Patent Application
20240401457
This disclosure generally relates to geosteering for oil and gas wells.
Geosteering can refer to the optimal placement of a wellbore based on the results of real-time downhole geological and geophysical logging measurements rather than three-dimensional targets in space. The objective is usually to keep a directional wellbore within a hydrocarbon pay zone defined in terms of its resistivity, density or even biostratigraphy. In mature areas, geosteering may be used to keep a wellbore in a particular reservoir section to minimize gas or water breakthrough and maximize economic production from the well. In the process of drilling a borehole, geosteering refers to the act of adjusting the borehole position (inclination and azimuth angles) on the fly to reach one or more geological targets. The adjustments are based on geological information gathered while drilling.
In one aspect, implementations provide a computer-implemented method that includes: receiving a plurality of streams of data encoding measurements taken from sensors on a drilling bit during a geosteering operation to place the drilling bit in a reservoir of hydrocarbons; applying a trained artificial intelligence (AI) engine to the measurements as the plurality of streams of data are received; based on, at least in part, applying the trained AI engine, detecting at least one drilling anomaly when placing the drilling bit in the reservoir of hydrocarbons during the geosteering operation; upon detecting the at least one drilling anomaly, generating an automated notification and a recommended action; and in response to a user feedback to the recommended action, adjusting the drilling bit in accordance with the user feedback.
Implementations may include one or more of the following features.
The trained AI engine may be configured to be trigged by a network of conditional logics that include range criteria for a plurality of parameters. The range criteria may provide ranges for the plurality of parameters. Each parameter may indicate a respective measurement, or a rage of change of a respective measurement. The trained AI engine may be generated utilizing historical measurements taken from one or more offset wells of the reservoir of hydrocarbons, wherein the historical measurements include: a measured reservoir parameter at the one or more offset wells, or a configuration parameter of the drilling bit at the one or more offset wells.
Detecting at least one drilling anomaly may include: detecting a trend of forecast reservoir parameters deviating from a target well planning requirement when placing the drill bit for the geosteering operation, wherein the trained AI engine generates, using current drilling parameters of the drilling bit and historical measurements taken from one or more offset wells of the reservoir of hydrocarbons, the forecast reservoir parameters. Generating the automated notification and the recommended action may include: sending, using a user-interactive chatbot interface, the automated notification to participants of the geo-steering operation, and conducting, using the user-interactive chatbot interface, an interactive session with the participants of the geo-steering operation so that the participants can concur on the recommended action. The sensors may include: a resistivity sensor, a gamma ray sensor, a sonic sensor, a nuclear magnetic resonance (NMR) tool, a pressure sensor, or a temperature sensor.
In another aspect, implementations provide a computer system comprising one or more hardware computer processors configured to perform operations of: receiving a plurality of streams of data encoding measurements taken from sensors on a drilling bit during a geosteering operation to place the drilling bit in a reservoir of hydrocarbons; applying a trained artificial intelligence (AI) engine to the measurements as the plurality of streams of data are received; based on, at least in part, applying the trained AI engine, detecting at least one drilling anomaly when placing the drilling bit in the reservoir of hydrocarbons during the geosteering operation; upon detecting the at least one drilling anomaly, generating an automated notification and a recommended action; and in response to a user feedback to the recommended action, adjusting the drilling bit in accordance with the user feedback.
Implementations may include one or more of the following features.
The trained AI engine may be configured to be trigged by a network of conditional logics that include range criteria for a plurality of parameters. The range criteria may provide ranges for the plurality of parameters. Each parameter may indicate a respective measurement, or a rage of change of a respective measurement. The trained AI engine may be generated utilizing historical measurements taken from one or more offset wells of the reservoir of hydrocarbons, wherein the historical measurements include: a measured reservoir parameter at the one or more offset wells, or a configuration parameter of the drilling bit at the one or more offset wells.
Detecting at least one drilling anomaly may include: detecting a trend of forecast reservoir parameters deviating from a target well planning requirement when placing the drill bit for the geosteering operation, wherein the trained AI engine generates, using current drilling parameters of the drilling bit and historical measurements taken from one or more offset wells of the reservoir of hydrocarbons, the forecast reservoir parameters. Generating the automated notification and the recommended action may include: sending, using a user-interactive chatbot interface, the automated notification to participants of the geo-steering operation, and conducting, using the user-interactive chatbot interface, an interactive session with the participants of the geo-steering operation so that the participants can concur on the recommended action. The sensors may include: a resistivity sensor, a gamma ray sensor, a sonic sensor, a nuclear magnetic resonance (NMR) tool, a pressure sensor, or a temperature sensor.
In yet another aspect, implementations provide a non-transitory computer-readable medium comprising software instructions that, when executed, cause a computer processor to perform operations of: receiving a plurality of streams of data encoding measurements taken from sensors on a drilling bit during a geosteering operation to place the drilling bit in a reservoir of hydrocarbons; applying a trained artificial intelligence (AI) engine to the measurements as the plurality of streams of data are received; based on, at least in part, applying the trained AI engine, detecting at least one drilling anomaly when placing the drilling bit in the reservoir of hydrocarbons during the geosteering operation; upon detecting the at least one drilling anomaly, generating an automated notification and a recommended action; and in response to a user feedback to the recommended action, adjusting the drilling bit in accordance with the user feedback.
Implementations may include one or more of the following features.
The trained AI engine may be configured to be trigged by a network of conditional logics that include range criteria for a plurality of parameters. The range criteria may provide ranges for the plurality of parameters. Each parameter may indicate a respective measurement, or a rage of change of a respective measurement. The trained AI engine may be generated utilizing historical measurements taken from one or more offset wells of the reservoir of hydrocarbons, wherein the historical measurements include: a measured reservoir parameter at the one or more offset wells, or a configuration parameter of the drilling bit at the one or more offset wells.
Detecting at least one drilling anomaly may include: detecting a trend of forecast reservoir parameters deviating from a target well planning requirement when placing the drill bit for the geosteering operation, wherein the trained AI engine generates, using current drilling parameters of the drilling bit and historical measurements taken from one or more offset wells of the reservoir of hydrocarbons, the forecast reservoir parameters. Generating the automated notification and the recommended action may include: sending, using a user-interactive chatbot interface, the automated notification to participants of the geo-steering operation, and conducting, using the user-interactive chatbot interface, an interactive session with the participants of the geo-steering operation so that the participants can concur on the recommended action.
Implementations according to the present disclosure may be realized in computer implemented methods, hardware computing systems, and tangible computer readable media. For example, a system of one or more computers can be configured to perform particular actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.
The details of one or more implementations of the subject matter of this specification are set forth in the description, the claims, and the accompanying drawings. Other features, aspects, and advantages of the subject matter will become apparent from the description, the claims, and the accompanying drawings.
Like reference numbers and designations in the various drawings indicate like elements.
The disclosed technology applies artificial intelligence and machine-learning (AI/ML) techniques to automatically detect and flag difficulties in passing during geosteering, and recommend actions to resolve, overcome and avoid such difficulties using real-time information acquired and transmitted while drilling (e.g., logging while drilling (LWD) data such as deep directional resistivity measurements and near bit gamma ray (GR) measurements). The disclosed system can be driven by an intricate network of control logics setting forth, e.g., specific criteria with thresholds. Using the intricate network of control logics, which can be built around training data and a static model, the disclosed system can achieve real-time detection of, e.g., progressing trends of deviation from target well planning requirements. After detection, the disclosed system can conduct real-time follow-up comparison with measurements taken from offset wells to provide a diagnosis of the detected progressing trend of deviation, as well as a remedial action plan. In some cases, the disclosed system may further refine the network of control logics based on results of the comparison.
For context, geosteering was originally developed with relatively crude direction tools, the advent of rotary steerable tools and an ever-increasing arsenal of geophysical tools have generated renewed interest to optimize the placement with ever-increasing accuracy. The goal is to direct the drilling bit to high-quality parts of the reservoir using petrophysical (i.e., relating to the physical qualities of rock) data. Often times a basic tool configuration can have directional and inclination sensors, along with a gamma ray tool. Other options can include devices for measuring neutron density, look ahead seismic data, and downhole pressure. Given the vast volume of data generated from the tools, only a small fraction of the available data are transmitted to the surface for real-time processing. In many cases, the available data is collected in memory using a data dump, which becomes accessible only when the tool assembly is brought back on the surface.
To address the technical challenge, implementations of the present disclosure can utilize the full complement of real-time drilling data including near bit gamma ray, azimuthal deep-reading resistivity (ADR), other logging-while-drilling (LWD) data, in addition to static data. The implementations can provide cohesive and proactive real-time monitoring of the geosteering operation and generate alerts to engineers and geoscientists when time is of essence By detecting anomalies and deviation during drilling, some implementations can generate improved predictions to facilitate the process of placing a wellbore over the carbohydrate pay zone. Specifically, the implementations incorporate AI/ML algorithms that can improve the analytical prediction using training data. For example, the implementations can update a static model while drilling in real-time to build a geological model. The geological model thus established can highlight inconsistency as formation comes deeper or shallower than the picks as per captured data while drilling comprising mainly of geological tops deviation. The implementations can automatically detect the progressing trend of deviation from the prognoses earlier provided by the geological model. Some implementations may monitor parameters such as porosity and resistivity during geosteering to assure the quality of well performance. The implementations may provide validation and assessment after landing of the well to evaluate the trajectory plan for guiding subsequent geosteering steps. For example, if the trajectory plan is no longer applicable, the implementations may generate a new trajectory plan to provide guidance for optimized well placement for the new geological picks. The implementations thus enable real-time, low-cost expert-level decisions with very little to no human intervention. Adopting the implementations for geosteering can result in cost saving through, for example, reduction of footage losses. The implementations can harness the available data for optimal decision making with improved objectiveness by weighing evidence in real-time without relying on operator's sole judgement, which can be prone to subjective bias, or even emotional whim. Detailed explanation of exemplary implementations are provided below in association with
Thereafter, the geosteering procedure may enter landing stage 110. For example, landing can refer to a landing point determination (LPD) stage when a wellbore is placed in a location based on available measurements. The goal is to have the wellbore landed in the desired location and at the correct angle to allow for efficient production. As illustrated, the geosteering procedure may determine whether the wellbore is landed according to the prognosis outlined in the report (111). In response to determining that the wellbore is landed according to plan, the drilling may be started. If not, the geosteering procedure may update the grid and drilling plan prior to starting drilling into the reservoir section (112). Thereafter, the drilling may start according to the plan, which may be updated.
The geosteering procedure may then enter online analysis stage 120. When drilling starts in the reservoir section (121), the logging while drilling (LWD) operation may generate multiple streams of measurement data in real-time from sensors and measurement devices mounted on the drilling tool. These measurements can reveal the geological formations and fluids surrounding the wellbore. For example, measurements can be taken from resistivity sensors that measure the electrical resistivity of the rock formations surrounding the wellbore. Such information can be used to determine the porosity and saturation of the rocks, which in turn can help estimate the presence and type of fluids in the formation. The measurements can also include gamma ray sensor measurements that record the natural gamma radiation emitted by the surrounding rock formations, which can be used to identify the mineral composition of the rocks and identify stratigraphic boundaries and formation thickness. The measurements can also include data from sonic/acoustic sensors that use sound waves to measure the velocity of sound and estimate the density of the rocks surrounding the wellbore. Such information can be used to determine the mechanical properties of the rocks and to help identify fractures, faults, and other geological features. The measurements may also be taken from magnetic sensors including, for example, nuclear magnetic resonance (NMR) tools that provide nonradioactive alternative for porosity measurements, which can help identify certain rock types and geological features. The measurements may also be taken from pressure and temperature sensors that measure the pressure and temperature of the fluids within the wellbore. Such measurements can be used to monitor wellbore stability and can help identify changes in fluid properties or pressure that may indicate the presence of hydrocarbons.
The geosteering procedure may determine whether a terminal point (e.g., total depth, or TD) has been reached (122). In response to determining no termination has been reached, the geosteering procedure may monitor real-time data input flow received from the LWD sensors (e.g., gamma ray, resistivity, image log, NMR, and other near bit tools). For context, near bit tools refer to tools installed close in distance from the drilling bit.
Implementations may invoke an AI engine for on-line analysis of the real-time measurements data (124). Referring to diagram 200 of
Diagram 200 also shows an example of a user interface 210 for achieving a chatbot-type of functionalities for user-interactive steps of 126/127/128 of
Further referring to diagram 300 of
In more detail and returning to
When the drilling operation reaches a termination, the geosteering procedure may determine whether the well has been completed in accordance with the drilling plan (131). In response to determining the well has not been completed, the geosteering procedure may proceed to make adjustments to, e.g., logging operation and plan design (132). In response to determining that the well has been completed in accordance with the plan, the geosteering procedure may complete the well (133). The geosteering procedure may conduct post-mortem meeting and generate a report for the well (141).
The process may then analyze the real-time data flow by applying a trained AI engine (402). As explained above in association with
When applying the trained AI engine to the real-time measurements data, the implementations can detect progressing trend of deviation during the current drilling operation (403). For example, the implementations can detect deviation from planned geological tops, or the prognoses. Upon detection, the process may generate automated notifications and provide prognoses of the current drilling operations (404). For example, the process may generate alerts and may provide recommended actions to respond to the detected anomalies. The process may then determine whether to adjust the trained AI engine (405). For example, the plan or the prognoses can be adjusted based on, for example, operator feedback in the form of concurrence of stakeholders. The adjusted plan or prognoses may iteratively impact the control logics of the AI engine for subsequent monitoring of the input data flow from sensors and measurement devices. In response to determining that the trained AI engine is to be adjusted, the process may update the control logics within the trained AI engine (406) to reflect, e.g., updated guidelines. Thereafter, the process may apply the newly updated AI engine to the real-time data flow (402). In response to determining no adjustment to the trained AI engine, the process may continue to use the same control logics of the trained AI engine (402). Such features introduce vigor into the geosteering process through modularity of structures, allowing human operators and experts to focus on events that are more likely to impact the outcome. The implementations can thus enhance the probability and frequency of wellbore placement that results in improved payout. In addition, the implementations also enhance the consistency and accuracy of such placement.
The computer 502 can serve in a role in a computer system as a client, network component, a server, a database or another persistency, another role, or a combination of roles for performing the subject matter described in the present disclosure. The illustrated computer 502 is communicably coupled with a network 530. In some implementations, one or more components of the computer 502 can be configured to operate within an environment, including cloud-computing-based, local, global, another environment, or a combination of environments.
The computer 502 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 502 can also include or be communicably coupled with a server, including an application server, e-mail server, web server, caching server, streaming data server, another server, or a combination of servers.
The computer 502 can receive requests over network 530 (for example, from a client software application executing on another computer 502) and respond to the received requests by processing the received requests using a software application or a combination of software applications. In addition, requests can also be sent to the computer 502 from internal users, external or third-parties, or other entities, individuals, systems, or computers.
Each of the components of the computer 502 can communicate using a system bus 503. In some implementations, any or all of the components of the computer 502, including hardware, software, or a combination of hardware and software, can interface over the system bus 503 using an application programming interface (API) 512, a service layer 513, or a combination of the API 512 and service layer 513. The API 512 can include specifications for routines, data structures, and object classes. The API 512 can 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 513 provides software services to the computer 502 or other components (whether illustrated or not) that are communicably coupled to the computer 502. The functionality of the computer 502 can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer 513, provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, another computing language, or a combination of computing languages providing data in extensible markup language (XML) format, another format, or a combination of formats. While illustrated as an integrated component of the computer 502, alternative implementations can illustrate the API 512 or the service layer 513 as stand-alone components in relation to other components of the computer 502 or other components (whether illustrated or not) that are communicably coupled to the computer 502. Moreover, any or all parts of the API 512 or the service layer 513 can be implemented as a child or a sub-module of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.
The computer 502 includes an interface 504. Although illustrated as a single interface 504 in
The computer 502 includes a processor 505. Although illustrated as a single processor 505 in
The computer 502 also includes a database 506 that can hold data for the computer 502, another component communicatively linked to the network 530 (whether illustrated or not), or a combination of the computer 502 and another component. For example, database 506 can be an in-memory, conventional, or another type of database storing data consistent with the present disclosure. In some implementations, database 506 can be a combination of two or more different database types (for example, a hybrid in-memory and conventional database) according to particular needs, desires, or particular implementations of the computer 502 and the described functionality. Although illustrated as a single database 506 in
The computer 502 also includes a memory 507 that can hold data for the computer 502, another component or components communicatively linked to the network 530 (whether illustrated or not), or a combination of the computer 502 and another component. Memory 507 can store any data consistent with the present disclosure. In some implementations, memory 507 can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to particular needs, desires, or particular implementations of the computer 502 and the described functionality. Although illustrated as a single memory 507 in
The application 508 is an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer 502, particularly with respect to functionality described in the present disclosure. For example, application 508 can serve as one or more components, modules, or applications. Further, although illustrated as a single application 508, the application 508 can be implemented as multiple applications 508 on the computer 502. In addition, although illustrated as integral to the computer 502, in alternative implementations, the application 508 can be external to the computer 502.
The computer 502 can also include a power supply 514. The power supply 514 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 514 can include power-conversion or management circuits (including recharging, standby, or another power management functionality). In some implementations, the power-supply 514 can include a power plug to allow the computer 502 to be plugged into a wall socket or another power source to, for example, power the computer 502 or recharge a rechargeable battery.
There can be any number of computers 502 associated with, or external to, a computer system containing computer 502, each computer 502 communicating over network 530. Further, the term “client,” “user,” or 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 502, or that one user can use multiple computers 502.
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, that is, 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, for example, a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to a 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. Configuring one or more computers means that the one or more computers have installed hardware, firmware, or software (or combinations of hardware, firmware, and software) so that when the software is executed by the one or more computers, particular computing operations are performed.
The term “real-time,” “real time,” “realtime,” “real (fast) time (RFT),” “near(ly) real-time (NRT),” “quasi 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 5 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, taking into account processing limitations of a described computing system and time required to, for example, gather, accurately measure, analyze, process, store, or transmit the data.
The terms “data processing apparatus,” “computer,” or “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware and 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 be, or further include special purpose logic circuitry, for example, a central processing unit (CPU), an FPGA (field programmable gate array), or an ASIC (application-specific integrated circuit). 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 an operating system of some type, for example LINUX, UNIX, WINDOWS, MAC OS, ANDROID, IOS, another operating system, or a combination of operating systems.
A computer program, which can also be referred to or described as a program, software, a software application, a unit, a module, a software module, a script, code, or other component can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and it can be deployed in any form, including, for example, as a stand-alone program, module, component, or subroutine, for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files, for example, files that store one or more modules, sub-programs, or portions of code. A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
While portions of the programs illustrated in the various figures can be illustrated as individual components, such as units or modules, that implement described features and functionality using various objects, methods, or other processes, the programs can instead include a number of sub-units, sub-modules, third-party services, components, libraries, and other components, as appropriate. Conversely, the features and functionality of various components can be combined into single components, as appropriate. Thresholds used to make computational determinations can be statically, dynamically, or both statically and dynamically determined.
Described methods, processes, or logic flows represent one or more examples of functionality consistent with the present disclosure and are not intended to limit the disclosure to the described or illustrated implementations, but to be accorded the widest scope consistent with described principles and features. The described methods, processes, or logic flows 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 data. 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.
Computers for the execution of a computer program can be based on general or special purpose microprocessors, both, or another type of CPU. Generally, a CPU will receive instructions and data from and write to a memory. The essential elements of a computer are a CPU, for performing or executing instructions, and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to, receive data from or transfer data to, or both, one or more mass storage devices for storing data, for example, magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable memory storage device.
Non-transitory computer-readable media for storing computer program instructions and data can include all forms of media and memory devices, magnetic devices, magneto optical disks, and optical memory device. Memory devices include semiconductor memory devices, for example, 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. Magnetic devices include, for example, tape, cartridges, cassettes, internal/removable disks. Optical memory devices include, for example, digital video disc (DVD), CD-ROM, DVD+/-R, DVD-RAM, DVD-ROM, HD-DVD, and BLURAY, and other optical memory technologies. The memory can store various objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories storing dynamic information, or other appropriate information including any parameters, variables, algorithms, instructions, rules, constraints, or references. Additionally, the memory can include other appropriate data, such as logs, policies, security or access data, or reporting files. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
To provide for interaction with a user, implementations of the subject matter described in this specification can be implemented on a computer having a display device, for example, a CRT (cathode ray tube), LCD (liquid crystal display), LED (Light Emitting Diode), or plasma monitor, for displaying information to the user and a keyboard and a pointing device, for example, a mouse, trackball, or trackpad by which the user can provide input to the computer. Input can also be provided to the computer using a touchscreen, such as a tablet computer surface with pressure sensitivity, a multi-touch screen using capacitive or electric sensing, or another type of touchscreen. Other types of devices can be used to interact with the user. For example, feedback provided to the user can be any form of sensory feedback. Input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with the user by sending documents to and receiving documents from a client computing device that is used by the user.
The term “graphical user interface,” or “GUI,” can be used in the singular or the plural to describe one or more graphical user interfaces and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including but not limited to, a web browser, a touch screen, or a command line interface (CLI) that processes information and efficiently presents the information results to the user. In general, a GUI can include a plurality of user interface (UI) elements, some or all associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements can be related to or represent the functions of the web browser.
Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, for example, as a data server, or that includes a middleware component, for example, an application server, or that includes a front-end component, for example, a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of wireline or wireless digital data communication (or a combination of data communication), for example, a communication network. Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) using, for example, 802.11 a/b/g/n or 802.20 (or a combination of 802.11x and 802.20 or other protocols consistent with the present disclosure), all or a portion of the Internet, another communication network, or a combination of communication networks. The communication network can communicate with, for example, Internet Protocol (IP) packets, Frame Relay frames, Asynchronous Transfer Mode (ATM) cells, voice, video, data, or other information between networks addresses.
The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what can be claimed, but rather as descriptions of features that can be specific to particular 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 sub-combination. Moreover, although previously described features can 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 can be directed to a sub-combination or variation of a sub-combination.
Particular 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 particular order shown or in sequential order, or that all illustrated operations be performed (some operations can be considered optional), to achieve desirable results. In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) can 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 it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Furthermore, any claimed implementation is considered to be 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.
