CN115542337A - Drilling return rock debris monitoring method and device and storage medium - Google Patents
Drilling return rock debris monitoring method and device and storage medium Download PDFInfo
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
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- H—ELECTRICITY
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- H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
- H04N13/20—Image signal generators
- H04N13/275—Image signal generators from 3D object models, e.g. computer-generated stereoscopic image signals
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Abstract
The application discloses a drilling well returned rock debris monitoring method, a drilling well returned rock debris monitoring device and a storage medium, which are used for monitoring the rock debris condition of an outlet of a vibrating screen in real time according to the volume of the rock debris, realizing quantification of the rock debris returned from a well, and further achieving the effect of judging the cleanliness of a drilling shaft or the underground abnormal condition. The method comprises the following steps: the method comprises the steps that a terminal obtains a scanning result of a laser radar on an outlet of a drilling vibrating screen, and obtains video stream data obtained by shooting rock debris at the outlet of the drilling vibrating screen through a camera; the terminal generates 3D point cloud data according to the scanning result; the terminal calculates through the 3D point cloud data to obtain a first volume of the rock debris; the terminal calculates according to the video stream data to obtain a second volume of the rock debris; and the terminal monitors the rock debris condition of the vibrating screen port according to the first volume and the second volume.
Description
Technical Field
The application relates to the technical field of petroleum drilling, in particular to a method and a device for monitoring rock debris returned by drilling and a storage medium.
Background
During drilling, after the underground rock is broken by the drill bit, the drilling fluid reaches the surface, and the rock fragments are called rock fragments and are also commonly called "sand samples". During drilling, drilling fluid circularly carries rock debris to return to the ground, and the rock debris in the drilling fluid needs to be separated by using a drilling vibrating screen.
In the drilling process, a worker can perform irregular observation on the outlet of the vibrating screen, know the state of rock debris returned from the well, including total amount estimation, size and shape estimation and the like, and judge whether an abnormal condition exists underground or whether the cleaning efficiency in the shaft exists or not by combining experience.
In the process that a worker observes the outlet of the vibrating screen, if abnormal conditions are not found in time, serious engineering accidents such as drilling sticking and even blowout are easily caused.
Disclosure of Invention
In order to solve the technical problem, the application provides a method, a device and a storage medium for monitoring rock debris returned from a drilling well,
the application provides a method for monitoring rock debris returned from a drilling well in a first aspect, which comprises the following steps:
the method comprises the steps that a terminal obtains a scanning result of a laser radar on an outlet of a vibrating screen, and video stream data obtained by shooting rock debris at the outlet of the vibrating screen through a camera is obtained;
the terminal generates 3D point cloud data according to the scanning result;
the terminal calculates through the 3D point cloud data to obtain a first volume of the rock debris;
the terminal calculates according to the video stream data to obtain a second volume of the rock debris;
and the terminal monitors the rock debris condition of the vibrating screen port according to the first volume and the second volume.
Optionally, the monitoring, by the terminal, the rock debris condition of the vibrating screen port according to the first volume and the second volume includes:
the terminal generates target rock debris monitoring data according to the first volume and the second volume;
the terminal inputs the video stream data into a machine learning model trained in advance, so that a prediction result is calculated, and the machine learning model is a converged model obtained through training of 3D point cloud data, the video stream data and data in drilling;
and the terminal monitors the rock debris condition of the vibrating screen opening according to the target rock debris monitoring data and the prediction result.
Optionally, the generating, by the terminal, target rock debris monitoring data according to the first volume and the second volume includes:
the terminal carries out difference average calculation on the first volume and the second volume;
and the terminal combines the data obtained by the average calculation of the difference values with the data in the current drilling well to obtain target rock debris monitoring data, and the data in the current drilling well is acquired in real time by the drilling working equipment and is sent to the terminal.
Optionally, the monitoring, by the terminal, the rock debris condition of the vibrating screen port according to the target rock debris monitoring data and the prediction result includes:
the terminal comprehensively judges whether the rock debris at the vibrating screen port is abnormal or not according to the target rock debris monitoring data and the prediction result;
if yes, the terminal sends an abnormal warning and continues monitoring;
and if not, the terminal continues monitoring.
Optionally, the terminal calculates through the 3D point cloud data to obtain a first volume of rock debris, including:
the terminal performs plane segmentation on the 3D point cloud data;
the terminal uses an outlier algorithm on the data after plane segmentation to remove redundancy of the data after plane segmentation;
the terminal piles the adjacent rock fragments of the target rock fragments into a same rock fragment pile by using a clustering algorithm on the data from which the redundancy is removed;
and the terminal respectively calculates each rock fragment pile to obtain a first volume.
Optionally, the calculating, by the terminal, through the video stream data to obtain the second volume of the rock debris includes:
the terminal processes the video stream data by using an image binarization algorithm to obtain processed video stream data;
the terminal establishes a target dynamic model for the processed video stream data;
the terminal processes the target dynamic model by using a method of segmentation and clustering, difference compensation and outlier removal to obtain a processed dynamic model;
the terminal combines the processed dynamic model with an integral algorithm to calculate a second volume;
and the terminal combines the first volume and the second volume, establishes a loss function system and further corrects the calculation result of the volume.
This application second aspect provides a rock debris monitoring devices that well drilling was returned, the device includes:
the terminal acquires a scanning result of the laser radar on the outlet of the vibrating screen and acquires video stream data obtained by shooting rock debris at the outlet of the vibrating screen by the camera;
the terminal generates 3D point cloud data according to the scanning result;
the terminal calculates through the 3D point cloud data to obtain a first volume of the rock debris;
the terminal calculates according to the video stream data to obtain a second volume of the rock debris;
and the terminal monitors the rock debris condition of the vibrating screen port according to the first volume and the second volume.
Optionally, the monitoring unit includes:
a generation subunit, wherein the terminal generates target rock debris monitoring data according to the first volume and the second volume;
the terminal inputs the video stream data into a machine learning model trained in advance so as to calculate a prediction result, and the machine learning model is a converged model obtained by training 3D point cloud data, video stream data and data in a well;
and the terminal monitors the rock debris condition of the vibrating screen port according to the target rock debris monitoring data and the prediction result.
The third aspect of the present application provides a drill cuttings monitoring apparatus, the apparatus comprising:
the system comprises a laser radar, a camera device, an explosion-proof shell, a processor, a memory, an input/output unit and a bus;
the laser radar, the camera shooting equipment and the processor are connected with the memory, the input/output unit and the bus to form intelligent central control;
the explosion-proof shell is arranged outside the laser radar, the camera equipment and the intelligent central control unit;
the memory holds a program that the processor calls to perform the method of any of the first aspect and the first aspect.
A fourth aspect of the present application provides a computer readable storage medium having a program stored thereon, the program when executed on a computer performs the method of drill return cuttings monitoring of any one of the first aspect and the first aspect as optional.
According to the technical scheme, the method has the following advantages:
the method has the advantages that 3D point cloud data of the rock debris with depth information can be generated by using the laser radar, the condition of the outlet of the vibrating screen can be recorded more accurately by using the camera equipment, meanwhile, the data are processed by using the terminal in an auxiliary mode, a judgment warning system is built, and the volume and the abnormal condition of the rock debris can be monitored in real time. If the abnormal condition appears in the detritus, the terminal can in time give alarm information, promotes the security in the drilling construction.
Drawings
In order to more clearly illustrate the technical solutions in the present application, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for monitoring cuttings returned from a well according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of a method for monitoring cuttings returned from a well according to another embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a drilling return debris monitoring device according to an embodiment of the present application;
FIG. 4 is a schematic diagram illustrating a structure of a drilling return rock debris monitoring device according to another embodiment of the present application;
fig. 5 is a schematic diagram illustrating an overall structure of a drilling return rock debris monitoring apparatus according to an embodiment of the present application.
Detailed Description
After the subterranean rock is broken by the drill bit, the drilling fluid reaches the surface, and the rock fragments are called cuttings, also commonly referred to as "sand samples". During the drilling process, geologists continuously collect and observe rock debris and recover the underground geological profile according to a certain sampling interval and late arrival time, which is called rock debris logging. The rock debris logging has the advantages of low cost, simplicity, convenience, practicability, timely understanding of underground conditions, strong data systematicness and the like, so the rock debris logging is widely applied to the exploration and development process of oil and gas fields.
In the prior art, rock debris sampling of rock debris logging is performed by a worker to a vibrating screen, a certain amount of falling rock debris is connected from the lower part of a guide plate of the vibrating screen by using a screen disc, and then, adhered slurry is washed off, a white porcelain disc is arranged, and the rock debris is brought back to a logging workshop for a logging engineer, a geological engineer and the like to perform rock sample qualitative analysis. When the footage on the well is fast, the worker needs to continuously run at the logging house and the vibrating screen;
the rock debris condition at the vibrating screen is monitored before and after the rock debris is taken back from the vibrating screen by workers, so that potential safety hazards are reduced, and engineering accidents are avoided.
Based on the method, the application provides a drilling return rock debris monitoring method which is used for monitoring rock debris abnormity at the outlet of the vibrating screen in real time and giving alarm information.
It should be noted that the method for monitoring the rock debris returned from the drilling well, provided by the application, can be applied to a terminal, a system and a server. For convenience of explanation, the terminal is taken as an execution subject for illustration in the present application.
It is also noted that the terms first, second and the like in the description and the drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order or importance.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of a method for monitoring drilling return rock debris provided by the present application, where the method for monitoring drilling return rock debris includes:
101. the method comprises the steps that a terminal obtains a scanning result of a laser radar on an outlet of a vibrating screen, and video stream data obtained by shooting rock debris at the outlet of the vibrating screen through a camera is obtained;
102. the terminal generates 3D point cloud data according to the scanning result;
in this embodiment, a lidar is used to scan the debris conditions at the exit of the shaker. After the terminal issues the monitoring command, the laser radar starts to execute a scanning modeling task at a fixed rate of 5 times per second. And after the scanning result is obtained, transmitting the scanning result to the terminal, and continuously generating 3D point cloud data by the terminal according to the scanning result.
The camera shooting equipment is used for shooting the whole condition of the outlet of the vibrating screen. And after the terminal issues a shooting instruction, shooting by the camera equipment. The frame rate of the acquired video stream data can reach 30 frames per second generally, and in the embodiment, data of 10 frames per second is taken for calculation.
The laser radar can scan to obtain original three-dimensional data, so that the data has depth information, the actual rock debris condition is digitized more specifically and accurately, and subsequent processing on a terminal is facilitated. The camera shooting equipment can be used for assisting laser radar, video stream data and 3D cloud point data are combined, and data close to the actual situation can be obtained more accurately.
103. The terminal calculates through the 3D point cloud data to obtain a first volume of the rock debris;
in this embodiment, the terminal needs to process the 3D point cloud data. For example, firstly, performing plane segmentation on the 3D point cloud data, removing part of information taking a vibrating screen as a plane, and highlighting a rock debris main body;
after the 3D point cloud data is subjected to plane segmentation, outlier algorithm is used for the data after the plane segmentation, outlier noise points which are inconsistent around or inside are removed, the calculation speed is increased, and redundancy of the data after the plane segmentation is removed; then, using a clustering algorithm to pile up rock debris close to the rock debris into the same cluster, and simplifying the calculation;
and finally, respectively calculating the volume of each cluster of rock debris piles, and constructing a convex hull according to the target point cloud to obtain rock debris volume data, namely the first volume.
104. The terminal calculates according to the video stream data to obtain a second volume of the rock debris;
in this embodiment, in order to assist the laser radar in performing numerical adjustment on the monitoring data, it is necessary to perform binarization processing on the rock debris images in the video stream data, and dynamically adjust the relevant threshold value, so that each video frame presents a corresponding result, and the result after the processing of the entire video stream data is the target dynamic model.
After the target dynamic model is obtained, target objects containing rock debris are presented in a video frame and distributed in an irregular state, and then calculation is performed by using a method including segmentation and clustering (tightly connected rock debris image piles are aggregated to form a plurality of pile-shaped distributions), difference compensation (enabling the image aggregated piles to be presented in a convex shape, and smoothing and cutting off partially concave compensation or partially large-fluctuation edges), outliers removal and integration ideas.
And setting the first volume as X and the second volume as Y, and constructing a volume calculation function V = aX + bY + beta, wherein a, b and beta are parameters continuously corrected according to the monitored real value, so that the overall result tends to be real.
The target dynamic model is generated to facilitate subsequent volume calculations. The model of the target object can be obtained after processing, namely the calculation content is obtained. After data processing is performed by using a plurality of methods, the rock fragment volume in the target object of the video stream data, namely the second volume, can be calculated more clearly.
After the 3D point cloud data and the video stream data are further processed, the 3D point cloud data and the video stream data are intuitively closer to real values by utilizing the calculation and correction of the volume.
105. And the terminal monitors the rock debris condition of the vibrating screen port according to the first volume and the second volume.
The rock debris 3D point cloud data with the depth information can be generated by using the laser radar, the outlet condition of the vibrating screen can be more accurately recorded by using the camera shooting equipment, meanwhile, the data is processed by using the terminal in an auxiliary manner, a judgment warning system is built, and the rock debris condition can be monitored in real time. If the abnormal condition of the rock debris is found, the terminal can give alarm information in time, and the safety in the drilling construction is improved.
In practice, the step of the terminal monitoring the debris condition of the shaker mouth in terms of the first volume and the second volume requires further processing, and this application provides another embodiment to elaborate this:
referring to fig. 2, the embodiment includes:
201. the method comprises the steps that a terminal obtains a scanning result of a laser radar on an outlet of a vibrating screen, and obtains video stream data obtained by shooting rock debris at the outlet of the vibrating screen through a camera;
202. the terminal generates 3D point cloud data according to the scanning result;
203. the terminal calculates through the 3D point cloud data to obtain a first volume of the rock debris;
204. the terminal calculates according to the video stream data to obtain a second volume of the rock debris;
205. The terminal generates target rock debris monitoring data according to the first volume and the second volume;
in this embodiment, the terminal first performs average calculation of the difference between the first volume and the second volume, and then combines the data obtained after the average calculation of the difference with the data in the current drilling well to obtain the target rock debris monitoring data. And acquiring data in the current well drilling in real time by the well drilling working equipment and transmitting the data to the terminal.
Because the laser radar scanning and the shooting of the camera equipment are both in fixed speed, the terminal obtains multiple groups of data in fixed time intervals. Therefore, a fixed interval time may be set to 1 to 2 seconds, and data obtained during this fixed interval time may be subjected to difference averaging.
Electronic data obtained by radar scanning and video shooting are different from real data, and after difference averaging processing is carried out, the electronic data can be corrected, so that the electronic data is close to the real data continuously, and errors are reduced.
206. The terminal inputs the video stream data into a machine learning model trained in advance, so that a prediction result is calculated, and the machine learning model is a converged model obtained through training of 3D point cloud data, video stream data and data in a well;
in the embodiment of the present application, the machine learning model is a model prepared by simulation before the start of the monitoring work, and the machine learning model can directly execute the work in the normal monitoring work. The machine learning model is an image processing model, image data is input into the model, namely video stream data is required to be sent into the machine learning model for calculation, and a prediction result is obtained.
For example, before monitoring, the data of the outlet of the vibrating screen needs to be scanned and photographed, then step 205 is executed to obtain a large amount of rock debris data of the current outlet of the vibrating screen, and the building training of the machine learning model is performed by using the large amount of rock debris data of the current outlet of the vibrating screen.
In the training process, the terminal uses a convolutional neural network (ResNet) as a backbone structure, combines optimization strategies such as cosine annealing and Warmup, uses a label to increase the generalization performance smoothly, introduces a plurality of data enhancement methods of CutMix and Mixup, and continuously adjusts parameters to obtain an optimal model.
The construction of the machine learning model can calculate the data of the rock debris at the outlet of the vibrating screen under the normal condition, so that the follow-up comparison with the monitoring data is facilitated, and whether the monitoring data is different or not is judged.
207. And the terminal monitors the rock debris condition of the vibrating screen port according to the target rock debris monitoring data and the prediction result.
In the embodiment, the terminal comprehensively judges whether the rock debris at the vibrating screen port is abnormal or not according to the target rock debris monitoring data and the prediction result; if so, the terminal sends an abnormal warning and continues monitoring; and if not, the terminal continues monitoring.
The laser radar can be used for generating the rock debris 3D point cloud data with depth information, the camera equipment can be used for more accurately recording the outlet condition of the vibrating screen, meanwhile, the terminal is used for assisting in processing data, a judgment warning system is built, and the rock debris condition can be monitored in real time. If the abnormal condition of the rock debris is found, the terminal can give alarm information in time, and the safety in the drilling construction is improved.
The above embodiment introduces the drilling return rock debris monitoring method provided by the present application, and the following describes embodiments of the drilling return rock debris monitoring device and the storage medium provided by the present application:
referring to fig. 3, the embodiment includes:
the acquiring unit 301 is used for acquiring a scanning result of the laser radar on the outlet of the vibrating screen and acquiring video stream data obtained by shooting rock debris at the outlet of the vibrating screen by the camera;
a generating unit 302, wherein the terminal generates 3D point cloud data according to the scanning result;
the first calculation unit 303 is used for calculating by the terminal through the 3D point cloud data to obtain a first volume of the rock debris;
the second calculation unit 304, the terminal calculates through the video stream data to obtain a second volume of the rock debris;
and the monitoring unit 305 is used for monitoring the rock debris condition of the vibrating screen port according to the first volume and the second volume.
Optionally, the monitoring unit 305 includes:
a generating subunit 3051, wherein the terminal generates target rock debris monitoring data according to the first volume and the second volume;
the prediction sub-unit 3052, the terminal inputs the video stream data into a machine learning model trained in advance, so as to calculate a prediction result, and the machine learning model is a converged model obtained through training of 3D point cloud data, video stream data and data in a well;
and the terminal monitors the rock debris condition of the vibrating screen port according to the target rock debris monitoring data and the prediction result.
Optionally, the generating subunit 3051 includes:
a difference average module 30511, configured to perform a difference average calculation on the first volume and the second volume by the terminal;
and the terminal combines the data obtained by the average calculation of the difference value with the data in the current drilling well to obtain target rock debris monitoring data, and the data in the current drilling well is acquired by the drilling working equipment in real time and is sent to the terminal by the combination module 30512.
Optionally, the monitoring subunit 3053 includes:
the judging module 30531, the terminal comprehensively judges whether the rock debris at the vibrating screen port is abnormal according to the target rock debris monitoring data and the prediction result;
the warning module 30532, the terminal sends out an abnormal warning and continues to monitor;
block 30533 is performed, where the terminal continues to monitor.
Optionally, the first calculating unit 303 includes:
a plane segmentation subunit 3031, wherein the terminal performs plane segmentation on the 3D point cloud data;
an outlier sub-unit 3032, the terminal uses an outlier algorithm on the plane-divided data to remove redundancy from the plane-divided data;
the clustering algorithm subunit 3033, the terminal uses a clustering algorithm on the data from which the redundancy is removed to stack adjacent rock fragments of the target rock fragments into a same rock fragment stack;
and a first calculating subunit 3034, which calculates a first volume for each rock fragment pile separately by the terminal.
Optionally, the second calculating unit 304 includes:
an image processing subunit 3041, where the terminal processes the video stream data by using an image binarization algorithm to obtain processed video stream data;
a dynamic model subunit 3042, where the terminal establishes a target dynamic model for the processed video stream data;
a model processing subunit 3043, where the terminal processes the target dynamic model by using a method of segmentation and clustering, difference compensation and outlier removal, to obtain a processed dynamic model;
a second calculating subunit 3044, where the terminal combines the processed dynamic model with an integration algorithm to calculate a second volume;
and a correction subunit 3045, where the terminal combines the first volume and the second volume, establishes a loss function system, and further corrects the calculation result of the volume.
Referring to fig. 4, the present application further provides a drill cuttings monitoring apparatus, comprising:
a laser radar 401, an image pickup apparatus 402, an explosion-proof enclosure 403, a processor 404, a memory 405, an input-output unit 406, and a bus 407;
the laser radar 401, the camera device 402 and the processor 404 are connected with the memory 405, the input/output unit 406 and the bus 407 to form an intelligent central control;
the explosion-proof housing 403 is arranged outside the laser radar 401, the camera device 402 and the intelligent central control;
the memory 405 holds a program that the processor 404 invokes to perform any of the well return cuttings monitoring methods described above.
Referring to fig. 5, the present application provides an overall structure of a rock debris device provided by an embodiment, including:
the system comprises a laser radar 401, a camera 402, an explosion-proof shell 403, a vibrating screen 501 and an explosion-proof sleeve 502;
the laser radar 401 and the intelligent central control are arranged inside the explosion-proof shell 403;
the laser radar 401 and the camera equipment 402 are placed above the vibrating screen 501 and used for scanning and shooting rock debris at the outlet of the vibrating screen 501;
wires such as bus 407 are disposed inside explosion proof sleeve 502.
The present application also relates to a computer-readable storage medium having a program stored thereon, the program being characterized in that, when run on a computer, it causes the computer to perform any of the methods described above.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, which are essential or part of the technical solutions contributing to the prior art, or all or part of the technical solutions, may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
Claims (10)
1. A method of monitoring cuttings returns from a wellbore, the method comprising:
the method comprises the steps that a terminal obtains a scanning result of a laser radar on an outlet of a vibrating screen, and obtains video stream data obtained by shooting rock debris at the outlet of the vibrating screen through a camera;
the terminal generates 3D point cloud data according to the scanning result;
the terminal calculates through the 3D point cloud data to obtain a first volume of the rock debris;
the terminal calculates according to the video stream data to obtain a second volume of the rock debris;
and the terminal monitors the rock debris condition of the vibrating screen port according to the first volume and the second volume.
2. The method of claim 1, wherein the terminal monitoring cuttings returned from the wellbore based on the first volume and the second volume comprises:
the terminal generates target rock debris monitoring data according to the first volume and the second volume;
the terminal inputs the video stream data into a machine learning model trained in advance, so that a prediction result is calculated, and the machine learning model is a converged model obtained through training of 3D point cloud data, the video stream data and data in drilling;
and the terminal monitors the rock debris condition of the vibrating screen port according to the target rock debris monitoring data and the prediction result.
3. The method of claim 2, wherein the terminal generating target cuttings monitoring data from the first volume and the second volume comprises:
the terminal carries out difference average calculation on the first volume and the second volume;
and the terminal combines the data obtained by the average calculation of the difference values with the data in the current drilling well to obtain target rock debris monitoring data, and the data in the current drilling well is acquired in real time by the drilling working equipment and is sent to the terminal.
4. The method for monitoring drill return cuttings as claimed in claim 2, wherein the monitoring of the cuttings at the shaker screen port by the terminal according to the target cuttings monitoring data and the prediction result comprises:
the terminal comprehensively judges whether the rock debris at the vibrating screen opening is abnormal or not according to the target rock debris monitoring data and the prediction result;
if so, the terminal sends an abnormal warning and continues monitoring;
and if not, the terminal continues monitoring.
5. The method of claim 1, wherein the terminal calculates from the 3D point cloud data to obtain a first volume of cuttings comprising:
the terminal performs plane segmentation on the 3D point cloud data;
the terminal uses an outlier algorithm on the data after the plane segmentation to remove redundancy of the data after the plane segmentation;
the terminal piles the adjacent rock fragments of the target rock fragments into a same rock fragment pile by using a clustering algorithm on the data from which the redundancy is removed;
and the terminal respectively calculates each rock fragment pile to obtain a first volume.
6. The method of claim 1, wherein the terminal calculates from the video stream data a second volume of cuttings comprising:
the terminal processes the video stream data by using an image binarization algorithm to obtain processed video stream data;
the terminal establishes a target dynamic model for the processed video stream data;
the terminal processes the target dynamic model by using a method of segmentation and clustering, difference compensation and outlier removal to obtain a processed dynamic model;
the terminal combines the processed dynamic model with an integral algorithm to calculate a second volume;
and the terminal combines the first volume and the second volume, establishes a loss function system and further corrects the calculation result of the volume.
7. A drilling return debris monitoring device, comprising:
the terminal acquires a scanning result of the laser radar on the outlet of the vibrating screen and acquires video stream data obtained by shooting rock debris at the outlet of the vibrating screen by the camera;
the terminal generates 3D point cloud data according to the scanning result;
the terminal calculates through the 3D point cloud data to obtain a first volume of rock debris;
the terminal calculates according to the video stream data to obtain a second volume of the rock debris;
and the terminal monitors the rock debris condition of the vibrating screen port according to the first volume and the second volume.
8. The apparatus for monitoring cuttings returning from a drilling well of claim 7, wherein the monitoring unit comprises:
a generation subunit, wherein the terminal generates target rock debris monitoring data according to the first volume and the second volume;
the terminal inputs the video stream data into a machine learning model trained in advance so as to calculate a prediction result, and the machine learning model is a converged model obtained by training 3D point cloud data, video stream data and data in a well;
and the terminal monitors the rock debris condition of the vibrating screen port according to the target rock debris monitoring data and the prediction result.
9. A drill cuttings monitoring apparatus for return of a borehole, the apparatus comprising:
the system comprises a laser radar, a camera device, an explosion-proof shell, a processor, a memory, an input/output unit and a bus;
the laser radar, the camera shooting equipment and the processor are connected with the memory, the input and output unit and the bus to form an intelligent central control;
the explosion-proof shell is arranged outside the laser radar, the camera equipment and the intelligent central control;
the memory holds a program that the processor invokes to perform the well-return cuttings monitoring method of any of claims 1-6.
10. A computer-readable storage medium having a program stored thereon, which when executed on a computer performs the well-return cuttings monitoring method of any of claims 1 to 6.
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