the rotation speed, rpm; D represents an outer diameter of the drill bit, m; and d represents an inner diameter of the drill bit, m;
a formula of a wellbore-formation temperature heat transfer model is as shown below:
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in the formula: T represents a temperature, *C; p represents a fluid density, kg/m3; C represents fluid specific heat capacity, J/(kg- C); k represents fluid thermal conductivity, W/(m-°C); t represents time, s;S represents the complex heat source term; r and z represent a radial direction and an axial direction respectively; and v represents a flow velocity, m/s;
for an annular heat transfer model, a linear equation after discretization using a fully implicit finite difference method is expressed as follows:
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in the formula, rpo represents an inner radius of a drill string, m; hpo represents a convective heat transfer coefficient of an outer wall of the drill string, W/(m2.°C); pm represents a density of the drilling fluid, kg/m3; q represents a flow rate, m3/s; Cm represents specific heat capacity of the drilling fluid, J/(kg.°C); hw represents a convective heat transfer coefficient of a well wall, W/(m2-°C); rw represents a radius of the well wall, m; j represents number of well depth nodes; n represents number of time nodes; Tn+1pj; represents a temperature of a wall of the drill string at a well depth j and time n+1, °C; Tn+1aj; represents an annular temperature at the well depth j and the time n+1, °C; Tn+1wj represents a temperature of the well wall at the well depth j and the time n+1, °C; Tnaj, represents an annular temperature at the well depth j and time n, °C; delta.z; represents a depth step; delta.t represents a time step; and Qsa represents heat generated by the complex heat source term, J.
See Step 2A Prong 1 above.
Under MPEP 2106.05(g) determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. In this case the this is mere data gathering and collecting data. No specific steps to determine what are the relevant parameters and how the initial data set is created from the relevant parameters is claimed. Specifics from specification [0135] cannot be imported into claim. Further even so the there is no disclosure how the relevance is determined or how combining is performed.
See Step 2A Prong 1 above.
See Step 2A Prong 1 above.
See Step 2A Prong 1 above.
See Step 2A Prong 1 above. The process for optimized relevant parameter (although not positively recited), if given weight would be an idea of solution (See MPEP 2106.05(f)(1)) where there is no methodology disclosed to optimize any of the relevant parameters (well depth, drilling time, circulation time, inlet temperature, mechanical drilling speed, drilling pressure, rotation speed, flow rate).
Under MPEP 2106.04(d)(I) and 2106.05(a), (h) and (f)(1): One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. In this case the wellbore temperature is calculated to guide the wellbore temperature. This is at best a field of use (2106.05(h)) as it does not detail how guiding the wellbore temperature is done for actual wellbore (what actions are taken to guide the wellbore temperature to specific value) and therefore is merely a field of use of computer temperature, akin to analysis presented for In re Flook in MPEP 2106.05(h).
Further under MPEP 2106.05(f)(1)) the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem (guide wellbore temperature) with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
The limitation present details of how the wellbore-formation transient heat transfer model calculates the wellbore temperature but does not show details what how (actual) wellbore temperature is guided based on this computation.
Taking considerations from above analysis the computation performed do not improve the technology/technical field to guide the wellbore temperature of an actual well and therefore does not provide a practical application.
See Step 2A Prong 1 above.
See Step 2A Prong 1 above.
See Step 2A Prong 1 above.
See Step 2A Prong 1 above.
As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B: As discussed above with respect to integration of the abstract idea into a practical application, the claim generically recites additional elements of data gathering and for the same reason does not add significantly more. As per MPEP 2106.05(d)(II):
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
Receiving wellsite data is well‐understood, routine, and conventional functions and is known in the art broadly as measurement-while-drilling (MWD), logging-while-drilling (LWD), and formation evaluation while drilling (FEWD). See e.g. US 20130341091 A1(¶[0019][0032][0034][0052]), US 20050222775 A1(¶[0008],[0039]), US 4547774 A (Claim 1).
Further amount to generally linking the use of the judicial exception to a particular environment of field of use which does not integrate the judicial exception into a practical application or provide significantly more than the abstract idea(See MPEP 2106.05(h)) because the steps may lead to better algorithm to predict wellbore temperature, it does not indicate how guide the wellbore temperature in actual well and thus does not improve performance of the wellbore based on the algorithm. The claims 1 therefore is considered to be patent ineligible.
Claims 2 recites wherein the relevant parameters constituting the initial data set comprise: well depth, drilling time, circulation time, inlet temperature, mechanical drilling speed, drilling pressure, rotation speed, flow rate, and wellbore temperature. This type of limitation merely confines the use of the abstract idea to a particular technological environment (wellbore data) and thus fails to add an inventive concept to the claims. MPEP 2106.05(g) & (h). The claim does not disclose any additional limitations that integrate the judicial exception into practical element or add significantly more.
Claims 3 recites wherein the wellbore temperature is a prediction label of the random forest algorithm, and the remaining parameters are feature labels, thereby further merely adding to abstract idea as claimed in claim 1. The claim does not disclose any additional limitations that integrate the judicial exception into practical element or add significantly more.
Claims 4 further recites wherein the comparative verification in step S7 comprises considering, if a mean square error between the temperature calculated by the model and the temperature predicted by machine learning is less than or equal to 2%, that the model has a calculation result with high accuracy and can be applied to on-site cooling calculation and analysis. This limitation adds to mathematical calculations pertaining to the algorithm and add merely to abstract idea as claimed in claim 1. Using the calculation on-site cooling calculation and analysis is field of use as this does not detail how the actual on-site inputs are varied based on temperature calculation. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
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Relevant Prior Art of Record
US 20250061255 A1 by Freestone; Benjamin shows in Fig.6A-6C is a flowchart of a method for preparing datasets. In FIG. 6B is a flowchart of a method for training machine learning models. In FIG. 6C is a flowchart of a method for generating and using analysis data of a machine learning model. Freestone does not specifically teach the wellbore-formation transient heat transfer model as claimed with the equations.
US 20070186640 A1 by Johnson; David O. et al. shows in Fig.6-8 generating a thermal wellbore model and determining temperature profile.
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Johnson also does not specifically teach wellbore-formation transient heat transfer model as claimed with the equations.
US 20190368339 A1 by HASHMI; Gibran Mushtaq et al. teaches data corresponding to wellbore characteristics, reservoir characteristics, and a preliminary pressure drop around a wellbore due to a Joule-Thomson (“J-T”) effect are obtained downhole. The data is then input into a wellbore fluid temperature model and/or reservoir fluid temperature model to calculate a wellbore fluid temperature profile and/or reservoir fluid temperature profile, respectively.
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Hashmi does not specifically teach wellbore-formation transient heat transfer model as claimed with the equations. Specifically Hashmi teaches temperature calculation to account based on thermal energy balance ([0018]-[0022]), using the fluid temperature model ([0023]-[0030]) it does not account for complex heat source term in Claim 1 step S1 and S7.
US 20210087918 A1 by Wang; Zhiyuan et al. shows wellbore annulus temperature model [0088]:
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Wang also does not specifically teach wellbore-formation transient heat transfer model as claimed with the equations.
US 20070234789 A1 by Glasbergen; Gerard et al. teaches method of determining fluid or flow rate distribution along a wellbore includes the steps of: monitoring a temperature distribution along the wellbore in real time; and determining in real time the fluid or flow rate distribution along the wellbore using the temperature distribution. A method of optimizing fluid or flow rate distribution includes the steps of: predicting in real time the fluid or flow rate distribution along the wellbore; comparing the predicted fluid or flow rate distribution to a desired fluid or flow rate distribution. See Figs.3, 9 and 10.
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US 20130132050 A1 by Parry; Andrew J. et al. teaches local and distributed heat-source terms can be accounted for within the context of a partitioned calculation domain as detailed in ¶[0043]:
Conclusion
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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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Thursday, November 20, 2025
1 Specificaiton ¶[0155] "...The determination coefficient of the optimized wellbore temperature prediction model is about 0.978, which proves that this model has a very high prediction accuracy. The comparison between the model prediction value and the true value of the wellbore temperature is as shown in FIG. 2 and FIG. 3...."
2 Specificaiton ¶[0155] "...The determination coefficient of the optimized wellbore temperature prediction model is about 0.978, which proves that this model has a very high prediction accuracy. The comparison between the model prediction value and the true value of the wellbore temperature is as shown in FIG. 2 and FIG. 3...."
3 Specificaiton ¶[0155] "...The determination coefficient of the optimized wellbore temperature prediction model is about 0.978, which proves that this model has a very high prediction accuracy. The comparison between the model prediction value and the true value of the wellbore temperature is as shown in FIG. 2 and FIG. 3...."
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