Patent Application
20240328281
An inflow control device (ICD) is a flow restricting device that is deployed as part of well completions to improve even distribution of the inflow into the producing wellbore. A flow control valve (FCV) is a device that prevents excessive flow by limiting flow to a pre-selected maximum rate, regardless of changing line pressure. Throughout this disclosure, the inflow-control device and the flow control valve are collectively referred to as the ICD. The ICD restricts flow by creating additional pressure drop and therefore adjusting wellbore pressure distribution to achieve an evenly distributed flow profile along a horizontal well or a horizontal wellbore. A horizontal wellbore is a section of high-angle wellbore drilled at an angle of at least eighty degrees to a vertical wellbore. A horizontal well is a drilled well that includes a portion that is a horizontal wellbore. Horizontal wells are drilled to enhance reservoir performance by placing a long wellbore section within the reservoir. A more evenly distributed flow profile reduces water or gas coning, prevents sand production, and solves other drawdown-related production problems. In general, ICDs are not adjustable. Once installed in the well, the location of the ICD and the relationship between resulting flow rate and pressure drop are fixed. The location of the ICD in the wellbore and the relationship between resulting flow rate and pressure drop along the wellbore are referred to as the design specification of the ICD.
In general, in one aspect, embodiments disclosed herein relate to a method to perform well completion in a field. The method includes obtaining a plurality of well logs of a candidate horizontal well and associated vertical offset wells, the plurality of well logs comprising petrophysical properties of a formation in the field, generating, based on the petrophysical properties of the plurality of well logs, a static geological model of the formation, generating, by performing reservoir simulation based on the static geological model, a dynamic geological model, and generating a plurality of inflow control device (ICD) models for the candidate horizontal well, generating, using a statistical algorithm and based on the dynamic geological model, a plurality of statistical predictions of production performance of the candidate horizontal well corresponding to the plurality of ICD models, selecting, based on a ranking of the plurality of statistical predictions of production performance of the candidate horizontal well, an optimal ICD model, and installing, based on the optimal ICD model, a plurality of ICDs in the candidate horizontal well.
In general, in one aspect, embodiments disclosed herein relate to an inflow control device (ICD) analyzer to perform well completion in a field. The ICD analyzer includes a computer processor and memory storing instructions, when executed by the computer processor comprising functionality for obtaining a plurality of well logs of a candidate horizontal well and associated vertical offset wells, the plurality of well logs comprising petrophysical properties of a formation in the field, generating, based on the petrophysical properties of the plurality of well logs, a static geological model of the formation, generating, by performing reservoir simulation based on the static geological model, a dynamic geological model, generating a plurality of inflow control device (ICD) models for the candidate horizontal well, generating, using a statistical algorithm and based on the dynamic geological model, a plurality of statistical predictions of production performance of the candidate horizontal well corresponding to the plurality of ICD models, selecting, based on a ranking of the plurality of statistical predictions of production performance of the candidate horizontal well, an optimal ICD model, and installing, based on the optimal ICD model, a plurality of ICDs in the candidate horizontal well.
In general, in one aspect, embodiments disclosed herein relate to a system that includes a wellsite for performing well production in a field, the wellsite comprising a candidate horizontal well and associated vertical offset wells, and an ICD analyzer comprising a computer processor and memory storing instructions, when executed by the computer processor comprising functionality for obtaining a plurality of well logs of the candidate horizontal well and the associated vertical offset wells, the plurality of well logs comprising petrophysical properties of a formation in the field, generating, based on the petrophysical properties of the plurality of well logs, a static geological model of the formation, generating, by performing reservoir simulation based on the static geological model, a dynamic geological model, generating a plurality of inflow control device (ICD) models for the candidate horizontal well, generating, using a statistical algorithm and based on the dynamic geological model, a plurality of statistical predictions of production performance of the candidate horizontal well corresponding to the plurality of ICD models, selecting, based on a ranking of the plurality of statistical predictions of production performance of the candidate horizontal well, an optimal ICD model, and installing, based on the optimal ICD model, a plurality of ICDs in the candidate horizontal well.
Other aspects and advantages of the claimed subject matter will be apparent from the following description and the appended claims.
Specific embodiments of the disclosed technology will now be described in detail with reference to the accompanying figures. Like elements in the various figures are denoted by like reference numerals for consistency.
In the following detailed description of embodiments of the disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to one of ordinary skill in the art that the disclosure may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.
Throughout the application, ordinal numbers (for example, first, second, third) may be used as an adjective for an element (that is, any noun in the application). The use of ordinal numbers is not to imply or create any particular ordering of the elements nor to limit any element to being only a single element unless expressly disclosed, such as using the terms “before”, “after”, “single”, and other such terminology. Rather, the use of ordinal numbers is to distinguish between the elements. By way of an example, a first element is distinct from a second element, and the first element may encompass more than one element and succeed (or precede) the second element in an ordering of elements.
In general, embodiments of the invention include a method and system for performing well completion operation of a horizontal well in a field. In one or more embodiments of the invention, inflow-control devices (ICDs) in the horizontal well are designed and specified using a single medium high-resolution dynamic model. Accordingly, the well completion operation of the horizontal well is performed according to the ICD specification. The high-resolution dynamic model incorporates actual petrophysical log data (e.g., logging-while-drilling (LWD) log) from a newly drilled horizontal well to ensure representative prediction of production performance of the horizontal well equipped with ICDs. In contrast to the current practice in the petroleum industry, the high-resolution dynamic model incorporates all geological heterogeneities in the formation and predicts production performance at contiguous time-steps.
Those skilled in the art will appreciate that while the disclosure refers to ICDs, embodiments disclosed herein may equally apply to FCVs or any other type of flow restriction apparatuses.
In some embodiments, the well system (106) includes a wellbore (120), a well sub-surface system (122), a well surface system (124), and a well control system (“control system”) (126). The area where the components of the well system (106) are located is referred to as a wellsite. The control system (126) may control various operations of the well system (106), such as well production operations, well completion operations, well maintenance operations, and reservoir monitoring, assessment and development operations. In some embodiments, the control system (126) includes a computer system that is the same as or similar to that of the computer system (400) described below in
The wellbore (120) may include a bored hole that extends from the surface (108) into a target zone of the hydrocarbon-bearing formation (104), such as the reservoir (102). An upper end of the wellbore (120), terminating at or near the surface (108), may be referred to as the “up-hole” end of the wellbore (120), and a lower end of the wellbore, terminating in the hydrocarbon-bearing formation (104), may be referred to as the “down-hole” end of the wellbore (120). As shown in
In some embodiments, during operation of the well system (106), the control system (126) collects and records wellhead data (140) for the well system (106). The wellhead data (140) may include, for example, a record of measurements of wellhead pressure (Pwh) (e.g., including flowing wellhead pressure), wellhead temperature (Twh) (e.g., including flowing wellhead temperature), wellhead production rate (Qwh) over some or all of the life of the well system (106), and water cut data. In some embodiments, the measurements are recorded in real-time, and are available for review or use within seconds, minutes or hours of the condition being sensed (e.g., the measurements are available within 1 hour of the condition being sensed). In such an embodiment, the wellhead data (140) may be referred to as “real-time” wellhead data (140). Real-time wellhead data (140) may enable an operator of the well system (106) to assess a relatively current state of the well system (106), and make real-time decisions regarding development of the well system (106) and the reservoir (102), such as on-demand adjustments in regulation of production flow from the well.
In some embodiments, the well sub-surface system (122) includes casing installed in the wellbore (120). For example, the wellbore (120) may have a cased portion and an uncased (or “open-hole”) portion. The cased portion may include a portion of the wellbore having casing (e.g., casing pipe and casing cement) disposed therein. The uncased portion may include a portion of the wellbore not having casing disposed therein. In some embodiments, the casing includes an annular casing that lines the wall of the wellbore (120) to define a central passage that provides a conduit for the transport of tools and substances through the wellbore (120). For example, the central passage may provide a conduit for lowering logging tools into the wellbore (120), a conduit for the flow of production (121) (e.g., oil and gas) from the reservoir (102) to the surface (108), or a conduit for the flow of injection substances (e.g., water) from the surface (108) into the hydrocarbon-bearing formation (104). In some embodiments, the well sub-surface system (122) includes production tubing installed in the wellbore (120). The production tubing may provide a conduit for the transport of tools and substances through the wellbore (120). The production tubing may, for example, be disposed inside casing. In such an embodiment, the production tubing may provide a conduit for some or all of the production (121) (e.g., oil and gas) passing through the wellbore (120) and the casing.
In some embodiments, the well surface system (124) includes a wellhead (130). The wellhead (130) may include a rigid structure installed at the “up-hole” end of the wellbore (120), at or near where the wellbore (120) terminates at the Earth's surface (108). The wellhead (130) may include structures for supporting (or “hanging”) casing and production tubing extending into the wellbore (120). Production (121) may flow through the wellhead (130), after exiting the wellbore (120) and the well sub-surface system (122), including, for example, the casing and the production tubing. In some embodiments, the well surface system (124) includes flow regulating devices that are operable to control the flow of substances into and out of the wellbore (120). For example, the well surface system (124) may include one or more production valves (132) that are operable to control the flow of production (121). For example, a production valve (132) may be fully opened to enable unrestricted flow of production (121) from the wellbore (120), the production valve (132) may be partially opened to partially restrict (or “throttle”) the flow of production (121) from the wellbore (120), and production valve (132) may be fully closed to fully restrict (or “block”) the flow of production (121) from the wellbore (120), and through the well surface system (124).
Keeping with
In some embodiments, the surface sensing system (134) includes a surface pressure sensor (136) operable to sense the pressure of production (121) flowing through the well surface system (124), after it exits the wellbore (120). The surface pressure sensor (136) may include, for example, a wellhead pressure sensor that senses a pressure of production (121) flowing through or otherwise located in the wellhead (130). In some embodiments, the surface sensing system (134) includes a surface temperature sensor (138) operable to sense the temperature of production (121) flowing through the well surface system (124), after it exits the wellbore (120). The surface temperature sensor (138) may include, for example, a wellhead temperature sensor that senses a temperature of production (121) flowing through or otherwise located in the wellhead (130), referred to as “wellhead temperature” (Twh). In some embodiments, the surface sensing system (134) includes a flow rate sensor (139) operable to sense the flow rate of production (121) flowing through the well surface system (124), after it exits the wellbore (120). The flow rate sensor (139) may include hardware that senses a flow rate of production (121) (Qwh) passing through the wellhead (130).
In some embodiments, the well system (106) includes an ICD analyzer (160). For example, the ICD analyzer (160) may include hardware and/or software with functionality for generating a high resolution dynamic geological model regarding the hydrocarbon-bearing formation (104) and/or performing one or more reservoir simulations using the high resolution dynamic geological model regarding the hydrocarbon-bearing formation (104) and/or performing one or more. For example, the ICD analyzer (160) may store well logs and geological data of the formation for generating the high resolution dynamic geological model. Accordingly, the high resolution dynamic geological model is used to perform reservoir simulations to generate and/or update one or more ICD specifications of candidate horizontal wells. A candidate horizontal well is a well with a horizontal wellbore section that has not been completed and is planned to be completed by installing ICDs according to the ICD specifications generated from the high resolution dynamic geological model. While the ICD analyzer (160) is shown at a wellsite, embodiments are contemplated where the ICD analyzer (160) is located away from wellsites. In some embodiments, the ICD analyzer (160) may include a computer system that is similar to the computer system (400) described below with regard to
In a typical reservoir simulation, a mathematical model of the reservoir includes a set of partial differential equations representing reservoir and well flows that are solved numerically. Numerical solution involves time and space/domain discretization replacing differential equations with difference equations. Time discretization refers to division of time into a sequence of time steps. In each time step, after discretization is solved iteratively, a non-linear system is linearized using Newton method, which may take several Newton iterations to converge. Space/domain discretization, also called grid generation, refers to division of the reservoir domain into a reservoir grid of small grid blocks. A grid is a tessellation of a set of contiguous polygonal (2D) or polyhedral (3D) objects referred to as grid blocks/cells/elements/control volumes. The geological model used by the ICD analyzer (160) represents the reservoir (102) based on a grid with corresponding petrophysical data.
As shown in
Initially in Step 200, a recently acquired logging-while-drilling (LWD) well log from the candidate horizontal well and petrophysical log data of associated vertical offset wells are obtained. An offset well is an existing well close to the candidate horizontal well that provides information for planning the candidate horizontal well. In one or more embodiments, the LWD well log includes petrophysical log data generated by a downhole sensor as the downhole sensor traverses a trajectory of the candidate horizontal well, such as various segments in a horizontal wellbore of the candidate horizontal well. The petrophysical data relates to physical and chemical rock properties and their interactions with fluids. The petrophysical log data represents petrophysical properties of the formation such as lithology, porosity, water saturation, permeability, saturation, etc. In some embodiments, petrophysical log data is obtained from three vertical offset wells within a pre-determined distance (e.g., 0.625 miles) from the candidate horizontal well that form a triangular enclosure of the candidate horizontal well. In some embodiments, petrophysical log data is obtained from four vertical offset wells within a pre-determined distance (e.g., 0.315 miles) from the candidate horizontal well that form a quadrilateral enclosure of the candidate horizontal well.
In Step 201, the petrophysical log data from the candidate horizontal well and the associated offset wells are analyzed to identity petrophysical properties including effective porosity, explicit permeability data including both fracture and matrix, current water saturation, and latest formation tops of all existing geological formations. Throughout this disclosure, the term “petrophysical properties” refers to the values of petrophysical properties.
In Step 202, the identified petrophysical properties (e.g., effective porosity, permeability, and water saturation) of the candidate horizontal well and the associated offset wells are upscaled. Upscaling is the process of assigning well log data to grid cells of a geological model. Each grid cell in the geological model has a single value for each petrophysical property. Because the grid cells are much larger than the well logs density, well log data is scaled up before being assigned to the grid cells. In one or more embodiments, the effective porosity and current water saturation are upscaled using the arithmetic method of nearest points. In other words, the values of the porosity and current water saturation of nearest points in the well log to a particular grid cell are averaged (i.e., arithmetic mean) and assigned to the grid cell. In contrast, the permeability is upscaled using a geometric method based on geometric mean.
In Step 203, geological surfaces are constructed using Kriging interpolation of formation tops from the candidate horizontal well and the associated offset wells in the triangular or quadrilateral enclosure of the candidate horizontal well. Kriging interpolation is a geostatistical interpolation technique that is based on both the distance and the degree of variation between known data points when estimating values in unknown areas. In some embodiments, the formation tops are extracted from the well log data. The constructed geological surfaces are used to represent horizons in the formation. A horizon is either a bedding surface where there is marked change in the lithology within a sequence of sedimentary or volcanic rocks, or a distinctive layer or thin bed with a characteristic lithology or fossil content within a sequence.
In Step 204, a three-dimensional (3D) static geological model based on a single-porosity medium is generated using the upscaled well log data and the geological surfaces as horizons. The 3D static geological model represents the reservoir at the time point (referred to as the starting time) when logging operations of the candidate horizontal well and the vertical offset wells are performed to obtain the well logs in Step 200. The single-porosity medium corresponds to non-fractured rocks in the reservoir. In other words, the enclosure defined by the vertical offset wells corresponds to a region of non-fractured rocks in the reservoir. In one or more embodiments, the dimension of each grid cell of the 3D static geological model is set at maximum of ten meters in X-direction by ten meters in Y-direction and by one-foot in Z-direction. The petrophysical properties of the candidate horizontal well and the associated offset wells are distributed in the 3D static geological model using sequential Gaussian simulation throughout the triangular or quadrilateral enclosure of the candidate horizontal well. In one or more embodiments, the sequential Gaussian simulation uses the kriging mean and variance from the Kriging interpolation to generate a Gaussian field of the petrophysical properties. The sequential Gaussian simulation generates multiple equally probable realizations that are post-processed to quantify and assess uncertainty. An example of the multiple equally probable realizations is shown in Block 301h depicted in
In Step 205, a high resolution dynamic geological model is generated from the 3D static geological model using the same grid cells without further upscaling. In particular, the 3D static geological model is used as an initial condition (i.e., starting point) of the reservoir simulation to generate time-varying (i.e., dynamic) petrophysical property values in the dynamic geological model. The time-varying petrophysical property values in the dynamic geological model correspond to a sequence of simulation time steps of the reservoir simulation. In one or more embodiments, the enumeration method is used to initialize the dynamic geological model while retaining petrophysical properties from the 3D static geological model. The enumeration method is an algorithm that enumerates the answers to a computational problem by taking an input and producing a list of solutions. Relative permeability is distributed using existing classification based on rock type or lithofacies. Existing fluid model in the 3D static geological model is also included in the dynamic geological model. Similarly, existing drive mechanism (e.g., injection scheme, aquifer and/or gas drive) of the 3D static geological model is retained in the dynamic geological model.
In Step 206, the reservoir simulation is run for at least 5 years of elapse time of the dynamic geological model to test/verify stability. In some embodiments, the criteria for stability include (i) no flow observed in the model when no well is activated for production and (ii) no cross flow between different layers in the reservoir and stable reservoir pressure. As time progresses from the starting time followed by a sequence of simulation time steps through the elapse time, the simulated values of petrophysical properties in the dynamic geological model change with respect to time due to fluid flows and other field operations, such as injection operation or other field stimulation operations. The output of the reservoir simulation includes oil and water production of the candidate horizontal well.
In Step 207, an ICD model for the candidate horizontal well is generated. The ICD model corresponds to a potential ICD design and installation specification that specifies the number and locations where the candidate horizontal well is divided into multiple segments using a multi-segmentation technique. Each segment of the candidate horizontal well in the ICD model includes a number and type of available ICDs, properties of ICD such as restrictions, area open to flow, and/or other characteristic parameters. The ICD model also includes a representative vertical lift model that represents a relationship between fluid flow rate and pressure.
In Step 208, multiple statistical realizations (e.g., five hundred or more) of production performance for the ICD model is generated using a statistical algorithm. In one or more embodiments, the Monte Carlo method is used as the statistical algorithm based on repeated random samplings to obtain a numerical result. Each realization is referred to as a Monte Carlo realization and corresponds to a particular set of petrophysical property values and other input parameters to the dynamic geological model. For example, other randomized input may include ICD restrictions, rock type, drilled length, fractures and high permeability streaks, etc. In one or more embodiments, values of each petrophysical property variable (e.g., porosity, permeability and water saturation) are uniformly distributed throughout repeated random samplings. In particular, these uniformly distributed porosity, permeability and water saturation values are generated from the 3D static model using Latin-cube Sampling techniques. In one or more embodiments, Latin cube sampling (LHS) is used as the statistical method for generating a near-random sample of petrophysical property values (e.g., porosity, permeability, and water saturation) and other model input parameters for performing the Monte Carlo method. To perform the Monte Carlo method, each random sample of petrophysical property values and other model input parameters is used as input to the reservoir simulation for one Monte Carlo realization. The distribution of values in high-resolution geological model is based on random sampling of petrophysical values but constrained to observed values at the wells.
In Step 209, a statistical prediction of production performance of the candidate horizontal well is generated. In one or more embodiments, the statistical prediction of production performance is identified from the outcomes of multiple Monte Carlo realizations. For example, the 50-percentile (denoted as P50) outcome of the Monte Carlo realizations is selected as the most-likely outcome of oil and water production for the ICD model. In other words, there is 50% probability that the actual oil and water production equals or exceeds the most-likely outcome. In another example, a mode (i.e., highest peak) of the histogram of Monte Carlo realizations is selected as the most-likely outcome of oil and water production for the ICD model.
In Step 210, a determination is made as to whether to generate an additional ICD model. The model parameters in the additional ICD model are controlled based on expected variables, which may include incidence of fractures or high-permeability streaks, non-reservoir zones, number of ICD compartments, number of ICDs and their respective restrictions, etc. If the determination is positive, i.e., at least one more ICD model is to be generated, the method returns to Step 207. If the determination is negative, i.e., no more ICD model is to be generated, the method proceeds to Step 211.
In Step 211, an optimal ICD model is selected according to a ranking of statistical predictions of production performance of the candidate horizontal well for all ICD models. For example, the most-likely (P50) outcomes of oil and water production for all the ICD models are compared to select the optimal ICD model that generates the highest value in all most-likely outcomes of oil and water production. The selected optimal ICD model is documented as the ICD specification for completing the candidate horizontal well.
In Step 212, the ICDs are installed in the candidate horizontal well to complete the candidate horizontal well into a producing well. Specifically, the ICDs are installed using the selected optimal ICD model as the ICD specification.
In Step 213, a production operation of the horizontal producing well is performed. In particular, the installed ICDs facilitate the production operation to achieve an evenly distributed flow profile through the horizontal wellbore of the producing well.
Block 301a shows a top view of the surface (108) in a region of the field (100) where four vertical offset wells (302b, 302c, 302d, 302e) are selected from existing wells to form a quadrilateral enclosure of the candidate horizontal well (302a).
Block 301b shows petrophysical well logs of the candidate horizontal well and the four vertical offset wells. For example, the scale markings of the petrophysical well logs are in porosity/permeability/water saturation unit per foot.
Block 301c shows the upscaled petrophysical well logs that are generated from the petrophysical well logs of Block 301b. For example, the scale markings of the upscaled petrophysical well logs are in porosity/permeability/water saturation unit per 3 feet.
Block 301d shows a fence diagram of porosity values in the 3D static geological model. The 3D static geological model represents petrophysical properties in the reservoir (102) where the fence diagram corresponds to the region depicted in Block 301a. In particular, the trajectory of the candidate horizontal well is enclosed in the quadrilateral enclosure formed by the four vertical offset wells (302b, 302c, 302d, 302e).
Block 301e shows an expanded view of a portion (303) of the 3D static geological model of Block 301c.
Block 301f shows a fence diagram of current water saturation values, initialized using the enumeration technique, in the high resolution dynamic geological model. The fence diagram helps to visualize the vertical and lateral distribution of water saturation in the model, it serves as good quality check/comparison with respect to observed values from the wells.
Block 301g shows a diagram of an example ICD model (304a) and corresponding predicted flow rate profile (304b) of the candidate horizontal well (302a). The possible ICD model (304a) and corresponding predicted flow rate profile (304b) are generated by simulations using the high resolution dynamic geological model of Block 301f. The ICD model (304a) specifies the location of ICDs (e.g., ICDs (304c, 304d)) along the trajectory of the horizontal wellbore. The predicted flow rate profile (304b) shows the predicted flow rate values along the trajectory of the horizontal wellbore. The horizontal axis of the diagram corresponds to the trajectory of the horizontal wellbore. The predicted flow rate profile (304b) shows substantially constant flow rate values along a section of the horizontal wellbore between the two ICDs (304c, 304d). In other words, the ICD specification (304a) achieves an evenly distributed flow profile.
Block 301h shows a histogram (304e) and a cumulative diagram (304f) of multiple equally probable realizations of production performance based on the ICD model of Block 301g for the candidate horizontal well (302a). In particular, these multiple equally probable realizations are related to the sequential Gaussian simulation descried above. The horizontal axis of the diagrams corresponds to a value of oil and water production of the candidate horizontal well (302a) to be completed based on this ICD model. The vertical axis corresponds to a tally of realizations in the histogram (304e) or a percentile value in the cumulative diagram (304f).
As noted above, a most-likely outcome (304g) of oil and water production for the ICD model is selected based on the histogram (304e) and the cumulative diagram (304f). For example, the most-likely outcome (304g) may be a 50-percentile oil and water production value of the cumulative diagram (304f). In another example, the most-likely outcome (304g) may be the mode (i.e., the highest peak) of the histogram (304e). The most-likely outcome (304g) of oil and water production for this ICD model is compared to many other ICD models to select the optimal ICD model as the ICD specification to complete the candidate horizontal well (302a) into a horizontal producing well.
Embodiments have the following advantages: (i) the expected production performance of horizontal oil producer wells may be evaluated in multiple time-steps, (ii) the geological heterogeneity of candidate horizontal wells are retained using high resolution dynamic geological model grid dimensions, (iii) short run-time from the dynamic model enables quick well completion decision making to minimize delays in drilling rig time, (iv) prevailing reservoir conditions are included in the evaluation by incorporating well logs of nearby wells, and (v) multiple realizations of the candidate horizontal well are evaluated in terms of production performance facilitate in well completion decision-making.
Embodiments may be implemented on a computer system.
The computer (402) can serve in a role as a client, network component, a server, a database or other persistency, or any other component (or a combination of roles) of a computer system for performing the subject matter described in the instant disclosure. The illustrated computer (402) is communicably coupled with a network (430). In some implementations, one or more components of the computer (402) may be configured to operate within environments, including cloud-computing-based, local, global, or other environment (or a combination of environments).
At a high level, the computer (402) is an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the described subject matter. According to some implementations, the computer (402) may also include or be communicably coupled with an application server, e-mail server, web server, caching server, streaming data server, business intelligence (BI) server, or other server (or a combination of servers).
The computer (402) can receive requests over network (430) from a client application (for example, executing on another computer (402)) and responding to the received requests by processing the said requests in an appropriate software application. In addition, requests may also be sent to the computer (402) from internal users (for example, from a command console or by other appropriate access method), external or third-parties, other automated applications, as well as any other appropriate entities, individuals, systems, or computers.
Each of the components of the computer (402) can communicate using a system bus (403). In some implementations, any or all of the components of the computer (402), both hardware or software (or a combination of hardware and software), may interface with each other or the interface (404) (or a combination of both) over the system bus (403) using an application programming interface (API) (412) or a service layer (413) (or a combination of the API (412) and service layer (413). The API (412) may include specifications for routines, data structures, and object classes. The API (412) may be either computer-language independent or dependent and refer to a complete interface, a single function, or even a set of APIs. The service layer (413) provides software services to the computer (402) or other components (whether or not illustrated) that are communicably coupled to the computer (402). The functionality of the computer (402) may be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer (413), provide reusable, defined business functionalities through a defined interface. For example, the interface may be software written in JAVA, C++, or other suitable language providing data in extensible markup language (XML) format or other suitable format. While illustrated as an integrated component of the computer (402), alternative implementations may illustrate the API (412) or the service layer (413) as stand-alone components in relation to other components of the computer (402) or other components (whether or not illustrated) that are communicably coupled to the computer (402). Moreover, any or all parts of the API (412) or the service layer (413) may be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of this disclosure.
The computer (402) includes an interface (404). Although illustrated as a single interface (404) in
The computer (402) includes at least one computer processor (405). Although illustrated as a single computer processor (405) in
The computer (402) also includes a memory (406) that holds data for the computer (402) or other components (or a combination of both) that can be connected to the network (430). For example, memory (406) can be a database storing data consistent with this disclosure. Although illustrated as a single memory (406) in
The application (407) is an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer (402), particularly with respect to functionality described in this disclosure. For example, application (407) can serve as one or more components, modules, applications, etc. Further, although illustrated as a single application (407), the application (407) may be implemented as multiple applications (407) on the computer (402). In addition, although illustrated as integral to the computer (402), in alternative implementations, the application (407) can be external to the computer (402).
There may be any number of computers (402) associated with, or external to, a computer system containing computer (402), each computer (402) communicating over network (430). Further, the term “client,” “user,” and other appropriate terminology may be used interchangeably as appropriate without departing from the scope of this disclosure. Moreover, this disclosure contemplates that many users may use one computer (402), or that one user may use multiple computers (402).
In some embodiments, the computer (402) is implemented as part of a cloud computing system. For example, a cloud computing system may include one or more remote servers along with various other cloud components, such as cloud storage units and edge servers. In particular, a cloud computing system may perform one or more computing operations without direct active management by a user device or local computer system. As such, a cloud computing system may have different functions distributed over multiple locations from a central server, which may be performed using one or more Internet connections. More specifically, cloud computing system may operate according to one or more service models, such as infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), mobile “backend” as a service (MBaaS), serverless computing, artificial intelligence (AI) as a service (AlaaS), and/or function as a service (FaaS).
Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments without materially departing from this invention. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims.
