Patent Application
20250237134
The present disclosure applies to techniques for exploring a subsurface for hydrocarbons based on seismic data from seismic sensors. Specifically, the present disclosure relates to improving the accuracy of seismic inversion using data from well logs.
In geology, sedimentary facies are bodies of sediment that are recognizably distinct from adjacent sediments that resulted from different depositional environments. Generally, geologists distinguish facies by aspects of the rock or sediment being studied. Seismic facies are groups of seismic reflections whose parameters (such as amplitude, continuity, reflection geometry, and frequency) differ from those of adjacent groups. Seismic facies analysis, a subdivision of seismic stratigraphy, plays a role in hydrocarbon exploration and is one key step in the interpretation of seismic data for reservoir characterization. The seismic facies in a given geological area can provide useful information, particularly about the types of sedimentary deposits and the anticipated lithology.
In reflection seismology, geologists and geophysicists perform seismic surveys to map and interpret sedimentary facies and other geologic features for applications, for example, identification of potential petroleum reservoirs. Seismic surveys are conducted by using a controlled seismic source (for example, a seismic vibrator or dynamite) to create a seismic wave. The seismic source is typically located at ground surface. The seismic wave travels into the ground, is reflected by subsurface formations, and returns to the surface where it is recorded by sensors called geophones. The geologists and geophysicists analyze the time it takes for the seismic waves to reflect off subsurface formations and return to the surface to map sedimentary facies and other geologic features. This analysis can also incorporate data from sources, for example, borehole logging, gravity surveys, and magnetic surveys.
The present disclosure describes techniques that can be used for improving the accuracy and precision of the determination of an elastic inversion volume based on localized measurements of wellbore data to correct for inaccuracies present in the inverted seismic volume. The increased accuracy of a corrected seismic inversion volume can be used for better well placement and reservoir modeling. The differences between the wellbore measurements and the seismic inversion volume are distributed in three-dimensions using an inverse distance squared interpolation method to generate a three-dimensional volume of residual values that are used to correct the seismic inversion volume evaluation of a physical property of the subsurface.
The one or more embodiments described in this specification can enable one or more of the following advantages, which include an improvement in the ability to determine well placements that will result in an increased productivity of hydrocarbons. The combination of potentially inaccurate yet spatially expansive seismic inversion data with a plurality of accurate yet spatially limited wellbore data provides an opportunity to improve the accuracy of the seismic inversion data in a large region. This can enable an improvement for determining well placements that will result in an increased productivity of hydrocarbons in the large region.
Embodiments of these systems and methods can include one or more of the following features.
In a general aspect, a method for determining a seismic inversion volume of a subsurface comprises obtaining seismic data of a subsurface that includes a plurality of wells, generating inverted seismic volume data from the seismic data, wherein the inverted seismic volume data comprises a three-dimensional representation of a first physical property of the subsurface, obtaining wellbore measurement data from the plurality of wells, wherein the wellbore measurement data comprise a localized measurement of a second physical property of the subsurface, determining residual values representing a difference between values of the wellbore measurement data and the inverted seismic volume data at the plurality wells, generating residual seismic volume data by transforming the residual values, the transforming comprising an interpolation of the residual values between the plurality of wells using an inverse distance squared function, and generating corrected seismic volume data based on a difference between values of the residual seismic volume data and the inverted seismic volume data.
In some implementations, a correlation between the seismic volume data and the wellbore measurement data increases as a function of a number of wells.
In some implementations, the residual values comprise the difference between the wellbore measurement data and the inverted seismic volume data at each well and at each of a plurality of depths.
In some implementations, the first physical property is the same as the second physical property.
In some implementations, the method comprises controlling drilling of a well based on the corrected seismic volume data, wherein the corrected seismic volume data specifies a region in the subsurface that has increased likelihood of a presence of a hydrocarbon deposit.
In some implementations, the corrected seismic volume data has an increased correlation to the wellbore measurement data, relative to a correlation of the inverted seismic volume data to the wellbore measurement data, the increased correlation representing an increased accuracy for representing subsurface features by the corrected seismic volume data.
The previously described implementation is implementable using a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer-implemented system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method, the instructions stored on the non-transitory, computer-readable medium.
The details of one or more embodiments are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings, and from the claims.
Like reference numbers and designations in the various drawings indicate like elements.
The systems and processes described herein are for the generating a corrected seismic inversion volume based on localized measurements of wellbore data to correct for inaccuracies present in an inverted seismic volume. Seismic inversion is the process of converting seismic data into a representation that describes the physical properties of a subsurface. Seismic data characterize the response of the subsurface to sound waves that travel through the subsurface and reflect at interfaces between layers. Properties of the subsurface such as sound wave velocity, density, and porosity can be reversed engineered by inverting, e.g., reverse engineering seismic data.
Seismic inversion data is subject to various sources of error. For example, seismic data can contain noise from various sources, including environmental noise, instrument noise, and artifacts due to data processing techniques. In addition, several assumptions of the underlying subsurface are made when performing the seismic inversion process. For example, seismic inversion can rely on simplified earth models that may not capture complex geological features accurately. In addition, the relationship between seismic properties and rock properties is governed by one or more rock physics models which are based on assumptions that may not hold true in every geological setting. Seismic inversion also includes estimation procedures with associated uncertainties to calculate parameters such as acoustic impedance.
In contrast to estimating the physical properties of a subsurface by inverting seismic data, the direct measurement of the physical properties of the subsurface in a wellbore is a more reliable technique. Wellbore measurement tools include calipers and ultrasonic transducers, which can provide insight into various physical properties of the subsurface. However, measurements in a wellbore only provide information about a one-dimensional subset of the entire subsurface. In other words, wellbore measurements provide information about the properties of the subsurface in the vicinity of the wellbore.
The combination of potentially inaccurate yet spatially expansive seismic inversion data with a plurality of accurate yet spatially limited wellbore data provides an opportunity to improve the accuracy of the seismic inversion data in a large region. This can enable an improvement for determining well placements that will result in an increased productivity of hydrocarbons in the large region.
Oil and gas tend to rise through permeable reservoir rock until further upward migration is blocked, for example, by the layer of impermeable cap rock 102. Seismic surveys attempt to identify locations where interaction between layers of the subterranean formation 100 are likely to trap oil and gas by limiting this upward migration. For example,
A seismic source 112 (for example, a seismic vibrator or an explosion) generates seismic waves 114 that propagate in the earth. The velocity of these seismic waves depends on properties, for example, density, porosity, and fluid content of the medium through which the seismic waves are traveling. Different geologic bodies or layers in the earth are distinguishable because the layers have different properties and, thus, different characteristic seismic velocities. For example, in the subterranean formation 100, the velocity of seismic waves traveling through the subterranean formation 100 will be different in the sandstone layer 104, the limestone layer 106, and the sand layer 108. As the seismic waves 114 contact interfaces between geologic bodies or layers that have different velocities, the interfaces reflect some of the energy of the seismic wave and refracts some of the energy of the seismic wave. Such interfaces are sometimes referred to as horizons.
The seismic waves 114 are received by a sensor or sensors 116. Although illustrated as a single component in
A control center 122 can be operatively coupled to the seismic control truck 120 and other data acquisition and wellsite systems. The control center 122 may have computer facilities for receiving, storing, processing, and analyzing data from the seismic control truck 120 and other data acquisition and wellsite systems. For example, computer systems 124 in the control center 122 can be configured to analyze, model, control, optimize, or perform management tasks of field operations associated with development and production of resources such as oil and gas from the subterranean formation 100. Alternatively, the computer systems 124 can be located in a different location than the control center 122. Some computer systems are provided with functionality for manipulating and analyzing the data, such as performing seismic interpretation or borehole resistivity image log interpretation to identify geological surfaces in the subterranean formation or performing simulation, planning, and optimization of production operations of the wellsite systems.
In some embodiments, results generated by the computer system 124 may be displayed for user viewing using local or remote monitors or other display units. One approach to analyzing seismic data is to associate the data with portions of a seismic cube representing represent the subterranean formation 100. The seismic cube can also be display results of the analysis of the seismic data associated with the seismic survey.
The process 400 includes a system that obtains (402) seismic data of a subsurface that includes a plurality of wells. The subsurface can include any number of wells. In some implementations, the density of wells in the subsurface from which the seismic data is obtained determines the accuracy of the process 400 described in relation to
The system can generate (404) inverted seismic volume data from the seismic data, where the inverted seismic volume data includes a three-dimensional representation of a physical property of the subsurface. Physical properties of the subsurface can include sound wave velocity, density, porosity, or any additional physical property that can be described by a physical model of the subsurface.
The system can obtain (406) wellbore measurement data from a plurality of wells, where the wellbore measurement data can include a localized measurement of a physical property of the subsurface. In some implementations, physical properties can be measured in a wellbore using mechanical calipers, resistivity calipers, acoustic imaging, or any other downhole method of evaluating the physical properties of the subsurface from inside the wellbore. Wellbore measurements can be considered to be the ground truth measurement of a physical property that can correct the seismic inversion evaluation of the same physical property. Since the wells are located in discrete positions and do not extend throughout the entire subsurface, the wellbore measurements can be used to anchor the values of the seismic inversion data at the specific well locations.
The system can determine (408) residual values representing a difference between the values of the wellbore measurement data and the inverted seismic volume data at the plurality of wellbore sites. The residual values between the wellbore measurement data and the inverted seismic volume data are the difference between the measurements of a specific subsurface property at the location of the wellbore. In other words, the system obtains both a wellbore measurement of a physical property and an inverted seismic volume data of the same physical property at the same region of the subsurface. The system can determine the residual values by comparing the evaluation of the same physical property by two different methods.
The system can generate (410) residual seismic volume data by transforming the residual values, the transforming comprising an interpolation of the residual values between the plurality of wellbore sites using an inverse distance squared function.
The inverse distance squared method assumes that points closer to a location of interest are more similar than points further away. For example, consider three points A, B, and C. Points A and B are ten times closer than A and C and ten times closer than B and C. The inverse distance squared method of interpolation assumes that the properties of A and B are more similar than between A and C as well as between B and C because of the proximity in space of A and B.
The inverse distance squared interpolation method is a technique used to estimate values at unsampled locations (e.g., away from the well sites) based on known values from surrounding sampled locations (e.g., the well sites). The method assigns weights to known data points inversely proportional to the square of the distance from the interpolation location. The value of an unsampled point in the subsurface can be estimated using inverse distance squared interpolation using the following equation,
where N is the number of known data points (number of well sites), zi is the measured value of the property at well site i, and dik is the distance from the interpolation location uk to the well site location i. A physical property at each point in the three-dimensional subsurface can be estimated if the physical property represented by the inverted seismic volume data is the same physical property represented by the wellbore measurements.
The system can generate (412) corrected seismic volume data by adding the residual seismic volume data to the inverted seismic volume data. The interpolated residual seismic volume data represents the error in the inverted seismic volume data. For example, if a point (x, y, z) of the subsurface has a residual of +a and the inverted seismic volume data for the subsurface property has a value of b, the corrected seismic volume data of the subsurface property at (x, y, z) is a+b. Similarly, if a point (x, y, z) of the subsurface has a residual of −a and the inverted seismic volume data for the subsurface property has a value of b, the corrected seismic volume data of the subsurface property at (x, y, z) is a−b.
As illustrated in the plot 500, the vertical axis 510 represents the subsurface depth. In the case of a wellbore measurement, the subsurface depth coincides with the depth of the wellbore where the acoustic impedance is measured. The horizontal axis 520 represents the acoustic impedance of the subsurface.
The trace 506 represents the inverted seismic data of the acoustic impedance of the subsurface at the location that coincides with a specific well site. The data represented by the trace 502 is the acoustic impedance along the depth of the wellbore evaluated with a method like acoustic imaging, or any method to extract the acoustic impedance of the subsurface at the well site. As illustrated in the plot 500, the acoustic impedance evaluated in the wellbore may exhibit noise due to environmental effects, instrument error, and data processing error. In some implementations, it can be assumed that a subsurface rock property like acoustic impedance does not change by large amounts over small distances as the wellbore data suggests. The trace 504 is a filtered version of the wellbore data 502 that represents the acoustic impedance of the subsurface. The high frequency component of the inverted seismic data can be removed with numerical techniques which can include a moving average or other signal processing techniques such as Fourier analysis to calculate an average value of the trace over larger distances to represent a more accurate representation of the subsurface property.
The residual values can be calculated and are represented by the trace 508. As illustrated, the trace 508 represents the difference between the trace 504 which is the filtered representation of the wellbore data trace 502 and the trace 506 which is the inverted seismic data. The region 530 of plot 500 represents a range of subsurface depths with a high residual value (trace 508) which indicates a large deviation between the inverted seismic data (trace 506) and the filtered wellbore data (trace 504) within the region 530. Alternatively, the region 532 of plot 500 represents a range of subsurface depths with a low residual value (trace 508) which indicates a small deviation (e.g., high degree of overlap) between the inverted seismic data (trace 506) and the filtered wellbore data (trace 504). The residual values represented by trace 508 describe the difference between the inverted seismic data and the wellbore data at the well site at a range of depths that coincide with the depth of the wellbore.
For example, the residual value 602 represents a point in the cross-section with a large residual value. A large residual value indicates a large deviation between the inverted seismic data and the wellbore data at that coordinate. Although the wellbore data was not measured continuously throughout the subsurface (it is only measured within the wellbore), the interpolation of the residual values allows the accuracy of the inverted seismic data to be evaluated at every coordinate in the three-dimensional volume. Similarly, the residual value 604 represents a point in the cross-section with a small residual value. A small residual value indicates a small deviation between the inverted seismic data and the wellbore data at that coordinate. At the coordinate that corresponds to residual value 604, the corrected inverted seismic data will be approximately equal to the raw inverted seismic data.
The plot 700 displays a linear fit line 708 which fits the data corresponding to the scatter plot that includes data point 706. Similarly, the plot 700 displays a linear fit line 704 which fits the data corresponding to the scatter plot that includes data point 702. The data that comprise the scatter plots can be fit to a line using a least squares method where the quality of the fit is described by an R2 value that can take a value between 0 and 1, where a value of 1 indicates a perfect linear fit and a value of 0 indicates no linear relationship between the variables. A perfect correlation between the corrected and uncorrected data (the wellbore measurement and the seismic inversion measurement) is indicated by a perfect linear relationship between the two measurements. An imperfect correlation between the two measurements is indicated by deviations from a linear relationship between the two variables. The data illustrated in
The correlation between the wellbore measurement data and the seismic inversion data after applying the correction procedure as described in relation to
A similar evaluation is illustrated on the right side 820 of
The horizontal axis 910 represents the acoustic impedance of the subsurface determined by the filtered wellbore data at a location that coincides with a well site that was not included in the interpolation procedure. The vertical axis 920 represents the acoustic impedance of the subsurface determined by the inverted seismic data at the well site. The plot 900 displays a scatter plot (e.g., data point 904) of the acoustic impedance evaluated from the inverted seismic data before correction and a scatter plot (e.g., data point 902) of the acoustic impedance evaluated from the corrected inverted seismic data, where the data is corrected using the process described in relation to
The plot 900 displays a linear fit line 908 which fits the data corresponding to the scatter plot that includes data point 904. Similarly, the plot 900 displays a linear fit line 906 which fits the data corresponding to the scatter plot that includes data point 902. The data that comprise the scatter plots can be fit to a line using a least squares method where the quality of the fit is described by an R2 value that can take a value between 0 and 1, where a value of 1 indicates a perfect linear fit and a value of 0 indicates no linear relationship between the variables. A perfect correlation, illustrated by the trace 912, between the corrected and uncorrected data (the wellbore measurement and the seismic inversion measurement) is indicated by a perfect linear relationship between the two measurements. An imperfect correlation between the two measurements is indicated by deviations from a linear relationship between the two variables. The data illustrated in
The correlation between the wellbore measurement data and the seismic inversion data after applying the correction procedure as described in relation to
Examples of field operations 1010 include forming/drilling a wellbore, hydraulic fracturing, producing through the wellbore, injecting fluids (such as water) through the wellbore, to name a few. In some implementations, methods of the present disclosure can trigger or control the field operations 1010. For example, the methods of the present disclosure can generate data from hardware/software including sensors and physical data gathering equipment (e.g., seismic sensors, well logging tools, flow meters, and temperature and pressure sensors). The methods of the present disclosure can include transmitting the data from the hardware/software to the field operations 1010 and responsively triggering the field operations 1010 including, for example, generating plans and signals that provide feedback to and control physical components of the field operations 1010. Alternatively, or in addition, the field operations 1010 can trigger the methods of the present disclosure. For example, implementing physical components (including, for example, hardware, such as sensors) deployed in the field operations 1010 can generate plans and signals that can be provided as input or feedback (or both) to the methods of the present disclosure.
Examples of computational operations 1012 include one or more computer systems 1020 that include one or more processors and computer-readable media (e.g., non-transitory computer-readable media) operatively coupled to the one or more processors to execute computer operations to perform the methods of the present disclosure. The computational operations 1012 can be implemented using one or more databases 1018, which store data received from the field operations 1010 and/or generated internally within the computational operations 1012 (e.g., by implementing the methods of the present disclosure) or both. For example, the one or more computer systems 1020 process inputs from the field operations 1010 to assess conditions in the physical world, the outputs of which are stored in the databases 1018. For example, seismic sensors of the field operations 1010 can be used to perform a seismic survey to map subterranean features, such as facies and faults. In performing a seismic survey, seismic sources (e.g., seismic vibrators or explosions) generate seismic waves that propagate in the earth and seismic receivers (e.g., geophones) measure reflections generated as the seismic waves interact with boundaries between layers of a subsurface formation. The source and received signals are provided to the computational operations 1012 where they are stored in the databases 1018 and analyzed by the one or more computer systems 1020.
In some implementations, one or more outputs 1022 generated by the one or more computer systems 1020 can be provided as feedback/input to the field operations 1010 (either as direct input or stored in the databases 1018). The field operations 1010 can use the feedback/input to control physical components used to perform the field operations 1010 in the real world.
For example, the computational operations 1012 can process the seismic data to generate three-dimensional (3D) maps of the subsurface formation. The computational operations 1012 can use these 3D maps to provide plans for locating and drilling exploratory wells. In some operations, the exploratory wells are drilled using logging-while-drilling (LWD) techniques which incorporate logging tools into the drill string. LWD techniques can enable the computational operations 1012 to process new information about the formation and control the drilling to adjust to the observed conditions in real-time.
The one or more computer systems 1020 can update the 3D maps of the subsurface formation as information from one exploration well is received and the computational operations 1012 can adjust the location of the next exploration well based on the updated 3D maps. Similarly, the data received from production operations can be used by the computational operations 1012 to control components of the production operations. For example, production well and pipeline data can be analyzed to predict slugging in pipelines leading to a refinery and the computational operations 1012 can control machine operated valves upstream of the refinery to reduce the likelihood of plant disruptions that run the risk of taking the plant offline.
In some implementations of the computational operations 1012, customized user interfaces can present intermediate or final results of the above-described processes to a user. Information can be presented in one or more textual, tabular, or graphical formats, such as through a dashboard. The information can be presented at one or more on-site locations (such as at an oil well or other facility), on the Internet (such as on a webpage), on a mobile application (or app), or at a central processing facility.
The presented information can include feedback, such as changes in parameters or processing inputs, that the user can select to improve a production environment, such as in the exploration, production, and/or testing of petrochemical processes or facilities. For example, the feedback can include parameters that, when selected by the user, can cause a change to, or an improvement in, drilling parameters (including drill bit speed and direction) or overall production of a gas or oil well. The feedback, when implemented by the user, can improve the speed and accuracy of calculations, streamline processes, improve models, and solve problems related to efficiency, performance, safety, reliability, costs, downtime, and the need for human interaction.
In some implementations, the feedback can be implemented in real-time, such as to provide an immediate or near-immediate change in operations or in a model. The term real-time (or similar terms as understood by one of ordinary skill in the art) means that an action and a response are temporally proximate such that an individual perceives the action and the response occurring substantially simultaneously. For example, the time difference for a response to display (or for an initiation of a display) of data following the individual's action to access the data can be less than 1 millisecond (ms), less than 1 second(s), or less than 10 s. While the requested data need not be displayed (or initiated for display) instantaneously, it is displayed (or initiated for display) without any intentional delay, accounting for processing limitations of a described computing system and time required to, for example, gather, accurately measure, analyze, process, store, or transmit the data.
Events can include readings or measurements captured by downhole equipment such as sensors, pumps, bottom hole assemblies, or other equipment. The readings or measurements can be analyzed at the surface, such as by using applications that can include modeling applications and machine learning. The analysis can be used to generate changes to settings of downhole equipment, such as drilling equipment. In some implementations, values of parameters or other variables that are determined can be used automatically (such as through using rules) to implement changes in oil or gas well exploration, production/drilling, or testing. For example, outputs of the present disclosure can be used as inputs to other equipment and/or systems at a facility. This can be especially useful for systems or various pieces of equipment that are located several meters or several miles apart or are in different countries or other jurisdictions.
The computer 1102 can serve in a role as a client, a network component, a server, a database, a persistency, or components of a computer system for performing the subject matter described in the present disclosure. The illustrated computer 1102 is communicably coupled with a network 1124. In some implementations, one or more components of the computer 1102 can be configured to operate within different environments, including cloud-computing-based environments, local environments, global environments, and combinations of environments.
At a high level, the computer 1102 is an electronic computing device operable to receive, transmit, process, store, and manage data and information associated with the described subject matter. According to some implementations, the computer 1102 can also include, or be communicably coupled with, an application server, an email server, a web server, a caching server, a streaming data server, or a combination of servers.
The computer 1102 can receive requests over network 1124 from a client application (for example, executing on another computer 1102). The computer 1102 can respond to the received requests by processing the received requests using software applications. Requests can also be sent to the computer 1102 from internal users (for example, from a command console), external (or third) parties, automated applications, entities, individuals, systems, and computers.
Each of the components of the computer 1102 can communicate using a system bus 1104. In some implementations, any or all of the components of the computer 1102, including hardware or software components, can interface with each other or the interface 1106 (or a combination of both), over the system bus 1104. Interfaces can use an application programming interface (API) 1114, a service layer 1116, or a combination of the API 1114 and service layer 1116. The API 1114 can include specifications for routines, data structures, and object classes. The API 1114 can be either computer-language independent or dependent. The API 1114 can refer to a complete interface, a single function, or a set of APIs.
The service layer 1116 can provide software services to the computer 1102 and other components (whether illustrated or not) that are communicably coupled to the computer 1102. The functionality of the computer 1102 can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer 1116, can provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, or a language providing data in extensible markup language (XML) format. While illustrated as an integrated component of the computer 1102, in alternative implementations, the API 1114 or the service layer 1116 can be stand-alone components in relation to other components of the computer 1102 and other components communicably coupled to the computer 1102. Moreover, any or all parts of the API 1114 or the service layer 1116 can be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.
The computer 1102 includes an interface 1106. Although illustrated as a single interface 1106 in
The computer 1102 includes a processor 1108. Although illustrated as a single processor 1108 in
The computer 1102 also includes a database 1120 that can hold data (such geomechanics data 1122) for the computer 1102 and other components connected to the network 1124 (whether illustrated or not). For example, database 1120 can be in-memory or a database storing data consistent with the present disclosure. In some implementations, database 1120 can be a combination of two or more different database types (for example, hybrid in-memory and conventional databases) according to implementations of the computer 1102 and the described functionality. Although illustrated as a single database 1120 in
The computer 1102 also includes a memory 1110 that can hold data for the computer 1102 or a combination of components connected to the network 1124 (whether illustrated or not). Memory 1110 can store any data consistent with the present disclosure. In some implementations, memory 1110 can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to implementations of the computer 1102 and the described functionality. Although illustrated as a single memory 1110 in
The application 1112 can be an algorithmic software engine providing functionality according to implementations of the computer 1102 and the described functionality. For example, application 1112 can serve as one or more components, modules, or applications. Further, although illustrated as a single application 1112, the application 1112 can be implemented as multiple applications 1118 on the computer 1102. In addition, although illustrated as internal to the computer 1102, in alternative implementations, the application 1112 can be external to the computer 1102.
The computer 1102 can also include a power supply 1118. The power supply 1118 can include a rechargeable or non-rechargeable battery that can be configured to be either user- or non-user-replaceable. In some implementations, the power supply 1118 can include power-conversion and management circuits, including recharging, standby, and power management functionalities. In some implementations, the power-supply 1118 can include a power plug to allow the computer 1102 to be plugged into a wall socket or a power source to, for example, power the computer 1102 or recharge a rechargeable battery.
There can be any number of computers 1102 associated with, or external to, a computer system including the computer 1102, with each computer 1102 communicating over network 1124. Further, the terms “client,” “user,” and other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one computer 1102 and one user can use multiple computers 1102.
Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs. Each computer program can include one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in/on an artificially generated propagated signal. The example, the signal can be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums.
The terms “data processing apparatus,” “computer,” and “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus can encompass all kinds of apparatus, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also include special purpose logic circuitry including, for example, a central processing unit (CPU), a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus or special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based). The apparatus can optionally include code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatuses with or without conventional operating systems, for example LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.
The methods, processes, or logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.
Computer readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data can include all forms of permanent/non-permanent and volatile/non-volatile memory, media, and memory devices. Computer readable media can include, for example, semiconductor memory devices such as random-access memory (RAM), read only memory (ROM), phase change memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer readable media can also include, for example, magnetic devices such as tape, cartridges, cassettes, and internal/removable disks.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented, in combination, in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations, separately, or in any suitable sub-combination. Moreover, although previously described features may be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can, in some cases, be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
Several implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations are depicted in the drawings or claims in a particular order, this should not be understood as requiring that such operations be performed in the order shown or in sequential order, or that all illustrated operations be performed (some operations may be considered optional), to achieve desirable results. In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and performed as deemed appropriate.
Moreover, the separation or integration of various system modules and components in the previously described implementations should not be understood as requiring such separation or integration in all implementations, and the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Accordingly, the previously described example implementations do not define or constrain the present disclosure. Other changes, substitutions, and alterations are also possible without departing from the scope of the present disclosure.
Furthermore, any claimed implementation is applicable to at least a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system comprising a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium.
Several embodiments have been described. Nevertheless, it will be understood that various modifications may be made without departing from the scope of the data processing system described herein. Accordingly, other embodiments are within the scope of the following claims.
