Intelligent measuring device and method for axial and cross-sectional distribution of wellbore cuttings bed

Abstract

The present invention provides an intelligent measuring device and method for the axial and cross-sectional distribution of a wellbore cuttings bed, which relates to the technical field of oil and natural gas extraction, and includes: a wellbore module, the wellbore module includes a mobile base, a simulated wellbore, and a pressure difference sensor; a drill rod module, the drill rod module includes a simulated drill rod, a torque sensor, and a rotating motor; a sand supply module, the sand supply module includes a sand output tank and a sand output pipeline; a liquid supply module, the liquid supply module includes a liquid supply pipeline; a suspension module, the suspension module includes a liftable suspension; and a tracking laser photography module, the tracking laser photography module includes a mobile vehicle, a laser instrument, a motion camera, and a curtain light, and the mobile vehicle can move along the simulated wellbore. The present invention can comprehensively simulate different drilling conditions through the wellbore module, the drill rod module, the sand supply module, the liquid supply module, and the suspension module, so as to study the migration law of cuttings under different conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploitation, and in particular to an intelligent measuring device and method for the axial and cross-sectional distribution of a wellbore cuttings bed. Background Art

[0002] With the continuous exploitation of oil resources in various places, the exploitation and reserves of conventional oil and gas reservoirs can no longer meet the world's development needs for oil and gas resources. At the same time, the exploitation technology of oil and gas resources is constantly improving, and the exploitation of unconventional oil and gas reservoirs is increasingly valued. In order to meet the exploitation needs of unconventional oil and gas reservoirs, long horizontal well exploitation technology continues to develop and progress. In order to improve the exploitation accuracy, drill pipe rotary steering and drilling targeting positioning technology have gradually become important technical means in the exploitation process of unconventional oil and gas reservoirs. However, the cuttings migration process in the development of long horizontal wells is significantly different from that of vertical wells, and unconventional oil and gas reservoirs are mostly sensitive formations with complex reservoir pore structures, low permeability, and drastic and sensitive changes in formation pressure.

[0003] During the drilling process, the drill bit and the centralizer will "carry" the cuttings accumulated on the cuttings bed. When the concentration of the "carried" cuttings reaches a certain value, the resistance to drilling will increase, making it difficult to drill and causing a stuck drill. When a stuck drill occurs, a cuttings accumulation area will form around the drill string, which reduces the space available for circulation in the wellbore, and may also cause the risk of pump blocking. As the cuttings accumulate at the bottom of the well, a stable cuttings bed will be formed, resulting in the inability to fully clean the wellbore during the well washing process. Therefore, in order to study the law of cuttings migration in large-displacement wells, it is necessary to determine the migration position of the cuttings bed at the bottom of the well through the law of changes in the axial pressure difference value. However, the cross-sectional structure distribution of the cuttings bed is asymmetric under rotary drilling conditions. The law obtained from the experiment can guide the distribution law of the cross-sectional structure of the downhole cuttings bed under actual drilling conditions. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the technical problem to be solved by the embodiments of the present invention is to provide an intelligent measurement device and method for the axial and cross-sectional distribution of a wellbore cuttings bed, which is used to study the influence of different factors on the cuttings migration effect in the annulus of rotary drilling of extended displacement wells.

[0005] The above-mentioned object of the present invention can be achieved by adopting the following technical solutions. The present invention provides an intelligent measuring device for the axial and cross-sectional distribution of a wellbore cuttings bed, comprising:

[0006] A wellbore module, the wellbore module comprising a mobile base, a simulated wellbore disposed on the mobile base, and at least one differential pressure sensor disposed on the simulated wellbore;

[0007] A drill rod module, the drill rod module comprising a simulated drill rod, a torque sensor disposed on the simulated drill rod, and a rotary motor, the simulated drill rod being inserted at one end of the simulated wellbore, and the rotary motor being drivably connected to one end of the simulated drill rod;

[0008] A sand supply module, the sand supply module comprising a sand production tank and a sand production pipeline, the sand production tank is connected to the other end of the simulated wellbore through the sand production pipeline;

[0009] A liquid supply module, the liquid supply module comprising a liquid supply pipeline, the liquid supply pipeline is respectively connected to two ends of the simulated wellbore;

[0010] A suspension module, the suspension module comprising a liftable suspension, one end of the wellbore module being connected to the suspension; and

[0011] A tracking laser photography module includes a mobile vehicle, and a laser instrument, a motion camera and a curtain light arranged on the mobile vehicle, and the mobile vehicle can move along the simulated wellbore.

[0012] In a preferred embodiment of the present invention, the intelligent measurement device for the axial and cross-sectional distribution of the wellbore cuttings bed also includes an intelligent control module, and the intelligent control module includes a main control computer, and the main control computer is electrically connected to the wellbore module, the drill rod module, the sand supply module, the liquid supply module, the suspension module and the tracking laser photography module.

[0013] In a preferred embodiment of the present invention, a sand filter tank, a liquid storage tank and a slurry pump are sequentially provided on the liquid supply pipeline along the path from one end of the simulated wellbore to the other end of the simulated wellbore.

[0014] In a preferred embodiment of the present invention, a cuttings pump is provided on the sand production pipeline.

[0015] In a preferred embodiment of the present invention, the mobile base includes a track and a mobile platform arranged on the track, and the simulated wellbore is arranged on the mobile platform.

[0016] In a preferred embodiment of the present invention, the simulated wellbore includes a plurality of spliced ​​acrylic glass tubes, and both ends of each of the glass tubes are provided with the differential pressure sensor for sensing the position of the cuttings bed to analyze the axial migration speed of the cuttings bed.

[0017] In a preferred embodiment of the present invention, the simulated wellbore is provided with longitudinal and transverse scale strips.

[0018] In a preferred embodiment of the present invention, a universal joint is provided on the mobile vehicle, and the laser instrument is adjustably arranged on the mobile vehicle via the universal joint.

[0019] In a preferred embodiment of the present invention, the suspension module further includes a winch, the suspension is connected to the winch, and the winch can drive the suspension to perform lifting motion.

[0020] In a preferred embodiment of the present invention, at least one eccentric ring centralizer is provided between the simulated drill pipe and the simulated wellbore, and the simulated drill pipe is eccentrically inserted into the simulated wellbore through the eccentric ring centralizer.

[0021] In a preferred embodiment of the present invention, the simulated drill rod comprises a plurality of spliced ​​drill rod connecting sections, and each of the drill rod connecting sections is provided with an eccentric ring centralizer.

[0022] The present invention also provides an experimental method using an intelligent measuring device for axial and cross-sectional distribution of a wellbore cuttings bed, comprising the following steps:

[0023] Install and initialize the intelligent measurement device for the axial and cross-sectional distribution of the wellbore cuttings bed;

[0024] Using the intelligent measuring device for the axial and cross-sectional distribution of the wellbore cuttings bed, the migration of the cuttings bed is simulated and a wellbore two-phase migration experiment under different drilling conditions is conducted;

[0025] Acquire and export experimental data, wherein the experimental data at least includes experimental conditions, the wetted perimeter of the cuttings bed, the inclination angle of the cuttings bed, data of a torque sensor, and data of a differential pressure sensor;

[0026] Based on the experimental data, intelligent model training and data calculation are carried out to study the migration law of cuttings beds.

[0027] In a preferred embodiment of the present invention, the training of the intelligent model and data calculation based on the experimental data specifically include the following steps:

[0028] Based on the experimental data, a data network is built;

[0029] Training the data network to obtain an intelligent model of rock cuttings transport;

[0030] When the training prediction result of the cuttings transport intelligent model is consistent with the law shown by the known experimental results, the cuttings transport intelligent model is saved;

[0031] inputting experimental conditions into the cuttings transport intelligent model;

[0032] When the training prediction results of the cuttings transport intelligent model are inconsistent with the known experimental results, the erroneous data is analyzed and the cuttings transport intelligent model is adjusted, and then the training prediction results are re-obtained and compared with the known experimental results until the training prediction results are consistent with the known experimental results.

[0033] The technical solution of the present invention has the following significant beneficial effects:

[0034] When the intelligent measuring device for the axial and cross-sectional distribution of the wellbore cuttings bed of the present invention is used, the fluid and sand sample can be separated by the liquid supply module, and the experimental fluid and sand sample can be recycled, which plays a role in saving experimental raw materials and costs. In addition, the migration process of cuttings in the simulated wellbore can be accurately recorded by tracking the laser photography module. During the use of the tracking laser photography module, the inclination measurement accuracy of the cuttings bed can be improved by adjusting the angle of the laser instrument, and the wet perimeter measurement of the cuttings bed can be more accurate by cooperating with the scale strip set on the simulated wellbore. The sand tank can be matched with the rotating motor to simulate the mechanical drilling speed, thereby meeting the test requirements of different drilling conditions. The liquid storage tank can be used to prepare the solution required for the experiment and the properties of the test solution. The suspension module can control the well inclination angle of the simulated wellbore, and can simulate a variety of well inclination angle conditions. The present invention can comprehensively simulate different drilling conditions through the wellbore module, the drill pipe module, the sand supply module, the liquid supply module and the suspension module, so as to study the migration law of cuttings under different conditions, that is, the distribution law of the cuttings bed cross section and axial direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] The accompanying drawings described herein are only for explanation purposes and are not intended to limit the scope of the present invention in any way. In addition, the shapes and proportional dimensions of the various components in the figures are only schematic, used to help understand the present invention, and are not specifically limited to the shapes and proportional dimensions of the various components of the present invention. Those skilled in the art can select various possible shapes and proportional dimensions to implement the present invention according to the teachings of the present invention.

[0037] Figure 1 A schematic diagram of a top view of the structure of the intelligent measuring device for the axial and cross-sectional distribution of a wellbore cuttings bed according to the present invention;

[0038] Figure 2 A side view structural schematic diagram of the intelligent measuring device for the axial and cross-sectional distribution of a wellbore cuttings bed according to the present invention;

[0039] Figure 3 A schematic diagram of an installation structure of the laser instrument of the present invention;

[0040] Figure 4 A schematic diagram of the process of training an intelligent model and calculating data based on experimental data according to the present invention;

[0041] Figure 5 It is a schematic diagram of the data acquisition process of the intelligent measurement device for the axial and cross-sectional distribution of the wellbore cuttings bed of the present invention;

[0042] Figure 6 A neural network intelligent training model of the present invention;

[0043] Figure 7 It is the intelligent model of rock cuttings transport that has been trained in the present invention.

[0044] Reference numerals in the above drawings:

[0045] 1. Simulated wellbore; 2. Track; 3. Moving platform; 4. Simulated drill pipe; 5. Eccentric ring stabilizer; 6. Torque sensor; 7. Sand output tank; 8. Rotating motor; 9. Slurry pump; 10. Cuttings pump; 11. Liquid storage tank; 12. Sand filter tank; 13. Suspension; 14. Laser instrument; 15. Motion camera; 16. Curtain light; 17. Main control computer; 18. Universal joint; 19. Scale strip. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Implementation Method 1

[0048] Please refer to Figure 1 and Figure 2As shown, in an embodiment of the present invention, there is provided an intelligent measuring device for the axial and cross-sectional distribution of a wellbore cuttings bed, comprising: a wellbore module, the wellbore module comprising a mobile base, a simulated wellbore 1 arranged on the mobile base, and at least one pressure difference sensor (not shown) arranged on the simulated wellbore 1; a drill rod module, the drill rod module comprising a simulated drill rod 4, a torque sensor 6 arranged on the simulated drill rod 4, and a rotary motor 8, the simulated drill rod 4 is inserted at one end of the simulated wellbore 1, and the rotary motor 8 is drivably connected to one end of the simulated drill rod 4; a sand supply mold The sand supply module comprises a sand output tank 7 and a sand output pipeline, and the sand output tank 7 is connected with the other end of the simulated wellbore 1 through the sand output pipeline; the liquid supply module comprises a liquid supply pipeline, and the liquid supply pipeline is respectively connected with the two ends of the simulated wellbore 1; the suspension module comprises a liftable suspension 13, and one end of the wellbore module is connected with the suspension 13; and the tracking laser photography module comprises a moving vehicle, and a laser instrument 14, a motion camera 15 and a curtain light 16 arranged on the moving vehicle, and the moving vehicle can move along the simulated wellbore 1.

[0049] On the whole, the intelligent measurement device for the axial and cross-sectional distribution of the wellbore cuttings bed can comprehensively simulate different drilling conditions through the wellbore module, drill pipe module, sand supply module, liquid supply module and suspension module, so as to study the migration law of cuttings under different conditions.

[0050] Among them, the movement process of cuttings in the simulated wellbore 1 can be accurately recorded by tracking the laser photography module. During use, the tracking laser photography module can measure the inclination of the cuttings bed by adjusting the angle of the laser instrument 14, with better measurement accuracy. The simulated wellbore 1 can be supplemented with light by the curtain light 16, and the image data of the cuttings bed in the simulated wellbore 1 can be captured in conjunction with the motion camera 15. The sand tank 7 cooperates with the rotating motor 8 to simulate the mechanical drilling speed, thereby meeting the test requirements of different drilling conditions. The fluid and sand samples can be separated by the liquid supply module, and the experimental fluid and sand samples can be recycled, which saves experimental raw materials and costs. The well inclination angle of the simulated wellbore 1 can be controlled by the suspension module, and a variety of well inclination conditions can be simulated.

[0051] In the embodiment of the present invention, the intelligent measuring device for the axial and cross-sectional distribution of the wellbore cuttings bed further comprises an intelligent control module, which comprises a main control computer 17, and the main control computer 17 is electrically connected to the wellbore module, the drill pipe module, the sand supply module, the liquid supply module, the suspension module and the tracking laser photography module. The intelligent control module can realize integrated control integrating experimental device control, experimental data acquisition and experimental data export.

[0052] Specifically, before use, each module is installed and tested, the simulated wellbore 1 is placed horizontally on the mobile base, and the gas in the simulated wellbore 1 is discharged. The intelligent control module is turned on to control the lifting process of the suspension 13 and adjust the well inclination angle between the simulated wellbore 1 and the horizontal plane; the flow rate of the liquid supply pipeline is controlled by the displacement control panel in the intelligent control module to obtain the required fluid displacement in the simulated wellbore 1; the speed of the rotating motor 8 and the sand supply of the sand tank 7 are controlled by the intelligent control module to simulate the mechanical drilling speed. In addition, the intelligent control module is also electrically connected to the differential pressure sensor, the torque sensor 6 and the tracking laser photography module to obtain various working conditions and use parameters during use, so as to analyze and study the migration law of the cuttings bed.

[0053] Furthermore, a software program is preset in the main control computer, and each module is controlled by the software program. For example, the drill rod module is controlled by the software program to adjust the drill rod rotation speed and collect the torque information of the drill rod. The sand supply module is controlled by the software program to adjust the supply speed of the cuttings, simulate drilling, well washing and other working conditions, and different mechanical drilling speeds. The fluid supply module is controlled by the software program to adjust the flow rate of the fluid supply, and the flow rate change information of each time period is collected in real time. The suspension module is controlled by the software program to adjust the well inclination. The tracking laser photography module is controlled by the software program to adjust the running speed of the mobile vehicle, track the movement position of the cuttings bed and collect images, and the collected image information is transmitted back to the main control computer. In other embodiments, the designer can also adjust the use method of the intelligent control module according to the simulation needs, and no specific restrictions are made here.

[0054] In an embodiment of the present invention, a sand filter tank 12, a liquid storage tank 11 and a slurry pump 9 are sequentially provided on the liquid supply pipeline from one end of the simulated wellbore 1 to the other end of the simulated wellbore 1.

[0055] The sand filter tank 12 can filter the fluid flowing back into the fluid supply pipeline from the simulated wellbore 1, thereby separating the fluid and the sand in the fluid. The filtered fluid can be stored in the liquid storage tank 11 for circulation. The slurry pump 9 can circulate the fluid in the liquid storage tank 11 into the simulated wellbore 1 to simulate the drilling condition. The designer can electrically connect the slurry pump 9 to the intelligent control module, so that the pumping volume of the slurry pump 9 can be better controlled by the intelligent control module.

[0056] In the embodiment of the present invention, a cuttings pump 10 is provided on the sand production pipeline. The cuttings pump 10 can pump the sand and gravel in the sand production tank 7 into the simulated wellbore 1, thereby simulating the cuttings working condition in the simulated wellbore 1. Designers can electrically connect the cuttings pump 10 to the intelligent control module, so that the sand feeding speed of the cuttings pump 10 can be better controlled by the intelligent control module to simulate the sand feeding speed under the same mechanical drilling speed.

[0057] In an embodiment of the present invention, the mobile base includes a track 2 and a mobile platform 3 arranged on the track 2 , and the simulated wellbore 1 is arranged on the mobile platform 3 .

[0058] By setting the mobile platform 3 on the track 2, when the simulated wellbore 1 is placed on the mobile platform 3, the mobile platform 3 can carry the simulated wellbore 1 to move along the track 2, so that different simulated wellbores 1 can be replaced according to experimental needs. And the simulated wellbore 1 can be transported to the suspension module by the mobile platform 3, and the inclination angle of the simulated wellbore 1 can be adjusted by connecting the suspension module to one end of the simulated wellbore 1 and lifting and lowering by the suspension 13, so that wellbores in different inclined states can be simulated.

[0059] In an embodiment of the present invention, the simulated wellbore 1 includes a plurality of spliced ​​acrylic glass tubes, and pressure difference sensors are provided at both ends of each glass tube.

[0060] The simulated wellbore 1 is formed by multiple glass tubes, and the working conditions inside the simulated wellbore 1 can be directly observed by using the glass tubes, which is convenient for observing and recording data. Specifically, the simulated wellbore 1 is formed by connecting six acrylic glass tubes. Each differential pressure sensor is electrically connected to the intelligent control module. By arranging differential pressure sensors at both ends of each acrylic glass tube, the differential pressure data in the simulated wellbore 1 can be quickly obtained, so as to analyze and study the migration law of the cuttings bed based on the differential pressure data.

[0061] Specifically, by arranging a plurality of differential pressure sensors at intervals on the simulated wellbore 1 , the plurality of differential pressure sensors can be used to collect information on changes in pressure consumption in the simulated wellbore 1 in real time.

[0062] Designers can adjust the acquisition frequency of the differential pressure sensor according to data acquisition needs. For example, the acquisition frequency can be adjusted to one second or several seconds, and no specific limitation is made here.

[0063] The collected pressure difference change information can be sent back to the main control computer in real time in the form of Excel, so as to analyze the migration speed of the cuttings bed directly through the pressure difference change signal stored in the main control computer. Based on the migration speed results of the cuttings bed, it can be used to infer the location distribution of the invisible cuttings bed in the actual drilling process.

[0064] In the embodiment of the present invention, longitudinal and transverse scale strips 19 are provided on the simulated wellbore 1. The scale strips 19 facilitate the experimenter to directly observe and measure the data of the cuttings bed in the simulated wellbore 1 from the outside of the simulated wellbore 1. The designer can determine the setting position and scale accuracy of the scale strips 19 according to the use requirements, and no specific restrictions are made here.

[0065] In an embodiment of the present invention, Figure 3In the embodiment shown, a universal joint 18 is provided on the mobile vehicle, and the laser instrument 14 is adjustably arranged on the mobile vehicle through the universal joint 18. The setting height and rotation angle of the laser instrument 14 can be adjusted through the universal joint 18, so that the inclination of the cuttings bed can be better measured in cooperation with the scale strip 19 arranged on the simulated wellbore 1. The designer can determine the number of joints and the joint positions of the universal joint 18 according to the use requirements, and no specific restrictions are made here.

[0066] In an embodiment of the present invention, the suspension module further includes a winch (not shown), the suspension 13 is connected to the winch, and the winch can drive the suspension 13 to perform lifting movements.

[0067] The suspension 13 can be pulled up and down by a winch. When one end of the simulated wellbore 1 is connected to the suspension 13, the winch can synchronously pull one end of the simulated wellbore 1 up and down, thereby adjusting the inclination angle of the simulated wellbore 1 to simulate wellbores in different inclined states.

[0068] In other embodiments, designers may also use a lifting rod instead of a winch, which is not specifically limited herein.

[0069] In an embodiment of the present invention, at least one eccentric ring centralizer 5 is provided between the simulated drill pipe 4 and the simulated wellbore 1 , and the simulated drill pipe 4 is eccentrically inserted into the simulated wellbore 1 through the eccentric ring centralizer 5 .

[0070] Specifically, the eccentric ring centralizer 5 includes a support frame and an propylene copper gasket arranged on the support frame. The simulated drill pipe 4 can be eccentrically inserted into the simulated wellbore 1 through the eccentric ring centralizer 5, thereby simulating the use condition of the drill pipe 4 in the actual wellbore environment.

[0071] In an embodiment of the present invention, the simulated drill rod 4 includes a plurality of drill rod connection sections that are spliced ​​together, and an eccentric ring stabilizer 5 is provided on each drill rod connection section. The simulated drill rod 4 is formed by splicing a plurality of drill rod connection sections, and the use length of the simulated drill rod 4 can be flexibly adjusted according to the use requirements. Moreover, each drill rod connection section is arranged in the simulated wellbore 1 through the eccentric ring stabilizer 5, thereby ensuring the eccentricity of the simulated drill rod 4. The designer can determine the eccentricity of the simulated drill rod 4 arranged in the simulated wellbore 1 according to the experimental requirements, and no specific limitation is made here.

[0072] Implementation Method 2

[0073] The embodiment of the present invention also provides a method for using the intelligent measurement device for the axial and cross-sectional distribution of a wellbore cuttings bed, comprising the following steps:

[0074] Step 101: Install and initialize an intelligent measuring device for the axial and cross-sectional distribution of a wellbore cuttings bed;

[0075] Step 102: using an intelligent measuring device for measuring the axial and cross-sectional distribution of a wellbore cuttings bed to simulate the migration of the cuttings bed and to conduct a wellbore two-phase migration experiment under different drilling conditions;

[0076] Step 103: Acquire and export experimental data, wherein the experimental data at least includes experimental conditions, wetted perimeter of the cuttings bed, inclination angle of the cuttings bed, data of the torque sensor 6 and data of the differential pressure sensor;

[0077] Step 104: Perform intelligent model training and data calculation based on experimental data to study the migration law of cuttings bed.

[0078] Specifically, the cuttings bed migration law is the law of the axial and cross-sectional distribution of the cuttings bed with different fluid displacement, drill pipe speed, mechanical drilling speed, fluid properties, and cuttings properties. By adjusting the experimental conditions, the axial and cross-sectional distribution laws of the cuttings bed under different experimental conditions can be obtained, so as to better study the influencing factors of different experimental conditions on the cuttings bed migration law.

[0079] Of course, designers can also adjust the research content of the cuttings bed migration law according to research needs, and no specific restrictions are made here.

[0080] In an embodiment of the present invention, the intelligent measurement device for the axial and cross-sectional distribution of the wellbore cuttings bed is used to simulate the migration of the cuttings bed and to simulate the wellbore two-phase migration experiment under different drilling conditions, including the following specific steps:

[0081] Place the simulated wellbore 1 horizontally on the mobile base, connect the modules, ensure that all valves on the modules are open, and connect the pipelines. Turn on the intelligent control module, and electrically connect the devices on the pipelines to the intelligent control module for data collection. Use the liquid supply pipeline to circulate the fluid medium in the simulated wellbore 1, discharge the gas in the simulated wellbore 1, and ensure that only liquid exists in the simulated wellbore 1.

[0082] According to the experimental conditions, the intelligent control module is used to control the lifting and lowering of the suspension module and adjust the inclination angle of the simulated wellbore 1. The speed of the slag propeller pump is controlled by the displacement control panel in the intelligent control module to obtain the required fluid displacement, and then the fluid displacement is no longer changed. After the displacement in the simulated wellbore 1 is stabilized, the drill pipe speed of the rotating motor 8 is controlled by the speed control panel in the intelligent control module, and the sand feeding speed of the cuttings pump 10 is adjusted by the sand adding control panel in the intelligent control module to simulate the mechanical drilling speed. The motion camera, the pressure difference sensor and the torque sensor 6 are turned on, and the cuttings movement state during the cuttings migration process in the simulated wellbore 1, the pressure difference in the simulated wellbore 1, and the torque change of the simulated drill pipe 4 are recorded through the recording system in the intelligent control module.

[0083] Specifically, the experimental conditions include at least different fluid displacement, mechanical drilling speed, drill pipe speed, fluid properties, cuttings properties, well inclination, etc. The data of the differential pressure sensor includes the pressure consumption data of the simulated wellbore 1. For example, Figure 5 The data collection process diagram shown in the figure includes the mechanical drilling speed controlled by the sand tank 7, the displacement controlled by the slurry pump 9, the rotation speed controlled by the rotary motor 8, the well inclination controlled by the suspension 13, and the properties of the injected cuttings and fluid, etc. The designer can determine the collection objects of the experimental conditions according to the experimental needs, and no specific restrictions are made here.

[0084] In an embodiment of the present invention, obtaining experimental data includes the following steps: waiting for the cuttings bed in the simulated wellbore 1 to reach a steady state, recording the time of reaching the steady state, and recording the wet perimeter length of the cuttings bed on both sides of the simulated wellbore 1 through the scale strip 19 on the simulated wellbore 1, such as Figure 3 In the embodiment shown, the wet perimeter lengths of the cuttings bed collected by two laser instruments are C1 and C2 respectively. The laser instruments 14 located on both sides of the simulated wellbore 1 are adjusted so that the laser of the laser instrument 14 is aligned with the top of the cuttings bed, and the inclination angles on both sides of the cuttings bed are recorded, wherein the elevation angle is + and the inclination angle is -. After the wet perimeter and inclination angle measurement of the cuttings bed are completed, the slag propeller pump is turned on to circulate the cuttings to the sand filter tank 12. The above-mentioned operation is repeated each time the simulated working condition of the intelligent measuring device for the axial and cross-sectional distribution of the wellbore cuttings bed is changed.

[0085] In an embodiment of the present invention, exporting experimental data includes the following steps: exporting experimental data mainly includes two parts: one part is a statistical table of the working conditions used in the experiment, including the experimental well inclination angle, drill pipe speed, mechanical drilling speed and fluid rheological properties, wherein the fluid rheological properties mainly refer to fluid viscosity and density. The other part is the experimental results collected artificially, which include the wetted perimeter of the cuttings bed and the inclination of the cuttings bed, as well as the torque sensor 6 installed on the simulated drill pipe 4 and the differential pressure sensor installed on the simulated wellbore 1. Among them, the torque and differential pressure matrix table is exported by computer in excel format.

[0086] In an embodiment of the present invention, Figure 4 The flowchart shown in the figure shows that the training and data calculation of the intelligent model based on experimental data specifically include the following steps:

[0087] Step 1041: Build a data network based on experimental data.

[0088] Step 1042: Train the data network to obtain an intelligent model of rock cuttings transport.

[0089] Step 1043: When the training prediction results of the cuttings transport intelligent model are consistent with the known experimental results, the cuttings transport intelligent model is saved.

[0090] Step 1044: Input experimental conditions into the cuttings transport intelligent model.

[0091] Step 1045: When the training prediction results of the cuttings transport intelligent model are inconsistent with the known experimental results, analyze the erroneous data and adjust the cuttings transport intelligent model, then re-obtain the training prediction results and compare them with the known experimental results until the training prediction results are consistent with the known experimental results.

[0092] Specifically, the data network includes a neural network intelligent training model. For example, Figure 6 The neural network intelligent training model shown, the cuttings transport intelligent model is obtained after training through the neural network intelligent training model, the neural network intelligent training model uses a BP-LSTM neural network with time series to learn the acquired experimental data, and trains the cuttings transport intelligent model to obtain the trained cuttings transport intelligent model.

[0093] The trained cuttings transport intelligent model is as follows: Figure 7 As shown, Figure 7 The intelligent algorithm deep learning module described in Figure 6 In the subsequent experiments, the experimental data collected by the main control computer 17 in real time are input into the trained rock cuttings transport intelligent model, so that the data results output by the rock cuttings transport intelligent model can be quickly obtained.

[0094] When the values ​​predicted by the trained cuttings transport intelligent model, i.e., the wetted perimeter of the cuttings bed, the wellbore inclination, the drill pipe torque, and the wellbore pressure difference, have a small error with the output data used for the training model, the network structure, weights, and bias values ​​of the model are saved. Given the known experimental results and the above output data, the data can be used to reflect the cross-sectional distribution of the cuttings bed and the axial movement of the cuttings bed, i.e., the wetted perimeter and the inclination reflect the cross-sectional distribution of the cuttings bed, and the pressure difference data reflects the axial position and migration speed of the cuttings bed.

[0095] In an embodiment of the present invention, a statistical table of experimental conditions, a table of manually collected data, and a torque and pressure difference matrix table are obtained, wherein the statistical table of experimental conditions and the table of manually collected data are multi-order matrices, and the torque data of the simulated drill pipe 4 and the pressure difference data of the simulated wellbore 1 are multi-order matrices.

[0096] According to the relevant experimental data table and the input parameters of the cuttings transport law analysis model, an input vector is constructed, wherein the input parameters include: the well inclination angle in the experimental condition statistical table, the rotation speed of the simulated drill pipe 4, the mechanical drilling speed and the fluid rheology (including fluid viscosity and fluid density), the wetted perimeter and inclination of the cuttings bed corresponding to each working condition in the artificially collected data table, and the torque matrix and the pressure difference matrix transmitted to the computer by the torque and pressure difference sensors. The obtained experimental data are used as input and output for model training to construct a cuttings transport intelligent model. The constructed cuttings transport intelligent model can be used for prediction of newly conducted experiments. The newly conducted experiments can use the input data transmitted back in real time by the cuttings transport intelligent model to predict the output data.

[0097] The following prediction process is performed: the input vector is input into the cuttings transport process parameter analysis model to obtain the drill pipe torque, pressure difference data and the wetted circumference and inclination of the cross-sectional cuttings bed; the prediction data is input into the cuttings transport process law analysis model to calculate the cross-sectional area, axial torque and pressure loss parameters of the cuttings bed at each position through the inclination and wetted circumference of the cuttings bed. Among them, the pressure loss parameter reflects the distribution of the axial cuttings bed by reflecting the changes of the pressure difference sensors at each position of the simulated wellbore 1.

[0098] Determine whether the difference between the cross-sectional area, torque and pressure loss of the predicted cuttings bed and the target data is within the preset range; if not, update the input vector and continue the prediction process; if so, use the obtained prediction data to study the migration law of the cuttings bed.

[0099] All articles and references disclosed, including patent applications and publications, are incorporated herein by reference for various purposes. The term "consisting essentially of ... " describing a combination should include determined elements, ingredients, parts or steps and other elements, ingredients, parts or steps that do not substantially affect the basic novel features of the combination. The use of the terms "comprising" or "including" to describe the combination of elements, ingredients, parts or steps here also contemplates the implementation method consisting essentially of these elements, ingredients, parts or steps. Here, by using the term "may", it is intended to illustrate that any attribute described that "may" includes is optional. Multiple elements, ingredients, parts or steps can be provided by a single integrated element, ingredient, part or step. Alternatively, a single integrated element, ingredient, part or step can be divided into separate multiple elements, ingredients, parts or steps. The disclosure "one" or "one" used to describe an element, ingredient, part or step is not said to exclude other elements, ingredients, parts or steps.

[0100] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the embodiments can be referred to each other. The above embodiments are only for illustrating the technical concept and features of the present invention. The purpose is to enable people familiar with this technology to understand the content of the present invention and implement it accordingly, and it cannot be used to limit the scope of protection of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be covered within the scope of protection of the present invention.

Claims

1. An intelligent measuring device for the axial and cross-sectional distribution of a wellbore cuttings bed, characterized in that: include: A wellbore module, the wellbore module comprising a mobile base, a simulated wellbore arranged on the mobile base, and at least one differential pressure sensor arranged on the simulated wellbore; the simulated wellbore comprises a plurality of spliced ​​acrylic glass tubes, each of the glass tubes having the differential pressure sensor at both ends, for sensing the position of the cuttings bed to analyze the axial migration speed of the cuttings bed; the simulated wellbore is provided with longitudinal and transverse scale strips; A drill rod module, the drill rod module comprising a simulated drill rod, a torque sensor disposed on the simulated drill rod, and a rotary motor, the simulated drill rod being inserted at one end of the simulated wellbore, and the rotary motor being drivably connected to one end of the simulated drill rod; A sand supply module, the sand supply module comprising a sand production tank and a sand production pipeline, the sand production tank is connected to the other end of the simulated wellbore through the sand production pipeline; A liquid supply module, the liquid supply module comprising a liquid supply pipeline, the liquid supply pipeline is respectively connected to two ends of the simulated wellbore; A suspension module, wherein the suspension module comprises a liftable suspension, and one end of the wellbore module is connected to the suspension; as well as A tracking laser photography module includes a mobile vehicle, and a laser instrument, a motion camera and a curtain light arranged on the mobile vehicle. The mobile vehicle can move along the simulated wellbore. A universal joint is provided on the mobile vehicle, and the laser instrument is adjustably arranged on the mobile vehicle through the universal joint.

2. The intelligent measuring device for the axial and cross-sectional distribution of a wellbore cuttings bed according to claim 1, characterized in that: The intelligent measuring device for the axial and cross-sectional distribution of the wellbore cuttings bed also includes an intelligent control module, which includes a main control computer. The main control computer is electrically connected to the wellbore module, the drill rod module, the sand supply module, the liquid supply module, the suspension module and the tracking laser photography module.

3. The intelligent measuring device for the axial and cross-sectional distribution of a wellbore cuttings bed according to claim 1, characterized in that: Along from one end of the simulated wellbore to the other end of the simulated wellbore, a sand filter tank, a liquid storage tank and a slurry pump are sequentially arranged on the liquid supply pipeline; and a cuttings pump is arranged on the sand outlet pipeline.

4. The intelligent measuring device for axial and cross-sectional distribution of wellbore cuttings bed according to claim 1, characterized in that: The mobile base includes a track and a mobile platform arranged on the track, and the simulated wellbore is arranged on the mobile platform.

5. The intelligent measuring device for axial and cross-sectional distribution of wellbore cuttings bed according to claim 1, characterized in that: The suspension module also includes a winch, the suspension is connected to the winch, and the winch can drive the suspension to perform lifting movements.

6. The intelligent measuring device for axial and cross-sectional distribution of wellbore cuttings bed according to claim 1, characterized in that: At least one eccentric ring centralizer is provided between the simulated drill pipe and the simulated wellbore, and the simulated drill pipe is eccentrically inserted into the simulated wellbore through the eccentric ring centralizer; the simulated drill pipe includes a plurality of spliced ​​drill pipe connecting sections, and each of the drill pipe connecting sections is provided with an eccentric ring centralizer.

7. A method for using the intelligent measuring device for axial and cross-sectional distribution of a wellbore cuttings bed according to any one of claims 1 to 6, characterized in that: The steps include: Install and initialize the intelligent measurement device for the axial and cross-sectional distribution of the wellbore cuttings bed; Using the intelligent measuring device for the axial and cross-sectional distribution of the wellbore cuttings bed, the migration of the cuttings bed is simulated and a wellbore two-phase migration experiment under different drilling conditions is conducted; Acquire and export experimental data, wherein the experimental data at least includes experimental conditions, the wetted perimeter of the cuttings bed, the inclination angle of the cuttings bed, data of a torque sensor, and data of a differential pressure sensor; Based on the experimental data, intelligent model training and data calculation are carried out to study the migration law of cuttings beds.

8. The method according to claim 7, characterized in that The training of the intelligent model and data calculation based on the experimental data specifically include the following steps: Based on the experimental data, a data network is built; Training the data network to obtain an intelligent model of rock cuttings transport; inputting experimental conditions into the cuttings transport intelligent model; When the training prediction result of the cuttings transport intelligent model is consistent with the law shown by the known experimental results, the cuttings transport intelligent model is saved; When the training prediction results of the cuttings transport intelligent model are inconsistent with the known experimental results, the erroneous data is analyzed and the cuttings transport intelligent model is adjusted, and then the training prediction results are re-obtained and compared with the known experimental results until the training prediction results are consistent with the known experimental results.

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