Automated equipment for acquiring drilling cuttings images

Abstract

An automatic image acquisition apparatus includes a housing including an image acquisition chamber. A tray is disposed in the image acquisition chamber and is configured to receive a drilling cuttings sample. At least one lamp is also disposed in the image acquisition chamber and disposed to illuminate the tray. A digital camera is disposed in the image acquisition chamber and is configured to acquire a digital image (e.g., a digital color image) of the chip sample. The apparatus also includes an electronic controller configured to instruct the digital camera to record a digital image of the drilling cuttings and to save the image to a digital memory or to communicate the image to an external computing device.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] Related Applications This application claims priority to European Patent Application No. 22305792.8 filed on May 31, 2022, the entire contents of which are incorporated herein by reference. Background Art

[0003] In underground drilling operations, such as for oil and gas exploration and production, formation cuttings are generated by the drilling activity. These cuttings have long been evaluated and characterized to better understand the properties and structure of the subsurface rock. The cuttings are typically collected after passing through a shale shaker and then cleaned and photographed for analysis. In recent years, it has been desirable to perform this evaluation and characterization automatically (or semi-automatically) to reduce labor costs and shorten the turnaround time for interpretation. However, achieving such automation is far from routine.

[0004] Electronic image data is often acquired and evaluated manually during routine drilling operations. Automated evaluation and characterization of such data is particularly challenging because cuttings include a wide variety of sizes, shapes, textures, and colors. Furthermore, cuttings are often jumbled together in the image, with individual particles touching each other and partially overlapping. Image lighting also tends to vary from image to image and from rig to rig, with shadows and other image artifacts making evaluation and characterization even more challenging. Improvements in image quality and consistency of cuttings images are needed in order to increase the effectiveness of automated machine learning algorithms to more accurately segment and label drilling cuttings images. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] For a more complete understanding of the disclosed subject matter and advantages thereof, reference is now made to the following description taken in conjunction with the accompanying drawings, in which:

[0006] Figure 1 An exemplary drilling rig including a system for acquiring digital photographic images of drill cuttings samples is depicted.

[0007] Figure 2 Describes a Figure 1 An exemplary apparatus for removing drill bit cuttings from a drilling fluid used in a drilling rig is depicted.

[0008] Figure 3 Depicted in Figure 1 An exemplary drill bit for use in a drilling machine depicted that generates drill chips.

[0009] Figure 4 An example process for obtaining chip images is depicted.

[0010] Figure 5A and 5B (collectively, FIG5 ) depicts an example embodiment of an automatic image acquisition apparatus.

[0011] Figure 6 Depicted is a block diagram of an example software module including a plurality of instruction blocks stored in a controller memory in the device shown in FIG. 5 .

[0012] Figure 7 Depicted for use Figure 6 The image acquisition block is shown as a flow chart of an example method for acquiring a digital image.

[0013] Figure 8 A flow chart of an example method for calibrating the image acquisition device shown in FIG. 5 is depicted. DETAILED DESCRIPTION

[0014] An automatic image acquisition device for acquiring digital photographic images of drilling cuttings samples is disclosed. The disclosed embodiments can advantageously improve the quality and consistency of the acquired images, as well as provide automatic or semi-automatic calibration of the device. In an example embodiment, the device includes a housing that includes an image acquisition chamber. A tray is deployed in the image acquisition chamber and is configured to receive the drilling cuttings sample. At least one light is also deployed in the image acquisition chamber and is configured to illuminate the tray. A digital camera is deployed in the image acquisition chamber and is configured to acquire a digital image (e.g., a digital color image) of the cuttings sample. The device also includes an electronic controller that is configured to automatically turn the light on and off, instruct the digital camera to record a digital photographic image of the drilling cuttings when the light is turned on, and save the image to a digital memory or transmit the image to an external computing device.

[0015] As is known to those of ordinary skill, drilling of underground boreholes (e.g., in oil and gas exploration and / or production operations) produces a large amount of rock cuttings that are available at the rig site. In current drilling operations, these cuttings are typically evaluated by geologists, petrophysicists, and / or other experts at the rig site, for example, to assess the local lithology and oil-bearing potential of the well. Drilling cuttings description and evaluation is typically a manual and repetitive task performed at the rig site. A commonly implemented workflow involves rig personnel identifying each lithology type by visual inspection and performing chemical and / or physical measurements on the cuttings samples. When identifying the lithology type in the cuttings sample, the operator describes the rock characteristics (color, hardness, grain size, etc.), for example, on paper. Photographs (digital images) can also be visually evaluated based on previous measurements and experience.

[0016] With the rise of image datasets in recent years and advances in machine learning (ML), attempts have been made to automatically perform cuttings identification using ML algorithms. Such attempts have included performing classification of cuttings from photographic images of drilling cuttings (e.g., using trained convolutional neural networks). Although such efforts are extensive, they have achieved only limited success. A particular difficulty has been accurately labeling multiple rock types within a single image, such as may occur during a transition from one lithology to another during drilling. A related difficulty has been accurately identifying changes from one lithology type to another in consecutive images (as may also occur when transitioning from one lithology type to another during drilling). These difficulties may be related in part to issues with the quality and consistency of the cutting images acquired.

[0017] Figure 1 An example drilling rig 10 is depicted, which includes an apparatus 100 configured to automatically acquire cuttings images generated during drilling operations. The apparatus can be deployed on the drilling rig, for example, in a field laboratory 60. In the depicted embodiment, a drill string 12 can be suspended at an upper end by a kelly 12 and a traveling block 14, and terminate at a drill bit 16 at a lower end. The drill string 12 and drill bit 16 can be rotated, for example, by a rotary table 18 on a drilling rig floor 20, thereby drilling a borehole (wellbore) 22 into a formation 24, wherein a portion of the borehole 22 can be casing 26. As shown, in some embodiments, a drilling fluid or drilling "mud" 28 can be pumped into the upper end of the hollow drill string 12 by a mud pump 30 through a connecting mud line 32. From there, the drilling fluid 28 may flow downward through the drill string 12, exit the drill string 12 through an opening in the drill bit 16, and may return to the surface through an annulus formed between the wall of the borehole 22 and the outer diameter of the drill string 12. Once at the surface, the drilling fluid 28 may be returned through a return line 34, such as via a bell neck 36. As shown, in certain embodiments, a blowout preventer 38 may be used to prevent a blowout from occurring during the drilling operation 10.

[0018] As in Figure 1 As further depicted above, drill bit cuttings formed by the drill bit 16 crushing the rock in the formation 24 can be removed from the returning drilling fluid 28 by a shale shaker 40 in the return line 34. The drilling fluid 28 can be reused and recirculated downhole. The shale shaker 40 can include a shale shaker pit 42 and a gas collector 44. Figure 2 Depicted are drill bit cuttings 46 that have been removed from the drilling fluid 28 in the shaker pit 42 of the shale shaker 40 before the drilling fluid 28 is delivered to the mud pit 48. In addition, Figure 3 Cuttings are depicted as being generated by the drill bit 16 during drilling and then flowing upward through the borehole annulus.

[0019] Now turn to Figure 4, depicts an example process 70 for obtaining a chip image. For example, using the above description of Figure 1 The drilling rig described herein drills a borehole at 72. The drilling operation generates cuttings, also as described above. The drilling cuttings are collected at 74, for example from a shale shaker 40, as described above. Figure 2 In an exemplary embodiment, the drilling cuttings may be prepared for analysis at 76, for example, by drying in an oven and / or screening or sieving the drilling cuttings (e.g., as shown at 90) to remove large or small particles. The sample of the obtained cuttings may then be placed in an image acquisition device 100, which may be configured to automatically acquire at least one photographic image of the cuttings (e.g., a first image and a second image illuminated with corresponding white light and ultraviolet light).

[0020] Figure 5A and Figure 5B (collectively, FIG. 5 ) depicts exterior and interior views of an image acquisition device 100 (also referred to herein as an image acquisition system 100 ). In certain embodiments, the device 100 may be a portable instrument and may be configured for use at substantially any suitable location on a rig site (e.g., in a modular lab or adjacent to a shale shaker 40 or other surface system or rig component). In the depicted example embodiment, the device includes an exterior housing 102 . The housing is intended to protect the internal components (e.g., cameras and other electronic circuitry) and to provide a controlled environment for image acquisition. The housing may be made of substantially any suitable construction material, such as sheet metal, plastic, etc. The disclosed embodiments are of course not limited in this regard.

[0021] The depicted example includes a first chamber 110 and a second chamber 120, which includes a lower image acquisition chamber 110 and an upper maintenance chamber 120. The image acquisition chamber 110 includes a door or hatch 112 (or entryway) that provides access to the chamber 110. The hatch 112 may include a seal or waterproofing (not shown) to provide better control of the image acquisition environment. The maintenance chamber 120 may also include a hatch 122 that provides maintenance access to various electronic (and other) components in the device 100.

[0022] Go to Figure 5B The image acquisition chamber 110 may include a sample tray 114 configured to receive cuttings (e.g., Figure 4 ). The apparatus 100 may include substantially any suitable tray, including, for example, a plastic or metal tray with a high contrast (vivid) background color to enhance subsequent particle identification and segmentation in the acquired image. The size and shape of the tray may be designed (in coordination with the camera lens system) to hold a sufficient amount of cuttings and fill the field of view in the image acquisition apparatus.

[0023] The chamber 110 may further include at least one light source, for example, including a white light source 116 and / or an ultraviolet light source 117 configured to illuminate the sample tray 114 (and the cuttings placed on the tray). The light sources 116, 117 may include, for example, light emitting diodes (LEDs) or other light sources. In certain embodiments, the white light source 116 may be configured to automatically turn on when the device 100 is powered on, or automatically turn on when the hatch 112 is opened, to provide internal lighting for the operator. In the depicted example embodiment, the light sources 116, 117 are disposed on the opposite inner walls of the chamber 110. The disclosed embodiments are not limited in this regard, as the light sources 116, 117 may be disposed in substantially any suitable one or more positions in the chamber 110 (e.g., above the tray).

[0024] The chamber 110 may further include a camera 118 and a corresponding lens 119, which are disposed above the tray 114, for example, mounted on or in a divider 130 disposed between the upper chamber 120 and the lower chamber 110. The camera may advantageously include a high-resolution color camera, for example, including a 10 or 20 (or more) megapixel image sensor. Substantially any suitable lens 119 may be utilized. The lens 119 may be configured to provide a sharp (focused) image to the image sensor, for example, including a 25 mm lens. The lens 119 may alternatively include a variable zoom lens. In some embodiments, the camera 118 and the corresponding lens 119 may advantageously be disposed in or on the divider 130, so that the lens extends downward into the lower chamber 110 and can be accessed from the lower chamber 110, and the camera electronics and connection ports are disposed in the upper chamber 120 and can be accessed from the upper chamber 120.

[0025] Continue to refer Figure 5B , the upper chamber 120 may include a controller 124, such as a computer board (or motherboard), which is configured to control the operation of the camera 118, lens 119 and / or lights 116, 117. The controller 124 may include one or more processors (e.g., microprocessors) that may be connected to one or more data storage devices (e.g., hard drives or solid-state memory). The controller may be configured to network (e.g., communicate) with external devices, such as via a hardwire or wireless connection. For example, the controller 124 may be configured to upload acquired images to a local (rig site) computer and / or a cloud-based computing system. The controller 124 may also include processor-executable instructions stored in a memory. As described below with respect to Figure 6 , Figure 7 and Figure 8 As described in more detail, the instructions may be configured to cause the apparatus 100 to automatically calibrate an image acquisition system, acquire at least one chip image, process the image, and / or upload the image to an external computer system.

[0026] The controller may also be configured to provide external access to internal software or firmware stored in the controller memory. Such external access (e.g., via a hardwired USB connection or via a wireless connection) may enable the software to be repaired and / or updated as needed via an external computer. In an example embodiment, the software may be stored on flash memory, and updating or repairing the software may include copying the software from the external computer (or other device) to the flash memory.

[0027] Although not depicted in FIG. 5 , it should be understood that the image acquisition device 100 may also include a user interface in communication with the controller 124. The user interface may include standard devices such as a keyboard, a mouse, and / or a display. The user interface may also (or alternatively) include an interface based on a mobile phone or a tablet computer. The user interface may, for example, enable a user to enter metadata associated with a cutting sample or a group of cutting samples, such as a hysteresis depth, a wellbore ID, a date, a time, and / or the like. It should be understood that the user interface may be dedicated to the device or may be independent of the device 100 (e.g., associated with a rig site computer). For example, the user interface may be associated with a cell phone, a tablet computer, a laptop, or other computerized device in communication with the controller 124. The disclosed embodiments are not limited thereto.

[0028] Figure 6 A block diagram of an example software module 140 including a plurality of instruction blocks stored in a controller memory is depicted. The software module 140 may include image acquisition instructions 142 configured to automatically acquire digital images of drilling cuttings samples. The image acquisition instructions may also be configured to automatically calibrate the image acquisition system, for example, including a camera, lens, and lighting settings. An image metadata block 144 may be configured to receive image metadata and associate the received data with the acquired image. Block 144 may also be configured to query a user, for example, via a suitable user interface.

[0029] The software module may optionally further include one or more image processing blocks 146, 148, and 152. For example, the image pre-processing block 146 may be configured to calibrate or standardize the image via color enhancement / calibration, cropping, filtering, etc. The image clustering or segmentation block 148 may be configured to generate a segmented image that identifies, highlights, and / or labels individual cutting particles in the image. Clustering or segmentation may be performed using any suitable algorithm, for example, including a trained neural network such as a pixel-by-pixel convolutional neural network (CNN) or other artificial intelligence and / or machine learning algorithm. In some embodiments, the algorithm may be configured to segment the image only (e.g., to highlight individual particles and different lithology types). In other embodiments, the algorithm may be configured to assign preliminary lithology type labels to the identified clusters (which may then be verified or corrected by an expert at an off-site location).

[0030] In embodiments with sufficient computer processing power (e.g., multiple processors), the software module 140 may also include an automated lithology block 152, which is configured to label individual cutting particles identified by the image segmentation block with a label including a lithology type (e.g., sandstone, limestone, shale, etc.). The automated lithology block 152 may include a trained neural network, such as a trained convolutional neural network (CNN). Such a CNN may be trained using, for example, a large number of annotated or labeled images including cutting images of various lithology types from around the world or from a local basin (oil field or region). Training may result in weight optimization of NN nodes and layers based on minimizing a loss function of the training data set. In other words, the weights of each node in a multi-layer NN may be adjusted so that the model minimizes error when classifying an input image (or a smaller subset thereof). It should be understood that such a CNN typically includes multiple layers, and the initial training is intended to train each layer (e.g., each node in each layer is assigned a weight to minimize the loss function). Initial training may require significant computing resources and may therefore advantageously be performed off-site, for example, using an initial training database that may then be transferred to the controller 140 .

[0031] Continue to refer Figure 6 , the software module also includes an image transmission and / or storage routine 154, which is configured to transmit image data to an external computer system and / or save the image to a local memory. Routine 154 can be configured to transmit images using any suitable hardware connection (e.g., including hardwired or wireless connections). Routine 154 can also be configured to transmit substantially any suitable image data, including, for example, raw image data from box 142, pre-processed image data from box 146, clustered or segmented image data from box 148, and / or segmented lithological interpretation image data from box 152.

[0032] Now turn to Figure 7 5, there is disclosed a method for, for example, using the image acquisition block 142 ( Figure 6 ) is a flowchart of an example method 160 for acquiring a digital image. The method 160 assumes, for example, that Figure 4 The prepared chip sample has been placed on a tray 114 in the image acquisition chamber 110. In this particular example, at 162, the camera settings are updated to configure the sensor for white light exposure. These settings may include white balance, specific exposure time, etc. A first image is acquired at 164 and saved to the controller memory at 166. The camera settings are then updated at 168 to configure the sensor for ultraviolet exposure. The ultraviolet light 117 is then turned on and the white light 116 is turned off at 172. A second image is acquired at 174 and saved to the controller memory at 176. The ultraviolet light is then turned off and the white light is turned on at 178. At 182, the first image and the second image are transmitted to an external computer resource for further processing. Such further processing may include, for example, clustering, segmentation, and automated lithology as described above. Image metadata may be entered at any time (e.g., before, during, or after execution of method 160). In addition, although not described in Figure 7 1, but it should be understood that the image acquisition block 142 can also be configured to automatically lock the lower hatch 112 when an image acquisition routine is initiated, and unlock the hatch 112 when the routine is completed (e.g., when an image has been transferred or when the UV light 117 has been turned off).

[0033] Now go to Figure 8 , and further referring to FIG. 5 , a flowchart of an exemplary method 200 for automatically calibrating the image acquisition device 100 to acquire a calibrated digital image is disclosed. Software module 140 ( Figure 6 ) may also include an automatic image calibration block, or a calibration routine may be stored in the image pre-processing block 146. A calibration target may be placed at a predefined location and in a predefined orientation on the tray 114, and a calibration procedure initiated (e.g., via a user interface). Substantially any suitable calibration target may be utilized, including, for example, the Rez Checker target available from Imatest (www.imatest.com / product / rez-checker / ).

[0034] At 202, a digital image of a calibration target may be acquired, and one or more image segments (or blocks) may be extracted from corresponding calibration areas in a calibration grid overlaying the digital image. The calibration areas are typically predefined and correspond to known edge, color, and / or grayscale areas in the calibration target. At 204, modeled (synthesized) image segments are obtained (or calculated) for corresponding areas of the calibration grid. The modeled image segments may be obtained using substantially any suitable technique, depending on the calibration grid areas as described in more detail below, and are typically stored in a memory in the image acquisition device 100. The modeled images obtained at 204 may include reference colors (e.g., including reference red, green, and blue values) corresponding to particular calibration areas in the calibration areas, reference intensities (or gray shades) corresponding to other calibration areas in the calibration areas, and / or modeled edge or wedge images corresponding to other calibration areas in the calibration target.

[0035] The method 200 also includes comparing the extracted image segments and the modeled image segments at 206. When the difference (or differences) between the image segment (or segments) and the modeled image (or segments) is less than a threshold, the image acquisition system is deemed to be optimized (or calibrated). When the difference (or differences) is greater than the threshold, the image acquisition system is adjusted at 208. The method may then return to 202 and acquire another calibration target image. The adjustment (or adjustments) to the image acquisition system at 208 may advantageously include updates to camera settings (such as exposure time and RGB gain), however, the disclosed embodiments are not limited in this regard as other adjustments may include, for example, lens settings such as focal plane adjustment and light intensity adjustment.

[0036] Continue to refer Figure 8 , comparing the extracted and modeled image segments at 206 may include comparing a single (single) extracted image segment and a corresponding single (single) modeled image segment, or may include comparing a plurality of extracted image segments (e.g., of a plurality of calibration regions) and a corresponding plurality of modeled images. For example, an image segment of each of a plurality of shading calibration regions may be compared to a corresponding modeled image of each of the same shading calibration regions. In such an embodiment, the comparison may include calculating a sum (or weighted sum) of the differences between the acquired image segments and the modeled image segments, and comparing the result to a corresponding threshold value. In another embodiment, image segments of a plurality of gray calibration regions may be compared to a corresponding modeled image segment of each of the same gray calibration regions. In yet another embodiment, one or more image segments of an edge or wedge region may be compared to a corresponding modeled image to calculate a spatial resolution of the image acquisition system, which may then be compared to a corresponding threshold value.

[0037] The modeled edge image can be calculated, for example, using a mathematical model. The modeled image can be calculated using essentially any suitable mathematical relationship, including, for example, Bessel functions or Gaussian functions. The difference between the image segment and the modeled image can be minimized by adjusting the model parameters to estimate the spatial resolution (e.g., spatial frequency resolution) of the digital imaging system. For example, the modulation transfer function 50% (MTF50) is a measure of the spatial frequency response of the imaging system (and an indicator of image sharpness). A calibration procedure may require that the MTF50 exceed a predefined threshold to ensure good focus. Otherwise, the system can be adjusted (e.g., by adjusting the lens focus) and the calibration repeated to evaluate the spatial resolution (e.g., the spatial frequency response of the system).

[0038] Calibration may also include calibrating image color and brightness properties. For example, calibration may evaluate the saturation of color image segments compared to a modeled (or ideal) saturation and the brightness or intensity of grayscale image segments compared to a modeled brightness. In one example embodiment, calibration may automatically update or adjust image exposure time until the saturation and / or brightness are within a threshold of the modeled saturation and / or brightness. For example, if the saturation or brightness exceeds the modeled saturation or brightness, the exposure time may be reduced. Calibration may also calculate ΔE (e.g., ΔE00) for color image segments and compare the calculated value to the modeled ΔE (it should be understood that ΔE is a standard measure of differences between displayed colors). In one example embodiment, calibration may automatically update or adjust the RGB gains in a digital camera until the calculated ΔE is within satisfactory limits of the modeled ΔE. Figure 8 As shown, the automatic calibration process can be repeated until an acceptable calibration is achieved. Meanwhile, the method 200 can also include reporting the calibration results to the user or saving them to a memory.

[0039] It should be understood that the present disclosure includes many embodiments. These embodiments include but are not limited to the following embodiments.

[0040] In a first embodiment, an apparatus for acquiring digital images of drill cuttings samples obtained during an underground drilling operation comprises: a housing defining an image acquisition chamber; a tray disposed in the image acquisition chamber and configured to receive the drill cuttings sample; a light disposed in the image acquisition chamber and configured to illuminate the tray; a digital camera disposed in the image acquisition chamber and configured to acquire digital images of the cuttings sample; and an electronic controller configured to automatically turn the light on and off, instruct the digital camera to acquire digital images of the drill cuttings when the light is turned on, and save the digital images to a digital memory or transmit the digital images to an external computing device.

[0041] A second embodiment may include the first embodiment, wherein the light comprises a first white light and a second ultraviolet light disposed in the image acquisition chamber and configured to illuminate the tray.

[0042] A third embodiment may include the second embodiment, wherein the electronic controller is further configured to (i) update settings in the digital camera for acquiring white light images; (ii) instruct the digital camera to record a first digital image of the drilling cuttings sample; (iii) update settings in the digital camera for acquiring ultraviolet light images; (iv) turn off the first white light and turn on the second ultraviolet light; (v) instruct the digital camera to record a second digital image of the drilling cuttings sample; (vi) turn on the first white light and turn off the second ultraviolet light; and (vii) transmit the first digital image and the second digital image of the drilling cuttings to an external computing device.

[0043] A fourth embodiment may include any one of the first to third embodiments, wherein the housing further defines a maintenance compartment, the maintenance compartment being located above the image acquisition chamber; and the electronic controller is disposed in the maintenance compartment.

[0044] A fifth embodiment may include the fourth embodiment, wherein the digital camera is deployed on a divider disposed between the image acquisition chamber and the maintenance chamber, wherein the digital camera includes a lens extending downward into the image acquisition chamber, and at least one connector port is disposed in the maintenance chamber.

[0045] A sixth embodiment may include any of the first to fifth embodiments, further comprising a hatch providing access to the image acquisition chamber, wherein the electronic controller is configured to automatically turn on the light when the hatch is opened.

[0046] A seventh embodiment may include any of the first to sixth embodiments, wherein the electronic controller includes an image pre-processing module configured to automatically adjust settings on the digital camera.

[0047] An eighth embodiment may include the seventh embodiment, wherein the image preprocessing module is configured to acquire a digital image of a calibration target; extract at least one image segment from a corresponding calibration area in the calibration target; compare at least one image segment with a corresponding modeled image segment; and adjust settings on the digital camera when a difference between at least one image segment and the corresponding modeled image segment exceeds a threshold.

[0048] The ninth embodiment may include any one of the first to eighth embodiments, wherein the electronic controller further comprises an image segmentation module, the image segmentation module being configured to generate a segmented image, the segmented image identifying individual cutting particles in the acquired digital image; and an automated lithology module being configured to label the identified individual cutting particles with a lithology type.

[0049] A tenth embodiment may include the ninth embodiment, wherein at least one of the image segmentation module and the automated lithology module comprises a trained convolutional neural network.

[0050] In an eleventh embodiment, a method for acquiring a digital image of a drill cuttings sample obtained during an underground drilling operation includes: placing the drill cuttings sample on a tray, the tray being deployed in a chamber in an automated image acquisition device, the automated image acquisition device including at least one light configured to illuminate the tray and a digital camera configured to automatically acquire a digital image of the drill cuttings sample; automatically turning on at least one light; automatically causing the digital camera to acquire a digital image of the drill cuttings sample; and automatically saving the acquired digital image to a memory or transmitting the acquired digital image to an external computing device.

[0051] The twelfth embodiment may include the eleventh embodiment, wherein automatically causing the digital camera to acquire the digital image further includes updating the settings in the digital camera for acquiring white light images; using the digital camera to record a first digital image of the drilling cuttings sample; updating the settings in the digital camera for acquiring ultraviolet light images; turning off the white light and turning on the ultraviolet light; using the digital camera to record a second digital image of the drilling cuttings sample; turning on the white light and turning on the ultraviolet light; and transmitting the first digital image and the second digital image to the external computing device.

[0052] A thirteenth embodiment may include any one of the eleventh to twelfth embodiments, further comprising generating a segmented image in which each cutting particle in the acquired digital image is identified; and marking each cutting particle identified in the segmented image with a lithology type label.

[0053] A fourteenth embodiment may include any one of the eleventh to thirteenth embodiments, further comprising placing a calibration target on the tray; acquiring a digital image of the calibration target; extracting at least one image segment from a corresponding calibration area in the calibration target; comparing at least one image segment with a corresponding modeled image segment; and automatically adjusting settings on the digital camera when a difference between at least one image segment and the corresponding modeled image segment exceeds a threshold.

[0054] A fifteenth embodiment may include the fourteenth embodiment, wherein the settings on the digital camera include exposure time and at least one of red, green, and blue gains.

[0055] In a sixteenth embodiment, an apparatus for acquiring digital images of drill cuttings samples obtained during an underground drilling operation comprises: a housing defining an upper maintenance chamber and a lower image acquisition chamber; a tray disposed in the lower image acquisition chamber and configured to receive the drill cuttings sample; a white light disposed in the lower image acquisition chamber and configured to illuminate the tray; an ultraviolet light disposed in the lower image acquisition chamber and configured to irradiate the tray; a digital camera disposed in the lower image acquisition chamber and configured to acquire digital images of the cuttings sample; and an electronic controller configured to automatically turn the light on and off, instruct the digital camera to acquire digital images of the drill cuttings when the light is turned on, and save the image to a digital memory or transmit the image to an external computing device.

[0056] The seventeenth embodiment may include the sixteenth embodiment, wherein the electronic controller is further configured to (i) update settings in the digital camera for acquiring white light images; (ii) instruct the digital camera to record a first digital image of the drilling cuttings sample; (iii) update settings in the digital camera for acquiring ultraviolet light images; (iv) turn off the white light and turn on the ultraviolet light; (v) instruct the digital camera to record a second digital image of the drilling cuttings sample; (vi) turn on the white light and turn off the ultraviolet light; and (vii) transmit the first digital image and the second digital image of the drilling cuttings to an external computing device.

[0057] An eighteenth embodiment may include any one of the sixteenth to seventeenth embodiments, wherein the digital camera is deployed on a divider disposed between the lower image acquisition chamber and the maintenance chamber, wherein the digital camera includes a lens extending downward into the lower image acquisition chamber and at least one connector port disposed in the maintenance chamber.

[0058] A nineteenth embodiment may include any one of the sixteenth to eighteenth embodiments, wherein the electronic controller includes an image preprocessing module, which is configured to acquire a digital image of a calibration target; extract at least one image segment from a corresponding calibration area in the calibration target; compare at least one image segment with a corresponding modeled image segment; and adjust settings on the digital camera when a difference between at least one image segment and the corresponding modeled image segment exceeds a threshold.

[0059] The twentieth embodiment may include any one of the sixteenth to nineteenth embodiments, wherein the electronic controller further comprises an image segmentation module configured to generate a segmented image that identifies individual cutting particles in the acquired digital image; and an automated lithology module configured to label the identified individual cutting particles with a lithology type.

[0060] Although the automated apparatus for drilling cuttings image acquisition has been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the disclosure as defined by the appended claims.

Claims

1. An apparatus for acquiring a digital image of a drill cuttings sample obtained during a subterranean drilling operation, the apparatus comprising: a housing defining an image acquisition chamber; a tray disposed in the image acquisition chamber and configured to receive the drill cuttings sample; a light disposed in the image acquisition chamber and configured to illuminate the tray; a digital camera disposed in the image acquisition chamber and configured to acquire a digital image of the chip sample; and An electronic controller is configured to automatically turn the light on and off, instruct the digital camera to acquire digital images of the drill cuttings when the light is on, and save the digital images to a digital memory or transmit the digital images to an external computing device.

2. The apparatus of claim 1, wherein the light comprises a first white light and a second ultraviolet light disposed in the image acquisition chamber and configured to illuminate the tray.

3. An apparatus according to claim 2, wherein the electronic controller is further configured to (i) update settings in the digital camera for acquiring white light images; (ii) instruct the digital camera to record a first digital image of the drilling cuttings sample; (iii) update settings in the digital camera for acquiring ultraviolet light images; (iv) turn off the first white light and turn on the second ultraviolet light; (v) instruct the digital camera to record a second digital image of the drilling cuttings sample; (vi) turn on the first white light and turn off the second ultraviolet light; and (vii) transmit the first digital image and the second digital image of the drilling cuttings to an external computing device.

4. The device according to claim 1, wherein: The housing further defines a maintenance compartment located above the image acquisition compartment; and The electronic controller is deployed in the maintenance room.

5. The apparatus of claim 4, wherein the digital camera is disposed on a divider disposed between the image acquisition chamber and the maintenance chamber, wherein the digital camera includes a lens extending downwardly into the image acquisition chamber, and at least one connector port is disposed in the maintenance chamber.

6. The apparatus of claim 1 further comprising a hatch providing access to the image acquisition chamber, wherein the electronic controller is configured to automatically turn on the light when the hatch is opened.

7. The apparatus of claim 1, wherein the electronic controller includes an image pre-processing module configured to automatically adjust settings on the digital camera.

8. The apparatus according to claim 7, wherein the image preprocessing module is configured to: acquiring a digital image of a calibration target; extracting at least one image segment from a corresponding calibration area in the calibration target; comparing at least one image segment to a corresponding modeled image segment; and When a difference between at least one image segment and a corresponding modeled image segment exceeds a threshold, settings on the digital camera are adjusted.

9. The apparatus of claim 1, wherein the electronic controller further comprises: an image segmentation module configured to generate a segmented image that identifies individual chip particles in the acquired digital image; and An automated lithology module configured to tag identified individual cuttings particles with a lithology type.

10. The apparatus of claim 9, wherein at least one of the image segmentation module and the automated lithology module comprises a trained convolutional neural network.

11. A method for acquiring a digital image of a drill cuttings sample obtained during a subterranean drilling operation, the method comprising: placing the drill cuttings sample on a tray disposed in a chamber in an automated image acquisition device, the automated image acquisition device comprising at least one light configured to illuminate the tray and a digital camera configured to automatically acquire a digital image of the drill cuttings sample; automatically turns on at least one light; automatically causing the digital camera to acquire a digital image of the drilling cuttings sample; and The acquired digital image is automatically saved to a memory or the acquired digital image is transmitted to an external computing device.

12. The method of claim 11, wherein automatically causing the digital camera to acquire the digital image further comprises: updating settings in the digital camera for acquiring white light images; recording a first digital image of the drill cuttings sample using the digital camera; Updated settings for acquiring UV images in digital cameras; Turn off the white light and turn on the UV light; recording a second digital image of the drill cuttings sample using the digital camera; Turn on the white light and switch on the UV light; and The first digital image and the second digital image are transmitted to the external computing device.

13. The method according to claim 11, further comprising: generating a segmented image in which individual chip particles in the acquired digital image are identified; and The identified individual cutting particles in the segmented image are labeled with lithology type labels.

14. The method according to claim 11, further comprising: placing a calibration target on the tray; acquiring a digital image of a calibration target; extracting at least one image segment from a corresponding calibration area in the calibration target; comparing at least one image segment to a corresponding modeled image segment; and When a difference between at least one image segment and a corresponding modeled image segment exceeds a threshold, settings on the digital camera are automatically adjusted.

15. The method of claim 14, wherein the settings on the digital camera include at least one of exposure time and red, green, and blue gains.

16. An apparatus for acquiring a digital image of a drill cuttings sample obtained during a subterranean drilling operation, the apparatus comprising: a housing defining an upper service chamber and a lower image acquisition chamber; a tray disposed in the lower image acquisition chamber and configured to receive the drill cuttings sample; a white light disposed in the lower image acquisition chamber and configured to illuminate the tray; an ultraviolet lamp disposed in the lower image acquisition chamber and configured to illuminate the tray; a digital camera disposed in the lower image acquisition chamber and configured to acquire a digital image of the chip sample; and An electronic controller is configured to automatically turn the light on and off, instruct the digital camera to acquire a digital image of the drill cuttings when the light is on, and save the image to a digital memory or transmit the image to an external computing device.

17. An apparatus according to claim 16, wherein the electronic controller is further configured to: (i) update settings in the digital camera for acquiring white light images; (ii) instruct the digital camera to record a first digital image of the drilling cuttings sample; (iii) update settings in the digital camera for acquiring ultraviolet light images; (iv) turn off the white light and turn on the ultraviolet light; (v) instruct the digital camera to record a second digital image of the drilling cuttings sample; (vi) turn on the white light and turn off the ultraviolet light; and (vii) transmit the first digital image and the second digital image of the drilling cuttings to an external computing device.

18. The apparatus of claim 16, wherein the digital camera is disposed on a divider disposed between the lower image acquisition chamber and the maintenance chamber, wherein the digital camera comprises a lens extending downwardly into the lower image acquisition chamber and at least one connector port disposed in the maintenance chamber.

19. The apparatus of claim 16, wherein the electronic controller comprises an image pre-processing module configured to: acquiring a digital image of a calibration target; extracting at least one image segment from a corresponding calibration area in the calibration target; comparing at least one image segment to a corresponding modeled image segment; and When a difference between at least one image segment and a corresponding modeled image segment exceeds a threshold, settings on the digital camera are adjusted.

20. The apparatus of claim 16, wherein the electronic controller further comprises: an image segmentation module configured to generate a segmented image that identifies individual chip particles in the acquired digital image; and An automated lithology module configured to tag identified individual cuttings particles with a lithology type.

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