Generating Pressure Tube Waves Downhole Using Electrical Pumping Units Safely

This application claims the priority of U.S. Provisional Patent Application No. 63/653,303, filed May 30, 2024, which is incorporated by reference in its entirety.

Hydrocarbons, such as oil and gas, are commonly obtained from subterranean formations that may be located onshore or offshore. The development of subterranean operations and the processes involved in removing hydrocarbons from a subterranean formation are complex. Subterranean operations involve a number of different steps such as, for example, drilling a wellbore at a desired well site, treating and stimulating the wellbore to optimize production of hydrocarbons, and performing the necessary steps to produce and process the hydrocarbons from the subterranean formation.

This disclosure relates to the field of seismic analysis and hydraulic fracture as well as hydraulic fracturing process monitoring and evaluation. In particular, monitoring hydraulic fracturing, currently, requires a large number of resources to evaluate downhole conditions via pressure pulse technology.

The present disclosure generally relates to systems and methods use flow rate and surface pressure information to properly align and scale a model of a tube wave and select the optimal parameters which maximizes similarity between modeled and measured tube wave.

is a diagram illustrating an example of a frac systemfor treatment operations, according to aspects of the present disclosure. Frac systemmay comprise a fluid management systemin fluid communication with a blender system. Blender systemmay in turn be in fluid communication with one or more pumping systemsthrough a fluid manifold system. Fluid manifold systemmay provide fluid communication between pumping systemsand a wellbore. In use, fluid management systemmay receive water or another fluid from a fluid source(e.g., a ground water source, a pond, one or more frac tanks), mix one or more fluid additives into the received water or fluid to produce a treatment fluid with a desired fluid characteristic, and provide the produced treatment fluid to blender system. Blender systemmay receive the produced treatment fluid from fluid management systemand mix the produced treatment fluid with a proppant, such as sand, or another granular materialto produce a final treatment fluid that is directed to fluid manifold. Pumping systemsmay then pressurize the final treatment fluid to generate pressurized final treatment fluid that is directed into wellbore, where the pressurized final treatment fluid generates fractures within a formation in fluid communication with wellbore.

An example one of pumping systemsmay comprise a first mover, a pump, and a drive train. As used herein, a mover may comprise any device that converts energy into mechanical energy to drive a pump. Example movers comprise, but are not limited to, electric pump motors, hydrocarbon-driven or steam engines, turbines, etc. Drive trainmay be removably coupled to first moverand pumpsthrough one or more drive shafts (not shown), and may comprise a transmissionwith one or more gears that transmits mechanical energy from first mover to the pump. For instance, to the extent pumpscomprise reciprocating pumps, the mechanical energy may comprise torque that drives pump

Drive trainmay further comprise an electric pump motor. As depicted, the electric pump motormay be coupled to transmissionbetween transmissionand pump. In the embodiment shown, electric pump motormay receive mechanical energy from first moverthrough transmissionand provide the received mechanical energy to pumpaugmented by mechanical energy generated by electric pump motor. It should be appreciated, however, that the orientation of electric pump motorwith respect to first mover, transmission, and the pumpis not limited to the embodiment shown. In other embodiments, electric pump motormay be positioned between transmissionand first mover, for instance, or between elements of transmissionitself. In yet other embodiments, electric pump motormay be incorporated into transmissionas part of a hybrid transmission system through which power from both first moverand electric pump motorare provided to pump

First moverand electric pump motormay receive energy or fuel in one or more forms from sources at the wellsite. The energy or fuel may comprise, for instance, hydrocarbon-based fuel, electrical energy, hydraulic energy, thermal energy, etc. The sources of energy or fuel may comprise, for instance, on-site fuel tanks, mobile fuel tanks delivered to the site, electrical generators, hydraulic pumping systems, etc. First moverand electric pump motormay then convert the fuel or energy into mechanical energy that can be used to drive associated pump

In the embodiment shown, first movermay comprise an internal combustion engine such as a diesel or dual fuel (e.g., diesel and natural gas) engine and electric pump motormay comprise an electric pump motor. First movermay receive a source of fuel from one or more fuel tanks (not shown) that may be located within the pumping systemand refilled as necessary using a mobile fuel truck driven on site. Electric pump motormay be electrically coupled to a source of electricity through a cable. Example sources of electricity comprise, but are not limited to, an on-site electrical generator, a public utility grid, one or more power storage elements, solar cells, wind turbines, other power sources, or one or more combinations of any of the previously listed sources.

As depicted, the source of electricity coupled electric pump motorcomprises a generatorlocated at the well site. The generator may comprise, for instance, a gas-turbine generator or an internal combustion engine that produces electricity to be consumed or stored on site. In the embodiment shown, generatormay receive and utilize natural gas from the wellboreor from another wellbore in the field (i.e., “wellhead gas”) to produce the electricity. As depicted, frac systemmay comprise gas conditioning systemsthat may receive the gas from wellboreor another source and condition the gas for use in the generator. Example gas conditioning systems comprise, but are not limited to, gas separators, gas dehydrators, gas filters, etc. In other embodiments, conditioned natural gas may be transported to the well site for use by the generator.

Frac systemmay further comprise one or more energy storage devicesthat may receive energy generated by generatoror other on-site energy sources and store in one or more forms for later use. For instance, storage devicesmay store the electrical energy from generatoras electrical, chemical, or mechanical energy, or in any other suitable form. Example storage devicescomprise, but are not limited to, capacitor banks, batteries, flywheels, pressure tanks, etc. In certain embodiments, energy storage devicesand generatormay be incorporated into a power grid located on site through which at least some of the fluid management system, blender system, pumping systems, and gas conditioning systemsmay receive power.

In use, first moverand electric pump motormay operate in parallel or in series to drive pump, with the division of power between the movers being flexible depending on the application. For instance, in a multi-stage well stimulation operation, the formation may be fractured (or otherwise stimulated) in one or more “stages,” with each stage corresponding to a different location within the formation. Each “stage” may be accompanied by an “active” period during which pumpmay be engaged and pressurized fluids are being pumped into wellboreto fracture the formation, and an “inactive” period during which the pumps are not engaged while other ancillary operations are taking place. The transition between the “inactive” and “active” periods may be characterized by a sharp increase in torque requirement.

In an embodiment in which first movercomprises a diesel engine and electric pump motorcomprises an electric pump motor, both the diesel engine and electric pump motor may be engaged to provide the necessary power, with the percentage contribution of each depending on the period in which frac systemis operating. For instance, during the “inactive” and “active” periods in which the torque requirements are relatively stable, the diesel engine, which operates more efficiently during low or near constant speed operations, may provide a higher percentage (or all) of the torque to the pump than the electric pump motor. In contrast, during transitions between “inactive” and “active” states, the electric pump motor may supplant the diesel engine as the primary source of torque to lighten the load on the diesel engine during these transient operations. In both cases, the electric pump motor reduces the torque required by the diesel engine, which reduces the amount of diesel fuel that must be consumed during the well stimulation operation. It should be noted that power sources could be used during continuous operation or intermittently as needed, including during transmission gear-shift events.

In addition to reducing the amount of diesel fuel needed to perform a well stimulation operation, the use of a first mover and an electric pump motor in a pumping system described herein may provide flexibility with respect to the types of movers that may be used. For instance, natural gas engines, i.e., internal combustion engines that use natural gas as their only source of combustion, are typically not used in oil field environments due to their limited torque capacity. By including two movers within pumping system, the torque capacity of the natural gas engine may be augmented to allow the use of a natural gas engine within pumping system. For instance, in certain embodiments, first movermay comprise a natural gas engine and electric pump motormay comprise an electric pump motor that operates in series or parallel with the natural gas engine to provide the necessary torque to power pump

In certain embodiments, pumping systemsmay be electrically coupled to an information handling systemthat directs the operation of first moversand electric pump motorsof pumping systems. Information handling systemmay further control at least a part of frac system. As illustrated, the information handling systemmay comprise any instrumentality or aggregate of instrumentalities operable to compute, estimate, classify, process, transmit, broadcast, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes. For example, an information handling systemmay be a personal computer, a network storage device, or any other suitable device and may vary in size, shape, performance, functionality, and price.

Information handling systemmay comprise a processing unit (e.g., microprocessor, central processing unit, etc.) that may process data from electric pump motor, discussed below, by executing software or instructions obtained from a local non-transitory computer readable media (e.g., optical disks, magnetic disks). The non-transitory computer readable media may store software or instructions of the methods described herein. Non-transitory computer readable media may comprise any instrumentality or aggregation of instrumentalities that may retain data and/or instructions for a period of time. Non-transitory computer readable media may comprise, for example, storage media such as a direct access storage device (e.g., a hard disk drive or floppy disk drive), a sequential access storage device (e.g., a tape disk drive), compact disk, CD-ROM, DVD, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), and/or flash memory; as well as communications media such wires, optical fibers, microwaves, radio waves, and other electromagnetic and/or optical carriers; and/or any combination of the foregoing. Information handling systemmay also comprise input device(s) (e.g., keyboard, mouse, touchpad, etc.) and output device(s) (e.g., monitor, printer, etc.). The input device(s) and output device(s) provide a user interface that enables an operator to interact with any device disposed or a part of frac systemand/or software executed by a processing unit.

For example, information handling systemmay send one or more control signals to pumping systemsto control the speed/torque output of first moversand electric pump motors. The control signals may take whatever form is necessary to communicate with the first moversand electric pump motors. For example, a control signal to electric pump motormay comprise an electrical control signal to a variable frequency drive (VFD), discussed below, coupled to electric pump motor, which may receive the control signal and alter the operation of the electric pump motor based on the control signal.

In certain embodiments, information handling systemmay also be electrically coupled to other elements of the system, including fluid management system, blender system, pumping systems, generator, and gas conditioning systemsin order to monitor and/or control the operation of frac system. In other embodiments, some or all of the functionality associated with information handling systemmay be located on the individual elements of the system, e.g., each of pumping systemsmay have individual controllers that direct the operation of the associated first moverand electric pump motors

illustrates an example pumping system, according to aspects of the present disclosure. Pumping systemmay be used, for instance, as one or more of pumping systemsdescribed above with reference to. As depicted, pumping systemcomprises a first moverin the form of a diesel engine coupled to reciprocating positive displacement pumpthrough a transmission systeminto which an electric pump motoris integrated. First mover, reciprocating positive displacement pump, and transmission systemmay be at least partially mounted on a trailercoupled to a truck. Truckmay comprise, for instance, a conventional engine that provides locomotion to truckand trailerthrough a transmission systemincorporating an electric pump motor. Transmission systemmay further comprise an electrical connection, such as a cable, between the transmission of truckand electric pump motorin transmission system.

In use, truckand trailerwith the pumping equipment mounted thereon may be driven to a well site at which a fracturing or other treatment operation will take place. In certain embodiments, truckand trailermay be one of many similar trucks and trailers that are driven to a well site. Once at the site, reciprocating positive displacement pumpmay be fluidically coupled to a wellbore(e.g., referring to), such as through a fluid manifold(e.g., referring to), to provide treatment fluid to wellbore. Reciprocating positive displacement pumpmay further be fluidically coupled to a source of treatment fluids to be pumped into the wellbore. When connected, the diesel engine may be started to provide a primary source of torque to reciprocating positive displacement pumpthrough the pump transmission system. Electric pump motorin pump transmission systemsimilar may be engaged to provide a supplemental source of torque to reciprocating positive displacement pump. As depicted, electric pump motorin pump transmission systemmay receive energy directly from the transmission of truck, such that truckitself operates as an electrical generator for the pumping operation. In addition to energy from truckand electric pump motorin pump transmission system, reciprocating positive displacement pumpmay receive electricity from other energy sources on the site, including a dedicated electrical generator on site or other pumping systems located on the site. During frac operations, measurements may be performed to determine downhole properties or wellboreand/or the formation. These measurements may be further processed by additional methods and systems that may utilize information handling system.

further illustrates an example information handling systemwhich may be employed to perform various steps, methods, and techniques disclosed herein. Persons of ordinary skill in the art will readily appreciate that other system examples are possible. As illustrated, information handling systemcomprises a processing unit (CPU or processor)and a system busthat couples various system components including system memorysuch as read only memory (ROM)and random-access memory (RAM)to processor. Processors disclosed herein may all be forms of this processor. Information handling systemmay comprise a cacheof high-speed memory connected directly with, in close proximity to, or integrated as part of processor. Information handling systemcopies data from memoryand/or storage deviceto cachefor quick access by processor. In this way, cacheprovides a performance boost that avoids processordelays while waiting for data. These and other modules may control or be configured to control processorto perform various operations or actions. Other system memorymay be available for use as well. Memorymay comprise multiple different types of memory with different performance characteristics. It may be appreciated that the disclosure may operate on information handling systemwith more than one processoror on a group or cluster of computing devices networked together to provide greater processing capability. Processormay comprise any general-purpose processor and a hardware module or software module, such as first module, second module, and third modulestored in storage device, configured to control processoras well as a special-purpose processor where software instructions are incorporated into processor. Processormay be a self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. Processormay comprise multiple processors, such as a system having multiple, physically separate processors in different sockets, or a system having multiple processor cores on a single physical chip. Similarly, processormay comprise multiple distributed processors located in multiple separate computing devices but working together such as via a communications network. Multiple processors or processor cores may share resources such as memoryor cacheor may operate using independent resources. Processormay comprise one or more state machines, an application specific integrated circuit (ASIC), or a programmable gate array (PGA) including a field PGA (FPGA).

Each individual component discussed above may be coupled to system bus, which may connect each and every individual component to each other. System busmay be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. A basic input/output (BIOS) stored in ROMor the like, may provide the basic routine that helps to transfer information between elements within information handling system, such as during start-up. Information handling systemfurther comprises storage devicesor computer-readable storage media such as a hard disk drive, a magnetic disk drive, an optical disk drive, tape drive, solid-state drive, RAM drive, removable storage devices, a redundant array of inexpensive disks (RAID), hybrid storage device, or the like. Storage devicemay comprise software modules,, andfor controlling processor. Information handling systemmay comprise other hardware or software modules. Storage deviceis connected to the system busby a drive interface. The drives and the associated computer-readable storage devices provide nonvolatile storage of computer-readable instructions, data structures, program modules and other data for information handling system. In one aspect, a hardware module that performs a particular function comprises the software component stored in a tangible computer-readable storage device in connection with hardware components, such as processor, system bus, and so forth, to carry out a particular function. In another aspect, the system may use a processor and computer-readable storage device to store instructions which, when executed by the processor, cause the processor to perform operations, a method or other specific actions. The basic components and appropriate variations may be modified depending on the type of device, such as whether information handling systemis a small, handheld computing device, a desktop computer, or a computer server. When processorexecutes instructions to perform “operations”, processormay perform the operations directly and/or facilitate, direct, or cooperate with another device or component to perform the operations.

As illustrated, information handling systememploys storage device, which may be a hard disk or other types of computer-readable storage devices which may store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, digital versatile disks (DVDs), cartridges, random access memories (RAMs), read only memory (ROM), a cable containing a bit stream and the like, may also be used in the exemplary operating environment. Tangible computer-readable storage media, computer-readable storage devices, or computer-readable memory devices, expressly exclude media such as transitory waves, energy, carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with information handling system, an input devicerepresents any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output devicemay also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems enable a user to provide multiple types of input to communicate with information handling system. Communications interfacegenerally governs and manages the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic hardware depicted may easily be substituted for improved hardware or firmware arrangements as they are developed.

As illustrated, each individual component described above is depicted and disclosed as individual functional blocks. The functions these blocks represent may be provided through the use of either shared or dedicated hardware, including, but not limited to, hardware capable of executing software and hardware, such as a processor, that is purpose-built to operate as an equivalent to software executing on a general-purpose processor. For example, the functions of one or more processors presented inmay be provided by a single shared processor or multiple processors. (Use of the term “processor” should not be construed to refer exclusively to hardware capable of executing software.) Illustrative embodiments may comprise microprocessor and/or digital signal processor (DSP) hardware, read-only memory (ROM)for storing software performing the operations described below, and random-access memory (RAM)for storing results. Very large-scale integration (VLSI) hardware embodiments, as well as custom VLSI circuitry in combination with a general-purpose DSP circuit, may also be provided.

illustrates an example information handling systemhaving a chipset architecture that may be used in executing the described method and generating and displaying a graphical user interface (GUI). Information handling systemis an example of computer hardware, software, and firmware that may be used to implement the disclosed technology. Information handling systemmay comprise a processor, representative of any number of physically and/or logically distinct resources capable of executing software, firmware, and hardware configured to perform identified computations. Processormay communicate with a chipsetthat may control input to and output from processor. In this example, chipsetoutputs information to output device, such as a display, and may read and write information to storage device, which may comprise, for example, magnetic media, and solid-state media. Chipsetmay also read data from and write data to RAM. A bridgefor interfacing with a variety of user interface componentsmay be provided for interfacing with chipset. Such user interface componentsmay comprise a keyboard, a microphone, touch detection and processing circuitry, a pointing device, such as a mouse, and so on. In general, inputs to information handling systemmay come from any of a variety of sources, machine generated and/or human generated.

Chipsetmay also interface with one or more communication interfacesthat may have different physical interfaces. Such communication interfaces may comprise interfaces for wired and wireless local area networks, for broadband wireless networks, as well as personal area networks. Some applications of the methods for generating, displaying, and using the GUI disclosed herein may comprise receiving ordered datasets over the physical interface or be generated by the machine itself by processoranalyzing data stored in storage deviceor RAM. Further, information handling systemreceives inputs from a user via user interface componentsand executes appropriate functions, such as browsing functions by interpreting these inputs using processor.

In examples, information handling systemmay also comprise tangible and/or non-transitory computer-readable storage devices for carrying or having computer-executable instructions or data structures stored thereon. Such tangible computer-readable storage devices may be any available device that may be accessed by a general purpose or special purpose computer, including the functional design of any special purpose processor as described above. By way of example, and not limitation, such tangible computer-readable devices may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other device which may be used to carry or store program code in the form of computer-executable instructions, data structures, or processor chip design. When information or instructions are provided via a network, or another communications connection (either hardwired, wireless, or combination thereof), to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be comprised within the scope of the computer-readable storage devices.

Computer-executable instructions comprise, for example, instructions and data which cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Computer-executable instructions also comprise program modules that are executed by computers in stand-alone or network environments. Generally, program modules comprise routines, programs, components, data structures, objects, and the functions inherent in the design of special-purpose processors, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.

In additional examples, methods may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Examples may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

illustrates an example of one arrangement of resources in a computing networkthat may employ the processes and techniques described herein, although many others are of course possible. As noted above, an information handling system, as part of their function, may utilize data, which comprises files, directories, metadata (e.g., access control list (ACLS) creation/edit dates associated with the data, etc.), and other data objects. The data on the information handling systemis typically a primary copy (e.g., a production copy). During a copy, backup, archive or other storage operation, information handling systemmay send a copy of some data objects (or some components thereof) to a secondary storage computing deviceby utilizing one or more data agents.

A data agentmay be a desktop application, website application, or any software-based application that is run on information handling system. As illustrated, information handling systemmay be disposed at any rig site (e.g., referring to), off site location, or repair and manufacturing center. The data agent may communicate with a secondary storage computing deviceusing communication protocolin a wired or wireless system. Communication protocolmay function and operate as an input to a website application. In the website application, field data related to pre- and post-operations, generated DTCs, notes, and the like may be uploaded. Additionally, information handling systemmay utilize communication protocolto access processed measurements, operations with similar DTCs, troubleshooting findings, historical run data, and/or the like. This information is accessed from secondary storage computing deviceby data agent, which is loaded on information handling system.

Secondary storage computing devicemay operate and function to create secondary copies of primary data objects (or some components thereof) in various cloud storage sitesA-N. Additionally, secondary storage computing devicemay run determinative algorithms on data uploaded from one or more information handling systems, discussed further below. Communications between the secondary storage computing devicesand cloud storage sitesA-N may utilize REST protocols (Representational state transfer interfaces) that satisfy basic C/R/U/D semantics (Create/Read/Update/Delete semantics), or other hypertext transfer protocol (“HTTP”)-based or file-transfer protocol (“FTP”)-based protocols (e.g., Simple Object Access Protocol).

In conjunction with creating secondary copies in cloud storage sitesA-N, the secondary storage computing devicemay also perform local content indexing and/or local object-level, sub-object-level or block-level deduplication when performing storage operations involving various cloud storage sitesA-N. Cloud storage sitesA-N may further record and maintain, EM logs, map DTC codes, store repair and maintenance data, store operational data, and/or provide outputs from determinative algorithms that are located in cloud storage sitesA-N. In a non-limiting example, this type of network may be utilized as a platform to store, backup, analyze, import, preform extract, transform and load (“ETL”) processes, mathematically process, apply machine learning models, and augment EM measurement data sets.

A machine learning model may be an empirically derived model which may result from a machine learning algorithm identifying one or more underlying relationships within a dataset. In comparison to a physics-based model, such as Maxwell's Equations, which are derived from first principles and define the mathematical relationship of a system, a pure machine learning model may not be derived from first principles. Once a machine learning model is developed, it may be queried in order to predict one or more outcomes for a given set of inputs. The type of input data used to query the model to create the prediction may correlate both in category and type to the dataset from which the model was developed.

The structure of, and the data contained within a dataset provided to a machine learning algorithm may vary depending on the intended function of the resulting machine learning model. The rows of data, or data points, within a dataset may contain one or more independent values. Additionally, datasets may contain corresponding dependent values. The independent values of a dataset may be referred to as “features,” and a collection of features may be referred to as a “feature space.” If dependent values are available in a dataset, they may be referred to as outcomes or “target values.” Although dependent values may be a component of a dataset for certain algorithms, not all algorithms require a dataset with dependent values. Furthermore, both the independent and dependent values of the dataset may comprise either numerical or categorical values.

While it may be true that machine learning model development is more successful with a larger dataset, it may also be the case that the whole dataset isn't used to train the model. A test dataset may be a portion of the original dataset which is not presented to the algorithm for model training purposes. Instead, the test dataset may be used for what may be known as “model validation,” which may be a mathematical evaluation of how successfully a machine learning algorithm has learned and incorporated the underlying relationships within the original dataset into a machine learning model. This may comprise evaluating model performance according to whether the model is over-fit or under-fit. As it may be assumed that all datasets contain some level of error, it may be important to evaluate and optimize the model performance and associated model fit by a model validation. In general, the variability in model fit (e.g.: whether a model is over-fit or under-fit) may be described by the “bias-variance trade-off.” As an example, a model with high bias may be an under-fit model, where the developed model is over-simplified, and has either not fully learned the relationships within the dataset or has over-generalized the underlying relationships. A model with high variance may be an over-fit model which has overlearned about non-generalizable relationships within training dataset which may not be present in the test dataset. In a non-limiting example, these non-generalizable relationships may be driven by factors such as intrinsic error, data heterogeneity, and the presence of outliers within the dataset. The selected ratio of training data to test data may vary based on multiple factors, including, in a non-limiting example, the homogeneity of the dataset, the size of the dataset, the type of algorithm used, and the objective of the model. The ratio of training data to test data may also be determined by the validation method used, wherein some non-limiting examples of validation methods comprise k-fold cross-validation, stratified k-fold cross-validation, bootstrapping, leave-one-out cross-validation, resubstituting, random subsampling, and percentage hold-out.

In addition to the parameters that exist within the dataset, such as the independent and dependent variables, machine learning algorithms may also utilize parameters referred to as “hyperparameters.” Each algorithm may have an intrinsic set of hyperparameters which guide what and how an algorithm learns about the training dataset by providing limitations or operational boundaries to the underlying mathematical workflows on which the algorithm functions. Furthermore, hyperparameters may be classified as either model hyperparameters or algorithm parameters.

Model hyperparameters may guide the level of nuance with which an algorithm learns about a training dataset, and as such model hyperparameters may also impact the performance or accuracy of the model that is ultimately generated. Modifying or tuning the model hyperparameters of an algorithm may result in the generation of substantially different models for a given training dataset. In some cases, the model hyperparameters selected for the algorithm may result in the development of an over-fit or under-fit model. As such, the level to which an algorithm may learn the underlying relationships within a dataset, including the intrinsic error, may be controlled to an extent by tuning the model hyperparameters.

Model hyperparameter selection may be optimized by identifying a set of hyperparameters which minimize a predefined loss function. An example of a loss function for a supervised regression algorithm may comprise the model error, wherein the optimal set of hyperparameters correlates to a model which produces the lowest difference between the predictions developed by the produced model and the dependent values in the dataset. In addition to model hyperparameters, algorithm hyperparameters may also control the learning process of an algorithm, however algorithm hyperparameters may not influence the model performance. Algorithm hyperparameters may be used to control the speed and quality of the machine learning process. As such, algorithm hyperparameters may affect the computational intensity associated with developing a model from a specific dataset.

Machine learning algorithms, which may be capable of capturing the underlying relationships within a dataset, may be broken into different categories. One such category may comprise whether the machine learning algorithm functions using supervised, unsupervised, semi-supervised, or reinforcement learning. The objective of a supervised learning algorithm may be to determine one or more dependent variables based on their relationship to one or more independent variables. Supervised learning algorithms are named as such because the dataset comprises both independent and corresponding dependent values where the dependent value may be thought of as “the answer,” that the model is seeking to predict from the underlying relationships in the dataset. As such, the objective of a model developed from a supervised learning algorithm may be to predict the outcome of one or more scenarios which do not yet have a known outcome. Supervised learning algorithms may be further divided according to their function as classification and regression algorithms. When the dependent variable is a label or a categorical value, the algorithm may be referred to as a classification algorithm. When the dependent variable is a continuous numerical value, the algorithm may be a regression algorithm. In a non-limiting example, algorithms utilized for supervised learning may comprise Neural Networks, K-Nearest Neighbors, Naïve Bayes, Decision Trees, Classification Trees, Regression Trees, Random Forests, Linear Regression, Support Vector Machines (SVM), Gradient Boosting Regression, and Perception Back-Propagation.

The objective of unsupervised machine learning may be to identify similarities and/or differences between the data points within the dataset which may allow the dataset to be divided into groups or clusters without the benefit of knowing which group or cluster the data may belong to. Datasets utilized in unsupervised learning may not comprise a dependent variable as the intended function of this type of algorithm is to identify one or more groupings or clusters within a dataset. In a non-limiting example, algorithms which may be utilized for unsupervised machine learning may comprise K-means clustering, K-means classification, Fuzzy C-Means, Gaussian Mixture, Hidden Markov Model, Neural Networks, and Hierarchical algorithms.

In examples to determine a relationship using machine learning, a neural network (NN), as illustrated in, may be utilized to help identify and/or control the flow rate produced by one or more electric pump motor(e.g., referring to).illustrates neural network (NN). NNmay operate utilizing one or more information handling systems(e.g., referring to) on computing network. Although a NN is illustrated, multiple models may be used with input output structures. These models may comprise flexible empirical models such as NN, gaussian processing methods, kriging methods, evolutionary methods such as genetic algorithms, classification methods, clustering methods empirical methods, or physics-based methods such as equations of state, thermodynamic models, geological, geochemistry, or chemistry models, or kinetic models or any combinations therein including recursive combinations of similar or dissimilar models and iterative model combinations. A NNis an artificial neural network with one or more hidden layersbetween input layerand output layer. In examples, NNmay be software on a single information handling system. In other examples, NNmay software running on multiple information handling systemsconnected wirelessly and/or by a hard-wired connection in a network of multiple information handling systems. Herein, NNmay be applied in a wide array of implementations.

During operations, input data may be given to neuronsin input layer. Neurons,, andare defined as individual or multiple information handling systemsconnected in a computing network. The output from neuronsmay be transferred to one or more neuronswithin one or more hidden layers. Hidden layerscomprises one or more neuronsconnected in a network that further process information from neurons. The number of hidden layersand neuronsin hidden layermay be determined by personnel that designs NN. Hidden layersis defined as a set of information handling systemsassigned to specific processing. Hidden layersspread computation to multiple neurons, which may allow for faster computing, processing, training, and learning by NN. Output from NNmay be computed by neurons. An information handling system(e.g., referring to) being utilized in a computing network, NN, or alone may control frac operations. Specifically, measurements from electric pump motorof parameters of electric pump motorbeing used for a frac operationmay be measured and sent to information handling systemfor further analysis. As discussed below, a tube wave, which may be generated during a frac operationmay be modeled before the tube wave is generated.

A tube wave may be generated by changing the flow rate in wellbore. Flow rate change may be created by ramping down electric pump motors. In other examples, the tube wave may also be generated by changing the flow rate of at least one pump, neutralizing all the pumps, opening or closing at least one of the surface valves. However, any change in pumping rate via an electric pump motormay create a change in inductive load, which may result in a change in back EMF and voltage change upstream. Further, if electric pump motorswas drawing a significant amount of current, or rapid change in current can create substantial back EMF and large voltage spikes. Further, quickly stopping electric pump motorsmay create a mismatch between the control frequency and speed of electric pump motors. This may cause electric pump motorsto become sub-synchronous, which may alter electric pump motorsto operate and function as a generator. This may also result in the higher voltage in the DC link and may even cause a change in frequency upstream. The back emf may be expressed as a function of

where Eis the back electric magnetic field (EMF) in volts, N=number of turns in a coil,

is the rate of change of magnetic flux, which is function of change in current. Additionally, voltage change may be represented as