BACKGROUND

[0001] Rendering methods and systems for wellbore trajectories are often based on tessellation and thus require lots of triangles to achieve an acceptable image quality for the wellbore. Furthermore, when multiple trajectories need to be rendered by such techniques, a plurality of triangles need to be generated which consumes a memory device of the graphical processing unit (GPU) rendering the wellbore trajectory image and thereby significantly impacts the framerate of the GPU.

SUMMARY

[0002] A method and a system for generating a wellbore image is disclosed. The method receives trajectory data captured by one or more sensors within a wellbore at a resource site. The trajectory data may include a plurality of boundary points of the wellbore in a first dimensional space. The method generates a trajectory segment using a center line segment of the wellbore such that the trajectory segment is bounded by a first point on the center line segment and a second point on the center line segment. The method generates a plurality of multi-dimensional points using the plurality of boundary points and the center line segment. The method generates a first image of the wellbore using the first plurality of multi-dimensional points. The first image of the wellbore, according to one embodiment, indicates an image of the wellbore in a second dimensional space that is of a higher order than the first dimensional space. BRIEF DESCRIPTION OF THE DRAWINGS

[0003] The disclosure is illustrated by way of example, and not by way of limitation in the figures of the accompanying drawings in which like reference numerals are used to refer to similar elements. It is emphasized that various features may not be drawn to scale and the dimensions of various features may be arbitrarily increased or reduced for clarity of discussion. [0004] Figure 1 illustrates an exemplary high-level flowchart for extracting and generating resolved data associated with a resource site.

[0005] Figure 2 illustrates a cross-sectional view of a resource site for which the process of Fig. 1 may be executed.

[0006] Figure 3 illustrates a high-level networked system diagram illustrating a communicative coupling of devices or systems associated with the resource site of Figure 2.

[0007] Figure 4 illustrates an exemplary detailed centerline segment with two consecutive points on said centerline segment.

[0008] Figure 5 illustrates an exemplary depiction of three envelopes that represent three segments of a wellbore trajectory.

[0009] Figures 6A and 6B illustrate an exemplary capping to resolve cracks at a junction between two consecutive envelopes.

[0010] Figures 7A and 7B illustrates exemplary depictions of the distance between a log value and a point on a trajectory line segment.

[0011] Figures 8A and 8B illustrate exemplary distances between 2 sample planes along a view direction.

[0012] Figure 8C illustrates the position of two points along a view direction relative to a point on a surface of the wellbore. [0013] Figure 9A illustrates an exemplary rendering of the multi-dimensional image using, from left to right, nearest neighbor interpolation, linear interpolation, and cubic interpolation, respectively.

[0014] Figure 9B illustrates additional exemplary renderings of multiple wellbores using a plurality of interpolation techniques.

[0015] Figures 10-11 illustrate exemplary flowcharts for generating a multidimensional image using trajectory data.

DETAILED DESCRIPTION

[0016] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0017] The disclosed systems and methods may be accomplished using interconnected devices and systems that obtain data a plurality of data associated with various parameters of interest at a resource site. The workfl ows/flowcharts described in this disclosure, according to some embodiments, implicate a new processing approach (e.g., hardware, special purpose processors, and specially programmed general-purpose processors) because such analyses are too complex and cannot be done by a person in the time available or at all. Thus, the described systems and methods are directed to tangible implementations or solutions to specific technological problems in exploring natural resources such as oil, gas, water well industries, and other mineral exploration operations. More specifically, the systems and methods presently disclosed may be applicable to exploring resources such as oil, natural gas, water, and Salar brines.

[0018] Attention is now directed to methods, techniques, infrastructure, and workflows for operations that may be carried out at a resource site. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined while the order of some operations may be changed. Some embodiments include an iterative refinement of one or more data associated with the resource site via feedback loops executed by one or more computing device processors and/or through other control devices/mechanisms that make determinations regarding whether a given action, template, or resource data, etc., is sufficiently accurate.

[0019] High-Level Flowchart

[0020] Figure 1 illustrates an exemplary high-level flowchart for generating a multidimensional image of a wellbore at a resource site. The exemplary flowchart shown begins with receiving, at block 102, traj ectory data captured by one or more sensors within the wellbore at the resource site. According to one embodiment, the trajectory data indicates a trajectory for the wellbore. Moreover, the trajectory may be defined by a center line segment that indicates a drilling direction for the wellbore. At block 104, a first trajectory segment and/or a second trajectory segment may be generated using the trajectory data. In addition, a plurality of multidimensional points may be generated, at block 106, using the first trajectory segment and/or the second trajectory segment. According to some embodiments, an image is generated, at block 108, using the plurality of multi-dimensional points. This image may depict a 3-dimensional image of the wellbore. These and other aspects are further discussed in association with the flowcharts of Figures 10 and 11. [0021] Resource Site

[0022] Figure 2 illustrates a cross-sectional view of a resource site 200 for which the process of Figure 1 may be executed. While the illustrated resource site 200 represents a subterranean formation, the resource site, according to some embodiments, may be below water bodies such as oceans, seas, lakes, ponds, wetlands, rivers, etc. According to one embodiment, various measurement tools capable of sensing one or more parameters such as seismic two-way travel time, density, resistivity, production rate, etc., of a subterranean formation and/or geological formations may be provided at the resource site. As an example, wireline tools may be used to obtain measurement information related to geological attributes (e.g., geological attributes of a wellbore and/or reservoir) including geophysical and/or geochemical information associated with the resource site 200. In some embodiments, various sensors may be located at various locations around the resource site 200 to monitor and collect data for executing the process of Figure 1.

[0023] Part, or all, of the resource site 200 may be on land, on water, or below water. In addition, while a resource site 200 is depicted, the technology described herein may be used with any combination of one or more resource sites (e.g., multiple oil fields or multiple wellsites, etc.), one or more processing facilities, etc. As can be seen in Figure 2, the resource site 200 may have data acquisition tools 202a, 202b, 202c, and 202d positioned at various locations within the resource site 200. The subterranean structure 204 may have a plurality of geological formations 206a-206d. As shown, this structure may have several formations or layers, including a shale layer 206a, a carbonate layer 206b, a shale layer 206c, and a sand layer 206d. A fault 207 may extend through the shale layer 206a and the carbonate layer 206b. The data acquisition tools, for example, may be adapted to take measurements and detect geophysical and/or geochemical characteristics of the various formations shown. [0024] While a specific subterranean formation with specific geological structures is depicted, it is appreciated that the oil field 200 may contain a variety of geological structures and/or formations, sometimes having extreme complexity. In some locations of a given geological structure, for example below a water line relative to the given geological structure, fluid may occupy pore spaces of the formations. Each of the measurement devices may be used to measure properties of the formations and/or other geological features. While each data acquisition tool is shown as being in specific locations in Figure 2, it is appreciated that one or more types of measurement may be taken at one or more locations across one or more sources of the resource site 200 or other locations for comparison and/or analysis. The data collected from various sources at the resource site 200 may be processed and/or evaluated and/or used as training data, and or used to generate high resolution result sets for characterizing a resource at the resource site, and/or used for generating resource models, and/or used for generating trajectory data which may be subsequently used to generate images of a wellbore at the resource site, etc.

[0025] Data acquisition tool 202a is illustrated as a measurement truck, which may include devices or sensors that take measurements of the subsurface through sound vibrations such as, but not limited to, seismic measurements. Drilling tool 202b may include a downhole sensor adapted to perform logging while drilling (LWD) data collection. Wireline tool 202c may include a downhole sensor deployed in a wellbore or borehole. Production tool 202d may be deployed from a production unit or Christmas tree into a completed wellbore. Examples of parameters that may be measured include weight on bit, torque on bit, subterranean pressures (e.g., underground fluid pressure), temperatures, flow rates, compositions, rotary speed, particle count, voltages, currents, and/or other parameters of operations as further discussed below. [0026] Sensors may be positioned about the oil field 200 to collect data relating to various oil field operations, such as sensors deployed by the data acquisition tools 202. The sensor may include any type of sensor such as a metrology sensor (e.g., temperature, humidity), an automation enabling sensor, an operational sensor e.g., pressure sensor, H2S sensor, thermometer, depth, tension), evaluation sensors, that can be used for acquiring data regarding the formation, wellbore trajectory, formation fluid/gas, wellbore fluid, gas/oil/water included in the formation/wellbore fluid, or any other suitable sensor. For example, the sensors may include accelerometers, flow rate sensors, pressure transducers, electromagnetic sensors, acoustic sensors, temperature sensors, chemical agent detection sensors, nuclear sensor, and/or any additional suitable sensors. In one embodiment, the data captured by the one or sensors may be used to characterize, or otherwise generate one or more parameter values for a high resolution result set used to, for example, generate a resource model.

[0027] Evaluation sensors may be featured in downhole tools such as tools 202b-202d and may include for instance electromagnetic, acoustic, nuclear, and optic sensors. Examples of tools including evaluation sensors that can be used in the framework of the current method include electromagnetic tools including imaging sensors such as FMI™ or QuantaGeo™ (mark of Schlumberger); induction sensors such as Rt Scanner™ (mark of Schlumberger), multifrequency dielectric dispersion sensor such as Dielectric Scanner™ (mark of Schlumberger); acoustic tools including sonic sensors, such as Sonic Scanner™ (mark of Schlumberger) or ultrasonic sensors, such as pulse-echo sensor as in UBI™ or PowerEcho™ (marks of Schlumberger) or flexural sensors PowerFlex™ (mark of Schlumberger); nuclear sensors such as Litho Scanner™ (mark of Schlumberger) or nuclear magnetic resonance sensors; fluid sampling tools including fluid analysis sensors such as InSitu Fluid Analyzer ™ (mark of Schlumberger); distributed sensors including fiber optic. Such evaluation sensors may be used in particular for evaluating the formation in which the well is formed (i.e., determining petrophysical or geological properties of the formation), for verifying the integrity of the well (such as casing or cement properties) and/or analyzing the produced fluid (flow, type of fluid, etc.).

[0028] As shown, data acquisition tools 202a-202d may generate data plots or measurements 208a-208d, respectively. These data plots are depicted within the resource site 200 to demonstrate that data generated by some of the operations executed at the resource site 200.

[0029] Data plots 208a-208c are examples of static data plots that may be generated by data acquisition tools 202a-202c, respectively. However, it is herein contemplated that data plots 208a-208c may also be data plots that may be generated and updated in real time. These measurements may be analyzed to better define properties of the formation(s) and/or determine the accuracy of the measurements and/or check for and compensate for measurement errors. The plots of each of the respective measurements may be aligned and/or scaled for comparison and verification purposes. In some embodiments, base data associated with the plots may be incorporated into site planning, modeling a test at the resource site 200. The respective measurements that can be taken may be any of the above.

[0030] Other data may also be collected, such as historical data of the resource site 200 and/or sites similar to the resource site 200, user inputs, information (e.g., economic information) associated with the resource site 200 and/or sites similar to the resource site 200, and/or other measurement data and other parameters of interest. Similar measurements may also be used to measure changes in formation aspects over time.

[0031] Computer facilities such as those discussed in association with Figure 3 may be positioned at various locations about the resource site 200 (e.g., a surface unit) and/or at remote locations. A surface unit (e.g., one or more terminals 320) may be used to communicate with the onsite tools and/or offsite operations, as well as with other surface or downhole sensors. The surface unit may be capable of sending commands to the oil field equipment/sy stems, and receiving data therefrom. The surface unit may also collect data generated during production operations and can produce output data, which may be stored or transmitted for further processing.

[0032] The data collected by sensors may be used alone or in combination with other data. The data may be collected in one or more databases and/or transmitted on or offsite. The data may be historical data, real time data, or combinations thereof. The real time data may be used in real time, or stored for later use. The data may also be combined with historical data or other inputs for further analysis or for modeling purposes to optimize production processes at the oil field 200. In one embodiment, the data is stored in separate databases, or combined into a single database.

[0033] High-Level Networked System

[0034] Fig. 3 illustrates a high-level networked system diagram illustrating a communicative coupling of devices or systems associated with the resource site 200. The system shown in the figure may include a set of processors 302a, 302b, and 302c for executing one or more processes discussed herein. The set of processors 302 may be electrically coupled to one or more servers (e.g., computing systems) including memory 306a, 306b, and 306c that may store for example, program data, databases, and other forms of data. Each server of the one or more servers may also include one or more communication devices 308a, 308b, and 308c. The set of servers may provide a cloud-computing platform 310. In one embodiment, the set of servers includes different computing devices that are situated in different locations and may be scalable based on the needs and workflows associated with the oil field 200. The communication devices of each server may enable the servers to communicate with each other through a local or global network such as an Internet network. In some embodiments, the servers may be arranged as a town 312, which may provide a private or local cloud service for users. A town may be advantageous in remote locations with poor connectivity. Additionally, a town may be beneficial in scenarios with large networks where security may be of concern. A town in such large network embodiments can facilitate implementation of a private network within such large networks. The town may interface with other towns or a larger cloud network, which may also communicate over public communication links. Note that cloud-computing platform 310 may include a private network and/or portions of public networks. In some cases, a cloud-computing platform 310 may include remote storage and/or other application processing capabilities.

[0035] The system of Fig. 3 may also include one or more user terminals 314a and 314b each including at least a processor to execute programs, a memory (e.g., 316a and 316b) for storing data, a communication device and one or more user interfaces and devices that enable the user to receive, view, and transmit information. In one embodiment, the user terminals 314a and 314b is a computing system having interfaces and devices including keyboards, touchscreens, display screens, speakers, microphones, a mouse, styluses, etc. The user terminals 314 may be communicatively coupled to the one or more servers of the cloudcomputing platform 310. The user terminals 314 may be client terminals or expert terminals, enabling collaboration between clients and experts through the system of Fig. 3.

[0036] The system of Fig. 3 may also include at least one or more oil fields 200 having, for example, a set of terminals 320, each including at least a processor, a memory, a communication device for communicating with other devices communicatively coupled to the cloud-computing platform 310. The resource site 200 may also have one or more sensors (e.g., one or more sensors described in association with Fig. 2) or sensor interfaces 322a and 322b communicatively coupled to the set of terminals 320 and/or directly coupled to the cloudcomputing platform 310. In some embodiments, data collected by the one or more sensors/sensor interfaces 322a and 322b may be processed to generate a one or more resource models and/or trajectory data for generating images of a wellbore, and/or one or more resolved data sets used to generate the resource model which may be displayed on a user interface associated with the set of terminals 320, and/or displayed on user interfaces associated with the set of servers of the cloud computing platform 310, and/or displayed on user interfaces of the user terminals 314. Furthermore, various equipment/devices discussed in association with the resource site 200 may also be communicatively coupled to the set of terminals 320 and or communicatively coupled directly to the cloud-computing platform 310. The equipment and sensors may also include one or more communication device(s) that may communicate with the set of terminals 320 to receive orders/instructions locally and/or remotely from the resource site 200 and also send statuses/updates to other terminals such as the user terminals 314.

[0037] The system of Fig. 3 may also include one or more client servers 324 including a processor, memory and communication device. For communication purposes, the client servers 324 may be communicatively coupled to the cloud-computing platform 310, and/or to the user terminals 314a and 314b, and/or to the set of terminals 320 at the resource site 200 and/or to sensors at the oil field, and/or to other equipment at the resource site 200.

[0038] A processor, as discussed with reference to the system of Fig. 3, may include a microprocessor, a graphical processing unit (GPU), a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, or another control or computing device. [0039] The memory/storage media discussed above in association with Figure 3 can be implemented as one or more computer-readable or machine-readable storage media that are non-transitory. In some embodiments, storage media may be distributed within and/or across multiple internal and/or external enclosures of a computing system and/or additional computing systems. Storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories; magnetic disks such as fixed, floppy and removable disks; other magnetic media including tape; optical media such as compact disks (CDs) or digital video disks (DVDs), BluRays or any other type of optical media; or other types of storage devices. “Non-transitory” computer readable medium refers to the medium itself (i.e., tangible, not a signal) and not data storage persistency (e.g., RAM vs. ROM).

[0040] Note that instructions can be provided on one computer-readable or machine- readable storage medium, or alternatively, can be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes and/or non-transitory storage means. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). The storage medium or media can be located either in a computer system running the machine- readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

[0041] It is appreciated that the described system of Figure 3 is an example that may have more or fewer components than shown, may combine additional components, and/or may have a different configuration or arrangement of the components. The various components shown may be implemented in hardware, software, or a combination of both, hardware and software, including one or more signal processing and/or application specific integrated circuits.

[0042] Further, the steps in the flowcharts described below may be implemented by running one or more functional modules in an information processing apparatus such as general-purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, GPUs or other appropriate devices associated with the system of Fig. 3. For example, the flowchart of Fig. 1 as well as the flowcharts below may be executed using a signal processing engine stored in memory 306a, 306b, or 306c such that the signal processing engine includes instructions that are executed by the one or more processors such as processors 302a, 302b, or 302c as the case may be. The various modules of Figure 3, combinations of these modules, and/or their combination with general hardware are included within the scope of protection of the disclosure. While one or more computing processors (e.g., processors 302a, 302b, or 302c) may be described as executing steps associated with one or more of the flowcharts described in this disclosure, the one or more computing device processors may be associated with the cloudbased computing platform 310 and may be located at one location or distributed across multiple locations. In one embodiment, the one or more computing device processors may also be associated with other systems of Fig. 3 other than the cloud-computing platform 310.

[0043] In some embodiments, a computing system or device is provided that includes at least one processor, at least one memory, and one or more programs stored in the at least one memory, such that the programs include instructions, which when executed by the at least one processor, are configured to perform any method disclosed herein.

[0044] In some embodiments, a computer readable storage medium is provided, which has stored therein one or more programs, the one or more programs including instructions, which when executed by a processor, cause the processor to perform any method disclosed herein. In some embodiments, a computing system is provided that includes at least one processor, at least one memory, and one or more programs stored in the at least one memory for performing any method disclosed herein. In some embodiments, an information processing apparatus for use in a computing system is provided for performing any method disclosed herein. [0045] Embodiments

[0046] Embodiments in this disclosure are directed to using a ray-casted approach to generate high-resolution geometry of wellbore trajectories using 1 -dimensional trajectory data (e.g., 1 -dimensional logs) captured by sensors within a wellbore. In particular, the technique relies on generating trajectory envelopes that are used as a ray-cast support. A fragment shader included in a signal processing engine and stored in a computer memory may implement a raycast loop to generate a multi-dimensional image (e.g., 3 -dimensional image) of the wellbore trajectory and renders same on a display device in real-time or near real-time. In particular, the processing stages associated with the generation of the multi-dimensional image include GPU data provisioning, computing or generating a plurality of envelops for the 1 -dimensional trajectory data captured within the wellbore, and ray-cast rendering of 1 -dimensional trajectory data to generate multi-dimensional images as further discussed in association with Figures 10- 11. The data provisioning stage may involve transmitting, the trajectory data captured by one or more sensors at the resource site 200 to a graphical processing unit (GPU) associated with an information processing apparatus or a computing device processor discussed in association with Figure 3. In particular, the trajectory data may be defined by a line segment representing a trajectory direction of the wellbore as shown in Figure 4. In one embodiment, a full set of logged data including temperature measurements, measured depth measurements, etc. may be incorporated in generating the multi-dimensional image. Moreover, the wellbore or borehole trajectory may be represented by a plurality of points that have a plurality of log values within the captured data.

[0047] At the stage where the envelopes are computed or generated for the 1- dimensional trajectory data captured within the wellbore, a ray-cast method may be employed in processing each pixel associated with the image to be generated. In particular, an optimization process may be employed to reduce the pixel set to only a subpart of the rendering screen based on a working zone around the wellbore trajectory. This is achieved using the enveloping discussed below in association with Figures 10-11. According to one embodiment, the envelop for each trajectory segment of the wellbore may include a section or segment between two consecutive points within the borehole or wellbore trajectory data that is sent to the GPU. As can be seen in Figure 4, the two consecutive points are indicated by the two "x's" on the center line segment shown in the figure. Considerations such as maximum radius and/or minimum radius associated with a given point within the wellbore are factored into the generation of the envelop for the trajectory segment.

[0048] Figure 5 illustrates an exemplary depiction of three envelopes that represent three segments of a wellbore trajectory. As can be seen in the figure, the three segments may have radii Rl, R2, and R3, respectively. Furthermore, in scenarios where the wellbore trajectory is non-linear, one or more cracks may appear at the junction between two consecutive envelopes. To address this problem, each consecutive envelope or working zone may be capped using a plane normal to a bisector of the segment direction as shown in Figures 6A and 6B.

[0049] In one embodiment, the ray-casting technique or computations involve the use of a signed distance field (SDF) operation. The SDF approach involves one or more of the following determinations associated with captured data or log values derived from sensors within a wellbore: if a determined distance d is greater than the log value, this means an associated multi-dimensional point of a given shape being generated is outside the given shape; if the distance is less than the log value, this means an associated multi-dimensional point of a given shape being generated is inside the shape; and if the distance is equal to the log value, this means an associated multi-dimensional point of a given shape being generated is on the shape surface of the given shape being generated.

[0050] In some embodiments, a custom or otherwise optimized SDF technique may be employed in generating a plurality of multi-dimensional points using 1 -dimensional captured trajectory data (e.g., log values). For example, the optimized SDF approach may involve the following: for any multi-dimensional point S _n , the point S _n is projected onto a point on a trajectory line segment to obtain p(S _n ). A distance d between the log value (e.g., a boundary point of the wellbore) corresponding to this point on the trajectory line segment and S _n may be determined as shown in Figures 7A and 7B. In some embodiments, the log value is indicative of a surface of the wellbore corresponding to the point on the trajectory line segment. A plurality of points S _n+1 may be iteratively generated using the relationship: S _n+1 = S _n + d * u, until d < E. For example, E may be less than or equal to about 0.01 meters. In addition, the number of iterations, according to some implementations is about 128 iterations, or less than about 128 iterations. It is appreciated that So may be initialized at a surface of an envelope according to one embodiment of this disclosure. Moreover, it is appreciated that the distance d may be signed, meaning that for d < 0, the surface of the wellbore is traversed beyond the surface's boundary.

[0051] When the angle between a view direction and a borehole trajectory becomes too small, some rendering artefacts may occur during the rendering process. To address this issue, a set of planes perpendicular to the wellbore or borehole trajectory may be generated. The distance between two planes included in the set of planes may be computed using a Nyquist frequency. In the illustrated example of Figures 8A and 8B, is used to indicate the distance between 2 sample planes along the view direction. When the view direction is aligned with the borehole trajectory, A becomes the sampling distance thus preventing ray-casting from missing intersections associated with the wellbore trajectory. When the view direction is perpendicular to trajecotry, becomes very big and no clamping occurs. According to some embodiments, when ray-casting results in sign changes, the surface intersection is between the two last computed points (e.g., S _n and S _n+1 ). Ray-cast iterations may be temporarily paused to switch to an alternate approach (e.g., a binary search approach) for such instances. In the exemplary example of Figure 8C, the point S _n is outside the surface of the wellbore while the point S _n+1 is inside the surface of the wellbore.

[0052] The level of detail applied to adjust the rendering quality of images generated using the above technique depends on the distance d, according to some embodiments. Log values (e.g., derived from logged or acquired data from the wellbore) may be associated with a multi-dimensional (e.g., a 3 -dimensional) trajectory position. Two consecutive log values may thus be associated with a multi-dimensional segment of the wellbore. In order to adjust the rendering quality, a multi-dimensional segment may have a rendered size in pixel. The log data may be processed as a uni-dimensional (e.g., 1 -dimensional) texture in a MIP map/pyramid including pre-calculated, optimized sequences of images, each of which may have a progressively lower resolution representation of a previous image. A log resolution may be selected in the pyramid and driven by the multi-dimensional segment with a rendered size in pixel. In some instances, the log rendering may be disabled with just the trajectory curve being rendered when, for example, the trajectory curve is far away.

[0053] In order to compute an interpolated log value between two input log values, texture interpolation may be employed. In particular, nearest or linear interpolation may be used since the log data or acquired data from the wellbore may be stored as a 1 -dimensional texture. Other exemplary interpolations may be employed as discussed in association with the flowcharts below. It is appreciated that changing interpolation for the log values has no impact on GPU memory consumption and, when the 1 -dimensional texture is interpolated, this has very low impact on GPU performance. For example, Figure 9A illustrates an exemplary rendering of the multi-dimensional image using, from left to right, nearest neighbor interpolation, linear interpolation, and cubic interpolation, respectively on the log values included in the captured data by one or more sensors within a given wellbore. All three interpolation techniques may be used during rendering of the multi-dimensional image with little to no impact on GPU memory consumption. Figure 9B illustrates additional exemplary rendering of multiple wellbores using a plurality of interpolation techniques as further discussed in the flowcharts below.

[0054] According to one implementation, a surface of the wellbore may be efficiently rendered as follows. An intersection between a view direction and an identified surface of the wellbore using the SDF technique may be first determined. This approach provides optimal results when the view direction is normal or otherwise perpendicular to the wellbore trajectory. Once the intersection is determined, one or more normal points included within the intersection are determined for shading. In one embodiment, a 3 -dimensional forward differential formula may be leveraged during the image generation process. Moreover, a 2-dimensional curve may be generated using the derivative of the 1 -dimensional log values included in the captured data, according to some implementations. One or more normal or perpendicular points relative to the 2-dimensional curve may then be generated as the referenced surface at this stage may be regarded as a circular surface. Following this one or more 3-dimensional normal points relative to the circular surface be generated to facilitate the circular coordinate conversion from a 2- dimensional space to a 3-dimensional space. This approach allows additional optimization of the SDF technique for normal computations since at least some of the surfaces of the wellbore is circular. In one embodiment, a 2-dimensional normal orientation for the view direction may be first determined for execution of the SDF technique before moving to a 3 -dimensional normal orientation indicating the view direction for the SDF technique to convert the cylindrical space (e.g., 2-dimensional space) to an object space (3 -dimensional space). This is a much faster approach for using the disclosed SDF technique in some embodiments.

[0055] It is appreciated that the foregoing method of rendering wellbore images is highly efficient as it does not employ triangle-based rendering or other rendering techniques that are computationally intensive. For example, triangle-based rendering techniques are limited by the number of generated polygons with artefacts appearing when getting close to the rendered surface. The disclosed process does not suffer from this limitation (e.g., residual artifacts after the rendering) but rather uses very minimal amounts of GPU memory during the rendering process for the wellbore in addition to having no undesirable residual artifacts. In some instances, triangle-based rendering techniques may require generating up to 32 vertices per log value to achieve a decently rendered image of a surface of the wellbore. It is appreciated that 1 vertex may contain 3 float values encoded into 4 bytes. This represents 32 * 3 * 4 = 384 bytes per log value, representing approximately 3 megabytes of GPU memory for 10,000 log values. The disclosed rendering technique, according to some embodiments, uses 4 bytes per log value plus up to 100 vertices to store the raw trajectory data. This represents about 50 kilobytes of GPU memory for 10,000 log values. Due to the low amount of GPU memory required because of the efficiency of the disclosed process, the framerate for generating wellbore images using the methods and systems provided by this disclosure is greatly improved. [0056] In some embodiments, the disclosed rendering method may be used in conjunction with a Unity 3D engine, a High-level shader language (HLSL) shader tool, a 3D engine supporting an Unreal shader engine, an OpenSceneGraph (OSG) shader engine, an Openinventor developer engine, an OpenGL shading language tool, a Vukan shader engine, a DirectX shader engine, etc.

[0057] Additional Flowcharts

[0058] Figures 10-11 illustrate exemplary flowcharts for generating a multidimensional image using trajectory data. At block 1002, the method includes receiving, using a computer processor, trajectory data captured by one or more sensors within a wellbore at the resource site. The trajectory data may indicate a wellbore trajectory for the wellbore and may include a plurality of boundary points in a first dimensional space, such that each boundary point included in the plurality of boundary points indicates a point on a surface of the wellbore. The wellbore trajectory may include a center line segment that indicates a direction in which the wellbore is drilled at the resource site. The method may further include generating, using the computer processor at block 1004, a first trajectory segment using the center line segment such that the first trajectory segment is bounded by a first point on the center line segment and a second point on the center line segment. The method may further include generating, using the computer processor at block 1006, a first plurality of multi-dimensional points using the plurality of boundary points and the center line segment. In addition, the method may include generating, using the computer processor at block 1008, a first image of the wellbore using the first plurality of multi-dimensional points. The first image of the wellbore may indicate an image of the wellbore in a second dimensional space that is of a higher order than the first dimensional space. [0059] According to some embodiments, generating the first plurality of multidimensional points includes determining, at block 1102, a first directional vector for the first trajectory segment. The first directional vector may indicate a first view direction for the wellbore within the first trajectory segment. The first directional vector may include a first vector line segment oriented in the first view direction. The method may, at block 1104, project, using the first directional vector, a first value included in the first vector line segment onto a first locus between the first point on the center line segment and the second point on the center line segment. The method may further determine, at block 1106, a first distance between the first value included in the first vector line segment and a first boundary point included in the plurality of boundary points in the first dimensional space. The method may also add (e.g., iteratively add) the first value included in the first vector line segment to a product of the first distance and the first directional vector until the first distance is less than a first threshold to generate the first plurality of multi-dimensional points

[0060] These and other implementations may each optionally include one or more of the following features. The first dimensional space, according to some implementations, is a 1-dimensional space while the second dimensional space is a 3-dimensional space. Moreover, the trajectory data, for example, includes a plurality of 1 -dimensional log values defining the plurality of boundary points such that each log value included in the plurality of 1 -dimensional log values maps a corresponding measured depth included in the plurality of measured depths to a numerical value. Furthermore, the computer processor discussed in conjunction with the flowcharts illustrated in Figures 10 and 11 include at least one graphical processing unit (GPU) with a corresponding memory device according to some embodiments.

[0061] In one embodiment, the method may generate a second trajectory segment using the center line segment such that the second trajectory segment is bounded by the second point on the center line segment and a third point on the center line segment. The method may further generate, using the computer processor, a second plurality of multi-dimensional points using the plurality of boundary points and the center line segment. Generating the second plurality of multi-dimensional points may include determining a second directional vector for the second trajectory segment. The second directional vector may indicate a second view direction for the wellbore within the second trajectory segment. The second directional vector, according to some embodiments, includes a second vector line segment oriented in the second view direction. Moreover, generating the second plurality of multi-dimensional points may include projecting, using the second directional vector, a first value included in the second vector line segment onto a first locus between the second point on the center line segment and the third point on the center line segment. In addition, generating the second plurality of multi-dimensional points includes determining a second distance between the first value included in the second vector line segment and a second boundary point included in the plurality of boundary points in the first dimensional space. Furthermore, generating the second plurality of multi-dimensional points may include adding (e.g., iteratively adding) the first value included in the second vector line segment to a product of the second distance and the second directional vector until the second distance is less than a second threshold to generate the second plurality of multidimensional points. The method may further include generating, using the computer processor, a second image of the wellbore using the second plurality of multi-dimensional points. The second image of the wellbore may indicate an image of the wellbore in a second dimensional space that is of a higher order than the first dimensional space.

[0062] It is appreciated that the first trajectory segment is parameterized by a first radius that is measured from the center line segment; and the second trajectory segment is also parameterized by a second radius that is measured from the center line segment. The first radius, in exemplary embodiments, is of a lesser magnitude relative to a magnitude of the second radius. Furthermore, the first trajectory segment and the second trajectory segment may be non-linearly concatenated at a first junction thereby forming a discontinuity artefact at the first junction. The discontinuity artefact may be resolved or otherwise corrected by capping, using: a first plane that is normal or perpendicular relative to a first bisector of a first trajectory direction within the first trajectory segment, and/or by a second plane that is normal or perpendicular relative to a second bisector of a second trajectory direction within the second trajectory segment.

[0063] According to some implementations, the first image and the second image are concatenated to form a 3 -dimensional image of the wellbore. In addition, the trajectory data may include temperature information for the plurality of boundary points, pressure information associated with the plurality of boundary points, radioactivity data associated with the plurality of boundary points, permeability data associated with the plurality of boundary points, and hygrometry data associated with the plurality of boundary points. In such cases, the 3- dimensional image may be coded (e.g., color-coded, numerically coded, textually coded, etc.) to visually indicate one or more of the temperature information, the pressure information, the radioactivity data, the permeability data, and the hygrometry data. Moreover, the first image and/or the second image respectively generated using the first plurality of multi-dimensional points and the second plurality of multi-dimensional points may be rendered on a graphical display device for viewing and interaction by a user. The plurality of boundary points include 1 -dimensional scalar values that are interpolated using one of: nearest-neighbor interpolation, nearest-neighbor interpolation with mipmapping, linear interpolation, linear interpolation with mipmap, cubic interpolation, and cubic interpolation with mipmapping.

[0064] The systems and methods described in this disclosure provide improvements in autonomous operations at resource sites such as oil and gas fields. The systems and methods described allow an ordered combination of new results in autonomous operations including wireline and testing operations with existing results. The systems and methods described cannot be performed manually in any useful sense. Simplified systems may be used for illustrative purposes but it will be appreciated that the disclosure extends to complex systems with many constraints thereby necessitating new hardware-based processing system described herein. The principles disclosed may be combined with a computing system to provide an integrated and practical application to achieve autonomous operations in oil and gas fields.

[0065] These systems, methods, processing procedures, techniques, and workflows increase effectiveness and efficiency. Such systems, methods, processing procedures, techniques, and workflows may complement or replace conventional methods for identifying, isolating, transforming, and/or processing various aspects of data that is collected from a subsurface region or other multi-dimensional space to enhance flow simulation prediction accuracy.

[0066] A benefit of the present disclosure is that more effective methods for downhole operations may be employed. It will be appreciated that the application and benefit of the disclosed techniques are not limited to subterranean wells and reservoirs and may also be applied to other types of energy explorations and/or other resource explorations (e.g., aquifers, Lithium/Salar brines, etc.).

[0067] While any discussion of or citation to related art in this disclosure may or may not include some prior art references, Applicant neither concedes nor acquiesces to the position that any given reference is prior art or analogous prior art.

[0068] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to use the invention and various embodiments with various modifications as are suited to the particular use contemplated.

[0069] It will also be understood that, although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the invention. The first object or step, and the second object or step, are both objects or steps, respectively, but they are not to be considered the same object or step.

[0070] The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in the description of the invention and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any possible combination of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

[0071] As used herein, the term “if’ may be construed to mean “when” or “upon” or

“in response to determining” or “in response to detecting,” depending on the context. [0072] Those with skill in the art will appreciate that while some terms in this disclosure may refer to absolutes, e.g., all source receiver traces, each of a plurality of objects, etc., the methods and techniques disclosed herein may also be performed on fewer than all of a given thing, e.g., performed on one or more components and/or performed on one or more source receiver traces. Accordingly, in instances in the disclosure where an absolute is used, the disclosure may also be interpreted to be referring to a subset.