Real-time pumping hydraulic fracturing monitoring using low-frequency acoustic data, machine learning, and hydraulic fracturing simulator

Abstract

Embodiments presented provide for hydraulic fracture monitoring. The monitoring uses real-time pumping data as well as low-frequency acoustic data that is fed into a machine learning algorithm to precisely determine hydraulic fracturing monitoring.

Description

REAL-TIME PUMPING HYDRAULIC FRACTURING MONITORING USING LOW-FREQUENCY ACOUSTIC DATA, MACHINE LEARNING, AND HYDRAULIC FRACTURING SIMULATORCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 678,125, entitled "REAL-TIME PUMPING HYDRAULIC FRACTURING MONITORING USING LOW-FREQUENCY ACOUSTIC DATA, MACHINE LEARNING, AND HYDRAULIC FRACTURING SIMULATOR" filed August 01 , 2024, the disclosure of which is hereby incorporated herein by reference in its entirety.FIELD OF THE DISCLOSURE

[0002] Aspects of the disclosure relate to hydraulic fracture monitoring. More specifically, aspects of the disclosure relate to real-time fracture monitoring using low- frequency acoustic data and machine learning algorithms.BACKGROUND

[0003] Hydraulic fracturing operations, as a widely used method for well stimulation, usually consists of three main steps: project design, laboratory tests, and execution of the in-field hydraulic fracturing with or without monitoring via secondary means for validation purposes (e.g., micro-seismic monitoring, cross-well strain monitoring from a nearby well or wells, etc.). Generally, project design is based on previous experience, successful jobs in the same area, recommendations from the oil field service company, and standardized rules. Laboratory testing is an important phase to better understand formation fluid properties in combination with the stimulation fluid and all types of the involved additives. Execution of the in-field hydraulic fracturing is a process, where the pre-designed project is implemented in correspondence with a designated pumping schedule. The pumping schedule may include lengths of time for pumping as well as rates, pressures and proppant concentrations.

[0004] Project design processes may be analyzed and refined through computer simulations. Lab testing may also be linked to the project design processes. Job design execution remains less dependent from the simulations of fracture propagating models, as there is a lack of data to be used in real-time for simulations and real-time schedule adjustments.

[0005] While the three-step process does have advantages, conventional methods and apparatus do not provide all of the necessary efficiency needed. For example, pre-project planning provides advantages over merely going to the field and performing operations; however, there is a lack of analysis capability using real-time data.

[0006] There is a need to provide an apparatus and methods that are as easy to operate as conventional analysis; however, an improvement is necessary to be able to use realtime data.

[0007] There is a further need to provide apparatus and methods that do not have the drawbacks discussed above, namely the inability to use real-time data as well as improvements in machine learning capabilities.

[0008] There is a still further need to reduce economic costs associated with operations and apparatus described above with conventional tools and overall economic costs associated with hydraulic fracture monitoring.SUMMARY

[0009] So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized below, may be had by reference to embodiments, some of which are illustrated in the drawings. It is to be noted that the drawings illustrate only typical embodiments of this disclosure and are therefore not to be considered limiting of its scope, for the disclosure may admit to other equally effective embodiments without specific recitation. Accordingly, the following summary provides just a few aspects of thedescription and should not be used to limit the described embodiments to a single concept.

[0010] In one example embodiment, a method for analyzing stimulation fluid flow distribution one of between and within perforation clusters during hydraulic fracturing is disclosed. The method may comprise obtaining proppant rate concentration data related to a wellbore. The method may further comprise using a machine learning algorithm to accept the proppant rate concentration data and predict a most probable fluid flow distribution between at least two clusters. The method may further comprise obtaining measured strain rate data related to geological stratum near the wellbore. The method may further comprise obtaining measured stress rate data related to the geological stratum near the wellbore. The method may further comprise using a strain rate inversion model to calculate strain rate simulation data and stress rate simulation data. The method may further com prise matching the calculated strain rate simulation data to the measured strain rate data and matching the calculated stress rate simulation data to the measured stress rate data and when the matching is below a threshold value proceeding with the method and when the matching is above a threshold value recalculating using the strain rate inversion model. The method may further comprise outputting results of both the machine learning algorithm and the strain rate inversion model.

[0011] In another example embodiment, an article of manufacture comprising a nonvolatile memory is disclosed. In this embodiment, the non-volatile memory is configured to have a list of instructions configured to be read by a computer, the list of instructions containing a method for analyzing stimulation fluid flow distribution one of between and within perforation clusters during hydraulic fracturing, The method may comprise obtaining proppant rate concentration data related to a wellbore. The method may further comprise using a machine learning algorithm to accept the proppant rate concentration data and predict a most probable fluid flow distribution between at least two clusters. The method may further comprise obtaining measured strain rate data related to geological stratum near the wellbore. The method may further comprise obtaining measured stressrate data related to the geological stratum near the wellbore. The method may further comprise using a strain rate inversion model to calculate strain rate simulation data and stress rate simulation data. The method may further comprise matching the calculated strain rate simulation data to the measured strain rate data and matching the calculated stress rate simulation data to the measured stress rate data and when the matching is below a threshold value proceeding with the method and when the matching is above a threshold value recalculating using the strain rate inversion model. The method may further comprise outputting results of both the machine learning algorithm and the strain rate inversion model.BRIEF DESCRIPTION OF THE DRAWINGS

[0012] So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the drawings. It is to be noted; however, that the appended drawings illustrate only typical embodiments of this disclosure and are therefore not be considered limiting of its scope, for the disclosure may admit to other equally effective embodiments.

[0013] FIG. 1 is a schematic dashboard view showing the measured well depth versus cluster acceptance efficiency versus designed and actual proppant consumption and fractures geometry.

[0014] FIG. 2 is a schematic workflow of DAS-IQ method assigning flow rates to clusters.

[0015] FIG. 3 is a typical arrangement of remote well strain rate monitoring with fiber optics.

[0016] FIG. 4 is a depiction of the result of the DAS-IQ workflow application.

[0017] FIG. 5 is a method of real-time pumping hydraulic fracturing monitoring using low frequency acoustic data, machine learning, and a hydraulic fracturing simulator.

[0018] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures (“FIGS”). It is contemplated that elements disclosed in one embodiment may be beneficially utilized on other embodiments without specific recitation.DETAILED DESCRIPTION

[0019] In the following, reference is made to embodiments of the disclosure. It should be understood; however, that the disclosure is not limited to specific described embodiments. Instead, any combination of the following features and elements, whether related to different embodiments or not, is contemplated to implement and practice the disclosure. Furthermore, although embodiments of the disclosure may achieve advantages over other possible solutions and / or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the disclosure. Thus, the following aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the claims except where explicitly recited in a claim. Likewise, reference to “the disclosure” shall not be construed as a generalization of inventive subject matter disclosed herein and should not be considered to be an element or limitation of the claims except where explicitly recited in a claim.

[0020] Although the terms first, second, third, etc., may be used herein to describe various elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms may be only used to distinguish one element, components, region, layer or section from another region, layer or section. Terms such as “first”, “second” and other numerical terms, when used herein, do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer or section discussed hereincould be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.

[0021] When an element or layer is referred to as being “on,” “engaged to,” “connected to,” or “coupled to” another element or layer, it may be directly on, engaged, connected, coupled to the other element or layer, or interleaving elements or layers may be present. In contrast, when an element is referred to as being “directly on,” “directly engaged to,” “directly connected to,” or “directly coupled to” another element or layer, there may be no interleaving elements or layers present. Other words used to describe the relationship between elements should be interpreted in a like fashion. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed terms.

[0022] Some embodiments will now be described with reference to the figures. Like elements in the various figures will be referenced with like numbers for consistency. In the following description, numerous details are set forth to provide an understanding of various embodiments and / or features. It will be understood; however, by those skilled in the art, that some embodiments may be practiced without many of these details, and that numerous variations or modifications from the described embodiments are possible. As used herein, the terms “above” and “below”, “up” and “down”, “upper” and “lower”, “upwardly” and “downwardly”, and other like terms indicating relative positions above or below a given point are used in this description to more clearly describe certain embodiments.

[0023] Aspects of the disclosure provide for a method for on-line / real-time fracture growth monitoring, slurry rate distribution over clusters determination, and, based on this data, the ability to make real-time field decisions. Monitoring of hydraulic fracturing projects at a wellsite is widely done using a pressure gauge located either at surface or downhole. Many times, a single pressure gauge is the only component used for such monitoring.

[0024] Micro-seismic acquisitions may be used to monitor evolution of stimulated rock volume; however, this is not the common practice. Thus, with multi-stage horizontal well fracturing, there is a strong need to know how perforation clusters (there are usually 5-10 per stage) accept slurry, how each induced fracture evolves, and what measures can be taken in real-time to optimize the treatment. It is further desired to be able to evaluate these measures with the use of hydraulic fracture modeling tools, preferably in real-time, to enable changes to be implemented as quickly as possible to optimize stimulation output.

[0025] In aspects of the disclosure, a method is presented where worksite operation data are incorporated with real-time data acquisition. This results in the leveraging of optical fiber data through the use of machine learning. A result of this analysis is the development of quantitative information about individual downhole slurry flows per cluster. The calculated slurry rates, as well as proppant concentrations, are used in hydraulic fracturing modeling tools to simulate individual fracture development. This method may be used continuously until the end of the project in the field.

[0026] As a result, a real-time product is produced to calculate slurry flow rate distribution per perforation and to provide a cluster quantitative assessment at a multistage hydraulic fracturing job. This technology allows real-time decisions for making optimal stage stimulations. Such method decisions may be to pump chemical diverters (to involve more clusters into the stimulation), change clean fluid rates or proppant concentrations, or to stop pumping altogether. The information received during stage stimulation may be used for an immediate design change.

[0027] Referring to FIG. 1 , the cepstrum from tube waves data analysis is illustrated, which shows how different perforation clusters accept fluid. This data may be placed into software in order to simulate factures in real-time.

[0028] A weak point of this approach is that the tube waves monitoring technology, when leveraging the ‘water hammer’ type effect without the use of optical fiber monitoring, doesnot provide sufficient accuracy in determining fluid acceptance per cluster. In this instance, neither exact volumes nor exact location of perforation clusters that accept fluid can be detected if they are close to each other. Also, the technology allows diversion evaluation between the nearest stages and generally suffers from comparatively low resolution to evaluate intra-stage diversion.

[0029] Referring to FIG. 1 , a schematic dashboard view showing the measured well depth versus cluster acceptance efficiency versus designed and actual proppant consumption and fractures geometry is presented. Variations of the dashboard view of the data generated and displayed are possible.

[0030] A solution to the above drawback is to place an optical fiber cable inside the stimulation well (or in other embodiments, to monitor fluid flow from a nearby well). The additional information may be used to locate active perforations that accept slurry. Distributed Acoustic Sensing (DAS) signal processing leveraging various interpretation algorithms provides both qualitative and quantitative information about the individual flows through perforations. Unfortunately, interpretation algorithms may be rather complex while requiring high-quality data with as little noise as possible.

[0031] Aspects of the disclosure use a fast DAS data pre-processing approach (phase unwrapping, preliminary filtering, etc.) in order to have a qualitative and distinctive signature of the signal noise level, helping to differentiate the most active perforations. In embodiments, the DAS data is processed with three types of filters: 1 ) tube wave filter, which dampens tube waves propagating along the well with known velocities (which can be easily detected from the 2d Fourier analysis of the DAS data) and with little attenuation; 2) high-pass filter, and 3) a white noise filter. Parameters of these filters are based on the analysis of DAS data and might be optimized for the specific job requirements. These parameters are selected in such a way that after filtering they maximize the amplitude of the signal in the well section near the stimulated clusters during pumping relative to remaining noise in the vertical section of the wellbore. A machine learning-based model for online stimulation efficiency monitoring can then be applied. This algorithm uses asinputs the wellbore completion and pressure-rate-concentration data acquired during the hydraulic fracturing job.

[0032] The stimulation efficiency algorithm can predict the uniformity of the flow rate distribution between the various perforation clusters, reported as UnSD (Unordered Slurry Distribution). The algorithm itself; however, is not able to assign these flow rates to each particular cluster. The algorithm provides flow rate breakdown among clusters without specifying their spatial order, so that, for example, in a 5-cluster stage; 10 percent, 10 percent, 10 percent, 40 percent, 30 percent, breakdown is undistinguishable from the 40 percent, 10 percent, 10 percent, 10 percent, 30 percent one.

[0033] Aspects of the disclosure provide a method, where DAS data from the stimulation well is used to re-distribute the flow rates sequence that has been predicted by the stimulation efficiency algorithm in a proper order, specifying the real locations of the successfully stimulated perforation clusters. Schematically, the method is shown in FIG. 2 and FIG. 5.

[0034] One concept behind the method is that optical fiber data, even low-resolution and low signal-to-noise optical fiber, can bear traces of flow rate distribution among the clusters, and thus can be used to dispense the UnSD to a properly spatially ordered SD (slurry distribution).

[0035] As will be understood, multiple types of fiber optics acquisition that can be used for that goal can be considered. In FIG. 2, cluster numbers 1 through 5 are illustrated. Data from the clusters are fed into the DAS-IQ system and individual outputs are generated. For the system presented in FIG. 2, hydraulic fractures are centered around the treatment well that has the optical fiber cable placed within the wellbore. Data may be read from the cable and output provided to the DAS-IQ system as illustrated.

[0036] In embodiments, the DAS flow and vibration noise acquisition occurs with fiber optic cable in the treatment well, covering the stimulated stage interval. The systemcompares the flow rates coming from the stimulation efficiency with the strength of the acoustic signal recorded along and around the stimulated stage. A target range may be established for the acoustical analysis. In one non-limiting embodiment, the target frequency range may be above, approximately 250 Hz. This range allows for the reduction of the contribution of pumping noise in the acoustic signal related to perforation flow. The acoustic signal is pre-processed to enhance high-frequency components related to perforation flow. The enhancement of the high-frequency components is performed at a stage of converting the optical reflections to acoustic signal by statistics-based adjustments in the raw optical data processing algorithms. Then, after the optical-to- acoustic processing, the DAS data is filtered based on the frequencies and patterns of pumping related and other signals identified in the well sections away from the stimulated stage to enhance the flow related signal in the stimulated well section. Then, the flow rate distribution from the stimulation efficiency monitoring, which is not tied to perforation cluster locations, is matched with the distribution of high-frequency acoustic noise in the stimulated well section to subsequently assign the best fit as being the main output.

[0037] One type of DAS signal and its interpretation may come from an induced strain acquisition with fiber optics located in nearby monitoring well or wells. For example, the unordered slurry distribution in the treatment well may be estimated UnSD « (100%, 0,0, 0,0), where 5 active clusters have been assumed. Fiber optic data resolves UnSD to SD, e.g. SD (100%, 0, 0,0,0), or (0,100%, 0,0), (0,0,100%, 0,0), (0,0,0,100%, 0), (0,0,0,0,100%), accordingly. In more complicated cases with more uniform distribution of slurry between clusters, machine learning techniques are used to help to order the UnSD data based on fiber optic pattern heterogeneity. This may be done with the use of the recurrent neural network or transformer architecture. In this case, the use of recurrency allows reaching more gradual distribution evolution prediction. In instances that the global maximum of fit between the predicted and the ground-truth distributions are not found, the good local minimum would show a reasonably good match with the distribution varying smoothly over time.

[0038] Referring to FIG. 3, a typical arrangement of remote well strain rate monitoring with fiber optics is illustrated. The left curve is a horizontal section of a monitoring well. The right curve with a stack of rectangles is a realization of one stimulation stage of a treatment well, with rectangles representing individual fractures.

[0039] The overall process for re-assigning the flow rates to particular clusters is the same as explained above with an additional constraint coming from a strain rate inversion algorithm and its associated accuracy limitations (based on the vicinity of clusters or large distance between monitoring and treating wells, etc.).

[0040] The closing loop of the workflow entails providing a user with a real-time flow rates distribution between perforation clusters. This simulation can be performed in realtime on the fractures propagation by comparing the field measurement-field modeling results with what was simulated initially without measurement and interpretation techniques. This may be performed upon taking on-site decisions, whether any modifications of surface pumping schedule is needed, or if any type of diversion technique must be applied in order to achieve a balance between flow rates (assuming equal distribution of flow through the clusters is what one is after). Referring to FIG. 4, a result from the DAS-IQ system is illustrated.

[0041] Referring to FIG. 5, a method 500 for analyzing stimulation fluid flow distribution one of between and within perforation clusters during hydraulic fracturing is disclosed. The method 500 may comprise, at 502, obtaining proppant rate concentration data related to a wellbore. The method may further comprise, at 504, using a machine learning algorithm to accept the proppant rate concentration data and predict a most probable fluid flow distribution between at least two clusters. The method may further comprise, at 506, obtaining measured strain rate data related to geological stratum near the wellbore. The method may further comprise, at 508, obtaining measured stress rate data related to the geological stratum near the wellbore. The method may further comprise, at 510, using a strain rate inversion model to calculate strain rate simulation data and stress rate simulation data. The method may further comprise, at 512, matching the calculated strainrate simulation data to the measured strain rate data and matching the calculated stress rate simulation data to the measured stress rate data and when the matching is below a threshold value proceeding with the method and when the matching is above a threshold value recalculating using the strain rate inversion model. The method may further comprise, at 514, outputting results of both the machine learning algorithm and the strain rate inversion model.

[0042] Example embodiments of the claims are described. The example embodiments should not be considered limiting. In one example embodiment, a method for analyzing stimulation fluid flow distribution one of between and within perforation clusters during hydraulic fracturing is disclosed. The method may comprise obtaining proppant rate concentration data related to a wellbore. The method may further comprise using a machine learning algorithm to accept the proppant rate concentration data and predict a most probable fluid flow distribution between at least two clusters. The method may further comprise obtaining measured strain rate data related to geological stratum near the wellbore. The method may further comprise obtaining measured stress rate data related to the geological stratum near the wellbore. The method may further comprise using a strain rate inversion model to calculate strain rate simulation data and stress rate simulation data. The method may further comprise matching the calculated strain rate simulation data to the measured strain rate data and matching the calculated stress rate simulation data to the measured stress rate data and when the matching is below a threshold value proceeding with the method and when the matching is above a threshold value recalculating using the strain rate inversion model. The method may further comprise outputting results of both the machine learning algorithm and the strain rate inversion model.

[0043] In another example embodiment, the method may be performed wherein the at least two clusters are assigned a fluid flow distribution rate higher than other clusters.

[0044] In another example embodiment, the method may be performed wherein the strain rate data is obtained from a monitoring wellbore near the wellbore.

[0045] In another example embodiment, the method may be performed wherein the strain rate data is obtained from a fiber optic cable in the monitoring wellbore.

[0046] In another example embodiment, the method may be performed wherein stress rate data is obtained from a monitoring wellbore near the wellbore.

[0047] In another example embodiment, the method may be performed wherein stress rate data is obtained from a fiber optic cable in the monitoring wellbore.

[0048] In another example embodiment, the method may be performed wherein the outputting of the results of both the machine learning algorithm and the strain rate inversion model is through one of visually depicting the data and saving the data to a nonvolatile memory.

[0049] In another example embodiment, the method may further comprise having a user input the threshold value.

[0050] In another example embodiment, an article of manufacture comprising a nonvolatile memory is disclosed. In this embodiment, the non-volatile memory is configured to have a list of instructions configured to be read by a computer, the list of instructions containing a method for analyzing stimulation fluid flow distribution one of between and within perforation clusters during hydraulic fracturing, The method may comprise obtaining proppant rate concentration data related to a wellbore. The method may further comprise using a machine learning algorithm to accept the proppant rate concentration data and predict a most probable fluid flow distribution between at least two clusters. The method may further comprise obtaining measured strain rate data related to geological stratum near the wellbore. The method may further comprise obtaining measured stress rate data related to the geological stratum near the wellbore. The method may further comprise using a strain rate inversion model to calculate strain rate simulation data and stress rate simulation data. The method may further comprise matching the calculatedstrain rate simulation data to the measured strain rate data and matching the calculated stress rate simulation data to the measured stress rate data and when the matching is below a threshold value proceeding with the method and when the matching is above a threshold value recalculating using the strain rate inversion model. The method may further comprise outputting results of both the machine learning algorithm and the strain rate inversion model.

[0051] In another embodiment, the article of manufacture may be configured wherein the article of manufacture is configured as one of a universal serial bus, a computer hard disk, and a solid-state device.

[0052] In another embodiment, the article of manufacture may be configured wherein the method further comprises having a user input the threshold value.

[0053] In another embodiment, the article of manufacture may be configured wherein the method may be performed in real-time during hydraulic fracturing.

[0054] The foregoing description of the embodiments has been provided for purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure. Individual elements or features of a particular embodiment are generally not limited to that particular embodiment, but, where applicable, are interchangeable and can be used in a selected embodiment, even if not specifically shown or described. The same may be varied in many ways. Such variations are not to be regarded as a departure from the disclosure, and all such modifications are intended to be included within the scope of the disclosure.

[0055] While embodiments have been described herein, those skilled in the art, having benefit of this disclosure, will appreciate that other embodiments are envisioned that do not depart from the inventive scope. Accordingly, the scope of the present claims or any subsequent claims shall not be unduly limited by the description of the embodiments described herein.

Claims

CLAIMSWhat is claimed is:1 . A method for analyzing stimulation fluid flow distribution one of between and within perforation clusters during hydraulic fracturing, comprising: obtaining proppant rate concentration data related to a wellbore; using a machine learning algorithm to accept the proppant rate concentration data and predict a most probable fluid flow distribution between at least two clusters; obtaining measured strain rate data related to geological stratum near the wellbore; obtaining measured stress rate data related to the geological stratum near the wellbore; using a strain rate inversion model to calculate strain rate simulation data and stress rate simulation data; matching the calculated strain rate simulation data to the measured strain rate data and matching the calculated stress rate simulation data to the measured stress rate data and when the matching is below a threshold value proceeding with the method and when the matching is above a threshold value recalculating using the strain rate inversion model; and outputting results of both the machine learning algorithm and the strain rate inversion model.

2. The method according to claim 1 , wherein the at least two clusters are assigned a fluid flow distribution rate higher than other clusters.

3. The method according to claim 1 , wherein the strain rate data is obtained from a monitoring wellbore near the wellbore.

4. The method according to claim 3, wherein the strain rate data is obtained from a fiber optic cable in the monitoring wellbore.

5. The method according to claim 1 , wherein the stress rate data is obtained from a monitoring wellbore near the wellbore.

6. The method according to claim 5, wherein the stress rate data is obtained from a fiber optic cable in the monitoring wellbore.

7. The method according to claim 1 , wherein the outputting of the results of both the machine learning algorithm and the strain rate inversion model is through one of visually depicting the data and saving the data to a non-volatile memory.

8. The method according to claim 1 , further comprising having a user input the threshold value.

9. The method according to claim 1 , wherein the method is performed in real-time during hydraulic fracturing.

10. An article of manufacture comprising a non-volatile memory, the non-volatile memory having a list of instructions configured to be read by a computer, the list of instructions containing a method for analyzing stimulation fluid flow distribution one of between and within perforation clusters during hydraulic fracturing, comprising: obtaining proppant rate concentration data related to a wellbore; using a machine learning algorithm to accept the proppant rate concentration data and predict a most probable fluid flow distribution between at least two clusters; obtaining measured strain rate data related to geological stratum near the wellbore;obtaining measured stress rate data related to the geological stratum near the wellbore; using a strain rate inversion model to calculate strain rate simulation data and stress rate simulation data; matching the calculated strain rate simulation data to the measured strain rate data and matching the calculated stress rate simulation data to the measured stress rate data and when the matching is below a threshold value proceeding with the method and when the matching is above a threshold value recalculating using the strain rate inversion model; and outputting results of both the machine learning algorithm and the strain rate inversion model.11 . The article of manufacture according to claim 10, wherein the article of manufacture is configured as one of a universal serial bus, a computer hard disk, and a solid-state device.

12. The article of manufacture according to claim 10, wherein the method may further comprise having a user input the threshold value.

13. The article of manufacture according to claim 10, wherein the method may be performed in real-time during hydraulic fracturing.

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