CN113885097A - A method and electronic device for extracting karst caves for three-dimensional in-situ stress field simulation of oil reservoirs - Google Patents

Abstract

A method for extracting karst caves and electronic equipment for simulating three-dimensional in-situ stress fields of oil reservoirs, the method comprises the steps of: acquiring logging data of a target oil and gas well; acquiring development characteristics of karst caves in the target oil and gas well according to the logging data; The logging data obtains quantitative judgment criteria for identifying the type of karst caves; obtains the distribution of the karst caves in the target oil and gas wells; and extracts the karst caves in the target oil and gas wells. The present application provides a method and electronic equipment for extracting karst caves for simulating three-dimensional in-situ stress fields of oil reservoirs, which comprehensively utilizes logging data to identify whether karst caves are developed in target oil and gas wells, judges the filling state of karst caves, and then proposes a quantitative judgment standard for karst cave type identification. , so as to facilitate the extraction of karst caves in the target oil and gas wells, and make up for the deficiency of the existing karst cave extraction methods.

Description

A method and electronic device for extracting karst caves for three-dimensional in-situ stress field simulation of oil reservoirs

technical field

The invention belongs to the technical field of fracture-cavity carbonate rock reservoirs, and particularly relates to a method for extracting karst caves and electronic equipment for simulating three-dimensional in-situ stress fields of oil reservoirs.

Background technique

Fractured-cavity carbonate reservoirs occupy a very important position in the world's oil and gas resources. According to statistics, more than one-third of the world's carbonate reservoirs are of the fractured-cavity type. The three-dimensional in-situ stress field of fractured-cavity carbonate reservoirs has a very important relationship with oil and gas accumulation, reservoir permeability, fracturing construction design and well location deployment. It has important theoretical guidance and practical significance. When simulating the three-dimensional in-situ stress field of fractured-cavity carbonate reservoirs, the influence of karst caves on the local stress field must be fully considered. Therefore, it is necessary to effectively identify the caves before simulating the three-dimensional in-situ stress field of fractured-cavity carbonate reservoirs.

In the invention patent "GST-based method for identifying and calibrating karst caves in heterogeneous carbonate rock reservoirs" (application number: 201710636436.3), a GST-based method for identifying and calibrating karst caves in heterogeneous carbonate rock reservoirs is disclosed , the method includes the steps of: constructing a cave identification operator of gradient structure tensor; using local gradient tensor analysis technology to strengthen the identification of cave attributes, and using the hole labeling algorithm to mark the cave number, location and extraction of characteristic parameters, so as to Achieve automatic identification of karst caves and quantitative description of the characteristics of distribution, scale and aggregation degree. A multi-scale karst cave identification method and system is disclosed in the invention patent "A multi-scale karst cave identification method and system" (application number: 201710942805.1). The method includes the steps of: performing forward modeling and analysis on karst caves of different scales, establishing an earthquake main The relationship between frequency, cave scale, and root mean square amplitude attributes; for the actual frequency division data volume, extract the root mean square amplitude attribute, analyze the relationship between the main frequency of the earthquake, the cave scale, and the root mean square amplitude attribute, and verify and forward simulation. The conclusions are consistent; the relationship between the main frequency of the earthquake, the scale of the karst cave, and the root mean square amplitude attribute is used to identify karst caves of different scales.

Existing karst cave identification methods mainly identify karst caves through the analysis of seismic data, ignoring the role of logging data, and have the defects of low recognition accuracy and inability to determine the size and filling conditions of karst caves. In fact, in the process of 3D in-situ stress field simulation of fractured-cavity reservoirs, the size and filling of caves have a great influence on the local distribution of in-situ stress. Therefore, the size and filling of caves must be determined by technical means to improve the three-dimensional in-situ stress field. Simulation accuracy.

SUMMARY OF THE INVENTION

In view of the above problems, the present invention provides a method for extracting karst caves and electronic equipment for simulating three-dimensional in-situ stress fields of oil reservoirs that overcome the above problems or at least partially solve the above problems.

In order to solve the above-mentioned technical problems, the present invention provides a method for extracting karst caves for simulating three-dimensional in-situ stress field of fractured-cave oil reservoirs, the method includes the steps:

Obtain logging data of target oil and gas wells;

Obtain the development characteristics of the karst cave in the target oil and gas well according to the well logging data;

Obtaining quantitative judgment criteria for karst cave type identification according to the logging data;

obtaining the distribution of the karst caves in the target oil and gas well;

The cave is extracted in the target oil and gas well.

Preferably, the acquiring the logging data of the target oil and gas well comprises the steps of:

acquiring imaging logging data of the target oil and gas well;

acquiring logging curve data of the target oil and gas well;

Acquire the seismic data volume of the target oil and gas well.

Preferably, the acquiring the logging curve data of the target oil and gas well comprises the steps of:

obtaining the deep lateral resistivity logging curve of the target oil and gas well;

obtaining the natural gamma logging curve of the target oil and gas well;

obtaining the compensated neutron logging curve of the target oil and gas well;

obtaining the density log curve of the target oil and gas well;

Acquiring the sonic travel log curve of the target oil and gas well.

Preferably, obtaining the development characteristics of the karst caves in the target oil and gas well according to the logging data includes the steps of:

Obtain the development characteristics of large-scale karst caves of the karst cave;

Obtaining the development characteristics of the dissolution pores of the karst cave;

Obtain the development characteristics of the dissolution fractures of the cave.

Preferably, the obtaining of the quantitative judgment criteria for the identification of the type of karst caves according to the logging data includes the steps of:

Set up the judgment standard database;

Get the cave type;

storing all the cave types in the judgment criteria database;

acquiring logging curve data in the logging data;

acquiring the logging curve data corresponding to each of the cave types;

The logging curve data is correspondingly stored in the judgment standard database.

Preferably, the obtaining the cave type includes the steps of:

Obtain the development characteristics of large karst caves;

Judging the development situation of the karst cave according to the development characteristics of the large karst cave;

Obtain imaging logging data;

judging the filling situation of the karst cave according to the imaging logging data;

The type of the karst cave is determined according to the development situation of the karst cave and the filling situation of the karst cave.

Preferably, the obtaining the distribution of the karst caves in the target oil and gas well comprises the steps of:

acquiring the seismic data volume in the logging data;

Obtain the formula for calculating the root mean square amplitude;

The distribution of karst caves within the acquisition range of the seismic data volume is calculated by using the root mean square amplitude calculation formula.

Preferably, the expression of the root mean square amplitude calculation formula is:

Among them, RMS represents the root mean square amplitude, a _i represents the amplitude value of the seismic data volume at the sampling point, and N represents the total number of samples of the seismic data volume in the sampling time window.

The present invention also provides an electronic device, the electronic device comprising:

at least one processor; and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute any one of the foregoing descriptions for use in crevice type oils A karst cave extraction method for 3D in-situ stress field simulation in Tibet.

The present invention also provides a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute any one of the aforementioned applications for fracture-cavity oil reservoirs A karst cave extraction method for 3D in-situ stress field simulation.

One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages: a method for extracting caves and electronic equipment for simulating three-dimensional in-situ stress fields of oil reservoirs provided by the present application, comprehensively utilize well logging data (such as: Deep lateral resistivity logging curve, natural gamma logging curve, compensated neutron logging curve, density logging curve, sonic time difference logging curve and seismic data volume) to identify whether there are karst caves in the target oil and gas wells, and to judge whether karst caves are developed in the target oil and gas wells Filling state, and then put forward a quantitative judgment standard for the identification of karst cave types, which facilitates the extraction of karst caves in target oil and gas wells and makes up for the shortcomings of existing karst cave extraction methods.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

FIG. 1 is a schematic flowchart of a method for extracting a karst cave for simulating a three-dimensional in-situ stress field of a fractured-cavity reservoir provided by an embodiment of the present invention;

2 is a deep lateral resistivity logging curve, a natural gamma logging curve, a compensated neutron logging curve, a density logging curve, and a sonic time difference logging curve in Embodiment 1;

Fig. 3 is the seismic data volume amplitude data in embodiment 1;

Fig. 4 is the root mean square amplitude data in embodiment 1;

5 is a deep lateral resistivity logging curve, a natural gamma logging curve, a compensated neutron logging curve, a density logging curve, and a sonic time difference logging curve in Example 2;

Fig. 6 is the seismic data volume amplitude data in embodiment 2;

Fig. 7 is the root mean square amplitude data in embodiment 2;

Fig. 8 is the karst cave of a certain block of the oil field extracted in Example 2;

9 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention;

FIG. 10 is a schematic structural diagram of a non-transitory computer-readable storage medium provided by an embodiment of the present invention.

Detailed ways

The present invention will be described in detail below with reference to specific embodiments and examples, and the advantages and various effects of the present invention will be more clearly presented therefrom. It should be understood by those skilled in the art that these specific embodiments and examples are used to illustrate the present invention, but not to limit the present invention.

Throughout the specification, unless specifically stated otherwise, terms used herein are to be understood as commonly used in the art. Therefore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In case of conflict, the present specification takes precedence.

Unless otherwise specified, various raw materials, reagents, instruments and equipment used in the present invention can be purchased from the market or can be prepared by existing methods.

As shown in FIG. 1 , in the embodiment of the present application, the present invention provides a method for extracting karst caves for 3D in-situ stress field simulation of fractured-cavity oil reservoirs, the method includes the steps:

S1: Obtain the logging data of the target oil and gas well;

In the embodiment of the present application, acquiring the logging data of the target oil and gas well in step S1 includes the steps:

acquiring imaging logging data of the target oil and gas well;

acquiring logging curve data of the target oil and gas well;

Acquire the seismic data volume of the target oil and gas well.

In the embodiment of the present application, when the logging data of the target oil and gas well is acquired, the imaging logging data, logging curve data and seismic data volume of the target oil and gas well are mainly acquired for subsequent analysis and use.

In the embodiment of the present application, the acquiring the logging curve data of the target oil and gas well includes the steps:

obtaining the deep lateral resistivity logging curve of the target oil and gas well;

obtaining the natural gamma logging curve of the target oil and gas well;

obtaining the compensated neutron logging curve of the target oil and gas well;

obtaining the density log curve of the target oil and gas well;

Acquiring the sonic travel log curve of the target oil and gas well.

In the embodiment of the present application, when acquiring the logging curve data of the target oil and gas well, it mainly acquires the deep lateral resistivity logging curve, natural gamma logging curve, compensated neutron logging curve, density logging curve of the target oil and gas well Logging curve and sonic time difference logging curve are used for subsequent analysis.

S2: obtaining the development characteristics of the karst caves in the target oil and gas well according to the logging data;

In the embodiment of the present application, acquiring the development characteristics of the karst caves in the target oil and gas well according to the logging data in step S2 includes the steps:

Obtain the development characteristics of large-scale karst caves of the karst cave;

Obtaining the development characteristics of the dissolution pores of the karst cave;

Obtain the development characteristics of the dissolution fractures of the cave.

In the embodiment of the present application, when the development characteristics of the karst caves in the target oil and gas well are obtained according to the logging data, the development characteristics of the karst caves are the development characteristics of large-scale karst caves, the development characteristics of dissolution pores and the development characteristics of dissolution fractures, and therefore the development characteristics of the karst caves are respectively The above three developmental characteristics can be obtained.

In the examples of this application, large-scale karst caves refer to reservoirs with large-scale karst caves as the main storage space, and large karst caves are easily developed at the intersection of conjugate fractures due to dissolution. Fractures and large karst caves are characterized by the following: the bilateral resistivity value is significantly reduced, showing a large difference; the natural gamma value is higher than that of the surrounding rock, and the karst cave has a "reverse bow" shape, and the uranium removal gamma value is also high. It is larger than the surrounding rock; the double-well diameter curve has obvious expansion phenomenon; the density value curve is "bow" at the cave, and the density value decreases greatly.

In the examples of this application, dissolution pores refer to reservoirs with dissolution pores as the main storage space, no large-scale dissolution pores are developed, and the development characteristics of dissolution pores are: the value of bilateral resistivity is significantly reduced, showing a small "negative" value. difference"; very low deuranium gamma values; slightly increased neutron porosity; slightly decreased density values.

In the examples of this application, dissolution fractures refer to reservoirs with dissolution fractures as the main reservoir space. Large-scale caves are not developed. The fractures are often dissolved, and some are even dissolved to form small dissolution holes. The development characteristics of dissolution fractures are: The shape of the imaging logging image is like a small round hole, and some dissolved pores and caves that are not filled with high-resistance minerals such as calcite are filled with mud during the drilling process, and the color on the imaging logging image is dark. It is represented as a black sine line; the development of cracks has a significant impact on the bilateral lateral values, which is manifested as a "difference" in the deep and shallow lateral values.

S3: obtaining a quantitative judgment standard for identifying the type of karst caves according to the logging data;

In the embodiment of the present application, in step S3, the quantitative judgment criteria for the identification of the type of karst caves obtained according to the logging data includes the steps of:

Set up the judgment standard database;

Get the cave type;

storing all the cave types in the judgment criteria database;

acquiring logging curve data in the logging data;

acquiring the logging curve data corresponding to each of the cave types;

The logging curve data is correspondingly stored in the judgment standard database.

In the embodiment of the present application, when obtaining the quantitative judgment criteria for karst cave type identification according to the logging data, an empty judgment criteria database is firstly set, and then the karst cave types are acquired. In this embodiment, there are four types of karst caves, which are as follows: Unfilled large karst caves, partially filled large karst caves, fully filled large karst caves and small karst caves, then store these four types of karst caves in the judgment standard database, and obtain the logging curve data corresponding to each type of karst cave, and then The logging curve data is correspondingly stored in the judgment standard database, and corresponds to the corresponding cave types one by one.

In the embodiment of the present application, the quantitative judgment criteria for the type identification of karst caves obtained from logging data are specifically as follows:

According to the above table, the cave can be well classified and identified.

In the embodiment of the present application, the obtaining the cave type includes the steps:

Obtain the development characteristics of large karst caves;

Judging the development situation of the karst cave according to the development characteristics of the large karst cave;

Obtain imaging logging data;

judging the filling situation of the karst cave according to the imaging logging data;

The type of the karst cave is determined according to the development situation of the karst cave and the filling situation of the karst cave.

In the embodiment of the present application, when obtaining the type of karst cave, first obtain the development characteristics of large-scale karst caves. Specifically, the development characteristics of large-scale karst caves are: the bilateral resistivity value is significantly reduced, showing a large difference; the natural gamma value is higher than the surrounding area. The rock increases in size and takes the shape of a "reverse bow" at the karst cave, and the deuranium gamma value is also higher than that of the surrounding rock; the double-well diameter curve has obvious diameter expansion; the density value curve is in the "bow" shape at the karst cave, Density values are greatly reduced. When the karst cave meets this characteristic, it is a large karst cave, otherwise it is a small karst cave; then obtain imaging logging data, and use this imaging logging data to judge the filling situation of the karst cave, that is, unfilled, partially filled or fully filled; Then, the type of the karst cave is judged according to the development of the karst cave and the filling of the karst cave, that is, it is judged that the karst cave is any one of the four types of unfilled large karst caves, partially filled large karst caves, fully filled large karst caves and small karst caves.

S4: obtaining the distribution of the karst caves in the target oil and gas well;

In the embodiment of the present application, obtaining the distribution of the karst caves in the target oil and gas well in step S4 includes the steps of:

acquiring the seismic data volume in the logging data;

Obtain the formula for calculating the root mean square amplitude;

The distribution of karst caves within the acquisition range of the seismic data volume is calculated by using the root mean square amplitude calculation formula.

In the embodiment of the present application, when obtaining the distribution of the karst caves in the target oil and gas well, the root mean square amplitude calculation formula is used for the seismic data volume to calculate the root mean square amplitude, so as to obtain the distribution of the karst caves within the acquisition range of the seismic data volume. Then, the distribution of karst caves on the trajectory of the target well can be obtained. The purpose of this step is to vertically smooth the amplitude data of all seismic data volumes in the sampling time window, so that the boundary of the cave is clearer, which is beneficial to the subsequent extraction steps.

In the embodiment of the present application, the expression of the root mean square amplitude calculation formula is:

Among them, RMS represents the root mean square amplitude, a _i represents the amplitude value of the seismic data volume at the sampling point, and N represents the total number of samples of the seismic data volume in the sampling time window.

S5: Extract the cave in the target oil and gas well.

In the embodiment of the present application, after obtaining the distribution of the karst caves on the target well trajectory, it can be easily extracted from the target oil and gas well. Specifically, by correlating the logging data with the seismic data volume, and then extracting karst caves from the seismic data volume for a certain block of the oilfield,

A method for extracting a karst cave in the present application for simulating a three-dimensional in-situ stress field of a fractured-cave oil reservoir will be described in detail below with specific examples.

Example 1:

Collect deep lateral resistivity logs, natural gamma logs, compensated neutron logs, density logs, sonic transit logs and seismic data volumes of target oil and gas wells. The deep lateral resistivity logging curve, natural gamma logging curve, compensated neutron logging curve, density logging curve, and sonic time difference logging curve are shown in Figure 2, and the amplitude data of the seismic data volume at a depth of 6328m are shown in Figure 2. shown in Figure 3.

According to the deep lateral resistivity logging curve, the natural gamma logging curve, the compensated neutron logging curve, the density logging curve and the sonic time difference logging curve, it is judged whether there is a cave and the filling of the cave. As shown in Figure 2, the compensated neutron parameter of the bedrock is 71.86, the density parameter of the bedrock is 2.682, and the acoustic time difference parameter of the bedrock is 51.49; at a depth of 6328m, the deep lateral resistivity is about 95, the natural gamma The horse parameter is about 10, the compensated neutron parameter is about 8, the density parameter is about 2.35, and the acoustic transit time parameter is about 53. According to the judgment method shown in Table 1, it is shown that unfilled large karst caves develop at a depth of 6328m.

The seismic data volume amplitude data is converted into the root mean square amplitude according to the root mean square amplitude calculation formula, as shown in FIG. 4 .

Example 2:

Collect deep lateral resistivity logs, natural gamma logs, compensated neutron logs, density logs, sonic transit logs and seismic data volumes of target oil and gas wells. The deep lateral resistivity logging curve, natural gamma logging curve, compensated neutron logging curve, density logging curve, and sonic time difference logging curve are shown in Figure 5, and the amplitude data of the seismic data volume at a depth of 6228m are shown in Figure 5. As shown in Figure 6;

According to the deep lateral resistivity logging curve, the natural gamma logging curve, the compensated neutron logging curve, the density logging curve and the sonic time difference logging curve, it is judged whether there is a cave and the filling of the cave. As shown in Figure 5, the compensated neutron parameter of the bedrock is 74.92, the density parameter of the bedrock is 2.727, and the acoustic time difference parameter of the bedrock is 58.64; at the depth of 6228m, the deep lateral resistivity is about 60, the natural gamma The horse parameter is about 10, the compensated neutron parameter is about 84, the density parameter is about 2.88, and the acoustic time difference parameter is about 80. According to the judgment method shown in Table 1, it shows that a small cave is developed at a depth of 6228m.

The seismic data volume amplitude data is converted into the root mean square amplitude according to the root mean square amplitude calculation formula, as shown in FIG. 7 . By correlating the logging data with the seismic data volume, karst caves are extracted from the seismic data volume for a certain block of the oilfield, as shown in Figure 8.

Referring to FIG. 9, an embodiment of the present disclosure further provides an electronic device 100, the electronic device includes:

at least one processor; and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, the instructions are executed by the at least one processor, so that the at least one processor can execute the three-dimensional in-situ stress field for fractured-cavity oil reservoirs in the foregoing method embodiments Simulated cave extraction method.

Embodiments of the present disclosure further provide a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the foregoing method embodiments.

Embodiments of the present disclosure also provide a computer program product, the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, when the program instructions are executed by a computer, make The computer executes the method for extracting karst caves for 3D in-situ stress field simulation of fractured-cavity oil reservoirs in the foregoing method embodiments.

The application provides a method and electronic device for extracting a cave for simulating a three-dimensional in-situ stress field of a reservoir, which comprehensively utilizes logging data (such as: deep lateral resistivity logging curve, natural gamma logging curve, compensated neutron logging curve, density log curve, sonic time difference log curve and seismic data volume) to identify whether there are karst caves in the target oil and gas wells, and to judge the filling state of the karst caves, and then put forward the quantitative judgment criteria for the type identification of karst caves, so as to facilitate the extraction of karst caves in the target oil and gas wells , making up for the shortcomings of the existing cave extraction methods.

Referring next to FIG. 9 , it shows a schematic structural diagram of an electronic device 100 suitable for implementing an embodiment of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), vehicle-mounted terminals (eg, mobile terminals such as in-vehicle navigation terminals), etc., and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in FIG. 9 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present disclosure.

As shown in FIG. 9 , the electronic device 100 may include a processing device (eg, a central processing unit, a graphics processor, etc.) 101 that may be loaded into random access according to a program stored in a read only memory (ROM) 102 or from a storage device 108 Various appropriate actions and processes are executed by the programs in the memory (RAM) 103 . In the RAM 103, various programs and data necessary for the operation of the electronic device 100 are also stored. The processing device 101 , the ROM 102 , and the RAM 103 are connected to each other through a bus 104 . An input/output (I/O) interface 105 is also connected to the bus 104 .

Typically, the following devices can be connected to the I/O interface 105: input devices 106 including, for example, a touch screen, touch pad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; including, for example, a liquid crystal display (LCD), speakers, An output device 107 of a vibrator or the like; a storage device 108 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 109 . Communication means 109 may allow electronic device 100 to communicate wirelessly or by wire with other devices to exchange data. Although the figures show electronic device 100 having various means, it should be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.

In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the method illustrated in the flowchart. In such an embodiment, the computer program may be downloaded and installed from the network via the communication device 109 , or from the storage device 108 , or from the ROM 102 . When the computer program is executed by the processing apparatus 101, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are executed.

10, which shows a schematic structural diagram of a computer-readable storage medium suitable for implementing an embodiment of the present disclosure, where the computer-readable storage medium stores a computer program, and the computer program can be executed by a processor. The method for extracting karst caves for three-dimensional in-situ stress field simulation of fractured-cave reservoirs as described in any one of the above is realized.

It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two. The computer readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with computer-readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device . Program code embodied on a computer readable medium may be transmitted using any suitable medium including, but not limited to, electrical wire, optical fiber cable, RF (radio frequency), etc., or any suitable combination of the foregoing.

The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or may exist alone without being assembled into the electronic device.

The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: acquires at least two Internet Protocol addresses; A node evaluation request for an Internet Protocol address, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein, the obtained The Internet Protocol address indicates an edge node in the content distribution network.

Alternatively, the above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; From the at least two Internet Protocol addresses, the Internet Protocol address is selected; the selected Internet Protocol address is returned; wherein, the received Internet Protocol address indicates an edge node in the content distribution network.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including object-oriented programming languages—such as Java, Smalltalk, C++, but also conventional Procedural programming language - such as the "C" language or similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through Internet connection).

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logical functions for implementing the specified functions executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented in dedicated hardware-based systems that perform the specified functions or operations , or can be implemented in a combination of dedicated hardware and computer instructions.

The units involved in the embodiments of the present disclosure may be implemented in a software manner, and may also be implemented in a hardware manner. Wherein, the name of the unit does not constitute a limitation of the unit itself under certain circumstances, for example, the first obtaining unit may also be described as "a unit that obtains at least two Internet Protocol addresses".

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.

It should be noted that, in this document, relational terms such as "first" and "second" etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply these There is no such actual relationship or sequence between entities or operations. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element. The above descriptions are only specific embodiments of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, this application is not intended to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

In a word, the above descriptions are only preferred embodiments of the technical solutions of the present invention, and are not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (10)

1. a karst cave extraction method of three-dimensional in-situ stress field simulation of oil reservoir, is characterized in that, described method comprises the steps: Obtain logging data of target oil and gas wells; Obtain the development characteristics of the karst cave in the target oil and gas well according to the well logging data; Obtaining quantitative judgment criteria for karst cave type identification according to the logging data; obtaining the distribution of the karst caves in the target oil and gas well; The cave is extracted in the target oil and gas well. 2. The method for extracting karst caves for three-dimensional in-situ stress field simulation of oil reservoirs according to claim 1, wherein the acquisition of logging data of target oil and gas wells comprises the steps of: acquiring imaging logging data of the target oil and gas well; acquiring logging curve data of the target oil and gas well; Acquire the seismic data volume of the target oil and gas well. 3. The method for extracting karst caves for three-dimensional in-situ stress field simulation of oil reservoirs according to claim 2, wherein the acquiring the logging curve data of the target oil and gas well comprises the steps of: obtaining the deep lateral resistivity logging curve of the target oil and gas well; obtaining the natural gamma logging curve of the target oil and gas well; obtaining the compensated neutron logging curve of the target oil and gas well; obtaining the density log curve of the target oil and gas well; Acquiring the sonic travel log curve of the target oil and gas well. 4. The method for extracting karst caves for three-dimensional in-situ stress field simulation of oil reservoirs according to claim 1, wherein the acquiring the development characteristics of karst caves in the target oil and gas well according to the logging data comprises the steps of: Obtain the development characteristics of large-scale karst caves of the karst cave; Obtaining the development characteristics of the dissolution pores of the karst cave; Obtain the development characteristics of the dissolution fractures of the cave. 5. The method for extracting karst caves for simulating three-dimensional in-situ stress fields of oil reservoirs according to claim 1, wherein said obtaining a quantitative judgment standard for karst cave type identification according to said well logging data comprises the steps of: Set up the judgment standard database; Get the cave type; storing all the cave types in the judgment criteria database; acquiring logging curve data in the logging data; acquiring the logging curve data corresponding to each of the cave types; The logging curve data is correspondingly stored in the judgment standard database. 6. The method for extracting karst caves for simulating three-dimensional in-situ stress fields of oil reservoirs according to claim 5, wherein the acquiring the karst cave types comprises the steps of: Obtain the development characteristics of large karst caves; Judging the development situation of the karst cave according to the development characteristics of the large karst cave; Obtain imaging logging data; judging the filling situation of the karst cave according to the imaging logging data; The type of the karst cave is determined according to the development situation of the karst cave and the filling situation of the karst cave. 7. The method for extracting karst caves for three-dimensional in-situ stress field simulation of oil reservoirs according to claim 1, wherein the obtaining the distribution of the karst caves in the target oil and gas well comprises the steps of: acquiring the seismic data volume in the logging data; Obtain the formula for calculating the root mean square amplitude; The distribution of karst caves within the acquisition range of the seismic data volume is calculated by using the root mean square amplitude calculation formula. 8. The method for extracting karst caves for three-dimensional in-situ stress field simulation of oil reservoirs according to claim 7, wherein the expression of the root mean square amplitude calculation formula is:

Among them, RMS represents the root mean square amplitude, a _i represents the amplitude value of the seismic data volume at the sampling point, and N represents the total number of samples of the seismic data volume in the sampling time window. 9. An electronic device, characterized in that the electronic device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the functions of any of the preceding claims 1-8. A method for extracting karst caves for 3D in-situ stress field simulation in fractured-cavity reservoirs. 10. A non-transitory computer-readable storage medium, characterized in that, the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the functions of any one of the preceding claims 1-8. A method for extracting karst caves for 3D in-situ stress field simulation in fractured-cavity reservoirs.

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