Methods and systems for integrated well construction

Abstract

One approach may include obtaining drilling rig operation data for various drilling rigs at different geographical locations via a computer processor. The approach may further include using the drilling rig operation data to generate a model identifying risk levels associated with the various drilling rig operations. The approach may further include using a computer processor and geographical location data and the model to simulate a sequence of drilling operations for constructing a portion of a wellbore drilled by a drilling rig at a desired geographical location.

Description

Background Technology

[0001] Before submitting a bid for a drilling project, a contractor can list all the costs of the project in preparation for submitting a formal bid. However, during the actual construction of the drilling project, cost overruns may occur due to unforeseen issues not included in the formal bid. For example, if certain control systems malfunction, non-productive time may occur during the construction of the drilling project. Therefore, for a given bid, contractors face various inherent risks in winning a drilling project. Consequently, there is a need for technology that can accurately quantify these risks. Summary of the Invention

[0002] In general, in one aspect, embodiments relate to a method that includes obtaining geographic location data relating to a desired geographic location of a drilling rig via a computer processor. The method includes obtaining drilling rig operation data for various drilling rigs at different geographic locations via a computer processor. The method includes using the drilling rig operation data to generate a model identifying risk levels associated with various drilling rig operations. The method includes using a computer processor and the geographic location data and the model to simulate a drilling rig operation sequence in which a drilling rig drills a portion of a wellbore at a desired geographic location.

[0003] In one aspect, embodiments relate to a system including a computer processor. The system includes memory coupled to and executable by the computer processor. The memory includes functionality for obtaining geographic location data relating to a desired geographic location of a drilling rig via the computer processor. The memory includes functionality for obtaining drilling rig operation data regarding various drilling rigs at different geographic locations via the computer processor. The memory includes functionality for generating models using the drilling rig operation data to identify risk levels associated with various drilling rig operations. The memory includes functionality for simulating a drilling rig operation sequence using the geographic location data and the model, the drilling rig operation sequence being used to construct a portion of a wellbore drilled by the drilling rig at a desired geographic location.

[0004] In general, in one aspect, embodiments relate to a non-transitory computer-readable medium storing instructions executable by a computer processor. The instructions include functionality for obtaining geographic location data regarding a desired geographic location of a drilling rig via the computer processor. The instructions include functionality for obtaining drilling rig operation data regarding various drilling rigs at different geographic locations via the computer processor. The instructions include functionality for generating models using the drilling rig operation data to identify risk levels associated with various drilling rig operations. These instructions include functionality for simulating a drilling rig operation sequence using the geographic location data and the model, the drilling rig operation sequence being used to construct a portion of a wellbore drilled by the drilling rig at the desired geographic location.

[0005] Other aspects of this disclosure will become apparent from the following description and the appended claims. Attached Figure Description

[0006] Specific embodiments of the disclosed technology will now be described in detail with reference to the accompanying drawings. For consistency, similar elements in the various drawings are indicated by similar reference numerals.

[0007] Figure 1 A block diagram of a system according to one or more embodiments is shown;

[0008] Figure 2 A block diagram of a system according to one or more embodiments is shown;

[0009] Figure 3 A block diagram of a system according to one or more embodiments is shown;

[0010] Figure 4 and 5 A flowchart according to one or more embodiments is shown;

[0011] Figure 6 Examples according to one or more embodiments are shown;

[0012] Figure 7.1 and 7.2 A computing system according to one or more embodiments is shown. Detailed Implementation

[0013] Specific embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. For consistency, similar elements in the various drawings are indicated by similar reference numerals.

[0014] In the following detailed description of embodiments of this disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to those skilled in the art that the disclosure may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.

[0015] Throughout the application, ordinal numbers (e.g., first, second, third, etc.) may be used as adjectives for elements (i.e., any noun in the application). Unless explicitly stated, such as by using terms like “before,” “after,” “single,” and other such terms, the use of ordinal numbers neither implies nor creates any particular order of elements, nor does it imply limiting any element to a single element. Rather, ordinal numbers are used to distinguish elements. For example, a first element is different from a second element, and a first element may contain more than one element, and the elements may be in the order they appear after (or before) the second element.

[0016] Typically, embodiments of this disclosure include systems and methods for using models to predict various operational parameters for a drilling project. In particular, operational parameters may include costs, projected timelines, and risks associated with constructing and / or operating wells at potential drilling locations. For example, multiple data sources are accessed and analyzed for drilling rig operation data relating to past drilling operations near or using similar well designs at potential drilling locations. A remote server may filter drilling rig operation data for use with a model to simulate the construction and / or operation of wells at potential drilling locations. The model may use one or more artificial intelligence algorithms to analyze the filtered drilling rig operation data and generate operational parameters at potential drilling locations. Thus, the remote server may collect well input parameters and location information for potential drilling locations from user equipment and, in response, send a report describing the operational parameters back to the user equipment. For example, the report may be a risk advisory message detailing the time, costs, and risks associated with constructing and / or operating one or more wells at a potential drilling location.

[0017] Turning Figure 1 , Figure 1 A block diagram of a system according to one or more embodiments is shown. Figure 1 A drilling system (10) according to one or more embodiments is shown. A drill string (58) is shown within a borehole (46). The borehole (46) may be located in soil (40) having a surface (42). The borehole (46) is shown as being cut by the action of a drill bit (54). The drill bit (54) may be located at the distal end of a bottom hole tool assembly (56), which is attached to and forms the lower part of the drill string (58). The bottom hole tool assembly (56) may include a number of devices, including various sub-assemblies. A measurement-while-drilling (MWD) sub-assembly may be included in a sub-assembly (62). Examples of MWD measurements may include direction, inclination, survey data, downhole pressure (internal and / or external and / or annular pressures of the drill pipe), resistivity, density, and porosity. The sub-assembly (62) may also include a sub-assembly for measuring torque and weight on the drill bit (54). Signals from the sub-assembly (62) may be processed in a processor (66). After processing, the information from the processor (66) can be transmitted to the pulse generator assembly (64). The pulse generator assembly (64) can convert the information from the processor (66) into pressure pulses in the drilling fluid. The pressure pulses can be generated in a specific pattern representing the data from the subassembly (62). The pressure pulses can propagate upward through the drilling fluid in the central opening of the drill string and toward the surface system. The subassemblies in the bottom hole assembly (56) may also include turbines or motors for powering the rotation and manipulation of the drill bit (54).

[0018] The drilling rig (12) may include, for example, a derrick (68) and a hoisting system, a rotary system, and / or a mud circulation system. The hoisting system may suspend the drill string (58) and may include a winch (70), a fast rope (71), a jack (75), a drill line (79), a traveling block and a hook (72), a swivel (74), and / or a dead rope (77). The rotary system may include a crisscross drill pipe (76), a rotary table (88), and / or an engine (not shown). The rotary system may apply rotational force to the drill string (58). Similarly, Figure 1 The illustrated embodiments are also applicable to top-drive drilling rigs. Although the drilling system (10) shown is on land, those skilled in the art will recognize that the described embodiments are equally applicable to marine environments.

[0019] A mud circulation system can pump drilling fluid down through openings in the drill string. The drilling fluid, referred to as mud, can be a mixture of water and / or diesel, special clay, and / or other chemicals. The mud can be stored in a mud pit (78). The mud can be drawn into a mud pump (not shown), which pumps the mud through a riser (86) and through a rotating element (74) into the square drill pipe (76), which may include a rotary seal. Similarly, the described technique can also be applied to underbalanced drilling. If underbalanced drilling is used, gas can be introduced into the mud at some point before entering the drill string using an injection system (not shown).

[0020] Mud can pass through the drill string (58) and the drill bit (54). As the teeth of the drill bit (54) grind the mud and chisel it into chips, mud may be ejected from the openings or nozzles of the drill bit (54). These mud jets can lift the chips from the bottom of the wellbore and away from the drill bit (54), and upward toward the surface in the annular space between the drill string (58) and the borehole (46) wall.

[0021] At the surface, mud and cuttings may exit the well through a side outlet in the blowout preventer (99) and a mud return line (not shown). The blowout preventer (99) includes pressure control devices and a rotary seal. The mud return line can feed mud into one or more separators (not shown) that can separate the mud from the cuttings. The mud can be returned from the separator to the mud pit (78) for storage and reuse.

[0022] Various sensors can be placed on the drilling rig (12) to measure drilling equipment. Specifically, hook load can be measured by a hook load sensor (94) mounted on the dead rope (77), and car position and associated car speed can be measured by a car sensor (95) that may be part of the winch (70). Surface torque can be measured by a sensor on the rotary table (88). Standpipe pressure can be measured by a pressure sensor (92) located on the standpipe (86). Signals from these measurements can be transmitted to a surface processor (96) or other network elements (not shown) arranged around the drilling rig (12). Additionally, mud pulses traveling up the drill string can be detected by the pressure sensor (92). For example, the pressure sensor (92) may include a transducer that converts mud pressure into an electronic signal. The pressure sensor (92) can be connected to the surface processor (96), which converts the signal from the pressure signal into a digital form, stores the digital signal, and demodulates it into usable MWD data. According to the various embodiments described above, the surface processor (96) can be programmed to automatically detect one or more drilling rig states based on the various input channels described. The processor (96) can be programmed to perform, for example, the automatic event detection described above. The processor (96) can send specific rig status and / or event detection information to a user interface system (97), which can be designed to alert individual drillers to events occurring on the rig and suggest activities to the drillers to avoid specific events.

[0023] Turning Figure 2 , Figure 2 A block diagram of a system according to one or more embodiments is shown. Figure 2 As shown, a cloud server (e.g., cloud server (210)) is connected via a network to various drilling rigs (e.g., drilling rig A (211), drilling rig B (212)), various drilling management networks (e.g., drilling management network A (231), drilling management network B (232)) and / or various user equipment (e.g., user equipment (290)). The cloud server may be a remote server, which includes hardware and / or software having functions for communicating via a network such as the Internet. The cloud server may include functions for automatically obtaining drilling rig operation data (e.g., drilling rig operation data A (271), drilling rig operation data B (272)) from various drilling rigs and / or drilling management networks. In one or more embodiments, the cloud server (210) may be similar to Figure 7.1 and 7.2 And the computer system (700) described in the attached description. The drilling management network (231, 232) can be similar to the one described below. Figure 3 And the drilling management network (330) described in the attached description.

[0024] In some embodiments, the cloud server includes functionality for obtaining drilling rig operation data (e.g., drilling rig operation data A (271), drilling rig operation data B (272)) from various drilling rigs (e.g., drilling rig A (211), drilling rig B (212)) and / or drilling management networks (e.g., drilling management network A (231), drilling management network B (232)). In one or more embodiments, the drilling rig operation data includes financial costs and / or time amounts associated with drilling operations on a particular drilling rig. In some embodiments, the drilling rig operation data may include information relating to equipment used for drilling or constructing a well, including surface and downhole equipment or any other equipment used in the drilling operation. Similarly, the drilling rig operation data may include information relating to sensor data and other measurements relating to such drilling equipment, drilling operations, maintenance operations, and / or other operations performed around the drilling rig. In some embodiments, the drilling rig operation data includes periodic drilling reports from the drilling site and data from publicly available databases. In other embodiments, drilling rig operation data includes interpolated and / or extrapolated data of desired drilling locations based on other drilling rig operation data (e.g., legacy data). Furthermore, for example, a cloud server (210) can obtain drilling rig operation data in real time or at periodic intervals such as daily, weekly, monthly, etc., from a drilling management network (231, 232). The cloud server can store filtered data and / or filtered and / or unfiltered drilling rig operation data from a local database (e.g., a drilling database (225)).

[0025] In some embodiments, the cloud server includes a risk prediction manager (e.g., risk prediction manager (220)). The risk prediction manager may be hardware and / or software that includes functionality for determining one or more operational parameters (e.g., operational parameter (223)). For example, the operational parameter may output data that predicts the individual or total cost of performing one or more drilling operations (e.g., drilling operations) at a hypothetical rig at a desired geographic location. Similarly, the operational parameter may also specify the time required to complete a particular operation. The operational parameter may also include a range of values ​​and a probability distribution of one of the values ​​occurring. Examples of operational parameters may include the amount of non-productive time at the rig, the cost of constructing the rig at a particular location (e.g., including equipment used in the drilling operation), the amount of time required to complete the construction of the rig, and / or the cost of drilling based on various well input parameters. Well input parameters may include the wellbore radius, the length of the wellbore, and / or other design parameters of the wellbore.

[0026] In some embodiments, the cloud server includes a model (e.g., model A (215)) that includes hardware and / or software having the capability to predict one or more operational parameters (e.g., operational parameters (223)). For example, model A (215) may include various data virtualization tools, search and knowledge discovery tools, stream analysis tools, relational databases, NoSQL databases, and various applications for generating outputs based on location information, well input parameters, drilling rig operation data, and other information. In some embodiments, the model includes the capability to determine the operational parameters based on one or more artificial intelligence algorithms. For example, artificial intelligence algorithms may include decision tree algorithms, one or more support vector machines, ensemble methods, and / or Naive Bayes classifier algorithms. In some embodiments, the model identifies risk levels associated with various drilling rig operations, such as one or more probabilities of completing a drilling operation without non-productive time or with non-productive time below a threshold amount.

[0027] Furthermore, the risk prediction manager (220) can determine various costs and / or time amounts for constructing and operating a drilling rig at a specific location (e.g., the desired geographic location submitted in the recommendation request). Thus, the risk prediction manager (220) can generate an overall estimate of the construction and estimates of various drilling operations to be performed at the rig. For example, these estimates can be included in a risk recommendation message (e.g., risk recommendation message (273)). Therefore, the risk prediction manager (220) can automatically or in response to a request from the user equipment send a risk recommendation message to the user equipment (e.g., user equipment (290)). For example, a user can select a desired geographic location for a potential drilling site via the user equipment and transmit various well input parameters for the drilling site to a cloud server. In response, the cloud server can generate a risk recommendation message and send it back to the user equipment.

[0028] User equipment (e.g., user equipment (290)) may include hardware and / or software for receiving input from and / or providing output to users. Furthermore, the user equipment may be connected to a drilling management network and / or a cloud server. For example, the user equipment may include functionality for presenting data and / or receiving input from users regarding various drilling and / or maintenance operations performed within the drilling management network. Examples of user equipment may include personal computers, smartphones, human-machine interfaces, and any other network-connected devices that, for example, obtain input from one or more users by providing a graphical user interface (GUI). Similarly, the user equipment may present data and / or receive control commands from users for operating the drilling rig.

[0029] Turning Figure 3 , Figure 3A block diagram of a system according to one or more embodiments is shown. Figure 3 As shown, the drilling management network (330) may include a human-machine interface (HMI) (e.g., HMI (333)), historical data (e.g., historical data (334)), and various network elements (e.g., network element (331)). The HMI may be hardware and / or software coupled to the drilling management network (330). For example, the HMI may allow operators to interact with the drilling system, such as sending commands to operate equipment or viewing sensor information from the drilling equipment. The HMI may include functions for presenting data and / or receiving input from users regarding various drilling operations and / or maintenance operations. For example, the HMI may include software that provides a graphical user interface (GUI) for presenting data and / or receiving control commands for operating the drilling rig. A network element (e.g., network element (331)) may refer to various hardware components within the network, such as switches, routers, hubs, or any other logical entities for combining one or more physical devices on the network. In particular, network elements, the HMI, and / or historical data may be similar to... Figure 7.1 and 7.2 And the computing system of the computing system (700) described in the appended description.

[0030] In one or more embodiments, a sensor device (e.g., sensor device X (320)) is coupled to a drilling management network (330). Specifically, the sensor device may include hardware and / or software that includes the ability to acquire one or more sensor measurements, such as sensor measurements of environmental conditions near the sensor device. The sensor device may process the sensor measurements into various types of sensor data (e.g., sensor data (315)). For example, sensor device X (320) may include the ability to convert sensor measurements acquired from sensor circuitry (e.g., sensor circuitry (324)) into a communication protocol format that can be transmitted over the drilling management network (330) via a communication interface (e.g., communication interface (322)). The sensor device may include pressure sensors, torque sensors, rotary switches, weight sensors, position sensors, microswitches, etc. The sensor device may include smart sensors. In some embodiments, the sensor device includes sensor circuitry without a communication interface or memory. For example, the sensor device may be coupled to a computer device that transmits sensor data over the drilling management network.

[0031] Furthermore, the sensor device may include a processor (e.g., processor (321)), a communication interface (e.g., communication interface (322)), a memory (e.g., memory (323)), and sensor circuitry (e.g., sensor circuitry (324)). The processor may be similar to the one described below. Figure 7.1 The computer processor (702) described in the appended description. The communication interface (322) may be similar to the one described below. Figure 7.1 The communication interface (712) described in the appended description. The memory (323) may be similar to the one described below. Figure 7.1 and the non-persistent memory (704) and / or persistent memory (706) described in the appended description. The sensor circuit (324) may be similar to Figure 1 And the various sensors described in the accompanying description (e.g., hook load sensor (94), vehicle sensor (95), pressure sensor (92), etc.).

[0032] In one or more embodiments, the drilling management network may include drilling equipment (e.g., drilling equipment (332)), such as winches (60), top drives, mud pumps, and the above-mentioned equipment. Figure 1 (and other components described in the accompanying drawings). The drilling management network (330) may further include various drilling operation control systems (e.g., drilling operation control system (335)) and various maintenance control systems (e.g., maintenance control system (336)). The drilling operation control system and / or maintenance control system may include, for example, a programmable logic controller (PLC), which includes hardware and / or software having the function of controlling one or more processes performed by the drilling rig, including but not limited to... Figure 1 The aforementioned components. Specifically, the programmable logic controller (PLC) can control the valve status, fluid level, pipeline pressure, warning alarms, and / or pressure release of the entire drilling rig. In particular, the PLC can be a rugged computer system capable of withstanding, for example, vibrations around the drilling rig, extreme temperatures, humid conditions, and / or dusty conditions. Without loss of generality, the term "control system" can refer to a drilling operation control system for operating and controlling equipment, a drilling data acquisition and monitoring system for acquiring and monitoring drilling process and equipment data, or a drilling interpretation software system for analyzing and understanding drilling events and progress.

[0033] Furthermore, the drilling operation control system and / or maintenance control system can refer to a control system that includes multiple PLCs within the drilling management network (330). For example, the control system may include controls for the above-mentioned PLCs. Figure 1 And the functions of operating within the systems, components, and / or sub-components described in the appended description. Thus, one or more drilling operation control systems (335) may include monitoring and / or performing functions relating to the mud circulation system, rotary system, hoisting system, pipe handling system, and / or Figure 1 And the functions of various other drilling activities described in the accompanying description. Similarly, one or more maintenance control systems (336) may include the functions of monitoring and / or performing various maintenance activities related to drilling equipment located around the drilling rig. Although in Figure 3 The drilling operation control system and maintenance control system are shown as separate devices, but in one or more embodiments, the programmable logic controller on the drilling rig and other drilling equipment (332) can be used in both the drilling operation control system and the maintenance control system.

[0034] In one or more embodiments, the sensor device includes functionality for establishing a network connection (e.g., network connection (340)) with one or more devices and / or systems (e.g., cloud server (210), drilling operation control system (335), maintenance control system (336)) on a drilling management network. In one or more embodiments, for example, network connection (340) may be an Ethernet connection establishing an Internet Protocol (IP) address for sensor device X (320). Therefore, one or more devices and / or systems on the drilling management network (330) can use the Ethernet network protocol to send data packets to and / or receive data packets from sensor device X (320). For example, sensor data (e.g., sensor data (315)) can be sent via the drilling management network (330) in data packets using a communication protocol. Sensor data may include sensor measurements, processed sensor data based on one or more base sensor measurements or parameters, and metadata related to the sensor device (e.g., timestamps and sensor device identification information, content attributes, sensor configuration information (e.g., offsets, conversion factors), etc.). Thus, sensor device X (320) can act as a host device on the drilling management network (330), for example, as a network node and / or endpoint on the drilling management network (330). In one embodiment, one or more sensors can be connected to the drilling management network via a Power over Ethernet (PoE) network.

[0035] In some embodiments, the drilling management network can collect drilling rig operation data from sensors, control systems, and / or other network devices surrounding the drilling rig. After collection, the drilling management network can then transmit the drilling rig operation data to a network similar to the one described above. Figure 2 And the remote server of the cloud server (210) described in the appended description. Similarly, the cloud server can send requests for specific drilling rig operation data to one or more network devices on the drilling management network. Therefore, the drilling management network may include functionality for automating the data collection process for collecting drilling rig operation data in order to update the drilling database on the cloud server periodically or in real time.

[0036] although Figure 1 , 2 Figures 3 and 4 illustrate various configurations of the components, but other configurations may be used without departing from the scope of this disclosure. For example, the components may be... Figure 1 , 2The various components in section 3 are combined to create a single component. As another example, a function performed by a single component can be performed by two or more components.

[0037] Turning Figure 4 , Figure 4 A flowchart according to one or more embodiments is shown. Specifically, Figure 4 A general method for simulating drilling rig operations in desired geographical locations is described. For example... Figure 1 , 2 And / or as described in 3, Figure 4 One or more boxes in the table can be executed by one or more components (e.g., the risk prediction manager (220)). Figure 4 The boxes in the diagram are shown and described sequentially. Those skilled in the art will understand that some or all of the boxes can be executed in different orders, can be combined or omitted, and can be executed in parallel. Furthermore, these boxes can be executed actively or passively.

[0038] In box 400, according to one or more embodiments, geographic location data regarding the desired geographic location of the drilling rig is obtained. For example, the geographic location data may correspond to Global Positioning System (GPS) coordinates or other information identifying a geographic area of ​​interest. The geographic location information may also include a radius that defines the coverage area of ​​the desired geographic location, for example, because multiple drilling locations are available for one or more drilling rigs. Furthermore, the desired geographic location may correspond to one or more potential drilling locations within a specific geological region (e.g., the Permian basin, the Bakken formation, etc.).

[0039] In box 410, according to one or more embodiments, drilling rig operation data for various drilling rigs in different geographical locations is obtained. In some embodiments, the cloud server collects information from various data sources, such as detailed daily drilling reports (DDR) from multiple drilling rigs, external drilling databases, etc. The risk prediction manager can parse data from different data sources to extract drilling rig operation data corresponding to one or more predetermined attributes. Examples of drilling rig operation data may include the amount of drilling time for a specific drilling operation and other attributes of the drilling operation. For example, drilling rig operation data may describe the drilling operation based on the wellbore size, the predetermined interval of the drilling, the basin type in which the wellbore is being drilled, the type of oil production including the wellbore, the drill string design (including but not limited to bottom hole assembly), the casing and completion design of the wellbore, and the surface equipment required for the operation. Drilling rig operation data may also include historical data on the drilling rig, such as clean time (i.e., well construction time without non-productive time), non-productive time (NPT) on the drilling rig, and / or identified causes related to non-productive time.

[0040] In some embodiments, drilling operation data includes data on various sequential drilling operations involved in constructing a wellbore. For example, drilling operation data may include total cost, wellbore completion time, etc., which may be more comprehensive than data on a single stage or facility (e.g., BHA, completion design, etc.) in the well construction process. Therefore, sequential drilling operations are designed to include different facilities as drilling progresses from one stage to another, while also being comprehensive at each drilling stage. Thus, drilling operation data can be a collection of data on the different stages or milestones involved in drilling and / or completing the entire wellbore at one or more drilling locations. Therefore, drilling operation data can provide an overview of all costs and risks associated with drilling a potential well.

[0041] Furthermore, drilling rig operation data can be stored in a database on a cloud server or at another remote location. In some embodiments, the risk prediction manager can use drilling rig operation data from different locations to generate drilling rig operation data for unexplored areas. For example, synthetic drilling rig operation data can be generated using data from similar well profiles and locations, employing one or more artificial intelligence algorithms based on well profiles and locations. Drilling rig operation data can also be obtained from various sensor devices deployed around the drilling management network. In particular, the sensor devices can be similar to... Figure 3 And the sensor device X (320) described in the appended description. In one or more embodiments, the sensor device is directly connected to the risk prediction manager. In some embodiments, various control systems in the drilling management network can provide drilling rig operation data directly to the risk prediction manager.

[0042] In box 420, a model is generated using drilling rig operation data, according to one or more embodiments. Specifically, the drilling rig operation data can be filtered to produce a sparse dataset, for example, a dataset smaller and more manageable than the data in a drilling database. After filtering, the model can analyze the dataset using geographic location data and well input parameters. For example, the geographic location data may correspond to one or more physical dimensions that define one or more locations of the proposed wellbore, where location information can narrow the model's area of ​​interest. Similarly, the model can also obtain various well input parameters for the proposed wellbore design. The model can be similar to the one described above. Figure 2 And the model A (215) described in the attached description.

[0043] In block 430, according to one or more embodiments, a drilling operation sequence for constructing a portion of a wellbore at a desired geographical location is simulated using geographic location data and models. For example, simulations may be performed prior to wellbore construction to determine various possible risks associated with the construction. In some embodiments, the risk prediction manager may use models to perform various Monte Carlo simulations. For example, Monte Carlo simulations may produce various possible outcomes corresponding to one or more operational parameters within the drilling operation sequence, such as cleanup time, various levels of risk for well construction and / or drilling operations, and the range of costs associated with the drilling rig. Furthermore, the drilling operation sequence may include specific drilling operations based on a particular well design, for example, simulating a drilling path for a well design defined by predetermined well input parameters. In some embodiments, the drilling operation sequence includes completing the wellbore, surface operations, and downhole operations.

[0044] Turning Figure 5 , Figure 5 A flowchart according to one or more embodiments is shown. Specifically, Figure 5 This paper describes a general method for using models to predict operational parameters of drilling rig operation sequences. For example... Figure 1 , 2 And / or as described in 3, Figure 5 One or more boxes in the process can be executed by one or more components (e.g., the risk prediction manager (220)). Figure 5 The boxes are displayed and described sequentially. Those skilled in the art will understand that some or all of the boxes can be executed in different orders, can be combined or omitted, and can be executed in parallel. Furthermore, these boxes can be executed actively or passively.

[0045] In box 500, according to one or more embodiments, geographic location data regarding the desired geographic location of the drilling rig is obtained. Box 500 may be similar to the above. Figure 4 And box 400 as described in the accompanying description. In some embodiments, the geographic location data may be part of a request for a risk advice message sent by the user equipment to a cloud server. For example, a user may request a risk advice message regarding the desired geographic location of a drilling rig. Thus, the user equipment may send geographic location data with various well input parameters to a risk prediction manager on the cloud server.

[0046] In block 510, according to one or more embodiments, drilling rig operation data is obtained from a drilling database. Block 510 can be similar to the above. Figure 4 And box 410 as described in the attached description. Drilling databases can be similar to those described above. Figure 2 And the drilling database described in the attached description (225).

[0047] In box 520, regression analysis is performed on drilling operation data using various well input parameters, according to one or more embodiments. In some embodiments, multiple regression analyses are performed on the drilling operation data, which are time-dependent and cost-dependent alone, to filter the drilling operation data into a specific dataset. For example, independent variables used to perform time analysis may include various drilling operation information, such as wellbore size at the wellbore, drilling interval at the wellbore, etc. Independent variables for cost estimation may include basin type and petroleum play to be drilled, wellbore casing design, etc.

[0048] Specifically, regression analysis can provide estimates of cleaning time and cost based on various well input parameters of the wellbore. For example, in box 500, well input parameters can be provided along with geographic location data. The risk prediction manager can use cleaning and non-productive times from rig operation data to validate the accuracy of the model output. In this way, the results of one or more regression analyses can be compared with user data to determine the likelihood of accuracy. In some embodiments, regression analysis generates a distribution of various rig operation data values ​​that take into account frequency and impact on other rig operation values.

[0049] In box 530, according to one or more embodiments, a series of drilling operations are simulated at a desired geographical location using models and regression analysis. This simulation can be similar to the one described above. Figure 4 The simulation performed in box 430 as described in the attached description.

[0050] In some embodiments, drilling operations are simulated multiple times using updated drilling rig operation data, geographic location data, and / or an updated model. This allows box 530 to be performed iteratively with or without repeated regression analysis. Similarly, drilling operations can be resimulated using the same or different drilling rig operation data. Therefore, by removing one or more boxes during additional simulations, the amount of time required to obtain the drilling rig's operating parameters at the desired geographic location can be reduced accordingly.

[0051] In box 540, according to one or more embodiments, a risk advisory message is sent to a user device based on one or more simulations of a series of drilling operations. In some embodiments, a risk advisory message is generated that may describe the total cost of the drilling project, various component costs of the drilling project, the total time required to complete the drilling project, and various individual times for completing different milestones related to the drilling project. The risk prediction manager may automatically generate the risk advisory message using various simulations performed at the desired geographical location in response to a request from the user device. Once generated, the risk prediction manager may send the risk advisory message to the user device, where it is presented on the user device's display.

[0052] Furthermore, risk recommendation messages can include various risks of a drilling project identified through simulations performed at the desired geographical location. Specifically, the risk prediction manager can use this model to determine the probabilities of various risks related to the costs and quantities of cleanup and / or non-productive time. For example, a risk recommendation message can describe the probability that a drilling project will exceed the recommended bid. Therefore, risk recommendation messages can be used to prepare bids for drilling projects. Similarly, for example, as part of a negotiation process for commercial comparisons of drilling projects, multiple risk recommendation messages can be generated for different well input parameters and desired geographical locations.

[0053] Turning Figure 6 , Figure 6 An example of a risk advice message is provided. This example is for illustrative purposes only and is not intended to limit the scope of the disclosed technology.

[0054] Go to Figure 6 , Figure 6 A risk recommendation message (685) for drilling rig X in the Permian Basin is displayed. Specifically, the risk recommendation message (685) describes various rig operation attributes (i.e., location attribute (610), cleanup time attribute (620), rig non-productive time attribute (630), total structural time attribute for a single well (640), risk attribute (650), activity-based risk characteristic attribute (655), completion cost attribute (660), and bid submission attribute (670)). For the location attribute (610), the values ​​of the various predicted operation parameters are GPS coordinates (X, Y) (611). For the cleanup time attribute (620), the corresponding predicted operation parameter value is 80% (621), where 80% of the structural time can be oriented towards structural drilling rigs and drilling wells. For the rig non-productive time attribute (630), the values ​​of the various predicted operation parameters are a range of 15% to 19% of the structural time for non-productive periods (631). For the total structural time attribute of a single well (640), the values ​​of each predicted operational parameter are values ​​within the range of 14 to 18 days for well completion (641).

[0055] For the risk attribute (650), the predicted operating parameter value is a 5% chance (651) that the wellbore configuration exceeds the value of the bid submission attribute (670). For the activity-based risk profile attribute (655), the predicted operating parameter value is a 60% chance (656) that the wellbore configuration will result in fluid loss, while the predicted operating parameter value is a 40% chance (656) that the wellbore configuration will result in pipe blockage. In a blocked pipe, the pipe cannot be released from the borehole without damage, nor exceeding the rig's maximum permissible hook load. For the completion cost attribute (660), the corresponding predicted operating parameter value ranges from $700,000 to $1,200,000 (661). For the bid submission attribute (670), the corresponding predicted operating parameter value is $1,100,000 (671).

[0056] and Figure 6 To maintain consistency, a risk advice message (685) can be displayed in the graphical user interface of the user equipment (not shown). Similarly, the user can provide various inputs to the user equipment to modify one or more attribute values ​​(611, 621, 631, 641, 651, 656, 661, 671). The risk prediction manager (not shown) can then automatically update other attributes accordingly. For example, based on the value of the total construction time attribute (640) for a single well, the user can adjust the value of the location attribute (610) to reduce the number of days to complete the well.

[0057] return Figure 5 In box 550, according to one or more embodiments, the model is updated using drilling rig operation data and one or more artificial intelligence methods. In some embodiments, the model is periodically updated as drilling rig operation data is further collected from various data sources. Furthermore, one or more artificial intelligence algorithms can adjust the model by comparing the model's operating parameters with actual drilling rig operation data. For example, a risk prediction manager can use a search method to iteratively adjust the model until the difference between the predicted operating parameters and the actual drilling rig operation data meets a predetermined criterion. For example, the predetermined criterion could be the convergence of the operating parameters at a local or global minimum. Various search methods can be used to adjust the model, such as gradient descent, the Newton-Raphson method, and various other types of search methods.

[0058] Regarding the above Figure 4 and 5The embodiments discussed herein automate various processes to provide faster delivery times compared to manual methods used for preparing and transmitting drilling project proposals. For example, proposals generated using offset well analysis might require up to a month to assess their time, cost, and risk. Even before receiving user data for well time assessment, business development discussions and signed confidentiality agreements may be necessary. Similarly, the aforementioned data analysis can provide a proactive approach to identifying potential drilling projects based on performance, permissions, and risk. Likewise, the process of automatically identifying risk magnitudes from multiple data sources can improve the consistency of risk allocation across drilling projects. Furthermore, by performing iterative quality checks between the output of predictive models and user data, issues in risk proposal messages can be identified before formal bid submission.

[0059] The implementation can be carried out on a computing system. Any combination of mobile, desktop, server, router, switch, embedded device, or other types of hardware can be used. For example, such as... Figure 7.1 As shown, the computing system (700) may include one or more computer processors (702), non-persistent memory (704) (e.g., volatile memory, such as random access memory (RAM), cache memory), persistent memory (706) (e.g., hard disk, optical drive, such as optical disc (CD) drive or digital multifunction disk (DVD) drive, flash memory, etc.), communication interface (712) (e.g., Bluetooth interface, infrared interface, network interface, optical interface, etc.), and many other components and functions.

[0060] The computer processor (702) may be an integrated circuit for processing instructions. For example, one or more computer processors may be one or more cores or microcores of a processor. The computing system (700) may also include one or more input devices (710), such as a touch screen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device.

[0061] The communication interface (712) may include an integrated circuit for connecting the computing system (700) to a network (not shown) (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, a mobile network, or any other type of network) and / or to another device (e.g., another computing device).

[0062] Furthermore, the computing system (700) may include one or more output devices (708), such as a screen (e.g., a liquid crystal display (LCD), plasma display, touch screen, cathode ray tube (CRT), monitor, projector, or other display device), printer, external storage device, or any other output device. One or more output devices may be the same as or different from input devices. Input and output devices may be locally or remotely connected to the computer processor (702), non-persistent memory (704), and persistent memory (706). Many different types of computing systems exist, and the aforementioned input and output devices may take other forms.

[0063] Software instructions in the form of computer-readable program code for performing embodiments of the present disclosure may be stored, in whole or in part, temporarily or permanently, on a non-transitory computer-readable medium, such as a CD, DVD, storage device, floppy disk, magnetic tape, flash memory, physical memory, or any other computer-readable storage medium. Specifically, the software instructions may correspond to computer-readable program code that, when executed by a processor, is configured to perform one or more embodiments of the present disclosure.

[0064] Figure 7.1 The computing system (700) in the system can be connected to a network or be part of a network. For example, such as Figure 7.2 As shown, the network (720) may include multiple nodes (e.g., node X (722), node Y (724)). Each node may correspond to a computing system, for example... Figure 7.1 The computing system shown, or a combination of nodes, can correspond to Figure 7.1 The computing system shown is illustrated. By way of one example, embodiments of this disclosure can be implemented on nodes of a distributed system connected to other nodes. By way of another example, embodiments of this disclosure can be implemented on a distributed computing system with multiple nodes, wherein each part of this disclosure can reside on a different node within the distributed computing system. Furthermore, one or more elements of the aforementioned computing system (700) can be located in remote locations and connected to other elements via a network.

[0065] Despite Figure 7.2 Not shown, but this node may correspond to a blade in a server chassis that connects to other nodes via a backplane. As another example, this node could correspond to a server in a data center. As yet another example, this node could correspond to a computer processor or a microcore of a computer processor with shared memory and / or resources.

[0066] Nodes in the network (720) (e.g., node X (722), node Y (724)) can be configured to provide services to client devices (726). For example, a node may be part of a cloud computing system. A node may include the ability to receive requests from client devices (726) and send responses to client devices (726). Client devices (726) may be computing systems, such as... Figure 7.1 The computing system shown. Furthermore, the client device (726) may include and / or perform all or part of one or more embodiments of this disclosure.

[0067] Figure 7.1 and 7.2 The computing systems or sets of computing systems described herein may include functionality that performs the various operations disclosed herein. For example, computing systems may perform communication between processes on the same or different systems. Various mechanisms employing some form of active or passive communication can facilitate data exchange between processes on the same device. Examples representing such inter-process communication include, but are not limited to, implementations of files, signals, sockets, message queues, pipes, semaphores, shared memory, message passing, and memory-mapped files. Further details relating to several of these non-limiting examples are provided below.

[0068] Based on the client-server network model, sockets can be used as interfaces or communication channel endpoints, allowing bidirectional data transfer between processes on the same device. First, following the client-server network model, a server process (e.g., a process providing data) creates a first socket object. Next, the server process binds the first socket object, associating it with a unique name and / or address. After creating and binding the first socket object, the server process then waits and listens for incoming connection requests from one or more client processes (e.g., processes seeking data). When a client process wants to obtain data from the server process, it initiates by creating a second socket object. The client process then continues to generate connection requests, which include at least the second socket object and the unique name and / or address associated with the first socket object. The client process then sends the connection request to the server process. Depending on availability, the server process can accept the connection request, establish a communication channel with the client process, or, if busy with other operations, queue the connection request in a buffer until the server process is ready. An established connection notifies the client process that communication can begin. In response, the client process can generate a data request specifying the data it desires. This data request is then transmitted to the server process. Upon receiving the data request, the server process analyzes the request and collects the requested data. Finally, the server process generates a response that includes at least the requested data and sends it to the client process. The data can more typically be transmitted as datagrams or character streams (e.g., bytes).

[0069] Shared memory refers to the allocation of virtual memory space to provide a mechanism for data to be communicated and / or accessed by multiple processes. In implementing shared memory, an initialization process first creates a shareable segment in persistent or non-persistent memory. After creation, the initialization process mounts the shareable segment and then maps it into the address space associated with the initialization process. After mounting, the initialization process proceeds to identify and grant access permissions to one or more authorized processes, which can also write data to or read data from the shareable segment. Changes made by one process to data in the shareable segment can immediately affect other processes that are also linked to the shareable segment. Furthermore, when one of the authorized processes accesses the shareable segment, the shareable segment is mapped into the address space of that authorized process. Typically, an authorized process can mount a shared segment at any given time, except during the initialization process.

[0070] Without departing from the scope of this disclosure, other techniques may be used to share data between processes, such as the various types of data described in this application. These processes may be part of the same or different applications and may be executed on the same or different computing systems.

[0071] A computing system performing one or more embodiments of this disclosure may include, in addition to sharing data between processes, the ability to receive data from a user. For example, in one or more embodiments, a user may submit data via a graphical user interface (GUI) on a user device. Data may be submitted via the GUI by the user selecting one or more GUI widgets or by inserting text and other data into the GUI widgets using a touchpad, keyboard, mouse, or any other input device. In response to the selection of a specific item, information about that specific item may be obtained by the computer processor from persistent or non-persistent memory. Once the user has selected an item, the content of the data obtained about that specific item may be displayed on the user device in response to the user's selection.

[0072] As another example, a request for data about a specific item can be sent to a server operatively connected to a user's device via a network. For instance, a user can select a Uniform Resource Locator (URL) link within a web client on their device, initiating a Hypertext Transfer Protocol (HTTP) or other protocol request to be sent to the web host associated with that URL. In response to this request, the server can retrieve data about the specific selected item and send that data to the device that initiated the request. Once the user device has received the data about the specific item, it can display the received data about that specific item on the user device in response to the user's selection. Beyond the above example, data received from the server after selecting a URL link can provide a Hypertext Markup Language (HTML) webpage, which can be rendered and displayed on the user's device by a web client.

[0073] Once data is obtained, for example, by using the techniques described above or from memory, the computing system, while executing one or more embodiments of this disclosure, can extract one or more data items from the obtained data. For example, extraction can be performed by... Figure 7.1The computational system (700) performs the following: First, it determines the data organization pattern (e.g., syntax, pattern, layout), which may be based on one or more of the following: position (e.g., position of a bit or column, the Nth token in the data stream, etc.), attribute (where the attribute is associated with one or more values), or hierarchy / tree structure (composed of layers of nodes with different levels of detail, such as in nested headers or nested document sections). Then, within the context of the organization pattern, the raw, unprocessed data symbol stream is parsed into a token stream (or hierarchical structure) (where each token may have an associated token "type").

[0074] Next, extraction criteria are used to extract one or more data items from the token stream or structure, where the extraction criteria are processed according to the organizational pattern to extract one or more tokens (or nodes from the hierarchical structure). For location-based data, tokens at the locations identified by the extraction criteria are extracted. For attribute / value-based data, tokens and / or nodes associated with attributes that satisfy the extraction criteria are extracted. For hierarchical / layered data, tokens associated with nodes that match the extraction criteria are extracted. Extraction criteria can be as simple as an identifier string, or they can be queries presented to a structured data repository (where the data repository can be organized according to a database schema or data format such as XML).

[0075] The extracted data can be used by the computing system for further processing. For example, Figure 7.1The computing system can perform data comparison while executing one or more embodiments of the present disclosure. Data comparison can be used to compare two or more data values (e.g., A, B). For example, one or more embodiments can determine that A > B, A = B, A!= B, A < B, etc. The comparison can be performed by submitting A, B, and an operation designation related to the comparison to an arithmetic logic unit (ALU) (i.e., a circuit that performs arithmetic and / or bitwise logical operations on two data values). The ALU outputs a numerical result of the operation and / or one or more status flags related to the numerical result. For example, the status flag can indicate whether the numerical result is positive, negative, zero, etc. By selecting an appropriate opcode and then reading the numerical result and / or status flag, the comparison can be performed. For example, to determine whether A > B, B can be subtracted from A (i.e., A - B), and the status flag can be read to determine whether the result is positive (i.e., if A > B, then A - B > 0). In one or more embodiments, if A = B or if A > B as determined using the ALU, then B can be considered a threshold, and A is considered to meet the threshold. In one or more embodiments of the present disclosure, A and B can be vectors, and comparing A with B includes comparing the first element of vector A with the first element of vector B, the second element of vector A with the second element of vector B, etc. In one or more embodiments, if A and B are strings, then the binary values of the strings can be compared.

[0076] Figure 7.1 The computing system in can implement and / or be connected to a data repository. For example, one type of data repository is a database. A database is a collection of information configured to simplify data retrieval, modification, reorganization, and deletion. A database management system (DBMS) is a software application that provides an interface for a user to define, create, query, update, or manage a database.

[0077] A user or software application can submit a statement or query to the DBMS. Then, the DBMS interprets the statement. The statement can be a select statement, update statement, create statement, delete statement, etc. for requesting information. In addition, the statement can include specifying data or data containers (database, table, record, column, view, etc.), identifiers, conditions (comparison operators), functions (e.g., concatenation, full concatenation, count, average, etc.), sorting (e.g., ascending, descending), or others. The DBMS can execute the statement. For example, the DBMS can access a storage buffer, a reference to a file, or an index for reading, writing, deleting, or any combination thereof in response to the statement. The DBMS can load data from persistent or non-persistent storage and perform calculations in response to a query. The DBMS can return the result to the user or software application.

[0078] Figure 7.1The computing system may include the ability to present raw and / or processed data (e.g., comparison results and other processing results). For example, data presentation can be accomplished through various presentation methods. Specifically, data can be presented through a user interface provided by the computing device. The user interface may include a GUI that displays information on a display device such as a computer monitor or a touchscreen on a handheld computer device. The GUI may include various GUI widgets that organize what data is displayed and how the data is presented to the user. Furthermore, the GUI may present data directly to the user, for example, data presented as actual data values ​​via text, or data presented as a visual representation of the data via the computing device, such as through a visualized data model.

[0079] For example, a GUI might first receive a notification from a software application requesting the rendering of a specific data object within the GUI. Next, the GUI might determine the data object type associated with the specific data object, for example, by obtaining data from data attributes within the data object that identify its type. Then, the GUI might determine any rules specifying the display of that data object type, such as rules specified by the software framework for a data object class, or based on any local parameters defined by the GUI for rendering that data object type. Finally, the GUI can obtain the data value from the specific data object and render a visual representation of that data value within a display device according to the rules specified for that data object type.

[0080] Data can also be presented using various audio methods. In particular, data can be presented as audio in an audio format and as sound through one or more speakers operatively connected to a computing device.

[0081] Data can also be presented to users through tactile methods. For example, tactile methods can include vibrations or other physical signals generated by a computing system. For instance, data can be conveyed to a user using vibrations generated by a handheld computer device for a predetermined duration and intensity.

[0082] The above description of the functions only presents the features provided by [the relevant entity / entity]. Figure 7.1 computing systems and Figure 7.2 Several examples of functions performed by nodes and / or client devices are provided. Other functions can be performed using one or more embodiments of this disclosure.

[0083] Although this disclosure has been described with respect to a limited number of embodiments, those skilled in the art who benefit from this disclosure will understand that other embodiments can be devised without departing from the scope of this disclosure as disclosed herein. Therefore, the scope of this disclosure should be limited only by the appended claims.

Claims

1. A method for constructing integrated wells, comprising: Geographical location data related to the desired geographic location of the first drilling rig is obtained through a computer processor; Drilling rig operation data related to multiple drilling rigs in different geographical locations is obtained through a computer processor; One or more regression analyses are performed on drilling rig operation data based on time, cost, or a combination thereof in order to filter the drilling rig operation data and generate estimates of cleaning time, non-productive time, and cost. Use drilling rig operation data to generate models that identify risk levels associated with multiple drilling rig operations; and Based at least in part on the model and one or more regression analyses, the drilling sequence of operations for constructing a portion of the wellbore drilled by the first drilling rig at a desired geographic location is simulated using a computer processor and geographic location data and the model. Based on the comparison of the model's operating parameters and drilling rig operating data, artificial intelligence algorithms are used to adjust the model; The accuracy of the model is determined by comparing the estimated ranges of cleaning time and non-productive time with the measured cleaning time and measured non-productive time, respectively. and Control signals are generated at least in part based on the results of the simulation to operate the device, wherein the operation of the device changes in response to receiving the control signals.

2. The method according to claim 1, in, Before constructing the wellbore at the desired geographical location, simulate the drilling rig operation sequence, and The drilling rig operation sequence includes surface operations and downhole operations.

3. The method according to claim 1, in, The model also identifies and calculates the total cost and time associated with the planned drilling operations used to construct this portion of the wellbore.

4. The method according to claim 1, in, The drilling operation data includes wellbore size, predetermined intervals for drilling out of the wellbore, basin type drilled out of the wellbore, type of oil zone including the wellbore, and drilling time for the predetermined drilling operation based on the casing design for the wellbore. The drilling rig operation data also includes the cost of the planned drilling operations.

5. The method according to claim 1, further comprising: Obtain multiple well input parameters for constructing the wellbore; and One or more regression analyses of the drilling rig operation data are performed based on multiple well input parameters.

6. The method according to claim 1, wherein, Cleaning costs include the total amount of time minus the non-productive time spent constructing the wellbore, and the costs include the cost of constructing the wellbore at the desired geographic location, the cost of operating the first drilling rig at the desired geographic location, or both.

7. The method according to claim 1, wherein, Generating the model includes: Obtain the model; and The model is updated in real time based on drilling rig operation data. In this process, a search method is used to iteratively update the model until it converges to a predetermined criterion.

8. The method according to claim 1, wherein, The simulated drilling rig operation sequence includes performing one or more Monte Carlo simulations to construct that portion of the wellbore.

9. The method according to claim 1, wherein, Generating the model involves using artificial intelligence algorithms on drilling rig operation data, wherein the artificial intelligence algorithms are selected from the group consisting of decision tree algorithms, support vector machines, ensemble methods, and naive Bayesian classifier algorithms.

10. The method according to claim 1, wherein, Obtaining the drilling rig operation data includes: Establish the first network connection from the remote server to the drilling management network at the second drilling rig. The drilling management network is configured to automatically operate multiple control systems at the second drilling rig, and The drilling rig operation data is obtained from the drilling management network.

11. The method according to claim 1, wherein, The simulated drilling rig operation sequence includes determining the amount of risk required to construct that portion of the wellbore using the first drilling rig.

12. The method according to claim 1, further comprising: The cleaning time and cost generated using one or more regression analyses will be compared with the observed cleaning time and cost; and The model is updated based on the comparison.

13. A system for constructing integrated wells, comprising: Computer processor; and Memory, which is connected to and executable by a computer processor, includes the following functions: Obtain geographic location data related to the desired geographic location of the first drilling rig; Obtain drilling rig operation data related to multiple drilling rigs in different geographical locations; One or more regression analyses are performed on drilling rig operation data based on time, cost, or a combination thereof in order to filter the drilling rig operation data and generate estimates of cleaning time, non-productive time, and cost. Use drilling rig operation data to generate models that identify risk levels associated with multiple drilling rig operations; Based at least in part on the model and one or more regression analyses, a drilling sequence for constructing a portion of a wellbore drilled by a first drilling rig at a desired geographic location is simulated using a computer processor and geographic location data and the model. The accuracy of the model is determined by comparing the estimated ranges of cleaning time and non-productive time with the measured cleaning time and measured non-productive time, respectively. Based on a comparison of model operating parameters and drilling rig operating data, artificial intelligence algorithms are used to adjust the model; and Control signals are generated at least in part based on the results of the simulation to operate the device, wherein the operation of the device changes in response to receiving the control signals.

14. The system according to claim 13, in, Before constructing the wellbore at the desired geographical location, simulate the drilling rig operation sequence, and The drilling rig operation sequence includes surface operations and downhole operations at the desired geographical location.

15. The system according to claim 13, wherein, Generating the model includes: Obtain the model; and The model is updated in real time based on drilling rig operation data. The model is iteratively updated using a search method until it converges to a predetermined criterion.

16. The system of claim 13, wherein generating the model includes using an artificial intelligence algorithm on drilling rig operation data, and wherein, The artificial intelligence algorithms are selected from the group consisting of: decision tree algorithms, support vector machines, ensemble methods, and naive Bayesian classifier algorithms.

17. The system according to claim 13, wherein, The simulated drilling rig operation sequence includes simulations based on the drilling rig operation sequence to determine the amount of risk required for the first drilling rig to construct that portion of the wellbore.

18. The system according to claim 13, wherein, The operation also includes adjusting the operation of the device in response to control signals.

19. A non-transitory computer-readable medium storing instructions executable by a computer processor, the instructions comprising the following functions: Obtain geographic location data related to the desired geographic location of the first drilling rig; Obtain drilling rig operation data related to multiple drilling rigs in different geographical locations; One or more regression analyses are performed on drilling rig operation data based on time, cost, or a combination thereof in order to filter the drilling rig operation data and generate estimates of cleaning time, non-productive time, and cost. Use drilling rig operation data to generate models that identify risk levels associated with multiple drilling rig operations; The accuracy of the model is determined by comparing the estimated ranges of cleaning time and non-productive time with the measured cleaning time and measured non-productive time, respectively. Based at least in part on the model and one or more regression analyses, a computer processor is used to simulate the drilling sequence for constructing a portion of the wellbore drilled by the first drilling rig in a desired geographic location, using geographic location data and the model. Based on the comparison of the model's operating parameters and drilling rig operating data, artificial intelligence algorithms are used to adjust the model; Control signals are generated at least in part based on the results of the simulation to operate the device, wherein the operation of the device changes in response to receiving the control signals; and Based at least in part on the results of simulations, generate risk advisory messages describing the probability that a drilling project will exceed the recommended bid. and Display risk advice messages to operators.

20. The non-transitory computer-readable medium according to claim 19, in, Before constructing the wellbore at the desired geographical location, simulate the drilling rig operation sequence, and The drilling rig operation sequence includes surface operations and downhole operations at the desired geographical location.

21. The non-transitory computer-readable medium according to claim 19, wherein, The simulated drilling rig operation sequence includes simulations based on the drilling rig operation sequence to determine the amount of risk required for the first drilling rig to construct that portion of the wellbore.

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