Electrical submersible pump sizing recommendation engine with variable optimization considerations

Abstract

Aspects of the subject technology relate to systems, methods, and computer-readable media for building electrically submersible pump (ESP) systems. ESP systems include parts that must be compatible with a wellbore environment for them to operate as desired. Parts that operate well when pumping oil may not operate well when pumping geothermal water. Parts of an ESP system must be able to fit into the wellbore and operate according to the expectations of a wellbore operator. This means that parts used to build an ESP system must be selected such that the ESP system may be operated according to an operational plan. Once an ESP system is designed and built, it may be operated in a manner that is consistent with an operational plan. Techniques of the present disclosure include machine learning processes that identify how best to design, build, and operate an ESP system based on a set of characteristics.

Description

ELECTRICAL SUBMERSIBLE PUMP SIZING RECOMMENDATION ENGINE WITH VARIABLE OPTIMIZATION CONSIDERATIONSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of U.S. Non-Pro visional Application No. 18 / 790,653 filed July 31, 2024, which is incorporated herein by reference.TECHNICAL FIELD

[0002] The present technology relates to identifying parts that may be best suited for deployment in a wellbore. More specifically, the present technology relates to building a wellbore system that includes an electrically submersible pump.BACKGROUND

[0003] In certain wellbore applications, like geothermal energy generation and oil and gas production, components may be deployed in a wellbore in instances where pumps must be used. For various reasons, pumps used in wellbore applications must be of a class of pump referred to as an electrically submersible pump (ESP). Such components may be selected based on their ability to withstand environments where they will be deployed. For example, a requirement of an ESP deployed in a geothermal application should be capable of pumping fluids that have temperatures that exceed 100 degrees Celsius (C). While certain ESPs may be able to withstand an operating environment in a manner that meets the needs of one operator or group or operators, those ESPs may not be able to meet the needs of another operator or group of operators. This may be true because the reliability and / or affordability of a particular ESP may vary depending on how that ESP is used. As such, components deployed in a wellbore must be able to perform a function according to expectations that are consistent expected environmental conditions and user preferences.BRIEF DESCRIPTION OF THE DRAWINGS

[0004] In order to describe the manner in which the features and advantages of this disclosure can be obtained, a more particular description is provided with reference to specific embodiments thereof which are illustrated in the appended drawings.Understanding that these drawings depict only exemplary embodiments of the disclosure and are not therefore to be considered limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings.

[0005] FIG. 1, illustrates a schematic representation of a well environment in a production phase, in accordance with various aspects of the subject technology.

[0006] FIG. 2 illustrates actions that may be performed when components included in an electrically submersible pump (ESP) system are identified, in accordance with various aspects of the subject technology.

[0007] FIG. 3 illustrates actions that may be performed when parts of an electrically submersible ESP system design are identified, in accordance with various aspects of the subject technology.

[0008] FIG. 4 is directed to models that may be used to implement the techniques described herein, in accordance with various aspects of the subject technology.

[0009] FIG. 5 illustrates an example of a well data sheet that may be included in a user interface, in accordance with various aspects of the subject technology.

[0010] FIG. 6 illustrates an example computing device architecture which can be employed to perform various steps, methods, and techniques disclosed herein.DETAILED DESCRIPTION

[0011] Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure.

[0012] Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the principles disclosed herein. The features and advantages of the disclosure may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosurewill become more fully apparent from the following description and appended claims or can be learned by the practice of the principles set forth herein.

[0013] It will be appreciated that for simplicity and clarity of illustration, where appropriate, reference numerals have been repeated among the different figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the embodiments described herein. However, it will be understood by those of ordinary skill in the art that the embodiments described herein can be practiced without these specific details. In other instances, methods, procedures, and components have not been described in detail so as not to obscure the related relevant feature being described. The drawings are not necessarily to scale and the proportions of certain parts may be exaggerated to better illustrate details and features. The description is not to be considered as limiting the scope of the embodiments described herein.

[0014] Aspects of the subject technology relate to systems, methods, and computer- readable media for building wellbore ESP systems. Such ESP systems include parts that must be compatible with a wellbore environment for them to operate as desired. Parts that operate well when pumping oil may not operate well when pumping geothermal water. Parts of an ESP system must be able to fit into the wellbore and operate according to the expectations of a wellbore operator. This means that a pump and other parts of an ESP system must be properly selected such that the system may be operated according to operational preferences, potentially according to an operational plan. Once an ESP system is designed and built, it may be operated in a manner that is consistent with the operational plan. Techniques of the present disclosure include machine learning processes that identify how best to design, build, and operate an ESP system based on a set of characteristics.

[0015] In certain wellbore applications (such as geothermal application and oil and gas applications), it may be important to operate pumps in specific ways. Wellbore applications can be managed based on application specific requirements using equipment that is best suited for a specific wellbore operation. Pumps that are powered by electrical energy that are placed in a wellbore are commonly capable of being submerged in fluids (e.g., oil, natural gas, or water). Such pumps may be referred to as electrical submersible pumps (ESPs). Reasons for selecting a pump may include one or more of safety, reliability, andaffordability. In certain environments (e.g., an environment that includes natural gas and oxygen) any spark or electrical arcing in a motor of the pump could create an explosion. In other instances, fluids like water can cause an electrical short circuit that can damage the pump’s motor if the motor was not isolated from the environment. As such, ESPs typically have sealed electrical interconnects and sealed motors.

[0016] Systems that use an ESP may be referred to as an ESP system and components (parts) of an ESP system must be able to fit into the wellbore and operate according to expectations of an operator of the wellbore. This means that an ESP and other parts of an ESP system must be properly selected. Criterion for selecting a proper set of parts may include part sizes (e.g., pipe size, pump body size / diameter, or other physical dimensions). This may be because only parts that fit in the wellbore may be deployed into the wellbore. These criteria may also include environmental characteristics (e.g., operating temperatures and / or pressures), pump operational characteristics (e.g., pump capacity, power consumption, and / or pump loss factors), reliability expectations (e.g., mean time between failure (MTBF), and / or other factors. While MTBF may be one reliability metric of a production system, other reliability metrics may include, one or more of, a failure rate, a probability or proportion of total time the system may be unavailable to perform a function, or a probability or proportion of total time that the system is likely be available to perform the function. In various embodiments, a system can include an ESP deployed downhole in a wellbore for pumping a substance out of the wellbore.

[0017] FIG. 1 illustrates a schematic representation of a well environment 100 in a production phase. Well environment 100 can represent an applicable environment in which a substance is pumped through wellbore 102 toward the surface. For example, well environment 100 can represent a hydrocarbon production environment in which hydrocarbons are pumped through wellbore 102 toward the surface. In another example, well environment 100 can represent a geothermal environment in which water / brine is pumped through wellbore 102 toward the surface.

[0018] Well environment 100 of FIG. 1 includes production system 104 disposed in relation to wellbore 102. Production system 104 includes a surface control system 106. Production system 104 also includes components disposed downhole in wellbore 102. Specifically, production system 104 includes gauge 108, motor 110, seal section 112, gasseparator 114, pump 116, and power cable 118. The components of production system 104, in combination, function to form various tasks related to pumping a substance through wellbore 102 toward the surface. In particular, surface control system 106 functions to control and interact with the various downhole components for performing various tasks related to pumping a substance through wellbore 102 towards the surface.

[0019] Gauge 108 functions to generate downhole data of one or more monitored parameters. Specifically, the downhole data can include applicable data that is capable of being measured downhole, for example by one or more sensors. When a first component or first point is described as being before a second component or second point, the first component or point can be positioned further in a wellbore than a second component or point. For example, gauge 108 can include a pressure gauge that is configured to identify a wellbore pressure before pump 116 (e.g. before a pump intake or gas separator). Further, gauge 108 can function to measure parameters for preventing or reducing formation damage caused by over-production through wellbore 102. Gauge 108 can communicate with the surface control system 106 in generating downhole data. Specifically, gauge 108 can provide the downhole data as telemetry data to surface control system 106, where the downhole data can be used in controlling production operation of production system 104.

[0020] Motor 110 functions to drive pump 116. Specifically, motor 110 can receive power from the surface through power cable 118 to drive pump 116 in lifting production substance towards the surface. Motor 110 can be an applicable motor that is capable of driving pump 116. Motor 110 and pump 116 may be sealed from the environment when included in an ESP system. Correspondingly, pump 116 can be an applicable pump that is capable of pumping production substances toward the surface of wellbore 102. Seal section 112 is disposed between motor 110 and the intake of pump 116. Seal section 112 functions to isolate motor 110 from downhole fluids. Seal section 112 also can function to equalize pressure in wellbore 102 with pressure in motor 110.

[0021] Gas separator 114 is positioned between pump 116 and sealing section 112 and motor 110 combination. Gas separator 114 can serve, at least in part, as an intake for pump 116. In particular, gas separator 114 can function to separate gas from fluid in wellbore 102 and allow for the entry of the separated fluid into pump 116. In turn, pump 116 can pump the separated fluid towards the surface as part of a production substance. Theseparated fluid that is fed to pump 116 can include portions of the separated gas that are broken down and incorporated into the fluid to form a more homogenized solution.

[0022] Applications where ESPs may be used include oil production, natural gas production, geothermal energy production systems, mining, water wells, drilling applications, cementing applications, carbon dioxide (CO2) sequestration, hydraulic fracturing, or other applications. Each of these different applications may be associated with different environments. Characteristics of various applications include temperature, temperature range, liquid specific gravity, gas specific gravity, measures of corrosivity or acidity (e.g., pH), percent hydrogen sulfide (H2S), percent CO2, percent N2, abrasiveness, viscosity, gas liquid mix ratio, and / or other characteristics.

[0023] Requirements that govern operation of a particular wellbore may correspond to preferences of a particular operator. For example, an operator of a first wellbore may prefer to minimize a running cost of production when that means reducing overall production of a particular well to a level that is less than a maximum production level for that first wellbore. This preference may be driven by an objective to operate the first wellbore as long as possible without encountering an equipment failure. Such an objective may also be driven by the fact that parts or personnel required to fix the equipment failure may be located far from the location where the first wellbore is located.

[0024] In another example, an operator of a second wellbore may prefer maximizing production even though that would increase the running costs of production. This preference may be driven by an objective to maximize revenue over a time period instead of minimizing costs. This objective may be driven by the fact that parts or personnel required to fix the equipment failure are located close to the location where the second wellbore is located.

[0025] The examples above show that operator preferences may be driven by running operational costs (e.g., the cost to power a pump system day to day), operational returns (e.g., revenues earned from production), and other cost factors (e.g., costs of personnel and parts require to repair a pump sting). More specifically, operator preferences may correspond to tradeoffs where one factor may be more important than another factor. Examples of such tradeoffs include * production volume over operating cost * operating cost over production volume * production volume over repair cost * repair cost overproduction volume * mean time between failure (MTBF) over per-unit-time production volume, and / or * per-unit-time production volume over MTBF.

[0026] In some instances, operator preference may include more than one of such tradeoffs, even when two different sets of preferences appear to conflict with each other. For example, operating preferences may include minimizing operating cost over production volume and maximizing per-unit-time production volume over MTBF. In such an instance, a production level may be selected based on day-to-day running costs even if that production level nominally reduces MTBF. Such a set of apparently contrasting preferences may allow for a pump to be chosen that provides an acceptable operating cost while maintaining a particular production volume over a time span when the MTBF of the pump system is projected not vary significantly (e.g., ±5%) with changes in production volume. As such an ML process may allow a pump system to operate at a specific production volume based on a small (less than threshold level) reduction in MTBF.

[0027] In the paragraphs above, one or more other metrics associated with the reliability of an ESP system besides MTBF include, a failure rate, a probability or proportion of total time the system may be unavailable to perform a function, or a probability or proportion of total time that the system is likely to be available to perform the function.

[0028] One metric related to failure rate may be based on a simple failure rate that corresponds to how often a system failure occurs. Other failure rates may be identified based cycle tests that help forecast failure rates in a population of similar systems. An example of such cycle test failure rate is a BX cycle test failure rate methodology, where a failure rate of X percent corresponds how long it takes for X percent of a group of systems to fail. For example, a failure rate of a B10 (X = 10 percent) cycle test methodology could correspond to the time it takes for 2 out of 20 motors of the same type to fail in a specific environment.

[0029] A reliability metric relating to system downtime may be identified by evaluating collected data to identify a probability or proportion of time that the system is not available for production. A downtime metric may identify that the system will likely be unavailable for one day out of every 100 days, for example. Another downtime metric may identify that there is a 90 percent probability that the system will experience at least one failure in 100 days of operation.

[0030] In another example, an availability metric may identify how available the system is likely to be. For example, an availability metric may indicate that the system is likely to operate for at least 60 days before experiencing a system shutdown. Another availability metric may indicate that the system has a 95 percent probability that the system will be available for more than 70 days before experiencing a failure that results in reduced production or shutdown of operation.

[0031] What this means is that for a particular job, there may be requirements that limit what parts can be used for a given set of environmental conditions (e.g., temperatures encountered when pumping geothermal water). This also means that for a given job, preferences relating to how the pump is used (i.e., use case preferences) may be used to identify parts that could be used to build the system. As such, use case preferences may be treated as operator settable criterion of a pump system design.

[0032] These preferences may identify one or more different selectable priorities that may stipulate that a set of longest lasting equipment be used, that only equipment capable of meeting a specified production level be used, or that maximizing a return is preferred. Preferences indicated by an operator may identify that an ESP system should never fail (or have a greatest MTBF) or that the equipment should run continuously (e.g., never be turned off). Preferences may be used to either prioritize production over reliability or prioritize reliability over production.

[0033] FIG. 2 illustrates actions that may be performed when components included in an ESP system are identified. At block 210 inputs associated with the ESP system are received. Such inputs may identify one or more requirements of components of the ESP system design, an application type of a plurality of application types, and a use case preference of a wellbore. The requirements of the ESP system design may identify a maximum power draw, an operating voltage, an electrical current, a volumetric flow rate, a flow rate versus efficiency, a pressure difference between a pump input and a pump output, pump material compatibility requirements, operational pressure range, part sizes, pipe diameter, or other metrics. Some or all of these metrics may be specified via a user interface of a computer. Users may also be allowed to enter information that identifies environmental requirements of the ESP system or information that identifies the application type of the ESP system. Alternatively, or additionally, the information thatidentifies the application type may be used to identify environmental requirements of the ESP system. Such environmental requirements may include values of the application characteristics discussed above. As such application characteristics may identify one or more of temperature, temperature range, pressure, pressure range, pressure drop, liquid specific gravity, gas specific gravity, measures corrosivity or acidity (e.g., pH), percent hydrogen sulfide (H2S), percent CO2, percent N2, abrasiveness, viscosity, gas liquid mix ratio, and / or other details relating to the environment where the ESP system will or may operate.

[0034] When use case preferences are part of the inputs received at block 210, these preferences may correspond to one or more of the preferences discussed above or may identify other preferences of a user that are relevant to an ESP system being planned. As mentioned above, these preferences may identify that running costs of production should be minimized (or be below a threshold level) or that production of a wellbore should be maximized (or be above another threshold level). Such preferences may be related to maximizing reliability (e.g., MTBF) instead of maximizing production or may include any other preference, such as prioritizing one metric over another metric. Here again operator preferences may be driven by running operational costs (e.g., the cost to power a pump system day to day), operational returns (e.g., revenues earned from production), and other cost factors (e g., costs of personnel and parts require to repair a pump sting). As such costs relevant to an ESP system may include one or more of an operational (running) cost, manpower costs to repair parts of the ESP system, and capital costs (e.g., costs of replacement parts). Furthermore, operator preferences may correspond to tradeoffs (or combinations of tradeoffs) where one factor may be more important than another factor. As mentioned above, examples of such tradeoffs include * production volume over operating cost * operating cost over production volume * production volume over repair cost * repair cost over production volume * mean time between failure over per-unit-time production volume, * per-unit-time production volume over mean time between failure, or combination thereof.

[0035] At block 220, wellbore data may be accessed. This wellbore data may identify specific types of applications where particular parts (e.g. a particular pump type or pump model) were used in a wellbore. Wellbore data may identify details of specific components(e.g., size, dimensions, pipe size, chemical capability, temperature limitations, pressure specifications or other details) that were used in the wellbore.

[0036] This wellbore data may include data that was collected from many different types of applications (e g., oil production, natural gas production, a geothermal application, mining, carbon sequestration, drilling, or hydraulic fracturing). As such, the wellbore data may be grouped into a plurality of datasets. Wellbore data may also include environmental data, run time information, failure data, production rates, temperatures, pressures, power usage, operating specified voltages, operating electrical current, the cost of power, or other information. For example, a first pump model may be selected for an oil production environment at a first location based on a preference of maximizing production volume over operating costs. In other instances, a second pump model may be selected for an oil production environment at a second location based on a preference for minimizing production cost even if that means reducing oil production rates.

[0037] The wellbore data accessed at block 220 may include data that was collected from many different types of wellbores where different applications (e g., drilling, oil production, natural gas production, a geothermal application, carbon sequestration, or hydraulic fracturing) were performed. As such, the wellbore data may be grouped into a plurality of datasets at block 230. These datasets may be grouped according to application type or a job type. One or more processors executing instructions out of a memory may perform actions of a machine learning (ML) process. These instructions may be part of one or more computer models that allow a computer to perform evaluations to identify parts that may be included in a particular ESP system. Because of this, a computer may access wellbore data and received input data to identify one or more parts of the ESP system that is being designed and built.

[0038] The actions of FIG. 2 may be used to identify parts that could be used to make an ESP system / apparatus that corresponds to characteristics of a job, the application type of the job, and one or more of a reliability metric and / or a cost metric associated with particular pieces or sets of equipment. As such, at block 240 one or more of the plurality of datasets that correspond to the characteristics of the job may be identified. The datasets identified at block 240 may also be associated with the application type of the job and the one or more of the reliability metric and the cost metric mentioned above.

[0039] Sets of wellbore data may be evaluated to identify how parts of an ESP system actually performed in an environment when mappings are made. Such mappings may cross-reference for each type of wellbore application, parts that were used, operational characteristics that identify how the parts were used (e.g., at a maximum pump power or conversely at a derated power level), and / or failures that were observed in real wellbore jobs. These mappings may be used to identify what may happen before a failure occurs and may be used to change the operation of the wellbore system according to a use plan. When a well management system used to monitor and control operation of an ESP system identifies that a failure precursor event has occurred, the well management system may control operation of the ESP system (for example by reducing production) until replacement parts and service personnel are at the jobsite. In other instances, these mappings may allow for a well management system or operator to identify that the wellbore job is proceeding on course with a low or acceptable probability of failure. Such functionality may be part of a machine learning (ML) computer model. The operation of such a computer model may compare data from other wellbore jobs when generating forecasts regarding use of a newly built ESP system. Such forecasts may be premised based on similarities between the design of an existing ESP system and the design of a new ESP system.

[0040] In instances when the new wellbore is to be used with a new set of conditions (conditions not previously observed), data from existing wellbores may still be used to identify an ESP system that should perform according to all known characteristics of the job that will be performed at the new wellbore. In such instances, scores assigned to ESP systems that use different pieces of equipment may be identified. Here, a highest score may correspond to a “best” or a “selected” ESP system design. Job characteristics may also identify one or more of a length of production run, a cost of operation, replacement / repair costs, or other operating constraints. In certain instances, costs of production may be calculated from current energy rates and / or labor rates.

[0041] At block 250, equipment listings may be generated that include equipment that corresponds to the characteristics of the job. These equipment listings may also correspond to the application type and the one or more of the reliability metric and the cost metric discussed above. This means that a computer may be used to identify which sets ofequipment best correspond to a set of job characteristics and use case preferences (e.g., one or more reliability metrics and / or one or more cost metrics, or combination thereof). Reliability metrics may identify a projected failure rate or forecast an MTBF of a particular ESP system. Cost metrics may include or be associated with an amount of electrical power used to power an ESP system. Alternatively, or additionally, cost metrics may identify or correspond to replacement and / or repair costs. Here a replacement cost may include the price of a new pump and costs of shipping that part to a location. Repair costs may also include the costs of repair personnel, travel costs of the repair personnel, and / or costs associated with lost production.

[0042] At block 260, the characteristics and potentially other information may be presented to a user over a user interface of a computer. The information presented to the user may identify types of parts included in particular ESP system that may be suitable for use in a wellbore. At block 270, input may be received that identifies an ESP system that was selected for use in the wellbore.

[0043] At this point in time, other processes may be initiated. For example, parts of the identified ESP system may be ordered and the ESP system may be built according to an equipment listing identified based on the actions of FIG. 2 being performed.

[0044] In “very high temperature” applications, where parts of an ESP system will be subjected to temperatures over 100 degrees Celsius (C) (e g., in geothermal applications or in steam assisted gravity drainage systems), for example, only parts that can withstand temperatures characteristic of those environments may be selected for use. In contrast, in applications that move water out of a mine, a pump may be used that has an operational temperature range that does not exceed 100 degrees C.

[0045] FIG. 3 illustrates actions that may be performed when parts of an ESP system are identified. At block 310 a compliance score may be assigned to each ESP system of a plurality of ESP systems. As such, each of the equipment datasets identified at block 240 of FIG. 2 may be assigned a compliance score. This compliance score is a representation of how closely operational characteristics and possibly use history of a previously deployed ESP system corresponds to a new ESP system design and use case preferences for a particular job.

[0046] This compliance score may have many factors, some that relate to parts used to build an ESP system, some that correspond to characteristics of the wellbore where the job will be performed (e.g., temperature, temperature range, liquid specific gravity, gas specific gravity, measures corrosivity or acidity (e.g., pH), percent hydrogen sulfide (H2S), percent CO2, percent N2, abrasiveness, viscosity, gas liquid mix ratio, and / or other characteristics), some that relate to either reliability or cost (e.g., a reliability metric and / or a cost metric), and / or some that relate to production.

[0047] It some instances, only some of a set of factors of an existing ESP system and the new ESP system may match, yet such a match may be the closest match available. For example, a new and existing ESP system may only share 20 of 40 factors with an existing ESP system, yet these shared factors may be more significant than the factors that are not shared between the new and existing ESP system. In an instance, where the new ESP system has a use case preference regarding minimizing operating costs instead of maximizing production, a compliance score comparing the existing ESP system to the new ESP system could be increased based on reliability data of the existing ESP system. This may be because an ESP system that is used at less than full capacity (e.g., at a derated power level) may be expected to be more reliable than an ESP system that is used at full capacity (e.g., at a maximum specified power). In such instances, a derated power level may be set at 80% of the maximum power specification of a pump. In such an instance, when this pump has a 1000 Watt (W) maximum power rating, the derated power level would be 800 W because 1000 W * 0.80 = 800 W.

[0048] Once each ESP system of the plurality of ESP systems have been assigned a compliance score at block 320, datasets that include information for each respective ESP system may be arranged in order by score (e.g., from highest score to lowest score). At block 330, these compliance scores may be provided to an operator via a user interface. In some instances, only scores greater than a threshold value may be provided to the operator. For example, only scores that are greater than a score of 59 out of 100 may be provided to the operator. At block 340 a user selection may be received. This user selection may indicate that the operator wishes to review parts included in the ESP system design or wishes to review other data associated with the job (e.g., environmental characteristics and / or compatibility data of the equipment included in the ESP system). The user maymake a selection that indicates that a particular ESP system has been selected for use in a wellbore.

[0049] At block 350, a use plan may be generated. This use plan may correspond to the operation of the selected ESP system. This use plan may be consistent with use case preferences that were defined previously. The use plan may also include conditional operational parameters, for example, when an event that is believed to be a precursor of a failure is detected. An example of such a precursor may include the tripping of a circuit breaker or a temporary automatic shutdown of a motor. For example, a motor that shuts down due to a thermal event or an overcurrent draw, may been observed before a motor of that type failed in an existing wellbore. In such an instance, the use plan may dictate that the pump be operated at a derated power level. A new motor or pump that uses that motor may also be provided to the site based on the observance of the failure precursor event (e.g., tripped circuit breaker or thermal shutdown). This may include a well management system sending automated messages to an operator of a wellbore or by automatic control of the well management system.

[0050] At block 360, the use plan may be provided to the operator, another operator, or to the well management system. The use plan may allow an operator or well management system maintain operation of the ESP system according to the use plan after the ESP system is built and deployed in the wellbore.

[0051] In FIG. 4, the disclosure now turns to a further discussion of models that can be used through the environments and techniques described herein. FIG. 4 is an example of a deep learning neural network 400 that can be used to implement all or a portion of the systems and techniques described herein (e.g., neural network 400 can be used to implement a perception module (or perception system) as discussed above). An input layer 410 can be configured to receive sensor data and / or data relating to an environment. The neural network 400 includes multiple hidden layers 420a, 420b, through 420n. The hidden layers 420a, 420b, through 420n include “n” number of hidden layers, where “n” is an integer greater than or equal to one. The number of hidden layers can be made to include as many layers as needed for the given application. The neural network 400 further includes an output layer 430 that provides an output resulting from the processing performed by the hidden layers 420a, 420b, through 420n.

[0052] The neural network 400 may be a multi-layer neural network of interconnected nodes. Each node can represent a piece of information. Information associated with the nodes is shared among the different layers and each layer retains information as information is processed. In some cases, the neural network 400 can include a feed-forward network, in which case there are no feedback connections where outputs of the network are fed back into itself. In some cases, the neural network 400 can include a recurrent neural network, which can have loops that allow information to be carried across nodes while reading in input.

[0053] Information can be exchanged between nodes through node-to-node interconnections between the various layers. Nodes of the input layer 410 can activate a set of nodes in the first hidden layer 420a. For example, as shown, each of the input nodes of input layer 410 is connected to each of the nodes of the first hidden layer 420a. The nodes of the first hidden layer 420a can transform the information of each input node by applying activation functions to the input node information. The information derived from the transformation can then be passed to and can activate the nodes of the next hidden layer 420b, which can perform their own designated functions. Example functions include convolutional, up-sampling, data transformation, and / or any other suitable functions. The output of the hidden layer 420b can then activate nodes of the next hidden layer, and so on. The output of the last hidden layer 420n can activate one or more nodes of the output layer 430, at which an output is provided. In some cases, while nodes in the neural network 400 are shown as having multiple output lines, a node can have a single output and all lines shown as being output from a node represent the same output value.

[0054] In some cases, each node or interconnection between nodes can have a weight that is a set of parameters derived from the training of the neural network 400. Once the neural network 400 is trained, it can be referred to as a trained neural network, which can be used to classify one or more activities. For example, an interconnection between nodes can represent a piece of information learned about the interconnected nodes. The interconnection can have a tunable numeric weight that can be tuned (e.g., based on a training dataset), allowing the neural network 400 to be adaptive to inputs and able to learn as more and more data is processed.

[0055] In an illustrative example, a metric of interest may be identified and data from a plurality of wells may be accessed and evaluated such that wells with similar characteristics can be classified. In one instance, the metric of interest may be wells that produced greater than a threshold level of oil over a time period or wellbore life. Data from wells that meet this metric may be clustered such that information associated with depts, fluid ratios, or other characteristics may be parsed such that components of a new ESP system can be identified. A neural network may be used to classify and cluster data associated with historic wells into appropriate clusters, then use a trained model to classify the new well of interest into one of those clusters. A neural network that performs such classifications may be referred to as a “classification neural network.”

[0056] The neural network 400 is pre-trained to process the features from the data in the input layer 420 using the different hidden layers 420a, 420b, through 420n in order to provide the output through the output layer 430. As such, input layer 420 may receive clustered data and may use the optimization metric (e.g., the wells that produced the greater than the threshold level of oil over the time period or life of the wellbore). Parts that may be used in a new ESP system that were used in similar wells may be identified based on operation of the trained model. For example, in an instance with motor X and motor Y were determined to perform better than other motors in similar wells. Both of these motors may be provided to a user as part of one or another new ESP system design. In certain instances, that user may be allowed to select either motor X or motor Y independently of other parts used in the new ESP system design.

[0057] In some cases, the neural network 400 can adjust the weights of the nodes using a training process called backpropagation. A backpropagation process can include a forward pass, a loss function, a backward pass, and a weight update. The forward pass, loss function, backward pass, and parameter / weight update is performed for one training iteration. The process can be repeated for a certain number of iterations for each set of training data until the neural network 400 is trained well enough so that the weights of the layers are accurately tuned.

[0058] To perform training, a loss function can be used to analyze errors in the output. Any suitable loss function definition can be used, such as a Cross-Entropy loss. Anotherexample of a loss function includes the mean squared error (MSE), defined as E total = X(l / 2 (target-output)A2). The loss can be set to be equal to the value of E total.

[0059] The loss (or error) will be high for the initial training data since the actual values will be much different than the predicted output. The goal of training may be to minimize the amount of loss so that the predicted output is the same as the training output. The neural network 400 can perform a backward pass by determining which inputs (weights) most contributed to the loss of the network and can adjust the weights so that the loss decreases and is eventually minimized.

[0060] The neural network 400 can include any suitable deep network. One example includes a Convolutional Neural Network (CNN), which includes an input layer and an output layer, with multiple hidden layers between the input and out layers. The hidden layers of a CNN include a series of convolutional, nonlinear, pooling (for down sampling), and fully connected layers. The neural network 400 can include any other deep network other than a CNN, such as an autoencoder, Deep Belief Nets (DBNs), Recurrent Neural Networks (RNNs), among others.

[0061] As understood by those of skill in the art, machine-learning based classification techniques can vary depending on the desired implementation. For example, machinelearning classification schemes can utilize one or more of the following, alone or in combination: hidden Markov models; RNNs; CNNs; deep learning; Bayesian symbolic methods; Generative Adversarial Networks (GANs); support vector machines; image registration methods; and applicable rule-based systems. Where regression algorithms are used, they may include but are not limited to: a Stochastic Gradient Descent Regressor, a Passive Aggressive Regressor, etc.

[0062] Machine learning classification models can also be based on clustering algorithms (e.g., a Mini -batch K-means clustering algorithm), a recommendation algorithm (e.g., a Minwise Hashing algorithm, or Euclidean Locality-Sensitive Hashing (LSH) algorithm), and / or an anomaly detection algorithm, such as a local outlier factor. Additionally, machine-learning models can employ a dimensionality reduction approach, such as, one or more of: a Mini -batch Dictionary Learning algorithm, an incremental Principal Component Analysis (PCA) algorithm, a Latent Dirichlet Allocation algorithm, and / or a Mini-batch K- means algorithm, etc.

[0063] FIG. 5 illustrates an example of a well data sheet that may be included in a user interface. The well data sheet 500 includes many fields that may be filled with wellbore data. These fields include client information, units of measurement, wellbore data, wellbore fluid information, rates of production (IPR), and additional information. The client information of well data sheet 500 may identify a customer, a State and County where a particular well is located, a well number, a field indicator, contact person, a phone number, a cell phone number, an email address, and a date when the well data sheet 500 was filled out or otherwise populated.

[0064] Units included in well data sheet 500 may identify units of measure of length (e.g., feet), units of measure of tubing or casing sizes (e.g., inches), a trajectory of the well (e.g., vertical), a rate of production model (e g., composite Vogel IPR), a liquid flow rate (e g., barrels per day (BPD)), gas flow rate (e.g., GOR: a ratio of a volumetric flow of produced gas to the volumetric flow of oil gas flow rate), a pressure unit of measure (e.g., pounds per square inch (PSI), and a temperature unit of measure (e.g., degrees Fahrenheit (F)).

[0065] Wellbore data included in well data sheet 500 may identify a casing size (e.g., 5 1 ” X 23 (lb / ft)), a tubing size (e.g. 27 / 8” x 6.5 (lb / ft)), perforation details (Top Perfs), a tubing length (feet), a Datum temperature (degrees F), a well head temperature (degrees F), a tubing pressure (PSI), and a casing pressure (PSI).

[0066] The fluid field of well data sheet 500 may be used to identify a specific gravity of oil of the wellbore (oil gravity), a specific gravity of water of the wellbore (water gravity), a specific gravity of gas (gas gravity), a gas flow rate (GOR), and percentages of other materials included in fluids output from the wellbore. These fluid percentages include a percentage of water (percent water cut), a percentage of hydrogen sulfide (H2S), a percentage of carbon dioxide (CO2), and a percentage of nitrogen (N2).

[0067] The rates of production (IPR) fields of the well data sheet 500 identify a static pressure (PSI), a well bottom hole pressure (Pwf) in PSI, location of a datum point (feet), a test liquid rate (BPD), a desired flow rate (BPD), a bubble point pressure (PSI), and other data (standing Calc’ Pb and Q max at datum).

[0068] Additional information that may be included in well data sheet 500 include an indication as to whether fluid in the wellbore is corrosive (Y / N), scale information, an indication regarding the presence of paraffin (Y / N), an indication as to whether thewellbore fluid is viscous (Y / N), a viscosity test indicator, temperature at which a viscosity test was conducted (degree F), and an indication as to whether a packer is located above a pump (Y / N). This additional information may also identify a type of surface controller, a surface voltage (e.g., a phase-to-phase voltage), a frequency of the surface voltage (Hertz), a measure of power available (KVA), downhole sensor information, an indication of a type of well head of the wellbore, and an indication of a type of penetrator of the wellbore. Well data sheet 500 also includes an area where notes can be added.

[0069] Well data sheet 500 of FIG. 5 may be provided to an operator via a user interface. In certain instances, some of the data included in a well data sheet may be automatically entered based on data accessible by a computer. In other instances, some or all of this data may be entered by the operator. Some of the data may identify dimension of a wellbore that parts of an ESP system will have to fit into (e.g., a casing or tubing size), other entries may identify fluid or wellbore (e.g., specific gravity, corrosiveness, wellbore temperatures or pressures), and still other information may correspond to user preferences (e.g., desired flow rate). As such, well data sheet 500 may be used to identify crucial characteristics of a job where an ESP system will be deployed and may be used to identify at least some preferences that an operator may have. One or more of the fields used to collect data may have selectable units of measure (e.g., inches, centimeters, feet, meters, PSI, degrees F, or degrees C). Some or all of the information included in data sheet 500 may be used by a processor that executes instructions of a computer model when that processor performs actions consistent with those discussed in respect to FIGS 2-3 of this disclosure.

[0070] FIG. 6 illustrates an example computing device architecture 600 which can be employed to perform various steps, methods, and techniques disclosed herein. The various implementations will be apparent to those of ordinary skill in the art when practicing the present technology. Persons of ordinary skill in the art will also readily appreciate that other system implementations or examples are possible.

[0071] As noted above, FIG. 6 illustrates an example computing device architecture 600 of a computing device which can implement the various technologies and techniques described herein. The components of the computing device architecture 600 are shown in electrical communication with each other using a connection 605, such as a bus. The example computing device architecture 600 includes a processing unit (CPU or processor)610 and a computing device connection 605 that couples various computing device components including the computing device memory 615, such as read only memory (ROM) 620 and random access memory (RAM) 625, to the processor 610.

[0072] The computing device architecture 600 can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor 610. The computing device architecture 600 can copy data from the memory 615 and / or the storage device 630 to the cache 612 for quick access by the processor 610. In this way, the cache can provide a performance boost that avoids processor 610 delays while waiting for data. These and other modules can control or be configured to control the processor 610 to perform various actions. Other computing device memory 615 may be available for use as well. The memory 615 can include multiple different types of memory with different performance characteristics. The processor 610 can include any general purpose processor and a hardware or software service, such as service 1 632, service 2 634, and service 3 636 stored in storage device 630, configured to control the processor 610 as well as a specialpurpose processor where software instructions are incorporated into the processor design. The processor 610 may be a self-contained system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

[0073] To enable user interaction with the computing device architecture 600, an input device 645 can represent 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 device 635 can also be one or more of a number of output mechanisms known to those of skill in the art, such as a display, projector, television, speaker device, etc. In some instances, multimodal computing devices can enable a user to provide multiple types of input to communicate with the computing device architecture 600. The communications interface 640 can generally govern and manage the user input and computing device output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

[0074] Storage device 630 is a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, suchas magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs) 625, read only memory (ROM) 620, and hybrids thereof. The storage device 630 can include services 632, 634, 636 for controlling the processor 610. Other hardware or software modules are contemplated. The storage device 630 can be connected to the computing device connection 605. In one aspect, a hardware module that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as the processor 610, connection 605, output device 635, and so forth, to carry out the function.

[0075] For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.

[0076] In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

[0077] Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer readable media. Such instructions can include, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or a processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, source code, etc. Examples of computer-readable media that may be used to store instructions, information used, and / or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.

[0078] Devices implementing methods according to these disclosures can include hardware, firmware and / or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personalcomputers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

[0079] The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functions described in the disclosure.

[0080] In the foregoing description, aspects of the application are described with reference to specific embodiments thereof, but those skilled in the art will recognize that the application is not limited thereto. Thus, while illustrative embodiments of the application have been described in detail herein, it is to be understood that the disclosed concepts may be otherwise variously embodied and employed, and that the appended claims are intended to be construed to include such variations, except as limited by the prior art. Various features and aspects of the above-described subject matter may be used individually or jointly. Further, embodiments can be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive. For the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate embodiments, the methods may be performed in a different order than that described.

[0081] Where components are described as being “configured to” perform certain operations, such configuration can be accomplished, for example, by designing electronic circuits or other hardware to perform the operation, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits) to perform the operation, or any combination thereof.

[0082] The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the examples disclosed herein may be implemented as electronic hardware, computer software, firmware, or combinations thereof. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software dependsupon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

[0083] The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices such as general purposes computers, wireless communication device handsets, or integrated circuit devices having multiple uses including application in wireless communication device handsets and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be realized at least in part by a computer- readable data storage medium comprising program code including instructions that, when executed, performs one or more of the method, algorithms, and / or operations described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials.

[0084] The computer-readable medium may include memory or data storage media, such as random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, and the like. The techniques additionally, or alternatively, may be realized at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and / or executed by a computer, such as propagated signals or waves.

[0085] Other embodiments of the disclosure 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. Embodiments may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (eitherby 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.

[0086] In the above description, terms such as "upper," "upward," "lower," "downward," "above," "below," "downhole," "uphole," "longitudinal," "lateral," and the like, as used herein, shall mean in relation to the bottom or furthest extent of the surrounding wellbore even though the wellbore or portions of it may be deviated or horizontal. Correspondingly, the transverse, axial, lateral, longitudinal, radial, etc., orientations shall mean orientations relative to the orientation of the wellbore or wellbore tool. Additionally, the illustrate embodiments are illustrated such that the orientation is such that the right-hand side is downhole compared to the left-hand side.

[0087] The term "coupled" is defined as connected, whether directly or indirectly through intervening components, and is not necessarily limited to physical connections. The connection can be such that the objects are permanently connected or releasably connected. The term "outside" refers to a region that is beyond the outermost confines of a physical object. The term "inside" indicates that at least a portion of a region is partially contained within a boundary formed by the object. The term "substantially" is defined to be essentially conforming to the particular dimension, shape or another word that substantially modifies, such that the component need not be exact. For example, substantially cylindrical means that the object resembles a cylinder, but can have one or more deviations from a true cylinder.

[0088] The term "radially" means substantially in a direction along a radius of the object, or having a directional component in a direction along a radius of the object, even if the object is not exactly circular or cylindrical. The term "axially" means substantially along a direction of the axis of the object. If not specified, the term axially is such that it refers to the longer axis of the object.

[0089] Although a variety of information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements, as one of ordinary skill would be able to derive a wide variety of implementations. Further and although some subject matter may have been described in language specific to structural features and / or method steps, it is to be understood that thesubject matter defined in the appended claims is not necessarily limited to these described features or acts. Such functionality can be distributed differently or performed in components other than those identified herein. The described features and steps are disclosed as possible components of systems and methods within the scope of the appended claims.

[0090] Moreover, claim language reciting “at least one of’ a set indicates that one member of the set or multiple members of the set satisfy the claim. For example, claim language reciting “at least one of A and B” means A, B, or A and B.

[0091] Statements of the disclosure include:

[0092] Statement 1: A method comprising receiving input associated with an electrical submersible pump (ESP) system design, wherein the input identifies characteristics of a job where a selected ESP system will be deployed; accessing wellbore data associated with operation of a plurality of different ESP system; arranging the wellbore data associated with the operation of the plurality of different ESP systems into a plurality of datasets based on an application type and one or more of a reliability metric or a cost metric; identifying one or more of the respective datasets of the plurality of datasets that correspond to the characteristics of the job, the application type, and the one or more of the reliability metric and the cost metric; generating equipment listings of a plurality of different ESP systems for performing the job based on one or more respective datasets that correspond to the characteristics of the job, the application type, and the one or more of the reliability metric and the cost metric; and presenting characteristics of the selected ESP system selected from the plurality of different ESP systems.

[0093] Statement 2: The method of statement 1, further comprising identifying ESP system components included in each respective ESP system of the plurality ESP systems based on an evaluation of the wellbore data; and identifying at least one of the reliability metrics or the cost metrics associated with each of the respective ESP systems based on the evaluation of the wellbore data.

[0094] Statement 3: The method of statements 1 or 2, wherein the reliability metric corresponds to at least one of a failure rate and a volume of production value.

[0095] Statement 4: The method of any of statements 1 through 3, wherein the cost metric includes at least one of an operational return value, a running operational cost value, and a cost of repair value.

[0096] Statement 5: The method of any of statements 1 through 4, further comprising generating a score for each ESP system of the plurality of different ESP systems; and identifying one or more ESP systems of the plurality of different ESP systems that have a score that at least meets an ESP system score threshold, wherein the selected ESP system is selected from the one or more identified ESP systems.

[0097] Statement 6: The method of any of statements 1 through 5, wherein the characteristics of the job include one or more requirements of components of the ESP system design, an application type of a plurality of application types, and a use case preference of a wellbore where the selected ESP system.

[0098] Statement 7 : The method of any of statements 1 through 6, wherein the use case preference prioritizes one or more of production volume over operating cost, the operating cost over the production volume, the production volume over repair cost, the repair cost over production the production volume, a reliability metric over per-unit-time production volume, and per-unit-time production volume over the reliability metric.

[0099] Statement 8: The method of any of statements 1 through 7, wherein the characteristics of the selected ESP system include one or more of an operational voltage, an operational current, an operational power, body size of a pump of the ESP system, a pipe size of the pump, a volumetric pump rate of the pump, and a flow rate versus efficiency of the pump.

[0100] Statement 9: An apparatus comprising: a memory; and one or more processors that execute instructions out of the memory to: receive input associated with an electrical submersible pump (ESP) system design, wherein the input identifies characteristics of a job where a selected ESP system will be deployed; access wellbore data associated with operation of a plurality of different ESP systems; arrange the wellbore data associated with the operation of the plurality of different ESP systems into a plurality of datasets based on an application type and one or more of a reliability metric or a cost metric; identify one or more of the respective datasets of the plurality of datasets that correspond to the characteristics of the job, the application type, and the one or more of the reliability metricand the cost metric; generate equipment listings of a plurality of different ESP systems for performing the job based on one or more respective datasets that correspond to the characteristics of the job, the application type, and the one or more of the reliability metric and the cost metric; and prepare to present characteristics of the selected ESP systems selected from the plurality of different ESP systems.

[0101] Statement 10: The apparatus of statement 9, wherein the one or more processors execute the instructions out of the memory to identify ESP system components included in each respective ESP system of the plurality ESP systems based on an evaluation of the wellbore data; and identify the at least one of the reliability metric or the cost metric associated with each of the respective ESP systems based on the evaluation of the wellbore data.

[0102] Statement 11 : The apparatus of statement 9 or 10, wherein the reliability metric corresponds to at least one of a failure rate and a volume of production value.

[0103] Statement 12: The apparatus of any of statements 9 through 11, wherein the cost metric includes at least one of an operational return value, a running operational cost value, and a cost of repair value.

[0104] Statement 13: The apparatus of any of statements 9 through 12, wherein the one or more processors execute the instructions out of the memory to generate a score for each ESP system of the plurality of different ESP systems; and identifying one or more ESP systems of the plurality of different ESP systems that have a score that at least meets an ESP system score threshold, wherein the selected ESP system is selected from the one or more identified ESP systems.

[0105] Statement 14: The apparatus of any of statements 9 through 13, wherein the characteristics of the job include one or more requirements of components of the ESP system design, an application type of a plurality of application types, and a use case preference of a wellbore where the selected ESP system.

[0106] Statement 15: A non-transitory computer-readable storage medium having embodied thereon instructions executable by one or more processors to receive input associated with an electrically submersible pump (ESP) system design, wherein the input identifies characteristics of a job where a selected ESP system will be deployed; access wellbore data associated with operation of a plurality of different ESP systems; arrange thewellbore data associated with the operation of the plurality of different ESP systems into a plurality of datasets based on an application type and one or more of a reliability metric or a cost metric; identify one or more of the respective datasets of the plurality of datasets that correspond to the characteristics of the job, the application type, and the one or more of the reliability metric and the cost metric; generate equipment listings of a plurality of different ESP systems for performing the job based on one or more respective datasets that correspond to the characteristics of the job, the application type, and the one or more of the reliability metric and the cost metric; and prepare characteristics of the selected ESP system selected from the plurality of different ESP systems for presentation.

[0107] Statement 16: The non-transitory computer-readable storage medium of statement 15, wherein the one or more processors execute the instructions to identify ESP system components included in each respective ESP system of the plurality ESP systems based on an evaluation of the wellbore data; and identify the at least one of the reliability metric or the cost metric associated with each of the respective ESP systems based on the evaluation of the wellbore data.

[0108] Statement 17: The non-transitory computer-readable storage medium of statements 15 or 16, wherein the reliability metric corresponds to at least one of a failure rate and a volume of production value.

[0109] Statement 18: The non-transitory computer-readable storage medium of any of statements 15 through 17, wherein the cost metric includes at least one of an operational return value, a running operational cost value, and a cost of repair value.

[0110] Statement 19: The non-transitory computer-readable storage medium of any of statements 15 through 18, wherein the one or more processors execute the instructions to generate a score for each ESP system of the plurality of different ESP systems; and identifying one or more ESP systems of the plurality of different ESP systems that have a score that at least meets an ESP system score threshold, wherein the selected ESP system is selected from the one or more identified ESP systems.

[0111] Statement 20: The non-transitory computer-readable storage medium of any of statements 15 through 19, wherein the characteristics of the job include one or more requirements of components of the ESP system design, an application type of a pluralityof application types, and a use case preference of a wellbore where the selected ESP system.

Claims

CLAIMSWHAT IS CLAIMED IS:

1. A method comprising: receiving input associated with an electrical submersible pump (ESP) system design, wherein the input identifies characteristics of a job where a selected ESP system will be deployed; accessing wellbore data associated with operation of a plurality of different ESP system; arranging the wellbore data associated with the operation of the plurality of different ESP systems into a plurality of datasets based on an application type and one or more of a reliability metric or a cost metric; identifying one or more of the respective datasets of the plurality of datasets that correspond to the characteristics of the job, the application type, and the one or more of the reliability metric and the cost metric; generating equipment listings of a plurality of different ESP systems for performing the job based on one or more respective datasets that correspond to the characteristics of the job, the application type, and the one or more of the reliability metric and the cost metric; and presenting characteristics of the selected ESP system selected from the plurality of different ESP systems.

2. The method of claim 1, further comprising: identifying ESP system components included in each respective ESP system of the plurality ESP systems based on an evaluation of the wellbore data; and identifying at least one of the reliability metrics or the cost metrics associated with each of the respective ESP systems based on the evaluation of the wellbore data.

3. The method of claim 1, wherein the reliability metric corresponds to at least one of a failure rate and a volume of production value.

4. The method of claim 1, wherein the cost metric includes at least one of an operational return value, a running operational cost value, and a cost of repair value.

5. The method of claim 1, further comprising: generating a score for each ESP system of the plurality of different ESP systems; and identifying one or more ESP systems of the plurality of different ESP systems that have a score that at least meets an ESP system score threshold, wherein the selected ESP system is selected from the one or more identified ESP systems.

6. The method of claim 1, wherein the characteristics of the job include: one or more requirements of components of the ESP system design, an application type of a plurality of application types, and a use case preference of a wellbore where the selected ESP system.

7. The method of claim 6, wherein the use case preference prioritizes one or more of: production volume over operating cost, the operating cost over the production volume, the production volume over repair cost, the repair cost over production the production volume, a reliability metric over per-unit-time production volume, and per-unit-time production volume over the reliability metric.

8. The method of claim 1, wherein the characteristics of the selected ESP system include one or more of an operational voltage, an operational current, an operational power, body size of a pump of the ESP system, a pipe size of the pump, a volumetric pump rate of the pump, and a flow rate versus efficiency of the pump.

9. An apparatus comprising: a memory; and one or more processors that execute instructions out of the memory to:receive input associated with an electrical submersible pump (ESP) system design, wherein the input identifies characteristics of a job where a selected ESP system will be deployed; access wellbore data associated with operation of a plurality of different ESP systems; arrange the wellbore data associated with the operation of the plurality of different ESP systems into a plurality of datasets based on an application type and one or more of a reliability metric or a cost metric; identify one or more of the respective datasets of the plurality of datasets that correspond to the characteristics of the job, the application type, and the one or more of the reliability metric and the cost metric; generate equipment listings of a plurality of different ESP systems for performing the job based on one or more respective datasets that correspond to the characteristics of the job, the application type, and the one or more of the reliability metric and the cost metric; and prepare to present characteristics of the selected ESP systems selected from the plurality of different ESP systems.

10. The apparatus of claim 9, wherein the one or more processors execute the instructions out of the memory to: identify ESP system components included in each respective ESP system of the plurality ESP systems based on an evaluation of the wellbore data; and identify the at least one of the reliability metric or the cost metric associated with each of the respective ESP systems based on the evaluation of the wellbore data.

11. The apparatus of claim 9, wherein the reliability metric corresponds to at least one of a failure rate and a volume of production value.

12. The apparatus of claim 9, wherein the cost metric includes at least one of an operational return value, a running operational cost value, and a cost of repair value.

13. The apparatus of claim 9, wherein the one or more processors execute the instructions out of the memory to: generate a score for each ESP system of the plurality of different ESP systems; and identifying one or more ESP systems of the plurality of different ESP systems that have a score that at least meets an ESP system score threshold, wherein the selected ESP system is selected from the one or more identified ESP systems.

14. The apparatus of claim 9, wherein the characteristics of the job include: one or more requirements of components of the ESP system design, an application type of a plurality of application types, and a use case preference of a wellbore where the selected ESP system.

15. A non-transitory computer-readable storage medium having embodied thereon instructions executable by one or more processors to: receive input associated with an electrically submersible pump (ESP) system design, wherein the input identifies characteristics of a job where a selected ESP system will be deployed; access wellbore data associated with operation of a plurality of different ESP systems; arrange the wellbore data associated with the operation of the plurality of different ESP systems into a plurality of datasets based on an application type and one or more of a reliability metric or a cost metric; identify one or more of the respective datasets of the plurality of datasets that correspond to the characteristics of the job, the application type, and the one or more of the reliability metric and the cost metric; generate equipment listings of a plurality of different ESP systems for performing the job based on one or more respective datasets that correspond to the characteristics of the job, the application type, and the one or more of the reliability metric and the cost metric; and prepare characteristics of the selected ESP system selected from the plurality of different ESP systems for presentation.

16. The non-transitory computer-readable storage medium of claim 15, wherein the one or more processors execute the instructions to: identify ESP system components included in each respective ESP system of the plurality ESP systems based on an evaluation of the wellbore data; and identify the at least one of the reliability metric or the cost metric associated with each of the respective ESP systems based on the evaluation of the wellbore data.

17. The non-transitory computer-readable storage medium of claim 15, wherein the reliability metric corresponds to at least one of a failure rate and a volume of production value.

18. The non-transitory computer-readable storage medium of claim 15, wherein the cost metric includes at least one of an operational return value, a running operational cost value, and a cost of repair value.

19. The non-transitory computer-readable storage medium of claim 15, wherein the one or more processors execute the instructions to: generate a score for each ESP system of the plurality of different ESP systems; and identifying one or more ESP systems of the plurality of different ESP systems that have a score that at least meets an ESP system score threshold, wherein the selected ESP system is selected from the one or more identified ESP systems.

20. The non-transitory computer-readable storage medium of claim 15, wherein the characteristics of the job include: one or more requirements of components of the ESP system design, an application type of a plurality of application types, and a use case preference of a wellbore where the selected ESP system.

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