CN113885097A - Karst cave extraction method for oil reservoir three-dimensional ground stress field simulation and electronic equipment - Google Patents
Karst cave extraction method for oil reservoir three-dimensional ground stress field simulation and electronic equipment Download PDFInfo
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Abstract
A karst cave extraction method for three-dimensional earth stress field simulation of an oil reservoir and electronic equipment are provided, wherein the method comprises the following steps: acquiring logging data of a target oil-gas well; acquiring the development characteristics of the karst cave in the target oil-gas well according to the logging data; obtaining a quantitative judgment standard for identifying the type of the karst cave according to the logging data; acquiring the distribution condition of the karst caves in the target oil-gas well; and extracting the karst cave from the target oil-gas well. According to the karst cave extraction method and the electronic equipment for simulating the three-dimensional geostress field of the oil reservoir, logging data are comprehensively utilized to identify whether a karst cave is developed in a target oil-gas well or not, the filling state of the karst cave is judged, and then a quantitative judgment standard for type identification of the karst cave is provided, so that the extraction of the karst cave in the target oil-gas well is facilitated, and the defects of the existing karst cave extraction method are overcome.
Description
Technical Field
The invention belongs to the technical field of fracture-cavity carbonate reservoirs, and particularly relates to a karst cave extraction method for reservoir three-dimensional ground stress field simulation and electronic equipment.
Background
The fracture-cavity carbonate rock oil reserves an important position in the world oil and gas resources, and according to statistics, more than one third of the carbonate rock reservoirs in the world are fracture-cavity type. The three-dimensional geostress field of the fracture-cavity type carbonate reservoir has very important relations with oil and gas reservoir formation, reservoir permeability, fracturing construction design, well location deployment and the like, and the simulation of the three-dimensional geostress field has important theoretical guidance and practical significance for the exploration and development of oil and gas. When the fracture-cavity carbonate reservoir three-dimensional ground stress field simulation is carried out, the influence of the karst cave on the local stress field must be fully considered. Therefore, the karst cave needs to be effectively identified before the fracture-cave type carbonate reservoir three-dimensional ground stress field simulation is carried out.
The invention discloses a method for identifying and calibrating karst caves in a GST-based carbonate heterogeneous reservoir (application number: 201710636436.3), which comprises the following steps: constructing a karst cave identification operator of the gradient structure tensor; the method is characterized in that a local gradient tensor analysis technology is utilized to carry out attribute strengthening identification on the karst cave, and a cave marking algorithm is utilized to carry out marking numbering, positioning and characteristic parameter extraction on the karst cave, so that the purposes of automatic identification and distribution of the karst cave and quantitative description of scale and aggregation degree characteristics are achieved. In the invention patent 'a multi-scale karst cave identification method and system' (application number: 201710942805.1), a multi-scale karst cave identification method and system are disclosed, the method comprises the following steps: forward simulation analysis is carried out aiming at different sizes of karst caves, and the relation of earthquake dominant frequency, karst cave size and root-mean-square amplitude attribute is established; extracting root mean square amplitude attributes aiming at actual frequency division data, analyzing the relation among the earthquake dominant frequency, the karst cave dimension and the root mean square amplitude attributes, and verifying the consistency with a forward modeling conclusion; and identifying different scales of karst caves by utilizing the relation among the seismic dominant frequency, the karst cave scale and the root mean square amplitude attribute.
The existing karst cave identification method mainly carries out karst cave identification through analysis of seismic data, ignores the action of logging data, and has the defects of low identification precision and incapability of determining the size and filling condition of the karst cave. In fact, in the process of simulating the three-dimensional ground stress field of the fracture-cavity type oil reservoir, the size and the filling condition of the karst cave have large influence on local distribution of ground stress, so that the size and the filling condition of the karst cave need to be determined by a technical means so as to improve the accuracy of simulating the three-dimensional ground stress field.
Disclosure of Invention
In view of the above problems, the present invention provides a solution cavity extraction method for three-dimensional earth stress field simulation of an oil reservoir and an electronic device, which overcome the above problems or at least partially solve the above problems.
In order to solve the technical problem, the invention provides a karst cave extraction method for simulating a three-dimensional ground stress field of a fracture-cavity type oil reservoir, which comprises the following steps:
acquiring logging data of a target oil-gas well;
acquiring the development characteristics of the karst cave in the target oil-gas well according to the logging data;
obtaining a quantitative judgment standard for identifying the type of the karst cave according to the logging data;
acquiring the distribution condition of the karst caves in the target oil-gas well;
and extracting the karst cave from the target oil-gas well.
Preferably, the acquiring logging data of the target hydrocarbon well comprises the following steps:
acquiring imaging logging data of the target oil-gas well;
acquiring logging curve data of the target oil-gas well;
and acquiring the seismic data volume of the target oil-gas well.
Preferably, the acquiring the logging curve data of the target hydrocarbon well comprises the following steps:
obtaining a deep lateral resistivity logging curve of the target oil and gas well;
acquiring a natural gamma ray logging curve of the target oil-gas well;
acquiring a compensated neutron logging curve of the target oil-gas well;
acquiring a density logging curve of the target oil-gas well;
and acquiring a sound wave time difference logging curve of the target oil-gas well.
Preferably, the step of obtaining the developmental characteristics of the karst cave in the target oil and gas well according to the logging data comprises the following steps:
acquiring the development characteristics of the large karst cave of the karst cave;
acquiring the development characteristics of the erosion cavities of the karst caves;
and acquiring the erosion crack development characteristics of the karst cave.
Preferably, the step of obtaining the quantitative judgment standard for identifying the type of the karst cave according to the logging data comprises the following steps:
setting a judgment standard database;
acquiring a karst cave type;
storing all the karst cave types into the judgment standard database;
acquiring logging curve data in the logging data;
acquiring logging curve data corresponding to each karst cave type;
and correspondingly storing the logging curve data into the judgment standard database.
Preferably, the obtaining of the karst cave type comprises the steps of:
acquiring the development characteristics of the large karst cave;
judging the karst cave development condition according to the large karst cave development characteristics;
acquiring imaging logging data;
judging the karst cave filling condition according to the imaging logging data;
and judging the karst cave type according to the karst cave development condition and the karst cave filling condition.
Preferably, the acquiring the distribution condition of the karst caves in the target oil and gas well comprises the following steps:
acquiring a seismic data volume in the logging data;
obtaining a root mean square amplitude calculation formula;
and calculating the karst cave distribution condition in the seismic data volume acquisition range by using the root-mean-square amplitude calculation formula.
Preferably, the root mean square amplitude calculation formula is expressed as:
where RMS denotes the root mean square amplitude, aiAnd N represents the total sampling number of the seismic data body in the sampling time window.
The present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of the previously described karst cave extraction methods for three-dimensional geostress field simulation of a fracture-cavity reservoir.
The invention also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to execute any one of the aforementioned karst cave extraction methods for three-dimensional geostress field simulation of a fracture-cavity reservoir.
One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages: according to the karst cave extraction method and the electronic equipment for simulating the three-dimensional geostress field of the oil reservoir, logging data (such as a deep lateral resistivity logging curve, a natural gamma logging curve, a compensated neutron logging curve, a density logging curve, an acoustic wave time difference logging curve and a seismic data volume) are comprehensively utilized to identify whether a karst cave is developed in a target oil and gas well, the filling state of the karst cave is judged, and further a quantitative judgment standard for identifying the type of the karst cave is provided, so that the extraction of the karst cave in the target oil and gas well is facilitated, and the defects of the existing karst cave extraction method are overcome.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a karst cave extraction method for three-dimensional geostress field simulation of a fracture-cavity type oil reservoir according to an embodiment of the present invention;
FIG. 2 is a deep lateral resistivity log, a natural gamma log, a compensated neutron log, a density log, and a sonic moveout log of example 1;
FIG. 3 is seismic data volume amplitude data from example 1;
FIG. 4 is the RMS amplitude data for example 1;
FIG. 5 is a deep lateral resistivity log, a natural gamma log, a compensated neutron log, a density log, and a sonic moveout log of example 2;
FIG. 6 is seismic data volume amplitude data from example 2;
FIG. 7 is the RMS amplitude data for example 2;
FIG. 8 shows a cavern in a section of an oilfield extracted in example 2;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a non-transitory computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
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 apparent therefrom. It will be understood by those skilled in the art that these specific embodiments and examples are for the purpose of illustrating the invention and are not to be construed as limiting the invention.
Throughout the specification, unless otherwise specifically noted, terms used herein should be understood as having meanings as commonly used in the art. Accordingly, unless defined otherwise, 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. If there is a conflict, the present specification will control.
Unless otherwise specifically stated, various raw materials, reagents, instruments, equipment and the like used in the present invention are commercially available or can be prepared by existing methods.
Referring to fig. 1, in an embodiment of the present application, the present invention provides a karst cave extraction method for three-dimensional geostress field simulation of a fracture-cavity type oil reservoir, where the method includes the steps of:
s1: acquiring logging data of a target oil-gas well;
in an embodiment of the present application, the acquiring of the logging data of the target hydrocarbon well in step S1 includes the steps of:
acquiring imaging logging data of the target oil-gas well;
acquiring logging curve data of the target oil-gas well;
and acquiring the seismic data volume of the target oil-gas well.
In the embodiment of the application, when the logging data of the target oil and gas well is obtained, the imaging logging data, the logging curve data and the seismic data volume of the target oil and gas well are mainly obtained for subsequent analysis and use.
In an embodiment of the present application, the acquiring logging curve data of the target hydrocarbon well includes:
obtaining a deep lateral resistivity logging curve of the target oil and gas well;
acquiring a natural gamma ray logging curve of the target oil-gas well;
acquiring a compensated neutron logging curve of the target oil-gas well;
acquiring a density logging curve of the target oil-gas well;
and acquiring a sound wave time difference logging curve of the target oil-gas well.
In the embodiment of the application, when the logging curve data of the target oil and gas well is obtained, the deep lateral resistivity logging curve, the natural gamma ray logging curve, the compensated neutron logging curve, the density logging curve and the sound wave time difference logging curve of the target oil and gas well are mainly obtained, and the deep lateral resistivity logging curve, the natural gamma ray logging curve, the compensated neutron logging curve, the density logging curve and the sound wave time difference logging curve are used for follow-up analysis.
S2: acquiring the development characteristics of the karst cave in the target oil-gas well according to the logging data;
in an embodiment of the present application, the step S2 of obtaining the developmental characteristics of the cavern in the target hydrocarbon well according to the logging data includes the steps of:
acquiring the development characteristics of the large karst cave of the karst cave;
acquiring the development characteristics of the erosion cavities of the karst caves;
and acquiring the erosion crack development characteristics of the karst cave.
In this application embodiment, when according to logging data obtains the development characteristic of solution cavity in the target oil gas well, the development characteristic of solution cavity is large-scale solution cavity development characteristic, solution cavity development characteristic and solution crack development characteristic, consequently obtain above-mentioned three development characteristic respectively can.
In the embodiment of the present application, the large cavern refers to a reservoir stratum with the large cavern as a main reservoir space, and a large cavern is easily developed at the intersection of the conjugate seams due to erosion, so the large cavern reservoir stratum is often accompanied by the erosion cavern and the erosion cracks, and the large cavern development characteristic is as follows: the bi-lateral resistivity values are obviously reduced and show large difference; the natural gamma value is increased compared with that of the surrounding rock, the karst cave is in a reverse arch shape, and the uranium removal gamma value is also increased compared with that of the surrounding rock; the double-well-diameter curve has obvious diameter expansion phenomenon; the density value curve is in a bow shape at the karst cave, and the density value is greatly reduced.
In the embodiment of the present application, an erosion cavern refers to a reservoir stratum with the erosion cavern as a main reservoir space, and a large-scale erosion cavern does not develop, and the erosion cavern is characterized by: the value of the bi-lateral resistivity is significantly reduced, exhibiting a small "negative difference"; the uranium removal gamma value is very low; neutron porosity is slightly increased; the density values decreased slightly.
In the embodiment of the present application, an erosion fracture refers to a reservoir stratum with an erosion fracture as a main reservoir stratum space, a large-scale karst cave is not developed, the fracture is often eroded, some fractures are even eroded to form a small erosion cave, and the erosion fracture development characteristic is as follows: filling the imaging log image with mud, wherein the imaging log image is shaped like a small round hole, and part of the imaging log image is not filled with high-resistance minerals such as karst pores and karst caves, and the imaging log image is dark in color, and the cracks displayed by the imaging log data are represented as black sine lines; the development of the crack has a more obvious influence on the bilateral values, and the development is shown as the 'difference' of the depth lateral values.
S3: obtaining a quantitative judgment standard for identifying the type of the karst cave according to the logging data;
in an embodiment of the present application, the step S3 of obtaining the quantitative determination criterion for identifying the type of the cavern according to the logging data includes the steps of:
setting a judgment standard database;
acquiring a karst cave type;
storing all the karst cave types into the judgment standard database;
acquiring logging curve data in the logging data;
acquiring logging curve data corresponding to each karst cave type;
and correspondingly storing the logging curve data into the judgment standard database.
In this embodiment of the present application, when obtaining a quantitative determination standard for identifying a karst cave type according to the logging data, a vacant determination standard database is first set, and then the karst cave type is obtained, where there are four karst cave types in this embodiment, which are: the method comprises the steps of filling unfilled large karst caves, partially filled large karst caves, fully filled large karst caves and small karst caves, storing the four karst cave types into a judgment standard database, acquiring logging curve data corresponding to each type of karst cave, and correspondingly storing the logging curve data into the judgment standard database and corresponding to the corresponding karst cave types one to one.
In the embodiment of the application, the quantitative judgment standard for acquiring the karst cave type identification by the logging data is specifically as follows:
according to the table, the karst cave can be well classified and identified.
In an embodiment of the present application, the obtaining of the karst cave type includes:
acquiring the development characteristics of the large karst cave;
judging the karst cave development condition according to the large karst cave development characteristics;
acquiring imaging logging data;
judging the karst cave filling condition according to the imaging logging data;
and judging the karst cave type according to the karst cave development condition and the karst cave filling condition.
In the embodiment of the present application, when acquiring a karst cave type, a large karst cave development characteristic is first acquired, and specifically, the large karst cave development characteristic is as follows: the bi-lateral resistivity values are obviously reduced and show large difference; the natural gamma value is increased compared with that of the surrounding rock, the karst cave is in a reverse arch shape, and the uranium removal gamma value is also increased compared with that of the surrounding rock; the double-well-diameter curve has obvious diameter expansion phenomenon; the density value curve is in a bow shape at the karst cave, and the density value is greatly reduced. When the characteristic is satisfied, the karst cave is a large karst cave, otherwise, the karst cave is a small karst cave; then acquiring imaging logging data, and judging the filling condition of the karst cave by using the imaging logging data, namely unfilled, partially filled or fully filled; and then judging the type of the karst cave according to the development condition and the filling condition of the karst cave, namely judging that the karst cave is any one of an unfilled large karst cave, a partially filled large karst cave, a fully filled large karst cave and a small karst cave.
S4: acquiring the distribution condition of the karst caves in the target oil-gas well;
in an embodiment of the present application, the obtaining of the distribution of the caverns in the target hydrocarbon well in step S4 includes:
acquiring a seismic data volume in the logging data;
obtaining a root mean square amplitude calculation formula;
and calculating the karst cave distribution condition in the seismic data volume acquisition range by using the root-mean-square amplitude calculation formula.
In the embodiment of the application, when the distribution condition of the karst caves in the target oil-gas well is obtained, the root mean square amplitude is calculated for the seismic data volume by adopting a root mean square amplitude calculation formula, so that the karst cave distribution condition in the seismic data volume acquisition range is obtained, and the karst cave distribution condition on the target well track is obtained. The step aims to perform vertical smoothing processing on the amplitude data of all seismic data bodies in the sampling time window, so that the boundary of the karst cave is clearer, and the subsequent extraction step is facilitated.
In the embodiment of the present application, the expression of the root-mean-square amplitude calculation formula is:
where RMS denotes the root mean square amplitude, aiAnd N represents the total sampling number of the seismic data body in the sampling time window.
S5: and extracting the karst cave from the target oil-gas well.
In the embodiment of the application, after the distribution condition of the karst caves on the target well track is obtained, the karst caves can be easily extracted from the target oil and gas well. Specifically, the method can be realized by correlating the logging data with the seismic data volume, then extracting a karst cave in the seismic data volume aiming at a certain block of the oil field,
the following describes a karst cave extraction method for simulating a three-dimensional geostress field of a fracture-cavity type oil reservoir in detail by using a specific embodiment.
Example 1:
and collecting a deep lateral resistivity logging curve, a natural gamma logging curve, a compensated neutron logging curve, a density logging curve, an acoustic time difference logging curve and a seismic data volume of the target oil-gas well. The deep lateral resistivity log, the natural gamma log, the compensated neutron log, the density log and the acoustic moveout log are shown in figure 2, and the seismic data volume amplitude data at the depth of 6328m is shown in figure 3.
And judging whether the karst cave exists or not and judging the filling condition of the karst cave according to a deep lateral resistivity logging curve, a natural gamma logging curve, a compensated neutron logging curve, a density logging curve and a sound wave time difference logging curve. As shown in fig. 2, the compensated neutron parameter of the bedrock is 71.86, the density parameter of the bedrock is 2.682, and the acoustic wave time difference parameter of the bedrock is 51.49; at depth 6328m, the deep lateral resistivity is about 95, the natural gamma parameter is about 10, the compensated neutron parameter is about 8, the density parameter is about 2.35, and the sonic time difference parameter is about 53. According to the judgment method shown in Table 1, it was revealed that unfilled large-sized caverns developed at a depth of 6328 m.
The seismic data volume amplitude data is converted to root mean square amplitude according to a root mean square amplitude calculation formula, as shown in figure 4.
Example 2:
and collecting a deep lateral resistivity logging curve, a natural gamma logging curve, a compensated neutron logging curve, a density logging curve, an acoustic time difference logging curve and a seismic data volume of the target oil-gas well. The deep lateral resistivity logging curve, the natural gamma logging curve, the compensated neutron logging curve, the density logging curve and the acoustic time difference logging curve are shown in the attached figure 5, and the seismic data volume amplitude data at the position of 6228m in depth is shown in the attached figure 6;
and judging whether the karst cave exists or not and judging the filling condition of the karst cave according to a deep lateral resistivity logging curve, a natural gamma logging curve, a compensated neutron logging curve, a density logging curve and a sound wave time difference logging curve. As shown in fig. 5, the compensation neutron parameter of the bedrock is 74.92, the density parameter of the bedrock is 2.727, and the acoustic wave time difference parameter of the bedrock is 58.64; at a depth of 6228m, the deep lateral resistivity was about 60, the natural gamma parameter was about 10, the compensated neutron parameter was about 84, the density parameter was about 2.88, and the acoustic wave time difference parameter was about 80, and it was shown that a small karst cave developed at a depth of 6228m according to the determination method shown in table 1.
The seismic data volume amplitude data is converted to root mean square amplitude according to a root mean square amplitude calculation formula, as shown in figure 7. By correlating the well log data with the seismic data volume, a cavern is extracted from the seismic data volume for a block in the oil field, as shown in fig. 8.
Referring to fig. 9, an embodiment of the present disclosure also provides an electronic device 100, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the solution cavity extraction method for three-dimensional geostress field simulation of a fracture-cavity reservoir in the above method embodiments.
The disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the foregoing method embodiments.
The disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the karst cave extraction method for three-dimensional geostress field simulation of a fracture-cavity reservoir in the aforementioned method embodiments.
According to the karst cave extraction method and the electronic equipment for simulating the three-dimensional geostress field of the oil reservoir, logging data (such as a deep lateral resistivity logging curve, a natural gamma logging curve, a compensated neutron logging curve, a density logging curve, an acoustic wave time difference logging curve and a seismic data volume) are comprehensively utilized to identify whether a karst cave is developed in a target oil and gas well, the filling state of the karst cave is judged, and further a quantitative judgment standard for identifying the type of the karst cave is provided, so that the extraction of the karst cave in the target oil and gas well is facilitated, and the defects of the existing karst cave extraction method are overcome.
Referring now to FIG. 9, a block diagram of an electronic device 100 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 9 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 9, the electronic device 100 may include a processing means (e.g., a central processing unit, a graphic processor, etc.) 101 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)102 or a program loaded from a storage means 108 into a Random Access Memory (RAM) 103. In the RAM 103, various programs and data necessary for the operation of the electronic apparatus 100 are also stored. The processing device 101, the ROM 102, and the RAM 103 are connected to each other via a bus 104. An input/output (I/O) interface 105 is also connected to bus 104.
Generally, the following devices may 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.; an output device 107 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 108 including, for example, magnetic tape, hard disk, etc.; and a communication device 109. The communication means 109 may allow the electronic device 100 to communicate wirelessly or by wire with other devices to exchange data. While the figures illustrate an electronic device 100 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment 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 embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 109, or installed from the storage means 108, or installed from the ROM 102. The computer program, when executed by the processing device 101, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
Referring now to fig. 10, there is shown a schematic diagram of a computer readable storage medium suitable for implementing an embodiment of the present disclosure, the computer readable storage medium storing a computer program which, when executed by a processor, is capable of implementing a karst cave extraction method for three-dimensional geostress field simulation of a fracture-cavity reservoir as described in any one of the above.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.
Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
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, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block 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 will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving 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 is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element. The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice 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 applied to other embodiments without departing from the spirit or scope of the application. Thus, the present 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 disclosed herein.
In short, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A karst cave extraction method for three-dimensional geostress field simulation of an oil reservoir is characterized by comprising the following steps:
acquiring logging data of a target oil-gas well;
acquiring the development characteristics of the karst cave in the target oil-gas well according to the logging data;
obtaining a quantitative judgment standard for identifying the type of the karst cave according to the logging data;
acquiring the distribution condition of the karst caves in the target oil-gas well;
and extracting the karst cave from the target oil-gas well.
2. The method for extracting the karst cave for simulating the three-dimensional geostress field of the oil reservoir according to claim 1, wherein the step of obtaining the logging data of the target oil and gas well comprises the following steps:
acquiring imaging logging data of the target oil-gas well;
acquiring logging curve data of the target oil-gas well;
and acquiring the seismic data volume of the target oil-gas well.
3. The method for extracting the karst cave for simulating the three-dimensional geostress field of the oil reservoir according to claim 2, wherein the step of obtaining the logging curve data of the target oil and gas well comprises the following steps:
obtaining a deep lateral resistivity logging curve of the target oil and gas well;
acquiring a natural gamma ray logging curve of the target oil-gas well;
acquiring a compensated neutron logging curve of the target oil-gas well;
acquiring a density logging curve of the target oil-gas well;
and acquiring a sound wave time difference logging curve of the target oil-gas well.
4. The method for extracting the karst cave in the three-dimensional geostress field simulation of the oil reservoir according to claim 1, wherein the step of obtaining the development characteristics of the karst cave in the target oil and gas well according to the logging data comprises the following steps:
acquiring the development characteristics of the large karst cave of the karst cave;
acquiring the development characteristics of the erosion cavities of the karst caves;
and acquiring the erosion crack development characteristics of the karst cave.
5. The method for extracting the karst cave in the three-dimensional geostress field simulation of the oil reservoir according to claim 1, wherein the step of obtaining the quantitative judgment standard for the type identification of the karst cave according to the logging data comprises the following steps:
setting a judgment standard database;
acquiring a karst cave type;
storing all the karst cave types into the judgment standard database;
acquiring logging curve data in the logging data;
acquiring logging curve data corresponding to each karst cave type;
and correspondingly storing the logging curve data into the judgment standard database.
6. The method for extracting the karst cave in the three-dimensional geostress field simulation of the oil reservoir according to claim 5, wherein the obtaining of the type of the karst cave comprises the following steps:
acquiring the development characteristics of the large karst cave;
judging the karst cave development condition according to the large karst cave development characteristics;
acquiring imaging logging data;
judging the karst cave filling condition according to the imaging logging data;
and judging the karst cave type according to the karst cave development condition and the karst cave filling condition.
7. The method for extracting the karst caves for simulating the three-dimensional geostress field of the oil reservoir according to claim 1, wherein the step of obtaining the distribution condition of the karst caves in the target oil and gas well comprises the following steps:
acquiring a seismic data volume in the logging data;
obtaining a root mean square amplitude calculation formula;
and calculating the karst cave distribution condition in the seismic data volume acquisition range by using the root-mean-square amplitude calculation formula.
8. The method for extracting the karst caves for simulating the three-dimensional geostress field of the oil reservoir according to claim 7, wherein the expression of the root-mean-square amplitude calculation formula is as follows:
where RMS denotes the root mean square amplitude, aiAnd N represents the total sampling number of the seismic data body in the sampling time window.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of karst cave extraction for three-dimensional geostress field simulation of a fracture-cavity reservoir as claimed in any one of claims 1 to 8.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of karst cave extraction for three-dimensional geostress field simulation of a fracture-vug reservoir as claimed in any one of claims 1 to 8.
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