Rock debris particle size prediction method and device

Abstract

The invention discloses a rock debris particle size prediction method and device, and relates to the technical field of oil and gas well drilling, and the method comprises the steps: obtaining drilling engineering information, and the success rate that rock debris is crushed again after being crushed every time; the drilling engineering information comprises drill rod rotating speed, drill rod joint radius, drilling fluid flow velocity and horizontal section length; according to the rotating speed of the drill rod, the radius of the drill rod joint and the flow velocity of drilling fluid, the sweep rate of the drill rod joint to rock debris is determined; according to the sweep rate of the drill rod joint on the rock debris, the length of the horizontal section and the success rate that the rock debris is crushed again after being crushed every time, a rock debris particle size distribution prediction model is adopted to determine a whole well section rock debris particle size distribution result; wherein the rock debris particle size distribution prediction model is a predetermined model for determining the rock debris particle size distribution according to the sweep rate of the drill rod joint on the rock debris and the success rate that the rock debris is crushed again after being crushed every time. The method can predict the particle size distribution of the rock debris in advance, and provides guidance for the design of a sand cleaning scheme.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas well drilling, and in particular to a method and a device for predicting rock cuttings particle size. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] Horizontal well drilling can greatly increase the contact area with the formation, significantly increase the oil leakage area, and greatly improve the production efficiency of oil and gas fields. It has now become the main force in the development of various oil fields. Although horizontal well drilling technology has great advantages in oil field development. However, the problem of cuttings migration is particularly prominent and may cause serious drilling accidents. In the horizontal well section, the direction of drilling fluid movement is perpendicular to the direction of gravity, and the difficulty of cuttings migration is higher than that of the vertical well section. In addition, cuttings will be deposited at the lower side of the wellbore to form a cuttings bed, which further increases the difficulty of wellbore cleaning.

[0004] The efficiency of cuttings transportation in horizontal wells is related to many factors, such as cuttings particle size, drilling fluid flow rate, drilling fluid viscosity, drilling speed, etc. Many scholars have conducted extensive research on the above factors, and determined the influence of each factor through numerical simulation and indoor experiments. The increase of drilling fluid flow rate and drilling fluid viscosity will improve the cleaning efficiency of cuttings, and too fast drilling speed will increase the difficulty of cuttings cleaning. Among many factors, the most critical is the influence of cuttings particle size, and on-site workers will also design sand cleaning plans based on this. In the current research, most of the experiments and simulations are carried out on several fixed particle sizes to determine the difficulty of starting and the transportation speed of cuttings. However, there are few reports on the relevant research progress of cuttings particle size distribution in horizontal sections. This makes the existing research lack the value of direct on-site application.

[0005] After the formation is broken by the drill bit, it forms cuttings with a roughly fixed particle size, and the particle size value can be determined experimentally. However, during its migration, the cuttings are repeatedly crushed into finer particles by the drill bit because the drill bit is not centered in the horizontal well section, which causes a huge change in particle size. It is particularly important to predict the change in particle size of cuttings in long horizontal sections of horizontal wells.

[0006] At present, there is an urgent need for a complete set of cuttings particle size prediction methods to guide the design of sand cleaning plans and ensure on-site drilling production. Summary of the invention

[0007] The embodiment of the present invention provides a method for predicting the particle size of rock cuttings, which is used to predict the distribution of the particle size of rock cuttings in advance and provide guidance for the design of a sand cleaning scheme. The method includes:

[0008] Obtain drilling engineering information and the success rate of re-crushing of cuttings after each crushing; wherein the drilling engineering information includes: drill pipe speed, drill pipe joint radius, drilling fluid flow rate, and horizontal section length;

[0009] Determine the penetration rate of the drill pipe joint to the cuttings according to the drill pipe speed, drill pipe joint radius and drilling fluid flow rate;

[0010] According to the sweep rate of the drill pipe joint to the cuttings, the length of the horizontal section, and the success rate of the cuttings being crushed again after each crushing, a cuttings particle size distribution prediction model is used to determine the cuttings particle size distribution results for the entire well section; wherein the cuttings particle size distribution prediction model is a predetermined model for determining the cuttings particle size distribution according to the sweep rate of the drill pipe joint to the cuttings, and the success rate of the cuttings being crushed again after each crushing.

[0011] The embodiment of the present invention further provides a rock cuttings particle size prediction device, which is used to predict the rock cuttings particle size distribution in advance and provide guidance for the design of sand cleaning schemes. The device includes:

[0012] The information acquisition module is used to obtain drilling engineering information and the success rate of the cuttings being crushed again after each crushing; wherein the drilling engineering information includes: drill pipe speed, drill pipe joint radius, drilling fluid flow rate, and horizontal section length;

[0013] The first processing module is used to determine the penetration rate of the drill pipe joint to the cuttings according to the drill pipe rotation speed, the drill pipe joint radius and the drilling fluid flow rate;

[0014] The second processing module is used to determine the rock cuttings particle size distribution result of the entire well section according to the sweep rate of the drill pipe joint to the rock cuttings, the length of the horizontal section, and the success rate of the rock cuttings being crushed again after each crushing, using a rock cuttings particle size distribution prediction model; wherein the rock cuttings particle size distribution prediction model is a predetermined model for determining the rock cuttings particle size distribution according to the sweep rate of the drill pipe joint to the rock cuttings, and the success rate of the rock cuttings being crushed again after each crushing.

[0015] An embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned rock cuttings particle size prediction method when executing the computer program.

[0016] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned rock cuttings particle size prediction method is implemented.

[0017] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned rock cuttings particle size prediction method is implemented.

[0018] In an embodiment of the present invention, drilling engineering information and the success rate of the cuttings being crushed again after each crushing are obtained; wherein the drilling engineering information includes: drill pipe speed, drill pipe joint radius, drilling fluid flow rate, and horizontal section length; according to the drill pipe speed, drill pipe joint radius, and drilling fluid flow rate, the sweep rate of the drill pipe joint to the cuttings is determined; according to the sweep rate of the drill pipe joint to the cuttings, the horizontal section length, and the success rate of the cuttings being crushed again after each crushing, the cuttings particle size distribution prediction model is used to determine the cuttings particle size distribution results of the entire well section; wherein the cuttings particle size distribution prediction model is a predetermined model for determining the cuttings particle size distribution according to the sweep rate of the drill pipe joint to the cuttings and the success rate of the cuttings being crushed again after each crushing. In this way, the cuttings particle size distribution can be predicted in advance, providing guidance for the design of sand cleaning schemes. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. 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. In the drawings:

[0020] Figure 1 A flow chart of a method for predicting rock cuttings particle size provided in an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of an application scenario of a cuttings particle size prediction method provided in an embodiment of the present invention;

[0022] Figure 3 A flow chart of a method for determining a rock cuttings particle size distribution prediction model provided in an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of the particle size distribution of rock cuttings provided in an embodiment of the present invention;

[0024] Figure 5 An example diagram of a curve of volume fraction of each cuttings particle size when there are 30 drill rods in a horizontal section provided in an embodiment of the present invention;

[0025] Figure 6 This is an example diagram of a curve of volume fraction of each cuttings particle size when there are 50 drill rods in a horizontal section provided in an embodiment of the present invention;

[0026] Figure 7 An example diagram of a curve of volume fraction of various rock cuttings particle sizes when there are 80 drill rods in a horizontal section provided in an embodiment of the present invention;

[0027] Figure 8 An example diagram of a curve of volume fraction of each cuttings particle size when there are 100 drill rods in a horizontal section provided in an embodiment of the present invention;

[0028] Fig. 9 An example of a particle size distribution diagram with horizontal section lengths of 270m and 900m provided in an embodiment of the present invention;

[0029] Fig.10 A schematic diagram of a rock cuttings particle size prediction device provided in an embodiment of the present invention;

[0030] Fig.11 A schematic diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0032] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0033] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0034] In the description of this specification, the terms "include", "including", "have", "contain", etc. are all open terms, which mean including but not limited to. The descriptions with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", etc. mean that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. The order of steps involved in each embodiment is used to schematically illustrate the implementation of the present application, and the order of steps is not limited and can be appropriately adjusted as needed.

[0035] Research has found that horizontal well drilling can greatly increase the contact area with the formation, significantly increase the oil leakage area, and greatly improve the efficiency of oil and gas field production. It has now become the main force in the development of various oil fields. Although horizontal well drilling technology has great advantages in oil field development. However, the problem of cuttings migration is particularly prominent and may cause serious drilling accidents. In the horizontal well section, the direction of drilling fluid movement is perpendicular to the direction of gravity, and the difficulty of cuttings migration is higher than that of the vertical well section. In addition, cuttings will be deposited at the lower side of the wellbore to form a cuttings bed, which further increases the difficulty of wellbore cleaning.

[0036] The efficiency of cuttings transportation in horizontal wells is related to many factors, such as cuttings particle size, drilling fluid flow rate, drilling fluid viscosity, drilling speed, etc. Many scholars have conducted extensive research on the above factors, and determined the influence of each factor through numerical simulation and indoor experiments. The increase of drilling fluid flow rate and drilling fluid viscosity will improve the cleaning efficiency of cuttings, and too fast drilling speed will increase the difficulty of cuttings cleaning. Among many factors, the most critical is the influence of cuttings particle size, and on-site workers will also design sand cleaning plans based on this. In the current research, most of the experiments and simulations are carried out on several fixed particle sizes to determine the difficulty of starting and the transportation speed of cuttings. However, there are few reports on the relevant research progress of cuttings particle size distribution in horizontal sections. This makes the existing research lack the value of direct on-site application.

[0037] After the formation is broken by the drill bit, it forms cuttings with a roughly fixed particle size, and the particle size value can be determined experimentally. However, during its migration, the cuttings are repeatedly crushed into finer particles by the drill bit because the drill bit is not centered in the horizontal well section, which causes a huge change in particle size. It is particularly important to predict the change in particle size of cuttings in long horizontal sections of horizontal wells.

[0038] At present, there is an urgent need for a complete set of cuttings particle size prediction methods to guide the design of sand cleaning plans and ensure on-site drilling production.

[0039] Regarding the above studies, Figure 1 As shown, an embodiment of the present invention provides a method for predicting rock cuttings particle size, comprising:

[0040] S101: Acquire drilling engineering information and the success rate of re-crushing of cuttings after each crushing; wherein the drilling engineering information includes: drill pipe rotation speed, drill pipe joint radius, drilling fluid flow rate, and horizontal section length;

[0041] S102: determining the penetration rate of the drill pipe joint to the cuttings according to the drill pipe rotation speed, the drill pipe joint radius, and the drilling fluid flow rate;

[0042] S103: According to the sweep rate of the drill pipe joint to the cuttings, the length of the horizontal section, and the success rate of the cuttings being crushed again after each crushing, a cuttings particle size distribution prediction model is used to determine the cuttings particle size distribution result of the entire well section; wherein the cuttings particle size distribution prediction model is a predetermined model for determining the cuttings particle size distribution according to the sweep rate of the drill pipe joint to the cuttings, and the success rate of the cuttings being crushed again after each crushing.

[0043] In an embodiment of the present invention, drilling engineering information and the success rate of the cuttings being crushed again after each crushing are obtained; wherein the drilling engineering information includes: drill pipe speed, drill pipe joint radius, drilling fluid flow rate, and horizontal section length; according to the drill pipe speed, drill pipe joint radius, and drilling fluid flow rate, the sweep rate of the drill pipe joint to the cuttings is determined; according to the sweep rate of the drill pipe joint to the cuttings, the horizontal section length, and the success rate of the cuttings being crushed again after each crushing, the cuttings particle size distribution prediction model is used to determine the cuttings particle size distribution results of the entire well section; wherein the cuttings particle size distribution prediction model is a predetermined model for determining the cuttings particle size distribution according to the sweep rate of the drill pipe joint to the cuttings and the success rate of the cuttings being crushed again after each crushing. In this way, the cuttings particle size distribution can be predicted in advance, providing guidance for the design of sand cleaning schemes.

[0044] The above-mentioned rock cuttings particle size prediction method is described in detail below.

[0045] like Figure 2 As shown, it is a schematic diagram of an application scenario of a method for predicting rock chip particle size provided in an embodiment of the present invention, wherein 1 is a rock chip bed prediction module; 2 is a rock chip collection device; 3 is a particle size observation device; 4 is a central processing computer; 5 is a client computer, and the central processing computer 4 is capable of executing the method for predicting rock chip particle size described in the embodiment of the present invention.

[0046] With respect to the above S101, the success rate of the cuttings of the initial well being crushed again after each crushing can be determined based on historical operating experience, for example. The success rate of the cuttings of other wells after the initial well being crushed again after each crushing can be obtained by correcting the success rate of the cuttings of the previous well being crushed again after each crushing according to the actual distribution results of the cuttings of the previous well.

[0047] Specifically, due to differences in drilling fluid properties and formations, the success rate β of re-crushing of oil and gas well cuttings at different well locations varies. Therefore, a cuttings collection device and a particle size observation device are set at the wellhead to measure the particle size of the returned particles and correct the success rate β of re-crushing.

[0048] In an embodiment of the present invention, for example, it also includes: receiving an observed particle size of rock chips sent by a particle size observation device; the observed particle size of rock chips is obtained by using a five-point sampling method after the particle size observation device takes photos of the rock chips collected by the rock chip collection device; based on the observed particle size of the rock chips, an actual distribution result of the rock chip particle size is determined; based on the actual distribution result of the rock chip particle size and a rock chip particle size distribution prediction model, the success rate of the rock chips being crushed again after each crushing.

[0049] In this way, the returned rock cuttings in the rock cutting collection device are photographed by the particle size observation device, and the outline of the rock cuttings is identified and the particle size of the rock cuttings is calculated by image processing. In the image processing, the five-point sampling method is adopted. Only part of the area needs to be identified to complete the particle size statistics, reducing the processing workload.

[0050] In the embodiment of the present invention, the calculation of the cuttings particle size distribution is completed in the central processing computer. In addition, for example, the cuttings cleaning plan design can also be completed. The cuttings processing plan generated by the central processing computer is transmitted to the client computer to guide the on-site cuttings cleaning operation.

[0051] For the above S102, the sweep rate of the drill pipe joint to the rock cuttings is determined according to the drill pipe rotation speed, the radius of the drill pipe joint, and the drilling fluid flow rate, for example, including: according to the drill pipe rotation speed, the radius of the drill pipe joint, and the drilling fluid flow rate, the sweep rate of the drill pipe joint to the rock cuttings is determined by the following formula:

[0052]

[0053] Among them, α is the sweep rate of the drill pipe joint to the cuttings; is the drill pipe rotation speed, unit: rad / min; r is the drill pipe joint radius, unit: m; v is the drilling fluid flow rate, unit: m / s.

[0054] With respect to the above S103, the rock cuttings particle size distribution prediction model is a predetermined model for determining the rock cuttings particle size distribution according to the sweep rate of the drill pipe joint to the rock cuttings and the success rate of the rock cuttings being crushed again after each crushing.

[0055] like Figure 3 As shown, a flow chart of a method for determining a rock cuttings particle size distribution prediction model provided by an embodiment of the present invention includes:

[0056] S301: Determine a particle size distribution formula of the cuttings after the cuttings are crushed by each drill bit according to the sweep rate of the drill pipe joint to the cuttings and the success rate of the cuttings being crushed again after each crushing.

[0057] S302: Integrate the particle size distribution formulas of the cuttings after being crushed by each drill bit to obtain a cuttings particle size distribution prediction model that determines the cuttings particle size distribution according to the sweep rate of the drill pipe joint to the cuttings and the success rate of the cuttings being crushed again after each crushing.

[0058] Specifically, the drill pipe is eccentric in the long horizontal section. During the migration of the cuttings, each time the cuttings pass through a drill pipe joint, they will be damaged. Considering the influence of the drill pipe joint on the crushing of the cuttings, the particle size of the cuttings in the long horizontal section is estimated. For example, the sweep rate of each drill pipe joint on the cuttings is α, and each time the cuttings are crushed, they are split into two small cuttings of the same size. After each time the cuttings are crushed, the success rate of being crushed again is β. It is assumed that the particle size of the cuttings produced after the drill bit breaks the formation is the same, and the initial particle size of the cuttings is 1. Based on the above assumptions, a series of cuttings particle size distribution is constructed, and based on this, the recursive formula for the cuttings particle size distribution in the long horizontal section is derived. After the cuttings are crushed three times, the particle size is small, and the volume fraction of the cuttings in this part is extremely small and can be ignored in the calculation. After the first drill pipe joint is destroyed, the cuttings particle size distribution is in the form of:

[0059]

[0060] Among them, S i is the volume ratio of rock fragments with a certain particle size, i is the particle size of rock fragments, i=1,

[0061] After the first crushing, rock cuttings with a volume fraction of α are affected, and smaller particles are crushed. The particle size of 1 is reduced by α, while the particle size of 1 / 2 is increased by α.

[0062] After the second drill pipe joint is broken, the particle size distribution of the cuttings is as follows:

[0063]

[0064] In the second crushing, the proportion of particles with a size of 1 is 1-α, the possibility of being touched by the drill rod is 1-α, and the probability of being crushed is α. The product of the three is the volume fraction of particles with a size of 1 that are crushed in the second crushing.

[0065] In the second crushing, only particles with a size of 1 / 2 can be crushed into particles with a size of 1 / 4, the proportion of particles with a size of 1 / 2 is α, the possibility of being touched by the drill rod is α, the probability of being crushed is α, and the success rate of crushing is β. The four are multiplied to get the volume fraction of particles with a size of 1 / 4 generated in the second crushing.

[0066] In the second crushing, particles with a particle size of 1 / 2 can be crushed into particles with a particle size of 1, and can also be crushed into particles with a particle size of 1 / 4. Considering the influence of the two, the volume fraction of particles with a particle size of 1 / 2 after the second crushing is obtained.

[0067] After the third drill pipe joint is broken, the particle size distribution of the cuttings is as follows:

[0068]

[0069] Therefore, after multiple crushing, the rock cuttings particle size distribution function becomes more complex and difficult to calculate. Therefore, the above formula is integrated and derived to obtain the rock cuttings particle size distribution prediction model. The rock cuttings particle size distribution prediction model is:

[0070]

[0071] In the above scheme, n is the number of drill pipe joints in the horizontal section, and the calculation formula is:

[0072]

[0073] Wherein, L is the length of the horizontal section, unit: m.

[0074] Therefore, for the above S103, according to the sweep rate of the drill pipe joint to the cuttings, the length of the horizontal section, and the success rate of the cuttings being crushed again after each crushing, a cuttings particle size distribution prediction model is used to determine the cuttings particle size distribution result of the entire well section, for example, including: according to the sweep rate of the drill pipe joint to the cuttings, the length of the horizontal section, and the success rate of the cuttings being crushed again after each crushing, the following cuttings particle size distribution prediction model is used to determine the cuttings particle size distribution result of the entire well section:

[0075]

[0076] Among them, S1 is the volume ratio of the cuttings with a particle size of 1, S 1 / 2 for Volume ratio of particle size cuttings, S 1 / 4 for Volume ratio of particle size cuttings, S 1 / 8 for The volume ratio of particle size cuttings, n is the number of drill pipe joints in the horizontal section, L is the length of the horizontal section, the penetration rate of the drill pipe joint to the cuttings, and the success rate of the cuttings being broken again after each breakage, a n is the volume ratio of the 1-particle cuttings passing through the nth drill pipe joint, b n for The volume ratio of particle size cuttings passing through the nth drill pipe joint, c n for The volume ratio of particle size cuttings passing through the nth drill pipe joint, d n for The volume ratio of particle size cuttings passing through the nth drill pipe joint, a n+1is the volume ratio of the 1-particle cuttings passing through the n+1th drill pipe joint, b n+1 for The volume ratio of particle size cuttings after passing through the n+1th drill pipe joint, c n+1 for The volume ratio of particle size cuttings after passing through the n+1th drill pipe joint, d n+1 for The volume ratio of particle size cuttings passing through the n+1th drill pipe joint.

[0077] In an embodiment of the present invention, the method further comprises: drawing a cuttings particle size distribution diagram according to the cuttings particle size distribution results of the entire well section, and characterizing the cuttings cleaning effect by the degree of particle size distribution skewness; and adjusting drilling parameters according to the cuttings cleaning effect.

[0078] Among them, the drilling parameters are adjusted according to the cuttings cleaning effect, including: when the particle size distribution diagram presents a normal distribution, it is determined that the cuttings cleaning effect is good; when the cuttings particle size distribution diagram presents a left-biased shape, it is determined that the cuttings cleaning effect is low, and the drilling parameters are adjusted; when the cuttings particle size distribution diagram presents a right-biased shape, it is determined that the cuttings cleaning effect is too high, and the drilling parameters are adjusted.

[0079] Specifically, the calculated results of the cuttings particle size distribution of the entire well section are plotted into a cuttings particle size distribution diagram, and the cuttings cleaning effect is characterized by the degree of particle size distribution skewness. A normal distribution of the particle size distribution diagram indicates that the adopted drilling parameters are reasonable and the cuttings cleaning effect is good. A left-skewed cutting distribution diagram indicates that the cuttings are over-crushed and the cuttings cleaning effect is low. A right-skewed cutting distribution diagram indicates that the cuttings cleaning effect is too high, the drilling parameters are too high, and energy is wasted. Based on this, the drilling parameters are optimized.

[0080] The method for predicting the particle size of rock cuttings according to the embodiment of the present invention is described in detail below with reference to a specific embodiment.

[0081] like Figure 2 As shown, the cuttings particle size prediction method described in the present invention is completed in the central processing computer 4, and the cuttings cleaning plan design can also be completed. The cuttings processing plan generated by the central processing computer 4 is transmitted to the client computer 5 to guide the on-site cuttings cleaning operation. The wellhead cuttings collection device 2 collects the cuttings returned from the wellhead, and the returned cuttings in the cuttings collection device 2 are photographed. Through image processing (for example, using a five-point sampling method), the cuttings contour is identified, the cuttings particle size is calculated, and the cuttings particle size is transmitted to the central processing computer 4 for calculation and processing.

[0082] In this way, only part of the area needs to be identified to complete the particle size statistics, reducing the processing workload.

[0083] In addition, due to the differences in drilling fluid properties and formations, the success rate β of re-crushing of oil and gas well cuttings at different well locations varies. The cuttings collection device 2 and particle size observation device 3 set at the wellhead position measure the particle size of the returned particles and correct the success rate β of re-crushing.

[0084] In addition, if Figure 4 As shown in the figure, the cuttings particle size distribution results of the entire well section are plotted into a cuttings particle size distribution diagram, and the cuttings cleaning effect is characterized by the degree of particle size distribution deviation. 6 is the normal distribution curve of cuttings; 7 is the left deviation distribution curve of cuttings; 8 is the right deviation distribution curve of cuttings. The particle size distribution presents a normal distribution of 6, indicating that the adopted drilling parameters are reasonable and the cuttings cleaning effect is good. The cuttings particle size distribution diagram presents a left deviation of 8, indicating that the cuttings are over-crushed and the cuttings cleaning effect is low. The cuttings particle size distribution diagram presents a right deviation of 7, indicating that the cuttings cleaning effect is too high, the drilling parameters are too high, and energy is wasted. Based on this, the drilling parameters are optimized.

[0085] In addition, the drilling fluid flow rate has an extreme value and cannot be increased continuously. After the flow rate design reaches a certain value, you can choose to add a cuttings cleaning device to increase the cuttings cleaning effect. Figures 5 to 8 As shown, 9 is the cuttings volume fraction curve with a particle size of 1; 10 is the cuttings volume fraction curve with a particle size of 1 / 2; 11 is the cuttings volume fraction curve with a particle size of 1 / 4, 12 is the cuttings volume fraction curve with a particle size of 1 / 8, and 13 is the cuttings total volume fraction curve with a particle size greater than 1 / 8. The drill pipe speed is 60 rpm, the drill pipe joint radius is 0.084m, and the drilling fluid flow rate is 30L / s. The drill pipe joint has a sweep rate α of about 10%. The success rate of re-crushing β is set to 50%. The horizontal section lengths are 270m, 450m, 720m, and 900m respectively. When the horizontal section lengths are 270m, 450m, 720m, and 900m, the corresponding average cuttings particle sizes are 0.55, 0.45, 0.37, and 0.34, respectively. When the drilling parameters remain unchanged, the longer the horizontal section length, the smaller the average cuttings particle size.

[0086] Among them, the average particle size of rock cuttings is calculated using the weighted average method, and the expression is:

[0087]

[0088] like Fig. 9 The figure shows the particle size distribution diagram for horizontal sections of 270m and 900m, 14 is the cuttings particle size distribution curve for horizontal section 270m, and 15 is the cuttings particle size distribution curve for horizontal section 900m. It can be seen that when the horizontal section is 270m, the cuttings particle size roughly conforms to the normal distribution, and the drilling parameter design is relatively reasonable. When the horizontal section is 900m, the cuttings particle size is biased to the left, the cuttings are over-broken, which is not conducive to cuttings cleaning. The drilling fluid flow rate should be appropriately increased or a cuttings cleaning device should be added.

[0089] The present invention also provides a device for predicting the particle size of rock cuttings, as described in the following embodiments. Since the principle of solving the problem by the device is similar to that of the method for predicting the particle size of rock cuttings, the implementation of the device can refer to the implementation of the method for predicting the particle size of rock cuttings, and the repeated parts will not be repeated.

[0090] like Fig.10 A schematic diagram of a rock cuttings particle size prediction device is shown, comprising:

[0091] The information acquisition module 1001 is used to acquire drilling engineering information and the success rate of the cuttings being crushed again after each crushing; wherein the drilling engineering information includes: drill pipe speed, drill pipe joint radius, drilling fluid flow rate, and horizontal section length;

[0092] The first processing module 1002 is used to determine the sweep rate of the drill pipe joint to the cuttings according to the drill pipe rotation speed, the drill pipe joint radius, and the drilling fluid flow rate;

[0093] The second processing module 1003 is used to determine the cuttings particle size distribution result of the entire well section according to the sweep rate of the drill pipe joint to the cuttings, the length of the horizontal section, and the success rate of the cuttings being crushed again after each crushing, using a cuttings particle size distribution prediction model; wherein the cuttings particle size distribution prediction model is a predetermined model for determining the cuttings particle size distribution according to the sweep rate of the drill pipe joint to the cuttings, and the success rate of the cuttings being crushed again after each crushing.

[0094] In a possible implementation, the invention further includes: a third processing module for determining a particle size distribution formula of rock cuttings after being crushed by each drill bit according to the sweep rate of the drill pipe joint to the rock cuttings and the success rate of the rock cuttings being crushed again after each crushing;

[0095] The cuttings particle size distribution formula after being crushed by each drill bit is integrated to obtain a cuttings particle size distribution prediction model that determines the cuttings particle size distribution according to the sweep rate of the drill pipe joint to the cuttings and the success rate of the cuttings being crushed again after each crushing.

[0096] In a possible implementation, the first processing module is specifically used to determine the sweep rate of the drill pipe joint to the cuttings according to the drill pipe rotation speed, the drill pipe joint radius, and the drilling fluid flow rate using the following formula:

[0097]

[0098] Among them, α is the sweep rate of the drill pipe joint to the cuttings; is the drill pipe rotation speed; r is the drill pipe joint radius; v is the drilling fluid flow rate.

[0099] In a possible implementation, the second processing module is specifically used to determine the cuttings particle size distribution result of the entire well section using the following cuttings particle size distribution prediction model according to the sweep rate of the drill pipe joint to the cuttings, the length of the horizontal section, and the success rate of the cuttings being crushed again after each crushing:

[0100]

[0101] Among them, S1 is the volume ratio of the cuttings with a particle size of 1, S 1 / 2 for Volume ratio of particle size cuttings, S 1 / 4 for Volume ratio of particle size cuttings, S 1 / 8 for The volume ratio of particle size cuttings, n is the number of drill pipe joints in the horizontal section, L is the length of the horizontal section, the penetration rate of the drill pipe joint to the cuttings, and the success rate of the cuttings being broken again after each breakage, a n is the volume ratio of the 1-particle cuttings passing through the nth drill pipe joint, b n for The volume ratio of particle size cuttings passing through the nth drill pipe joint, c n for The volume ratio of particle size cuttings passing through the nth drill pipe joint, d n for The volume ratio of particle size cuttings passing through the nth drill pipe joint, a n+1 is the volume ratio of the 1-particle cuttings passing through the n+1th drill pipe joint, b n+1 for The volume ratio of particle size cuttings after passing through the n+1th drill pipe joint, c n+1 for The volume ratio of particle size cuttings after passing through the n+1th drill pipe joint, d n+1 for The volume ratio of particle size cuttings passing through the n+1th drill pipe joint.

[0102] In a possible implementation, the method further includes: a fourth processing module for drawing a cuttings particle size distribution diagram according to the cuttings particle size distribution results of the entire well section, and characterizing the cuttings cleaning effect by the degree of particle size distribution skewness; and adjusting drilling parameters according to the cuttings cleaning effect.

[0103] In one possible implementation, the fourth processing module is specifically used to determine that the rock cuttings cleaning effect is good when the particle size distribution graph presents a normal distribution; to determine that the rock cuttings cleaning effect is low and adjust the drilling parameters when the rock cuttings particle size distribution graph presents a left-biased shape; and to determine that the rock cuttings cleaning effect is too high and adjust the drilling parameters when the rock cuttings particle size distribution graph presents a right-biased shape.

[0104] In a possible implementation, it also includes: a fifth processing module, used to receive the observed particle size of rock chips sent by the particle size observation device; the observed particle size of rock chips is obtained by taking pictures of the rock chips collected by the rock chip collection device by the particle size observation device, using a five-point sampling method; based on the observed particle size of rock chips, the actual distribution result of the rock chip particle size is determined; based on the actual distribution result of the rock chip particle size and the rock chip particle size distribution prediction model, the success rate of the rock chips being crushed again after each crushing.

[0105] Based on the above invention concept, Fig.11 As shown, the present invention also proposes a computer device 1100, including a memory 1110, a processor 1120, and a computer program 1130 stored in the memory 1110 and executable on the processor 1120, wherein the processor 1120 implements the aforementioned rock chip particle size prediction method when executing the computer program 1130.

[0106] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned rock cuttings particle size prediction method is implemented.

[0107] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned rock cuttings particle size prediction method is implemented.

[0108] In an embodiment of the present invention, drilling engineering information and the success rate of the cuttings being crushed again after each crushing are obtained; wherein the drilling engineering information includes: drill pipe speed, drill pipe joint radius, drilling fluid flow rate, and horizontal section length; according to the drill pipe speed, drill pipe joint radius, and drilling fluid flow rate, the sweep rate of the drill pipe joint to the cuttings is determined; according to the sweep rate of the drill pipe joint to the cuttings, the horizontal section length, and the success rate of the cuttings being crushed again after each crushing, the cuttings particle size distribution prediction model is used to determine the cuttings particle size distribution results of the entire well section; wherein the cuttings particle size distribution prediction model is a predetermined model for determining the cuttings particle size distribution according to the sweep rate of the drill pipe joint to the cuttings and the success rate of the cuttings being crushed again after each crushing. In this way, the cuttings particle size distribution can be predicted in advance, providing guidance for the design of sand cleaning schemes.

[0109] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0113] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting rock cuttings particle size, characterized in that: include: Obtain drilling engineering information and the success rate of re-crushing of cuttings after each crushing; wherein the drilling engineering information includes: drill pipe speed, drill pipe joint radius, drilling fluid flow rate, and horizontal section length; Determine the penetration rate of the drill pipe joint to the cuttings according to the drill pipe speed, drill pipe joint radius and drilling fluid flow rate; According to the sweep rate of the drill pipe joint to the cuttings, the length of the horizontal section, and the success rate of the cuttings being crushed again after each crushing, a cuttings particle size distribution prediction model is used to determine the cuttings particle size distribution results for the entire well section; wherein the cuttings particle size distribution prediction model is a predetermined model for determining the cuttings particle size distribution according to the sweep rate of the drill pipe joint to the cuttings, and the success rate of the cuttings being crushed again after each crushing.

2. The method for predicting rock chip particle size according to claim 1, characterized in that: Before obtaining drilling engineering information, it also includes: According to the penetration rate of the drill pipe joint to the cuttings and the success rate of the cuttings being crushed again after each crushing, the formula for the distribution of the cuttings particle size after the cuttings are crushed by each drill bit is determined; The cuttings particle size distribution formula after being crushed by each drill bit is integrated to obtain a cuttings particle size distribution prediction model that determines the cuttings particle size distribution according to the sweep rate of the drill pipe joint to the cuttings and the success rate of the cuttings being crushed again after each crushing.

3. The method for predicting rock cuttings particle size according to claim 1, characterized in that: According to the drill pipe speed, drill pipe joint radius, and drilling fluid flow rate, determine the penetration rate of the drill pipe joint to the cuttings, including: According to the drill pipe speed, drill pipe joint radius, and drilling fluid flow rate, the following formula is used to determine the sweep rate of the drill pipe joint to the cuttings: Among them, α is the sweep rate of the drill pipe joint to the cuttings; is the drill pipe rotation speed; r is the drill pipe joint radius; v is the drilling fluid flow rate.

4. The method for predicting rock cuttings particle size according to claim 1, characterized in that: According to the penetration rate of the drill pipe joint to the cuttings, the length of the horizontal section, and the success rate of the cuttings being crushed again after each crushing, the cuttings particle size distribution prediction model is used to determine the cuttings particle size distribution results of the entire well section, including: According to the penetration rate of the drill pipe joint to the cuttings, the length of the horizontal section, and the success rate of the cuttings being crushed again after each crushing, the following cuttings particle size distribution prediction model is used to determine the cuttings particle size distribution results for the entire well section: Among them, S1 is the volume ratio of the cuttings with a particle size of 1, S 1 / 2 for Volume ratio of particle size cuttings, S 1 / 4 for Volume ratio of particle size cuttings, S 1 / 8 for The volume ratio of particle size cuttings, n is the number of drill pipe joints in the horizontal section, L is the length of the horizontal section, the penetration rate of the drill pipe joint to the cuttings, and the success rate of the cuttings being broken again after each breakage, a n is the volume ratio of the 1-particle cuttings passing through the nth drill pipe joint, b n for The volume ratio of particle size cuttings passing through the nth drill pipe joint, c n for The volume ratio of particle size cuttings passing through the nth drill pipe joint, d n for The volume ratio of particle size cuttings passing through the nth drill pipe joint, a n+1 is the volume ratio of the 1-diameter cuttings after passing through the n+1th drill pipe joint, b n+1 for The volume ratio of particle size cuttings after passing through the n+1th drill pipe joint, c n+1 for The volume ratio of particle size cuttings after passing through the n+1th drill pipe joint, d n+1 for The volume ratio of particle size cuttings passing through the n+1th drill pipe joint.

5. The method for predicting rock cuttings particle size according to claim 1, characterized in that: Also includes: According to the cuttings particle size distribution results of the entire well section, a cuttings particle size distribution diagram is drawn, and the cuttings cleaning effect is characterized by the degree of particle size distribution deviation; Adjust drilling parameters according to the cuttings cleaning effect.

6. The method for predicting rock cuttings particle size according to claim 5, characterized in that: Adjust drilling parameters based on cuttings cleaning results, including: When the particle size distribution graph shows a normal distribution, it is determined that the cuttings cleaning effect is good; When the cuttings particle size distribution graph shows a left-biased front shape, it is determined that the cuttings cleaning effect is low and the drilling parameters are adjusted; When the cuttings particle size distribution diagram shows a right-leaning shape, it is determined that the cuttings cleaning effect is too high and the drilling parameters are adjusted.

7. The method for predicting rock cuttings particle size according to claim 1, characterized in that: Also includes: Receiving the observed particle size of rock cuttings sent by the particle size observation device; The observed particle size of the cuttings is obtained by using a five-point sampling method after the particle size observation device takes photos of the cuttings collected by the cuttings collection device; According to the observed particle size of the cuttings, the actual distribution result of the particle size of the cuttings is determined; According to the actual distribution results of the rock cuttings particle size and the prediction model of the rock cuttings particle size distribution, the success rate of the rock cuttings being crushed again after each crushing is corrected.

8. A device for predicting rock cuttings particle size, characterized in that: include: The information acquisition module is used to obtain drilling engineering information and the success rate of the cuttings being crushed again after each crushing; wherein the drilling engineering information includes: drill pipe speed, drill pipe joint radius, drilling fluid flow rate, and horizontal section length; The first processing module is used to determine the penetration rate of the drill pipe joint to the cuttings according to the drill pipe rotation speed, the drill pipe joint radius and the drilling fluid flow rate; The second processing module is used to determine the rock cuttings particle size distribution result of the entire well section according to the sweep rate of the drill pipe joint to the rock cuttings, the length of the horizontal section, and the success rate of the rock cuttings being crushed again after each crushing, using a rock cuttings particle size distribution prediction model; wherein the rock cuttings particle size distribution prediction model is a predetermined model for determining the rock cuttings particle size distribution according to the sweep rate of the drill pipe joint to the rock cuttings, and the success rate of the rock cuttings being crushed again after each crushing.

9. The rock chip particle size prediction device according to claim 8, characterized in that: Also includes: The third processing module is used to determine the particle size distribution formula of the rock cuttings after being crushed by each drill bit according to the sweep rate of the drill pipe joint to the rock cuttings and the success rate of the rock cuttings being crushed again after each crushing; The cuttings particle size distribution formula after being crushed by each drill bit is integrated to obtain a cuttings particle size distribution prediction model that determines the cuttings particle size distribution according to the sweep rate of the drill pipe joint to the cuttings and the success rate of the cuttings being crushed again after each crushing.

10. The rock chip particle size prediction device according to claim 8, characterized in that: The first processing module is specifically used to determine the sweep rate of the drill pipe joint to the cuttings according to the drill pipe rotation speed, the drill pipe joint radius, and the drilling fluid flow rate using the following formula: Among them, α is the sweep rate of the drill pipe joint to the cuttings; is the drill pipe rotation speed; r is the drill pipe joint radius; v is the drilling fluid flow rate.

11. The rock chip particle size prediction device according to claim 8, characterized in that: The second processing module is specifically used to determine the cuttings particle size distribution result of the entire well section using the following cuttings particle size distribution prediction model according to the sweep rate of the drill pipe joint to the cuttings, the length of the horizontal section, and the success rate of the cuttings being crushed again after each crushing: Among them, S1 is the volume ratio of the cuttings with a particle size of 1, S 1 / 2 for Volume ratio of particle size cuttings, S 1 / 4 for Volume ratio of particle size cuttings, S 1 / 8 for The volume ratio of particle size cuttings, n is the number of drill pipe joints in the horizontal section, L is the length of the horizontal section, the penetration rate of the drill pipe joint to the cuttings, and the success rate of the cuttings being broken again after each breakage, a n is the volume ratio of the 1-particle cuttings passing through the nth drill pipe joint, b n for The volume ratio of particle size cuttings passing through the nth drill pipe joint, c n for The volume ratio of particle size cuttings passing through the nth drill pipe joint, d n for The volume ratio of particle size cuttings passing through the nth drill pipe joint, a n+1 is the volume ratio of the 1-diameter cuttings after passing through the n+1th drill pipe joint, b n+1 for The volume ratio of particle size cuttings after passing through the n+1th drill pipe joint, c n+1 for The volume ratio of particle size cuttings after passing through the n+1th drill pipe joint, d n+1 for The volume ratio of particle size cuttings passing through the n+1th drill pipe joint.

12. The rock chip particle size prediction device according to claim 8, characterized in that: Also includes: The fourth processing module is used to draw a cuttings particle size distribution diagram based on the cuttings particle size distribution results of the entire well section, and to characterize the cuttings cleaning effect by the degree of particle size distribution bias; Adjust drilling parameters according to the cuttings cleaning effect.

13. The rock chip particle size prediction device according to claim 12, characterized in that: A fourth processing module is specifically used to determine that the rock cuttings cleaning effect is good when the particle size distribution diagram presents a normal distribution; When the cuttings particle size distribution graph shows a left-biased front shape, it is determined that the cuttings cleaning effect is low and the drilling parameters are adjusted; When the cuttings particle size distribution diagram shows a right-leaning shape, it is determined that the cuttings cleaning effect is too high and the drilling parameters are adjusted.

14. The rock chip particle size prediction device according to claim 8, characterized in that: Also includes: A fifth processing module, used for receiving the observed particle size of the cuttings sent by the particle size observation device; The observed particle size of the cuttings is obtained by using a five-point sampling method after the particle size observation device takes photos of the cuttings collected by the cuttings collection device; According to the observed particle size of the cuttings, the actual distribution result of the particle size of the cuttings is determined; According to the actual distribution results of the rock cuttings particle size and the prediction model of the rock cuttings particle size distribution, the success rate of the rock cuttings being crushed again after each crushing is corrected.

15. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

17. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images