Method, device, terminal equipment and storage medium for dividing fracture-vug features

Abstract

The application relates to a method and device for classifying fracture-cave features, a terminal device and a readable storage medium. The method comprises the following steps: calculating statistical interval values according to a preset algorithm based on a basic data table, wherein the basic data table represents drilling mud loss and well position coordinates; counting the number of wells in which mud loss occurs in different intervals based on the statistical interval values and the basic data table to obtain the distribution characteristics of the wells in which mud loss occurs in each interval; classifying mud loss data according to the distribution characteristics of the wells in which mud loss occurs in each interval; and determining fracture-cave development feature scale information of the wells in which mud loss occurs based on the classification result, so that karst fracture-cave distribution classification representation can be realized with rich mud loss well data.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of oil and gas exploration, and relates to voice recognition technology, in particular to a method and device for classifying fracture-vug characteristics, a terminal device and a storage medium. BACKGROUND

[0002] When describing the fracture-vug distribution characteristics of fracture-vug carbonate rocks, various indirect data and methods are used for characterization, including drilling core data, seismic data, logging data, mud logging data, well kick, blowout, emptying, mud loss data during drilling, and other development dynamic data (cumulative oil production, cumulative gas production) accumulated during development. Seismic and logging are the main means, but due to the influence of resolution and the measurement means itself, the characterization is not comprehensive. SUMMARY

[0003] Therefore, it is necessary to provide a method and device for classifying fracture-vug characteristics, a terminal device and a storage medium to effectively avoid the problem of incomplete fracture-vug characterization due to the influence of resolution and the measurement means itself in the prior art. The present application can realize hierarchical characterization of karst fracture-vug distribution with the aid of rich mud loss well data.

[0004] The present application provides a method for classifying fracture-vug characteristics in the first aspect, which comprises:

[0005] Based on the basic data table, the statistical interval value is calculated according to a preset algorithm, wherein the basic data table represents drilling mud loss and well location coordinates;

[0006] Based on the statistical interval value and the basic data table, the number of wells with mud loss in different intervals is counted to obtain the distribution characteristics of wells with mud loss in each interval;

[0007] According to the distribution characteristics of wells with mud loss in each interval, the mud loss data is classified;

[0008] Based on the classification result, the fracture-vug development characteristic scale information of the well with mud loss is determined.

[0009] Optionally, the construction of the basic data table comprises:

[0010] Based on oil and gas field dynamic data and oil and gas field static data, a single well mud loss data table and a well location data table are obtained;

[0011] The basic data table is constructed according to the single well mud loss data table and the well location data table.

[0012] Optionally, the method further comprises obtaining the basic data table representing drilling mud loss and well location coordinates.

[0013] Optionally, the method for calculating the data statistical interval value comprises:

[0014] X=L max / 100

[0015] Wherein, X is the data statistical interval to be determined, L max is the maximum value of the mud loss amount;

[0016] A number sequence (Y) is selected, and the expression is as follows:

[0017] Y=10 i (i=0, 1, 2, 3,...)

[0018] Wherein, Y is the reference number sequence of the data statistical interval;

[0019]

[0020] Wherein, Z is the data statistical interval to be determined, The function is to take the value closest to X from the number sequence Y, divide by n, and then assign it to Z; and n is an adjustment coefficient, taking the value of 1 or 2.

[0021] Optionally, the distribution characteristics of the wells with mud loss in each interval include one or more of the following:

[0022] The data distribution of adjacent intervals has continuous and discontinuous distribution characteristics;

[0023] Or, the interval with continuous data distribution has a large change in the number of wells;

[0024] Or, the smaller the drilling mud loss amount in an interval, the more wells in the interval, and the larger the drilling mud loss amount in an interval, the fewer wells in the interval.

[0025] Optionally, the mud loss amount data is classified according to the distribution characteristics of the wells with mud loss in each interval, comprising:

[0026] According to the distribution characteristics of each interval, the target mud loss amount is determined, wherein the target mud loss amount is used to demarcate the intervals with continuous distribution and the intervals with discontinuous distribution;

[0027] Based on the target mud loss amount, the intervals are distinguished to obtain a first interval with continuous distribution;

[0028] The first interval is divided again based on dichotomy to obtain two second intervals and a second mud loss amount, wherein the second mud loss amount is a demarcation point of the two second intervals, and a proportion of the second mud loss amount in the total number of mud loss wells in the first interval is within a preset standard range or adjacent to the preset standard range.

[0029] Optionally, based on the grading result, the fracture-cave development feature scale information of the well with the mud loss amount is determined, including:

[0030] Based on the target mud loss amount and the second mud loss amount, the fracture-cave development feature scale information of the well with the mud loss amount is determined, wherein the fracture-cave development feature scale information includes one or more of the following:

[0031] The distribution of the well with the mud loss amount less than the second mud loss amount reflects a first scale of fracture-cave development feature;

[0032] Or, the distribution of the well with the mud loss amount greater than or equal to the second mud loss amount but less than the target mud loss amount reflects a second scale of fracture-cave development feature;

[0033] Or, the distribution of the well with the mud loss amount greater than or equal to the target mud loss amount reflects a third scale of fracture-cave development feature;

[0034] Wherein, the first scale is less than the second scale, and the second scale is less than the third scale.

[0035] The second aspect of the present application provides a device for dividing fracture-cave features, the device comprising:

[0036] An interval value determination module is configured to calculate a statistical interval value according to a preset algorithm based on a basic data table, wherein the basic data table represents the drilling mud loss amount and the well location coordinates;

[0037] A distribution feature determination module is configured to count the number of wells with mud loss in different intervals based on the statistical interval value and the basic data table to obtain the distribution features of the wells with mud loss in each interval;

[0038] A grading module is configured to grade the mud loss amount data according to the distribution features of the wells with mud loss in each interval;

[0039] A fracture-cave development feature determination module is configured to determine the fracture-cave development feature scale information of the well with the mud loss amount based on the grading result.

[0040] The third aspect of the present application provides a terminal device, comprising a processor and a memory; the memory is used for storing computer instructions, and the processor is used for running the computer instructions stored in the memory to realize the method for dividing the fracture-cave feature.

[0041] The fourth aspect of the present application provides a computer readable storage medium, the computer readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to realize the method for dividing the fracture-cave feature.

[0042] The present application has the following advantages: through extensive investigation and programming processing of a large amount of data of drilling mud loss, and proposing hierarchical representation, the accurate representation of the fracture-cave karst development degree can be realized, and good results are obtained. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 It is an application environment diagram of a method for dividing a fracture-cave feature in an embodiment;

[0044] Figure 2 It is a flowchart of a method for dividing a fracture-cave feature in another embodiment (I);

[0045] Figure 3 It is a flowchart of a method for dividing a fracture-cave feature in another embodiment (II);

[0046] Figure 4 It is a drilling mud loss statistical table of Tahe oilfield in another embodiment;

[0047] Figure 5 It is a large domain, domain, sub-domain and small domain division result of single well initial production fluid volume of Tahe oilfield in another embodiment;

[0048] Figure 6 It is a mud loss amount domain representation diagram;

[0049] Figure 7 It is a structure block diagram of a device for dividing a fracture-cave feature in another embodiment;

[0050] Figure 8 It is an internal structure diagram of a terminal device in another embodiment. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0052] Figure 1An application environment diagram of a method for dividing fracture-cave characteristics in an embodiment; Figure 2 A flowchart of a method for dividing fracture-cave characteristics in another embodiment (I); Figure 3 A flowchart of a method for dividing fracture-cave characteristics in another embodiment (II); Figure 4 A drilling mud loss amount statistical table of Tahe Oilfield in another embodiment; Figure 5 A division result of single-well initial fluid production amount of Tahe Oilfield in another embodiment; Figure 6 A mud loss amount division representation diagram; Figure 7 A structural block diagram of a device for dividing fracture-cave characteristics in another embodiment; Figure 8 An internal structure diagram of a terminal device in another embodiment.

[0053] The method for dividing fracture-cave characteristics provided in the application can be applied in an application environment as shown in Figure 1 . In the application environment, a terminal 102 communicates with a server 104 through a network. The terminal 102 acquires a basic data table, wherein the basic data table represents drilling mud loss amount and well location coordinates; and transmits the basic data table to the server 104. The server 104 calculates statistical interval values based on the basic data table according to a preset algorithm, wherein the basic data table represents drilling mud loss amount and well location coordinates. The server 104 counts the number of wells in which mud loss occurs in different intervals based on the statistical interval values and the basic data table, and obtains distribution characteristics of the wells in which mud loss occurs in each interval. The server 104 classifies mud loss amount data according to the distribution characteristics of the wells in which mud loss occurs in each interval. The server 104 determines fracture-cave development characteristic scale information of the wells in which mud loss occurs based on the classification result. The terminal 102 can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0054] In another embodiment, as shown in Figure 2 , a method for dividing fracture-cave characteristics is provided. The method is taken as an example of the server 104 in Figure 1 for illustration, and includes the following steps:

[0055] Step S201: calculating statistical interval values based on a basic data table according to a preset algorithm;

[0056] The basic data table represents drilling mud loss amount and well location coordinates.

[0057] According to the data basic table, an existing basic data table can be directly acquired. Of course, the data basic table can also be constructed in real time. Specifically, the data basic table is constructed according to the following steps:Figure 3 As shown, based on oil and gas field dynamic data and oil and gas field static data, a single well mud loss data table and a well site data table are obtained; and a basic data table is constructed according to the single well mud loss data table and the well site data table. In this embodiment, the source of the basic data table is not limited, as long as it meets the requirements of this embodiment.

[0058] For example, a drilling mud loss and well site coordinate basic data table is constructed.

[0059] According to the "Tahar Oilfield (1900-2014) Well Site Data Table", the total number of drilled wells is about 2080, and according to the "Tahar Oilfield March 2018 Single Well-Blowdown, Loss-Production Test Data Table", the number of wells with mud loss data is 792. The amount of single well drilling mud loss is very different, ranging from 8217m 3 to only 0.2m 3 .

[0060] In addition, the calculation method of the data statistical interval includes but is not limited to the following contents:

[0061] X = L max / 100

[0062] Wherein, X is the data statistical interval to be determined, L max is the maximum value of the mud loss amount;

[0063] A sequence (Y) is selected, and its expression is as follows:

[0064] Y = 10 i (i = 0, 1, 2, 3,...)

[0065] Wherein, Y is the reference sequence of the data statistical interval;

[0066]

[0067] Wherein, Z is the data statistical interval to be determined, The function is to take the value closest to X from the sequence Y, divide by n, and then assign it to Z; and n is an adjustment coefficient, taking the value of 1 or 2.

[0068] Step S202: Based on the statistical interval value and the basic data table, the number of wells with mud loss in different intervals is counted to obtain the distribution characteristics of wells with mud loss in each interval.

[0069] The distribution characteristics of the wells with mud loss in each interval include, but are not limited to, one or more of the following: the data distribution of adjacent intervals has continuous and discontinuous distribution characteristics; or, the interval with continuous data distribution has a large change in the number of wells; or, the smaller the amount of drilling mud loss in an interval, the more wells in the interval, and the larger the amount of mud loss, the fewer the number of wells in the interval.

[0070] Step S203: grading the mud loss amount data according to the distribution characteristics of the wells with mud loss in each interval;

[0071] Step S204: determining the fracture-cave development characteristic scale information of the wells with mud loss based on the grading result.

[0072] The fracture-cave of the well with mud loss includes, but is not limited to, a carbonate rock fracture-cave.

[0073] Specifically, through the step S204, the fracture-cave development characteristic scale information of the well with mud loss can be determined through the grading result. Moreover, the relationship between the actual research area and the fracture-cave development distribution characteristics can be summarized in combination with other relevant information, and the understanding of the carbonate rock fracture-cave development and distribution law can be deepened.

[0074] In the embodiment, after obtaining the statistical interval value through the basic data table, the number of wells with mud loss in different intervals is counted according to the statistical interval value and the basic data table, so as to obtain the distribution characteristics of the wells with mud loss in each interval. Then, the mud loss amount data is graded according to the distribution characteristics of the wells with mud loss in each interval. The fracture-cave development characteristic scale information of the well with mud loss is determined based on the grading result. The accurate characterization of the fracture-cave karst development degree is realized, and good results are achieved.

[0075] In another embodiment, the step S203 includes, but is not limited to, the following steps:

[0076] Step S2031: determining a target mud loss amount according to the distribution characteristics of each interval;

[0077] The target mud loss amount is used to distinguish the intervals with continuous distribution and the intervals with discontinuous distribution.

[0078] Step S2032: distinguishing the intervals based on the target mud loss amount to obtain a first interval with continuous distribution.

[0079] Step S2033: performing secondary division on the first interval based on the bisection method to obtain two second intervals and a second mud loss amount.

[0080] The secondary mud loss amount is the demarcation point of the two secondary intervals, and the proportion of the secondary mud loss amount in the total number of mud loss wells in the primary interval is within the preset standard range or adjacent to the preset standard range.

[0081] Specifically, because the distribution characteristics of the wells with mud loss in each interval include one or more of the following: the data distribution of adjacent intervals has continuous and discontinuous distribution characteristics; or, the interval with continuous data distribution has a large change in the number of wells; or, the smaller the drilling mud loss amount of the interval, the more the number of wells in the interval, and the larger the drilling mud loss amount of the interval, the fewer the number of wells in the interval. Therefore, in this embodiment, the drilling mud loss amount is classified according to the distribution characteristics of each interval, so as to determine two numbers, which are referred to as the secondary mud loss amount J and the target mud loss amount K (unit, m 3 ) in this patent. The demarcation value of the continuous interval and the discontinuous interval is K, and the classified limit value within the continuous interval in the range of 90±1% (or adjacent to the range) of the total number of wells with mud loss is J.

[0082] For example, the target mud loss amount is determined to be 2400 (m 3 ), and is the demarcation point of continuous and discontinuous data distribution, according to which two primary intervals (0-2400, 2400-8250) are divided, and the K value is determined to be 2400 (m3). 0-2400 is a continuous interval, which is divided into two secondary intervals (0-1200, 1200-2400) by the bisection method. Among them, 1200 is the secondary mud loss amount, and is adjacent to the range of 90±1% of the total number of wells with mud loss, so the J value is determined to be 1200 (see Table 2).

[0083] In another embodiment, the step S204 includes but is not limited to the following steps:

[0084] Step S2041: determining the fracture-cave development characteristic scale information of the well with mud loss based on the target mud loss amount and the secondary mud loss amount;

[0085] The fracture-cave development characteristic scale information includes one or more of the following:

[0086] The distribution of the well with mud loss less than the secondary mud loss amount reflects the first scale of fracture-cave development characteristics;

[0087] Or, the distribution of the well with mud loss greater than or equal to the secondary mud loss amount but less than the target mud loss amount reflects the second scale of fracture-cave development characteristics;

[0088] Or, the distribution of the well with mud loss greater than or equal to the target mud loss amount reflects the third scale of fracture-cave development characteristics;

[0089] wherein the first scale is smaller than the second scale, and the second scale is smaller than the third scale.

[0090] That is, the distribution of wells with mud loss less than 1200 (m 3 ) reflects the development characteristics of small-scale fracture-cave, the distribution of wells with mud loss greater than or equal to 1200 (m 3 ) and less than 2400 (m 3 ) reflects the development characteristics of medium-scale fracture-cave, and the distribution of wells with mud loss greater than or equal to 2400 (m 3 ) reflects the development characteristics of large-scale fracture-cave.

[0091] It should be understood that, although each step in the flowchart of Figure 2 is shown in sequential order following as indicated by the arrows, the steps need not be performed in the order indicated by the arrows. Unless explicitly stated otherwise, the steps of the method can be performed in any order, and the execution of some steps can be concurrent, in addition, at least some of the steps in Figure 2 may include multiple sub-steps or stages, which need not be performed at the same time, but can be performed at different times, and the order of the sub-steps or stages need not be sequential, but can be performed in rotation or alternation with other steps or sub-steps or stages of other steps.

[0092] In another embodiment, as shown in Figure 3 , a method of classifying fracture-cave characteristics is provided.

[0093] Drilling mud loss is not only an important drilling engineering problem, but also an important oil production technology problem, and an important geological problem. Drilling mud loss data can effectively serve the research of reservoir fracture, low-order fault, unconformity structure and oil and gas reservoir type, and the discovery of subtle gas reservoir. For oilfields with developed karst phenomena, such as Tahe Oilfield, drilling mud loss data are particularly rich and widely used in karst research.

[0094] During drilling in oil and gas field exploration and development, when a dissolution fracture-cave of carbonate rock (or other soluble rock) is drilled, well leakage, blowout, emptying and mud loss may occur. The distribution of wells with these phenomena on a plan view can reflect the development and distribution characteristics of dissolution fracture-cave. Among these phenomena, mud loss occurs most frequently and the most data can be obtained. Therefore, mud loss data are most commonly used to reflect the development and distribution characteristics of fracture-cave. The existing method is to display all wells with mud loss on a plan view at one time to reflect the development and distribution characteristics of fracture-cave.

[0095] For the karst study of Ordovician carbonate reservoirs in Tahe Oilfield, previous studies mainly analyzed the relevant phenomena or assisted in solving related problems based on the distribution characteristics of drilling mud loss wells in the plane, without considering the size of the drilling mud loss. From a statistical point of view, the more developed the karst phenomenon in the drilled section and its vicinity, the larger the mud loss will be during drilling. Therefore, based on the premise of distinguishing the size of the drilling mud loss, analyzing the distribution characteristics of the mud loss wells in the plane will more deeply and carefully reflect the development characteristics of the reservoir karst. Literature research results show that there is no similar research at present.

[0096] Therefore, in this embodiment, Tahe Oilfield is taken as an example to analyze the distribution characteristics of the mud loss wells in the plane based on the premise of distinguishing the size of the drilling mud loss.

[0097] Specifically, 1) a drilling mud loss and well location coordinate basic data table is constructed;

[0098] According to the "Tahe Oilfield (1900-2014) well location data table", the total number of drilled wells is about 2080, and according to the "Tahe Oilfield 2018 March single well-bleeding, loss-production test data table", the number of mud loss data wells is 792. The size of the single well drilling mud loss is very different, with a maximum of 8217m 3 and a minimum of only 0.2m 3 .

[0099] 2) According to a certain algorithm, select the appropriate statistical interval value, and count the number of mud loss wells in different intervals, and summarize the distribution characteristics of the mud loss wells in each interval;

[0100] The data statistical interval calculation formula is as follows:

[0101] X=L max / 100 (1)

[0102] In formula (1), X is the data statistical interval to be determined, L max is the maximum value of the mud loss.

[0103] Select a sequence (Y), whose expression is as follows:

[0104] Y=10 i (i=0, 1, 2, 3,...) (2)

[0105] In formula (2), Y is the reference sequence of the data statistical interval. And define:

[0106]

[0107] In formula (3), Z is the data statistical interval to be determined, The function of the function is to take the value closest to X from the series Y, divide by n, and then assign the value to Z. n is an adjustment factor, taking the value of 1 or 2.

[0108] For example, the maximum value of the mud loss of an oilfield is 8217 m 3 , and from equation (1), X = 8217 / 100 = 82.17, which is closest to 10+2, and n is taken as 2, so the data statistics is determined to be 50 (m 3 ).

[0109] Therefore, according to the algorithm of this embodiment, the statistical interval value is calculated to be 50, and the drilling mud loss data is statistically analyzed, and the distribution characteristics of each interval are: ① the data distribution of adjacent intervals has continuous and discontinuous distribution characteristics; ② the interval with continuous data distribution has a large change in the number of wells; ③ the smaller the mud loss of the interval, the more wells in the interval, and the larger the mud loss of the interval, the fewer wells in the interval (see Table 1).

[0110] 3) According to the distribution characteristics of the wells with mud loss in each interval, the mud loss data is classified;

[0111] 2400 (m 3 ) is the boundary point of continuous and discontinuous data distribution, and according to this, two primary intervals (0-2400, 2400-8250) are divided, and the K value is determined to be 2400 (m 3 ). 0-2400 is a continuous interval, which is divided into two secondary intervals (0-1200, 1200-2400) by the bisection method. Among them, 1200 is close to the total number of mud loss wells 90±1%, so the J value is determined to be 1200 (see Table 2).

[0112] 4) Classify the distribution of the wells with mud loss on the plane;

[0113] The distribution of the wells with mud loss less than 1200 (m 3 ) reflects the characteristics of small-scale fracture-vug development, the distribution of the wells with mud loss greater than or equal to 1200 (m 3 ) and less than 2400 (m 3 ) reflects the characteristics of medium-scale fracture-vug development, and the distribution of the wells with mud loss greater than or equal to 2400 (m 3 ) reflects the characteristics of large-scale fracture-vug development.

[0114] 5) Combine the actual situation of the study area with other relevant information, summarize the relationship between them and the fracture-vug development distribution characteristics, and deepen the understanding of the fracture-vug development and distribution rules of carbonate rocks.

[0115] The distribution of the wells with mud loss greater than or equal to 1200 (m 3The distribution of the wells with mud loss of less than 1200 (m 3 ) reflects the development of small-scale fracture-cave system, which is characterized by a large distribution range, a much larger number of wells in the north of the pinch-out line of the Sangtamu Formation than in the south of the pinch-out line, and a north-northeast linear distribution of the wells in the south of the pinch-out line (see Figure 2 ) Figure 1 shows the (eroded) pinch-out line of the Sangtamu Formation of the Upper Ordovician in the Tahe Oilfield, and the (eroded) pinch-out line of the Yijianfang Formation of the Upper Ordovician.

[0116] In another embodiment, as shown in Figure 7 , a device for classifying fracture-cave features is provided, which comprises: an interval value determination module 101 configured to calculate a statistical interval value according to a preset algorithm based on a basic data table, wherein the basic data table represents the drilling mud loss and the well location coordinates; a distribution feature determination module 102 configured to count the number of wells with mud loss in different intervals based on the statistical interval value and the basic data table, and obtain the distribution features of the wells with mud loss in each interval; a grading module 103 configured to grade the mud loss data according to the distribution features of the wells with mud loss in each interval; and a fracture-cave development feature determination module 104 configured to determine the fracture-cave development feature scale information of the wells with mud loss based on the grading result.

[0117] Optionally, the construction of the basic data table comprises:

[0118] obtaining a single-well mud loss data table and a well location data table based on dynamic data and static data of the oil and gas field;

[0119] constructing a basic data table according to the single-well mud loss data table and the well location data table.

[0120] Optionally, the basic data table representing the drilling mud loss and the well location coordinates is obtained.

[0121] Optionally, the calculation method of the data statistical interval value comprises:

[0122] X = L max / 100

[0123] wherein X is the data statistical interval value to be determined, L max is the maximum value of the mud loss, and L

[0124] Select a sequence (Y), the expression is as follows:

[0125] Y = 10 i (i = 0, 1, 2, 3,...)

[0126] Wherein, Y is the reference sequence of data statistical interval;

[0127]

[0128] Wherein, Z is the data statistical interval to be determined, The function is to take the value closest to X from the sequence Y, divided by n, and then assigned to Z; and, n is an adjustment coefficient, taking the value of 1 or 2.

[0129] Optionally, the well where the mud loss occurs has the following one or more distribution characteristics in each interval:

[0130] The data distribution of adjacent intervals has continuous and discontinuous distribution characteristics;

[0131] Or, the interval with continuous data distribution has a large change in the number of wells;

[0132] Or, the smaller the interval of drilling mud loss, the more wells in the interval, and the larger the interval of mud loss, the fewer wells in the interval.

[0133] Optionally, the grading module 103 is specifically configured to: determine a target mud loss amount according to the distribution characteristics of each interval, wherein the target mud loss amount is used to demarcate the intervals with continuous distribution and the intervals with discontinuous distribution; based on the target mud loss amount, the intervals are distinguished to obtain a first interval with continuous distribution; based on the bisection method, the first interval is divided twice to obtain two second intervals and a second mud loss amount, wherein the second mud loss amount is the demarcation point of the two second intervals, and the proportion of the second mud loss amount in the total number of mud loss wells in the first interval is within a preset standard range or adjacent to the preset standard range.

[0134] Optionally, the fracture and cave development feature determination module 104 is specifically configured to:

[0135] Determine the fracture and cave development feature scale information of the well where the mud loss occurs based on the target mud loss amount and the second mud loss amount, wherein the fracture and cave development feature scale information includes one or more of the following:

[0136] The distribution of wells with mud loss less than the second mud loss amount reflects the first scale of fracture and cave development characteristics;

[0137] Or, the distribution of wells greater than or equal to the secondary mud loss amount but less than the target mud loss amount reflects the second scale fracture-cavity development characteristics;

[0138] Or, the distribution of wells greater than or equal to the target mud loss amount reflects the third scale fracture-cavity development characteristics.

[0139] Wherein, the first scale is smaller than the second scale, and the second scale is smaller than the third scale.

[0140] The specific limitations of the above device can be referred to the limitations of the two methods in the above, which will not be repeated here. Each module in the above two devices can be realized by software, hardware and combination thereof, in whole or in part. The above modules can be embedded in or independent of the processor in the terminal device in hardware form, or stored in the memory in the terminal device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above modules.

[0141] In another embodiment, a terminal device is provided, which can be a server, and its internal structure diagram can be as shown in Figure 8 The terminal device includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the terminal device is used to provide computing and control capabilities. The memory of the terminal device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the terminal device is used to store related data. The network interface of the terminal device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a method for dividing fracture-cavity characteristics.

[0142] Those skilled in the art can understand that Figure 8 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the terminal device to which the scheme of the present application is applied. The specific terminal device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0143] In another embodiment, a terminal device is provided, which includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the above method for dividing fracture-cavity characteristics.

[0144] The terms involved in the terminal device in this embodiment and the implementation principle can be referred to the method for dividing fracture-cavity characteristics in the embodiment of the present application, which will not be repeated here.

[0145] In another embodiment, a computer readable storage medium is provided, having stored thereon a computer program, which when executed by a processor implements the method of dividing a fracture-cave feature.

[0146] The nouns and implementation principles involved in the computer readable storage medium in this embodiment can be referred to the method of dividing a fracture-cave feature in the embodiment of the application, which will not be described here.

[0147] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the computer program can include the processes of the above-mentioned embodiments. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM) and the like.

[0148] The technical features of the above embodiments can be combined in any way. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0149] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method of partitioning a fracture-vug feature, characterized by, The method comprises: calculating a data statistical interval value according to a preset algorithm based on a basic data table, wherein the basic data table represents drilling mud loss and well location coordinates; based on the data statistical interval value and the basic data table, the number of wells with mud loss in different intervals is counted to obtain the distribution characteristics of wells with mud loss in each interval; determining a target mud loss amount according to the distribution characteristics of each interval, wherein the target mud loss amount is used to demarcate the continuous and discontinuous intervals; based on the target mud loss amount, the intervals are distinguished to obtain a continuous first-level interval; based on the bisection method, the first-level interval is divided twice to obtain two second-level intervals and a second-level mud loss amount, wherein the second-level mud loss amount is the demarcation point of the two second-level intervals, and the proportion of the second-level mud loss amount in the total number of mud loss wells in the first-level interval is within a preset standard range or adjacent to the preset standard range; based on the target mud loss amount and the second-level mud loss amount, the fracture and cave development characteristic scale information of the well with mud loss is determined, wherein the fracture and cave development characteristic scale information includes one or more of the following: the distribution of wells with mud loss less than the second-level mud loss amount reflects a first scale of fracture and cave development characteristics; or, the distribution of wells with mud loss greater than or equal to the second-level mud loss amount but less than the target mud loss amount reflects a second scale of fracture and cave development characteristics; or, the distribution of wells with mud loss greater than or equal to the target mud loss amount reflects a third scale of fracture and cave development characteristics; wherein the first scale is smaller than the second scale, and the second scale is smaller than the third scale; the method for calculating the data statistical interval value comprises: X = L max / 100 Wherein, X is the data statistics interval pending value, L max is the maximum value of the mud loss; selecting a sequence (Y) with the following expression: Y = 10 i (i = 0, 1, 2, 3,...... ) wherein Y is a reference sequence of data statistical intervals; Z = ( X, Y ) / n wherein Z is a data statistical interval to be determined, The function of the (X, Y) function is to take the value in the number series Y closest to X, divide it by n, and assign the result to Z; and n is an adjustment coefficient, taking the value of 1 or 2.

2. The method of claim 1, wherein, the construction of the basic data table comprises: based on oil and gas field dynamic data and oil and gas field static data, a single-well mud loss data table and a well location data table are obtained; a basic data table is constructed according to the single-well mud loss data table and the well location data table.

3. The method of claim 1, wherein, The method further comprises obtaining the basic data table representing drilling mud loss and well location coordinates.

4. The method of claim 1, wherein, The distribution characteristics of wells with mud loss in each interval include one or more of the following: the data distribution of adjacent intervals has continuous and discontinuous distribution characteristics; or, the interval with continuous data distribution has a large number of wells; or, the smaller the drilling mud loss, the more wells in the interval, and the larger the mud loss, the fewer wells in the interval.

5. An apparatus for implementing the method of partitioning a fracture-vug feature according to any one of claims 1 to 4, characterized in that, The device comprises: an interval value determination module for calculating a data statistical interval value according to a preset algorithm based on a basic data table, wherein the basic data table represents drilling mud loss and well location coordinates; a distribution characteristic determination module for counting the number of wells with mud loss in different intervals based on the data statistical interval value and the basic data table to obtain the distribution characteristics of wells with mud loss in each interval; The grading module is used for grading the mud loss amount data according to the distribution characteristics of the wells in which mud loss occurs in each interval; The fracture-vug development feature determination module is used for determining the fracture-vug development feature scale information of the well in which the mud loss amount occurs based on the grading result.

6. A terminal device, characterized by comprising: comprise a processor and a memory; The memory is used for storing computer instructions, and the processor is used for running the computer instructions stored in the memory to implement the method for dividing fracture-vug features in any one of claims 1 to 4.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method for dividing fracture-vug features in any one of claims 1 to 4.

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