CN112395745A - Underground river reservoir body geological model establishing method and processing equipment - Google Patents
Underground river reservoir body geological model establishing method and processing equipment Download PDFInfo
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Abstract
The specification provides a method and a device for establishing a geological model of an underground river reservoir body. In one method embodiment, modeling data may be obtained that includes at least fracture development probability map data corresponding to fractures in a fracture group, probability density functions of fracture angles and lengths, probability density functions of cavern widths corresponding to fractures of each group; carrying out main stem simulation of the underground river karst cave based on the modeling data; performing underground river karst cave branch simulation based on the modeling data; and stopping corresponding simulation processing when the simulation ending condition of the trunk simulation or the branch simulation is reached. By utilizing the embodiment of the invention, the underground river reservoir body form and scale can be effectively simulated, the simulation effect has good continuity, the crack distribution in a research area is highly matched, the operation is simple, convenient and efficient, a plurality of simulation implementations can be formed, the uncertainty analysis of the underground river reservoir body development is further carried out, and the distribution prediction precision of the underground river reservoir body is improved.
Description
Technical Field
The invention relates to the technical field of petroleum and natural gas exploration and development, in particular to a method for establishing a geological model of an underground river reservoir body and processing equipment.
Background
Underground river is a river below the ground, mainly developed in limestone and developed in both modern and ancient times. Underground rivers which develop in geological history period (such as 4 hundred million years ago) are likely to be filled with sand, mud or gravel, and then buried by later deposition and buried underground (such as 400 meters underground) to become ancient underground rivers. When oil and gas are transported into underground rivers, the entire underground river is called an underground river reservoir.
In a karst cave type carbonate oil reservoir, the underground river reservoir body greatly contributes to the productivity, so that the underground river reservoir body distribution prediction and three-dimensional geological modeling are very important. In the field of underground river reservoir modeling technology, methods such as sequential indication simulation methods and multipoint geostatistical-based modeling methods have been proposed. Although the methods achieve certain effects in the aspect of carbonate underground river modeling, the underground river reservoir body simulated by the methods still has larger deviation with the underground river reservoir body which is actually developed in the research area.
Disclosure of Invention
The purpose of the description is to provide an underground river reservoir body geological model establishing method and processing equipment, which can effectively simulate the shape and scale of the underground river reservoir body, have good simulation effect continuity, are highly matched with the crack distribution of a research area, are simple, convenient and efficient to operate, can form a plurality of simulation implementations, further perform uncertainty analysis on the development of the underground river reservoir body, and improve the distribution prediction precision of the underground river reservoir body.
The method and the processing equipment for establishing the geological model of the underground river reservoir provided by the embodiment of the specification are at least realized in the following modes:
a geological model building method for underground river reservoir bodies comprises the following steps:
obtaining modeling data, wherein the modeling data at least comprises fracture development probability map data corresponding to fractures in fracture group systems, probability density functions of fracture angles and lengths, and probability density functions of karst cave widths corresponding to fractures of each group system;
carrying out main stem simulation of the underground river karst cave based on the modeling data;
performing underground river karst cave branch simulation based on the modeling data;
and stopping corresponding simulation processing when the simulation ending condition of the trunk simulation or the branch simulation is reached.
In a preferred embodiment, when the trunk simulation or branch simulation of the underground river karst cave is performed, the method comprises the following steps:
randomly generating a first crack, and simulating a first section karst cave formed by the crack;
randomly generating a next crack connected with the first crack, and simulating a next section of karst cave formed by the next crack;
and sequentially and circularly simulating the next section of karst cave, and stopping the corresponding main simulation or branch simulation of the underground river karst cave until the end standard of the main simulation or branch simulation is reached.
In a preferred embodiment, the randomly generating a first crack and simulating a first section of karst cave formed by the first crack comprises:
randomly selecting a crack group;
when the main trunk of the underground river karst cave is simulated, randomly selecting a point from a first line as the starting point of a first crack by taking the numerical value of the first line of a crack development probability map of the selected crack group system as a standard;
randomly selecting a numerical value from the probability density functions of the crack length and the crack angle of the crack group system as the angle and the length of a first crack to generate the first crack;
randomly selecting a numerical value from the karst cave width probability density function corresponding to the crack group system as the width of the first section of karst cave, and generating a preferred embodiment, when the first section of karst cave is simulated in the simulation of underground river karst cave branches, the initial point of the first crack is selected from the point set where the crack in the main trunk of the underground river karst cave is located.
In a preferred embodiment, the simulating a next stage cavern formed by the next fracture comprises:
randomly selecting one crack group as the crack group of the next crack connected with the first crack and marking as a new crack group in other crack groups except the crack group where the first crack is located;
acquiring a crack probability value of a point set where a first crack is located on a crack development probability graph corresponding to the new crack group system, randomly selecting a point as a starting point of a next crack according to the value, wherein the probability that the point with the larger probability value is selected as the starting point of the next crack is larger;
randomly selecting a numerical value from the probability density functions of the crack length and the crack angle of the new crack group system as the angle and the length of the next crack, and generating the next crack;
and randomly selecting a numerical value from the karst cave width probability density function corresponding to the new fracture group as the width of the next section of karst cave, and generating the next section of karst cave.
In a preferred embodiment, the ending criteria for the underground river cavern trunk simulation includes that the newly simulated cavern segment meets the left, lower and right boundaries of the study area.
In a preferred embodiment, the termination criteria for the simulation of the underground river karst cave branch include:
the newly simulated karst cave sections meet four boundaries of a research area or the number of the karst cave sections in underground river branches reaches a certain preset value.
In a preferred embodiment, a plurality of underground river branches extend from a main trunk of the underground river, and the number of the branches is preset.
A processing apparatus for underground river reservoir geological modeling, comprising:
the data acquisition module is used for acquiring modeling data, wherein the modeling data at least comprises fracture development probability map data corresponding to fractures in a fracture group system, probability density functions of fracture angles and lengths and probability density functions of karst cave widths corresponding to fractures of each group system;
the main stem simulation module is used for carrying out main stem simulation on the underground river karst cave based on the modeling data;
the branch simulation module is used for carrying out underground river karst cave branch simulation based on the modeling data;
and the ending module is used for stopping corresponding simulation processing when the simulation ending condition of the trunk simulation or the branch simulation is reached.
A processing apparatus for underground river reservoir geological modeling, comprising: at least one processor and a memory for storing processor-executable instructions, which when executed by the processor perform the steps of any one of the method embodiments described herein.
The underground river reservoir body geological model building method and the processing equipment provided by the embodiment of the specification can effectively simulate the shape and scale of the underground river reservoir body, have good simulation effect continuity, are highly matched with the crack distribution of a research area, are simple and efficient to operate, can form a plurality of simulation implementations, further perform uncertainty analysis on the development of the underground river reservoir body, and improve the distribution prediction precision of the underground river reservoir body.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is a schematic flow chart diagram of an embodiment of a subsurface river reservoir geological model creation method provided herein;
FIG. 2 is a schematic diagram of a main trunk (or branch) simulation process of a subsurface river karst cave provided by the present specification;
FIG. 3 is a schematic diagram of the first section of karst cave simulation (the background is a crack development probability diagram) according to the present invention;
FIG. 4 is a schematic diagram of the present invention for performing a second stage karst cave simulation based on the first stage karst cave in FIG. 3 (the background is a probability diagram of crack development)
FIG. 5 is a schematic diagram of two example underground river reservoirs simulated using embodiments of the present description;
FIG. 6 is a block diagram of a server device hardware architecture to which an embodiment of a method of subsurface river reservoir geological model creation of the present invention is applied;
fig. 7 is a schematic block diagram of an embodiment of a processing device for underground river reservoir geological model building provided by the present specification.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.
In a karst cave type carbonate oil reservoir, the underground river reservoir body greatly contributes to the productivity, so that the underground river reservoir body distribution prediction and three-dimensional geological modeling are very important. The applicant has found that from a causal point of view, underground rivers are actually formed by erosion of groundwater along interconnected fractures widely distributed in the rock, and therefore the development of underground rivers has a strong dependence on the development of interconnected fractures.
On the other hand, some methods which are proposed at present, such as a sequential indication simulation method and a multipoint geostatistics-based modeling method, achieve certain effects in the aspect of carbonate rock underground river modeling, and on the one hand, because the methods do not consider the strong control effect of the connected cracks on the underground river, the simulated underground river reservoir body has larger deviation with the underground river reservoir body which is actually developed in a research area. If the carbonate rock underground river reservoir body is simulated by applying the sequential indication simulation method, the current very mature sequential indication simulation algorithm based on the variation function is mainly adopted to randomly simulate the spatial distribution of the underground river reservoir body, but the underground river simulated by the method has poor continuity, strong randomness, disordered geometric form and quite far difference from the actual situation.
The carbonate rock underground river reservoir body is simulated by a modeling method based on multipoint geostatistics (see patent document with application number of CN201811265621.7, entitled 'method, device and system for establishing underground river reservoir body geological model'). The main steps in some implementations include: firstly, scanning a prepared training image to obtain a multipoint statistical rule; then, acquiring underground river development data (template) of surrounding pixels for each pixel to be simulated, searching the possibility of underground river development at the target pixel from a multipoint statistical rule, and randomly assigning values at the target pixel according to the possibility; and finally, repeating the previous step for each pixel to finish the random simulation of the underground river. The underground river simulated by the method has poor connectivity, and meanwhile, because the influence of the cracks on the distribution of the underground river is not considered, the situation that the simulated underground river is not matched with the distribution of the cracks actually developed underground often occurs. In the invention, the whole underground river karst cave is divided into a plurality of sections of small karst caves, and each section of small karst cave is enlarged from the crack, so that the control effect of the crack on the underground river is fully embodied by some embodiment schemes in the specification. Of course, the fractures described in this specification are generally connected fractures and are not independent.
Therefore, the description provides a new underground river reservoir modeling idea and a specific method, so that the simulated underground river reservoir has good continuity, and the consistency with the underground real crack development rule is improved.
The following description is provided with a specific implementation scenario of the target-based underground river cavern reservoir modeling. Specifically, fig. 1 is a schematic flow chart of an embodiment of a subsurface river reservoir geological model building method provided in this specification. Although the present specification provides method operational steps or devices, system configurations, etc., as illustrated in the following examples or figures, more or less operational steps or modular units may be included in the methods or devices, as may be conventional or may be part of the inventive subject matter, based on conventional or non-inventive considerations. In the case of steps or structures which do not logically have the necessary cause and effect relationship, the execution sequence of the steps or the module structure of the apparatus is not limited to the execution sequence or structure shown in the embodiment or the drawings in this specification. When the apparatus, server, system or end product of the method or system architecture is applied in an actual device, server, system or end product, the method or module architecture according to the embodiment or the drawings may be executed sequentially or executed in parallel (for example, in an environment of parallel processors or multi-thread processing, or even in an environment of distributed processing, server clustering, or implementation in combination with cloud computing or block chain technology).
Of course, the following description of the embodiments does not limit other scalable solutions obtained based on the embodiments of the present disclosure. Specifically, an embodiment of a method for establishing a geologic model of a subsurface river reservoir provided in the present specification is shown in fig. 1, and may include:
s0: obtaining modeling data, wherein the modeling data at least comprises fracture development probability map data corresponding to the fractures in each fracture group, probability density functions of fracture angles and lengths, and probability density functions of karst cave widths corresponding to the fractures of each group. The acquisition methods of these types of data can be implemented by those skilled in the art in geological modeling. For example, the main basis for obtaining the fracture development probability map is that the fracture development probability is in a negative correlation with the distance to the nearby large fault, and the main steps can include: counting the functional relation between the crack development frequency (probability) and the distance to a nearby large fault in the field outcrop; manually interpreting subsurface faults from the artificial seismic data; applying the function relation to the periphery of the underground fault to obtain the data of the underground crack development probability map; the probability density functions of the crack angle, the length and the karst cave width are obtained by field outcrop statistics.
S2: carrying out main stem simulation of the underground river karst cave based on the modeling data;
s4: performing underground river karst cave branch simulation based on the modeling data;
s6: and stopping corresponding simulation processing when the simulation ending condition of the trunk simulation or the branch simulation is reached.
In general, the angles, lengths, and other characteristics of the cracks formed at different times are different, and a plurality of cracks with similar characteristics of the angles, lengths, and other characteristics formed at a certain time may be referred to as a crack group, or simply a group.
Fractures are typically distributed in the subsurface over 2 families, with the angle, length, etc. properties of the fractures in each family being significantly different. For each cluster of fractures, a probability density function of its corresponding fracture development probability map (a type of fracture development probability map data), fracture angle, and length may be obtained. Since the karst cave is formed by the dissolution and development of the cracks, the karst cave widths formed by the cracks of different sets are different, so that the probability density function of the karst cave width corresponding to each set of cracks can be obtained in the embodiment.
The karst cave is formed by dissolving and expanding a plurality of connected cracks. As shown in fig. 2, when the trunk or branch simulation of the underground river karst cave is performed, the method may include:
s20: randomly generating a first crack, and simulating a first section karst cave formed by the crack;
s22: randomly generating a next crack connected with the first crack, and simulating a next section of karst cave formed by the next crack;
s24: and sequentially and circularly simulating the next section of karst cave, and stopping the corresponding main simulation or branch simulation of the underground river karst cave until the end standard of the main simulation or branch simulation is reached.
As shown in fig. 3, in the first section of karst cave simulation, first, a fracture set may be randomly selected; next, a point is randomly selected from the top line (the first line shown by L) as the starting point of the first crack, based on the top line numerical value of the group of crack growth probability maps. Generally, the higher the fracture probability value, the higher the probability that a point is selected as the starting point; then, randomly selecting a numerical value from the probability density functions of the crack length and the crack angle of the crack group system as the angle and the length of the initial crack, and generating the initial crack; further, a value can be randomly selected from the probability density function of the solution cavity width corresponding to the fracture group as the width of the first-segment solution cavity, and the first-segment solution cavity is generated. Thus, in another embodiment of the method, the randomly generating a first fracture and simulating a first section of the cavern formed by the first fracture may comprise:
s200: randomly selecting a crack group;
s202: when the main trunk of the underground river karst cave is simulated, randomly selecting a point from a first line as the starting point of a first crack by taking the numerical value of the first line of a crack development probability map of the selected crack group system as a standard;
s204: randomly selecting a numerical value from the probability density functions of the crack length and the crack angle of the crack group system as the angle and the length of a first crack to generate the first crack;
s206: randomly selecting a numerical value from the karst cave width probability density function corresponding to the crack group as the width of the first section of karst cave, and generating the first section of karst cave.
As shown in fig. 4, in other fracture groups than the first fracture group, one fracture group may be randomly selected as the fracture group of the next fracture connected to the first fracture, and is recorded as a new fracture group; then, acquiring a crack probability value of a point set where the first crack is located on a crack development probability graph corresponding to the new crack group system, randomly selecting a point as a starting point of the next crack according to the value of the crack probability value, wherein the probability that the point with the larger probability value is selected as the starting point of the next crack is also larger; then, randomly selecting a numerical value from the probability density functions of the crack length and the crack angle of the new crack group system as the angle and the length of the next crack, and generating the next crack; and finally, randomly selecting a numerical value from the karst cave width probability density function corresponding to the new crack group as the width of the next section of karst cave, and generating the next section of karst cave. Thus, in another embodiment of the method described herein, the simulating a next stage cavern formed by the next fracture may comprise:
s220: randomly selecting one crack group as the crack group of the next crack connected with the first crack and marking as a new crack group in other crack groups except the crack group where the first crack is located;
s222: acquiring a crack probability value of a point set where a first crack is located on a crack development probability graph corresponding to the new crack group system, randomly selecting a point as a starting point of a next crack according to the value, wherein the probability that the point with the larger probability value is selected as the starting point of the next crack is larger;
s224: randomly selecting a numerical value from the probability density functions of the crack length and the crack angle of the new crack group system as the angle and the length of the next crack, and generating the next crack;
s226: and randomly selecting a numerical value from the karst cave width probability density function corresponding to the new fracture group as the width of the next section of karst cave, and generating the next section of karst cave.
And sequentially and circularly simulating the next section of karst cave according to the mode of S22 until the ending standard is reached and the simulation of the karst cave trunk is ended. In one embodiment of the present description, the termination criteria for the cavern trunk simulation may include that the newly simulated cavern segment hits the left, lower, and right boundaries of the study area.
The foregoing describes a processing method for simulation of the main trunk of the underground river karst cave. The simulation step of the underground river karst cave branch is similar to the simulation of the underground river karst cave trunk, and may also include a first section of karst cave simulation, a next section of karst cave simulation, and the completion of the karst cave branch simulation when the completion criterion is reached, which may specifically refer to the related description of the foregoing steps S2, S20-S24, or S200-S206. In other embodiments, the simulation step of the underground river karst cave branch and the simulation of the underground river karst cave main trunk may also perform different processes, including:
(1) when the first section karst cave is simulated, the initial point of the first crack is not selected from the upper boundary (first row) of the research area, but is selected from the point set where the crack in the main trunk of the underground river karst cave is located;
(2) the end standard of the underground river karst cave branch simulation is changed into: the newly simulated karst cave sections meet four boundaries (upper, lower, left and right) of a research area or the number of the karst cave sections in underground river branches reaches a certain preset value. In addition, a plurality of underground river branches can be extended from one underground river trunk, and the number of the branches can be manually preset.
The following description will take a specific oil field in northwest of china as an example to illustrate the implementation and technical effects of the present specification. The development of the underground river reservoir body of the oil field utilizes the method of the embodiment of the specification to carry out the random simulation work of the underground river reservoir body in the region. Three groups of cracks are distributed in the oil field, angles are respectively about 20 degrees, 80 degrees and 130 degrees, density probability functions of the lengths of the cracks are normally distributed, the average value of the density probability functions is 4 meters, the standard deviation of the density probability functions is 1.4 meters, and karst cave widths are normally distributed, the average value of the karst cave widths is 7 meters, and the standard deviation of the karst cave widths is 2.4 meters. The two subsurface river reservoirs obtained from the simulation, example 1 and example 2, are shown in figure 5. As can be seen from fig. 5, the trend of the karst cave simulated by the embodiment of the present disclosure has a very high angle fit with the fracture, and the continuity of the simulation result is better.
Therefore, the underground river reservoir body geological model building method provided by the embodiment of the specification can effectively simulate the form and scale of the underground river reservoir body, has good simulation effect continuity, is highly matched with the crack distribution of a research area, is simple, convenient and efficient to operate, can form a plurality of simulation implementations, further performs uncertainty analysis on the development of the underground river reservoir body, and improves the distribution prediction precision of the underground river reservoir body.
In the present specification, each embodiment of the method is described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. Reference is made to the description of the method embodiments.
It is understood that all or part of the steps of the method described in the above embodiments may be executed on a computing device of a certain participant or performed by computing and communication among multiple participants, may be executed by a server of a third party, or may be executed by a third server and one or more participants together (e.g., a platform used by the participants together).
The method embodiments provided in the embodiments of the present specification may be executed in a handheld terminal, a computer terminal, a server cluster, a mobile terminal, a blockchain node, a distributed network, or a similar computing device. The apparatus may include a system (including a distributed system), software (applications), modules, components, servers, clients, etc. that employ embodiments of the present description in conjunction with any necessary hardware for implementation. Taking a server running on a server as an example, fig. 6 is a hardware structure block diagram of a server device to which an embodiment of the underground river reservoir geological model building method of the present invention is applied. As shown in fig. 6, the server 10 may include one or more (only one shown) processors 100 (the processors 100 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 200 for storing data, and a transmission module 300 for communication functions. It will be understood by those skilled in the art that the structure shown in fig. 6 is merely illustrative and is not intended to limit the structure of the electronic device. For example, the server 10 may also include more or fewer components than shown in FIG. 6, and may also include other processing hardware, such as an internal bus, memory, database or multi-level cache, a display, or have other configurations than shown in FIG. 6, for example.
The memory 200 may be used to store software programs and modules of application software, and the processor 100 executes various functional applications and data processing by operating the software programs and modules stored in the memory 200. Memory 200 may include high speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 200 may further include memory located remotely from processor 100, which may be connected to server 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission module 300 is used for receiving or transmitting data via a network. Examples of such networks may include a blockchain private network of the server 10 or a network provided by the world wide web or a communications provider. In one example, the transmission module 300 includes a Network adapter (NIC) that can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission module 300 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
Based on the description of the embodiment of the underground river reservoir body geological model building method, the specification also provides underground river reservoir body geological model building processing equipment. The device may be used in a multi-party participating data sharing application scenario. The apparatus may include systems (including distributed systems), software (applications), modules, components, servers, clients, etc. that use the methods described in the embodiments of the present specification in conjunction with any necessary apparatus to implement the hardware. Based on the same innovative conception, embodiments of the present specification provide apparatuses in one or more embodiments as described in the following embodiments. Since the implementation scheme for solving the problem of the device is similar to that of the method, the specific implementation of the device in the embodiment of the present specification may refer to the implementation of the foregoing method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Specifically, fig. 7 is a schematic block diagram of an embodiment of a processing apparatus for underground river reservoir geological model building provided in this specification, and as shown in fig. 7, the apparatus may include:
a data obtaining module 70, configured to obtain modeling data, where the modeling data at least includes fracture development probability map data corresponding to fractures in a fracture group, probability density functions of fracture angles and lengths, and probability density functions of karst cave widths corresponding to fractures of each group;
a trunk simulation module 71, configured to perform trunk simulation of the underground river karst cave based on the modeling data;
a branch simulation module 72, configured to perform branch simulation of the underground river karst cave based on the modeling data;
a termination module 73, configured to stop corresponding simulation processing when a simulation termination condition of the trunk simulation or the branch simulation is reached
It should be noted that the above-mentioned descriptions of the apparatus according to the method embodiment may also include other implementations, or may also include, for example, a processing module (shown in dashed lines in fig. 7) to implement relevant steps of the method embodiment. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.
In the present specification, the embodiments of the apparatus described above are described in a progressive manner, and the same and similar parts among the embodiments are mutually referred to or described with reference to the corresponding method embodiments, and each embodiment focuses on the differences from the other embodiments. Reference is made to the description of the method embodiments. The specific details can be obtained according to the descriptions of the foregoing method embodiments, and all of them should fall within the scope of the implementation protected by this application, and no further description is given to implementation schemes of the embodiments one by one.
The method or the device for establishing the geological model of the underground river reservoir body, provided by the embodiments of the present specification, may be implemented by a processor executing corresponding program instructions in a computer, for example, implemented in a PC end using a C + + language of a Windows operating system, implemented based on a Linux system, or implemented in an intelligent terminal using Android and iOS system programming languages, or implemented in a server cluster, a cloud processing/cloud computing/cloud server, a block chain, and processing logic based on quantum computing, etc. Based on the description of the embodiment of the method, the specification also provides another processing device for establishing the geological model of the underground river reservoir body. In one embodiment, the processing device may include: at least one processor and a memory for storing processor-executable instructions, the instructions when executed by the processor implementing the steps of the method of any one of the present descriptions.
The processing device may comprise a device using any one of the method embodiments of the present description or comprising any one of the apparatus embodiments of the present description in combination with the necessary implementation hardware.
Storage media that store processor-executable instructions may include physical devices used to store information, typically digitized information and then stored using electrical, magnetic, or optical media. The storage medium may include: devices that store information using electrical energy, such as various types of memory, e.g., RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, tapes, core memories, bubble memories, and usb disks; devices that store information optically, such as CDs or DVDs. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth.
As mentioned above, the specific implementation manner of the embodiment of the processing device described above may refer to the description of the foregoing method embodiment. The description according to the method related embodiment may further include other embodiments, and the specific implementation may refer to the description of the corresponding method embodiment, which is not described in detail herein.
The foregoing description has been directed to specific embodiments of this disclosure. The embodiments described based on the above embodiments are extensible and still fall within the scope of implementations provided in the present specification. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.
The underground river reservoir body geological model building method and the processing equipment provided by the embodiment of the specification can effectively simulate the shape and scale of the underground river reservoir body, have good simulation effect continuity, are highly matched with the crack distribution of a research area, are simple and efficient to operate, can form a plurality of simulation implementations, further perform uncertainty analysis on the development of the underground river reservoir body, and improve the distribution prediction precision of the underground river reservoir body.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The server, the apparatus, and the module illustrated in the above embodiments may be implemented by a computer chip or an entity, or implemented by an article with certain functions. One typical implementation device is a server system. Of course, this application does not exclude that with future developments in computer technology, the computer implementing the functionality of the above described embodiments may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device or a combination of any of these devices.
Although one or more embodiments of the present description provide method operational steps as described in the embodiments or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive approaches. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or end product executes, it may execute sequentially or in parallel (e.g., parallel processors or multi-threaded environments, or even distributed data processing environments) according to the method shown in the embodiment or the figures. 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, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded. For example, if the terms first, second, etc. are used to denote names, they do not denote any particular order.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, when implementing one or more of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, etc. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage, graphene storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
As will be appreciated by one skilled in the art, one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the present specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description of the specification, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is merely exemplary of one or more embodiments of the present disclosure and is not intended to limit the scope of one or more embodiments of the present disclosure. Various modifications and alterations to one or more embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present specification should be included in the scope of the claims.
Claims (10)
1. A geological model building method for underground river reservoir bodies comprises the following steps:
obtaining modeling data, wherein the modeling data at least comprises fracture development probability map data corresponding to fractures in each fracture group, probability density functions of fracture angles and lengths, and probability density functions of karst cave widths corresponding to fractures of each group;
carrying out main stem simulation of the underground river karst cave based on the modeling data;
performing underground river karst cave branch simulation based on the modeling data;
and stopping corresponding simulation processing when the simulation ending condition of the trunk simulation or the branch simulation is reached.
2. The method of claim 1, when performing underground river karst cave trunk simulation or underground river karst cave branch simulation, comprising:
randomly generating a first crack, and simulating a first section karst cave formed by the crack;
randomly generating a next crack connected with the first crack, and simulating a next section of karst cave formed by the next crack;
and sequentially and circularly simulating the next section of karst cave, and stopping the corresponding main simulation or branch simulation of the underground river karst cave until the end standard of the main simulation or branch simulation is reached.
3. The method of claim 2, wherein randomly generating a first fracture and simulating a first section of a cavern formed by the first fracture comprises:
randomly selecting a crack group;
when the main trunk of the underground river karst cave is simulated, randomly selecting a point from a first line as the starting point of a first crack by taking the numerical value of the first line of a crack development probability map of the selected crack group system as a standard;
randomly selecting a numerical value from the probability density functions of the crack length and the crack angle of the crack group system as the angle and the length of a first crack to generate the first crack;
randomly selecting a numerical value from the karst cave width probability density function corresponding to the crack group as the width of the first section of karst cave, and generating the first section of karst cave.
4. The method of claim 3, wherein in the simulation of the underground river karst cave branch, when the first section karst cave is simulated, the starting point of the first crack is selected from the point set where the crack in the main trunk of the underground river karst cave is located.
5. The method of claim 2, the simulating a next-stage cavern formed by the next fracture comprising:
randomly selecting one crack group as the crack group of the next crack connected with the first crack and marking as a new crack group in other crack groups except the crack group where the first crack is located;
acquiring a crack probability value of a point set where a first crack is located on a crack development probability graph corresponding to the new crack group system, randomly selecting a point as a starting point of a next crack according to the value, wherein the probability that the point with the larger probability value is selected as the starting point of the next crack is larger;
randomly selecting a numerical value from the probability density functions of the crack length and the crack angle of the new crack group system as the angle and the length of the next crack, and generating the next crack;
and randomly selecting a numerical value from the karst cave width probability density function corresponding to the new fracture group as the width of the next section of karst cave, and generating the next section of karst cave.
6. The method of claim 5, wherein the termination criteria for the underground river cavern trunk simulation includes that the newly simulated cavern segment meets the left, lower and right boundaries of the research area.
7. The method of claim 5, wherein the termination criteria for the simulation of the underground river karst cave branch comprises:
the newly simulated karst cave sections meet four boundaries of a research area or the number of the karst cave sections in underground river branches reaches a certain preset value.
8. The method of claim 7, wherein a trunk of the underground river has a plurality of underground river branches extending therefrom, and the number of branches is predetermined.
9. A processing apparatus for underground river reservoir geological modeling, comprising:
the data acquisition module is used for acquiring modeling data, wherein the modeling data at least comprises fracture development probability map data corresponding to fractures in a fracture group system, probability density functions of fracture angles and lengths and probability density functions of karst cave widths corresponding to fractures of each group system;
the main stem simulation module is used for carrying out main stem simulation on the underground river karst cave based on the modeling data;
the branch simulation module is used for carrying out underground river karst cave branch simulation based on the modeling data;
and the ending module is used for stopping corresponding simulation processing when the simulation ending condition of the trunk simulation or the branch simulation is reached.
10. A processing apparatus for underground river reservoir geological modeling, comprising: at least one processor and a memory for storing processor-executable instructions, the processor implementing the steps of the method of any one of claims 1-8 when executing the instructions.
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