CN117908098A - Method, device and storage medium for predicting thermal storage of down-the-hill rock solution hole seam - Google Patents
Method, device and storage medium for predicting thermal storage of down-the-hill rock solution hole seam Download PDFInfo
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Abstract
A method, a device and a storage medium for predicting thermal storage of a down-the-hole seam type of a down-the-hole rock, wherein the method comprises the following steps: acquiring seismic data of a down-the-mine rock solution hole seam type reservoir; acquiring drilled down-the-hill well data of the down-the-hill rock solution hole seam type reservoir; processing the seismic data and obtaining processed data; acquiring karst reservoir distribution of the down-the-hill rock karst pore seam type reservoir according to the processing data; quantifying the space spread of the down-the-hill karst reservoir of the down-the-hill karst pore seam type reservoir according to the karst reservoir distribution; acquiring spectral features of drilled thermal reservoirs according to the seismic data and the drilled down-the-hole data; and predicting the thermal storage of the karst aperture seam type of the down-the-hill rock according to the spatial spread of the karst reservoir of the down-the-hill rock and the frequency spectrum characteristics. The prediction of the thermal storage of the down-the-hill rock karst aperture seam is optimized, and the prediction of geothermal resources is of guiding significance; the geophysical parameters of geothermal resources are quantized, a technical process of thermal storage prediction is established, and the prediction accuracy of the thermal storage of the down-the-hill rock solution hole seam type is improved.
Description
Technical Field
The invention belongs to the technical field of geothermal resources, and particularly relates to a method and a device for predicting thermal storage of a down-the-hill rock karst pore seam type and a storage medium.
Background
Carbon emissions from humans are a major cause of global warming, and as the climate warms, the earth's ecosystem is being destroyed, and it has become a common public knowledge for humans to maintain ecological balance with reduced carbon emissions.
The geothermal resource is safe and environment-friendly, and can realize zero emission of carbon content, thereby being a very high-quality clean energy. Geothermal resources are widely distributed underground, and the geothermal resource has the characteristics of multiple heat storage distribution layers and regional enrichment. Through the application and analysis of the North China geothermal resource in recent years, the subsurface geothermal resource is considered to have the characteristics of high formation water temperature, large water yield, good recharging condition and considerable economic benefit, and is the most ideal subsurface geothermal resource.
The searching of the thermal storage of the submarine mountain is a key for developing geothermal resources of the submarine mountain, the thermal storage of the submarine mountain has the characteristics of strong concealment and local development, and a geophysical method aiming at the thermal storage of the submarine mountain is imperative to be established. The most similar research aiming at the down-the-hole seam type reservoir prediction of the down-the-hole seam type has been carried out for many years, but most of research objects are oil-gas-containing reservoirs, and the research on geothermal reservoir prediction is very few. The prediction of the underground mountain geothermal reservoirs at present mainly comprises the steps of extracting sensitive parameters, wave impedance or geostatistical inversion of various reservoirs of earthquakes, and then establishing a threshold value by combining the actual development degree of the single-well underground reservoirs and the oil, gas and water yielding conditions, so that the aim of the underground mountain thermal reservoir prediction is finally achieved. Although the method has a certain effect on the prediction of the down-the-country rock solution pore seam type reservoir, the method does not have good effect on the prediction of the fluid properties in the reservoir.
Disclosure of Invention
In view of the above, the present invention provides a method, apparatus and storage medium for predicting thermal storage of a down-the-hill rock solution pore seam which overcomes or at least partially solves the above-mentioned problems.
In order to solve the technical problems, the invention provides a thermal storage prediction method for a down-the-mine rock karst pore seam, which comprises the following steps:
acquiring seismic data of a down-the-mine rock solution hole seam type reservoir;
acquiring drilled down-the-hill well data of the down-the-hill rock solution hole seam type reservoir;
processing the seismic data and obtaining processed data;
acquiring karst reservoir distribution of the down-the-hill rock karst pore seam type reservoir according to the processing data;
Quantifying the space spread of the down-the-hill karst reservoir of the down-the-hill karst pore seam type reservoir according to the karst reservoir distribution;
acquiring spectral features of drilled thermal reservoirs according to the seismic data and the drilled down-the-hole data;
And predicting the thermal storage of the karst aperture seam type of the down-the-hill rock according to the spatial spread of the karst reservoir of the down-the-hill rock and the frequency spectrum characteristics.
Preferably, said processing said seismic data and obtaining processed data comprises the steps of:
Performing multi-window dip angle scanning on the seismic data;
acquiring stratum dip angle and azimuth angle;
and constructing the guided filtering of the seismic data.
Preferably, said constructing guided filtering of said seismic data comprises the steps of:
Acquiring the stratum inclination angle and the azimuth angle;
carrying out underground space orientation analysis according to the stratum inclination angle and the azimuth angle;
Performing continuous edge detection according to the stratum inclination angle and the azimuth angle;
and performing edge protection directional filtering according to the stratum inclination angle and the azimuth angle.
Preferably, the step of obtaining the karst reservoir distribution of the downhill rock karst pore type reservoir according to the processing data comprises the following steps:
Acquiring the processing data;
Extracting seismic coherence properties of the processed data;
Extracting seismic curvature attributes of the processed data;
and acquiring the karst reservoir distribution according to the seismic coherence attribute and the seismic curvature attribute.
Preferably, the extracting the seismic curvature attribute of the processed data comprises the steps of:
Acquiring the processing data;
Constructing an average curvature according to the processed data;
and acquiring the minimum amplitude negative curvature according to the processing data.
Preferably, the step of quantifying the space spread of the down-the-hill karst reservoir of the down-the-hill karst pore seam type reservoir according to the karst reservoir distribution comprises the steps of:
obtaining a coherence parameter, constructing an average curvature and a minimum amplitude negative curvature;
proportional transformation of the coherence parameter, the construction average curvature and the minimum amplitude negative curvature into a preset value range respectively;
Establishing a karst reservoir relation expression according to the coherence parameters, the constructed average curvature and the minimum amplitude negative curvature;
acquiring each weight parameter in the karst reservoir relation expression;
preferably, the karst reservoir relational expression is:
y＝-δ1ε+δ2Kmean+δ3(kA)min，
Where y represents karst reservoir distribution, ε represents a coherence parameter, K mean represents a constructed average curvature, (K A)min represents a minimum amplitude negative curvature, and δ 1、δ2 and δ 3 represent a coherence parameter, a weight parameter constructing the average curvature and the minimum amplitude negative curvature, respectively, δ 1+δ2+δ3 =1.
The application also provides a down-the-hill rock solution hole seam type heat storage prediction device, which comprises:
The seismic data acquisition module is used for acquiring seismic data of the down-the-hole seam type reservoir of the down-the-hole rock;
The drilled down-the-hole data acquisition module is used for acquiring the drilled down-the-hole data of the down-the-hole rock karst seam type reservoir;
The processing data acquisition module is used for processing the seismic data and obtaining processing data;
the karst reservoir distribution acquisition module is used for acquiring karst reservoir distribution of the down-the-hill rock karst pore seam type reservoir according to the processing data;
the down-the-hill karst reservoir space spreading quantification module is used for quantifying down-the-hill karst reservoir space spreading of the down-the-hill karst pore seam type reservoir according to the karst reservoir distribution;
the spectrum characteristic acquisition module is used for acquiring spectrum characteristics of drilled thermal storage according to the seismic data and the drilled down-the-hole data;
And the down-the-hill rock karst pore type thermal storage prediction module is used for predicting down-the-hill rock karst pore type thermal storage according to the down-the-hill karst reservoir space spread and the frequency spectrum characteristics.
The application also provides an electronic device, which comprises:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of the down-the-hill karst pore thermal storage prediction methods described above.
The application also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to execute any of the aforementioned method for predicting thermal storage of downhill rock solution pores.
One or more technical solutions in the embodiments of the present application at least have the following technical effects or advantages: the method, the device and the storage medium for predicting the thermal storage of the down-the-country rock karst aperture slot optimize the prediction of the thermal storage of the down-the-country rock karst aperture slot, and have guiding significance for geothermal resource prediction; meanwhile, the geophysical parameters of geothermal resources are quantized, a set of thermal storage prediction technical flow is established, and the prediction accuracy of the thermal storage of the down-the-hill rock karst aperture seam is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for predicting thermal storage of a down-the-hill rock karst aperture seam provided by the embodiment of the invention;
FIG. 2 is a schematic structural diagram of a thermal storage prediction device for a down-the-hill rock karst aperture seam according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of an electronic device according to the present invention;
fig. 4 is a schematic structural diagram of a non-transitory computer readable storage medium according to the present invention.
Detailed Description
The advantages and various effects of the present invention will be more clearly apparent from the following detailed description and examples. It will be understood by those skilled in the art that these specific embodiments and examples are intended to illustrate the invention, not to limit the invention.
Throughout the specification, unless specifically indicated otherwise, the terms used herein should be understood as meaning as commonly used in the art. Accordingly, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In case of conflict, the present specification will control.
Unless otherwise specifically indicated, the various raw materials, reagents, instruments, equipment and the like used in the present invention are commercially available or may be prepared by existing methods.
Referring to fig. 1, in an embodiment of the present application, the present application provides a method for predicting thermal storage of a downhill rock karst pore seam, the method comprising the steps of:
S1: acquiring seismic data of a down-the-mine rock solution hole seam type reservoir;
S2: acquiring drilled down-the-hill well data of the down-the-hill rock solution hole seam type reservoir;
in the embodiment of the application, the seismic data of the down-the-country rock karst seam type reservoir and the drilled down-the-country well data can be acquired through relevant documents of the down-the-country rock karst seam type reservoir or through corresponding detection equipment.
S3: processing the seismic data and obtaining processed data;
in an embodiment of the present application, the processing the seismic data and obtaining processed data includes the steps of:
Performing multi-window dip angle scanning on the seismic data;
acquiring stratum dip angle and azimuth angle;
and constructing the guided filtering of the seismic data.
In the embodiment of the application, because the stratum inclination angle and the signal to noise ratio can cause great adverse effect on the coherent result of the seismic signals, multi-window inclination angle scanning and structure-oriented filtering processing are required to be carried out on the seismic data.
In the embodiment of the application, multi-window inclination angle scanning is the basis of constructing guide filtering, and the multi-window inclination angle scanning is to search through a plurality of grids (the same in number and size) containing analysis points, and find the window with the largest similarity degree in each window as the inclination angle and azimuth angle of the analysis point. And through multi-window scanning, an estimation window is obtained, and the accuracy of the inclination angle and the azimuth angle of the analysis point is improved. Tilt and azimuth angles can be used to estimate discontinuities in the local reflecting surface, and typically using vertical windows to estimate tilt and azimuth angles provides more stable estimates than at the picked-up horizon. The fault information on the slice of the multi-window scanning dip angle data body is more abundant, and the small fracture identification is more advantageous, but the fault edge identification is poor.
In an embodiment of the present application, the constructing the guided filtering of the seismic data includes the steps of:
Acquiring the stratum inclination angle and the azimuth angle;
carrying out underground space orientation analysis according to the stratum inclination angle and the azimuth angle;
Performing continuous edge detection according to the stratum inclination angle and the azimuth angle;
and performing edge protection directional filtering according to the stratum inclination angle and the azimuth angle.
In the embodiment of the application, the formation-oriented filtering is to perform directional filtering along the stratum by using the stratum inclination angle and the stratum azimuth angle, and the formation-oriented filtering can be roughly divided into three steps, namely, performing underground space directional analysis, then performing continuous edge detection, and finally performing edge protection directional filtering. Filtering is simply smoothing the signal parallel to the direction of the seismic event axis, while the seismic signals in other directions will not be processed. The effect of calculating and extracting the coherent body on the structure-oriented filtering data body is good, and because the structure-oriented filtering can improve the signal to noise ratio of the seismic data, the continuity and the discontinuous characteristic of the same phase axis of the seismic data are more obvious, and therefore the coherent body can clearly describe faults.
S4: acquiring karst reservoir distribution of the down-the-hill rock karst pore seam type reservoir according to the processing data;
in an embodiment of the present application, the obtaining the karst reservoir distribution of the downhill karst pore type reservoir according to the processing data includes the steps of:
Acquiring the processing data;
Extracting seismic coherence properties of the processed data;
Extracting seismic curvature attributes of the processed data;
and acquiring the karst reservoir distribution according to the seismic coherence attribute and the seismic curvature attribute.
In the embodiment of the application, the development mechanism of the down-the-hill rock karst pore type reservoir and massive seismic data experiments show that the coherence and curvature attribute performance better reflects the distribution range of the reservoir, so that the karst reservoir distribution can be obtained through the seismic coherence attribute and the seismic curvature attribute.
In the embodiment of the application, the extraction of the seismic coherence attribute and the seismic curvature attribute is performed on the basis of the processed data obtained by multi-window dip angle scanning and construction guide filtering of the seismic data, so that the accuracy of the seismic attribute is improved.
In the embodiment of the application, the fracture and the microcrack are the basis of the karst of the down-the-hill, and the fracture is the arrival of the karst. The coherence attribute is to find the correlation between seismic signals, and the similarity of waveforms is closely related to the continuity of the stratum. The correlation coefficient can be obtained by analyzing the similarity of adjacent seismic trace waveforms in a time window with a target point as a center, the size of the correlation coefficient reflects the horizontal and longitudinal continuity of the stratum and is also a main expression form of fracture, and the fracture of the region with the small correlation coefficient intensively develops.
In the embodiment of the application, the seismic coherence attribute extraction is performed by adopting a coherence algorithm based on covariance matrix eigenvalue, and the method does not need horizon restraint, is a coherence algorithm with inclination angle and azimuth angle, and can improve the transverse resolution of fracture while ensuring the anti-noise capability.
In the embodiment of the present application, let the seismic signal be a time-dependent function f (t), and the seismic signal be a convolution of the wavelet and the reflection coefficient, where f (t) =x (t) ×r (t). One wavelet commonly used in seismic exploration is the Rake wavelet, which can be expressed as x (t) =e -at sin (βt) u (t), whereAs a unit step signal, the seismic signal may be expressed as:
f(t)＝e-atsin(βt)u(t)*R(t)， (1)；
Discretizing the seismic signal within a certain period of time t+2t and setting the seismic signal as an n-dimensional random variable, i.e., f (T) = e -atsin(βt)u(t)*R(t)＝(X(t)1,X(t)2,...,X(t)n)T, the seismic signal covariance can be expressed as:
let the eigenvalue of the covariance matrix of the seismic signal be lambda and the eigenvector be x, then we can get: cx=λx, (3);
Substituting the formulas (1) and (2) into the formula (3) can obtain the eigenvalue lambda of the covariance matrix, and calculate the corresponding eigenvector x. Setting the coherence parameter calculated based on the eigenvalue of the covariance matrix of the seismic signal as epsilon
In an embodiment of the present application, the extracting the seismic curvature attribute of the processed data includes the steps of:
Acquiring the processing data;
Constructing an average curvature according to the processed data;
and acquiring the minimum amplitude negative curvature according to the processing data.
In the embodiment of the application, the curvature is the bending degree of any point on the description curve, which is the reciprocal of the radius of a circle, and the magnitude can reflect the bending degree of an arc shape, and the larger the curvature is, the more the curvature is. For a down-the-hill karst reservoir, the extent of fracture development is proportional to the extent of bending, so a curvature method can be used to evaluate the fracture.
Let a certain point curvature value be k, then it can be obtained:
The construction curvature is the curvature calculated from the horizon of the three-dimensional seismic interpretation, reflecting the degree of curvature of any point on the interpreted horizon.
The layer curvature is achieved by constructing a surface fit and calculating surface equation coefficients based on gridding the interpreted horizon. According to the method of Roberts (2001), the general expression of the quadratic trend surface fitted by the construction surface is:
f(x,y)＝ax2+by2+cxy+dx+ey+f。
it follows that the expression for constructing the mean curvature is:
in embodiments of the application, the amplitude curvature is derived from a transverse second order derivative of the amplitude of the seismic data, and like the formation curvature, the amplitude curvature can also provide a number of useful geological information. Amplitude curvature is extracted to account for reservoirs that are more sensitive to amplitude variations. The maximum positive curvature characterizes the morphology and the internal structure of the biological reef, the minimum negative curvature can characterize the distribution of rock karst slits, and the minimum amplitude negative curvature is selected for the submerged karst reservoir research.
Let a certain point amplitude curvature value be k A, then it can be obtained:
wherein the expression of the minimum amplitude negative curvature is:
s5: quantifying the space spread of the down-the-hill karst reservoir of the down-the-hill karst pore seam type reservoir according to the karst reservoir distribution;
in an embodiment of the present application, the quantification of the spatial distribution of the down-the-hill karst reservoir of the down-the-hill karst pore-gap reservoir according to the karst reservoir distribution includes the steps of:
obtaining a coherence parameter, constructing an average curvature and a minimum amplitude negative curvature;
proportional transformation of the coherence parameter, the construction average curvature and the minimum amplitude negative curvature into a preset value range respectively;
Establishing a karst reservoir relation expression according to the coherence parameters, the constructed average curvature and the minimum amplitude negative curvature;
acquiring each weight parameter in the karst reservoir relation expression;
In the embodiment of the application, through seismic attribute optimization of the down-the-hill rock karst seam reservoir, the coherence and curvature attribute are found to jointly reflect karst reservoir distribution, but the weight ratio is not yet determined.
In the embodiment of the application, the spatial spread of the down-the-hill karst reservoir is quantified by establishing a coherent parameter epsilon, constructing a weight relation of average curvature K mean and minimum amplitude negative curvature (K A)min).
Firstly, the coherent parameter epsilon, the construction average curvature K mean and the minimum amplitude negative curvature (K A)min) are respectively and proportionally transformed into the range of (0, 1) values, then a karst reservoir relation expression is established, then the development condition of a well-drilled rock-solvent-seam reservoir is combined, meanwhile, y=1 is enabled according to the actual seismic data condition, a weight parameter delta 1、δ2、δ3 of the development of the high-quality rock-solvent-seam reservoir is comprehensively established, and then the reservoir distribution research is quantified according to the specific gravity of the weight parameter.
In the embodiment of the application, the karst reservoir relation expression is:
y＝-δ1ε+δ2Kmean+δ3(kA)min，
Where y represents karst reservoir distribution, ε represents a coherence parameter, K mean represents a constructed average curvature, (K A)min represents a minimum amplitude negative curvature, and δ 1、δ2 and δ 3 represent a coherence parameter, a weight parameter constructing the average curvature and the minimum amplitude negative curvature, respectively, δ 1+δ2+δ3 =1.
S6: acquiring spectral features of drilled thermal reservoirs according to the seismic data and the drilled down-the-hole data;
In the embodiment of the application, when the seismic signals pass through the rock karst pore reservoir, resonance scattering and resonance amplification benefits occur in the low-frequency part of the seismic waves, so that the energy of the low-frequency part is enhanced, and the energy of the high-frequency part is relatively weakened. Based on the sensitivity of frequency data to underground oil gas and geothermal resource response, the application is to qualitatively and quantitatively develop geothermal resource spectrum characteristic research by deepening the well-drilled spectrum analysis on the basis of the down-the-hole rock karst pore reservoir prediction, thereby achieving the purpose of establishing heat storage.
In the embodiment of the application, the seismic data are time domain signals, and the time domain data are converted into frequency domain data by discrete Fourier transform, wavelet transform, generalized S transform and other methods, so that the research of the frequency spectrum characteristics can be carried out. The application adopts discrete Fourier transform to develop the research of frequency spectrum data, and can be expressed as if the frequency spectrum of the seismic signal is F (w)In the middle ofLet the amplitude spectrum of the seismic signal be A (w) and the phase spectrum be phi (w), then/>
S7: and predicting the thermal storage of the karst aperture seam type of the down-the-hill rock according to the spatial spread of the karst reservoir of the down-the-hill rock and the frequency spectrum characteristics.
In the embodiment of the application, the spectrum analysis results of a large number of time windows of the downhole seam reservoir of the down-the-hill rock show that the geothermal reservoir and the oil-gas reservoir have large difference in frequency spectrum, particularly in amplitude spectrum. Compared with an oil gas reservoir, when the geothermal reservoir generates low-frequency resonance and high-frequency attenuation effects, the amplitude spectrum trend is overall slower, the low-frequency resonance frequency band is relatively wider, and the high-frequency attenuation trend is relatively slower. Through the spectral characteristics of the drilled heat storage, a proper development time window of the down-the-hill rock karst pore seam reservoir is selected, and the development area in the regional heat reservoir can be accurately predicted.
As shown in fig. 2, the present application further provides a thermal storage prediction device for a down-the-hill rock karst aperture seam, comprising:
the seismic data acquisition module 10 is used for acquiring the seismic data of the down-the-hole seam type reservoir of the down-the-mine rock;
a drilled down-the-hole data acquisition module 20 for acquiring drilled down-the-hole data of the down-the-hole rock karst slot reservoir;
A processing data acquisition module 30, configured to process the seismic data and obtain processed data;
a karst reservoir distribution acquisition module 40 for acquiring a karst reservoir distribution of the downhill karst pore type reservoir according to the processing data;
A down-the-hill karst reservoir space spread quantization module 50 for quantizing down-the-hill karst reservoir space spread of the down-the-hill karst pore-gap reservoir according to the karst reservoir distribution;
A spectral feature acquisition module 60, configured to acquire spectral features of drilled thermal storage according to the seismic data and the drilled down-the-hole data;
The down-the-hill rock solution hole seam type thermal storage prediction module 70 is used for predicting the down-the-hill rock solution hole seam type thermal storage according to the down-the-hill karst reservoir space spread and the frequency spectrum characteristics.
The application provides a down-the-country rock solution pore type thermal storage prediction device which can execute the method for predicting the down-the-country rock solution pore type thermal storage.
Referring now to fig. 3, a schematic diagram of an electronic device 100 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 3 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 3, the electronic device 100 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 101 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 102 or a program loaded from a storage means 108 into a Random Access Memory (RAM) 103. In the RAM 103, various programs and data necessary for the operation of the electronic apparatus 100 are also stored. The processing device 101, ROM 102, and RAM 103 are connected to each other by a bus 104. An input/output (I/O) interface 105 is also connected to bus 104.
In general, the following devices may be connected to the I/O interface 105: input devices 106 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 107 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 108 including, for example, magnetic tape, hard disk, etc.; and a communication device 109. The communication means 109 may allow the electronic device 100 to communicate wirelessly or by wire with other devices to exchange data. While an electronic device 100 having various means is shown in the figures, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 109, or from the storage means 108, or from the ROM 102. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 101.
Referring now to fig. 4, there is illustrated a schematic diagram of a computer readable storage medium suitable for use in implementing embodiments of the present disclosure, the computer readable storage medium storing a computer program that, when executed by a processor, is capable of implementing a method of thermal storage prediction of a down-the-hill rock karst seam as described in any of the above.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects an internet protocol address from the at least two internet protocol addresses and returns the internet protocol address; receiving an Internet protocol address returned by the node evaluation equipment; wherein the acquired internet protocol address indicates an edge node in the content distribution network.
Or the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
The method, the device and the storage medium for predicting the thermal storage of the down-the-country rock karst aperture slot optimize the prediction of the thermal storage of the down-the-country rock karst aperture slot, and have guiding significance for geothermal resource prediction; meanwhile, the geophysical parameters of geothermal resources are quantized, a set of thermal storage prediction technical flow is established, and the prediction accuracy of the thermal storage of the down-the-hill rock karst aperture seam is improved.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explanation of the principles of the present invention and are in no way limiting of the invention. Accordingly, any modification, equivalent replacement, improvement, etc. made without departing from the spirit and scope of the present invention should be included in the scope of the present invention. Furthermore, the appended claims are intended to cover all such changes and modifications that fall within the scope and boundary of the appended claims, or equivalents of such scope and boundary.
Claims (10)
1. The method for predicting the thermal storage of the down-the-hill rock solution hole seam is characterized by comprising the following steps:
acquiring seismic data of a down-the-mine rock solution hole seam type reservoir;
acquiring drilled down-the-hill well data of the down-the-hill rock solution hole seam type reservoir;
processing the seismic data and obtaining processed data;
acquiring karst reservoir distribution of the down-the-hill rock karst pore seam type reservoir according to the processing data;
Quantifying the space spread of the down-the-hill karst reservoir of the down-the-hill karst pore seam type reservoir according to the karst reservoir distribution;
acquiring spectral features of drilled thermal reservoirs according to the seismic data and the drilled down-the-hole data;
And predicting the thermal storage of the karst aperture seam type of the down-the-hill rock according to the spatial spread of the karst reservoir of the down-the-hill rock and the frequency spectrum characteristics.
2. The method for predicting thermal storage of a down-the-hill rock solution pore type according to claim 1, wherein the processing the seismic data and obtaining processed data comprises the steps of:
Performing multi-window dip angle scanning on the seismic data;
acquiring stratum dip angle and azimuth angle;
and constructing the guided filtering of the seismic data.
3. The method for predicting thermal storage of a down-the-hill rock open-pit joint of claim 2, wherein said constructing guided filtering of said seismic data comprises the steps of:
Acquiring the stratum inclination angle and the azimuth angle;
carrying out underground space orientation analysis according to the stratum inclination angle and the azimuth angle;
Performing continuous edge detection according to the stratum inclination angle and the azimuth angle;
and performing edge protection directional filtering according to the stratum inclination angle and the azimuth angle.
4. The method for predicting thermal storage of a downhill rock open-pit type according to claim 1, wherein the step of obtaining a karst reservoir distribution of the downhill rock open-pit type reservoir from the processing data comprises the steps of:
Acquiring the processing data;
Extracting seismic coherence properties of the processed data;
Extracting seismic curvature attributes of the processed data;
and acquiring the karst reservoir distribution according to the seismic coherence attribute and the seismic curvature attribute.
5. The method for predicting thermal storage of a down-the-hill rock solution pore type of claim 4, wherein said extracting the seismic curvature attribute of the processed data comprises the steps of:
Acquiring the processing data;
Constructing an average curvature according to the processed data;
and acquiring the minimum amplitude negative curvature according to the processing data.
6. The method for predicting thermal storage of a down-the-hill karst pore type according to claim 1, wherein said quantifying the down-the-hill karst reservoir spatial spread of the down-the-hill karst pore type reservoir according to the karst reservoir distribution comprises the steps of:
obtaining a coherence parameter, constructing an average curvature and a minimum amplitude negative curvature;
proportional transformation of the coherence parameter, the construction average curvature and the minimum amplitude negative curvature into a preset value range respectively;
Establishing a karst reservoir relation expression according to the coherence parameters, the constructed average curvature and the minimum amplitude negative curvature;
acquiring each weight parameter in the karst reservoir relation expression;
And quantifying karst reservoir distribution according to the specific gravity of each weight parameter.
7. The method for predicting thermal storage of a down-the-hill rock solution pore seam of claim 6, wherein the karst reservoir relational expression is:
y＝-δ1ε+δ2Kmean+δ3(kA)min，
Where y represents karst reservoir distribution, ε represents a coherence parameter, K mean represents a constructed average curvature, (K A)min represents a minimum amplitude negative curvature, and δ 1、δ2 and δ 3 represent a coherence parameter, a weight parameter constructing the average curvature and the minimum amplitude negative curvature, respectively, δ 1+δ2+δ3 =1.
8. The utility model provides a down-the-hill rock karst aperture seam formula thermal storage prediction unit which characterized in that includes:
The seismic data acquisition module is used for acquiring seismic data of the down-the-hole seam type reservoir of the down-the-hole rock;
The drilled down-the-hole data acquisition module is used for acquiring the drilled down-the-hole data of the down-the-hole rock karst seam type reservoir;
The processing data acquisition module is used for processing the seismic data and obtaining processing data;
the karst reservoir distribution acquisition module is used for acquiring karst reservoir distribution of the down-the-hill rock karst pore seam type reservoir according to the processing data;
the down-the-hill karst reservoir space spreading quantification module is used for quantifying down-the-hill karst reservoir space spreading of the down-the-hill karst pore seam type reservoir according to the karst reservoir distribution;
the spectrum characteristic acquisition module is used for acquiring spectrum characteristics of drilled thermal storage according to the seismic data and the drilled down-the-hole data;
And the down-the-hill rock karst pore type thermal storage prediction module is used for predicting down-the-hill rock karst pore type thermal storage according to the down-the-hill karst reservoir space spread and the frequency spectrum characteristics.
9. An electronic device, the electronic device comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of thermal storage prediction of downhill rock open-hole joints of any of the preceding claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of thermal storage prediction of a downhill rock solution pore seam of any one of the preceding claims 1-7.
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