Patent Publication Number: US-11047221-B2

Title: System and console for monitoring and managing well site operations

Description:
This application claims benefit of and priority to U.S. Provisional Application No. 61/841,383, filed Jun. 30, 2013, and is entitled to benefit of that priority date. The specification, figures, appendices and complete disclosure of U.S. Provisional Application No. 61/841,383 are incorporated herein in their entireties by specific reference for all purposes. This application is the result of activities and work undertaken within the scope of a joint research agreement, dated Apr. 16, 2007, entered into by BP International Limited and its affiliates, including BP Corporation North America, Inc. (collectively referred to as “BP”), and Sense Intellifield AS, a wholly-owned subsidiary of Kongsberg Maritime, whose name was subsequently changed to Kongsberg Intellifield AS as of Apr. 30, 2007, and later to Kongsberg Oil and Gas Technologies AS, for the performance of experimental, developmental, or research work in the field of the present invention. 
    
    
     FIELD OF INVENTION 
     This invention relates generally to oil and gas well drilling and production, and related operations. More particularly, this invention relates to a computer-implemented system for monitoring and managing well drilling and related operations. 
     BACKGROUND OF THE INVENTION 
     It is well-known that the drilling of an oil or gas well, and related operations, is responsible for a significant portion of the costs related to oil and gas exploration and production. In particular, as new wells are being drilled into remote or less-accessible reservoirs, the complexity, time and expense to drill a well have substantially increased. 
     Accordingly, it is critical that drilling operations be completed safely, accurately, and efficiently. With directional drilling techniques, and the greater depths to which wells are being drilled, many complexities are added to the drilling operation, and the cost and effort required to respond to a problem during drilling are high. This requires a high level of competence from the driller or drilling engineer at the drilling rig (or elsewhere) to safely drill the well as planned. 
     A “well plan” specifies a number of parameters for drilling a well, and is developed, in part, based on a geological model. A geological model of various subsurface formations is generated by a geologist from a variety of sources, including seismic studies, data from wells drilled in the area, core samples, and the like. A geological model typically includes depths to the various “tops” that define the formations (the term “top” generally refers to the top of a stratigraphic or biostratigraphic boundary of significance, a horizon, a fault, a pore pressure transition zone, change in rock type, or the like. Geological models usually include multiple tops, thereby defining the presence, geometry and composition of subsurface features. 
     The well plan specifies drilling parameters as the well bore advances through the various subsurface features. Parameters include, but are not limited to, mud weight, drill bit rotational speed, and weight on bit (WOB). The drilling operators rely on the well plan to anticipate tops and changes in subsurface features, account for drilling uncertainties, and adjust drilling parameters accordingly. 
     In many cases, the initial geological model may be inaccurate. The depth or location of a particular top may be off by a number of feet. Further, since some geological models recite distances based on the distance between two tops, an error in the absolute depth of one top can result in errors in the depths of multiple tops. Thus, a wellbore can advance into a high pressure subsurface formation before anticipated. 
     Such errors thus affect safety as well as cost and efficiency. It is fundamental in the art to use drilling “mud” circulating through the drill string to remove cuttings, lubricate the drill bit (and perhaps power it), and control the subsurface pressures. The drilling mud returns to the surface, where cuttings are removed, and is then recycled. 
     In some cases, the penetration of a high pressure formation can cause a sudden pressure increase (or “kick”) in the wellbore. If not detected and controlled, a “blowout” can occur, which may result in failure of the well. Blowout preventers (“BOP”) are well known in the art, and are used to protect drilling personnel and the well site from the effects of a blowout. A variety of systems and methods for BOP monitoring and testing are known in the art, including “Blowout Preventer Testing System and Method,” U.S. Pat. No. 7,706,890, and “Monitoring the Health of Blowout Preventer,” US 2012/0197527, both of which are incorporated herein in their entireties by specific reference for all purposes. 
     Conversely, if the mud weight is too heavy, or the wellbore advances into a particularly fragile or fractured formation, a “lost circulation” condition may result where drilling mud is lost into the formation rather than returning to the surface. This leads not only to the increased cost to replace the expensive drilling mud, but can also result in more serious problems, such as stuck drill pipe, damage to the formation or reservoir, and blowouts. 
     Similar problems and concerns arise during other well operations, such as running and cementing casing and tubulars in the wellbore, wellbore completions, or subsurface formation characterizations. 
     Drills strings and drilling operations equipment include a number of sensors and devices to measure, monitor and detect a variety of conditions in the wellbore, including, but not limited to, hole depth, bit depth, mud weight, choke pressure, and the like. This data can be generated in real-time, but can be enormous, and too voluminous for personnel at the drilling site to review and interpret in sufficient detail and time to affect the drilling operation. Some of the monitored data may be transmitted back to an engineer or geologist at a remote site, but the amount of data transmitted may be limited due to bandwidth limitations. Thus, not only is there a delay in processing due to transmission time, the processing and analysis of the data may be inaccurate due to missing or incomplete data. Drilling operations continue, however, even while awaiting the results of analysis (such as an updated geological model). 
     A real-time drilling monitor (RTDM) workstation is disclosed in “Drilling Rig Advisor Console,” U.S. application Ser. No. 13/312,646. The RTDM receives sensor signals from a plurality of sensors and generates single graphical user interface with dynamically generated parameters based on the sensor signals. 
     Likewise, an intelligent drilling advisor system is disclosed in “Intelligent Drilling Advisor,” U.S. Pat. No. 8,121,971, which is incorporated herein by specific reference for all purposes. The intelligent advisor system comprises an information integration environment that accesses and configures software agents that acquire data from sensors at a drilling site, transmit that data to the information integration environment, and drive the drilling state and the drilling recommendations for drilling operations at the drilling site. 
     SUMMARY OF INVENTION 
     In various embodiments, the present invention comprises a well advisor system for monitoring and managing well drilling and production operations. The system may be accessed through one or more workstations, or other computing devices. A workstation comprises one or more computers or computing devices, and may be located at a well site or remotely. The system can be implemented on a single computer system, multiple computers, a computer server, a handheld computing device, a tablet computing device, a smart phone, or any other type of computing device. 
     The system is in communication with and receives input from various sensors. In general, the system collects real-time sensor data sampled during operations at the well site, which may include drilling operations, running casing or tubular goods, completion operations, or the like. The system processes the data, and provides nearly instantaneous numerical and visual feedback through a variety of graphical user interfaces (“GUIs”). 
     The GUIs are populated with dynamically updated information, static information, and risk assessments, although they also may be populated with other types of information. The users of the system thus are able to view and understand a substantial amount of information about the status of the particular well site operation in a single view, with the ability to obtain more detailed information in a series of additional views. 
     In one embodiment, the system is installed at the well site, and thus reduces the need to transmit date to a remote site for processing. The well site can be an offshore drilling platform or land-based drilling rig. This reduces delays due to transmitting information to a remote site for processing, then transmitting the results of that processing back to the well site. It also reduces potential inaccuracies in the analysis due to the reduction in the data being transmitted. The system thus allows personnel at the well site to monitor the well site operation in real time, and respond to changes or uncertainties encountered during the operation. The response may include comparing the real time data to the current well plan, and modifying the well plan. 
     In yet another embodiment, the system is installed at a remote site, in addition to the well site. This permits users at the remote site to monitor the well-site operation in a similar manner to a user at the well-site installation. 
     In some exemplary embodiments, the system is a web-enabled application, and the system software may be accessed over a network connection such as the Internet. A user can access the software via the user&#39;s web browser. In some embodiments, the system performs all of the computations and processing described herein and only display data is transmitted to the remote browser or client for rendering screen displays on the remote computer. In another embodiment, the remote browser or software on the remote system performs some of the functionality described herein. 
     Sensors may be connected directly to the workstation at the well site, or through one or more intermediate devices, such as switches, networks, or the like. Sensors may comprise both surface sensors and downhole sensors. Surface sensors include, but are not limited to, sensors that detect torque, revolutions per minute (RPM), and weight on bit (WOB). Downhole sensors include, but are not limited to, gamma ray, pressure while drilling (PWD), and resistivity sensors. The surface and downhole sensors are sampled by the system during drilling or well site operations to provide information about a number of parameters. Surface-related parameters include, but are not limited to, the following: block position; block height; trip/running speed; bit depth; hole depth; lag depth; gas total; lithography percentage; weight on bit; hook load; choke pressure; stand pipe pressure; surface torque; surface rotary; mud motor speed; flow in; flow out; mud weight; rate of penetration; pump rate; cumulative stroke count; active mud system total; active mud system change; all trip tanks; and mud temperature (in and out). Downhole parameters include, but are not limited to, the following: all FEMWD; bit depth; hole depth; PWD annular pressure; PWD internal pressure; PWD EMW; PWD pumps off (min, max and average); drill string vibration; drilling dynamics; pump rate; pump pressure; slurry density; cumulative volume pumped; leak off test (LOT) data; and formation integrity test (FIT) data. Based on the sensed parameters, the system causes the processors or microprocessor to calculate a variety of other parameters, as described below. 
     In several embodiments, the system software comprises a database/server, a display or visualization module, one or more smart agents, one or more templates, and one or more “widgets.” The database/server aggregates, distributes and manages real-time data being generated on the rig and received through the sensors. The display or visualization module implements a variety of GUI displays, referred to herein as “consoles,” for a variety of well site operations. The information shown on a console may comprise raw data and calculated data in real time. 
     Templates defining a visual layout may be selected or created by a user to display information in some portions of or all of a console. In some embodiments, a template comprises an XML file. A template can be populated with a variety of information, including, but not limited to, raw sensor data, processed sensor data, calculated data values, and other information, graphs, and text. Some information may be static, while other information is dynamically updated in real time during the well site operation. In one embodiment, a template may be built by combining one or more display “widgets” which present data or other information. Smart agents perform calculations based on data generated through or by one or more sensors, and said calculated data can then be displayed by a corresponding display widgets. 
     In one exemplary embodiment, the system provides the user the option to implement a number of consoles corresponding to particular well site operations. In one embodiment, consoles include, but are not limited to, rig-site fluid management, BOP management, cementing, and casing running. A variety of smart agents and other programs are used by the consoles. Smart agents and other programs may be designed for use by a particular console, or may be used by multiple consoles. A particular installation of the system may comprise a single console, a sub-set of available consoles, or all available consoles. 
     Agents can be configured, and configuration files created or modified, using the agent properties display. The same properties are used for each agent, whether the agent configuration is created or imported. The specific configuration information (including, but not limited to, parameters, tables, inputs, and outputs) varies depending on the smart agent. Parameters represent the overall configuration of the agent, and include basic settings including, but not limited to, start and stop parameters, tracing, whether data is read to a log, and other basic agent information. Tables comprise information appearing in database tables associated with the agent. Inputs and outputs are the input or output mnemonics that are being tracked or reported on by the agent. For several embodiments, in order for data to be tracked or reported on, each output must have an associated output. This includes, but is not limited to, log and curve information. 
     In one embodiment, the system comprises a “No Drilling Surprises” Console provides information related to geologic uncertainty concerning a well being drilled, with a focus on the safety of the drilling operation. In several embodiments, the No Drilling Surprises Console comprises at least two widgets (Correlation, and Correlation Table), at least three agents (D-Exponent, Gas Interpretation, and Normalized Gas), and at least two import wizards (Formation Market, and Risk). The smart agents receive and pass information to these programs. The smart agents must be configured with parameter, table, input and output settings for the desired operation. 
     The Correlation Widget allows the user to compare historical data from offset wells to real-time data from the active well (e.g., the well being drilled). The data displayed includes, but is not limited to, log, formation marker, risk, and annotation data. 
     The Correlation Table Widget displays the formation markers on an active wellbore in a table with configurable columns, and provides the user the ability to set a threshold value to alert the user of significant deviations between actual marker picks as compared to the prognosis marker picks. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a view of a system in accordance with an embodiment of the present invention. 
         FIG. 2  shows a software architecture in accordance with various embodiments of the present invention. 
         FIG. 3  shows a smart agent management toolbar. 
         FIG. 4  shows a smart agent management menu. 
         FIG. 5  shows a smart agent configuration file import menu. 
         FIG. 6  shows a smart agent configuration display screen. 
         FIG. 7  shows a smart agent configuration file export menu. 
         FIG. 8  shows a smart agent configuration file download display screen. 
         FIG. 9  shows a smart agent configuration file copy menu. 
         FIG. 10  shows a No Drilling Surprises Console display screen. 
         FIG. 11  shows a display produced by the Correlation Widget. 
         FIG. 12  shows a Correlation Widget Properties general settings screen. 
         FIG. 13  shows a Correlation Widget Properties tracks and curves settings screen. 
         FIG. 14  shows another Correlation Widget Properties tracks and curves settings screen. 
         FIG. 15  shows a Curve Appearance Properties settings screen. 
         FIG. 16  shows a Curve Data Source Properties settings screen. 
         FIG. 17  shows an Index Track Properties settings screen. 
         FIG. 18  shows a Markers settings screen. 
         FIG. 19  shows a Risk settings screen. 
         FIG. 20  shows an Annotations settings screen. 
         FIG. 21  shows a menu display. 
         FIG. 22  shows a display produced by the Correlation Table Widget. 
         FIG. 23A  shows a Correlation Table Properties General Settings screen. 
         FIG. 23B  shows a Correlation Table Properties Columns screen. 
         FIG. 23C  shows a Correlation Table Properties Marker Thresholds screen. 
         FIG. 24  shows a D-Exponent Agent configuration page. 
         FIG. 25  shows a D-Exponent Agent menu with “Add” option. 
         FIG. 26  shows a Wellbore Selector dialog window. 
         FIG. 27  shows the Inputs/Outputs section of the D-Exponent Agent configuration page. 
         FIG. 28  shows a Select Mnemonic dialog box. 
         FIG. 29  shows an Output Mnemonics and Formulas screen. 
         FIG. 30  shows a Gas Interpretation Agent configuration page. 
         FIG. 31  shows the Inputs/Outputs section of the Gas Interpretation Agent configuration page. 
         FIG. 32  shows a Select Mnemonic dialog box. 
         FIG. 33  shows an Output Mnemonics screen. 
         FIG. 34  shows an Output Formulas screen. 
         FIG. 35A  shows a Normalized Gas Agent configuration page. 
         FIG. 35B  shows the Inputs/Outputs section of the Normalized Gas Agent configuration page. 
         FIG. 36  shows a Select Mnemonic dialog box. 
         FIG. 37  shows an Output Mnemonics and Formulas screen. 
         FIG. 38  shows a Formation Marker Import file import screen. 
         FIG. 39  shows a Formation Marker Import data mapping screen. 
         FIG. 40  shows a Formation Marker Import file import status screen. 
         FIG. 41  shows a Risk Import file import screen. 
         FIG. 42  shows a Risk Import data verification screen. 
         FIG. 43  shows a Risk Import file import status screen. 
     
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     Computing Environment Context 
     The following discussion is directed to various exemplary embodiments of the present invention, particularly as implemented into a situationally-aware distributed hardware and software architecture in communication with one or more operating drilling rigs. However, it is contemplated that this invention may provide substantial benefits when implemented in systems according to other architectures, and that some or all of the benefits of this invention may be applicable in other applications. For example, while the embodiments of the invention may be described herein in connection with wells used for oil and gas exploration and production, the invention also is contemplated for use in connection with other wells, including, but not limited to, geothermal wells, disposal wells, injection wells, and many other types of wells. One skilled in the art will understand that the examples disclosed herein have broad application, and that the discussion of any particular embodiment is meant only to be exemplary of that embodiment, and not intended to suggest that the scope of the disclosure, including the claims, is limited to that embodiment. 
     In order to provide a context for the various aspects of the invention, the following discussion provides a brief, general description of a suitable computing environment in which the various aspects of the present invention may be implemented. A computing system environment is one example of a suitable computing environment, but is not intended to suggest any limitation as to the scope of use or functionality of the invention. A computing environment may contain any one or combination of components discussed below, and may contain additional components, or some of the illustrated components may be absent. Various embodiments of the invention are operational with numerous general purpose or special purpose computing systems, environments or configurations. Examples of computing systems, environments, or configurations that may be suitable for use with various embodiments of the invention include, but are not limited to, personal computers, laptop computers, computer servers, computer notebooks, hand-held devices, microprocessor-based systems, multiprocessor systems, TV set-top boxes and devices, programmable consumer electronics, cell phones, personal digital assistants (PDAs), network PCs, minicomputers, mainframe computers, embedded systems, distributed computing environments, and the like. 
     Embodiments of the invention may be implemented in the form of computer-executable instructions, such as program code or program modules, being executed by a computer or computing device. Program code or modules may include programs, objections, components, data elements and structures, routines, subroutines, functions and the like. These are used to perform or implement particular tasks or functions. Embodiments of the invention also may be implemented in distributed computing environments. In such environments, tasks are performed by remote processing devices linked via a communications network or other data transmission medium, and data and program code or modules may be located in both local and remote computer storage media including memory storage devices. 
     In one embodiment, a computer system comprises multiple client devices in communication with at least one server device through or over a network. In various embodiments, the network may comprise the Internet, an intranet, Wide Area Network (WAN), or Local Area Network (LAN). It should be noted that many of the methods of the present invention are operable within a single computing device. 
     A client device may be any type of processor-based platform that is connected to a network and that interacts with one or more application programs. The client devices each comprise a computer-readable medium in the form of volatile and/or nonvolatile memory such as read only memory (ROM) and random access memory (RAM) in communication with a processor. The processor executes computer-executable program instructions stored in memory. Examples of such processors include, but are not limited to, microprocessors, ASICs, and the like. 
     Client devices may further comprise computer-readable media in communication with the processor, said media storing program code, modules and instructions that, when executed by the processor, cause the processor to execute the program and perform the steps described herein. Computer readable media can be any available media that can be accessed by computer or computing device and includes both volatile and nonvolatile media, and removable and non-removable media. Computer-readable media may further comprise computer storage media and communication media. Computer storage media comprises media for storage of information, such as computer readable instructions, data, data structures, or program code or modules. Examples of computer-readable media include, but are not limited to, any electronic, optical, magnetic, or other storage or transmission device, a floppy disk, hard disk drive, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, EEPROM, flash memory or other memory technology, an ASIC, a configured processor, CDROM, DVD or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium from which a computer processor can read instructions or that can store desired information. Communication media comprises media that may transmit or carry instructions to a computer, including, but not limited to, a router, private or public network, wired network, direct wired connection, wireless network, other wireless media (such as acoustic, RF, infrared, or the like) or other transmission device or channel. This may include computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism. Said transmission may be wired, wireless, or both. Combinations of any of the above should also be included within the scope of computer readable media. The instructions may comprise code from any computer-programming language, including, for example, C, C++, C#, Visual Basic, Java, and the like. 
     Components of a general purpose client or computing device may further include a system bus that connects various system components, including the memory and processor. A system bus may be any of several types of bus structures, including, but not limited to, a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus. 
     Computing and client devices also may include a basic input/output system (BIOS), which contains the basic routines that help to transfer information between elements within a computer, such as during start-up. BIOS typically is stored in ROM. In contrast, RAM typically contains data or program code or modules that are accessible to or presently being operated on by processor, such as, but not limited to, the operating system, application program, and data. 
     Client devices also may comprise a variety of other internal or external components, such as a monitor or display, a keyboard, a mouse, a trackball, a pointing device, touch pad, microphone, joystick, satellite dish, scanner, a disk drive, a CD-ROM or DVD drive, or other input or output devices. These and other devices are typically connected to the processor through a user input interface coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, serial port, game port or a universal serial bus (USB). A monitor or other type of display device is typically connected to the system bus via a video interface. In addition to the monitor, client devices may also include other peripheral output devices such as speakers and printer, which may be connected through an output peripheral interface. 
     Client devices may operate on any operating system capable of supporting an application of the type disclosed herein. Client devices also may support a browser or browser-enabled application. Examples of client devices include, but are not limited to, personal computers, laptop computers, personal digital assistants, computer notebooks, hand-held devices, cellular phones, mobile phones, smart phones, pagers, digital tablets, Internet appliances, and other processor-based devices. Users may communicate with each other, and with other systems, networks, and devices, over the network through the respective client devices. 
     By way of further background, the term “software agent” refers to a computer software program or object that is capable of acting in a somewhat autonomous manner to carry out one or more tasks on behalf of another program or object in the system. Software agents can also have one or more other attributes, including mobility among computers in a network, the ability to cooperate and collaborate with other agents in the system, adaptability, and also specificity of function (e.g., interface agents). Some software agents are sufficiently autonomous as to be able to instantiate themselves when appropriate, and also to terminate themselves upon completion of their task. 
     The term “expert system” refers to a software system that is designed to emulate a human expert, typically in solving a particular problem or accomplishing a particular task. Conventional expert systems commonly operate by creating a “knowledge base” that formalizes some of the information known by human experts in the applicable field, and by codifying some type of formalism by way the information in the knowledge base applicable to a particular situation can be gathered and actions determined. Some conventional expert systems are also capable of adaptation, or “learning”, from one situation to the next. Expert systems are commonly considered to be in the realm of “artificial intelligence.” 
     The term “knowledge base” refers to a specialized database for the computerized collection, organization, and retrieval of knowledge, for example in connection with an expert system. The term “rules engine” refers to a software component that executes one or more rules in a runtime environment providing among other functions, the ability to: register, define, classify, and manage all the rules, verify consistency of rules definitions, define the relationships among different rules, and relate some of these rules to other software components that are affected or need to enforce one or more of the rules. Conventional approaches to the “reasoning” applied by such a rules engine in performing these functions involve the use of inference rules, by way of which logical consequences can be inferred from a set of asserted facts or axioms. These inference rules are commonly specified by means of an ontology language, and often a description language. Many reasoners use first-order predicate logic to perform reasoning; inference commonly proceeds by forward chaining and backward chaining. 
     The present invention may be implemented into an expert computer hardware and software system, implemented and operating on multiple levels, to derive and apply specific tools at a drilling site from a common knowledge base, including, but not limited to, information from multiple drilling sites, production fields, drilling equipment, and drilling environments. At a highest level, a knowledge base is developed from attributes and measurements of prior and current wells, information regarding the subsurface of the production fields into which prior and current wells have been or are being drilled, lithology models for the subsurface at or near the drilling site, and the like. In this highest level, an inference engine drives formulations (in the form of rules, heuristics, calibrations, or a combination thereof) based on the knowledge base and on current data. An interface to human expert drilling administrators is provided for verification of these rules and heuristics. These formulations pertain to drilling states and drilling operations, as well as recommendations for the driller, and also include a trendologist function that manages incoming data based on the quality of that data, such management including the amount of processing and filtering to be applied to such data, as well as the reliability level of the data and of calculations therefrom. 
     At another level, an information integration environment is provided that identifies the current drilling sites, and drilling equipment and processes at those current drilling sites. Based upon that identification, and upon data received from the drilling sites, servers access and configure software agents that are sent to a host client system at the drilling site; these software agents operate at the host client system to acquire data from sensors at the drilling site, to transmit that data to the information integration environment, and to derive the drilling state and drilling recommendations for the driller at the drilling site. These software agents include one or more rules, heuristics, or calibrations derived by the inference engine, and called by the information integration environment. In addition, the software agents sent from the information integration environment to the host client system operate to display values, trends, and reliability estimates for various drilling parameters, whether measured or calculated. 
     The information integration environment is also operative to receive input from the driller via the host client system, and to act as a knowledge base server to forward those inputs and other results to the knowledge base and the inference engine, with verification or input from the drilling administrators as appropriate. 
     According to another aspect of the invention, the system develops a knowledge base from attributes and measurements of prior and current wells, and from information regarding the subsurface of the production fields into which prior and current wells have been or are being drilled. According to this aspect of the invention, the system self-organizes and validates historic, real time, and/or near real time depth or time based measurement data, including information pertaining to drilling dynamics, earth properties, drilling processes and driller reactions. This drilling knowledge base suggests solutions to problems based on feedback provided by human experts, learns from experience, represents knowledge, instantiates automated reasoning and argumentation for embodying best drilling practices. 
     According to yet another aspect of the invention, the system includes the capability of virtualizing information from a well being drilled into a collection of metalayers, such metalayers corresponding to a collection of physical information about the layer (material properties, depths at a particular location, and the like) and also information on how to successfully drill through such a layer, such metalayers re-associating as additional knowledge is acquired, to manage real-time feedback values in optimizing the drilling operation, and in optimizing the driller response to dysfunction. Normalization into a continuum, using a system of such metalayers, enables real-time reaction to predicted downhole changes that are identified from sensor readings. 
     According to another aspect of the invention, the system is capable of carrying out these functions by creating and managing a network of software agents that interact with the drilling environment to collect and organize information for the knowledge base, and to deliver that information to the knowledge base. The software agents in this network are persistent, autonomous, goal-directed, sociable, reactive, non-prescriptive, adaptive, heuristic, distributed, mobile and self-organizing agents for directing the driller toward drilling optimization, for collecting data and information, and for creating dynamic transitional triggers for metalayer instantiation. These software entities interact with their environment through an adaptive rule-base to intelligently collect, deliver, adapt and organize information for the drilling knowledge base. According to this aspect of the invention, the software agents are created, modified and destroyed as needed based on the situation at the drilling rig, within the field, or at any feasible knowledge collection point or time instance within the control scope of any active agent. 
     According to another aspect of the invention, the software agents in the network of agents are controlled by the system to provide the recommendations to the drillers, using one or more rules, heuristics, and calibrations derived from the knowledge base and current sensor signals from the drilling site, and as such in a situationally aware manner. In this regard, the software agents interact among multiple software servers and hardware states in order to provide recommendations that assist human drillers in the drilling of a borehole into the earth at a safely maximized drilling rate. The software “experts” dispatch agents, initiate transport of remote memory resources, and provide transport of knowledge base components including rules, heuristics, and calibrations according to which a drilling state or drilling recommendation is identified responsive to sensed drilling conditions in combination with a selected parameter that is indicative of a metalayer of the earth, and in combination with selected minimums and maximums of the drilling equipment sensor parameters. The software experts develop rules, heuristics, and calibrations applicable to the drilling site derived from the knowledge base that are transmitted via an agent to a drilling advisor application, located at the drilling site, that is coupled to receive signals from multiple sensors at the drilling site, and also to one or more servers that configure and service multiple software agents. 
     According to another aspect of the invention, the system is applied to circulation actors to optimize circulation, hydraulics at the drill bit point of contact with the medium being drilled, rationalization of distributed pressure and temperature measurements and to provide recommendations to avoid or recover from loss of circulation events. 
     In addition, while this invention is described in connection with a multiple level hardware and software architecture system, in combination with drilling equipment and human operators, it is contemplated that several portions and facets of this invention are separately and independently inventive and beneficial, whether implemented in this overall system environment or if implemented on a stand-alone basis or in other system architectures and environments. Those skilled in the art having reference to this specification are thus directed to consider this description in such a light. 
     Well Advisor System and Consoles 
       FIG. 1  illustrates a workstation showing a well advisor system  100  in accordance with various exemplary embodiments of the present invention. The workstation comprises one or more computers or computing devices, and may be located at a well site or remotely. The system can be implemented on a single computer system, multiple computers, a computer server, a handheld computing device, a tablet computing device, a smart phone, or any other type of computing device. 
     The system is in communication with and receives input from various sensors  120 ,  130 . In general, the system collects real-time sensor data sampled during operations at the well site, which may include drilling operations, running casing or tubular goods, completion operations, or the like. The system processes the data, and provides nearly instantaneous numerical and visual feedback through a variety of graphical user interfaces (GUIs). 
     The GUIs are populated with dynamically updated information, static information, and risk assessments, although they also may be populated with other types of information, as described below. The users of the system thus are able to view and understand a substantial amount of information about the status of the particular well site operation in a single view, with the ability to obtain more detailed information in a series of additional views. 
     In one embodiment, the system is installed at the well site, and thus reduces the need to transmit date to a remote site for processing. The well site can be an offshore drilling platform or land-based drilling rig. This reduces delays due to transmitting information to a remote site for processing, then transmitting the results of that processing back to the well site. It also reduces potential inaccuracies in the analysis due to the reduction in the data being transmitted. The system thus allows personnel at the well site to monitor the well site operation in real time, and respond to changes or uncertainties encountered during the operation. The response may include comparing the real time data to the current well plan, and modifying the well plan. 
     In yet another embodiment, the system is installed at a remote site, in addition to the well site. This permits users at the remote site to monitor the well-site operation in a similar manner to a user at the well-site installation. 
     The architecture of the system workstation shown in  FIG. 1  is only one example of multiple possible architectures. In one embodiment, the workstation comprises one or more processors or microprocessors  102  coupled to one or more input devices  104  (e.g., mouse, keyboard, touchscreen, or the like), one or more output devices  106  (e.g., display, printer, or the like), a network interface  108 , and one or more non-transitory computer-readable storage devices  110 . In some embodiments, the input and output devices may be part of the workstation itself, while in other embodiment such devices may be accessible to the workstation through a network or other connection. 
     In one exemplary embodiment, the network interface may comprise a wire-based interface (e.g., Ethernet), or a wireless interface (e.g., BlueTooth, wireless broadband, IEEE 802.11x WiFi, or the like), which provides network connectivity to the workstation and system to enable communications across local and/or wide area networks. For example, the workstation can receive portions of or entire well or cementing plans or geological models  117  from a variety of locations. 
     The storage devices  110  may comprise both non-volatile storage devices (e.g., flash memory, hard disk drive, or the like) and volatile storage devices (e.g., RAM), or combinations thereof. The storage devices store the system software  115  which is executable by the processors or microprocessors to perform some or all of the functions describe below. The storage devices also may be used to store well plans, geological models  117 , configuration files and other data. 
     In some exemplary embodiments, the system is a web-enabled application, and the system software may be accessed over a network connection such as the Internet. A user can access the software via the user&#39;s web browser. In some embodiments, the system performs all of the computations and processing described herein and only display data is transmitted to the remote browser or client for rendering screen displays on the remote computer. In other embodiments, the remote browser or software on the remote system performs some of the functionality described herein. 
     Sensors  120 ,  130  may be connected directly to the workstation at the well site, or through one or more intermediate devices, such as switches, networks, or the like. Sensors may comprise both surface sensors  120  and downhole sensors  130 . Surface sensors include, but are not limited to, sensors that detect torque, revolutions per minute (RPM), and weight on bit (WOB). Downhole sensors include, but are not limited to, gamma ray, pressure while drilling (PWD), and resistivity sensors. The surface and downhole sensors are sampled by the system during drilling or well site operations to provide information about a number of parameters. Surface-related parameters include, but are not limited to, the following: block position; block height; trip/running speed; bit depth; hole depth; lag depth; gas total; lithography percentage; weight on bit; hook load; choke pressure; stand pipe pressure; surface torque; surface rotary; mud motor speed; flow in; flow out; mud weight; rate of penetration; pump rate; cumulative stroke count; active mud system total; active mud system change; all trip tanks; and mud temperature (in and out). Downhole parameters include, but are not limited to, the following: all FEMWD; bit depth; hole depth; PWD annular pressure; PWD internal pressure; PWD EMW; PWD pumps off (min, max and average); drill string vibration; drilling dynamics; pump rate; pump pressure; slurry density; cumulative volume pumped; leak off test (LOT) data; and formation integrity test (FIT) data. Based on the sensed parameters, the system causes the processors or microprocessor to calculate a variety of other parameters, as described below. 
       FIG. 2  provides an example of the system software architecture. The system software comprises a database/server  150 , a display or visualization module  152 , one or more smart agents  154 , one or more templates  156 , and one or more “widgets”  160 . The database/server  150  aggregates, distributes and manages real-time data being generated on the rig and received through the sensors. The display or visualization module  152  implements a variety of graphical user interface displays, referred to herein as “consoles,” for a variety of well site operations. The information shown on a console may comprise raw data and calculated data in real time. 
     Templates  156  defining a visual layout may be selected or created by a user to display information in some portions of or all of a console. In some embodiments, a template comprises an XML file. A template can be populated with a variety of information, including, but not limited to, raw sensor data, processed sensor data, calculated data values, and other information, graphs, and text. Some information may be static, while other information is dynamically updated in real time during the well site operation. In one embodiment, a template may be built by combining one or more display “widgets”  160  which present data or other information. Smart agents  154  perform calculations based on data generated through or by one or more sensors, and said calculated data can then be displayed by a corresponding display widgets. 
     In one exemplary embodiment, the system provides the user the option to implement a number of consoles corresponding to particular well site operations. In one embodiment, consoles include, but are not limited to, rig-site fluid management, BOP management, cementing, and casing running. A variety of smart agents and other programs are used by the consoles. Smart agents and other programs may be designed for use by a particular console, or may be used by multiple consoles. A particular installation of the system may comprise a single console, a sub-set of available consoles, or all available consoles. 
     In various embodiments, smart agents in the system can be managed with a toolbar  200  (as seen in  FIG. 3 ) or by a drop-down menu  210  (as seen in  FIG. 4 ), which may be activated by clicking on a smart agent icon, right-click on a mouse button, or the like. Functions include, but are not limited to, adding a new agent  202   a , copying an agent configuration  202   b , importing  202   c  or exporting  202   d  an agent configuration file, deleting an agent  202   e , refreshing the status of an agent  202   f , or starting or stopping an agent. 
     For certain smart agents, an agent configuration file must be imported  220  to use the smart agent, as seen in  FIG. 5 . In one embodiment, configuration files are denominated as *.agent files. Selecting the import option provides the user the option to enter the configuration file name, or browse to a location where the configuration file is stored. 
     Agents can be configured, and configuration files created or modified, using the agent properties display, as seen in  FIG. 6 . The same properties are used for each agent, whether the agent configuration is created or imported. The specific configuration information (including, but not limited to, parameters, tables, inputs, and outputs) varies depending on the smart agent. Parameters  232  represent the overall configuration of the agent, and include basic settings including, but not limited to, start and stop parameters, tracing, whether data is read to a log, and other basic agent information. Tables  234  comprise information appearing in database tables associated with the agent. Inputs  236  and outputs  238  are the input or output mnemonics that are being tracked or reported on by the agent. For several embodiments, in order for data to be tracked or reported on, each output must have an associated output. This includes, but is not limited to, log and curve information. 
     Users can export an agent configuration file for other users to import and use. The export configuration button in the toolbar can be used for a selected agent, or the agent can be right-clicked on and the export configuration option  240  chosen, as shown in  FIG. 7 . The user confirms  242  the action to download the file to a local hard drive or other file storage location, as seen in  FIG. 8 . The user may name the file as desired. Once downloaded, the file can be copied, emailed, or otherwise transferred to another user for importation and use. 
     Copying an agent configuration  244 , as seen in  FIG. 9 , allows the user to copy an agent configuration file and rename it. This saves the user from having to perform an initial setup of the agent properties or create a new configuration file multiple times, if the user has agent configurations that are similar. In one embodiment, the user right clicks on the desired agent, selects the copy option, and identifies the wellbore for which the configuration is to be used. The user can name or rename the new agent configuration. 
     No Drilling Surprises Console 
     The GUI display for an embodiment of a No Drilling Surprises Console  300  is shown in  FIG. 10 . The No Drilling Surprises Console provides information related to geologic uncertainty concerning a well being drilled, with a focus on the safety of the drilling operation. This display generally shows real-time information from the active wellbore  302  in comparison to historical data from one or more offset wells  304 . In this embodiment, the No Drilling Surprises Console comprises at least two widgets (Correlation, and Correlation Table), at least three agents (D-Exponent, Gas Interpretation, and Normalized Gas), and at least two import wizards (Formation Market, and Risk). The smart agents receive and pass information to these programs. The smart agents must be configured with parameter, table, input and output settings for the desired operation. 
       FIG. 11  shows an example of a visual display produced by the Correlation Widget  310 . The Correlation Widget allows the user to compare historical data from offset wells  304  to real-time data from the active well  302  (e.g., the well being drilled). The data displayed includes, but is not limited to, log, formation marker, risk  314 , and annotation data. The widget display can be customized by right-clicking on the widget, and selecting “Properties” from the menu. An example of a Correlation Widget Properties screen  320  is shown in  FIG. 12 . In one embodiment, the Properties screen has five tabs: General  322 ; Tracks and Curve  324 ; Markers  326 ; Risks  328 ; and Annotations  330 . 
     From the General tab settings screen, as seen in  FIG. 12 , the user can change the overall look of the log, including units  332  (the unit of measure to use for the index of the log tracks), decimals  334  (how many significant digits are displayed for the index values), a “fit width” check box  336  (if checked, all of the tracks, active and offsets, are fitted to the width of the widget display), and a “fit height” check box  338  (if checked, all of the tracks, active and offsets, are fitted to the height of the widget display). 
     From the Tracks and Curves tab settings screens, as seen in  FIGS. 13 and 14 , the user can set properties for each track on the log. The configuration of tracks by wellbore is shown in the window  340  on the left. The user can then add and modify wellbore reference information  342  (as seen in  FIG. 13 ), or specific curve information  344  for the selected wellbore (as seen in  FIG. 14 ). 
     As seen in  FIG. 13 , the “Add Offset” option allows the user to add an offset well track group to the configuration, while the “X” or “Remove” option allows the user to remove an offset well track group from the configuration. The up and down arrows move the selected (highlighted) wellbore track group up or down in the list, which affects the order of wellbore display on the widget. In one embodiment, the wellbore track groups are displayed from left to right in the order from top to bottom shown on this settings screen. The “Reference” option allows the user to set the wellbore reference for a given offset wellbore (there is only one option in the dropdown option list for the active wellbore; the active wellbore can be changed by using the “My Wells” tab or the WITSML Tree tab). The “Trajectory” option allows the user to select the trajectory to use for the display of the curve data (in one embodiment, TVD is used as the index of the widget so a trajectory is required to display curve data). The “TVD Offset” value is used to shift the display of curve data, formation markers, and risks on the index track (a positive value shifts them up, while a negative value shifts them down). In one embodiment, this value (in the selected index unit of measure) should be set to the elevation different in the default datum per the WellDatm values of the selected well and mean sea level. The “Track Group Header Color” allows the user to set the color of the header for the wellbore. The “Copy Settings” option provides the capability to copy the entire configuration (including all wellbore, track, and curve properties) of a different wellbore into the currently selected wellbore. 
     As seen in  FIG. 14 , the “Add Curve” option allows the user to access the selector dialog. The “Title” option changes the name of the track shown on the display. The “Background Color” option changes the background color of the track. The “Show Grid” check box is used to activate the “Grid Type” options, allowing the user to decide the type of chart (linear or logarithmic), and the number and type of grid lines (i.e., number of primary thick lines, number of secondary thin lines, start value for logarithmic chart, number of decades, line width and color). 
     The “Edit Curves” option  350  allows the user to access the curve settings tabs: Appearance, Data Source, and Alarm (as seen in  FIGS. 15 and 16 ).  FIG. 15  shows an example of the Curve Appearance Properties screen  360 , which allows the user to select and modify a variety of data governing the appearance of the curved, such as title, mnemonic form, units, ranges, and font and line styles and color. For example, the “Shape” option  362  allows the user to choose how the curve is drawn, and the “Scale Wrap” click box  364  allows the user to determine if the curve will wrap. Similarly,  FIG. 16  shows an example of the Curve Data Source Properties screen  370 , which allows the user to select and specify the source of the curve data, as well as properties thereof. 
       FIG. 17  shows an example of an Index Track Properties setting screen  180  under the Tracks and Curves tab. In one embodiment, this allows the user to change the title of the Measured Depth (MD) track  182 , and the title of the True Vertical Depth (TVD) track  184 . 
       FIG. 18  shows an example of a Markers tab settings screen  190 , which allows the user to change how markers are displayed. The user can set the visibility of a marker  192 , display the marker names  194 , display a preview of the line style and color  196 , and select the line style and color  198 . 
       FIG. 19  shows an example of a Risks tab settings screen  200 , which allows the user to change how risks are displayed. The user can set the visibility of a risk  202 , display the risk category names  204 , display a preview of the risk color  206 , and select the risk color  208 . 
       FIG. 20  shows an example of an Annotations tab setting screen  210 , which allows the user to set filtering options for annotations. The filters include user name  212  (to filter out annotations made by a particular user), user role  214  (to filter out annotations made by users with a selected role, e.g., Wellsite Geologists, Operations Geologist), and category  216  (to filter out annotations by category, e.g., Geologic, Drilling, General). 
     Many of the above options also can be activated by right-clicking on the widget, and accessing the appropriate option, as seen in the right-click menu  220  in  FIG. 21 . 
       FIG. 22  shows an example of a visual display produced by the Correlation Table Widget  230 . This displays the formation markers on an active wellbore in a table with configurable columns, including name and depths (original, updated, and actual), and provides the user the ability to set a threshold value to alert the user of significant deviations between actual marker picks as compared to the prognosis marker picks. 
     The display can be customized by right-clicking on the widget, and selecting “Properties” from the menu to open the Correlation Table Properties dialog, which has three sections: General Settings, Columns, and Marker Thresholds. General Settings  240 , as seen in  FIG. 23A , allows the user to change the overall look of the table (e.g., title, font size, depth unit, and TVD offset). Columns  242 , as seen in  FIG. 23B , allows the user to select which columns display and in which order (by moving the highlighted column up or down the list). Marker Thresholds  244 , as seen in  FIG. 23C , allows the user to set marker threshold values that determine if an alert should be displayed for particular markers, if the box is checked for that marker. 
     In one embodiment, the console comprises three early gas detection agents: D-Exponent, Gas Interpretation, and Normalized Gas. Each agent requires certain input and creates specific output that can be viewed using the log widget. 
     The D-Exponent Agent is used to calculate the drilling exponent and the corrected drilling exponent for a given set of wellbore data. An example of the D-Exponent Agent configuration page  250  is seen in  FIG. 24 . In one embodiment, this configuration page is initiated from the “My Agents” table in the Well Advisor system. The user right-clicks on the D-Exponent Agent node and selects the “Add” option  252  in the menu, as seen in  FIG. 25 . This displays the wellbore selector dialog window  254 , as seen in  FIG. 26 , which prompts the user to select the desired wellbore to configure. Selecting the wellbore causes the configuration page to open. 
     The Inputs/Outputs section  260  of the configuration page, as seen in  FIG. 27 , prompts the user to configure the desired input mnemonics and values. For certain input parameters, the user can open a Select Mnemonic dialog screen  262 , as seen in  FIG. 28 , to select a mnemonic to use. Once all of the input mnemonics and values have been defined, the agent is ready to begin calculating its output mnemonics. The user can validate and save the configuration, and start the agent. The agent output mnemonics and formulas  264  used for the calculations are shown in  FIG. 29 . In one exemplary embodiment, two outputs (“lastBitDia” and “dB”) are calculated based on the constant values provided, and are not written to output mnemonics. These values are used in the calculation of the two outputs that are written to output mnemonics (“dExp” and “dExpCorr”), as indicated by checking the “Use as Output” boxes  266  for those outputs. 
     The Gas Interpretation Agent is used to calculate a variety of outputs based on the component gases and the total drilling gas. An example of the Gas Interpretation Agent configuration page  270  is seen in  FIG. 30 . This configuration page can be initiated and modified in a similar manner as the D-Exponent Agent configuration page. The Inputs/Outputs section  280  of the configuration page is seen in  FIG. 31 . The Select Mnemonic dialog box  290  is seen in  FIG. 32 . The output mnemonics  300  can be seen in  FIG. 33 , and the agent calculation formulas  310  can be seen in  FIG. 34 . 
     The Normalized Gas Agent is used to calculate the m3 rock cut per minute and the normalized gas equation. An example of the Normalized Gas Agent configuration page  320  is seen in  FIG. 35A . This configuration page can be initiated and modified in a similar manner as the D-Exponent Agent configuration page. The Inputs/Outputs section  322  of the configuration page is seen in  FIG. 35B . The Select Mnemonic dialog box  334  is seen in  FIG. 36 . The output mnemonics  336  and the agent calculation formulas  338  can be seen in  FIG. 37 . 
     Formation marker data can be imported with the Formation Marker Import wizard. An example of a file import selection screen  340  is shown in  FIG. 38 . Once selected, the user maps (assigns) the data contained in the file to properties of the formation marker, as seen in  FIG. 39 .  FIG. 40  shows an example of an import status window  344  upon completion of the import process. 
     Similarly, risk data can be imported with the Risk Import wizard. An example of a file import selection screen  350  is shown in  FIG. 41 . Once selected, the user verifies the source file risk settings and destination properties  352 , as seen in  FIG. 42 .  FIG. 43  shows an example of an import status window  354  upon completion of the import process. 
     Thus, it should be understood that the embodiments and examples described herein have been chosen and described in order to best illustrate the principles of the invention and its practical applications to thereby enable one of ordinary skill in the art to best utilize the invention in various embodiments and with various modifications as are suited for particular uses contemplated. Even though specific embodiments of this invention have been described, they are not to be taken as exhaustive. There are several variations that will be apparent to those skilled in the art.