CN113327070B - Method and device for intelligently surveying coal-based gas and electronic equipment - Google Patents
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Abstract
The invention discloses a method and a device for intelligently surveying coal-based gas and electronic equipment. The method comprises the following steps: acquiring coal-based gas exploration data of a target coal bed, wherein the coal-based gas exploration data comprises gas logging data, sampling test data and conventional logging data; analyzing the coal-based gas exploration data, and determining coal-based gas characteristic parameters of a target coal bed, wherein the coal-based gas characteristic parameters comprise: the thickness of the coal measure stratum, the structural development grade, the lithology of the top and bottom plates, the thickness of the top and bottom plates and the characteristic value of the aquifer; determining an evaluation value of a target coal bed by adopting a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameters; and determining the coal-based gas evaluation result of the target coal bed based on the evaluation value. The traditional exploration means is combined with an intelligent analysis algorithm, so that the evaluation accuracy of the coal-based gas mineral resources can be improved, and a basis is provided for the comprehensive development of the coal-based gas.
Description
Technical Field
The invention relates to the field of geological exploration, in particular to a method and a device for intelligently exploring coal-based gas and electronic equipment.
Background
Coal-based gas generally refers to various natural gases existing in coal-based reservoirs, and is mainly unconventional natural gases, such as coal bed gas, coal-based shale gas and coal-based sandstone gas, and also includes coal-based carbonate gas, natural gas hydrate and the like. Due to different characteristics of different mineral resource combination types, the applicability of the method to various exploration technical means is different.
In the related art, when the comprehensive exploration is carried out on the mineral products of different combination types, the accuracy of evaluating the coal resources and other mineral product resources of the coal-bearing rock series by different exploration technical means is difficult to guarantee.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus and an electronic device for improving accuracy of evaluating coal-based gas mineral resources.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for intelligently surveying coal-based gas, comprising: acquiring coal-based gas exploration data of a target coal bed, wherein the coal-based gas exploration data comprises gas logging data, sampling test data and conventional logging data; analyzing the coal-based gas exploration data, and determining coal-based gas characteristic parameters of a target coal bed, wherein the coal-based gas characteristic parameters comprise: the thickness of the coal measure stratum, the structural development grade, the lithology of the top and bottom plates, the thickness of the top and bottom plates and the characteristic value of the aquifer; determining an evaluation value of a target coal bed by adopting a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameters; and determining the coal-based gas evaluation result of the target coal bed based on the evaluation value.
In some embodiments, the method further comprises the step of obtaining coal-based gas survey data by: acquiring seismic exploration data of a target area, and interpreting the seismic exploration data; if the seismic exploration data contains characteristic data of a coal-series gas reservoir, drilling in a target area to obtain conventional logging data of the target area, and constructing a geological profile of the target area based on the conventional logging data; adding gas logging in a target area to obtain gas logging data of the target area; determining whether coal-based gas exists in a target area based on gas logging data; if the coal-series gas exists in the target area, determining a target coal bed where the coal-series gas reservoir is located based on the geological profile; and collecting parallel samples from the reservoir where the target coal seam is located, and performing test analysis on the parallel samples to obtain sampling test data of the target coal seam.
In some embodiments, the geological profile is constructed via the steps of: inputting conventional logging data into a pre-trained coal measure stratum analysis model to obtain coal measure stratum parameters of a target area, wherein the coal measure stratum parameters comprise: the buried depth of the coal seam, the thickness of the coal seam and the structure of the coal seam; and constructing a geological profile of the target area based on the coal measure stratum parameters.
In some embodiments, inputting the conventional logging data into a pre-trained coal measure formation analysis model to obtain coal measure formation parameters of the target region, including: correcting the depth parameter in the conventional logging data to obtain corrected logging data; preprocessing the corrected logging data to obtain preprocessed logging data; and inputting the preprocessed logging data into a pre-trained target convolutional neural network to obtain coal measure stratum parameters.
In some embodiments, the corrected well log data is preprocessed to obtain preprocessed well log data, including at least one of: correcting the inclined well curve into a straight well curve; performing curve smoothing treatment; normalizing the numerical value; and influence errors of the external environment on the detecting instrument are eliminated.
In some embodiments, parallel samples are collected from a reservoir in which the target coal seam is located, and the parallel samples are subjected to test analysis to obtain sampled test data of the target coal seam, and the method further includes: and carrying out a drainage and mining test on the target coal seam.
In some embodiments, determining an evaluation value of the target coal seam by using a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameters includes: and determining the weight sum of the coal measure stratum thickness, the structure development grade, the top and bottom plate lithology, the top and bottom plate thickness and the aquifer characteristic value as the evaluation value of the target coal bed based on the preset weight.
In a second aspect, the present invention provides an apparatus for intelligently surveying coal-based gas, the apparatus comprising: the data acquisition unit is configured to acquire coal-based gas exploration data of a target coal seam, wherein the coal-based gas exploration data comprises gas logging data, sampling test data and conventional logging data; the data analysis unit is configured to analyze the coal-based gas exploration data and determine coal-based gas characteristic parameters of a target coal bed, and the coal-based gas characteristic parameters comprise: the thickness of the coal measure stratum, the structural development grade, the lithology of the top and bottom plates, the thickness of the top and bottom plates and the characteristic value of the aquifer; the numerical value determining unit is configured to determine an evaluation numerical value of the target coal seam by adopting a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameters; and the result evaluation unit is configured to determine a coal-series gas evaluation result of the target coal seam based on the evaluation value.
In a third aspect, the invention provides an electronic device comprising one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement the method for intelligent exploration of coal-based gas in any of the embodiments described above.
In a fourth aspect, the present invention provides a computer readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements a method for intelligently surveying coal-based gas as in any of the above embodiments.
The method for intelligently surveying the coal-based gas provided by the invention has the beneficial effects that:
the method provided by the invention is used for analyzing the coal-based gas characteristic parameters from the coal-based gas exploration data of the target coal bed, then determining the evaluation value of the target coal bed by adopting a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameters, and determining the coal-based gas evaluation result of the target coal bed based on the evaluation value. The traditional exploration means is combined with an intelligent analysis algorithm, so that the evaluation accuracy of the coal-based gas mineral resources can be improved, and a basis is provided for the comprehensive development of the coal-based gas.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings.
FIG. 1 is a flow diagram of one embodiment of a method for intelligently surveying coal-based gas according to the present invention;
FIG. 2 is a flow chart of yet another embodiment of a method for intelligently surveying coal-based gas according to the present invention;
FIG. 3 is a flow chart of a method of intelligently surveying coal-based gas to construct a geological profile in one embodiment in accordance with the present invention;
FIG. 4 is a schematic view of one embodiment of an apparatus for intelligently surveying coal-based gas according to the present invention;
FIG. 5 is a schematic block diagram of an electronic device suitable for use in implementing embodiments of the present invention.
Detailed Description
The present invention is described below based on embodiments, and it will be understood by those of ordinary skill in the art that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.
Unless the context clearly requires otherwise, throughout the description and the claims, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".
FIG. 1 shows a flow diagram 100 of one embodiment of a method for intelligent exploration of coal-based gas, in accordance with the present invention. The process 100 includes the following steps:
The coal-series gas exploration data comprises gas logging data, sampling test data and conventional logging data.
Typically, conventional well log data is obtained by a worker through drilling operations, including, for example, the depth of the coal seam, the thickness of the coal seam, the structure of the coal seam, and the degree of deterioration of the coal, and is used to characterize the geological features of the coal-based gas reservoir. The gas logging data is data obtained by detecting and recording gas collected in the drilling fluid and is used for judging whether a coal-based gas reservoir exists in the area where the drilling well is located. The sampling test data is obtained by testing a sample collected from the target coal seam, and may include, for example, the gas content of the coal-based gas, and the like, for characterizing the prospect of reservoir formation and the mining performance of the coal-based gas.
And 102, analyzing the coal-based gas exploration data and determining the coal-based gas characteristic parameters of the target coal bed.
Wherein, the coal-series gas characteristic parameters comprise: coal measure stratum thickness, tectonic development grade, top and bottom plate lithology, top and bottom plate thickness and aquifer characteristic value.
In this embodiment, the coal-based gas characteristic parameter is determined based on the gas logging data, the conventional logging data, and the sampling test data acquired in step 101, and the coal-based gas parameter may be determined according to a plurality of factors associated with the coal-based gas parameter.
As an example, parameters associated with the coal-based gas parameter (for example, the thickness of the coal seam) may be extracted from the gas logging data, the conventional logging data, and the sampling test data by a worker, and then the weight of each parameter is set according to the empirical data, and the weighted sum of each parameter is determined as the coal-based gas parameter.
For another example, a statistical method may be used to analyze the association relationship between the coal-based gas parameter and each associated parameter from experimental data (including historical gas logging data, historical conventional logging data, and historical sampling test data), and then determine the coal-based gas parameter based on the gas logging data, the conventional logging data, and the sampling test data obtained in step 101.
And 103, determining an evaluation value of the target coal seam by adopting a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameters.
The Multiple Regression Analysis (Multiple Regression Analysis) is a statistical Analysis method in which one variable of a plurality of related variables is regarded as a dependent variable, one or more other variables are regarded as independent variables, a linear or nonlinear mathematical model quantitative relation between the variables is established, and sample data is used for Analysis.
In this embodiment, the coal-based gas characteristic parameters can represent the geological characteristics of the coal-based gas from multiple dimensions, and the geological characteristics of different dimensions can be coupled together by using a multiple regression analysis algorithm, so that the obtained evaluation values can represent the geological characteristics of the coal-based gas in the target coal seam more comprehensively and scientifically.
As an example, an initial machine learning model (for example, a polynomial regression model or a logistic regression model) may be constructed, and then the coal gas characteristic parameters and their corresponding evaluation values are extracted from the historical data, so as to construct training samples. And then training an initial machine learning model based on the constructed sample data so that the initial machine learning model learns the corresponding relation between the characteristic parameters of the coal-based gas and the evaluation values. And then, inputting the coal-based gas characteristic parameters obtained in the step 102 into the trained machine learning model to obtain the evaluation value of the target coal seam.
In some optional implementation manners of this embodiment, determining an evaluation value of the target coal seam by using a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameters includes: and determining the weight sum of the coal measure stratum thickness, the structure development grade, the top and bottom plate lithology, the top and bottom plate thickness and the aquifer characteristic value as the evaluation value of the target coal bed based on the preset weight.
In this implementation manner, the weight of the coal-based gas characteristic parameter represents the influence of the parameter on the evaluation value of the target coal seam. For example, if the influence of the coal measure formation thickness, the structural development grade, the top and bottom plate lithology, the top and bottom plate thickness and the aquifer characteristic value on the evaluation value decreases sequentially, the corresponding weights also decrease sequentially.
As an example, a statistical analysis method may be adopted to analyze the weight of each coal-series gas characteristic parameter from the historical data. The weight of each coal-based gas characteristic parameter can also be set according to the experience of workers.
In a preferred embodiment of the present example, it was found that the weights of the coal measure formation thickness, the structural development grade, the roof-floor lithology, the roof-floor thickness and the aquifer characteristic value are 0.45, 0.22, 0.15, 0.12 and 0.06 in this order, and the influence of each characteristic parameter on the evaluation value can be more accurately characterized.
And 104, determining a coal-series gas evaluation result of the target coal seam based on the evaluation value.
In this embodiment, the evaluation value may be divided into a plurality of intervals, each interval corresponds to a different evaluation result, and the evaluation result may represent the prospect of coal-based gas formation and the mining performance.
As an example, when the evaluation value is in the interval of 90 to 100, the evaluation result of the coal-based gas in the target coal seam can be determined to be excellent, which indicates that the coal-based gas in the target coal seam has excellent prospect and extraction property; when the evaluation value is in the interval of 80-90, the evaluation result of the coal-based gas in the target coal bed can be determined to be good, and the coal-based gas in the target coal bed has better prospect and mining performance; when the evaluation value is in the interval of 60 to 80, the evaluation result of the coal-based gas in the target coal seam can be determined to be general, and the prospect of the coal-based gas in the target coal seam is shown to be general; when the evaluation value is less than 60, it may be determined that the evaluation result of the coal-based gas in the target coal seam is not good, which indicates that the prospect of reservoir formation and the mineability of the coal-based gas in the target coal seam are poor.
The method provided by the invention is used for analyzing the coal-based gas characteristic parameters from the coal-based gas exploration data of the target coal bed, then determining the evaluation value of the target coal bed by adopting a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameters, and determining the coal-based gas evaluation result of the target coal bed based on the evaluation value. The traditional exploration means is combined with an intelligent analysis algorithm, so that the evaluation accuracy of the coal-based gas mineral resources can be improved, and a basis is provided for the comprehensive development of the coal-based gas.
Referring next to fig. 2, fig. 2 illustrates a flow chart of yet another embodiment of the method of the present invention for intelligent exploration of coal-based gas. The process 200 includes the following steps:
Generally, a geophysical exploration technology can be adopted to acquire seismic exploration data of a target area, and then the seismic exploration data are interpreted through a seismic attribute technology, a seismic inversion technology or an azimuth anisotropy technology and other methods, so that azimuth anisotropy characteristics of seismic waves on various geologic bodies are extracted, and the geological characteristics of the target area are determined. For example, coal seam thickness, fault and other formation distributions, coal seam burial depth, coal seam dip and outcrop positions, and the like may be included.
In practice, the main purpose of coal logging is to obtain a geological profile of a coal-bearing formation, so as to accurately determine the morphology of the coal bed. The target coal seam refers to a coal seam and other rock formations with high organic carbon content, which belong to a small part of a geological profile and are generally distributed near the coal seam.
In this embodiment, if the seismic data includes characteristic data of a coal-based gas reservoir, drilling operations may be performed in the target area to take a profile of a formation in the target area, extract a core sample, and determine conventional logging data of the target area through experimental measurements. As an example, conventional well log data may include: the buried depth of the coal seam, the thickness of the coal seam, the structure of the coal seam, the metamorphic grade of the coal and the physical property of the rock stratum can be constructed into a geological section of a target area according to conventional logging data, so that the position of the target coal seam in the geological section of the target area is determined.
And 203, adding gas logging in the target area, and acquiring gas logging data of the target area.
In this embodiment, the gas logging data may provide a basis for determining whether a coal-based gas reservoir exists in the target region.
As an example, gas logging may be added in the target area, gas in the drilling fluid is collected, then the gas is detected, and the detection result of the gas is recorded, so that gas logging data of the target area can be obtained.
And 204, determining whether coal-based gas exists in the target area or not based on the gas logging data.
In practice, if the recorded components of the gas in the drilling fluid in the gas logging data include coal-based gas, it indicates that coal-based gas exists in the target area.
In this embodiment, the geological profile constructed in step 202 may represent a real geological structure of the target region, so that the location of the coal-derived gas reservoir may be determined according to the geological profile, and the location may be determined as the target coal seam.
And step 206, collecting parallel samples from the reservoir where the target coal seam is located, and performing test analysis on the parallel samples to obtain sampling test data of the target coal seam.
In this embodiment, the sampling test data may represent a reservoir formation parameter of the coal-based gas in the target coal seam, and may include, for example, a gas content, which is used to determine a reservoir formation prospect and a mineability of the coal-based gas in the target coal seam.
In some optional implementations of this embodiment, parallel samples are collected from a reservoir where the target coal seam is located, and the parallel samples are subjected to test analysis to obtain sampled test data of the target coal seam, where the method further includes: and carrying out a drainage and mining test on the target coal seam. In the exploration stage, before the target coal bed is subjected to sampling test, the authenticity of the coal-based gas in the target coal bed can be verified through a drainage test.
And step 207, analyzing the coal-based gas exploration data and determining the coal-based gas characteristic parameters of the target coal bed.
And 208, determining an evaluation value of the target coal seam by adopting a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameters.
And step 209, determining the coal-series gas evaluation result of the target coal seam based on the evaluation value.
In this embodiment, steps 207 to 209 correspond to steps 102 to 104, and are not described herein again.
As can be seen from fig. 2, the embodiment shown in fig. 2 embodies a complete operation process from exploration to generation of an evaluation result, and determines a coal-based gas evaluation result of a target coal seam by using an intelligent analysis evaluation strategy according to conventional well logging data, gas well logging data and sampling well logging data obtained by exploration, so that organic combination of a traditional exploration means and intelligent analysis evaluation is realized, the accuracy of evaluation of coal-based gas resources can be further improved, and a reference basis can be provided for exploitation of coal-based gas.
With continued reference to FIG. 3, FIG. 3 is a flow chart illustrating the steps of constructing a geological profile in one embodiment of the intelligent method of surveying coal-based gas of the present invention. The process 300 includes the following steps:
In this embodiment, the coal measure formation analysis model represents a corresponding relationship between the conventional logging data and the coal measure formation parameters, for example, a deep learning model (e.g., a deep neural network, a cyclic neural network, etc.) may be adopted, and the accuracy of the coal measure formation parameters may be improved by using the capability of mining potential correlations of data by the deep learning model.
In a specific example, a deep neural network may be used as a coal measure stratum analysis model, conventional logging data in historical data is used as a training sample, coal measure stratum parameters corresponding to the conventional logging data are used as sample labels of the training sample, then the training sample is input into a pre-constructed initial deep neural network, and the sample labels are used as expected outputs to train the initial deep neural network so as to learn a corresponding relationship between the conventional logging data and the coal measure stratum parameters. And finishing the training when the iteration times reach the preset times or are converged. And then, the acquired conventional logging data of the target area can be input into the trained deep neural network, and the coal measure stratum parameters of the target area can be output.
In some optional implementations of this embodiment, step 301 may further include steps 3011 to 3013:
and step 3011, correcting the depth parameter in the conventional logging data to obtain corrected logging data.
In practice, the logging depth in conventional logging data is the position of the logging tool in the borehole, which is obtained by measuring the length of the cable or the drill pipe. Due to the reasons of borehole environment, cable stretching, drill pipe stretching and the like, the measurement depth is not matched with the real depth of an instrument probe, so that the depth parameter cannot accurately describe the formation information.
In the embodiment, the measurement error of the logging depth is reduced by correcting the depth parameter, so that the accuracy of the description of the logging depth on the formation information of the target area is improved.
And step 3012, preprocessing the corrected logging data to obtain preprocessed logging data.
As an example, the corrected log data may be subjected to a depth alignment process such that a plurality of sample data of each log data are consistent in depth. In this way, noise data in the well log data is facilitated to be reduced.
In some optional implementations of this embodiment, the preprocessing is performed on the corrected logging data to obtain preprocessed logging data, where the preprocessed logging data includes at least one of: correcting the inclined well curve into a straight well curve; performing curve smoothing treatment; normalizing the numerical value; and influence errors of the external environment on the detecting instrument are eliminated.
The curve smoothing process may reject error or noise data in the log data that is not due to formation causes. Numerical normalization can eliminate systematic errors. In the implementation mode, the preprocessing modes can be selected or combined according to actual requirements, adverse factors in the logging data can be eliminated, and the accuracy of the evaluation result can be improved.
And 3013, inputting the preprocessed logging data into a pre-trained target convolutional neural network to obtain coal measure stratum parameters.
In the implementation mode, the target convolutional neural network represents the corresponding relation between the preprocessed logging data and the coal measure stratum parameters. The target convolutional neural network can extract the data characteristics of the processed logging data according to the learned strategy, mine the potential relation between the data and finally output the coal measure stratum parameters.
As can be seen from fig. 3, the embodiment shown in fig. 3 embodies the steps of mining the conventional logging data through the deep learning model and outputting the coal-based formation parameters, so that the geological structure of the target region can be more accurately described according to the geological profile constructed by the coal-based formation parameters, and the accuracy of evaluation can be further improved by evaluating the prospect and the mineability of the coal-based gas resources through the secondary data mining bureau in combination with the subsequent multiple regression analysis algorithm.
Referring next to fig. 4, fig. 4 is a schematic diagram 400 of an embodiment of the system for intelligently surveying coal-based gas according to the present invention, and as shown in fig. 4, an apparatus for intelligently surveying coal-based gas according to the present invention includes: a data obtaining unit 401 configured to obtain coal-based gas exploration data of a target coal seam, where the coal-based gas exploration data includes gas logging data, sampling test data, and conventional logging data; a data analysis unit 402 configured to analyze the coal-based gas exploration data and determine coal-based gas characteristic parameters of the target coal seam, where the coal-based gas characteristic parameters include: the thickness of the coal measure stratum, the structural development grade, the lithology of the top and bottom plates, the thickness of the top and bottom plates and the characteristic value of the aquifer; a value determining unit 403, configured to determine an evaluation value of the target coal seam by using a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameter; and a result evaluation unit 404 configured to determine a coal-based gas evaluation result of the target coal seam based on the evaluation value.
According to the device for intelligently surveying the coal-based gas, the coal-based gas characteristic parameters are analyzed from the coal-based gas exploration data of the target coal bed, then the evaluation value of the target coal bed is determined by adopting a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameters, and the coal-based gas evaluation result of the target coal bed is determined based on the evaluation value. The traditional exploration means is combined with an intelligent analysis algorithm, so that the evaluation accuracy of the coal-based gas mineral resources can be improved, and a basis is provided for the comprehensive development of the coal-based gas.
Referring now to fig. 5, a schematic diagram of an electronic device (e.g., the server or terminal device of fig. 1) 500 suitable for use in implementing embodiments of the present invention is shown. The terminal device in the embodiment of the present invention may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 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. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the invention 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 via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program performs the above-described functions defined in the methods of the embodiments of the invention when executed by the processing device 501. It should be noted that the computer readable medium in the embodiments of the present invention may 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 embodiments of the invention, 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 embodiments of the present invention, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, 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 coal-based gas exploration data of a target coal bed, wherein the coal-based gas exploration data comprises gas logging data, sampling test data and conventional logging data; analyzing the coal-based gas exploration data, and determining coal-based gas characteristic parameters of a target coal bed, wherein the coal-based gas characteristic parameters comprise: the thickness of the coal measure stratum, the structural development grade, the lithology of the top and bottom plates, the thickness of the top and bottom plates and the characteristic value of the aquifer; determining an evaluation value of a target coal bed by adopting a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameters; and determining the coal-based gas evaluation result of the target coal bed based on the evaluation value.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like 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 invention. 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.
Those skilled in the art will readily appreciate that the above-described preferred embodiments may be freely combined, superimposed, without conflict.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. 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 (7)
1. A method for intelligently surveying coal-based gas, the method comprising:
acquiring coal-based gas exploration data of a target coal bed, wherein the coal-based gas exploration data comprises gas logging data, sampling test data and conventional logging data, and the method comprises the following steps: acquiring seismic exploration data of a target area, and interpreting the seismic exploration data; if the seismic exploration data contains characteristic data of a coal-based gas reservoir, drilling in the target area to obtain conventional logging data of the target area, and constructing a geological profile of the target area based on the conventional logging data, wherein the conventional logging data comprises the burial depth of the coal seam, the thickness of the coal seam, the structure of the coal seam and the metamorphism degree of coal; adding gas logging in the target area to obtain gas logging data of the target area, wherein the gas logging data is obtained by detecting and recording gas in drilling fluid; determining whether coal-series gas exists in the target area based on the gas logging data; if the coal-series gas exists in the target area, determining a target coal bed where the coal-series gas reservoir is located based on the geological profile; collecting parallel samples from a reservoir where the target coal seam is located, and performing test analysis on the parallel samples to obtain sampling test data of the target coal seam;
analyzing the coal-based gas exploration data, and determining coal-based gas characteristic parameters of the target coal bed, wherein the coal-based gas characteristic parameters comprise: the thickness of the coal measure stratum, the structural development grade, the lithology of the top and bottom plates, the thickness of the top and bottom plates and the characteristic value of the aquifer;
determining an evaluation value of the target coal seam by adopting a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameters, wherein the evaluation value comprises the following steps: determining the weight sum of the coal measure stratum thickness, the structure development grade, the top and bottom plate lithology, the top and bottom plate thickness and the aquifer characteristic value as an evaluation numerical value of the target coal seam;
determining a coal-series gas evaluation result of the target coal seam based on the evaluation value;
wherein the geological profile is constructed by: inputting the conventional logging data into a pre-trained coal measure stratum analysis model to obtain coal measure stratum parameters of the target area, wherein the coal measure stratum parameters comprise: the buried depth of the coal seam, the thickness of the coal seam and the structure of the coal seam; and constructing a geological profile of the target area based on the coal measure stratum parameters.
2. The method of claim 1, wherein inputting the conventional well log data into a pre-trained coal-based formation analysis model to obtain coal-based formation parameters of the target region comprises:
correcting the depth parameter in the conventional logging data to obtain corrected logging data;
preprocessing the corrected logging data to obtain preprocessed logging data;
and inputting the preprocessed logging data into a pre-trained target convolutional neural network to obtain the coal measure stratum parameters.
3. The method of claim 2, wherein pre-processing the corrected well log data to obtain pre-processed well log data comprises at least one of:
correcting the inclined well curve into a straight well curve;
performing curve smoothing treatment;
normalizing the numerical value;
and influence errors of the external environment on the detecting instrument are eliminated.
4. The method of any one of claims 1 to 3, wherein parallel samples are taken from the reservoir in which the target coal seam is located, and the parallel samples are subjected to test analysis to obtain sampled test data of the target coal seam, and wherein the method further comprises:
and carrying out a drainage and mining test on the target coal seam.
5. An apparatus for intelligently surveying coal-based gas, the apparatus comprising:
the data acquisition unit is configured to acquire coal-based gas exploration data of a target coal seam, wherein the coal-based gas exploration data comprises gas logging data, sampling test data and conventional logging data;
the data acquisition unit includes: a data interpretation module configured to acquire seismic survey data for a target area and interpret the seismic survey data; a conventional logging module configured to drill in the target region to obtain conventional logging data of the target region if characteristic data of a coal-based gas reservoir exists in the seismic exploration data, and construct a geological profile of the target region based on the conventional logging data, wherein the conventional logging data comprises a burial depth of a coal seam, a thickness of the coal seam, a structure of the coal seam and a deterioration degree of the coal; the gas logging module is configured to add gas logging in the target area and acquire gas logging data of the target area, wherein the gas logging data is obtained by detecting and recording gas in drilling fluid; a sampling module configured to: determining whether coal-series gas exists in the target area based on the gas logging data; if the coal-series gas exists in the target area, determining a target coal bed where the coal-series gas reservoir is located based on the geological profile; collecting parallel samples from a reservoir where the target coal seam is located, and performing test analysis on the parallel samples to obtain sampling test data of the target coal seam;
a data analysis unit configured to analyze the coal-based gas exploration data and determine a coal-based gas characteristic parameter of the target coal seam, where the coal-based gas characteristic parameter includes: the thickness of the coal measure stratum, the structural development grade, the lithology of the top and bottom plates, the thickness of the top and bottom plates and the characteristic value of the aquifer;
a value determining unit configured to determine an evaluation value of the target coal seam by using a preset multiple regression analysis algorithm based on the coal-based gas characteristic parameter, including: determining the weight sum of the coal measure stratum thickness, the structure development grade, the top and bottom plate lithology, the top and bottom plate thickness and the aquifer characteristic value as an evaluation numerical value of the target coal seam; determining a coal-series gas evaluation result of the target coal seam based on the evaluation value;
a result evaluation unit configured to determine a coal-based gas evaluation result of the target coal seam based on the evaluation value;
wherein the conventional logging module, including a profile construction sub-module, is configured to: the geological profile is constructed by the following steps: inputting the conventional logging data into a pre-trained coal measure stratum analysis model to obtain coal measure stratum parameters of the target area, wherein the coal measure stratum parameters comprise: the buried depth of the coal seam, the thickness of the coal seam and the structure of the coal seam; and constructing a geological profile of the target area based on the coal measure stratum parameters.
6. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method for intelligent exploration of coal-based gas as recited in any of claims 1-4.
7. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for intelligent exploration of coal-based gases according to any one of claims 1 to 4.
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