DETAILED ACTION
Claims 1 – 12, 15 – 24 and 26 have been presented for examination. Claims 1, 8 - 10, 19, 21 – 22, 24 and 26 are currently amended. Claims 13 – 14, 25 and 27 – 28 are cancelled.
This Office Action is in response to the amendments dated 07/28/2025
The instant Office Action relies on Dupont et al. (US 2018/0335538) which is cited on the IDS.
Rejection of Claims 1-12 and 15-23 under 35 U.S.C. § 112(b)
Applicant’s amendments overcome the 112(b) rejection. Therefore, it is withdrawn.
Response to Rejection of Claims 1-12, 15-24 and 26 under 35 U.S.C. 101
Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues: “Amended Claim 1, therefore, integrates the purported judicial exceptions into the practical application of "adjusting" specific controllable features that include flow rate and proppant concentration, which are identified by the prediction model for the specific KPIs of residual and actual production.
Additionally, amended Claim 1 includes that the adjusted flow rate or the adjusted proppant concentration are "adjusted in a direction according to the prediction model to improve the at least one selected KPI". As such, amended Claim 1 also provides additional limitations on how the flow rate and the proppant concentration are adjusted, which can be done by increasing or decreasing a pump rate for adjusting the flow rate and adding or reducing the amount of proppant added in a blender for changing the proppant concentration”
Applicant argues that the recited “adjusting” amounts to integrating the abstract idea into a practical application. The requirements for subject matter eligibility analysis are discussed in MPEP 2111. Examiner notes that the adjusting is with regard to “how the flow rate and the proppant concentrations are adjusted”. However, said adjustment process and direction of adjustment amounts to merely using the results of the abstract idea by operating generic “well site equipment”. Specifically, the “adjusting” amounts to reciting the words “apply it” since it requires no more than ordinary equipment operated in its order capacity and merely based on the result of the abstract idea (see Claim Rejections - 35 USC § 101).
Applicant argues: “Additionally, amended Claim 1 includes "executing the HF job plan using one or more of the adjusted flow rate or the adjusted proppant concentration". As such, amended Claim 1 also provides an additional limitation that is significantly more than the purported judicial exceptions. Thus, amended Claim 1 is also patent eligible under Pathway C denoted in MPEP 2106 via a Step 2B analysis.”
Applicant argues that the recited “executing the HF job plan …” amounts to integrating the abstract idea into a practical application. The requirements for subject matter eligibility analysis are discussed in MPEP 2111. The recited “executing the HF job plan” amounts to merely using the results of the abstract idea by operating generic “well site equipment”. Specifically, the “executing” amounts to reciting the words “apply it” since it requires no more than ordinary equipment operated in its order capacity and merely based on the result of the abstract idea (see Claim Rejections - 35 USC § 101). Looking at the combination of elements, the “executing” does not achieve a particular result since it is merely in response to when the well site data set fails to satisfy the one or more KPIs according to the prediction model which does not limit the “executing” to any specific outcome.
Response to Rejection of Claims 1-12 and 19-22 under 35 U.S.C. § 103
Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues: “The hydrodynamic formation model of Kabannik, however, does not appear to: (1) identify a combination of controllable features that impact one or more KPIs of well production, (2) where the controllable features are from time-series pumping data gathered during pumping operations or during shutdown operations performed at other well before the executing of the HF job plan for the well. Instead of identifying, the hydrodynamic formation model appears to be used to calculate production indicators. (See page 13, lines 20-21 of Kabbanik.)”
Applicant argues that the model of Kabbanik does not appear to be used to identify controllable features. Examiner notes that Kabbanik explicitly teaches using the model to compute sensitivities for treatment parameters related to a KPI (see Page 12, Lines 13 - 15 and Figure 3C)
Applicant argues: “Additionally, the applied Kabannik-Xu-Dupont-Voneiff combination does not appear to teach or suggest … However, paragraph 174 of Xu discloses analyzing a job design if an evaluation of a fracture operation is not satisfied based on a proppant placement simulation and determining the adjustments that may be made. Thus, Xu as applied appears to disclose analyzing what to adjust instead of already knowing what to adjust as in amended Claim 1 that adjusts an already "identified combination of controllable features from the prediction model".”
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Specifically the “identifies a combination” is taught by Kabannik.
Response to Rejection of Claim 15-18 and 23 under 35 U.S.C. § 103
Applicant’s arguments are not persuasive based on the preceding remarks.
Response to Rejection of Claim 24 and 26 under 35 U.S.C. § 103
Applicant’s arguments are not persuasive based on the preceding remarks.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 12, 15 – 24 and 26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Independent claim 1 recites a statutory category (i.e. a process) method of directing operations of well site equipment at a well for executing a hydraulic fracturing (HF) job plan, comprising: extracting a response feature set from time-series pumping data gathered during pumping operations or during shutdown operations performed at other wells before the executing of the HF job plan for the well, wherein the response feature set includes controllable features; building a prediction model for potential well production, using the controllable features of the response feature set, that identifies a combination of the controllable features from the response feature set that impacts one or more key performance indicators (KPIs) for well production; selecting a KPI for the well, wherein the selected KPI for the well is the residual production or the actual production. At Step 2A, Prong I the recited limitations in part, alone or in combination, amount to steps that, under their broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “extracting” and “identifies” and “selecting” amounts to an analytical process recited at a high-level of generality that could be performed mentally based on previously gathered time-series data. The “building” recites constructing a model at a high-level of generality such that it is not precluded from being performed mentally. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: receiving a well site data set, where the well site data set is from one or more treatment cycles of the well during the executing of the HF job plan by the well site equipment; adjusting one or more controllable features at the well for the HF job plan when the selected KPI is not satisfied, wherein the one or more controllable features at the well are the identified combination of controllable features from the prediction model for the selected KPI and includes flow rate and proppant concentration; and executing the HF job plan using one or more of the adjusted flow rate or the adjusted proppant concentration that are adjusted in a direction according to the prediction model to improve the at least one selected KPI. The “receiving” amounts to insignificant data gathering since it is recited at a high-level of generality with regard to how the specific data is received (see MPEP 2106.05(g)). The “adjusting” and “executing” uses ordinary well site equipment operating in its ordinary capacity to achieve the desired result merely according to the one or more of the KPIs. Therefore, it amounts to reciting the words “apply it”. The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The “receiving” amount(s) to well understood, routine, conventional activity since it covers electronics means of receiving the desired data (see MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network”). The “adjusting” and “executing” uses ordinary well site equipment operating in its ordinary capacity to achieve the desired result merely “according to the one or more of the KPIs” and “using the selected one or more features”. The additional elements do not add anything more when considered in combination than when considered individually since the “adjusting” and “executing” does not affect how the data is gathered. For at least these reasons, the claim is not patent eligible.
Dependent claim 2 – 9 and 12 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s): in claim 2 wherein the response feature set is one or more of a pressure response data, flow distribution data, microseismic response data, and fracture dimension data; in claim 3 wherein the response feature set is one or more of first and second derivative of pressure with time, non-linear spline fits of pressure responses, proppant pumping, diversion cycle properties, and shape of entire treatment cycles; and in claim 4 wherein the response feature set for the shutdown operations are one or more of instantaneous shut-in pressure, pressure decline slope post pumping, or water hammer amplitude, frequency, and decay; and in claim 5 wherein the prediction model decouples a location of the well from the controllable features; and in claim 6 wherein the response feature set includes a response feature from historical KPI and time-series data from one or more past executed jobs; and in claim 7 wherein the extracting further comprises: aggregating uncontrollable features, wherein the uncontrollable features are uncontrollable for HF jobs; aggregating the controllable features, wherein the controllable features are controllable for HF jobs; and the response feature set comprises the uncontrollable features and the controllable features; and in claim 8 wherein for different ones of the one or more KPIs for well production, the prediction model identifies different combinations of the controllable features that impact; and in claim 12 wherein the extracting and the building utilize an ensemble model utilizing a single stage predictive model or a multiple stage predictive model, and wherein the ensemble model consolidates one or more modeling techniques and data sources. At Step 2A, Prong I the recited limitations, alone or in part, amounts to steps that that, under their broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “response feature set is” and “response feature set for the shutdown operations are” and “response feature set includes” and “response feature set comprises” further limit the parent claim “extracting” and “identifying”. The “aggregating” and “aggregating over” and “identigies” are recited at a high-level of generality such that they could be performed mentally. The “controllable features are” further limits the “aggregating” without precluding performance in the mind. The recited limitations in part, amount to step(s) that, under its broadest reasonable interpretation, cover mathematical concepts (see MPEP 2106.04(a)(2)(I)). For example, the “prediction decouples” and “prediction model utilizes” and “utilize an ensemble model” and “ensemble model consolidates one or more modeling techniques” further limits the parent claim “building” to use specific data recite and further models, said models encompassing mathematical models as recited in the parent claim. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since there are no further recited limitations. The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception since there are no further recited limitations. For at least these reasons, the claim is not patent eligible.
Dependent claim 9 - 11 and 15 – 18 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s): in claim 15 identifying a first event set, wherein the first event set comprises event time intervals, utilizing the first time-series pumping data set; and estimating a second event set, utilizing the second time-series pumping data set and the machine learning model; and in claim 16 wherein the first event set and the second event set further comprise event property data; and in claim 17 wherein the first event set and the second event set comprise event types of one or more of treatments, diversion cycles, san slug, minifrac, step-up, step-down, instantaneous shut-in pressure, breakdown, and screenouts, and in claim 18 wherein the first event set and the second event set further comprise user defined data. At Step 2A, Prong I the recited limitations, alone or in part, amounts to steps that that, under their broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “identifying” and “estimating” amounts to an analytical process recited at a high-level of generality that could be performed mentally based on data and/or models. The “first event set and the second event set further comprise” and “first event set and the second event set comprise” and “first event set and the second event set further comprise” further limit the parent claim “identifying” and “estimating” without precluding performance in the mind. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: in claim 9 wherein adjusting the flow rate is performed by increasing or decreasing, depending on the direction according to the prediction model, a pump rate of a fracturing fluid pump; in claim 10 wherein the adjusting is in real-time; in claim 11 wherein the receiving is real-time HF job data; and in claim 15 wherein the extracting the response feature set further comprises: receiving a first time-series pumping data set, wherein the first time-series pumping data set comprises one or more of treating pressure, slurry rate, and proppant concentration; receiving a second time-series pumping data set, wherein the second time-series pumping data set comprises one or more of treating pressure, slurry rate, and proppant concentration; training a machine learning model utilizing the first event set; and in claim 18 wherein the first time-series pumping data set and the second time-series pumping data further comprise user defined data. For example, the “adjusting” further limits the parent claim adjusting without specifying how to achieve the specific end result. The “receiving” amounts to insignificant data gathering since it is recited at a high-level of generality with regard to how the specific data is received (see MPEP 2106.05(g)). The “receiving is“ and “first time-series pumping data set and the second time-series pumping data further comprise” further limits the data received, without further limiting how it is received. The “training” amounts to reciting the words “apply it” since it amounts to using a computer to train a model at a high-level of generality. The claim is directed to an abstract idea.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the “adjusting” and “training” amounts to reciting the words “apply it”. The “receiving” and “receiving is” and “first time-series pumping data set and the second time-series pumping data further comprise” amount(s) to well understood, routine, conventional activity since it covers electronics means of receiving the desired data (see MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network”). The additional elements do not add anything more when considered in combination than when considered individually since the claim 15 “training” does not affect how the data is gathered in the “receiving”. For at least these reasons, the claim is not patent eligible.
Independent claim 19 recites a statutory category (i.e. a manufacture) non-transitory computer-readable medium having a series of operating instructions stored thereon to perform operations for directing well site equipment of a well, the operations comprising: extracting a response feature set from time-series pumping data gathered during prior pumping operations or during prior shutdown operations performed at other wells before the execution of the HF job plan at the well, wherein the response feature set includes controllable features; building a prediction model for potential well production, using the controllable features of the response feature set, that identifies a combination of the controllable features from the response feature set that impacts one or more key performance indicators (KPIs) for well production; selecting at least one KPI for the well from the one or more KPIs for well production, wherein the at least one KPI for the well includes residual production and actual production; determining if the at least one selected KPI for the well is satisfied based on the well site data set. At Step 2A, Prong I the recited limitations in part, alone or in combination, amount to steps that, under their broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “extracting” and “identifies” and “selecting” and “determining” amounts to an analytical process recited at a high-level of generality that could be performed mentally based on previously gathered time-series data. The “building” recites constructing a model at a high-level of generality such that it is not precluded from being performed mentally. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: having a non-transitory computer-readable medium having a series of operating instructions stored thereon that directs a data processing apparatus when executed thereby; and receiving a well site data set, where the well site data set is from one or more treatment cycles of the well during the execution of the HF job plan by the well site equipment; and adjusting one or more controllable features at the well for the HF job when the at least one selected KPI is not satisfied, wherein the one or more controllable features at the well are the identified combination of controllable features from the prediction model for the at least one selected KPI and include flow rate and proppant concentration; executing the HF job plan using one or more of the adjusted flow rate or the adjusted proppant concentration that are adjusted in a direction according to the prediction model to improve the at least one selected KPI. The “non-transitory computer-readable medium” are recited at a high-level of generality such that they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). The “receiving” amounts to insignificant data gathering since it is recited at a high-level of generality with regard to how the specific data is received (see MPEP 2106.05(g)). The “adjusting” and “executing” uses ordinary well site equipment operating in its ordinary capacity to achieve the desired result merely according to the one or more of the KPIs. Therefore, it amounts to reciting the words “apply it”. The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “non-transitory computer-readable medium” amount to no more than mere instructions to apply the judicial exception using generic computer components. The additional elements do not amount to a particular machine (see MPEP 2106.05(b)(I)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The “receiving” amount(s) to well understood, routine, conventional activity since it covers electronics means of receiving the desired data (see MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network”). The “adjusting” and “executing” uses ordinary well site equipment operating in its ordinary capacity to achieve the desired result merely “according to the one or more of the KPIs” and “using the selected one or more features”. The additional elements do not add anything more when considered in combination than when considered individually since the “executing” does not affect how the data is gathered. Further, the “receiving” requires nothing more than generic computer hardware and functions. For at least these reasons, the claim is not patent eligible.
Dependent claim 20 - 22 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s): in claim 2 wherein the response feature set is one or more of a pressure response data, flow distribution data, microseismic response data, and fracture dimension data; in claim 20 wherein the extracting further comprises: aggregating uncontrollable features, wherein the uncontrollable features are uncontrollable for HF jobs; aggregating the controllable features, wherein the controllable features are controllable for HF jobs; and the response feature set comprises the uncontrollable features and the controllable features;; and in claim 22 wherein the revising further comprises: determining a subset of the controllable features to be adjusted in the HF job plan to improve satisfaction of the KPI. At Step 2A, Prong I the recited limitations, alone or in part, amounts to steps that that, under their broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “response feature set comprises” further limit the parent claim “extracting” and “identifying”. For example, the “aggregating” and “determining” are recited at a high-level of generality such that they could be performed mentally. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: in claim 21 wherein the wherein adjusting the proppant concentration is performed in real-time by increasing an amount of proppant being added. For example, the “adjusting” further limits the parent claim adjusting without specifying how to achieve the specific end result. The claim is directed to an abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the “adjusting” and “training” amounts to reciting the words “apply it”. At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. For at least these reasons, the claim is not patent eligible.
Dependent claim 23 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s): in claim 15 identifying a first event set, wherein the first event set comprises event time intervals, utilizing the first time-series pumping data set; and estimating a second event set, utilizing the second time-series pumping data set and the machine learning model. At Step 2A, Prong I the recited limitations, alone or in part, amounts to steps that that, under their broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “identifying” and “estimating” amounts to an analytical process recited at a high-level of generality that could be performed mentally based on data and/or models. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: in claim 15 wherein the extracting the response feature set further comprises: receiving a first time-series pumping data set, wherein the first time-series pumping data set comprises one or more of treating pressure, slurry rate, and proppant concentration; receiving a second time-series pumping data set, wherein the second time-series pumping data set comprises one or more of treating pressure, slurry rate, and proppant concentration; training a machine learning model utilizing the first event set. For example, the “receiving” amounts to insignificant data gathering since it is recited at a high-level of generality with regard to how the specific data is received (see MPEP 2106.05(g)). The “training” amounts to reciting the words “apply it” since it amounts to using a computer to train a model at a high-level of generality. The claim is directed to an abstract idea.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the “training” amounts to reciting the words “apply it”. The “receiving” amount(s) to well understood, routine, conventional activity since it covers electronics means of receiving the desired data (see MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network”). The additional elements do not add anything more when considered in combination than when considered individually since the claim 15 “training” does not affect how the data is gathered in the “receiving”. For at least these reasons, the claim is not patent eligible.
Independent claim 24 recites a statutory category (i.e. a machine) to revise a hydraulic fracturing (HF) job plan to direct operations of well site equipment for a well, comprising: generate cleaned data sets by analyzing, cleaning, correcting, and removing outlier data elements from received data sets that are obtained from other wells before execution of the HF job plan at the well, wherein the received data sets include controllable features; build prediction models for potential well production, using controllable features of the received data set, that identify a combination of the controllable features from the received data set that impact key performance indicators (KPis) for well production, wherein the KPIs include residual production and actual production; selecting a KPI for the well, wherein the selected KPI for the well is the residual production or the actual production. At Step 2A, Prong I the recited limitations in part, alone or in combination, amount to steps that, under their broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “build” and “selecting” amounts to an analytical process recited at a high-level of generality that could be performed mentally based on previously gathered time-series data. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: one or more processors operable to; adjusting one or more controllable features at the well for the HF job plan when the selected KPI is not satisfied, wherein the one or more controllable features at the well are the identified combination of controllable features from the prediction model for the selected KPI and includes flow rate and proppant concentration; and executing the HF job plan using one or more of the adjusted flow rate or the adjusted proppant concentration that are adjusted in a direction according to the prediction model to improve the selected KPI. The “processors” amount to generic computer components, such that they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). The “adjusting” and “executing” uses ordinary well site equipment operating in its ordinary capacity to achieve the desired result merely according to the one or more of the KPIs. Therefore, it amounts to reciting the words “apply it”. The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the “processors” amount to no more than mere instructions to apply the judicial exception using generic computer components. The additional elements do not amount to a particular machine (see MPEP 2106.05(b)(I)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The “adjusting” and “executing” uses ordinary well site equipment operating in its ordinary capacity to achieve the desired result merely according to the final predictive model. The additional elements do not add anything more when considered in combination than when considered individually since the “adjusting” and “executing” does not affect how the “processors” operate and do not require anything more than generic computer functions. For at least these reasons, the claim is not patent eligible.
Dependent claim 26 recite(s) the same statutory category at Step 1 as the parent claim(s). Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further recites: a well controller, operable to execute the HF job plan using the one or more of the adjusted flow rate or the adjusted proppant concentration. The “execute” amounts to reciting the words “apply-it” since the manner in which the job plan is executed by the controller is recited at a high-level of generality. The “controller” does not amount to particular machine since it is only nominally used in the execution of the system since its structure is recited at a high-level of generality, and since it is used to implement insignificant extra-solution activity. The claim is directed to an abstract idea.
At Step 2B the claims do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the “execute” amounts to reciting the words “apply-it”, and the “controller” does not amount to a particular machine. The additional elements do not add anything more when considered in combination than when considered individually since the “execute” requires no more than generic computer components performing their normal functions. For at least these reasons, the claims are not patent eligible.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 12 and 19 - 22 are rejected under 35 U.S.C. 103 as being unpatentable over Kabanni et al. (WO 2016/076746) (henceforth “Kabannik (746)”) in view of Xu, W. (US 2014/0151033) (henceforth “Xu (033)”), and further in view of Dupont et al. (US 2018/0335538) (henceforth “Dupont (538)”), and further in view of Voneiff et al. “A Well Performance Model Based on Multivariate Analysis of Completion and Production Data from Horizontal Wells in the Montney Formation in British Columbia” (henceforth “Voneiff”). Kabannik (746) and Xu (033) and Dupont (538) and Voneiff are analogous art because they solve the same problem of simulating a hydraulic fracturing operation, and because they are in the same field of oil and gas exploration.
With regard to claim 1, Kabannik (746) teaches a method of directing operations of well site equipment at a well for executing a hydraulic fracturing (HF) job plan, comprising: (Kabannik (746) Figure 4A an hydraulic fracturing process is determined which can then be performed)
building a prediction model for potential well production, using controllable features of a response feature set; (Kabannik (746) Page 10, Lines 15 – 29 static formation model is generated from production data (prediction model), where the geological formation can include previous hydraulic fracturing process (using controllable features))
that identifies a combination of the controllable features from the response feature set that impacts one or more key performance indicators (KPIs) for well production; (Kabannik (746) Page 12, Lines 13 - 15 and Figure 3C the treatment parameters that are most sensitive are identified from the static model (identifying a combination of features from the response features), and Page 14, Lines 1 - 3 said treatment parameters are used to optimize the well production in view of treatment cost (to satisfy a KPI))
receiving a well site data set, where the well site data set is from one or more treatment cycles of the well; and (Kabannik (746) Figure 4A an initial treatment plan is evaluated using the dynamic formation model (receiving well site data set from treatment cycles))
selecting at least one KPI for the well from the one or more KPIs for well production; determining if the at least one selected KPI for the well is satisfied based on the well site data set; (Kabannik (746) Page 20, Lines 25 – 26 production indicators can be desirably utilized “The method may maximize production indicators of a well by minimizing the cost of a hydraulic fracturing process while maximizing production.”)
adjusting one or more controllable features at the well for the HF job when the at least one selected KPI is not satisfied, wherein the one or more controllable features at the well are the identified combination of controllable features from the prediction model for the at least one selected KPI; executing the HF job plan using one or more of the controllable parameters that are adjusted in a direction according to the prediction model to improve the at least one selected KPI, (Kabannik (746) Figure 4A a modified hydraulic fracturing process is performed according to production indicator meeting a goal)
Kabannik (746) does not appear to explicitly disclose: extracting a response feature set from time-series pumping data gathered during pumping operations or during shutdown operations performed at other wells before the executing of the HF job plan for the well, wherein the response feature set includes controllable features; that the receiving a well site data set is during the executing of the HF job plan by the well site equipment; wherein the one or more controllable features at the well includes flow rate and proppant concentration; that the executing the HF job plan is using one or more of the adjusted flow rate or the adjusted proppant concentration that are adjusted in a direction according to the prediction model.
However, Xu (033) teaches:
extracting a response feature set from time-series pumping data gathered during pumping operations or during shutdown operations (Xu (033) Figure 16 and Paragraph 76 and 125 the pumping rate and job parameters are measured)
performed before the executing of a HF job plan for a well, (Xu (033) Paragraph 174 previous fracturing jobs are analyzed in relation to future adjust jobs (before the executing of a HF job plan) “An analysis 1444 of the simulation, for example, by comparison of actual with simulated results to evaluate the fracture operation 1400. If satisfied, a production operation may be executed 1446. If not, job design may be analyzed 1448, and adjustments to one or more of the job parameters may be made 1450. The fracture operation may then be repeated.”)
wherein the response feature set includes controllable features; (Xu (033) Figure 5.1 flow rate of hydraulic fluid supplied is obtained over the time period (controllable features)
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receiving a well site data set, where the well site data set is from one or more treatment cycles of the well during the executing of the HF job plan by well site equipment (Xu (033) Paragraph 174 actual results obtained from the fracturing operation are compared with simulated results and used to adjust said fracturing operation “An analysis 1444 of the simulation, for example, by comparison of actual with simulated results to evaluate the fracture operation 1400. If satisfied, a production operation may be executed 1446. If not, job design may be analyzed 1448, and adjustments to one or more of the job parameters may be made 1450. The fracture operation may then be repeated.”)
selecting, using an identified combination of controllable features, one or more features of the well site data set to adjust when the well site data set fails to satisfy KPIs according to a prediction model; and executing the HF job plan according to the one or more of the KPIs by operating the well site equipment using the selected one or more features of the well site data set (Xu (033) Paragraph 174 fracturing operations are desirably adjusted (selecting one or more features to adjust) if actual results do not match simulated results (fail to satisfy KPI according to a prediction model), where one of ordinary skill in the art is motivated to optimize result effective variables, and then performing the fracturing operation (executing the HF job plan) “An analysis 1444 of the simulation, for example, by comparison of actual with simulated results to evaluate the fracture operation 1400. If satisfied, a production operation may be executed 1446. If not, job design may be analyzed 1448, and adjustments to one or more of the job parameters may be made 1450. The fracture operation may then be repeated.”)
wherein the selected one or more features are flow rate and proppant concentration; and wherein one or more controllable features at the well includes flow rate and proppant concentration; and executing an HF job plan using one or more of adjusted flow rate or adjusted proppant concentration that are adjusted in a direction according to a prediction model (Xu (033) Figure 14 and Paragraph 173 slurry flow rate and proppant concentration can be desirably adjusted as part of the fracturing operation in combination with a model (according to a prediction model) “1432-obtaining job parameters relating to stimulation parameters, such as pumping (e.g., flow rate, time), fluid (e.g., viscosity, density) and proppant parameters (e.g., dimension, material). The fracture operation 1400 also includes 1434-generating plots of formation parameters 1436 (e.g, slurry rate and proppant concentration over time) from the obtained parameters.”
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It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) with the steps of extracting well pressure information and treatment parameters during a hydraulic fracturing operations disclosed by Xu (033). One of ordinary skill in the art would have been motivated to make this modification in order to obtain response information about a reservoir in order to optimize fracturing operations (Xu (033) Paragraph 129).
Kabannik (746) in view of Xu (033) does not appear to explicitly disclose: that the extracted response feature set from time-series pumping data gathering during pumping operations or during shutdown operating is performed at other wells;
However, Dupont (538) teaches:
extracting a response feature set from time-series pumping data gathered during pumping operations or during shutdown operations performed at other wells before the executing of a HF job plan for a well, (Dupont (538) Paragraph 52 various data from factors associated with existing wells are received (at other wells) “FIG. 3 shows an example of a method 300 that includes a reception block 304 for receiving data where the data can include data for a plurality of factors associated with a plurality of wells”, and Paragraph 34 the factors include pumping data of a stimulation treatment (time-series pumping data gathered during pumping operations) “As an example, a stimulation treatment may include pumping fluid into a formation via a wellbore at pressure and rate sufficient to cause a fracture to open”, and Figure 2 modeling is based on existing wells in the same field, which can then be applied to new wells including stimulation treatments (before executing of a HF job plan for a well)
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It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) in view of Xu (033) with the ensemble modelling disclosed by Dupont (538). One of ordinary skill in the art would have been motivated to make this modification in order to obtain more accurate modeling results (Dupont (538) Paragraph 51 “With demand for unconventional well products expected to rise worldwide, there is considerable value in faster and more accurate prediction methods”)
Kabannik (746) in view of Xu (033), and further in view of Dupont (538) does not appear to explicitly disclose: wherein the at least one selected KPI for the well includes residual production and actual production.
However, Voneiff teaches:
a KPIs is residual production and actual production (Voneiff Page 16, Top a residual is used to evaluate predicted production after performing pumping operations “A residual plot graphs an independent variable on the x-axis, and the difference between each actual and predicted point on the y-axis. Sometimes the regression model predicts too high, sometimes too low. Ideally, the residual will be normally distributed around the zero value with no apparent trend along the x-axis … This tells us the model can likely be improved upon by making some changes to the model equation,”)
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) in view of Xu (033), and further in view of Dupont (538) with the model analysis using residuals disclosed by Voneiff. One of ordinary skill in the art would have been motivated to make this modification in order to analyze the results of models for well production (Voneiff Page 16).
With regard to claim 19, it recites substantially similar steps as in claim 1, which is taught by Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff. Claim 19 further recites: a computer program product having a series of operating instructions stored on a non-transitory computer-readable medium that directs a data processing apparatus when executed thereby to perform operations to design a hydraulic fracturing (HF) job plan to direct operations of well site equipment of a well, having operations comprising those in claim 1.
Kabannik (746) teaches:
teaches a computer program product having a series of operating instructions stored on a non-transitory computer-readable medium that directs a data processing apparatus when executed thereby to perform operations to design a hydraulic fracturing (HF) job plan to direct operations of well site equipment of a well, having operations comprising: (Kabannik (746) Page 18, Lines 23 – 27 steps can be performed by computer readable program stored on non-transitory computer readable storage medium)
With regard to claim 2, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 1, and further teaches:
wherein the response feature set is one or more of a pressure response data, flow distribution data, microseismic response data, and fracture dimension data. (Xu (033) Paragraph 119 pressure measurements are obtained during well monitoring (response feature set are instantaneous pressure), and Paragraph 132 properties of the well can be obtained during a shut-in period (shut-in pressure))
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) with the steps of extracting well pressure information and treatment parameters during a hydraulic fracturing operations disclosed by Xu (033). One of ordinary skill in the art would have been motivated to make this modification in order to obtain response information about a reservoir in order to optimize fracturing operations (Xu (033) Paragraph 129)
With regard to claim 3, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 1, and further teaches:
wherein the response feature set is one or more of first and second derivative of pressure with time, non-linear spline fits of pressure responses, proppant pumping, diversion cycle properties, and shape of entire treatment cycles. (Xu (033) Paragraph 5 the fluid injected can contain proppants (proppant pumping), and the pumping rate is measured (response feature set is))
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) with the steps of extracting treatment parameters during a hydraulic fracturing operations disclosed by Xu (033). One of ordinary skill in the art would have been motivated to make this modification in order to obtain response information about a reservoir in order to optimize fracturing operations (Xu (033) Paragraph 129)
With regard to claim 4, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 1, and further teaches:
wherein the response feature set for the shutdown operations are one or more of instantaneous shut-in pressure, pressure decline slope post pumping, or water hammer amplitude, frequency, and decay. (Xu (033) Paragraph 119 pressure measurements are obtained during well monitoring (response feature set are instantaneous pressure), and Paragraph 132 properties of the well can be obtained during a shut-in period (shut-in pressure))
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) with the steps of extracting well pressure information and treatment parameters during a hydraulic fracturing operations disclosed by Xu (033). One of ordinary skill in the art would have been motivated to make this modification in order to obtain response information about a reservoir in order to optimize fracturing operations (Xu (033) Paragraph 129)
With regard to claim 5, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 1, and further teaches:
wherein the prediction model decouples a location of the well from the controllable features. (Dupont (538) Paragraph 86 location coordinates can be a separate factor in a production model “As an example, factors can include for a well, its initial production rate, its azimuth, its location (e.g., longitude and latitude, etc.), its total fluid, its total proppant, its TVD and its lateral length.”, and Figure 2 modeling is based on existing wells in the same field, which can then be applied to new wells
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It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) in view of Xu (033) with the ensemble modelling disclosed by Dupont (538). One of ordinary skill in the art would have been motivated to make this modification in order to obtain more accurate modeling results (Dupont (538) Paragraph 51 “With demand for unconventional well products expected to rise worldwide, there is considerable value in faster and more accurate prediction methods”)
With regard to claim 6, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 1, and further teaches:
wherein the response feature set includes a response feature from historical KPI and (Kabannik (746) Line 14, Lines 1 - 3 the production indicator is maximized and compares to the previous production indicator)
time-series data from one or more past executed jobs. (Xu (033) the sensor data can be historical data from previous jobs)
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) with the steps of extracting well pressure information and treatment parameters during a hydraulic fracturing operations disclosed by Xu (033). One of ordinary skill in the art would have been motivated to make this modification in order to obtain response information about a reservoir in order to optimize fracturing operations (Xu (033) Paragraph 129).
With regard to claim 7 and 20, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 1 and 19, and further teaches:
aggregating uncontrollable features, wherein the uncontrollable features are uncontrollable for HF jobs; (Xu (755) Figure 5.1 microseismic events producing from fracturing operations are processed over time intervals (aggregating uncontrollable features)
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aggregating the controllable features, wherein the controllable features are controllable for HF jobs; and (Xu (033) Figure 5.1 flow rate of hydraulic fluid supplied is obtained over the time period (aggregating controllable features)
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the response feature set comprises the uncontrollable features and the controllable features. (Xu (033) Figure 5.1 a model is obtained based on the microseismic events and flow rates of hydraulic fluids
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It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) with the steps of hydraulic fracture modeling based on microseismic events and observed hydraulic treatment flow rates disclosed by Xu (033). One of ordinary skill in the art would have been motivated to make this modification in order to obtain response information about a reservoir in order to optimize fracturing operations (Xu (033) Paragraph 129).
With regard to claim 8 and 21, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 7 and 20, and further teaches:
wherein for different ones of the one or more KPIs for well production, the prediction model identifies different combinations of the controllable features that impact (Kabannik (746) Page 13, Lines 20 – 21 various production indicators can be calculated, where each could reasonably have their own identified controllable features)
With regard to claim 9 and 22, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 1 and 20, and further teaches:
wherein adjusting the flow rate is performed by increasing or decreasing, depending on the direction according to the prediction model, a pump rate of a fracturing fluid pump (Kabannik (746) Page 19, Lines 27 to Page 20, Line 1 pump rate of a hydraulic fracturing process can be desirably optimized (increasing or decreasing) ”To perform the parametric study, each parameter, e.g. the pump rate and pulse time, was varied in”)
With regard to claim 10, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 7, and further teaches:
wherein the adjusting is in real-time (Kabannik (746) Figure 4C and Page 1, Line 1 the adjustments of the hydraulic fracturing process pertain to real-time optimizations)
With regard to claim 11, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 1, and further teaches:
wherein the receiving is real-time HF job data. (Xu (033) Paragraph 141 the receiving data and related modeling operations are performed in real-time for interactive optimization with a user)
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) with the steps of hydraulic fracture modeling based on microseismic events and observed hydraulic treatment flow rates disclosed by Xu (033). One of ordinary skill in the art would have been motivated to make this modification in order to obtain response information about a reservoir in order to optimize fracturing operations (Xu (033) Paragraph 129).
With regard to claim 12, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 1, and further teaches:
wherein the extracting and the building utilize an ensemble model utilizing a single stage predictive model or a multiple stage predictive model, and wherein the ensemble model consolidates one or more modeling techniques and data sources. (Dupont (538) Paragraph 81 a weighted sample of models can be used to form the prediction, where using any desired weighted combination of a plurality of fracture design programs of analytical simulators Kabannik (746) has predictable results “In such an example, ensembles of these trees can be built and a weighted sample taken as a prediction ( e.g., a predicted value).”)
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) in view of Xu (033) with the ensemble modelling disclosed by Dupont (538). One of ordinary skill in the art would have been motivated to make this modification in order to obtain more accurate modeling results (Dupont (538) Paragraph 51 “With demand for unconventional well products expected to rise worldwide, there is considerable value in faster and more accurate prediction methods”)
Claims 15 – 18 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff, and further in view of Ma et al. (WO 2014/061993) (henceforth “Ma (993)”). Kabannik (746) and Xu (033) and Dupont (538) and Voneiff and Ma (993) are analogous art because they solve the same problem of simulating a hydraulic fracturing operation, and because they are in the same field of oil and gas exploration.
With regard to claim 15 and 23, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 1 and 19, and further teaches:
receiving a first time-series pumping data set, wherein the first time-series pumping data set comprises one or more of treating pressure, slurry rate, and proppant concentration; receiving a second time-series pumping data set, wherein the second time-series pumping data set comprises one or more of treating pressure, slurry rate, and proppant concentration; (Xu (033) Paragraph 119 pressure measurements are obtained during well monitoring of one or more hydraulic fracturing processes (treating pressure)
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) with the steps of extracting well pressure information and treatment parameters during a hydraulic fracturing operations disclosed by Xu (033). One of ordinary skill in the art would have been motivated to make this modification in order to obtain response information about a reservoir in order to optimize fracturing operations (Xu (033) Paragraph 129).
Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff does not appear to explicitly disclose wherein the extracting the response feature set further comprises: identifying a first event set, wherein the first event set comprises event time intervals, utilizing the first time-series pumping data set; training a machine learning model utilizing the first event set; and estimating a second event set, utilizing the second time-series pumping data set and the machine learning model.
However, Ma (993) teaches:
receiving a first time-series pumping data set, wherein the first time-series pumping data set comprises one or more of treating pressure, slurry rate, and proppant concentration; receiving a second time-series pumping data set, wherein the second time-series pumping data set comprises one or more of treating pressure, slurry rate, and proppant concentration; (Ma (993) Page 3, Lines 1 - 3 pressure (treating pressure) during fracturing treatment can be continuously monitored (a first and second time-series))
identifying a first event set, wherein the first event set comprises event time intervals, utilizing the first time-series pumping data set; (Ma (993) Page 13, Lines 5 -14 the hydraulic fracturing operation (utilizing the first time-series pumping data set) produces microseismic events which have their locations derived (identifying a first event set))
training a machine learning model utilizing the first event set; and (Ma (993) Figure 7 - 8 and Page 11, Lines 25 – 27 a model is trained using a computer (a machine learning model) based on the microseismic events (utilizing the first event set))
estimating a second event set, utilizing the second time-series pumping data set and the machine learning model. (Ma (993) Page 13, Lines 27 - 30 the prediction of fractures is based on the fitted fracture planes (utilizing the machine learning model) and a pumping schedule which can be desirably defined (utilizing the second time-series pumping data set), and Page 12, Lines 18 – 20 the fracture propagation is directly related to the microseismic events (estimating a second event set))
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff with the microseismic event modeling and prediction disclosed by Ma (993). One of ordinary skill in the art would have been motivated to make this modification in order to control the propagation of fractures in view of a specific hydraulic fracturing treatment (Ma (993) Page 12, Lines 26 – 28 pumping schedule can be modified to desirably control the fracture propagation).
With regard to claim 16, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff, and further in view of Ma (993) teaches all the elements of the parent claim 15, and further teaches:
wherein the first event set and the second event set further comprise event property data. (Ma (993) Page 13, Lines 5 -14 the hydraulic fracturing operation produces microseismic events which have their locations derived and projected on a fracture plane and have a time-dependent distributed (event property data))
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff with the microseismic event modeling and prediction disclosed by Ma (993). One of ordinary skill in the art would have been motivated to make this modification in order to control the propagation of fractures in view of a specific hydraulic fracturing treatment (Ma (993) Page 12, Lines 26 – 28 pumping schedule can be modified to desirably control the fracture propagation).
With regard to claim 17, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff, and further in view of Ma (993) teaches all the elements of the parent claim 15, and further teaches:
wherein the first event set and the second event set comprise event types of one or more of treatments, diversion cycles, san slug, minifrac, step-up, step-down, instantaneous shut-in pressure, breakdown, and screenouts. (Ma (993) Figure 8 events are classified by type and are from treatments (event types of treatments or minifrac))
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff with the microseismic event modeling and prediction disclosed by Ma (993). One of ordinary skill in the art would have been motivated to make this modification in order to control the propagation of fractures in view of a specific hydraulic fracturing treatment (Ma (993) Page 12, Lines 26 – 28 pumping schedule can be modified to desirably control the fracture propagation).
With regard to claim 18, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff, and further in view of Ma (993) teaches all the elements of the parent claim 15, and further teaches:
wherein the first time-series pumping data set and the second time-series pumping data further comprise user defined data, and (Ma (993) Page 5, Lines 18 - 20 the data used for the modeling is made available by a user (pumping data comprise user defined data) “In this fashion, the user is able to retrieve the well data and make it available for simulation of flow in a network of fractures”)
wherein the first event set and the second event set further comprise user defined data. (Ma (993) Page 11, Lines 4 – 8 the events are classified according to some user-defined categories “Those events proximate to the envelopes may be classified as tip-related. More specifically, the events within, say, 10% of envelope 702 may be classified as length-tip events, while those events within 10% of envelopes 704 and 706 may be classified as height-tip events. These classifications need not be mutually exclusive.”)
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff with the microseismic event modeling and prediction disclosed by Ma (993). One of ordinary skill in the art would have been motivated to make this modification in order to control the propagation of fractures in view of a specific hydraulic fracturing treatment (Ma (993) Page 12, Lines 26 – 28 pumping schedule can be modified to desirably control the fracture propagation).
With regard to 22, Kabannik (746) in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 1 and 20, and further teaches:
wherein the wherein adjusting the proppant concentration is performed in real-time by increasing an amount of proppant being added (Kabannik (746) Figure 4C and Page 1, Line 1 the adjustments of the hydraulic fracturing process pertain to real-time optimizations)
Claim 24 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Kabannik (746) in view of Mu et al. (US 62755803 as referenced from US 11913446, with section references to US 62755803) (henceforth “Mu (62755803)”), and further in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff. Kabannik (746) and Mu (62755803) and Xu (033) and Dupont (538) and Voneiff are analogous art because they solve the same problem of simulating a hydraulic fracturing operation, and because they are in the same field of oil and gas exploration.
With regard to claim 24, Kanabbik (746) teaches a system to revise a hydraulic fracturing (HF) job plan to direct operations of well site equipment for a well, comprising: one or more processors operable to: (Kabannik (746) Figure 4A an hydraulic fracturing process is iteratively updated and then performed, and Page 18, Lines 4 – 27 steps can be performed by computer readable program stored on non-transitory computer readable storage medium and executed by a processor)
generate data sets (Kabannik (746) Page 10, Lines 26 – 29 data is obtained from production wells (data analyzer operable to generate data sets, and Figure 3A and 4A hydraulic fracturing parameters are determined (generate data sets))
build prediction models for potential well production, using controllable features of received data set, (Kabannik (746) Page 10, Lines 15 – 29 static formation model is generated from production data (prediction model), where the geological formation can include previous hydraulic fracturing process (using controllable features))
that identify a combination of the controllable features from the received data set that impact key performance indicators (KPIs) for well production
(Kabannik (746) Page 12, Lines 13 - 15 and Figure 3C the treatment parameters that are most sensitive are identified from the static model (identifying a combination of features from the response features), and Page 14, Lines 1 - 3 said treatment parameters are used to optimize the well production in view of treatment cost (to satisfy a KPI))
selecting a KPI for the well, wherein the selected KPI for the well is residual production or actual production; (Kabannik (746) Page 20, Lines 25 – 26 production indicators can be desirably utilized “The method may maximize production indicators of a well by minimizing the cost of a hydraulic fracturing process while maximizing production.”)
executing the HF job plan using one or more of the controllable parameters that are adjusted in a direction according to the prediction model to improve the at least one selected KPI, (Kabannik (746) Figure 4A a modified hydraulic fracturing process is performed according to production indicator meeting a goal)
Kabannik (746) does not appear to explicitly disclose: generate cleaned data sets by analyzing, cleaning, correcting, and removing outlier data elements from received data sets; and that the generated data sets are of the received data sets.
However, Mu (62755803) teaches:
generate cleaned data sets by analyzing, cleaning, correcting, and removing outlier data elements from received data sets; and that generated data sets are of the received data sets (Mu (62755803) Paragraph 51 and 26 a filter and average process (to generate cleaned data sets by) removes outlier pressure measurements which can be averaged (analyzing, cleaning, correcting, and removing outlier data elements), where the pressures of Kabannik (746) could be operated on in a predictable manner)
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) with the steps of filtering and averaging received pressure data disclosed by Mu (62755803). One of ordinary skill in the art would have been motivated to make this modification in order to enhance the quality of received data when multiple measurements are available (Mu (62755803) Paragraph 26)
Kabannik (746) in view of Mu (62755803) does not appear to explicitly disclose: the cleaned data sets that are obtained from other wells before execution of the HF job plan at the well, wherein the received data sets include controllable features; wherein the one or more controllable features at the well includes flow rate and proppant concentration
However, Xu (033) teaches:
data sets that are obtained before execution of a HF job plan at a well, (Xu (033) Paragraph 174 previous fracturing jobs are analyzed in relation to future adjust jobs (before the executing of a HF job plan) “If satisfied, a production operation may be executed 1446. If not, job design may be analyzed 1448, and adjustments to one or more of the job parameters may be made 1450. The fracture operation may then be repeated.”)
wherein the received data sets include controllable features; (Xu (033) Figure 5.1 flow rate of hydraulic fluid supplied is obtained over the time period (controllable features)
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wherein one or more controllable features at the well includes flow rate and proppant concentration (Xu (033) Figure 14 and Paragraph 173 slurry flow rate and proppant concentration can be desirably adjusted as part of the fracturing operation in combination with a model (according to a prediction model) “1432-obtaining job parameters relating to stimulation parameters, such as pumping (e.g., flow rate, time), fluid (e.g., viscosity, density) and proppant parameters (e.g., dimension, material). The fracture operation 1400 also includes 1434-generating plots of formation parameters 1436 (e.g, slurry rate and proppant concentration over time) from the obtained parameters.”
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It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) in view of Mu (62755803) with the steps of extracting well pressure information and treatment parameters during a hydraulic fracturing operations disclosed by Xu (033). One of ordinary skill in the art would have been motivated to make this modification in order to obtain response information about a reservoir in order to optimize fracturing operations (Xu (033) Paragraph 129).
Kabannik (746) in view of Mu (62755803), and further in view of Xu (033) does not appear to explicitly disclose: that the received data sets are obtained from other wells.
However, Dupont (538) teaches:
generate data sets from received data sets that are obtained from other wells before execution of an HF job plan, wherein the received data sets include controllable features (Dupont (538) Paragraph 52 various data from factors associated with existing wells are received (received data sets obtained from other wells) “FIG. 3 shows an example of a method 300 that includes a reception block 304 for receiving data where the data can include data for a plurality of factors associated with a plurality of wells”, and Paragraph 34 the factors include pumping data of a stimulation treatment (include controllable features) “As an example, a stimulation treatment may include pumping fluid into a formation via a wellbore at pressure and rate sufficient to cause a fracture to open”, and Figure 2 modeling is based on existing wells in the same field, which can then be applied to new wells including stimulation treatments (before executing of a HF job plan)
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290
550
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)
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) in view of Mu (62755803), and further in view of Xu (033) with the ensemble modelling disclosed by Dupont (538). One of ordinary skill in the art would have been motivated to make this modification in order to obtain more accurate modeling results (Dupont (538) Paragraph 51 “With demand for unconventional well products expected to rise worldwide, there is considerable value in faster and more accurate prediction methods”)
Kabannik (746) in view of Mu (62755803), and further in view of Xu (033), and further in view of Dupont (538) does not appear to explicitly disclose: wherein the KPIs include residual production and actual production.
However, Voneiff teaches:
a KPIs is residual production and actual production (Voneiff Page 16, Top a residual is used to evaluate predicted production after performing pumping operations “A residual plot graphs an independent variable on the x-axis, and the difference between each actual and predicted point on the y-axis. Sometimes the regression model predicts too high, sometimes too low. Ideally, the residual will be normally distributed around the zero value with no apparent trend along the x-axis … This tells us the model can likely be improved upon by making some changes to the model equation,”)
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) in view of Mu (62755803), and further in view of Xu (033), and further in view of Dupont (538) with the model analysis using residuals disclosed by Voneiff. One of ordinary skill in the art would have been motivated to make this modification in order to analyze the results of models for well production (Voneiff Page 16).
With regard to claim 26, Kabannik (746) in view of Mu (62755803), and further in view of Xu (033), and further in view of Dupont (538), and further in view of Voneiff teaches all the elements of the parent claim 24, and further teaches:
a well controller, operable to execute the HF job plan, (Kabannik (746) Figure 4A a plurality of dynamic formation models are formed with updated hydraulic fracturing processes, and final one is used for updating a treatment schedule, which is used to perform the hydraulic fracturing process (a well controller operable to, and execute the HF job plan))
using the one or more of the adjusted flow rate or the adjusted proppant concentration (Xu (033) Figure 14 and Paragraph 173 slurry flow rate and proppant concentration can be desirably adjusted as part of the fracturing operation in combination with a model
It would have been obvious to one of ordinary skill in the art to combine the method of optimizing a hydraulic fracturing job using predictive modeling disclosed by Kabannik (746) in view of Mu (62755803) with the steps of extracting well pressure information and treatment parameters during a hydraulic fracturing operations disclosed by Xu (033). One of ordinary skill in the art would have been motivated to make this modification in order to obtain response information about a reservoir in order to optimize fracturing operations (Xu (033) Paragraph 129).
Examiner General Comments
With regard to the prior art rejection(s), any cited portion of the relied upon reference(s), either by pointing to specific sections or as quotations, is intended to be interpreted in the context of the reference(s) as a whole as would be understood by one of ordinary skill in the art. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention since the entire reference is considered to provide disclosure relating to the cited portions. Further, the claims and only the claims form the metes and bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent and spirit of compact prosecution.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALFRED H. WECHSELBERGER whose telephone number is (571)272-8988. The examiner can normally be reached M - F, 10am to 6pm.
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/ALFRED H. WECHSELBERGER/ExaminerArt Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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