DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is made final.
Claims 1-23 filed on 10/24/2025 have been reviewed and considered by this office action.
Claims 1-3, 5, 12-15, 17, 18, and 20 have been amended.
Claims 21-23 have been newly added.
Claim Objections
Claim 18 is objected to because of the following informalities:
Claim 18 recites “saturation index and precipitation amount using real-data.” Examiner believes this was meant to read “a saturation index and a precipitation amount using real-time data,” as in claim 5.
Appropriate correction is required.
Response to Arguments
Applicant’s amended claims, filed 10/24/2025, have overcome the rejections under 35 U.S.C. § 101.
Applicant’s amended claims have overcome the rejections under 35 U.S.C. § 102 and 103. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Zhang.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 9, 10, 14, and 15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhang et al. (US 2019/0292881 A1).
Regarding claim 1, Zhang discloses a scale prediction and control system, comprising:
one or more processors and storage media comprising processor-executable instructions ([0011]: “control unit 122 includes a processor 124 and a memory storage device 126 that stores therein various program that when accessed by the processor 124, enable the processor 124 to determine a scale parameter such as scale volume, scale concentration, or scale production at a downhole location from sensor measurements and fluid parameter measurements obtained by the sensor 120 and/or associated fluid parameter sensors”) that, when executed by the one or more processors, cause the one or more processors to:
receive real-time data from one or more sensors associated with equipment of a hydrocarbon well production system ([0018]: “real-time scale deposition and additional fluid parameter measurements are obtained at a second location”);
predict scale precipitation in the hydrocarbon well production system in real-time based at least in part on the real-time data ([0018]: “the measurements of the additional fluid parameters at the second location and the measurement of real-time scale deposition at the second location are provided to a model that includes transfer functions that determine a value of the scale deposition real-time at the first location”);
compute a target concentration of scale ([0015]: “The calculated values of these parameters at the first location can be used (along with the scale measurements obtained at the second location) to determine an amount or concentration of scale in the fluid at the first location”):
calculate a target scale inhibitor injection rate based on the target concentration of scale ([0015]: “From the calculated amount of scale at the first location, the processor 124 can determine an amount of scale inhibitor to pump to the first location and control the injection unit 130 accordingly”); and
automatically operate one or more chemical injection pumps of a chemical injection system by adjusting one or more operating parameters of the equipment based at least in part on the real-time predicted scale precipitation to adjust an injection rate of chemicals from the one or more chemical injection pumps in a self-correcting closed loop control based on the target scale inhibitor injection rate to maintain a scale precipitation in the hydrocarbon well production system that is less than or equal to the target concentration of scale ([0016]: “the control unit 122 performs a closed-loop process in which the sensor 120 and control unit 122 are continuously monitoring the scale measurements and fluid parameter values during the production process. The control unit 122 can therefore provide continuous adjustments to the amount of scale inhibitor that is being delivered downhole, thereby optimizing scale inhibitor delivery and reducing the possibility of over-dosing or under-dosing the wellbore formation”).
Regarding claim 2, Zhang discloses the scale prediction and control system of claim 1.
Zhang further discloses wherein the automatically operating the one or more chemical injection pumps of the chemical injection system by adjusting the one or more operating parameters of the equipment comprises: determining a scale inhibitor injection rate setpoint based at least in part on the predicted scale precipitation ([0015]: “From the calculated amount of scale at the first location, the processor 124 can determine an amount of scale inhibitor to pump to the first location and control the injection unit 130 accordingly”); and
automatically adjusting a speed of one or more chemical injection pumps of the chemical injection system in accordance with the scale inhibitor injection rate setpoint ([0012]: “The control unit 122 controls the pumping rate of injection unit 130 to deliver an amount of scale inhibitor 132 that is determined by the processor 124 based on a calculated amount of downhole scale volume determined by the processor 124”).
Regarding claim 9, Zhang discloses the scale prediction and control system of claim 1.
Zhang further discloses wherein the one or more sensors comprise one or more surface sensors disposed at a surface of a well of the hydrocarbon well production system ([0013]: “The sensor 120 is located at an uphole or surface location (also referred to herein as 'a second location') and obtains scale measurements as well as measurements of fluid parameter at the second location”).
Regarding claim 10, Zhang discloses the scale prediction and control system of claim 9.
Zhang further discloses wherein the real-time data comprises data relating to pressure, temperature, flow rate, or some combination thereof, of produced fluid that is produced from the well of the hydrocarbon well production system ([0022]: “the parameter includes at least one of: (i) temperature at or near the second location; (ii) pressure at or near the second location; (iii) water cut at or near the second location; (iv) a pH at or near the second location; (v) a salt content at or near the second location; (vi) a flow rate; and a surface roughness at or near the second location”).
Regarding claim 14, Zhang discloses a method, comprising:
receiving, via a scale prediction and control system, real-time data from one or more sensors associated with equipment of a hydrocarbon well production system ([0018]: “real-time scale deposition and additional fluid parameter measurements are obtained at a second location”);
predicting, via the scale prediction and control system, scale precipitation in the hydrocarbon well production system in real-time based at least in part on the real-time data ([0018]: “the measurements of the additional fluid parameters at the second location and the measurement of real-time scale deposition at the second location are provided to a model that includes transfer functions that determine a value of the scale deposition real-time at the first location”);
computing a target concentration of scale ([0015]: “The calculated values of these parameters at the first location can be used (along with the scale measurements obtained at the second location) to determine an amount or concentration of scale in the fluid at the first location”);
calculating a target scale inhibitor injection rate based on the target concentration of scale ([0015]: “From the calculated amount of scale at the first location, the processor 124 can determine an amount of scale inhibitor to pump to the first location and control the injection unit 130 accordingly”); and
automatically operating one or more chemical injection pumps of a chemical injection system by adjusting, via the scale prediction and control system, one or more operating parameters of the equipment based at least in part on the predicted scale precipitation to adjust an injection rate of chemicals from the one or more chemical injection pumps in a self-correcting closed loop control based on the target scale inhibitor injection rate to maintain a scale precipitation in the hydrocarbon well production system that is less than or equal to the target concentration of scale ([0016]: “the control unit 122 performs a closed-loop process in which the sensor 120 and control unit 122 are continuously monitoring the scale measurements and fluid parameter values during the production process. The control unit 122 can therefore provide continuous adjustments to the amount of scale inhibitor that is being delivered downhole, thereby optimizing scale inhibitor delivery and reducing the possibility of over-dosing or under-dosing the wellbore formation”).
Regarding claim 15, Zhang discloses the method of claim 14.
Zhang further discloses wherein the automatically operating the one or more chemical injection pumps of the chemical injection system by adjusting, via the scale prediction and control system, the one or more operating parameters of the equipment comprises: determining, via the scale prediction and control system, a scale inhibitor injection rate setpoint based at least in part on the predicted scale precipitation ([0015]: “From the calculated amount of scale at the first location, the processor 124 can determine an amount of scale inhibitor to pump to the first location and control the injection unit 130 accordingly”); and
automatically adjusting, via the scale prediction and control system, a speed of the one or more chemical injection pumps of the chemical injection system in accordance with the scale inhibitor injection rate setpoint ([0012]: “The control unit 122 controls the pumping rate of injection unit 130 to deliver an amount of scale inhibitor 132 that is determined by the processor 124 based on a calculated amount of downhole scale volume determined by the processor 124”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3, 4, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 2019/0292881 A1), in view of Ige et al. (US 2013/0175030 A1).
Regarding claim 3, Zhang discloses the scale prediction and control system of claim 1.
Zhang further teaches wherein the processor-executable instructions, when executed by the one or more processors, further cause the one or more processors to: predict the scale precipitation in the hydrocarbon well production system based at least in part on the additional real-time data ([0015]: “The calculated values of these parameters at the first location can be used (along with the scale measurements obtained at the second location) to determine an amount or concentration of scale in the fluid at the first location”).
Zhang does not explicitly teach “infer additional real-time data from a first machine learning model using simulation algorithms.”
Ige further teaches infer additional real-time data from a first machine learning model using simulation algorithms ([0075]: “The production control framework 410 can include features to provide flow rate or production estimates, for example, based on measured data (e.g., temperature, pump curves, downhole gauges, other downhole measurements, etc.) and optionally modeling algorithms (e.g., one or more learning algorithms), where flow rate and production estimates correspond to additional real-time data”; [0078-0080]: “In the example of FIG. 5, the ESP layer 520 includes one or more ESP controllers that can operate according to one or more control algorithms such as the learning algorithms described with respect to the system 400 of FIG. 4, … one or more neural network algorithms, one or more other algorithms, and optionally information from one or more simulation frameworks”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the scale prediction and control system of Zhang to incorporate the teachings of Ige so as to include inferring additional real-time data from a first machine learning model using simulation algorithms. Doing so would allow well conditions to be predicted with the aim of managing production goals and maximizing uptime (Ige, [0025]: “various technologies, techniques, etc., may be implemented to manage production goals, for example, by being cognizant of factors such as lifting cost (e.g., electricity cost, cost of well treatments, etc.). Various approaches may include maximizing uptime by predicting, detecting and reacting to changing well conditions and extending equipment life through process adjustment”).
Regarding claim 4, Zhang in view of Ige teaches the scale prediction and control system of claim 3.
Zhang does not explicitly teach “wherein the first machine learning model is configured to estimate the additional real-time data using inputs from the one or more sensors or from one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system.”
Ige further teaches wherein the first machine learning model is configured to estimate the additional real-time data using inputs from the one or more sensors or from one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system ([0052]: “The well 303 may include one or more well sensors 320… Such sensors are fiber-optic based and can provide for real time sensing of temperature, for example, in SAGD or other operations… Measurements of temperature along the length of the well can provide for feedback, for example, to understand conditions downhole of an ESP. Well sensors may extend thousands of feet into a well (e.g., 4,000 feet or more) and beyond a position of an ESP”' [0075]: “The production control framework 410 can include features to provide flow rate or production estimates, for example, based on measured data (e.g., temperature, pump curves, downhole gauges, other downhole measurements, etc.) and optionally modeling algorithms (e.g., one or more learning algorithms)”).
Regarding claim 16, Zhang discloses the method of claim 14.
Zhang does not explicitly teach “inferring, via the scale prediction and control system, additional real-time data from a first machine learning model using simulation algorithms.”
Ige further teaches comprising inferring, via the scale prediction and control system, additional real-time data from a first machine learning model using simulation algorithms ([0075]: “The production control framework 410 can include features to provide flow rate or production estimates, for example, based on measured data (e.g., temperature, pump curves, downhole gauges, other downhole measurements, etc.) and optionally modeling algorithms (e.g., one or more learning algorithms), where flow rate and production estimates correspond to additional real-time data”; [0078-0080]: “In the example of FIG. 5, the ESP layer 520 includes one or more ESP controllers that can operate according to one or more control algorithms such as the learning algorithms described with respect to the system 400 of FIG. 4, … one or more neural network algorithms, one or more other algorithms, and optionally information from one or more simulation frameworks”).
The reasons to combine Ige into Zhang are the same as articulated in the rejection of claim 3 above.
Regarding claim 17, Zhang in view of Ige teaches the method of claim 16.
Zhang does not explicitly teach “wherein the first machine learning model is configured to estimate the additional real-time data using inputs from the one or more sensors or from one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system.”
Ige further teaches wherein the first machine learning model is configured to estimate the additional real-time data using inputs from the one or more sensors or from one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system ([0052]: “The well 303 may include one or more well sensors 320… Such sensors are fiber-optic based and can provide for real time sensing of temperature, for example, in SAGD or other operations… Measurements of temperature along the length of the well can provide for feedback, for example, to understand conditions downhole of an ESP. Well sensors may extend thousands of feet into a well (e.g., 4,000 feet or more) and beyond a position of an ESP”' [0075]: “The production control framework 410 can include features to provide flow rate or production estimates, for example, based on measured data (e.g., temperature, pump curves, downhole gauges, other downhole measurements, etc.) and optionally modeling algorithms (e.g., one or more learning algorithms)”).
The reasons to combine Ige into Zhang are the same as articulated in the rejection of claim 3 above.
Claims 5 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 2019/0292881 A1), in view of Ige et al. (US 2013/0175030 A1), in view of Al-Hajri et al. (US 2020/0364623 A1), and in view of Chang et al. (US 2020/0127598 A1).
Regarding claim 5, Zhang in view of Ige teaches the scale prediction and control system of claim 3.
Zhang does not explicitly teach “wherein a second machine learning model is a neural network model that computes a saturation index and a precipitation amount using real-time data received from the one or more sensors or from one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system, real-time data received from one or more surface sensors disposed at a surface of the well of the hydrocarbon well production system, and an output of the first machine learning model.”
Ige further teaches wherein a second machine learning model is a neural network model ([0079]: “the ESP layer 520 includes one or more ESP controllers that can operate according to one or more control algorithms such as… one or more neural network algorithms, one or more other algorithms, and optionally information from one or more simulation frameworks”) that computes amount ([0141-0142]: a model “can predict thermodynamic precipitation point of waxes and asphaltenes based on reservoir fluid compositions information” and “it can predict the mass of precipitated wax”) using real-time data received from the one or more sensors or from one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system, real-time data received from one or more surface sensors disposed at a surface of the well of the hydrocarbon well production system, and an output of the first machine learning model ([0122]: “a method that includes learning can include sensing at two or more locations with respect to a well. For example, such a method can include sensing downhole and sensing at the surface”).
Ige does not explicitly teach computing saturation index. Additionally, while Ige teaches that a machine learning module may receive closed loop feedback ([0068]: “a control strategies module or modules 420, which may include one or more learning algorithms, which may allow for closed-loop control. As an example, the control strategies 420 may respond to receipt of data via the interface 402 by updating one or more of the modules for modeling. In such an approach, the modules are subject to feedback in a closed-loop manner”), Ige does not explicitly teach using “an output of the first machine learning model.”
Al-Hajri further teaches wherein a second machine learning model is a neural network model that computes saturation index and precipitation amount ([0035] “different machine learning techniques such as … neural networks, or a convolutional neural network (CNN) can be used” to train the machine learning model 116; [0025]: “the feature extraction module 104 uses the training data 120 to estimate a Langelier Saturation Index of one or more aqueous samples 132”; [0031]: “the features used can include the pH, temperature, the ionic strength of an aqueous sample, the calcium cation concentration, the bicarbonate anion concentration, and the CO2 mole fraction when the water mixture is saturated with gas containing CO2 to evaluate the effect of solution conditions on the tendency and extent of precipitation”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the scale prediction and control system of Zhang in view of Ige to incorporate the teachings of Al-Hajri so as to include a second machine learning model is a neural network model that computes saturation index and precipitation amount using real-data received from one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system, real-time data received from one or more surface sensors disposed at a surface of the well of the hydrocarbon well production system, and an output of the first machine learning model. Doing so would allow an integrated and flexible scale prediction system with the aim of improving scale inhibition (Al-Hajri, [0013]: “Prediction of the calcium carbonate scale formation using an accurate machine learning model reduces computational power. The cost of designing a calcium carbonate scale inhibition program is thus reduced using the procedural workflow that alternates different machine learning classification and probability models to achieve a global cost minima”).
Chang, addressing the same problem of predictive modeling using machine learning, further teaches using an output of the first machine learning model as the input of a second machine learning model (FIG. 1A and [0041]: “The output nodes of the first neural network 11 are connected to the input nodes of the second neural network 12”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the scale prediction and control system of Zhang in view of Ige to incorporate the teachings of Chang so as to include an output of the first machine learning model. Doing so would allow the second machine learning model to be tuned without changing the first machine learning model (Chang, [0038]: “the programmer only needs to fine-tune and train the second neural network again without changing the first neural network, so as to reduce time consumption and facilitate the programmer to solve the problem”).
Regarding claim 18, Zhang in view of Ige teaches the method of claim 16.
Zhang does not explicitly teach “wherein a second machine learning model is a neural network model that computes saturation index and precipitation amount using real-data received from one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system, real-time data received from the one or more sensors or from one or more surface sensors disposed at a surface of the well of the hydrocarbon well production system, and an output of the first machine learning model.”
Ige further teaches wherein a second machine learning model is a neural network model ([0079]: “the ESP layer 520 includes one or more ESP controllers that can operate according to one or more control algorithms such as… one or more neural network algorithms, one or more other algorithms, and optionally information from one or more simulation frameworks”) that computes ([0141-0142]: a model “can predict thermodynamic precipitation point of waxes and asphaltenes based on reservoir fluid compositions information” and “it can predict the mass of precipitated wax”) using real-data received from one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system, real-time data received from the one or more sensors or from one or more surface sensors disposed at a surface of the well of the hydrocarbon well production system, and an output of the first machine learning model ([0122]: “a method that includes learning can include sensing at two or more locations with respect to a well. For example, such a method can include sensing downhole and sensing at the surface”).
Ige does not explicitly teach computing saturation index. Additionally, while Ige teaches that a machine learning module may receive closed loop feedback ([0068]: “a control strategies module or modules 420, which may include one or more learning algorithms, which may allow for closed-loop control. As an example, the control strategies 420 may respond to receipt of data via the interface 402 by updating one or more of the modules for modeling. In such an approach, the modules are subject to feedback in a closed-loop manner”), Ige does not explicitly teach using “an output of the first machine learning model.”
Al-Hajri further teaches wherein a second machine learning model is a neural network model that computes saturation index and precipitation amount ([0035] “different machine learning techniques such as … neural networks, or a convolutional neural network (CNN) can be used” to train the machine learning model 116; [0025]: “the feature extraction module 104 uses the training data 120 to estimate a Langelier Saturation Index of one or more aqueous samples 132”; [0031]: “the features used can include the pH, temperature, the ionic strength of an aqueous sample, the calcium cation concentration, the bicarbonate anion concentration, and the CO2 mole fraction when the water mixture is saturated with gas containing CO2 to evaluate the effect of solution conditions on the tendency and extent of precipitation”).
Chang, addressing the same problem of predictive modeling using machine learning, further teaches using an output of the first machine learning model as the input of a second machine learning model (FIG. 1A and [0041]: “The output nodes of the first neural network 11 are connected to the input nodes of the second neural network 12”).
The reasons to combine Al-Hajri and Chang into Zhang in view of Ige are the same as articulated in the rejection of claim 5 above.
Claims 6-8, 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 2019/0292881 A1), in view of Thigpen et al. (US 2007/0289740 A1).
Regarding claim 6, Zhang discloses the scale prediction and control system of claim 1.
Zhang does not explicitly teach “wherein the one or more sensors comprise one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system.”
Thigpen further teaches wherein the one or more sensors comprise one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system ([0022]: “a variety of sensors are placed at suitable locations in the well 50 to provide measurements or information relating to a number of downhole parameters of interest”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the scale prediction and control system of Zhang to incorporate the teachings of Thigpen so as to include the one or more sensors comprising one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system. Doing so would allow measurements of parameters to be obtained that may help to improve scale inhibition with the aim of maximizing production and increasing the life of equipment (Thigpen, [0045]: “it becomes desirable to proactively alter the chemical injection to inhibit the formation of scale, corrosion, asphaltene, emulsion and hydrate to mitigate their potential affects. It also is desirable to inject the optimum quantities of additives that will increase the life of the equipment and provide enhanced or maximum production of hydrocarbons”).
Regarding claim 7, Zhang in view of Thigpen teaches the scale prediction and control system of claim 6.
Zhang does not explicitly teach “wherein the real-time data comprises data relating to operating parameters of an electric submersible pump disposed downhole within the well of the hydrocarbon well production system.”
Thigpen further teaches wherein the real-time data comprises data relating to operating parameters of an electric submersible pump disposed downhole within the well of the hydrocarbon well production system ([0020]: “the controller 130 receives signals from sensors SE (FIG. 2A) relating to the actual pump frequency, flow rate through the ESP, fluid pressure and temperature associated with the ESP 30 measurements,” which correspond to operating parameters of an electrical submersible pump (ESP)).
Regarding claim 8, Zhang in view of Thigpen teaches the scale prediction and control system of claim 6.
Zhang does not explicitly teach “wherein the real-time data comprises data relating to a reservoir through which the well of the hydrocarbon well production system extends.”
Thigpen further teaches wherein the real-time data comprises data relating to a reservoir through which the well of the hydrocarbon well production system extends ([0023]: “sensors may be suitably placed in the well 50 to obtain measurements” including “sensors for measuring pressures corresponding to each production zone, … sensors for measuring fluid flow rates corresponding to each of the production zones,” where “production zones” refers to reservoirs, as supported by [0008] and the measurements correspond to data relating to a reservoir).
Regarding claim 11, Zhang discloses the scale prediction and control system of claim 1.
Zhang does not explicitly teach “wherein the one or more sensors comprise a corrosion probe configured to determine a corrosion rate based at least in part on one or more chemical properties of produced fluid that is produced from a well of the hydrocarbon well production system.”
Thigpen further teaches wherein the one or more sensors comprise a corrosion probe configured to determine a corrosion rate based at least in part on one or more chemical properties of produced fluid that is produced from a well of the hydrocarbon well production system ([0022]: “a variety of sensors are placed at suitable locations in the well 50 to provide measurements or information relating to a number of downhole parameters of interest. In one aspect, one or more gauge or sensor carriers, such as a carrier 15, may be placed in the production tubing to house any number of suitable sensors. The carrier 15 may include… chemical sensors that provide information about scale, corrosion”; [0048]: “The downhole sensor measurements 222 include, but are not limited to: information relating to… chemical characteristics or compositions of fluids, including the presence, amount and location of corrosion”).
The reasons to combine Thigpen into Zhang are the same as articulated in the rejection of claim 6 above.
Regarding claim 13, Zhang discloses the scale prediction and control system of claim 1.
Zhang does not explicitly teach “wherein the processor-executable instructions, when executed by the one or more processors, cause the one or more processors to synchronize operation of a submersible pump with a status of chemical injection equipment.”
Thigpen further teaches wherein the processor-executable instructions, when executed by the one or more processors, further cause the one or more processors to synchronize operation of a submersible pump with a status of the chemical injection equipment ([0035]: “if the flow rate drops to an undesirable level from a particular production zone, the central controller 150 may close a corresponding choke, stop chemical injection to that zone and alter the ESP pump speed. In another aspect, the central controller 150 may analyze the effects of a chemical buildup, such as corrosion, asphaltenes and may alter the amount and type of chemicals to be supplied and/or alter the ESP pump speed and/or reduce the flow fluid flow or cut off the flow from a particular zone or cause the well to shut down”).
The reasons to combine Thigpen into Zhang are the same as articulated in the rejection of claim 6 above.
Claims 12, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 2019/0292881 A1), in view of Bello et al. (US 2016/0356125 A1).
Regarding claim 12, Zhang discloses the scale prediction and control system of claim 1.
While Zhang teaches obtaining flow rate ([0010]: “The sensor 120 shown in FIG. 1 can refer to a scale sensor or a cluster of sensors that includes the scale sensor and additional sensors that measure additional fluid parameters such as… flow rate”), Zhang does not explicitly teach “wherein the processor-executable instructions, when executed by the one or more processors, further cause the one or more processors to determine a water rate using a virtual flow meter.”
Bello further teaches wherein the processor-executable instructions, when executed by the one or more processors, further cause the one or more processors to determine a water rate using a virtual flow meter ([0042]: “system 60 executes an analysis and modeling application… that utilizes a set of models collectively referred to as a virtual flow metering (VFM) model… For example, the modeling engine calculates hourly oil, gas and water flow rates for each individual well and/or clusters of wells in real time”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the scale prediction and control system of Zhang to incorporate the teachings of Bello so as to include causing the one or more processors to determine a water rate using a virtual flow meter. Doing so would allow the calculation of water rate with the motivation to improve monitoring and forecasting of well performance (Bello, [0022]: “The systems and methods described herein provide critical information on a well's capabilities... also provide advanced model calibration and uncertainty quantification techniques to provide online probabilistic-based estimation of gas-oil-water flow rates and effective reservoir management through continuous (real-time) reservoir production monitoring, production allocation, performance forecasting (look ahead) and virtual individual well testing”).
Regarding claim 19, Zhang discloses the method of claim 14.
While Zhang teaches obtaining flow rate ([0010]: “The sensor 120 shown in FIG. 1 can refer to a scale sensor or a cluster of sensors that includes the scale sensor and additional sensors that measure additional fluid parameters such as… flow rate”), Zhang does not explicitly teach “wherein the processor-executable instructions, when executed by the one or more processors, further cause the one or more processors to determine a water rate using a virtual flow meter.”
Bello further teaches comprising determining, via the scale prediction and control system, a water rate using a virtual flow meter ([0042]: “system 60 executes an analysis and modeling application… that utilizes a set of models collectively referred to as a virtual flow metering (VFM) model… For example, the modeling engine calculates hourly oil, gas and water flow rates for each individual well and/or clusters of wells in real time”).
The reasons to combine Bello into Zhang are the same as articulated in the rejection of claim 12 above.
Regarding claim 21, Zhang discloses the scale prediction and control system of claim 1.
Zhang further teaches the automatically operating the one or more chemical injection pumps of the chemical injection system by adjusting the one or more operating parameters of the equipment comprises: determining a scale inhibitor injection rate setpoint based at least in part on the predicted scale precipitation and the determined water rate ([0015]: “the model is used to calculate such parameters as scaling risk, based on fluid temperature (T1) at the first location, fluid pressure (P1) at the first location, water cut (WC1) at the first location, fluid pH (pH1) at the first location, salt content of the fluid at the first location, flow rate at the first location, surface roughness, etc. The calculated values of these parameters at the first location can be used (along with the scale measurements obtained at the second location) to determine an amount or concentration of scale in the fluid at the first location. From the calculated amount of scale at the first location, the processor 124 can determine an amount of scale inhibitor to pump to the first location and control the injection unit 130 accordingly”); and
automatically adjusting a speed of the one or more chemical injection pumps of the chemical injection system in accordance with the scale inhibitor injection rate setpoint ([0012]: “The control unit 122 controls the pumping rate of injection unit 130 to deliver an amount of scale inhibitor 132 that is determined by the processor 124 based on a calculated amount of downhole scale volume determined by the processor 124”).
While Zhang teaches obtaining flow rate ([0010]: “The sensor 120 shown in FIG. 1 can refer to a scale sensor or a cluster of sensors that includes the scale sensor and additional sensors that measure additional fluid parameters such as… flow rate”), Zhang does not explicitly teach “wherein the processor-executable instructions, when executed by the one or more processors, further cause the one or more processors to determine a water rate using a virtual flow meter.”
Bello further teaches wherein: the processor-executable instructions, when executed by the one or more processors, cause the one or more processors to determine a water rate using a virtual flow meter ([0042]: “system 60 executes an analysis and modeling application… that utilizes a set of models collectively referred to as a virtual flow metering (VFM) model… For example, the modeling engine calculates hourly oil, gas and water flow rates for each individual well and/or clusters of wells in real time”).
The reasons to combine Bello into Zhang are the same as articulated in the rejection of claim 12 above.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 2019/0292881 A1), in view of Al-Hajri et al. (US 2020/0364623 A1).
Regarding claim 20, Zhang teaches a scale prediction and control system, comprising:
one or more processors and storage media comprising processor-executable instructions ([0011]: “The control unit 122 includes a processor 124 and a memory storage device 126 that stores therein various program”) that, when executed by the one or more processors, cause the one or more processors to:
receive real-time data from one or more sensors associated with equipment of a hydrocarbon well production system ([0018]: “real-time scale deposition and additional fluid parameter measurements are obtained at a second location”);
([0018]: “the measurements of the additional fluid parameters at the second location and the measurement of real-time scale deposition at the second location are provided to a model that includes transfer functions that determine a value of the scale deposition real-time at the first location”);
compute a target concentration of scale ([0015]: “The calculated values of these parameters at the first location can be used (along with the scale measurements obtained at the second location) to determine an amount or concentration of scale in the fluid at the first location”);
calculate a target scale inhibitor injection rate based on the target concentration of scale ([0015]: “From the calculated amount of scale at the first location, the processor 124 can determine an amount of scale inhibitor to pump to the first location and control the injection unit 130 accordingly”); and
automatically operate one or more chemical injection pumps of a chemical injection system by adjusting one or more operating parameters of the equipment based at least in part on the predicted scale precipitation to adjust an injection rate of chemicals from the one or more chemical injection pumps in a self-correcting closed loop control based on the target scale inhibitor injection rate to maintain a scale precipitation in the hydrocarbon well production system that is less than or equal to the target concentration of scale ([0016]: “the control unit 122 performs a closed-loop process in which the sensor 120 and control unit 122 are continuously monitoring the scale measurements and fluid parameter values during the production process. The control unit 122 can therefore provide continuous adjustments to the amount of scale inhibitor that is being delivered downhole, thereby optimizing scale inhibitor delivery and reducing the possibility of over-dosing or under-dosing the wellbore formation”).
Zhang does not explicitly teach “utilize cloud-based computing software to predict scale precipitation in the hydrocarbon well production system based at least in part on the real-time data.”
Al-Hajri further teaches utilize cloud-based computing software to predict scale precipitation in the hydrocarbon well production system in real-time based at least in part on the real-time data ([0003]: “Methods for prediction and inhibition of calcium carbonate scale in hydrocarbon wells using machine learning are disclosed”; [0077]: “the network link 520 provides a connection through the local network 522 to a host computer 524 or to a cloud data center or equipment operated by an Internet Service Provider (ISP) 526... In an embodiment, the network 520 contains the cloud 502 or a part of the cloud 502 described above”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the scale prediction and control system of Zhang to incorporate the teachings of Al-Hajri so as to include utilizing cloud-based computing software. As set forth in MPEP § 2143, by combining the known technique of providing data communication through a network to other data devices such as a cloud data center as taught by Al-Hajri with the scale prediction and control system of Zhang, one of ordinary skill would expect to achieve the predictable result of utilizing cloud-based computing software to predict scale precipitation in the hydrocarbon well production system based at least in part on the real-time data.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 2019/0292881 A1), in view of Bello et al. (US 2016/0356125 A1), and in view of Rustad et al. (US 2016/0115395 A1).
Regarding claim 22, Zhang in view of Bello teaches the scale prediction and control system of claim 21.
Zhang and Bello do not explicitly teach “wherein: the processor-executable instructions, when executed by the one or more processors, cause the one or more processors to determine a concentration of the scale inhibitor; and the determining the scale inhibitor injection rate setpoint is further based at least in part on the determined concentration of the scale inhibitor.”
Rustad further teaches wherein: the processor-executable instructions, when executed by the one or more processors, cause the one or more processors to determine a concentration of the scale inhibitor ([0016]: “the additive management system may be configured to inject a chemical into a fluid flow of the hydrocarbon extraction system and to determine a ratio of the injected chemical to relative water in the fluid flow. For example, the additive management system may inject one or more chemicals, such as hydrate inhibitors (e.g., thermodynamic inhibitors and/or kinetic inhibitors), pH modifiers, and/or scale inhibitors”; [0028]: “the controller 24 may be configured to determine an injected chemical content in the fluid flow, which may be a proportion, percentage, or concentration of the injected chemical relative to the fluid flow”); and
the determining the scale inhibitor injection rate setpoint is further based at least in part on the determined concentration of the scale inhibitor ([0040]: “the controller 24 may determine the flow rate (e.g., mass flow rate) of hydrate inhibitor to inject based on the determined concentration of hydrate inhibitor and the flow rate (e.g., mass flow rate) of water in the fluid flow”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the scale prediction and control system of Zhang in view of Bello to incorporate the teachings of Rustad so as to include causing the one or more processors to determine a concentration of the scale inhibitor and the determining the scale inhibitor injection rate setpoint being further based at least in part on the determined concentration of the scale inhibitor. Doing so would allow precise monitoring and control of scale with the aim of reducing its formation while minimizing cost (Rustad, [0003]: “Hydrate formation in hydrocarbon extraction operations is an industry wide concern… Unfortunately, hydrate inhibitors may be used excessively to prevent hydrate formation, which unnecessarily increases the cost of hydrocarbon extraction operations”; [0015] the additive management system may enable precise and/or targeted monitoring and/or control of hydrate formation throughout the hydrocarbon extraction system”).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 2019/0292881 A1), in view of Bello et al. (US 2016/0356125 A1), in view of Rustad et al. (US 2016/0115395 A1), and in view of Al-Hajri et al. (US 2020/0364623 A1).
Regarding claim 23, Zhang in view of Bello and Rustad teaches the scale prediction and control system of claim 22.
Zhang further teaches wherein: the processor-executable instructions, when executed by the one or more processors, cause the one or more processors to determine a scale risk assessment ([0015]: “Using the model, the processor 124 calculates an environmental condition at the first location (e.g., at a downhole location at which fluid flows from the formation in the production tubing). In other words, the model is used to calculate such parameters as scaling risk”; [0018]: “the measurements of the additional fluid parameters at the second location and the measurement of real-time scale deposition at the second location are provided to a model that includes transfer functions that determine a value of the scale deposition real-time at the first location”),
the scale risk assessment being based at least in part on the received real-time data from the one or more sensors ([0018]: “the measurements of the additional fluid parameters at the second location and the measurement of real-time scale deposition at the second location are provided to a model that includes transfer functions that determine a value of the scale deposition real-time at the first location”) and ([0013]: “The sensor 120 is located at an uphole or surface location (also referred to herein as 'a second location') and obtains scale measurements as well as measurements of fluid parameter at the second location”; [0022]: “the parameter includes at least one of: (i) temperature at or near the second location; (ii) pressure at or near the second location; (iii) water cut at or near the second location; (iv) a pH at or near the second location; (v) a salt content at or near the second location; (vi) a flow rate; and a surface roughness at or near the second location”); and
the determining the scale inhibitor injection rate setpoint is further based at least in part on the determined scale risk assessment ([0015]: “From the calculated amount of scale at the first location, the processor 124 can determine an amount of scale inhibitor to pump to the first location and control the injection unit 130 accordingly”).
Zhang does not explicitly teach “a saturation index determined using real-time data received from the one or more sensors or from one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system.”
Al-Hajri further teaches the scale risk assessment being based at least in part on a saturation index determined using real-time data received from the one or more sensors or from one or more downhole sensors disposed downhole within a well of the hydrocarbon well production system ([0025]: “the feature extraction module 104 uses the training data 120 to estimate a Langelier Saturation Index of one or more aqueous samples 132”; [0031]: “the features used can include the pH, temperature, the ionic strength of an aqueous sample, the calcium cation concentration, the bicarbonate anion concentration, and the CO2 mole fraction when the water mixture is saturated with gas containing CO2 to evaluate the effect of solution conditions on the tendency and extent of precipitation”; [0028]: “The Oilfield Scale Prediction Model uses experimental solubility data to determine the saturation index. Critical saturation indices beyond which scaling occurs can be used. The Oilfield Scale Prediction Model uses the flow characteristics and experimental kinetic data to predict the calcium carbonate scale deposition profile from the bottomhole to the surface once the critical saturation index is exceeded”).
The reasons to combine Al-Hajri into Zhang in view of Bello and Rustad are the same as articulated in the rejection of claim 5 above.
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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/M.I.K./Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117
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