DETAILED ACTION
Status of the Application
The following is a Final Office Action. In response to Examiner's communication of December 10, 2025, Applicant, on January 14, 2026, amended claims 1, 2, 5, 9, 11, 15, & 18 and canceled claims 3, 6, 16, & 19. Claim 1, 2, 4, 5, 7-15, 17, 18, & 20 are now pending in this application and have been rejected below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Amendment
Applicant's amendments are sufficient to overcome the 35 USC 101 rejections set forth in the previous action. Therefore, these rejections are withdrawn.
Applicant's amendments render moot the 35 USC 102 rejections set forth in the previous action. Therefore, new grounds for rejection necessitated by Applicant’s amendments are set forth below.
Response to Arguments - 35 USC § 101
Applicant’s arguments and amendments with respect to the 35 USC 101 rejections have been fully considered, and they are persuasive. Therefore, the 35 USC 101 rejections are withdrawn.
Response to Arguments - 35 USC § 102
Applicant’s arguments with respect to the prior art rejections have been fully considered, but they are now moot in view of new grounds for rejection necessitated by Applicant’s amendments.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1, 2, 4, 5, 7-15, 17, 18, & 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 9, 15 recite the limitation "the plurality of devices including ….” In this limitation, there is insufficient antecedent basis for “the plurality of devices.” The claim previously recites “one or more devices,” not a “plurality of devices.” It is not clear whether “the plurality of devices” refers to different devices. For the purposes of examination, examiner interprets the recitation of “the plurality of devices” to mean “the one or more of devices.”
Claims 2, 4, 5, 7, & 8 depend on claim 1 and does not cure the aforementioned deficiencies, and thus, these claims are rejected for the reasons set forth above.
Claims 10-14 depend on claim 9 and does not cure the aforementioned deficiencies, and thus, these claims are rejected for the reasons set forth above.
Claims 17, 18, & 20 depend on claim 15 and does not cure the aforementioned deficiencies, and thus, these claims are rejected for the reasons set forth above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4, 5, 7, 9-13, 15, 17, & 18 are rejected under 35 U.S.C. 103 as being unpatentable over by Hajizadeh, et al. (WO 2017059152 A1), hereinafter Hajizadeh, in view of Sofronov, et al. (WO 2018117890 A1), hereinafter Sofronov.
Regarding claim 1, Hajizadeh discloses a method, comprising (Abstract, [0042]):
receiving a plurality of datasets from one or more devices in a resource extraction site, the plurality of datasets including sensor data and/or operational data from the one or more devices ([0042]-[0043], the method depicted in FIGS. 2.1 and 2.2 may be practiced using the E&P computer system 118 described above, wherein in Block 201 of FIG. 2.1, first data from one or more first downhole sensors of a first well and second data from a second computing unit associated with a second well are received by a first computing unit (i.e. sensors, first and second well, second computing unit – other devices), e.g., the first data may include reservoir property data, fluid property data, and production data of the first well, and the second data is reservoir property data, fluid property data, and production data of the second well, [0013], a computing unit associated with a well in the field receives data from one or more downhole sensors of the well, e.g., the data may relate to temperature, pressure, flow rate, etc., [0019], [0024], each of the wellsite system A 114-1, B 114-2, C 114-3 is associated with a rig, a wellbore, and other wellsite equipment configured to perform wellbore operations, such as logging, drilling, fracturing, production, etc., and the E&P computer system 118 includes a number of computing units, such as computing unit A 223, B 224, C 225),
the plurality of devices including a valve, manifold, and/or pump of chemical injection equipment, water flooding equipment, artificial lift equipment, liquid metering equipment, power generation equipment, or a combination thereof ([0018]-[0019], the field includes data acquisition tools 102-2, 102-3, 102-4 positioned along the field are located in the wells and include downhole sensors and wellsite systems A 114-1, B 114-2, C 114-3 each associated with a rig, a wellbore, and other wellsite equipment configured to perform wellbore operations, the wellbore operations extract fluids from and/or inject fluids into the subterranean formation, survey operations and wellbore operations are referred to as field operations, data acquisition tools and wellsite equipment are referred to as field operation equipment, and [0026], the field operation result data (232) of performing the field operation in the field includes production data (e.g., pressures, flow rates, etc.) or other performance indicators (e.g., a measure of water flooding or gas lift in an injection operation, etc.) (i.e. water flooding, chemical injection, artificial lift systems - Examiner notes, artificial lift includes gas lift) of the well associated with the computing unit A (223), wherein the performance indicator may be obtained from the aforementioned downhole sensors located in the wellbore and stored as the field operation result data (232), [0039], the computing unit A includes the field task engine 230 that is configured to generate a control signal based on the control data (233-2) to modulate a control device of the wellbore 103, e.g., the control device may be a piece of drilling equipment, an actuator, a fluid valve, or other electrical and/or mechanical devices located in or otherwise associated with the wellbore, [0020], the E&P computer system 118 may be provided with functionally for actuating mechanisms, e.g., modulating a flow control valve or other control devices, at the field 100));
… a respective machine learning model for each context identified in the plurality of datasets, wherein each machine learning model is associated with respective equipment of the resource extraction site ([0045], in block 202, a first predictive model for the first well is built based at least on the first data and the second data, wherein the first and second data is used to form a training set, and based on the training set, the first predictive model is built using ML to determine a statistical dependence of the production data of the first well based on the reservoir and fluid properties of the first well and determine an additional statistical dependence of the production data of the first well based on the second data in the first predictive model, [0024]-[0025], [0035], [0039], computing unit A includes a data analyzer 227 that uses machine learning (ML) to build the predictive model 238 for the wellbore 103 based on the training set 234 during training and then generates the predicted results of the field operation, and a field task engine 230 generates a control signal to modulate a control device of the wellbore 103 and control the field operation equipment, (i.e. building a model executed by the data analyzer of the computing unit of the well site in the operating phase to predict production data of field operation equipment of the first and second well, which is ultimately used to control equipment of the first and second well – ML models associated with respective equipment at the site));
determining one or more updated operational parameters for the one or more devices using a respective machine learning model and the plurality of datasets ([0046], in block 203, In Block 203, an input value set (e.g., control data, such as FCV control data) of the first predictive model is determined by the first computing unit for achieving a target result of the field operation, e.g., the target result is evaluated based on an objective function, and the first predictive model is inversed to determine the control data (e.g., FCV control data) as constrained by the local sensor data and the remote data, [0052], in Block 209, the input value set of the first predictive model is adjusted by the first computing unit using the further updated value of the second data {i.e., remote data), and in response to updating the first input value set, the method returns to Block 204 where the first control signal is adjusted based on the updating of the first input value set, [0025], [0036], the optimizer (228) of the computing unit A (223) determines, based on the objective function (237), a set of values for the input value set (233) as the input of the predictive model 238 for achieving a target result of the field operation, e.g., the objective function (237) may be a maximized hydrocarbon production quantity of the wellbore 103 and the target result may correspond to the maximized value (e.g., value A (237-1)) of the objective function (237));
generating one or more commands for implementing the one or more updated operational parameters for the one or more devices ([0047], in Block 204, the first control device (e.g., FCV) is modulated by the first computing unit based on the input value set, or more particularly the control data (e.g., FCV control data), wherein a first control signal is generated by the first computing unit based on the input value set); and
sending ([0019], the field operation equipment may be controlled by a field operation control signal sent from the surface unit 112 and the E&P computer system 118) the one or more commands to the one or more devices to cause the one or more devices to operate according to the one or more operations based on the one or more updated operational parameters ([0047], in Block 204, the first control signal is applied to the first control device to modulate the first control device, e.g., the flow rate may be adjusted, e.g., using the FCV control signal, in real time in response to the local sensor data and the remote data being streamed from the downhole sensors and remote wells, [0028], control data used to generate a control signal for modulating or adjusting a control device, which may be a flow control valve (FCV), an inflow control valve (ICV), or an inflow control device (ICD) where the corresponding control data may be used to generate the FCV, ICV, or ICD control signal).
While Hajizadeh discloses all of the above, including … a respective machine learning model for each context identified in the plurality of datasets, wherein each machine learning model is associated with respective equipment of the resource extraction site (as above), Hajizadeh does not necessarily expressly disclose the remaining elements of the following limitation, which however, are taught by further teachings in Sofronov.
Sofronov teaches selecting a respective machine learning model for each context identified in the plurality of datasets, wherein each machine learning model is associated with respective equipment of the resource extraction site ([0228], [0233], the method receives data acquired by one or more pieces of field equipment during a hydraulic fracturing operation at a field site and accesses information associated with the field site, the method uses one of a plurality of different trained machine learning algorithms of a computing system, wherein the method selects the trained machine learning algorithm based at least in part on data, the information, or the data and the information, [0165], information input, such as equipment specifications, reservoir conditions, near wellbore conditions, etc., may be utilized to select and/or adjust a model of the predictor portion 860).
Hajizadeh and Sofronov are analogous fields of invention because both address the problem of managing hydrocarbon extraction systems. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Hajizadeh the ability to select a respective machine learning model for each context identified in the plurality of datasets, as taught by Sofronov, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of selecting a respective machine learning model for each context identified in the plurality of datasets, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Hajizadeh with the aforementioned teachings of Sofronov in order to produce the added benefit of improving hydrocarbon reservoir productivity. [0111].
Regarding claim 2, the combined teachings of Hajizadeh and Sofronov teaches the method of claim 1 (as above). Further, while Hajizadeh discloses wherein the … machine learning models are trained using datasets associated with one or more additional devices in one or more resource extraction sites ([0024]-[0025], [0035], computing unit A includes a data analyzer 227 that use ML to build the predictive model 238 for the wellbore 103 based on the training set 234 during training and generates the predicted results of the field operation in the operating phase, [0045], in block 202, a first predictive model for the first well is built based at least on the first data and the second data, wherein the first and second data is used to form a training set, and based on the training set, the first predictive model is built using ML to determine a statistical dependence of the production data of the first well based on the reservoir and fluid properties of the first well and determine an additional statistical dependence of the production data of the first well based on the second data in the first predictive model), Hajizadeh does not necessarily expressly disclose the remaining elements of the following limitation, which however, are taught by further teachings in Sofronov.
Sofronov teaches the selected machine learning models are trained using datasets associated with one or more additional devices in one or more resource extraction sites ([0228], [0233], the method receives data acquired by one or more pieces of field equipment during a hydraulic fracturing operation at a field site and accesses information associated with the field site, the method uses one of a plurality of different trained machine learning algorithms of a computing system, wherein the method selects the trained machine learning algorithm based at least in part on data, the information, or the data and the information, [0165], information input, such as equipment specifications, reservoir conditions, near wellbore conditions, etc., may be utilized to select and/or adjust a model of the predictor portion 860).
Hajizadeh and Sofronov are analogous fields of invention because both address the problem of managing hydrocarbon extraction systems. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Hajizadeh the ability to select a respective machine learning model for each context identified in the plurality of datasets, as taught by Sofronov, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of selecting a respective machine learning model for each context identified in the plurality of datasets, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Hajizadeh with the aforementioned teachings of Sofronov in order to produce the added benefit of improving hydrocarbon reservoir productivity. [0111].
Regarding claim 4, the combined teachings of Hajizadeh and Sofronov teaches the method of claim 1 (as above). Further, Hajizadeh discloses wherein the plurality of datasets comprises pressure data, temperature data, speed data, frequency data, operational data, position data, or any combination thereof ([0013], data from one or more downhole sensors of the well may relate to temperature, pressure, flow rate, etc., [0018], the data acquisition tools located in the wells include downhole sensors, such as temperature sensors, pressure sensors, flow rate sensors, acoustic sensors, pH sensors, water cut sensor, gas fraction sensors, one or more gauges, etc.).
Regarding claim 5, the combined teachings of Hajizadeh and Sofronov teaches the method of claim 1 (as above). Further, Hajizadeh discloses wherein each machine learning model is trained to identify at least one relationship between at least one of the plurality of datasets and at least one of the one or more updated operational parameters for the one or more devices ([0024]-[0025], [0035], computing unit A includes a data analyzer 227 that use ML to build the predictive model 238 for the wellbore 103 based on the training set 234 during training and generates the predicted results of the field operation in the operating phase, [0045], in block 202, a first predictive model for the first well is built based at least on the first data and the second data, wherein the first and second data is used to form a training set, and based on the training set, the first predictive model is built using ML to determine a statistical dependence of the production data of the first well based on the reservoir and fluid properties of the first well and determine an additional statistical dependence of the production data of the first well based on the second data in the first predictive model, [0046], in block 203, In Block 203, an input value set (e.g., control data, such as FCV control data) of the first predictive model is determined by the first computing unit for achieving a target result of the field operation, e.g., the target result is evaluated based on an objective function, and the first predictive model is inversed to determine the control data constrained by the local sensor data and the remote data), wherein the at least one relationship corresponds to increased production in the resource extraction site ([0025], [0036], the objective function (237) may be a maximized hydrocarbon production quantity of the wellbore 103 and the target result may correspond to the maximized value (e.g., value A (237-1)) of the objective function (237)).
Regarding claim 7, the combined teachings of Hajizadeh and Sofronov teaches the method of claim 1 (as above). Further, Hajizadeh discloses wherein the one or more commands are configured to cause the one or more devices to actuate ([0019]-[0020], the field operation equipment may be controlled by a field operation control signal sent from the E&P computer system 118, and the E&P computer system 118 may be provided with functionally for actuating mechanisms, e.g., modulating a flow control valve or other control devices, at the field 100, [0028], [0039], a control signal modulates a control device of the wellbore 103, e.g., the control device may be a flow control valve (FCV), an inflow control valve (ICV), or an inflow control device (ICD), a piece of drilling equipment, an actuator, a fluid valve, or other electrical and/or mechanical devices located in or otherwise associated with the wellbore, [0047], the first control signal is applied to the first control device to modulate the first control device, e.g., the flow rate may be adjusted, using the FCV control signal).
Regarding claim 9, Hajizadeh discloses a system, comprising (Abstract, [0042], [0086]):
one or more devices in a resource extraction site, wherein the one or more devices is configured to acquire a plurality of datasets associated with a resource extraction site, the plurality of datasets including sensor data and/or operational data from the one or more devices ([0042]-[0043], the method depicted in FIGS. 2.1 and 2.2 may be practiced using the E&P computer system 118 described above, wherein in Block 201 of FIG. 2.1, first data from one or more first downhole sensors of a first well and second data from a second computing unit associated with a second well are received by a first computing unit (i.e. sensors, first and second well, second computing unit – other devices), e.g., the first data may include reservoir property data, fluid property data, and production data of the first well, and the second data is reservoir property data, fluid property data, and production data of the second well, [0013], a computing unit associated with a well in the field receives data from one or more downhole sensors of the well, e.g., the data may relate to temperature, pressure, flow rate, etc., [0019], [0024], each of the wellsite system A 114-1, B 114-2, C 114-3 is associated with a rig, a wellbore, and other wellsite equipment configured to perform wellbore operations, such as logging, drilling, fracturing, production, etc., and the E&P computer system 118 includes a number of computing units, such as computing unit A 223, B 224, C 225),
the plurality of devices including a valve, manifold, and/or pump of chemical injection equipment, water flooding equipment, artificial lift equipment, liquid metering equipment, power generation equipment, or a combination thereof ([0018]-[0019], the field includes data acquisition tools 102-2, 102-3, 102-4 positioned along the field are located in the wells and include downhole sensors and wellsite systems A 114-1, B 114-2, C 114-3 each associated with a rig, a wellbore, and other wellsite equipment configured to perform wellbore operations, the wellbore operations extract fluids from and/or inject fluids into the subterranean formation, survey operations and wellbore operations are referred to as field operations, data acquisition tools and wellsite equipment are referred to as field operation equipment, and [0026], the field operation result data (232) of performing the field operation in the field includes production data (e.g., pressures, flow rates, etc.) or other performance indicators (e.g., a measure of water flooding or gas lift in an injection operation, etc.) (i.e. water flooding, chemical injection, artificial lift systems - Examiner notes, artificial lift includes gas lift) of the well associated with the computing unit A (223), wherein the performance indicator may be obtained from the aforementioned downhole sensors located in the wellbore and stored as the field operation result data (232), [0039], the computing unit A includes the field task engine 230 that is configured to generate a control signal based on the control data (233-2) to modulate a control device of the wellbore 103, e.g., the control device may be a piece of drilling equipment, an actuator, a fluid valve, or other electrical and/or mechanical devices located in or otherwise associated with the wellbore, [0020], the E&P computer system 118 may be provided with functionally for actuating mechanisms, e.g., modulating a flow control valve or other control devices, at the field 100)); and
a remote computing system configured to ([0024], the E&P computer system 118 includes a number of computing units, such as computing unit A 223, B 224, C 225 each associated with, respectively, the wellsite systems A 114-1, B 114-2, C 114-3, [0042]-[0043], the method depicted in FIGS. 2.1 and 2.2 may be practiced using the E&P computer system 118 described above, wherein a first computing unit receives data from sensors of a first well and second data from a second computing unit):
receive the plurality of datasets ([0042]-[0043], the method depicted in FIGS. 2.1 and 2.2 may be practiced using the E&P computer system 118 described above, wherein in Block 201 of FIG. 2.1, first data from one or more first downhole sensors of a first well and second data from a second computing unit associated with a second well are received by a first computing unit (i.e. sensor, first and second well, second computing unit – other devices), e.g., the first data may include reservoir property data, fluid property data, and production data of the first well, and the second data is reservoir property data, fluid property data, and production data of the second well, [0013], a computing unit associated with a well in the field receives data from one or more downhole sensors of the well, e.g., the data may relate to temperature, pressure, flow rate, etc.,);
… a machine learning model for each context identified in the plurality of datasets, wherein each machine learning model is associated with one or more operations of the one or more devices based on the plurality of datasets, wherein each machine learning model is trained to identify one or more additional devices configured to perform one or more additional operations within the resource extraction site based on the plurality of datasets ([0045], in block 202, a first predictive model for the first well is built based at least on the first data and the second data, wherein the first and second data is used to form a training set, and based on the training set, the first predictive model is built using ML to determine a statistical dependence of the production data of the first well based on the reservoir and fluid properties of the first well and determine an additional statistical dependence of the production data of the first well based on the second data in the first predictive model, [0024]-[0025], [0035], [0039], computing unit A includes a data analyzer 227 that uses machine learning (ML) to build the predictive model 238 for the wellbore 103 based on the training set 234 during training and then generates the predicted results of the field operation, and a field task engine 230 generates a control signal to modulate a control device of the wellbore 103 and control the field operation equipment, (i.e. building a model executed by the data analyzer of the computing unit of the well site in the operating phase to predict production data of field operation equipment of the first and second well, which is ultimately used to control equipment of the first and second well – ML models associated with respective equipment at the site));
determine one or more updated operational parameters for the one or more devices, one or more settings for the one or more additional devices, or both based on a corresponding machine learning model and the plurality of datasets ([0046], in block 203, In Block 203, an input value set (e.g., control data, such as FCV control data) of the first predictive model is determined by the first computing unit for achieving a target result of the field operation, e.g., the target result is evaluated based on an objective function, and the first predictive model is inversed to determine the control data (e.g., FCV control data) as constrained by the local sensor data and the remote data, [0052], in Block 209, the input value set of the first predictive model is adjusted by the first computing unit using the further updated value of the second data {i.e., remote data), and in response to updating the first input value set, the method returns to Block 204 where the first control signal is adjusted based on the updating of the first input value set, [0025], [0036], the optimizer (228) of the computing unit A (223) determines, based on the objective function (237), a set of values for the input value set (233) as the input of the predictive model 238 for achieving a target result of the field operation, e.g., the objective function (237) may be a maximized hydrocarbon production quantity of the wellbore 103 and the target result may correspond to the maximized value (e.g., value A (237-1)) of the objective function (237));
generate one or more commands for implementing the one or more updated operational parameters for the one or more devices, the one or more settings for the one or more additional devices, or both ([0047], in Block 204, the first control device (e.g., FCV) is modulated by the first computing unit based on the input value set, or more particularly the control data (e.g., FCV control data), wherein a first control signal is generated by the first computing unit based on the input value set); and
send ([0019], the field operation equipment may be controlled by a field operation control signal sent from the surface unit 112 and the E&P computer system 118) the one or more commands to the one or more devices, the one or more additional devices, or both, wherein the one or more devices are configured to adjust the one or more operations based on the one or more updated operational parameters, and wherein the one or more additional devices are configured to operate according to the one or more settings ([0047], in Block 204, the first control signal is applied to the first control device to modulate the first control device, e.g., the flow rate may be adjusted, e.g., using the FCV control signal, in real time in response to the local sensor data and the remote data being streamed from the downhole sensors and remote wells, [0028], control data used to generate a control signal for modulating or adjusting a control device, which may be a flow control valve (FCV), an inflow control valve (ICV), or an inflow control device (ICD) where the corresponding control data may be used to generate the FCV, ICV, or ICD control signal).
While Hajizadeh discloses all of the above, including … a machine learning model for each context identified in the plurality of datasets, wherein each machine learning model is associated with one or more operations of the one or more devices based on the plurality of datasets, wherein each machine learning model is trained to identify one or more additional devices configured to perform one or more additional operations within the resource extraction site based on the plurality of datasets (as above), Hajizadeh does not necessarily expressly disclose the remaining elements of the following limitation, which however, are taught by further teachings in Sofronov.
Sofronov teaches select a machine learning model for each context identified in the plurality of datasets, wherein each machine learning model is associated with one or more operations of the one or more devices based on the plurality of datasets, wherein each machine learning model is trained to identify one or more additional devices configured to perform one or more additional operations within the resource extraction site based on the plurality of datasets ([0228], [0233], the method receives data acquired by one or more pieces of field equipment during a hydraulic fracturing operation at a field site and accesses information associated with the field site, the method uses one of a plurality of different trained machine learning algorithms of a computing system, wherein the method selects the trained machine learning algorithm based at least in part on data, the information, or the data and the information, [0165], information input, such as equipment specifications, reservoir conditions, near wellbore conditions, etc., may be utilized to select and/or adjust a model of the predictor portion 860).
Hajizadeh and Sofronov are analogous fields of invention because both address the problem of managing hydrocarbon extraction systems. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Hajizadeh the ability to select a respective machine learning model for each context identified in the plurality of datasets, as taught by Sofronov, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of selecting a respective machine learning model for each context identified in the plurality of datasets, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Hajizadeh with the aforementioned teachings of Sofronov in order to produce the added benefit of improving hydrocarbon reservoir productivity. [0111].
Regarding claim 10, this claim is substantially similar to claim 4, and is, therefore, rejected on the same basis as claim 4. While claim 10 is directed toward a system, Hajizadeh discloses a system as claimed. Abstract, [0042], [0086].
Regarding claim 11, the combined teachings of Hajizadeh and Sofronov teaches the system of claim 9 (as above). Further, while Hajizadeh discloses wherein the … machine learning models are configured to identify at least one relationship between at least one of the plurality of datasets and at least one of the one or more updated operational parameters for the one or more devices ([0045], in block 202, a first predictive model for the first well is built based at least on the first data and the second data using ML to determine a statistical dependence of the production data of the first well based on the reservoir and fluid properties of the first well and determine an additional statistical dependence of the production data of the first well based on the second data in the first predictive model, [0046], in block 203, In Block 203, an input value set (e.g., control data, such as FCV control data) of the first predictive model is determined by the first computing unit for achieving a target result of the field operation, e.g., the target result is evaluated based on an objective function, and the first predictive model is inversed to determine the control data constrained by the local sensor data and the remote data), wherein the at least one relationship corresponds to increased production in the resource extraction site ([0025], [0036], the objective function (237) may be a maximized hydrocarbon production quantity of the wellbore 103 and the target result may correspond to the maximized value (e.g., value A (237-1)) of the objective function (237)), Hajizadeh does not necessarily expressly disclose the remaining elements of the following limitation, which however, are taught by further teachings in Sofronov.
Sofronov teaches selected machine learning models are configured to identify at least one relationship between at least one of the plurality of datasets and at least one of the one or more updated operational parameters for the one or more devices ([0228], [0233], the method receives data acquired by one or more pieces of field equipment during a hydraulic fracturing operation at a field site and accesses information associated with the field site, the method uses one of a plurality of different trained machine learning algorithms of a computing system, wherein the method selects the trained machine learning algorithm based at least in part on data, the information, or the data and the information, [0165], information input, such as equipment specifications, reservoir conditions, near wellbore conditions, etc., may be utilized to select and/or adjust a model of the predictor portion 860).
Hajizadeh and Sofronov are analogous fields of invention because both address the problem of managing hydrocarbon extraction systems. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Hajizadeh the ability to select a respective machine learning model for each context identified in the plurality of datasets, as taught by Sofronov, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of selecting a respective machine learning model for each context identified in the plurality of datasets, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Hajizadeh with the aforementioned teachings of Sofronov in order to produce the added benefit of improving hydrocarbon reservoir productivity. [0111].
Regarding claim 12, the combined teachings of Hajizadeh and Sofronov teaches the system of claim 9 (as above). Further, Hajizadeh discloses wherein the one or more devices are configured to inject one or more chemicals into one or more oil production wells ([0018]-[0019], the field includes data acquisition tools 102-2, 102-3, 102-4 positioned along the field are located in the wells and include downhole sensors and wellsite systems A 114-1, B 114-2, C 114-3 each associated with a rig, a wellbore, and other wellsite equipment configured to perform wellbore operations, the wellbore operations extract fluids from and/or inject fluids into the subterranean formation, survey operations and wellbore operations are referred to as field operations, data acquisition tools and wellsite equipment are referred to as field operation equipment, and [0026], the field operation result data (232) of performing the field operation in the field includes production data (e.g., pressures, flow rates, etc.) or other performance indicators (e.g., a measure of water flooding or gas lift in an injection operation, etc.) (i.e. chemical injection; Examiner notes, gas lift in an injection operation injects gas into a well, and fluid injection injects water, gas, and other chemicals) of the well associated with the computing unit A (223), wherein the performance indicator may be obtained from the aforementioned downhole sensors located in the wellbore and stored as the field operation result data (232)).
Regarding claim 13, the combined teachings of Hajizadeh and Sofronov teaches the system of claim 9 (as above). Further, Hajizadeh discloses wherein the one or more devices are configured to inject water into one or more water injector or one or more water disposal wells ([0018]-[0019], the field includes data acquisition tools 102-2, 102-3, 102-4 positioned along the field are located in the wells and include downhole sensors and wellsite systems A 114-1, B 114-2, C 114-3 each associated with a rig, a wellbore, and other wellsite equipment configured to perform wellbore operations, the wellbore operations extract fluids from and/or inject fluids into the subterranean formation, survey operations and wellbore operations are referred to as field operations, data acquisition tools and wellsite equipment are referred to as field operation equipment, and [0026], the field operation result data (232) of performing the field operation in the field includes production data (e.g., pressures, flow rates, etc.) or other performance indicators (e.g., a measure of water flooding in an injection operation, etc.) (i.e. inject water into a water injector) of the well associated with the computing unit A (223), wherein the performance indicator may be obtained from the aforementioned downhole sensors located in the wellbore and stored as the field operation result data (232)).
Regarding claim 15, Hajizadeh discloses a non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processing system to perform operations comprising (Abstract, [0042], [0086]):
receiving a plurality of datasets from one or more devices in a resource extraction site, the plurality of datasets including sensor data and/or operational data from the one or more devices ([0042]-[0043], the method depicted in FIGS. 2.1 and 2.2 may be practiced using the E&P computer system 118 described above, wherein in Block 201 of FIG. 2.1, first data from one or more first downhole sensors of a first well and second data from a second computing unit associated with a second well are received by a first computing unit (i.e. sensors, first and second well, second computing unit – other devices), e.g., the first data may include reservoir property data, fluid property data, and production data of the first well, and the second data is reservoir property data, fluid property data, and production data of the second well, [0013], a computing unit associated with a well in the field receives data from one or more downhole sensors of the well, e.g., the data may relate to temperature, pressure, flow rate, etc., [0019], [0024], each of the wellsite system A 114-1, B 114-2, C 114-3 is associated with a rig, a wellbore, and other wellsite equipment configured to perform wellbore operations, such as logging, drilling, fracturing, production, etc., and the E&P computer system 118 includes a number of computing units, such as computing unit A 223, B 224, C 225),
the plurality of devices including a valve, manifold, and/or pump of chemical injection equipment, water flooding equipment, artificial lift equipment, liquid metering equipment, power generation equipment, or a combination thereof ([0018]-[0019], the field includes data acquisition tools 102-2, 102-3, 102-4 positioned along the field are located in the wells and include downhole sensors and wellsite systems A 114-1, B 114-2, C 114-3 each associated with a rig, a wellbore, and other wellsite equipment configured to perform wellbore operations, the wellbore operations extract fluids from and/or inject fluids into the subterranean formation, survey operations and wellbore operations are referred to as field operations, data acquisition tools and wellsite equipment are referred to as field operation equipment, and [0026], the field operation result data (232) of performing the field operation in the field includes production data (e.g., pressures, flow rates, etc.) or other performance indicators (e.g., a measure of water flooding or gas lift in an injection operation, etc.) (i.e. water flooding, chemical injection, artificial lift systems - Examiner notes, artificial lift includes gas lift) of the well associated with the computing unit A (223), wherein the performance indicator may be obtained from the aforementioned downhole sensors located in the wellbore and stored as the field operation result data (232), [0039], the computing unit A includes the field task engine 230 that is configured to generate a control signal based on the control data (233-2) to modulate a control device of the wellbore 103, e.g., the control device may be a piece of drilling equipment, an actuator, a fluid valve, or other electrical and/or mechanical devices located in or otherwise associated with the wellbore, [0020], the E&P computer system 118 may be provided with functionally for actuating mechanisms, e.g., modulating a flow control valve or other control devices, at the field 100));
… a machine learning model for each context identified in the plurality of datasets, wherein each machine learning model is associated with one or more operations of the one or more devices, wherein each machine learning model is trained using datasets associated with one or more additional devices in one or more resource extraction sites ([0045], in block 202, a first predictive model for the first well is built based at least on the first data and the second data, wherein the first and second data is used to form a training set, and based on the training set, the first predictive model is built using ML to determine a statistical dependence of the production data of the first well based on the reservoir and fluid properties of the first well and determine an additional statistical dependence of the production data of the first well based on the second data in the first predictive model, [0024]-[0025], [0035], [0039], computing unit A includes a data analyzer 227 that uses machine learning (ML) to build the predictive model 238 for the wellbore 103 based on the training set 234 during training and then generates the predicted results of the field operation, and a field task engine 230 generates a control signal to modulate a control device of the wellbore 103 and control the field operation equipment, [0019], field operations are performed as directed by a surface unit 112 and/or the E&P computer system 118, e.g., the field operation equipment may be controlled by a field operation control signal from the surface unit 112/the E&P computer system 118, (i.e. building a model executed by the data analyzer of the computing unit of the well site in the operating phase to predict production data of field operation equipment of the first and second well, which is ultimately used to control equipment of the first and second well – ML models associated with respective equipment at the site));
determining one or more updated operational parameters for the one or more devices based on a … machine learning model and the plurality of datasets ([0046], in block 203, an input value set (e.g., control data, such as FCV control data) of the first predictive model is determined by the first computing unit for achieving a target result of the field operation, e.g., the target result is evaluated based on an objective function, and the first predictive model is inversed to determine the control data (e.g., FCV control data) as constrained by the local sensor data and the remote data, [0052], in Block 209, the input value set of the first predictive model is adjusted by the first computing unit using the further updated value of the second data {i.e., remote data), and in response to updating the first input value set, the method returns to Block 204 where the first control signal is adjusted based on the updating of the first input value set, [0025], [0036], the optimizer (228) of the computing unit A (223) determines, based on the objective function (237), a set of values for the input value set (233) as the input of the predictive model 238 for achieving a target result of the field operation, e.g., the objective function (237) may be a maximized hydrocarbon production quantity of the wellbore 103 and the target result may correspond to the maximized value (e.g., value A (237-1)) of the objective function (237));
generating one or more commands for implementing the one or more updated operational parameters for the one or more devices ([0047], in Block 204, the first control device (e.g., FCV) is modulated by the first computing unit based on the input value set, or more particularly the control data (e.g., FCV control data), wherein a first control signal is generated by the first computing unit based on the input value set); and
sending ([0019], the field operation equipment may be controlled by a field operation control signal sent from the surface unit 112 and the E&P computer system 118) the one or more commands to the one or more devices, wherein the one or more devices are configured to adjust the one or more operations based on the one or more updated operational parameters ([0047], in Block 204, the first control signal is applied to the first control device to modulate the first control device, e.g., the flow rate may be adjusted, e.g., using the FCV control signal, in real time in response to the local sensor data and the remote data being streamed from the downhole sensors and remote wells, [0028], control data used to generate a control signal for modulating or adjusting a control device, which may be a flow control valve (FCV), an inflow control valve (ICV), or an inflow control device (ICD) where the corresponding control data may be used to generate the FCV, ICV, or ICD control signal).
While Hajizadeh discloses all of the above, including … a machine learning model for each context identified in the plurality of datasets, wherein each machine learning model is associated with one or more operations of the one or more devices, wherein each machine learning model is trained using datasets associated with one or more additional devices in one or more resource extraction sites;
determining one or more updated operational parameters for the one or more devices based on a … machine learning model and the plurality of datasets (as above), Hajizadeh does not necessarily expressly disclose the remaining elements of the following limitation, which however, are taught by further teachings in Sofronov.
Sofronov teaches selecting a machine learning model for each context identified in the plurality of datasets, wherein each machine learning model is associated with one or more operations of the one or more devices, wherein each machine learning model is trained using datasets associated with one or more additional devices in one or more resource extraction sites;
a selected machine learning model ([0228], [0233], the method receives data acquired by one or more pieces of field equipment during a hydraulic fracturing operation at a field site and accesses information associated with the field site, the method uses one of a plurality of different trained machine learning algorithms of a computing system, wherein the method selects the trained machine learning algorithm based at least in part on data, the information, or the data and the information, [0165], information input, such as equipment specifications, reservoir conditions, near wellbore conditions, etc., may be utilized to select and/or adjust a model of the predictor portion 860).
Hajizadeh and Sofronov are analogous fields of invention because both address the problem of managing hydrocarbon extraction systems. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Hajizadeh the ability to select a respective machine learning model for each context identified in the plurality of datasets, as taught by Sofronov, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of selecting a respective machine learning model for each context identified in the plurality of datasets, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Hajizadeh with the aforementioned teachings of Sofronov in order to produce the added benefit of improving hydrocarbon reservoir productivity. [0111].
Regarding claims 17 & 18, these claims are substantially similar to claims 4 & 11, respectively, and are, therefore, rejected on the same basis as claims 4 & 11. While claims 17 & 18 are directed toward a non-transitory computer-readable medium comprising computer-executable instructions executed to cause a processing system to perform operations, Hajizadeh discloses a non-transitory computer-readable medium as claimed. Abstract, [0042], [0086].
Claims 8, 14, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over by Hajizadeh, et al. (WO 2017059152 A1), hereinafter Hajizadeh, in view of Sofronov, et al. (WO 2018117890 A1), hereinafter Sofronov, in further in view of Oehring, et al. (US 10408031 B2), hereinafter Oehring.
Regarding claim 8, the combined teachings of Hajizadeh and Sofronov teaches the method of claim 1 (as above). Further, while Hajizadeh discloses all of the above and wherein the plurality of datasets is received via a communication … and the one or more commands is sent via the communication … ([0039], the computing unit A includes the field task engine 230 that is configured to generate a control signal based on the control data 233-2 to modulate a control device of the wellbore 103, e.g., the control device may be a piece of drilling equipment, an actuator, a fluid valve, or other electrical and/or mechanical devices located in or otherwise associated with the wellbore, [0020], the E&P computer system 118 may be provided with functionally for actuating mechanisms, e.g., modulating a flow control valve or other control devices, at the field 100), and strongly suggests the communication uses a communication protocol ([0034], the input/output interface obtains data from the downhole sensors and/or exchange data with other computing units using a wireless transceiver of the computing unit A 223, e.g., the input/output interface 226 may wirelessly receive data streamed from the downhole sensors and/or other computing units, [0082]-[0084], computing systems of a swarm of intelligent wells include a communication interface, e.g., Bluetooth interface, infrared interface, network interface, optical interface), Hajizadeh does not appear to expressly disclose the remaining elements of the following limitation, which however, are taught by further teachings in Oehring.
Oehring teaches wherein the plurality of datasets is received via a communication protocol and the one or more commands is sent via the communication protocol (cl. 7, ln. 34-cl. 8, ln. 15, in fig. 3, in a connected automated fracturing system, components 42 of a fracturing system, such as a pump 112, blender 114, hydration unit 116, etc., and one or more other components 124, may include communication devices for transmitting and receiving data with each other over a communication network 126, wherein the communication network may include various types of wired or wireless communication protocols, and sensors and control devices may be integrated into these components allowing them to communicate with the rest of the system, and in fig. 4, components 138 may also be communicative with a control center 132 over the communication network 140, such as described above with respect to FIG. 3, control center 132 may receive data from any of the components 138, analyze the received data, and generate control instructions for one or more of the components based at least in part on the data, and the control center 132 controls an aspect of one component based on a condition of another component, cl. 6, ln. 38-47, the sensors may transmit data to a data van 38 for collection and analysis, other components, the central processing unit, devices and control units remote from the site, wherein the communication means may include WiFi, Bluetooth, cellular, nearfield, Internet-based, etc.).
Hajizadeh and Oehring are analogous fields of invention because both address the problem of managing hydrocarbon extraction systems. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Hajizadeh the ability for data to be received and sent via a communication, as taught by Oehring, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of datasets are received via a communication protocol and commands are sent via the communication protocol, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Hajizadeh with the aforementioned teachings of Oehring in order to produce the added benefit to manage all components so that each is performing its job the most efficiently. cl. 6, ln. 34-37, 56-67.
Regarding claim 14, this claim is substantially similar to claim 8, and is, therefore, rejected on the same basis as claim 8. While claim 14 is directed toward a system, Hajizadeh discloses a system as claimed. Abstract, [0042], [0086].
Regarding claim 20, this claim is substantially similar to claim 8, and is, therefore, rejected on the same basis as claim 8. While claim 20 is directed toward a non-transitory computer-readable medium comprising computer-executable instructions executed to cause a processing system to perform operations, Hajizadeh discloses a non-transitory computer-readable medium as claimed. Abstract, [0042], [0086].
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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CHARLES GUILIANO
Primary Examiner
Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623