DETAILED ACTIONS
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/20/2026 has been entered.
Response to Arguments
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 05/04/26.
Status of the Claims
Claims 1- 8, 10-11, and 14-25 are pending.
Claims 1, 10, and 17-20 are amended.
Claims 9, and 12-13 are canceled.
Response to Arguments
Rejections Under 35 U.S.C. §103
Applicant's argument/amendment, see remarks pages 8-12, filed 05/04/2026.with respect to the rejection(s) of Claims under 35 U.S.C. §103 have been fully considered and are moot because the amendment has necessitated new ground of rejections. The rejections are set forth below.
Claim Rejections - 35 USC §103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 10, and 14-17,19-21 are rejected under 35 U.S.C. §103 as being unpatentable over of Hsu et al. (US 2009/0150079 A1, hereinafter Hsu) and in view of Gattu et al. (US 2020/0241517 A1, hereinafter Gattu), and further in view of James HAUGHS (US 2022/0275695 A1, hereinafter, Haughs).
Regarding Claim 1, Hsu teaches
A method for drilling fluid management, the method (Hsu, Figure 5-6) comprising:
receiving a plurality of fluid property measurements of a drilling fluid (Hsu, Figure 5-6, [0002] “measuring formation fluids and, more particularly, to methods and apparatus to monitor contamination levels in a formation fluid”; at a plurality of measurement times over a period of time (Hsu, Figure 5, step 512, controller measures contamination values over time at “time- based intervals” see [0084], and a time stamp is recorded for each measurement.[0063] “The timestamp information can be used during a playback phase to determine the time at which each measurement was acquired”).
determining contaminant concentrations respectively corresponding to each of the plurality of measurement times based on two or more of the plurality of fluid property measurements at each measurement time, ;(HSU, Figure (Hsu, Figure 5, step 512, Store time stamp value. [0063] “The timestamp information can be used during a playback phase to determine the time at which each measurement was acquired”). [0084] the controller 33 2 and/or the processor 146 (FIG. 1) at the surface may be configured to analyze the measured OD values at predetermined intervals (e.g., time-based intervals, (…) etc.) to determine the contamination levels in fluid samples” NOTE: measurement is conducted at multiple time intervals during real time, for example, ever 5 min. see [0082]).
determining rate of change of an increase in the contaminant concentrations over the period of time;(Hsu, [0008] “A contamination level in the fluid is determined based on the rate of change value”. [0037], Assuming that the contamination η changes with respect to a pumping time during which fluid is extracted from a formation, the values of the optical densities ODλ of the extracted fluid samples will reflect the contamination levels in the fluid samples” [0039], Figure 7, The example methods and apparatus described herein are configured to determine a buildup exponent value (α). The buildup exponent value α defines a rate of change indicative of an amount of change in the optical densities ODλ of measured fluid samples relative to the amount (i.e., volume) of fluid that has been extracted from a formation” NOTE: the rate of change of concentration level measured over duration of pumping. The amount of mud filtrate in a formation fluid sample indicates the contamination level (i.e., the amount of contamination) of the formation fluid sample”
HSU determines rate of change of concentration and compare the contamination value with a threshold value, also set a trend data (HSU, Figure 5-6).
Hsu is silent on extrapolating, from the rate of change of the increase in the contaminant concentrations, when the contaminant concentrations will reach or exceed a threshold contaminant concentration; and determining a treatment plan for the contaminant based on the extrapolation
However, Gattu teaches extrapolating, from the rate of change of the increase in the contaminant concentrations, when the contaminant concentrations will reach or exceed a threshold contaminant concentration; and determining a treatment plan for the contaminant based on the extrapolation (Gattu, Figure 1, [0015] “a mean variance rule can verify the rate of change of the signal and whether there is a change in the mean value and variance for the rate of change, When the change in mean and variance is considerable, exceeds a threshold, and/or is outside of a predetermined range, the mean variance rule can be violated”. Gattu further teaches predicting/ estimating new data outside the range see [0016], “When a data
sample is removed, it can be desirable to estimate the missing data to continue estimating asset operation. Estimating missing data samples can allow asset monitoring even when data can be sparse and/or intermittently lost. Missing data can be estimated using missing data estimation techniques, such as interpolation and/or extrapolation. Extrapolation can construct new data samples outside the range of the discrete set of existing data”. Therefore, extrapolation predicts new data when threshold exceed and maintenance alert is provided. See [0018] [0018] When estimated data can be assessed as a good fit, for example when an estimate quality metric exceeds a pre-determined threshold value or an error metric is below a pre-determined threshold value, at 140, a maintenance analysis on the data samples including the estimated data can be executed”. [0024] In some implementations where predictive maintenance analyses can generate an alert about a potential future failure of an oil and gas industrial asset, uncertainty
can be included in the diagnosis. Accurate maintenance analysis records and data can be desirable because, for example, shutting down a compressor can be a multi-million dollar decision” see [0010]. NOTE: the predictive method based on time series measured data analysis, and apply for any system maintenance or for predicting contamination by extrapolating the “rate of change” of time series contamination value).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Hsu’s method for predicting contamination level to incorporate a method of using time series data “rate of change” (increase or decreasing ) and extrapolate to predict new data exceeding a predetermined threshold value with the benefit of providing asset monitoring ,diagnosis, analyses and/or analytic solutions to diagnose asset health ahead of time and efficient management of alerts as taught by (Gattu [0010]). It would have been obvious to a person of ordinary skill to include the well-known time series data prediction model using extrapolation of rate of change data and predicting an anomaly using known algorithm/machine learning network, in order to yield the predicted results of generating accurate prediction and treatment or maintenance alert, yet with higher accuracy (KSR).
Hsu and Gattu are silent on
the plurality of fluid property measurements comprising at least two of: plastic viscosity (PV), yield point (YP), 10-second Gel (10s Gel), 10-minute Gel (10m Gel), high-temperature high-pressure (HTHP) fluid loss, or pH;
the determining the contaminant concentrations being further based on identifying a contaminant signature based on changes over time in at least two of the at least two of: the PV, the YP, the lOs Gel, the lOim Gel, the HTHP fluid loss, or the pH;
However, Haughs teaches the plurality of fluid property measurements comprising at least two of: plastic viscosity (PV), yield point (YP), 10-second Gel (10s Gel), 10-minute Gel (10m Gel), high-temperature high-pressure (HTHP) fluid loss, or pH; (Haughs, [0015] The properties may include one or more of: viscosity;
density; a rheological property; filtrate data; a measured concentration of one or more ions; a measured concentration of one or more chemicals; and filtration/fluid loss. Additionally or alternatively, the properties may include
funnel viscosity, plastic viscosity, thixotropic index, yield point, gel strength, electrical stability, and encapsulation rates”).
the determining the contaminant concentrations being further based on identifying a contaminant signature based on changes over time in at least two of the at least two of: the PV, the YP, the lOs Gel, the lOim Gel, the HTHP fluid loss, or the pH; (Haughs, Figure 5, [0007] The method may further comprise determining
a concentration of each of a plurality of components of the drilling fluid. The concentration may be determined by inputting the measured properties to a fluid model that relates the properties of the drilling fluid to the concentrations of each of the components. [0071] Depending on the desired periodicity, the process can be carried out continuously to provide live, real-time monitoring of the
drilling fluid composition and borehole conditions”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Hsu’s method for measuring fluid properties such as plastic viscosity, Yield point etc and predicting contamination level based on the fluid properties with the benefit of providing fluid property and contamination level monitoring for an optimal drilling operation as taught by (Haughs [0071). It would have been obvious to a person of ordinary skill to include the well-known fluid property measurement and incorporating these property to estimate fluid contaminant composition concentration using known algorithm/model, in order to yield the predicted results of generating accurate prediction and treatment or maintenance alert, yet with higher accuracy (KSR).
Regarding Claim 2, Combination of Hsu, Gattu, and Haughs teaches the method of claim 1,
Hsu is silent on further comprising applying the treatment plan to the drilling fluid.
However, Gattu teaches further comprising applying the treatment plan to the drilling fluid. (Gattu, Figure 1, step 140, [0018] “When estimated data can be assessed as a good fit, for example when an estimate quality metric exceeds a
pre-determined threshold value or an error metric is below a pre-determined threshold value, at 140, a maintenance analysis on the data samples including the estimated data can be executed. If an out of range and/or not an error (NaN)
test failure is observed, rules down the line that use these tags can generate an alert for data quality violations, but can use the estimated data to execute the maintenance rules that are dependent on these tag” .NOTE: a maintenance plan can be executed based on estimated new data.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Hsu’s method for predicting contamination level to incorporate a method of using time series data “rate of change” (increase or decreasing ) and extrapolate to predict new data exceeding a predetermined threshold value with the benefit of providing asset monitoring ,diagnosis, analyses and/or analytic solutions to diagnose asset health ahead of time and efficient management of alerts as taught by (Gattu [0010]). It would have been obvious to a person of ordinary skill to include the well-known time series data prediction model using extrapolation of rate of change data and predicting an anomaly using known algorithm/machine learning network, in order to yield the predicted results of generating accurate prediction and treatment or maintenance alert, yet with higher accuracy (KSR).
Regarding Claim 3, Combination of Hsu, Gattu, and Haughs teaches the method of claim 1,
Hsu further teaches wherein the receiving the plurality of fluid property measurements includes receiving the plurality of fluid property measurements from a drilling fluid return. (Hsu, Figure 5, steps 502-512, [0080] Turning to FIG. 5, initially the port 304a of the probe 302a (FIG. 3A) begins extracting (or admitting) fluid from the formation F (block 502). In other example implementations both of the ports 304a-b can be configured to simultaneously extract fluid samples from the formation F”).
Regarding Claim 4, Combination of Hsu, Gattu, and Haughs teaches the method of claim 1,
Hsu further teaches wherein the receiving the plurality of fluid property measurements includes receiving different fluid property measurements at a time (Hsu, [0006] In accordance with a disclosed example, an example method to measure fluid properties involves obtaining first property data indicative of a first fluid property of a formation fluid and second property data indicative of a second fluid property of the formation fluid”).
Regarding Claim 5, Combination of Hsu, Gattu, and Haughs teaches the method of claim 1,
Hsu further teaches wherein a collection frequency of the plurality of fluid property measurements is a measurement every one to five minutes. (Hsu, Figure 1-3, [0082], “The spectrometer 324 may be configured to measure the fluid extracted by the probe 302a (FIG. 3A) from the formation F (FIG. 1) at predetermined time-based intervals (e.g., every 5 minutes) or cumulative volume-based intervals (e.g., every 5,000 cubic centimeters of extracted fluid)”).
Regarding Claim 6, Combination of Hsu, Gattu, and Haughs teaches the method of claim 1,
Hsu is silent on wherein the determining the treatment plan includes determining a treatment type.
However, Gattu teaches wherein the determining the treatment plan includes determining a treatment type (Gattu, [0018] “Further, identifying the data quality and estimating the missing data can help in detecting specific failure modes for the asset because detecting specific failure modes can require the ability to assess
trends in individual sensor data.” [0034] The computing device 220 also includes a display 250. The display 250 can include a graphical user interface (not shown). The display 250 can provide the results of the maintenance analysis, any alerts generated by the predictive analytic system 230, and operational data associated with the operation of the industrial asset 210 and/or the sensor 215 to a user or operator of the predictive analytic system 230” Note, specific problem can be mitigated based on predicting data and implemented for Hsu method.).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Hsu’s method for predicting contamination level to incorporate a method of using time series data “rate of change” (increase or decreasing ) and extrapolate to predict new data exceeding a predetermined threshold value with the benefit of providing asset monitoring ,diagnosis, analyses and/or analytic solutions to diagnose asset health ahead of time and efficient management of alerts as taught by (Gattu [0010]). It would have been obvious to a person of ordinary skill to include the well-known time series data prediction model using extrapolation of rate of change data and predicting an anomaly using known algorithm/machine learning network, in order to yield the predicted results of generating accurate prediction and treatment or maintenance alert, yet with higher accuracy (KSR).
Regarding Claim 7, Combination of Hsu, Gattu, and Haughs teaches the method of claim 6,
Hsu is silent on wherein the determining the treatment plan includes determining at least one of a treatment mass of the treatment type or a treatment schedule of the treatment type.
However, Gattu teaches wherein the determining the treatment plan includes determining at least one of a treatment mass of the treatment type or a treatment schedule of the treatment type (Gattu, “the maintenance analysis executed by the data quality engine 235 in operation 140 of FIG. 1. The controller 240 can be configured to modify operations such as powering on or powering off the industrial asset 210, adjusting a rate of speed of the industrial asset 210, modifying a frequency of operation of the industrial asset 210, or the like”. NOTE: Maintenance type can is a design choice. Gattu teaches that operation 140 instruct the user a maintenance timeline and types of problem to mitigate. treatment action is a design and operation choice. It depends on individual wellbore and the formation fluid. It is not an inventive step. [0010]. Overall system performance can be determined by the health status of sensors. Sensor and/or instrument health can be relied on by predictive maintenance analyses and/or analytic solutions to diagnose asset health, in some cases, before problems can arise”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Hsu’s method for predicting contamination level to incorporate a method of using time series data “rate of change” (increase or decreasing ) and extrapolate to predict new data exceeding a predetermined threshold value with the benefit of providing asset monitoring ,diagnosis, analyses and/or analytic solutions to diagnose asset health ahead of time and efficient management of alerts as taught by (Gattu [0010]). It would have been obvious to a person of ordinary skill to include the well-known time series data prediction model using extrapolation of rate of change data and predicting an anomaly using known algorithm/machine learning network, in order to yield the predicted results of generating accurate prediction and treatment or maintenance alert, yet with higher accuracy (KSR).
Regarding Claim 10,
A method for drilling fluid management (Hsu, Figure 5-6), comprising:
measuring a first measurement of each of at least two drilling fluid property of a drilling fluid at a first time; ”( Hsu, [0007.] a data interface configured to obtain first property data indicative of a first fluid property of a formation fluid”).
determining a first contaminant concentration of a contaminant based on the first measurement; [0084] the controller 33 2 and/or the processor 146 (FIG. 1) at the surface may be configured to analyze the measured OD values at predetermined intervals (e.g., time-based intervals, (…) etc.) to determine the contamination levels in fluid samples” NOTE: measurement is conducted at multiple time intervals during real time, for each time interval fluid property and concentration measurement is done for example, ever 5 min.. See fig. 6A-6B, [0084], “a time stamp is recorded for each measurement for each measurement”. Therefore, first contamination measurement is related to first property data measurement). measuring a second measurement of each of the at least two drilling fluid property at a second time (Hsu, [0006] “measure fluid properties involve obtaining second first property data indicative of a first fluid property of a formation fluid”).
determining a second contaminant concentration of the contaminant based on the second measurement (Hsu, See fig. 6A-6B, [0084], “a time stamp is recorded for each measurement for each measurement”. Therefore, second contamination measurement is related to second property data measurement);
measuring a third measurement of each of the at least two the drilling fluid property at a third time; determining a third second contaminant concentration of the contaminant based on the third measurement; (Hsu, In some embodiments, the collection frequency may be in a range having a lower value, an upper value, or lower and upper values including any of a measurement every 1 second, 5 seconds 15 seconds, 30 seconds, 1 minute, 5 minutes, 10 minutes, 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 12 hours, 18 hours, 24 hours, or any value therebetween” Therefore, any third measurement could be representing at 15th second measurement. It is operation choice to set the number of time intervals of collecting and measuring data).
diagnosing a rate of change of total contaminant concentration based on a first difference between: a ratio of a second difference between the first contaminant concentration and the second contaminant concentration to a third difference between the first time and the second time; a ratio of a fourth difference between the second contaminant concentration and the third contaminant concentration to a fifth difference between the second time and the third time (Hsu, [0006], “A correlation between the first and second property data is then generated. Third data is fitted to the correlation. A fitting parameter is determined based on the third data indicative of an amount of change of the first property data relative to an amount of change of the second property data.”. Hsu, figures 5, 6A-6B, [0009], The example apparatus also includes a contamination value generator configured to determine a contamination level in the fluid based on the rate of change value. rate of change value is determined based on the linear relationship. A contamination level in the fluid is determined based on the rate of change value. [0074] the example apparatus 400 is provided with a data relationship processor 414. In the illustrated example, the data relationship processor 414 is configured to determine rate of change values (i.e., slopes) and intercept values associated with linear and/or non-linear relationships between data” NOTE: a ratio of difference between first and second contamination value and time difference is simply rate of change / slope of concentration value over time. A time stamped series of measurement data is collected, therefore, real-time generating first, second contamination values at first, second interval is an operation/ analysis choice/ algorithm/ model, not inventive step.) and
HSU determines rate of change of concentration and compare the contamination value with a threshold value, also set a trend data (HSU, Figure 5-6).
Hsu is silent on extrapolating, from the rate of change of the increase in the contaminant concentrations, when the contaminant concentrations will reach or exceed a threshold contaminant concentration; and determining a treatment plan for the contaminant based on the extrapolation. , the determining the treatment plan comprising selecting the treatment plan from a plurality of contaminant-specific treatment options based on the contaminant signature.
However, Gattu teaches extrapolating, from the rate of change of the increase in the contaminant concentrations, when the contaminant concentrations will reach or exceed a threshold contaminant concentration; and determining a treatment plan for the contaminant based on the extrapolation (Gattu, Figure 1, [0015] “a mean variance rule can verify the rate of change of the signal and whether there is a change in the mean value and variance for the rate of change, When the change in mean and variance is considerable, exceeds a threshold, and/or is outside of a predetermined range, the mean variance rule can be violated”. Gattu further teaches predicting/ estimating new data outside the range see [0016], “When a data
sample is removed, it can be desirable to estimate the missing data to continue estimating asset operation. Estimating missing data samples can allow asset monitoring even when data can be sparse and/or intermittently lost. Missing data can be estimated using missing data estimation techniques, such as interpolation and/or extrapolation. Extrapolation can construct new data samples outside the range of the discrete set of existing data”. Therefore, extrapolation predicts new data when threshold exceed and maintenance alert is provided. See [0018] [0018] When estimated data can be assessed as a good fit, for example when an estimate quality metric exceeds a pre-determined threshold value or an error metric is below a pre-determined threshold value, at 140, a maintenance analysis on the data samples including the estimated data can be executed”. [0024] In some implementations where predictive maintenance analyses can generate an alert about a potential future failure of an oil and gas industrial asset, uncertainty
can be included in the diagnosis. Accurate maintenance analysis records and data can be desirable because, for example, shutting down a compressor can be a multi-million dollar decision” see [0010]. NOTE: the predictive method based on time series measured data analysis, and apply for any system maintenance or for predicting contamination by extrapolating the “rate of change” of time series contamination value). the determining the treatment plan comprising selecting the treatment plan from a plurality of contaminant-specific treatment options based on the contaminant signature. (Gattu, “the maintenance analysis executed by the data quality engine 235 in operation 140 of FIG. 1. The controller 240 can be configured to modify operations such as powering on or powering off the industrial asset 210, adjusting a rate of speed of the industrial asset 210, modifying a frequency of operation of the industrial asset 210, or the like”. NOTE: Maintenance type can is a design choice. Gattu teaches that operation 140 instruct the user a maintenance timeline and types of problem to mitigate. treatment action is a design and operation choice. It depends on individual wellbore and the formation fluid. It is not an inventive step. [0010]. Overall system performance can be determined by the health status of sensors. Sensor and/or instrument health can be relied on by predictive maintenance analyses and/or analytic solutions to diagnose asset health, in some cases, before problems can arise”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Hsu’s method for predicting contamination level to incorporate a method of using time series data “rate of change” (increase or decreasing ) and extrapolate to predict new data exceeding a predetermined threshold value with the benefit of providing asset monitoring ,diagnosis, analyses and/or analytic solutions to diagnose asset health ahead of time and efficient management of alerts as taught by (Gattu [0010]). It would have been obvious to a person of ordinary skill to include the well-known time series data prediction model using extrapolation of rate of change data and predicting an anomaly using known algorithm/machine learning network, in order to yield the predicted results of generating accurate prediction and treatment or maintenance alert, yet with higher accuracy (KSR).
Hsu and Gattu are silent on
measuring a first measurement of each of at least two drilling fluid properties of a drilling fluid at a first time, the at least two drilling fluid properties comprising at least: plastic viscosity (PV), yield point (YP), 10-second Gel (10s Gel), 10-minute Gel (10m Gel), high-temperature high-pressure (HTHP) fluid loss, or pH;
determining a second contaminant concentration of the contaminant based on the second measurement, the determining the second contaminant concentration being further based on identifying a contaminant signature based on changes over time in at least two of the at least two of: the PV, the YP, the 1Os Gel, the lOim Gel, the HTHP fluid loss, or the pH;
the determining the third contaminant concentration being further based on identifying the contaminant signature based on changes over time in the at least two of: the PV, the YP, the lOs Gel, the lOm Gel, the HTHP fluid loss, and the pH;
However, Haughs teaches measuring a first measurement of each of at least two drilling fluid properties of a drilling fluid at a first time, the at least two drilling fluid properties comprising at least: plastic viscosity (PV), yield point (YP), 10-second Gel (10s Gel), 10-minute Gel (10m Gel), high-temperature high-pressure (HTHP) fluid loss, or pH;; (Haughs, [0015] The properties may include one or more of: viscosity;
density; a rheological property; filtrate data; a measured concentration of one or more ions; a measured concentration of one or more chemicals; and filtration/fluid loss. Additionally or alternatively, the properties may include
funnel viscosity, plastic viscosity, thixotropic index, yield point, gel strength, electrical stability, and encapsulation rates”).
determining a second contaminant concentration of the contaminant based on the second measurement, the determining the second contaminant concentration being further based on identifying a contaminant signature based on changes over time in at least two of the at least two of: the PV, the YP, the 1Os Gel, the lOim Gel, the HTHP fluid loss, or the pH;the determining the third contaminant concentration being further based on identifying the contaminant signature based on changes over time in the at least two of: the PV, the YP, the lOs Gel, the lOm Gel, the HTHP fluid loss, and the pH; (Haughs, Figure 5, [0007] The method may further comprise determining
a concentration of each of a plurality of components of the drilling fluid. The concentration may be determined by inputting the measured properties to a fluid model that relates the properties of the drilling fluid to the concentrations of each of the components. [0071] Depending on the desired periodicity, the process can be carried out continuously to provide live, real-time monitoring of the drilling fluid composition and borehole conditions” NOTE: the process is repeated continuously, the measurement steps can be defined as first, second, third .. measurements. It is not an inventive step).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Hsu’s method for measuring fluid properties such as plastic viscosity, Yield point etc and predicting contamination level based on the fluid properties with the benefit of providing fluid property and contamination level monitoring for an optimal drilling operation as taught by (Haughs [0071). It would have been obvious to a person of ordinary skill to include the well-known fluid property measurement and incorporating these property to estimate fluid contaminant composition concentration using known algorithm/model, in order to yield the predicted results of generating accurate prediction and treatment or maintenance alert, yet with higher accuracy (KSR).
Regarding Claim 14, Combination of Hsu, Gattu, and Haughs teaches the method of claim 10,
Hsu further teaches wherein the diagnosing the rate of change of the concentration of the contaminant includes determining a probability of the contaminant comprising a particular contaminant type a contaminant type. (Hsu, [0030] FIG. 20 depicts a chart having a curve plot corresponding to the contamination levels in fluid samples extracted from a formation and another curve plot corresponding to uncertainty values indicative of statistical variations of the contamination levels caused by noise in OD data”).
Regarding Claim 15, Combination of Hsu, Gattu, and Haughs teaches the method of claim 10,
Hsu further teaches wherein the diagnosing the rate of change of the concentration of the contaminant (Hsu, figures 5, 6A-6B, [0009], The example apparatus also includes a contamination value generator configured to determine a contamination level in the fluid based on the rate of change value.” includes identifying a contaminant type and a contaminant concentration (Hsu, [0080], “the extracted fluid typically contains a mixture of the oil 127 (FIG. 1) and the mud filtrate 125 (i.e., the contaminant”.)
Regarding Claim 16, Combination of Hsu, Gattu, and Haughs teaches the method of claim 15, Hsu is silent on wherein the determining the treatment plan includes determining at least one of a treatment mass of the treatment type or a treatment schedule of the treatment type.
However, Gattu teaches wherein the determining the treatment plan includes determining at least one of a treatment mass of the treatment type or a treatment schedule of the treatment type (Gattu, “the maintenance analysis executed by the data quality engine 235 in operation 140 of FIG. 1. The controller 240 can be configured to modify operations such as powering on or powering off the industrial asset 210, adjusting a rate of speed of the industrial asset 210, modifying a frequency of operation of the industrial asset 210, or the like”. NOTE: Maintenance type can is a design choice. Gattu teaches that operation 140 instruct the user a maintenance timeline and types of problem to mitigate. treatment action is a design and operation choice. It depends on individual wellbore and the formation fluid. It is not an inventive step. [0010]. Overall system performance can be determined by the health status of sensors. Sensor and/or instrument health can be relied on by predictive maintenance analyses and/or analytic solutions to diagnose asset health, in some cases, before problems can arise”).
Regarding Claim 17,
A system, comprising:
a sensor(Hsu, Figure 1, 136, 137 a-c, [0048], The formation tester 136 includes one or more probe(s) 137a-c,); a processor (Hsu, Figure 1, processor 146); and computer-readable media, the computer-readable media including instructions which, when accessed by the processor, cause the processor to (Hsu, [0079], In some example implementations, the flowcharts can be representative of example machine readable instructions and the example methods of the flowcharts may be implemented entirely or in part by executing the machine readable instructions”):
to receive a plurality of fluid property measurements of a drilling fluid Hsu, Figure 5-6, [0002] “measuring formation fluids and, more particularly, to methods and apparatus to monitor contamination levels in a formation fluid”; at a plurality of measurement times over a period of time from the sensor(Hsu, Figure 5, step 512, controller measures contamination values over time at “time- based intervals” see [0084], and a time stamp is recorded for each measurement.[0063] “The timestamp information can be used during a playback phase to determine the time at which each measurement was acquired”);
determine contaminant concentrations respectively corresponding to each of the plurality of measurement times based on one or more of the plurality of fluid property measurements at each measurement time (Hsu, Figure (Hsu, Figure 5, step 512, Store time stamp value. [0063] “The timestamp information can be used during a playback phase to determine the time at which each measurement was acquired”). [0084] the controller 33 2 and/or the processor 146 (FIG. 1) at the surface may be configured to analyze the measured OD values at predetermined intervals (e.g., time-based intervals, (…) etc.) to determine the contamination levels in fluid samples” NOTE: measurement is conducted at multiple time intervals during real time, for example, ever 5 min. see [0082]).
determining rate of change of an increase in the contaminant concentrations over the period of time;(Hsu, [0008] “A contamination level in the fluid is determined based on the rate of change value”. [0037], Assuming that the contamination η changes with respect to a pumping time during which fluid is extracted from a formation, the values of the optical densities ODλ of the extracted fluid samples will reflect the contamination levels in the fluid samples” [0039], Figure 7, The example methods and apparatus described herein are configured to determine a buildup exponent value (α). The buildup exponent value α defines a rate of change indicative of an amount of change in the optical densities ODλ of measured fluid samples relative to the amount (i.e., volume) of fluid that has been extracted from a formation” NOTE: the rate of change of concentration level measured over duration of pumping. The amount of mud filtrate in a formation fluid sample indicates the contamination level (i.e., the amount of contamination) of the formation fluid sample”
HSU determines rate of change of concentration and compare the contamination value with a threshold value, also set a trend data (HSU, Figure 5-6).
Hsu is silent on extrapolating, from the rate of change of the increase in the contaminant concentrations, when the contaminant concentrations will reach or exceed a threshold contaminant concentration; and determining a treatment plan for the contaminant based on the extrapolation
However, Gattu teaches extrapolating, from the rate of change of the increase in the contaminant concentrations, when the contaminant concentrations will reach or exceed a threshold contaminant concentration; and determining a treatment plan for the contaminant based on the extrapolation (Gattu, Figure 1, [0015] “a mean variance rule can verify the rate of change of the signal and whether there is a change in the mean value and variance for the rate of change, When the change in mean and variance is considerable, exceeds a threshold, and/or is outside of a predetermined range, the mean variance rule can be violated”. Gattu further teaches predicting/ estimating new data outside the range see [0016], “When a data
sample is removed, it can be desirable to estimate the missing data to continue estimating asset operation. Estimating missing data samples can allow asset monitoring even when data can be sparse and/or intermittently lost. Missing data can be estimated using missing data estimation techniques, such as interpolation and/or extrapolation. Extrapolation can construct new data samples outside the range of the discrete set of existing data”. Therefore, extrapolation predicts new data when threshold exceed and maintenance alert is provided. See [0018] [0018] When estimated data can be assessed as a good fit, for example when an estimate quality metric exceeds a pre-determined threshold value or an error metric is below a pre-determined threshold value, at 140, a maintenance analysis on the data samples including the estimated data can be executed”. [0024] In some implementations where predictive maintenance analyses can generate an alert about a potential future failure of an oil and gas industrial asset, uncertainty
can be included in the diagnosis. Accurate maintenance analysis records and data can be desirable because, for example, shutting down a compressor can be a multi-million dollar decision” see [0010]. NOTE: the predictive method based on time series measured data analysis, and apply for any system maintenance or for predicting contamination by extrapolating the “rate of change” of time series contamination value).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Hsu’s method for predicting contamination level to incorporate a method of using time series data “rate of change” (increase or decreasing ) and extrapolate to predict new data exceeding a predetermined threshold value with the benefit of providing asset monitoring ,diagnosis, analyses and/or analytic solutions to diagnose asset health ahead of time and efficient management of alerts as taught by (Gattu [0010]). It would have been obvious to a person of ordinary skill to include the well-known time series data prediction model using extrapolation of rate of change data and predicting an anomaly using known algorithm/machine learning network, in order to yield the predicted results of generating accurate prediction and treatment or maintenance alert, yet with higher accuracy (KSR).
Hsu and Gattu are silent on
the plurality of fluid property measurements comprising at least two of: plastic viscosity (PV), yield point (YP), 10-second Gel (10s Gel), 10-minute Gel (10m Gel), high-temperature high-pressure (HTHP) fluid loss, or pH;
the determining the contaminant concentrations being further based on identifying a contaminant signature based on changes over time in at least two of the at least two of: the PV, the YP, the lOs Gel, the lOim Gel, the HTHP fluid loss, or the pH;
However, Haughs teaches the plurality of fluid property measurements comprising at least two of: plastic viscosity (PV), yield point (YP), 10-second Gel (10s Gel), 10-minute Gel (10m Gel), high-temperature high-pressure (HTHP) fluid loss, or pH; (Haughs, [0015] The properties may include one or more of: viscosity;
density; a rheological property; filtrate data; a measured concentration of one or more ions; a measured concentration of one or more chemicals; and filtration/fluid loss. Additionally or alternatively, the properties may include
funnel viscosity, plastic viscosity, thixotropic index, yield point, gel strength, electrical stability, and encapsulation rates”).
the determining the contaminant concentrations being further based on identifying a contaminant signature based on changes over time in at least two of the at least two of: the PV, the YP, the lOs Gel, the lOim Gel, the HTHP fluid loss, or the pH; (Haughs, Figure 5, [0007] The method may further comprise determining
a concentration of each of a plurality of components of the drilling fluid. The concentration may be determined by inputting the measured properties to a fluid model that relates the properties of the drilling fluid to the concentrations of each of the components. [0071] Depending on the desired periodicity, the process can be carried out continuously to provide live, real-time monitoring of the
drilling fluid composition and borehole conditions”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Hsu’s method for measuring fluid properties such as plastic viscosity, Yield point etc and predicting contamination level based on the fluid properties with the benefit of providing fluid property and contamination level monitoring for an optimal drilling operation as taught by (Haughs [0071). It would have been obvious to a person of ordinary skill to include the well-known fluid property measurement and incorporating these property to estimate fluid contaminant composition concentration using known algorithm/model, in order to yield the predicted results of generating accurate prediction and treatment or maintenance alert, yet with higher accuracy (KSR).
Regarding Claim 19, Combination of Hsu, Gattu, and Haughs teaches the system of claim 17,
Hsu teaches wherein the sensor is located in-line in one or more pipes transporting drilling fluid to a drill string (Hsu, Figure 1, 136, 137 a-c, [0048], The formation tester 136 includes one or more probe(s) 137a-c, Drill string 104);
Regarding Claim 20, Combination of Hsu, Gattu, and Haughs teaches the system of claim 17,
Hsu teaches wherein the sensor has a collection frequency of at least a measurement every hour (Hsu, Figure 1-3, [0080], the extracted fluid typically contains a mixture of the oil 127 (FIG. 1) and the mud filtrate 125 (i.e., the contaminant) that permeates the formation F during a drilling process.
After some time (e.g., minutes, hours, etc.) of pumping or extracting fluid from the formation F, [0082], “The spectrometer 324 may be configured to measure the fluid extracted by the probe 302a (FIG. 3A) from the formation F (FIG. 1) at predetermined time-based intervals (e.g., every 5 minutes) or cumulative volume-based intervals (e.g., every 5,000 cubic centimeters of extracted fluid)”).
Regarding Claim 21, Combination of Hsu, Gattu, and Haughs teaches the method of claim 1, Hsu in is silent on further comprising:
HSU determines rate of change of concentration and compare the contamination value with a threshold value, also set a trend data (HSU, Figure 5-6).
Hsu is silent on predicting one or more consequences of not treating the contamination based on the extrapolating, the one or more consequences including a damage to drilling equipment or a failure of the drilling equipment; and
predicting when the one or more consequences will occur, wherein the treatment plan is further determined based on the predicting when the one or more consequences will occur.
However, Gattu teaches predicting one or more consequences of not treating the contamination based on the extrapolating, the one or more consequences including a damage to drilling equipment or a failure of the drilling equipment; and predicting when the one or more consequences will occur, wherein the treatment plan is further determined based on the predicting when the one or more consequences will occur. (Gattu, Figure 1, [0015] “a mean variance rule can verify the rate of change of the signal and whether there is a change in the mean value and variance for the rate of change, When the change in mean and variance is considerable, exceeds a threshold, and/or is outside of a predetermined range, the mean variance rule can be violated”. Gattu further teaches predicting/ estimating new data outside the range see [0016], “When a data sample is removed, it can be desirable to estimate the missing data to continue estimating asset operation. Estimating missing data samples can allow asset monitoring even when data can be sparse and/or intermittently lost. Missing data can be estimated using missing data estimation techniques, such as interpolation and/or extrapolation. Extrapolation can construct new data samples outside the range of the discrete set of existing data”. Therefore, extrapolation predicts new data when threshold exceed and maintenance alert is provided. See [0018] When estimated data can be assessed as a good fit, for example when an estimate quality metric exceeds a pre-determined threshold value or an error metric is below a pre-determined threshold value, at 140, a maintenance analysis on the data samples including the estimated data can be executed”. [0024] In some implementations where predictive maintenance analyses can generate an alert about a potential future failure of an oil and gas industrial asset, uncertainty can be included in the diagnosis. Accurate maintenance analysis records and data can be desirable because, for example, shutting down a compressor can be a multi-million dollar decision” see [0010]. NOTE: the predictive method based on time series measured data analysis, and apply for any system maintenance or for predicting contamination by extrapolating the “rate of change” of time series contamination value. If the correct maintenance is not predicted , there is huge cost (a multi-million dollar) or hazard for drilling system).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Hsu’s method for predicting contamination level to incorporate a method of using time series data “rate of change” (increase or decreasing ) and extrapolate to predict new data exceeding a predetermined threshold value with the benefit of providing asset monitoring ,diagnosis, analyses and/or analytic solutions to diagnose asset health ahead of time and efficient management of alerts as taught by (Gattu [0010]). It would have been obvious to a person of ordinary skill to include the well-known time series data prediction model using extrapolation of rate of change data and predicting an anomaly using known algorithm/machine learning network, in order to yield the predicted results of generating accurate prediction and treatment or maintenance alert, yet with higher accuracy (KSR).
Regarding Claim 24, Combination of Hsu, Gattu, and Haughs teaches the method of claim 1,
Hsu further teaches further comprising: predicting that the contaminant will be above the contaminant threshold before a next measurement will be taken, (Hsu, Figure 3- 5, [0087], [ 0087] After determining the contamination levels, the controller 332 (or the processor 146) determines whether the contamination levels in the most recently measured fluid samples are sufficiently low to test the formation oil 127 (FIG. 1) (block 524). For example, the controller 332 (or the processor 146) can be provided with a threshold value that defines the maximum contamination level allowable in a fluid sample [0110] The example apparatus 400 determines a fitting interval or a fitting range (e.g., a straight-line fit range) and the buildup exponent value (a) (block 624) based on the combined OD data. The example apparatus 400 can be configured to determine the fitting interval and the buildup exponent value (a) using a derivative technique and/or a Bayesian Information Criterion (BIC) technique
Hsu is silent on wherein the treatment plan is further based on the determining that the contaminant will be above the contaminant threshold before a next measurement will be taken; and starting the treatment plan before the next measurement is taken.
However, wherein the treatment plan is further based on the determining that the contaminant will be above the contaminant threshold before a next measurement will be taken; and starting the treatment plan before the next measurement is taken.(Gattu, Figure 1, [0015] “a mean variance rule can verify the rate of change of the signal and whether there is a change in the mean value and variance for the rate of change, When the change in mean and variance is considerable, exceeds a threshold, and/or is outside of a predetermined range, the mean variance rule can be violated”. Gattu further teaches predicting/ estimating new data outside the range see [0016], “When a data sample is removed, it can be desirable to estimate the missing data to continue estimating asset operation. [0018] When estimated data can be assessed as a good fit, for example when an estimate quality metric exceeds a pre-determined threshold value or an error metric is below a pre-determined threshold value, at 140, a maintenance analysis on the data samples including the estimated data can be executed”.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Hsu’s method for predicting contamination level to incorporate a method of using time series data “rate of change” (increase or decreasing ) and extrapolate to predict new data exceeding a predetermined threshold value with the benefit of providing asset monitoring ,diagnosis, analyses and/or analytic solutions to diagnose asset health ahead of time and efficient management of alerts as taught by (Gattu [0010]). It would have been obvious to a person of ordinary skill to include the well-known time series data prediction model using extrapolation of rate of change data and predicting an anomaly using known algorithm/machine learning network, in order to yield the predicted results of generating accurate prediction and treatment or maintenance alert, yet with higher accuracy (KSR).
Claims 8,11, 18, and 25 are rejected under 35 U.S.C. §103 as being unpatentable over Hsu and in view of Gattu as applied to claim 6, and in further view of Bartetzko et al. (US 2017 /0234127 A1, hereinafter Bartetzko, previously cited).
Regarding Claim 8, Combination of Hsu, Gattu, and Haughs teaches the method of claim 6,
Hsu and Gattu are silent on wherein the determining the treatment plan includes determining a return time for a treated fluid treated according to the treatment plan.
However, Bartetzko teaches wherein the determining the treatment plan includes determining a return time for a treated fluid treated according to the treatment plan (Bartetzko, Figure 4, [0057], “100 may predict the composition and properties of the drilling fluid exiting the well and compare the predicted properties to the properties of the current sample of the fluid leaving the well. Act 412 and act 414 may be repeated at regular or irregular intervals and the results may be stored in memory 120. In some embodiments, act 412 and act 414 are repeated every hour, every several hours, every day, or as otherwise desired”).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Hsu methods in view of Bartetzko treatment action plan to mitigate the concentration of contaminant level as taught by Bartetzko to provide an accurate detection of contaminant and a timely mitigation action with the benefit of reducing degradation of at least one wellbore component. (Bartetzko, [0007]).
Regarding Claim 11, Combination of Hsu, Gattu, and Haughs teaches the method of claim 10,
Hsu is silent on further comprising applying the treatment plan to the drilling fluid.
However, Bartetzko teaches further comprising applying the treatment plan to the drilling fluid. (Bartetzko, Figure 8, step 822, 824)
It would have been obvious to a person of ordinary skill before the effective filing date to modify Hsu and Gattu’s methods in view of Bartetzko treatment action plan to mitigate the concentration of contaminant level of drilling fluid as taught by Bartetzko to provide an accurate detection of contaminant and a timely mitigation action with the benefit of reducing degradation of at least one wellbore component. (Bartetzko, [0007]).
Regarding Claim 18, Combination of Hsu, Gattu, and Haughs teaches the system of claim 17,
Hsu and Gattu are silent on wherein the sensor includes a rheometer.
However, Bartetzko teaches wherein the sensor includes a rheometer (Bartetzko, Figure 1, [0030] The user interface 140 may include input device and output devices operably coupled to the processor 110. In some embodiments, the input devices include temperature sensors, pressure sensors, sensors for assessing the density, viscosity, electrical conductivity, and pH of a fluid, and sensors for measuring the chemical composition of a fluid in real-time. In other embodiments a pH, density, viscosity, electrical conductivity, and the concentration of chemical species of a fluid may be measured in a laboratory and input into the user interface 140 through an input device” NOTE: a rheometer measures fluid properties and Bartetzko teaches input device measures different properties of fluid equivalent to measurement by a rheometer).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Hsu and Gisolf methods in view of Bartetzko treatment action plan to mitigate the concentration of contaminant level as taught by Bartetzko to provide an accurate detection of contaminant and a timely mitigation action with the benefit of reducing degradation of at least one wellbore component. (Bartetzko, [0007]).
Regarding Claim 25, Combination of Hsu, Gattu, and Haughs teaches the method of claim 14,
Hsu and Gattu are silent on wherein the probability of the contaminant comprising a particular contaminant type corresponds to the a probability of the contaminant comprising one or more of: cement, hardened cement, green cement, anhydrite, gypsum, magnesium, a salt, a halide, a halite, a formate, a carbonate, a bicarbonate, an acidic gas, hydrogen sulfide, carbon dioxide, a low gravity solid, a drilled solid, a fine solid, a colloidal solid, or another contaminant material
However, Bartetzco teaches wherein the probability of the contaminant comprising a particular contaminant type corresponds to the a probability of the contaminant comprising one or more of: cement, hardened cement, green cement, anhydrite, gypsum, magnesium, a salt, a halide, a halite, a formate, a carbonate, a bicarbonate, an acidic gas, hydrogen sulfide, carbon dioxide, a low gravity solid, a drilled solid, a fine solid, a colloidal solid, or another contaminant material (Bartetzko,[0049] the drilling fluid may interact with acid gases such as CO2, H2S, or other materials present within the formation. In other embodiments, chloride ions may be dissolved into the drilling fluid. The CO2, H2S, or chloride ions may dissolve in the drilling fluid and cause further reactions with the drilling fluid. For example, the acid gases may consume acid gas scavengers, or may reduce
the concentration of bases such as magnesium oxide or other additives in the drilling fluid or mud, or may alter physical properties of the drilling fluid by degrading viscosifiers present in the drilling fluid. materials such as H2S or CO2 may corrode or degrade construction material such as metallic materials,
composite materials, hard metals such as cemented carbide, elastomers, sealing materials, polytetrafluoroethylene (PTFE), polyetheretherketone (PEEK), and cement in the well.).
Claims 22, 23 are rejected under 35 U.S.C. §103 as being unpatentable over Hsu and in view of Gattu as applied to claim 1, and in further view of Todd W. Benson (US 2019/0309614 A1, hereinafter Benson).
Regarding Claim 22, Combination of Hsu, Gattu, and Haughs teaches the method of claim 1, Hsu is silent on further comprising predicting one or more consequences of not treating the contamination based on the extrapolating,
However, Gattu teaches further comprising predicting one or more consequences of not treating the contamination based on the extrapolating , the one or more consequences including an associated cost of the treatment plan (Gattu, figure 1, step 140, [0011] an alert can be generated notifying users of the asset monitoring and diagnosis systems of a potentially anomalous asset, but uncertainty can be included in the predictive maintenance analysis diagnosis generating the alert of potential future failure. [0026] Updating models due to process changes and/or diagnosis false positives can include a significant cost and can be cumbersome to the user. It can be desirable to maintain the models based on process conditions and/or when a diagnosis is incorrect. [0026], “Accurate maintenance analysis records and data can be desirable because, for example, shutting down a compressor can be a multi-million dollar decision” NOTE: not predicting correct time of maintenance can cost a lot)
Hsu and Gattu are silent on the one or more consequences including an associated cost of the treatment plan.
However, Benson teaches the one or more consequences including an associated cost of the treatment plan. (Benson, Figure 8, step 806-808, [0132] In step 806, a cost is calculated for each remaining solution vector. As illustrated in FIG. 7C, the costs may be represented as a cost matrix (that may or may not be weighted) with each solution vector having corresponding costs in the cost matrix. In step 808, a minimum of the solution vectors may be taken to identify the lowest cost solution vector. It is understood that the minimum cost is one
way of selecting the desired solution vector, and that other ways may be used. Accordingly, step 808 is concerned with selecting an optimal solution vector based on a set of target parameters, which may include one or more of a financial
cost, a time cost, a reliability cost, and/or any other factors, such as an engineering cost like dogleg severity, that may be used to narrow the set of solution vectors to the optimal solution vector”also see figure 32.
It would have been obvious to a person of ordinary skill before the effective filing date to modify Bartetzko treatment action plan and incorporate Bensons treatment cost estimation plan to mitigate consequences of untreated contaminant level as taught by Benson to provide an optimized cost estimation for the operation of a drill well with the benefit of maintaining the well degradation. (Benson, [0132]-[0134).
Regarding Claim 23, Combination of Hsu, Gattu, and Haughs teaches the method of claim 1,
Hsu, Gisolf, and Bartetzko are silent on wherein: the treatment plan is a first treatment plan among a plurality of proposed treatment plans, each of the plurality of proposed treatment plans having a respective associated cost; and the method further comprises selecting the first treatment plan from among the plurality of proposed treatment plans due to the associated cost of the first treatment plan being lower than the associated costs of other treatment plans among the plurality of proposed treatment plans.
However, Benson teaches the treatment plan is a first treatment plan among a plurality of proposed treatment plans (Benson, Figure 8A,step 802, calculate multiple solutions vector) each of the plurality of proposed treatment plans having a respective associated cost (Benson Figure 8A, step 806); and the method further comprises selecting the first treatment plan from among the plurality of proposed treatment plans due to the associated cost of the first treatment plan being lower than the associated costs of other treatment plans among the plurality of proposed treatment plans.( (Benson Figure 8A, step 808, 810, [0132], In step 808, a minimum of the
solution vectors may be taken to identify the lowest cost solution vector. It is understood that the minimum cost is one way of selecting the desired solution vector, and that other ways may be used. Accordingly, step 808 is concerned with
selecting an optimal solution vector based on a set of target parameters, which may include one or more of a financial cost, a time cost, a reliability cost, and/or any other factors, such as an engineering cost like dogleg severity, that may be
used to narrow the set of solution vectors to the optimal solution vector.);
It would have been obvious to a person of ordinary skill before the effective filing date to modify Bartetzko treatment action plan and incorporate Bensons treatment cost estimation plan to mitigate consequences of untreated contaminant level as taught by Benson to provide an optimized cost estimation for the operation of a drill well with the benefit of maintaining the well degradation. (Benson, [0132]-[0134).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Al-Rubaii et al. (US 2021/0140300 A1) recites Methods, systems, and computer-readable medium to perform operations including determining, in real-time, values of drilling parameters of a drilling system drilling a wellbore.
The operations further include calculating, based on the values of the drilling parameters, a cuttings concentration in an annulus of the wellbore (CCA). Further, the operations include calculating, based on the calculated CCA
and a mud weight (MW) of a drilling fluid, an effective mud weight (MWeff) of the drilling fluid. Yet further, the operations include calculating, based on the effective mud weight, a slip velocity of the cuttings. In addition, the operations include calculating, based on the slip velocity, a transport ratio (TR) of the cuttings” (Abstract).
Bi Hongfeng (US 10845285 B1) discloses “A fast response fluid monitoring system (300) used for fast evaluations and predictions of the properties of a drilling fluid or a fracturing fluid (204) onsite of an oilfield operation, by measuring the fluid properties under two shear rates at current temperature, predicting the fluid properties under other shear rates and under an elevated standard testing temperature, and comparing and updating results of the test to the predicted results to optimize next-time predicting practice” (abstract).
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supervisor, EMAN ALKAFAWI can be reached on (571) 272-4448. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DILARA SULTANA/Examiner, Art Unit 2858
06/26/2026
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858
6/30/2026
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