Patent Application
20190145183
This disclosure relates generally to the field of drill bit wear, and more specifically to the field of predicting drill bit wear.
The current dull grading process was developed by the International Association of Drilling Contractors (IADC) in 1987 and is meant to provide a “mental picture” of the wear on a used drill bit. An evaluator visually inspects the used drill bit and describes the observed wear using a standardized eight field code and descriptors. However, the IADC eight field code has poor resolution, accuracy, and repeatability. IADC eight field codes are not easily integrated into other drilling data, which makes advanced analytics for predicting drill bit wear difficult. Further, the determinations of wear are subjective and linear, while oftentimes wear volume is non-linear. The result is that the full value of drill bit forensics is lost. Thus, what is needed is an improved method of determining drill bit wear to facilitate prediction of drill bit wear.
In one embodiment, a method for improving drill bit performance is described. A method for improving drill bit performance includes obtaining a wear report for a drill bit, wherein the wear report includes wear characteristics of the drill bit and one or more drill operating parameters under which the drill bit was used; comparing the wear characteristics of the drill bit to a threshold for acceptable drill bit wear; and adjusting one or more drill operating parameters based on the wear characteristics of the drill bit. In some embodiments, obtaining the wear report for the drill bit includes analyzing one or more images of the drill bit to identify one or more wear characteristics of the drill bit; identifying one or more wear patterns based on the one or more wear characteristics of the drill bit; identifying one or more probable drilling conditions based on the one or more wear patterns; and generating the wear report for the drill bit based on the one or more images of the drill bit, the one or more wear characteristics of the drill bit, and the one or more probable drilling conditions.
In other embodiments, the method may be embodied in computer executable program code and stored in a non-transitory storage device. In yet other embodiments, the method may be implemented by a system.
This disclosure is directed to systems, methods, and computer readable media for improving drill bit performance. A system for improving drill bit performance includes one or more processors and a memory coupled to the processors. The memory stores instructions to obtain a wear report for a drill bit, compare wear characteristics of the drill bit from the wear report to a threshold for acceptable drill bit wear, and adjust drill operating parameters based on the wear characteristics of the drill bit. The memory may also store instructions to analyze images of the drill bit to identify wear characteristics of the drill bit, identify wear patterns based on the wear characteristics of the drill bit, identify probable drilling conditions based on the wear patterns, and generate the wear report for the drill bit based on the images of the drill bit, the wear characteristics of the drill bit, and the probable drilling conditions. The images of the drill bit may be 3D scans of the drill bit before and after use and may be analyzed using image processing or computer vision. The adjustments to the drill operating parameters should improve drill bit performance.
In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the disclosed concepts. As part of this description, some of this disclosure's drawings represent structures and devices in block diagram form in order to avoid obscuring the novel aspects of the disclosed embodiments. In this context, it should be understood that references to numbered drawing elements without associated identifiers (e.g., 100) refer to all instances of the drawing element with identifiers (e.g., 100a and 100b). Further, as part of this description, some of this disclosure's drawings may be provided in the form of a flow diagram. The actions in any particular flow diagram may be presented in a particular order. However, it should be understood that the particular flow of any flow diagram is used only to exemplify one embodiment. In other embodiments, any of the various actions depicted in the flow diagram may not be required, some actions may be performed simultaneously, or other actions may be added, according to various embodiments. In other embodiments, any of the various actions may be taken by alternate components. The language used in this disclosure has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the disclosed subject matter. Reference in this disclosure to “one embodiment” or to “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment, and multiple references to “one embodiment” or to “an embodiment” should not be understood as necessarily all referring to the same embodiment or to different embodiments.
It should be appreciated that in the development of any actual implementation (as in any development project), numerous decisions must be made to achieve the developers' specific goals (e.g., compliance with system and business-related constraints), and that these goals will vary from one implementation to another. It will also be appreciated that such development efforts might be complex and time consuming, but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure.
For purposes of this disclosure, the term 3D scanning system refers to any assembly by which distance data may be collected or calculated and reconstructed to extrapolate the shape of an object. 3D scanning system may refer to any kind of 3D scanning system, including a contact 3D scanner or a non-contact 3D scanner such as a time-of-flight 3D laser scanner, a triangulation 3D scanner, a structured light 3D scanner, and the like. Further, in one or more embodiments, the 3D scanning system may be an internal component of an electronic device or a separate external component connected to an electronic device at will by a user.
Referring to
The condition of a used drill bit's inner cutting structure and outer cutting structure is described in fields 115 and 120 with a linear scale of zero to eight. For example, the condition of steel tooth bits is described as a measure of lost tooth height due to abrasion, damage, or both, where zero indicates there has been no loss of tooth height and eight indicates total loss of tooth height. As another example, the condition of insert bits is described as a measure of total cutting structure reduction due to lost, worn, and/or broken inserts, where zero indicates no lost, worn, and/or broken inserts and eight indicates all inserts are lost, worn, and/or broken. As a further example, the condition of fixed cutter bits is described as a measure of lost, worn, and/or broken cutting structure, where zero indicates no lost, worn, and/or broken cutting structure and eight indicates all the cutting structure is lost, worn, and/or broken. The dull characteristics field 125 contains a standardized code describing the type of wear on the drill bit. An example set of standardized codes is shown in Table 1, and a user may select the appropriate descriptive code for the dull characteristics field 125 of the IADC code.
The fourth field 130 describes the location of the wear on the drill bit. For example, if the drill bit has experienced broken cones, the location field 130 indicates which cone or cones have been broken. Table 2 shows example codes used to indicate the location of the wear on the drill bit.
The fifth field 150 describes the bearing wear of roller cone bits and contains an “X” when a fixed cutter bit is used. For example, the condition of non-sealed bearings is described on a linear scale of zero to eight estimating the bearing life used, where zero indicates no bearing life is used and eight indicates all bearing life has been used and no bearing life remains. As another example, the condition of sealed bearings is described using standardized codes, examples of which are shown in Table 3.
The sixth field 160 indicates gauge measurement in fractions of an inch. For example, an I is used where the drill bit is in gauge, 1/16 is used when the drill bit is 1/16th of an inch out of gauge, etc. The seventh field 170 of the IADC code describes additional dull characteristics of the drill bit. This portion of the IADC code provides an opportunity for the user to indicate a secondary dull characteristic to the primary dull characteristic described in the third field 125 of the IADC code and uses the same standardized codes, examples of which are shown in Table 1. The eighth field 180 describes the reasons the drill bit was retired from use or the run terminated using standardized codes, examples of which are shown in Table 4.
As discussed previously, the IADC eight field code is subjective and doesn't provide resolution, accuracy, or repeatability. Without quantitative measurements of wear, two different evaluators may determine two different IADC codes to describe the same drill bit. Evaluators may have less, more, or different training than the next user to determine the IADC eight field code. Further, evaluators are limited to describing only two dull characteristics with the IADC code, regardless of how many are present on the drill bit. Further, the IADC eight field code has poor resolution, in that there may be dozens of cutters within a region with different amounts of wear lumped together under a single IADC code and a single determination of wear. Oftentimes wear volume is non-linear while the IADC dull grading system is linear. In addition, an IADC eight field code is difficult to integrate with other drilling data to provide a holistic view of drill performance and enable advanced analytics for predicting bit wear and optimizing drill bit and drilling parameter selection.
Some processes for determining wear characteristics of drill bits are limited to determining an IADC code and capturing images of the top of the drill bit, the side of the drill bit, each blade of the drill bit, and closeups of any particular wear, drill bit images 310A-310N, which are stored with the IADC code describing the drill bit and drill bit use information, such as the well name, drilling assembly number, start and end times and depth, total footage drilled, and the like.
At step 330, the image processing module determines patterns in the drill bit wear determined in step 320. Any number of wear metrics may be calculated using the calculated wear for each individual cutter and the region in which each cutter resides, such as overall wear of the drill bit, minimum and maximum values of wear, average wear, wear on each blade, wear on each region, and the like. At step 340, the image processing module identifies probable forms of drilling dysfunction and other less than optimal drilling conditions based on the patterns of drill bit wear determined in step 330. For example, if at step 330 the image processing module determines the drill bit has moderate wear in the gauge region, the probable form drilling dysfunction it will identify in step 340 is whirl. At step 350, the image processing module generates a wear report 360 for the drill bit. In some embodiments, wear report 360 includes a header summarizing key metadata about the drill bit, ranked distributions of wear in each region, and drill bit images 310A-310N with the wear of each individual cutter in each drill bit image. In some embodiments, the header includes the drill bit make, model, and serial number, the depth drilled, the start and stop dates and times, the configuration of drilling parameters used, expected lithology, and the like. Wear report 360 may be any format for ease of integration and analysis with other drilling data.
In some embodiments, determining drill bit wear at step 410 includes steps 412, 414, and 416. At step 412, the machine learning modules included in the computer vision module identify wear characteristics in each drill bit image. At step 414, the computer vision module eliminates false positives from the wear characteristics identified in step 412. In one embodiment, the machine learning modules output a confidence score between zero and one associated with each identified wear characteristic and the computer vision module filters the identified wear characteristics based on the associated confidence score and a threshold confidence score. Different threshold confidence scores may be used for each wear characteristic, based on the number of training images for the wear characteristic, the uniqueness of the wear characteristic, and the like. For example, nozzles are a unique feature and a large number of training images are available so the nozzle characteristic is associated with a threshold confidence score of 0.95. In contrast, broken cutters and worn cutters look similar and the machine learning modules struggle to distinguish the two characteristics, so the broken cutters and worn cutters characteristics are associated with a threshold confidence score of 0.7 and 0.8, respectively. In some embodiments, the computer vision module presents the identified wear characteristics to a user to eliminate false positives. At step 416, the computer vision module eliminates overlapping features. Where the machine learning modules identify wear characteristics independently, the same portion of a drill bit image may be identified as multiple characteristics. In some embodiments, the computer vision module eliminates overlapping features by ranking the identified characteristics by confidence score, calculating the overlap ratio between the identified characteristics, and eliminating the identified characteristic associated with a lower confidence score when the overlap ratio exceeds a threshold value.
At step 420, the computer vision module determines patterns in the drill bit wear determined in step 410. As described previously with reference to
The processes for analyzing drill bit images 310A-310N using image processing and computer vision described herein with reference to
At step 630, the 3D image analysis module determines patterns in the drill bit wear determined in step 620. As described previously with reference to
At step 730, the drilling parameter adjustment module determines whether the drill bit wear described in the wear report meets or exceeds a threshold of acceptable drill bit wear. The threshold of acceptable drill bit wear may be selected based on the wellbore characteristics, drilling tool characteristics, business considerations (e.g., cost to implement adjustments to drilling parameters), and the like. If the drill bit wear described in the wear report exceeds the threshold of acceptable drill bit wear, the drilling parameter adjustment module adjusts the configuration of drilling parameters specified in the well plan based on the wear report at step 740. For example, if the wear report indicates whirl is a probable drilling dysfunction present during use of the drill bit, the drilling parameter adjustment module determines adjustments to drilling parameters to reduce the likelihood of whirl. Then, the drilling parameter adjustment module returns to step 730 and determines if the adjustments to the configuration of drilling parameters reduce expected wear on a drill bit to within the threshold of acceptable drill bit wear. For example, the drilling parameter adjustment module uses a model of drill bit wear to predict expected drill bit wear under the adjusted configuration of drilling parameters. If the drill bit wear described in the wear report or expected drill bit wear based on the adjusted configuration of drilling parameters is within the threshold of acceptable drill bit wear, the drilling parameter adjustment module implements the configuration of drilling parameters at step 750. Where the drill bit wear described in the wear report is within the threshold of acceptable drill bit wear, the drilling parameter adjustment module implements the configuration of drilling parameters specified in the well plan without adjustments. Where the drill bit wear described in the wear report exceeds the threshold of acceptable drill bit wear, the drilling parameter adjustment module implements the adjusted configuration of drilling parameters that results in expected drill bit wear within the threshold of acceptable drill bit wear. Whether the drilling parameter adjustment module adjusts the configuration of drilling parameters or implements them as-is, it stores the wear report and associated drill bit specifications, configuration of drilling parameters during use of the drill bit, and lithology information in a database at step 760. When the database reaches a threshold number of entries, a model of drill bit wear may be generated. The threshold number of entries in the database may depend on the method of generating the model of drill bit wear.
When the model of drill bit wear reaches the threshold level of accuracy, the drilling parameter adjustment module continues to step 820, where it predicts expected drill bit wear using the model of drill bit wear generated in step 810. In some embodiments, the drilling parameter adjustment module inputs the specifications of the drill bit to be used, the configuration of drill bit parameters to be used, and the expected lithology to the machine learning modules. The machine learning modules use the inputs and the model of drill bit wear to predict expected drill bit wear. At step 830, the drilling parameter adjustment module determines whether the expected drill bit wear meets or exceeds a threshold of acceptable drill bit wear. The threshold of acceptable drill bit wear may be selected based on wellbore characteristics, drill bit characteristics, business considerations, and the like, as described herein with references to
The process for drilling parameter adjustment based on drill bit wear described herein as example process 700 in reference to
Referring now to
Processor 905 may execute instructions necessary to carry out or control the operation of many functions performed by device 900 (e.g., such as the prediction of drill bit wear as disclosed herein). Processor 905 may, for instance, drive display 910 and receive user input from user interface 915. User interface 915 may allow a user to interact with device 900. For example, user interface 915 can take a variety of forms, such as a button, keypad, dial, a click wheel, keyboard, display screen and/or a touch screen. Processor 905 may also, for example, be a system-on-chip such as those found in mobile devices and include a dedicated graphics processing unit (GPU). Processor 905 may be based on reduced instruction-set computer (RISC) or complex instruction-set computer (CISC) architectures or any other suitable architecture and may include one or more processing cores. Graphics hardware 920 may be special purpose computational hardware for processing graphics and/or assisting processor 905 to process graphics information. In one embodiment, graphics hardware 920 may include a programmable GPU.
Image capture circuitry 950 may include lens 980. Lens assembly may have an associated sensor element 990. Image capture circuitry 950 may capture three-dimensional, still and/or video images. Output from image capture circuitry 950 may be processed, at least in part, by video codec(s) 955 and/or processor 905 and/or graphics hardware 920, and/or a dedicated image processing unit or pipeline incorporated within circuitry 950. Images so captured may be stored in memory 960 and/or storage 965. Microphone 930 may capture audio recordings that may be processed, at least in part, by audio codec(s) 935 and/or processor 905. Audio recordings so captured may be stored in memory 960 and/or storage 965.
Memory 960 may include one or more different types of media used by processor 905 and graphics hardware 920 to perform device functions. For example, memory 960 may include memory cache, read-only memory (ROM), and/or random access memory (RAM). Storage 965 may store media (e.g., audio, image and video files), preference information, device profile information, and any other suitable data. Storage 965 may store computer program instructions or software such as the image processing module described herein with reference to
The scope of the disclosed subject matter should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.”
This application claims priority to U.S. Provisional Application No. 62/585,168 filed Nov. 13, 2017 and hereby incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 62585168 | Nov 2017 | US |
