CN110208861B - Prediction method and device for constructing soft coal development area - Google Patents
Prediction method and device for constructing soft coal development area Download PDFInfo
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Abstract
The application provides a prediction method and a prediction device for constructing a soft coal development area, wherein the method comprises the following steps: acquiring three-dimensional seismic data and a logging curve in a research area; analyzing the three-dimensional seismic data by using an ant colony tracking algorithm to obtain an ant data body; performing seismic inversion according to the three-dimensional seismic data and the logging curve to obtain a logging data volume; extracting ant attribute values and well logging attribute values of all target points in the target coal bed from the ant data body and the well logging data body respectively, and fusing the ant attribute values and the well logging attribute values corresponding to all the target points to obtain fused values of all the target points of the target coal bed; and dividing at least one region on the plane of the target coal bed according to the relation between the fusion value of each target point and the set threshold value, comparing the region with the position where the constructed soft coal exists, and determining the development region of the constructed soft coal from the at least one region.
Description
Technical Field
The application relates to the technical field of geological exploration, in particular to a prediction method and a prediction device for a soft coal development area.
Background
The structural soft coal refers to coal which deforms under the action of structural stress, namely, under different stress-strain environments and structural stress, the physical structure, the chemical structure, the optical characteristics and the like of the coal are obviously changed, so that structural deformed coal with different structural characteristics and different types is formed. At present, the prediction research aiming at constructing soft coal development areas is relatively lacked.
Disclosure of Invention
The embodiment of the application aims to provide a prediction method and a prediction device for constructing a soft coal development area so as to predict the development area for constructing soft coal in an underground rock stratum.
In a first aspect, an embodiment of the present application provides a prediction method for constructing a soft coal development area, including: acquiring three-dimensional seismic data and a logging curve in a research area; analyzing the three-dimensional seismic data by using an ant colony tracking algorithm to obtain an ant data body, wherein the ant data body is a set of ant attribute values at each position in the stratum of the research area, and the ant attribute values represent whether faults exist at the position; performing seismic inversion with the logging curve as constraint according to the three-dimensional seismic data and the logging curve to obtain a logging data volume, wherein the logging data volume is a set of logging attribute values at each position in the stratum of the research area; extracting ant attribute values and well logging attribute values of all target points in the target coal bed from the ant data body and the well logging data body respectively, and fusing the ant attribute values and the well logging attribute values corresponding to all the target points to obtain fused values of all the target points of the target coal bed; dividing at least one region on the plane of the target coal bed according to the relation between the fusion value of each target point and a set threshold value, comparing the position where the constructed soft coal exists with the position of the at least one region, and determining the development region of the constructed soft coal from the at least one region according to the comparison result.
The ant data body can reflect the fracture structure condition in the underground rock stratum, and the inventor analyzes the positions of a plurality of structure soft coals disclosed in the field to find that the structure soft coals are mostly positioned at the fault development or the fault edge position, namely, the ant data body can reflect the development of the structure soft coals to a certain extent, and the ant data body and the logging data body are fused, so that the prediction of the structure soft coals is carried out by combining the distribution of the faults and the lithological change shown by logging, and compared with the prediction result obtained by a single attribute value, the prediction result is more accurate.
In an optional implementation manner of the first aspect, the well log includes a density curve and a natural gamma curve, and performing a seismic inversion with the well log as a constraint according to the three-dimensional seismic data and the well log to obtain a well log data volume, including: performing seismic inversion with a density curve as constraint according to the three-dimensional seismic data and the density curve to obtain a density data volume, and performing seismic inversion with a natural gamma curve as constraint according to the three-dimensional seismic data and the natural gamma curve to obtain a natural gamma data volume, wherein the density data volume and the natural gamma data volume are respectively a set of density values and a set of natural gamma values at each position in a stratum of a research area; the method for extracting the ant attribute values and the logging attribute values of all target points in the target coal seam from the ant data body and the logging data body respectively and fusing the ant attribute values and the logging attribute values corresponding to all the target points comprises the following steps: and extracting ant attribute values, density values and natural gamma values of all target points in the target coal bed from the ant data volume, the density data volume and the natural gamma data volume respectively, and fusing the ant attribute values, the density values and the natural gamma values corresponding to all the target points.
The inventor respectively tests and discovers that density values of a plurality of gas outburst points (namely, structural soft coal can be considered to exist) disclosed in a research area are relatively large, based on the discovery, the density values of target points of a target coal bed are compared with a density distribution interval of the structural soft coal, and the obtained prediction result is better matched with the actually uncovered gas outburst points, so that the density change is used as one index of the structural soft coal prediction; on the other hand, the natural gamma value of the rock at the top of the coal seam is lower in the development area of the soft coal, and the natural gamma has an indicating effect on the sand-mud ratio of the rock stratum, so that the development condition of the soft coal can be indirectly indicated. Therefore, the prediction is carried out by combining the two logging attributes, and the accuracy of the prediction result is high.
In an optional implementation of the first aspect, before performing natural gamma curve-constrained seismic inversion from the three-dimensional seismic data and the natural gamma curve, the method further comprises: and smoothing the natural gamma curve.
After the natural gamma curve is subjected to smoothing treatment, the distribution of sand and mud thick layers can be highlighted, and meanwhile, the inversion speed can be improved.
In an alternative embodiment of the first aspect, performing a log-constrained seismic inversion from the three-dimensional seismic data and the log comprises: and performing seismic inversion on the three-dimensional seismic data and the logging curve by utilizing a probabilistic neural network model.
In an optional implementation of the first aspect, before performing seismic inversion on the three-dimensional seismic data and the well log using the probabilistic neural network model, the method further comprises: and training and cross-verifying the probabilistic neural network model by using the density curve and the natural gamma curve of each drill hole in the research area to determine the parameters of the probabilistic neural network model.
In an alternative embodiment of the first aspect, the training of the probabilistic neural network model using the density curve and the natural gamma curve of each borehole in the study area comprises: and training the probabilistic neural network model by using a density curve and a natural gamma curve of each drill hole in the research area, wherein the density curve and the natural gamma curve meet preset conditions, and the preset conditions mean that the change degree of the curves is greater than the preset degree.
Curves with small change degree in the logging curves can be considered as not reflecting formation lithology changes, and the curves do not need to participate in training of the neural network model, so that negative effects on prediction results are avoided.
In a second aspect, an embodiment of the present application provides a prediction apparatus for constructing a soft coal development area, including: the data acquisition module is used for acquiring three-dimensional seismic data and a logging curve in a research area; the data processing module is used for analyzing the three-dimensional seismic data by utilizing an ant colony tracking algorithm to obtain an ant data body, and performing seismic inversion with a logging curve as a constraint according to the three-dimensional seismic data and the logging curve to obtain a logging data body, wherein the ant data body is a set of ant attribute values at each position in a stratum of a research area, the ant attribute values represent whether a fault exists at the position, and the logging data body is a set of logging attribute values at each position in the stratum of the research area; the data fusion module is used for extracting ant attribute values and logging attribute values of all target points in the target coal seam from the ant data body and the logging data body respectively, and fusing the ant attribute values and the logging attribute values corresponding to all the target points to obtain fusion values of all the target points of the target coal seam; and the prediction module is used for dividing at least one region on the plane of the target coal bed according to the relation between the fusion value of each target point and a set threshold value, comparing the position where the constructed soft coal exists, which is obtained in advance, with the position of the at least one region, and determining the development region for constructing the soft coal from the at least one region according to the comparison result.
In an optional implementation manner of the second aspect, the well log curve includes a density curve and a natural gamma curve, and the data processing module is specifically configured to: performing seismic inversion with a density curve as constraint according to the three-dimensional seismic data and the density curve to obtain a density data volume, and performing seismic inversion with a natural gamma curve as constraint according to the three-dimensional seismic data and the natural gamma curve to obtain a natural gamma data volume, wherein the density data volume and the natural gamma data volume are respectively a set of density values and a set of natural gamma values at each position in a stratum of a research area; the data fusion module is specifically configured to: and extracting ant attribute values, density values and natural gamma values of all target points in the target coal bed from the ant data volume, the density data volume and the natural gamma data volume respectively, and fusing the ant attribute values, the density values and the natural gamma values corresponding to all the target points.
In an optional implementation manner of the second aspect, the data processing module is further configured to: and smoothing the natural gamma curve.
In an optional implementation manner of the second aspect, the data processing module is specifically configured to: and performing seismic inversion on the three-dimensional seismic data and the logging curve by utilizing a probabilistic neural network model.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 is a flow chart of a prediction method for constructing soft coal development zones according to an embodiment of the present application;
fig. 2 is a plan view reflecting a fracture situation on a target coal seam, which is obtained according to an ant data body;
FIG. 3 is a schematic diagram of a prediction device for constructing a soft coal development zone according to an embodiment of the present disclosure;
fig. 4 is a schematic view of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Examples
The structural soft coal is deformed coal in which a coal bed is broken or deformed with strong toughness and shrinkage under the action of structural stress, and the special physical and chemical structure determines the characteristics of high gas content and low gas permeability. Coal bed gas is generally filled in broken gaps for constructing soft coal, so that coal mining areas with the soft coal are often dangerous areas with gas outburst, and the mining safety of mines can be damaged. In view of this, the embodiment of the present application provides a prediction method for constructing a soft coal development area, which can provide guidance for actual application scenarios such as gas outburst prediction of a mine and coal bed gas exploration and development, and with reference to fig. 1, the method includes the following steps:
step 101: three-dimensional seismic data and well logs within the study area are obtained.
The research area is a geographical area where the method is to be executed to construct soft coal development area prediction, the three-dimensional seismic data is processed seismic data (such as post-stack migration data, pre-stack time migration data and the like) obtained by a detector after shooting and exciting a seismic wave during field acquisition, the logging curve is a curve formed by measuring geophysical parameters of a rock stratum by logging equipment in a drilling process, and the logging characteristics of different lithologies and different layers are reflected.
Step 102: and analyzing the three-dimensional seismic data by using an ant colony tracking algorithm to obtain an ant data body.
The ant data body is a set of ant attribute values at each position in the underground rock stratum of the research area, and each ant attribute value represents whether a fault exists at the position or not, which means that the fault and crack conditions of the underground of the whole research area can be reflected by using the three-dimensional ant data body.
The ant colony tracking algorithm is characterized in that a crawling track behavior is marked by simulating natural ant colonies to optimally search a food path, ants are placed in a seismic data body formed by three-dimensional seismic data to search faults possibly existing underground, the ant colonies capture fracture information in the seismic data body, and fracture responses describing the underground faults are formed, namely the ant data body. The basic principle of the algorithm is as follows: a large number of ants are scattered in the seismic data body, when some ants find fracture traces meeting preset fracture conditions, certain signals (which can be understood as pheromones) are released, ants in other areas are gathered to concentrate on the fracture to track the fracture until the fracture is tracked and identified, and other positions which do not meet the preset fracture conditions are not marked and signals are released, so that the ant data body with clear fracture traces is formed finally. As shown in fig. 2, fig. 2 shows a plan view of a fracture situation on a target coal seam extracted from an ant data body along the target coal seam, from which the fault distribution on the target coal seam can be clearly seen, and the depth of a fracture trace in the plan view indicates the severity of fracture damage in the coal seam.
The reason for generating the structure soft coal is that the coal bed is broken, so that the original primary coal is damaged to form the structure soft coal, and because the coal bed gas is filled in the broken gap of the structure soft coal, the gas outburst is easy to occur, so that the primary coal and the structure soft coal can be distinguished according to whether the gas outburst phenomenon occurs or not, namely, when a gas outburst point occurs at a certain position in the mining process, the point can be considered to have the structure soft coal. The inventor researches and discovers that a plurality of gas outburst points uncovered in a certain research area are mostly located at fault development or fault edge positions, and the distribution of visible fracture and crack can indicate the damage position of stress concentration on a coal seam, so that the fault distribution and the development of the constructed soft coal can be considered to have a certain relation, therefore, the fault can be used as one of prediction indexes when the constructed soft coal is predicted, and the ant data body can be used for explaining the distribution condition of underground faults, so that the ant data body can be used for predicting the constructed soft coal.
Step 103: and performing seismic inversion with the logging curve as constraint according to the three-dimensional seismic data and the logging curve to obtain a logging data volume.
The logging data volume represents a three-dimensional set of logging attribute values at each position in the stratum of the study region, and when the logging curve is a density curve and/or a natural gamma curve, the obtained logging data volume is also a density data volume and/or a natural gamma data volume, which are specifically described below by taking the density data volume and the natural gamma data volume as examples.
The seismic inversion method can be in various manners, including but not limited to any possible manner of recursive inversion, sparse impulse inversion, feature inversion, neural network inversion, and the like, and specifically, how to perform inversion according to three-dimensional seismic data and well logs can refer to the prior art, which is not described herein. In this embodiment, a Probabilistic Neural Network (PNN) model may be used for seismic inversion. Before inversion is carried out, firstly, a density curve and a natural gamma curve of each drill hole in a research area are used for training and cross validation on a probability neural network model so as to determine parameters (including learning parameters and hyper-parameters) of the probability neural network model, attention needs to be paid to the fact that the density curve and the natural gamma curve which participate in training meet certain conditions, namely the change degree of the curves is larger than a preset degree, if the well logging attribute values on the obtained well logging curves do not have certain changes, the curves can be considered not to reflect the change of the lithology of the stratum, and the curves are not allowed to participate in the training of the neural network model so as to avoid negative effects on a prediction result.
Before this, the inventor carries out long-term tests on density data volumes and natural gamma data volumes respectively, extracts target coal seam level information from the obtained density data volumes, and analyzes target coalAnd (3) obtaining the plane spread of the target coal bed by the change condition of the average density value (arithmetic mean of the density values in the thickness range with the target points as the centers) in the thickness range represented by the inversion time windows of the layer and the upper and lower 2ms of the layer, comparing the density values in the plane spread of the target coal bed with the density change range of the constructed soft coal, and if the density values are in the density change range of the constructed soft coal, determining that the constructed soft coal exists at the point. In a certain test process, comparing 7 gas outburst points disclosed in a research area, the density values of the 7 gas outburst points are all relatively large values (the density value is between 1.43 and 1.47 g/cm)3While the density change of the target coal seam is 1.379-1.466g/cm3In between), this shows that the method for constructing soft coal prediction by using density value is feasible, and the finally obtained prediction result is better matched with the revealed gas outburst point, so the embodiment uses the change of density as one index for constructing soft coal prediction. On the other hand, after the inventor conducts research on the natural gamma data body, the inventor finds that in a development area of the constructed soft coal, the natural gamma value of the rock at the top of the coal seam is low, namely the sandstone develops, and the natural gamma has an indicating effect on the sand-mud ratio of the rock seam and can also indirectly indicate the development condition of the constructed soft coal. The combination of the two logging attributes of density and natural gamma ray can ensure that the accuracy of the prediction result is higher.
Step 104: and respectively extracting the ant attribute value and the logging attribute value of each target point in the target coal seam from the ant data body and the logging data body.
Step 105: and fusing the ant attribute value corresponding to each target point with the logging attribute value to obtain a fused value of each target point of the target coal bed.
The method comprises the steps of extracting attribute values along a coal seam from a three-dimensional ant data body, a density data body and a natural gamma data body to obtain ant attribute values, density values and natural gamma values corresponding to target points on a target coal seam, further adopting a multi-information fusion algorithm, associating, correlating and integrating three attribute values corresponding to the target points and reflecting the change of the structural soft coal according to certain conditions to form a new fusion value, wherein the fusion value can more accurately reflect the change of the structural soft coal and form a planar distribution of the target coal seam, each target point on the planar distribution of the coal seam has a fusion value, the fusion values of the coal seam at various positions are matched with the structural soft coal found in the actual mining process one by one, and further the development condition of the structural soft coal can be reflected from the change of the fusion values.
The above-mentioned multi-information fusion algorithm can refer to the prior art embodiments, and will not be described here.
Step 106: and dividing at least one region on the plane of the target coal bed according to the relation between the fusion value of each target point and the set threshold value.
Firstly, the relation between the fusion value of each target point and a set threshold is judged in turn, if the fusion values in a certain range are all larger than the set threshold, the target points in the range are divided into the same area, if the fusion value in a certain range is not more than the set threshold value, the target point in the range is divided into another area, after the judgment of each target point on the target coal seam is completed, at least one area can be divided on the plane of the target coal seam, the at least one area may be understood as an area defined by all target points larger than a set threshold (the area may include a plurality of areas discontinuous with each other), and an area formed by all the target points not greater than the set threshold is defined as another area, i.e., the two areas represent a development area and an under-development area of the constructed soft coal referred to below.
It should be noted that, since the variation ranges of the fusion values of different research regions may be different, the set threshold value needs to be adjusted according to the actual geological condition of the research region, so that the divided regions can be closer to the actual condition.
Step 107: comparing the position where the soft coal is constructed and the position of at least one region, and determining the development region of the soft coal from the at least one region according to the comparison result.
At least one area divided on the target coal seam represents an actual geographical area in the research area, so that the geographical position of each area can be compared with the geographical positions of a plurality of gas outburst points which are actually uncovered, if all the gas outburst points are found to be located in one area (or the ratio of the number of the gas outburst points in one area to the total number of the gas outburst points which are actually uncovered is found to be higher than a certain proportion, such as 80%), the area can be determined as a development area for constructing soft coal, and the other area is determined as an underdevelopment area for constructing the soft coal (the area is represented as raw coal with better coal seam maintenance).
Optionally, after determining the development conditions of the soft coal structure represented by the two regions, the obtained prediction results may be visually displayed, that is, a color picture is formed, the development regions of the soft coal structure are highlighted with a relatively obvious color in the picture, and the underdeveloped regions of the soft coal structure are represented with another color, so that the prediction results can be more intuitively reflected.
The inventor carries out field prediction on a certain research area by applying the prediction method provided by the embodiment, compares the mining data, finds that the prediction result is better matched with 3 actually seen collapse columns and 6 actually seen faults, proves that the precision of the prediction result is higher, finds that the structural soft coal in the research area covers the area above about 3/4 in the whole research area according to the prediction result reflected in the picture, and has a wider development range. Therefore, the prediction method provided by the embodiment can accurately depict the distribution of the constructed soft coal, and can provide important theoretical and technical support for practical application scenes such as mine gas outburst prediction, coal bed gas exploration and development and the like based on the distribution of the constructed soft coal.
Further, in a test, the whole inversion process is analyzed under the condition of the same computing power, and the inventor finds that: during density inversion, a density curve is smooth and less, the inversion takes 7 days, the inversion result is clear in reflection of a coal seam with the depth of more than 2m and can reflect sand bodies with the depth of about 3-5m, the inversion result is high in reflection precision of a thin layer, and the inversion method can be used for micro change of a target coal seam and lithology change of a coal seam roof in a near range; during natural gamma inversion, the natural gamma curve is subjected to smoothing treatment, the identification capability of the smoothed natural gamma curve on a thin layer is reduced to a certain extent, the distribution of sand and mud thick layers is highlighted, the inversion speed is improved, and the inversion time is shortened to 4 hours.
It should be noted that, in this embodiment, based on the analysis of the cause of formation of the structural soft coal, the distribution of the fault is used to reflect the structural soft coal, but if prediction is performed by using only the ant data body, if no fault occurs at a certain position, it is not possible to accurately determine whether structural soft coal exists at the position, and even if a fault exists at a certain position, it is impossible to accurately determine which region of the fault range the structural soft coal is located in, and it is seen that there is a certain deficiency in the prediction method based on the single attribute, so this embodiment provides a scheme of prediction after multi-attribute fusion, and simultaneously reflects the development of the structural soft coal by using the distribution of the fault, the density and the change of the natural gamma, and since the fusion value cannot be compared with the uniform dimension between the structural soft coals, this embodiment also uses the change of the fusion value to match with the gas outburst of the real uncovering, thereby defining the development area for constructing the soft coal.
It should be noted that, because the three-dimensional seismic data are applied in the above scheme, compared with data such as coal samples, boreholes, and well logs, which can only represent one point or one line, the three-dimensional seismic data volume can improve the accuracy of lateral prediction, and the distribution of the structural soft coal in a certain area (or a coal seam unexposed area) far away from the borehole can also realize accurate prediction.
Based on the same inventive concept, the embodiment of the present application further provides a prediction apparatus for constructing a soft coal development area, and with reference to fig. 3, the apparatus includes:
a data acquisition module 201, configured to acquire three-dimensional seismic data and a log in a research area;
the data processing module 202 is configured to analyze the three-dimensional seismic data by using an ant colony tracking algorithm to obtain an ant data volume, and perform seismic inversion with a logging curve as a constraint according to the three-dimensional seismic data and the logging curve to obtain a logging data volume; the ant data body is a set of ant attribute values at each position in the stratum of the research area, the ant attribute values represent whether faults exist at the position, and the logging data body is a set of logging attribute values at each position in the stratum of the research area;
the data fusion module 203 is configured to extract the ant attribute values and the logging attribute values of the target points in the target coal seam from the ant data volume and the logging data volume, and fuse the ant attribute values corresponding to each target point with the logging attribute values to obtain fusion values of the target points in the target coal seam;
the prediction module 204 is configured to partition at least one region on the plane of the target coal seam according to a relationship between the fusion value of each target point and a set threshold, compare a position where the constructed soft coal exists, which is obtained in advance, with the position of the at least one region, and determine a development region where the soft coal is constructed from the at least one region according to a comparison result.
Optionally, the log curve includes a density curve and a natural gamma curve, and the data processing module 202 is specifically configured to: performing seismic inversion with the density curve as constraint according to the three-dimensional seismic data and the density curve to obtain a density data volume, and performing seismic inversion with the natural gamma curve as constraint according to the three-dimensional seismic data and the natural gamma curve to obtain a natural gamma data volume, wherein the density data volume and the natural gamma data volume are respectively a set of density values and a set of natural gamma values at each position in the stratum of the research area; the data fusion module 203 is specifically configured to: and extracting ant attribute values, density values and natural gamma values of all target points in the target coal bed from the ant data volume, the density data volume and the natural gamma data volume respectively, and fusing the ant attribute values, the density values and the natural gamma values corresponding to all the target points.
Optionally, the data processing module 202 is further configured to: and smoothing the natural gamma curve.
Optionally, the data processing module 202 is specifically configured to: and performing seismic inversion on the three-dimensional seismic data and the logging curve by using the probabilistic neural network model.
The basic principle and the technical effects of the prediction apparatus for constructing a soft coal development area provided above are the same as those of the previous method embodiment, and for the sake of brief description, no part of this embodiment is mentioned, and reference may be made to the corresponding contents in the above method embodiment, which is not described herein again.
Referring to fig. 4, the present embodiment provides an electronic device 300, which includes a processor 301 and a memory 302, where the memory 302 stores at least one instruction, at least one program, code set, or instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor 301 to implement the prediction method for constructing a soft coal development area according to the foregoing embodiment. The electronic device 300 may further comprise a communication interface 303 and a communication bus 304, wherein the processor 301, the memory 302 and the communication interface 303 communicate with each other via the communication bus 304.
The memory 302 may include high-speed random access memory (as a cache) and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. The communication bus 304 is a circuit that connects the described elements and enables transmission between the elements. For example, the processor 301 receives commands from other elements through the communication bus 304, decodes the received commands, and performs calculations or data processing according to the decoded commands. The communication interface 303 connects the electronic device 300 with other network devices, user equipment, networks. For example, the communication interface 303 may be connected to a network by wire or wirelessly to connect to external other network devices or user devices. The wireless communication may include at least one of: WIFI, bluetooth, cellular communication, Global System for mobile communication (GSM), etc., the wired communication may include at least one of the following: universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), asynchronous transfer Standard Interface (RS-232), and the like.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
It should be noted that the functions, if implemented in the form of software functional modules and sold or used as independent products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution or parts of the technical solution of the present application may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (8)
1. A prediction method for constructing a soft coal development zone, comprising:
acquiring three-dimensional seismic data and a logging curve in a research area;
analyzing the three-dimensional seismic data by using an ant colony tracking algorithm to obtain an ant data body, wherein the ant data body is a set of ant attribute values at each position in the stratum of the research area, and the ant attribute values represent whether faults exist at the position;
performing seismic inversion with the logging curve as constraint according to the three-dimensional seismic data and the logging curve to obtain a logging data volume, wherein the logging data volume is a set of logging attribute values at each position in the stratum of the research area;
extracting ant attribute values and well logging attribute values of all target points in the target coal bed from the ant data body and the well logging data body respectively, and fusing the ant attribute values and the well logging attribute values corresponding to all the target points to obtain fused values of all the target points of the target coal bed;
dividing at least one region on the plane of the target coal bed according to the relation between the fusion value of each target point and a set threshold value, comparing the position where the constructed soft coal exists, which is obtained in advance, with the position of the at least one region, and determining a development region for constructing the soft coal from the at least one region according to the comparison result;
the logging curve comprises a density curve and a natural gamma curve, seismic inversion with the logging curve as constraint is carried out according to the three-dimensional seismic data and the logging curve, and a logging data body is obtained, and the method comprises the following steps:
performing seismic inversion with a density curve as constraint according to the three-dimensional seismic data and the density curve to obtain a density data volume, and performing seismic inversion with a natural gamma curve as constraint according to the three-dimensional seismic data and the natural gamma curve to obtain a natural gamma data volume, wherein the density data volume and the natural gamma data volume are respectively a set of density values and a set of natural gamma values at each position in a stratum of a research area;
the method for extracting the ant attribute values and the logging attribute values of all target points in the target coal seam from the ant data body and the logging data body respectively and fusing the ant attribute values and the logging attribute values corresponding to all the target points comprises the following steps:
and extracting ant attribute values, density values and natural gamma values of all target points in the target coal bed from the ant data volume, the density data volume and the natural gamma data volume respectively, and fusing the ant attribute values, the density values and the natural gamma values corresponding to all the target points.
2. The method of claim 1, wherein prior to performing natural gamma curve-constrained seismic inversion from the three-dimensional seismic data and the natural gamma curve, the method further comprises:
and smoothing the natural gamma curve.
3. The method of claim 1, wherein performing a log-constrained seismic inversion from the three-dimensional seismic data and the log comprises:
and performing seismic inversion on the three-dimensional seismic data and the logging curve by utilizing a probabilistic neural network model.
4. The method of claim 3, wherein prior to performing seismic inversion on the three-dimensional seismic data and well logs using a probabilistic neural network model, the method further comprises:
and training and cross-verifying the probabilistic neural network model by using the density curve and the natural gamma curve of each drill hole in the research area to determine the parameters of the probabilistic neural network model.
5. The method of claim 4, wherein training the probabilistic neural network model using the density curve and the natural gamma curve for each borehole in the region of interest comprises:
and training the probabilistic neural network model by using a density curve and a natural gamma curve of each drill hole in the research area, wherein the density curve and the natural gamma curve meet preset conditions, and the preset conditions mean that the change degree of the curves is greater than the preset degree.
6. A prediction apparatus for constructing a soft coal development zone, comprising:
the data acquisition module is used for acquiring three-dimensional seismic data and a logging curve in a research area;
the data processing module is used for analyzing the three-dimensional seismic data by utilizing an ant colony tracking algorithm to obtain an ant data body, and performing seismic inversion with a logging curve as a constraint according to the three-dimensional seismic data and the logging curve to obtain a logging data body, wherein the ant data body is a set of ant attribute values at each position in a stratum of a research area, the ant attribute values represent whether a fault exists at the position, and the logging data body is a set of logging attribute values at each position in the stratum of the research area;
the data fusion module is used for extracting ant attribute values and logging attribute values of all target points in the target coal seam from the ant data body and the logging data body respectively, and fusing the ant attribute values and the logging attribute values corresponding to all the target points to obtain fusion values of all the target points of the target coal seam;
the prediction module is used for dividing at least one region on the plane of the target coal bed according to the relation between the fusion value of each target point and a set threshold value, comparing the position where the constructed soft coal exists, which is obtained in advance, with the position of the at least one region, and determining a development region for constructing the soft coal from the at least one region according to the comparison result;
the logging curve comprises a density curve and a natural gamma curve, and the data processing module is specifically used for:
performing seismic inversion with a density curve as constraint according to the three-dimensional seismic data and the density curve to obtain a density data volume, and performing seismic inversion with a natural gamma curve as constraint according to the three-dimensional seismic data and the natural gamma curve to obtain a natural gamma data volume, wherein the density data volume and the natural gamma data volume are respectively a set of density values and a set of natural gamma values at each position in a stratum of a research area;
the data fusion module is specifically configured to: and extracting ant attribute values, density values and natural gamma values of all target points in the target coal bed from the ant data volume, the density data volume and the natural gamma data volume respectively, and fusing the ant attribute values, the density values and the natural gamma values corresponding to all the target points.
7. The apparatus of claim 6, wherein the data processing module is further configured to: and smoothing the natural gamma curve.
8. The apparatus of claim 6, wherein the data processing module is specifically configured to: and performing seismic inversion on the three-dimensional seismic data and the logging curve by utilizing a probabilistic neural network model.
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