CN110208861A - A kind of prediction technique and device of tectonic soft coal development area - Google Patents
A kind of prediction technique and device of tectonic soft coal development area Download PDFInfo
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- G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
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Abstract
The application provides the prediction technique and device of a kind of tectonic soft coal development area, wherein method includes: the 3D seismic data and log obtained in survey region；3D seismic data is analyzed using ant colony tracing algorithm, obtains ant data volume；Seismic inversion is carried out according to 3D seismic data and log, obtains log data body；The ant attribute value and well log attributes value of each target point in target coal seam are extracted from ant data volume and log data body respectively, and the corresponding ant attribute value of each target point is merged with well log attributes value, the fusion value of acquisition each target point of target coal seam；Mark off at least one region in the plane of target coal seam according to the relationship of the fusion value of each target point and given threshold, and be obtained ahead of time there are the positions of tectonic soft coal to compare, and then the development region of tectonic soft coal is determined from least one region.
Description
Technical field
This application involves technical field of geological exploration, in particular to a kind of prediction technique of tectonic soft coal development area
And device.
Background technique
Tectonic soft coal refers to the coal being deformed under tectonic stress effect, i.e., in different stress strain environment and construction
Under stress, significant changes will all occur for physical structure, chemical structure and its photosensitiveness feature of coal etc., so that being formed has not
Same structure feature, different types of structural deformation coal.The forecasting research for tectonic soft coal development region also relatively lacks at this stage
It is weary.
Summary of the invention
The prediction technique and device for being designed to provide a kind of tectonic soft coal development area of the embodiment of the present application, with predictably
The development region of tectonic soft coal in lower rock stratum.
In a first aspect, the embodiment of the present application provides a kind of prediction technique of tectonic soft coal development area, comprising: obtain research area
3D seismic data and log in domain；The 3D seismic data is analyzed using ant colony tracing algorithm, is obtained
Ant data volume is obtained, the ant data volume is the set of the ant attribute value in survey region stratum at each position, described
Ant attribute value indicates at the position with the presence or absence of tomography；According to the 3D seismic data and the log carry out with
Log is the seismic inversion of constraint, obtains log data body, and the log data body is each in survey region stratum
Set the set of the well log attributes value at place；Each target point in target coal seam is extracted from ant data volume and log data body respectively
Ant attribute value and well log attributes value, and the corresponding ant attribute value of each target point is merged with well log attributes value,
Obtain the fusion value of each target point of target coal seam；According to the relationship of the fusion value of each target point and given threshold in the target coal
Layer plane on mark off at least one region, by be obtained ahead of time there are the positions of tectonic soft coal and at least one described region
Position compare, and according to comparing result from least one described region determine tectonic soft coal development region.
Ant data volume is able to reflect the rift structure situation in subterranean strata, multiple deposits through inventor to what is disclosed on the spot
It is analyzed in the position of tectonic soft coal, finds tectonic soft coal multidigit in mature fault or tomography marginal position, that is to say, that
Ant data volume is able to reflect the development of tectonic soft coal to a certain extent, also, by ant data volume and log data body into
Row fusion carries out the prediction of tectonic soft coal with shown variation of lithological is logged well in conjunction with the distribution of tomography, compared to by list
The prediction result that one attribute value obtains can be more accurate.
In a kind of optional embodiment of first aspect, the log includes density curve and natural gamma
Curve is carried out taking log as the seismic inversion constrained according to the 3D seismic data and the log, be obtained
Log data body, comprising: carried out taking density curve as the ground constrained according to the 3D seismic data and the density curve
Inverting is shaken, density data body is obtained, and, it is carried out according to the 3D seismic data and the gamma ray curve with nature
Gamma curve is the seismic inversion of constraint, obtains nature gamma data body, the density data body and the natural gamma data
Body is respectively the set of the density value in survey region stratum at each position and the set of natural gamma value；It is described respectively from ant
The ant attribute value and well log attributes value of each target point in target coal seam are extracted in ant data volume and log data body, and will be every
The corresponding ant attribute value of one target point is merged with well log attributes value, comprising: respectively from ant data volume, density data body
And ant attribute value, density value and the natural gamma value of each target point in target coal seam are extracted in natural gamma data volume,
And the corresponding ant attribute value of each target point, density value and natural gamma value are merged.
It is directed to density data body respectively through inventor and natural gamma data volume carries out test discovery, disclosed in survey region
Multiple gas outburst points (can think that there are tectonic soft coals) density value it is relatively large, based on this discovery by target coal
The density value of each target point of layer and the Density Distribution section of tectonic soft coal compare, and finds the prediction result obtained and takes off in fact
Gas outburst point matching is preferable, therefore the one of them index that the variation of density is predicted as tectonic soft coal；On the other hand, also
It was found that in the development region of tectonic soft coal, the natural gamma value of coal seam rock crown is lower, since natural gamma is for rock stratum
Sand mud ratio has indicative function, also can indication structure cherry coal indirectly developmental state.Therefore, in conjunction with above two well log attributes into
Row prediction, the accuracy of prediction result are higher.
In a kind of optional embodiment of first aspect, according to the 3D seismic data and the natural gal
Before the progress of horse curve is the seismic inversion constrained with gamma ray curve, the method also includes: it is bent to the natural gamma
Line is smoothed.
After gamma ray curve smoothing processing, the distribution of sand, mud thick-layer can be highlighted, while inversion speed can be brought
It is promoted.
In a kind of optional embodiment of first aspect, according to the 3D seismic data and the log
Carry out with log be constraint seismic inversion, comprising: utilize probabilistic neural network model to the 3D seismic data with
And log carries out seismic inversion.
In a kind of optional embodiment of first aspect, in utilization probabilistic neural network model to the 3-D seismics
Before data and log carry out seismic inversion, the method also includes: the density respectively to drill in research on utilization region is bent
Line and gamma ray curve is trained to probabilistic neural network model and cross validation, with the determination probabilistic neural network
The parameter of model.
In a kind of optional embodiment of first aspect, the density curve and nature that respectively drill in research on utilization region
Gamma curve is trained probabilistic neural network model, comprising: what is respectively drilled in research on utilization region meets preset condition
Density curve and gamma ray curve are trained the probabilistic neural network model, wherein the preset condition refers to song
The variation degree of line is greater than predeterminable level.
The little curve of variation degree in log, it is believed that it does not reflect that formation lithology changes, these curves
The training for needing not participate in neural network model avoids the negative effect to prediction result.
Second aspect, the embodiment of the present application provide a kind of prediction meanss of tectonic soft coal development area, comprising: data acquisition mould
Block, for obtaining 3D seismic data and log in survey region；Data processing module, for being tracked using ant colony
Algorithm analyzes the 3D seismic data, obtains ant data volume, and, according to the 3D seismic data and institute
State log and carry out be with log constraint seismic inversion, acquisition log data body, wherein the ant data volume is
The set of ant attribute value in survey region stratum at each position, the ant attribute value indicate to whether there is at the position
Tomography, the log data body are the set of the well log attributes value in survey region stratum at each position；Data fusion module,
For respectively from the ant attribute value of each target point and well logging in extraction target coal seam in ant data volume and log data body
Attribute value, and the corresponding ant attribute value of each target point is merged with well log attributes value, obtain each target of target coal seam
The fusion value of point；Prediction module, for according to the fusion value of each target point and the relationship of given threshold in the target coal seam
At least one region is marked off in plane, by be obtained ahead of time there are the positions of the position of tectonic soft coal and at least one region
It sets and compares, and determine the development region of tectonic soft coal from least one described region according to comparing result.
In a kind of optional embodiment of second aspect, the log includes density curve and natural gamma
Curve, the data processing module are specifically used for: being carried out according to the 3D seismic data and the density curve with density
Curve is the seismic inversion of constraint, obtains density data body, and, according to the 3D seismic data and the natural gamma
Curve carries out taking gamma ray curve as the seismic inversion constrained, obtains nature gamma data body, the density data body and institute
Stating natural gamma data volume is respectively the set of the density value in survey region stratum at each position and the collection of natural gamma value
It closes；The data fusion module is specifically used for: mentioning from ant data volume, density data body and natural gamma data volume respectively
Take ant attribute value, density value and the natural gamma value of each target point in target coal seam, and by the corresponding ant of each target point
Ant attribute value, density value and natural gamma value are merged.
In a kind of optional embodiment of second aspect, the data processing module is also used to: to the natural gal
Horse curve is smoothed.
In a kind of optional embodiment of second aspect, the data processing module is specifically used for: utilizing probability mind
Seismic inversion is carried out to the 3D seismic data and log through network model.
Detailed description of the invention
Technical solution in ord to more clearly illustrate embodiments of the present application will make below to required in the embodiment of the present application
Attached drawing is briefly described, it should be understood that the following drawings illustrates only some embodiments of the application, therefore should not be seen
Work is the restriction to range, for those of ordinary skill in the art, without creative efforts, can be with
Other relevant attached drawings are obtained according to these attached drawings.
Fig. 1 is a kind of flow chart of the prediction technique of tectonic soft coal development area provided by the embodiments of the present application；
Fig. 2 is the plan view of the crack conditions in the reflection target coal seam obtained according to ant data volume；
Fig. 3 is a kind of schematic diagram of the prediction meanss of tectonic soft coal development area provided by the embodiments of the present application；
Fig. 4 is the schematic diagram of a kind of electronic equipment provided by the embodiments of the present application.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application is described.
Embodiment
Tectonic soft coal is the deformation coal that broken or strong tough contracting deformation occurs under tectonic stress effect for coal seam, special
Physics and chemical structure determine its characteristic with high air content and low-permeable.It is general in the broken gap of tectonic soft coal
It is filled with coal bed gas, makes that mine may be endangered there are the danger zone that the mine district of tectonic soft coal often becomes Gas Outburst
Digging safety.In view of this, the embodiment of the present application provides a kind of prediction technique of tectonic soft coal development area, it can be mine
The practical application scenes such as gas outbursts Prediction and CBM exploration and development provide guidance, and referring to Fig.1, this method includes following step
It is rapid:
Step 101: obtaining the 3D seismic data and log in survey region.
Wherein, survey region is the geographic area that will be executed the method and carry out tectonic soft coal development regional prediction, three-dimensional
Seismic data is (such as to be folded after blowing out earthquake-wave-exciting when field acquisition by the seismic data by processing that wave detector obtains
Offset data, pre-stack time migration data etc. afterwards), log is the rock obtained in drilling process using logging equipment measurement
The geophysical parameters of layer are formed by curve, reflect the logging character of different lithology, different layers position, in the present embodiment, survey
Well curve can choose at least one of density curve, gamma ray curve, certainly, also be not excluded for that other classes can also be used
The scheme that the log of type is predicted.
Step 102: 3D seismic data being analyzed using ant colony tracing algorithm, obtains ant data volume.
Ant data volume is the set of the ant attribute value in survey region subterranean strata at each position, each ant category
Property value expression be meant that the position with the presence or absence of there is tomography, it means that be able to reflect out using three-dimensional ant data volume whole
The tomography of a survey region underground, crack situation.
Ant colony tracing algorithm is by simulation nature ant colony in order to which Optimizing Search food path marks track of creeping
Ant is put into and is formed by seismic data cube by 3D seismic data to search for underground tomography that may be present, ant by behavior
Group will capture fracture information in seismic data cube, form the fracture response of description subsurface fault situation, i.e. ant data volume.It should
The basic principle of algorithm are as follows: a large amount of ant is disseminated in seismic data cube, when the discovery of certain ants meets default failure condition
Splitting traces when will discharge certain signal (can be understood as pheromones), convene the ant in other regions to concentrate on breaking part pair
It is tracked, until complete the tracking and identification of the breaking part, and other positions for being unsatisfactory for default failure condition will not be into
Rower note and release signal, ultimately form the ant data volume with clear splitting traces.As shown in Fig. 2, Fig. 2 indicate from
The plan view for reflecting the crack conditions in the target coal seam obtained is extracted in ant data volume along target coal seam, from the plan view
In can clearly see the tomography distribution in target coal seam, the depth of splitting traces indicates that the serious of damage layer is interrupted in coal seam in figure
Degree.
Tectonic soft coal Producing reason is coal seam fractrue, so that the primary coal of script is destroyed to form tectonic soft coal, and by
It is filled with coal bed gas in the broken gap of tectonic soft coal, therefore Gas Outburst easily occurs, thus can be according to whether occurring
Gas Outburst phenomenon distinguishes primary coal and tectonic soft coal, that is to say, that when a certain position appearance watt of discovery in mining process
This projecting point, it can think that there are tectonic soft coals for the point.Through inventor the study found that takes off in fact in a certain survey region is multiple
Gas outburst point multidigit is in mature fault or tomography marginal position, it is seen that fracture, the distribution in crack being capable of index stress concentrations
To the destruction position in coal seam, thus it can consider that tomography distribution and the development of tectonic soft coal are contacted in the presence of certain, therefore, pre-
Can be using tomography as one of them prediction index when surveying tectonic soft coal, and ant data physical efficiency is for explaining that subsurface fault is distributed
Situation, therefore can use ant data volume to carry out the prediction of tectonic soft coal.
Step 103: carrying out with log being the seismic inversion constrained according to 3D seismic data and log, obtain
Obtain log data body.
Log data body surface shows the three-dimensional set of the well log attributes value in survey region stratum at each position, bent in well logging
When line is density curve and/or gamma ray curve, the corresponding log data body of acquisition is also density data body and/or nature
Gamma data body is illustrated by taking density data body and natural gamma data volume as an example in detail below.
Seismic inversion mode can there are many, including but not limited to recurrence inversion, Sparse Pulse Inversion, characteristic retrieval, mind
Through any possible mode such as network inverting, specifically how carrying out inverting according to 3D seismic data and log can join
According to the prior art, no explanation is provided here.In the present embodiment, probabilistic neural network (Probabilistic can be used
Neural Network, PNN) model progress seismic inversion.Before carrying out inverting, first with what is respectively drilled in survey region
Density curve and gamma ray curve is trained to probabilistic neural network model and cross validation, to determine probabilistic neural net
The parameter (including learning parameter and hyper parameter) of network model, it is noted that participate in trained density curve and gamma ray curve
It should meet some requirements, i.e., the variation degree of curve should be greater than preset degree, if on acquired log
Well log attributes value there is not certain variation, it may be considered that these curves do not reflect formation lithology change, then do not allow
Its training for participating in neural network model, in order to avoid cause the negative effect to prediction result.
Before this, inventor is directed to density data body respectively and natural gamma data volume has carried out long term test, from obtaining
The extraction of target coal seam layer position information, analysis target coal seam and window institute when its upper and lower 2ms inverting are carried out in the density data body obtained
Average density value in the thickness range of expression is (to the density in the thickness range in target coal seam centered on each target point
Value seeks arithmetic average) situation of change, obtain the planar distribution of target coal seam, then will be each close on target coal seam planar distribution
Angle value and the variable density range of tectonic soft coal compare, and think if density value is located within the scope of tectonic soft coal variable density
There are tectonic soft coals for the point.During certain primary test, 7 gas outburst points disclosed in comparative study region find 7
The density value of gas outburst point is that (density value is located at 1.43-1.47g/cm to relatively large value3Between, and target coal seam
Variable density is in 1.379-1.466g/cm3Between), this shows that the method for carrying out tectonic soft coal prediction using density value is feasible
, moreover, the prediction result finally obtained and the matching for the gas outburst point taken off in fact are also preferable, therefore the present embodiment is by density
Change the one of them index predicted as tectonic soft coal.On the other hand, inventor studies for natural gamma data volume
After find, in the development region of tectonic soft coal, the natural gamma value of coal seam rock crown is lower namely sandstone is more developed,
And natural gamma has indicative function for the sand mud ratio of rock stratum, also can indication structure cherry coal indirectly developmental state.In conjunction with
Density and both well log attributes of natural gamma, can make the accuracy of prediction result higher.
Step 104: respectively from the ant category for extracting each target point in target coal seam in ant data volume and log data body
Property value and well log attributes value.
Step 105: the corresponding ant attribute value of each target point being merged with well log attributes value, obtains target coal seam
The fusion value of each target point.
Mentioning for attribute value is carried out along coal seam from three-dimensional ant data volume, density data body and natural gamma data volume
It takes, obtains ant attribute value, density value and natural gamma value corresponding to each target point in target coal seam, further, this
Embodiment uses multi-information fusion algorithm, by the attribute value of the corresponding three kinds of reflections tectonic soft coal variation of each target point according to certain
Condition is associated, is related and comprehensive, forms a new fusion value, which can the more accurate change for reflecting tectonic soft coal
Change, and form the planar distribution of target coal seam, each target point on the planar distribution of coal seam all has a fusion value, by coal seam
The fusion value located at various locations and the tectonic soft coal found in practical recovery process match one by one, and then can be from fusion value
The developmental state of variation reflection tectonic soft coal.
Above-mentioned multi-information fusion algorithm is referred to the embodiment of the prior art, and no explanation is provided here.
Step 106: being marked off in the plane of target coal seam according to the relationship of the fusion value of each target point and given threshold
At least one region.
The fusion value of each target point and the relationship of given threshold are successively judged first, if there is melting within the scope of some
Conjunction value is all larger than given threshold, then the target point within the scope of this is divided into the same region, if melting in a certain range
Target point within the scope of this is then divided into another region no more than given threshold by conjunction value, is completed to target coal seam
After the judgement of upper each target point, at least one region can be marked off in the plane of target coal seam, at least one region
It is to be understood that all target points greater than given threshold, which are formed by region, is defined as a region (in this region
May include discontinuous multiple regions each other), and all target points no more than given threshold are formed by region and determine
Justice is another region, i.e., the two regions indicate the development region of tectonic soft coal hereinafter referred to as and owe to educate region.
It should be noted that the variation range of different survey region fusion values may be different, therefore above-mentioned given threshold
It needs to be adjusted according to the practical geological condition of survey region, the region marked off is enable to be more nearly actual conditions.
Step 107: by be obtained ahead of time there are the positions of tectonic soft coal and the position at least one region to compare, and
The development region of tectonic soft coal is determined from least one region according to comparing result.
What at least one region marked off in target coal seam indicated is the actual geographic region in survey region, therefore can
To compare the geographical location in each region and the geographical location for the multiple gas outburst points taken off in fact, if it find that all watts
This projecting point be respectively positioned in wherein some region (or the gas outburst point in a certain region of discovery quantity and real to take off gas prominent
The ratio for the total quantity put out, which is higher than certain proportion, such as 80%) can then be determined as this region the development area of tectonic soft coal
Domain, another region are then determined as owing for tectonic soft coal and educate region (indicating that the region is that coal seam keeps preferable primary coal).
It optionally, can be by the prediction knot of acquisition after the developmental state for determining tectonic soft coal representated by the two regions
Fruit carries out visualization display, i.e. one color image of formation, by the development region of tectonic soft coal with more apparent face in picture
Color highlights, and the deficient development area of tectonic soft coal is indicated with another color, enable prediction result more intuitively by
It reflects.
Inventor predicts certain survey region using prediction technique provided in this embodiment on the spot, and compares digging
Data, discovery prediction result match preferably with 3 karst collapse col umns and 6 tomographies seen in fact, it was demonstrated that and the precision of prediction result is higher, and
Find that the tectonic soft coal in the survey region covers entire survey region about 3/4 or more according to the prediction result reflected in picture
Region, development range are wider.As it can be seen that prediction technique provided in this embodiment can accurately portray the distribution of tectonic soft coal, it is based on
The distribution of tectonic soft coal can also provide weight for practical application scenes such as Gas Outburst prediction and CBM exploration and developments
The theory and technical support wanted.
Further, in primary test, entire refutation process, invention human hair are analyzed in the identical situation of computing capability
Existing: when inversion of Density, density curve is smoothly less, and the inverting used time 7 days, inversion result reflected the coal seam of 2m or more clear
It is clear, it can reflect the sand body of 3-5m or so, inversion result is higher for thin layer reflection precision, can be used for the small change of target coal seam
The variation of lithological of change and the nearly range of roof；When natural gamma inverting, smoothing processing has been carried out to gamma ray curve, has been put down
Gamma ray curve after cunning has a degree of decline for the recognition capability of thin layer, but has highlighted point of sand, mud thick-layer
Cloth, while the promotion of inversion speed is also brought, inverse time used shorten to 4 hours, therefore, is utilizing natural gamma
Before curve carries out seismic inversion, this method further includes the steps that being smoothed gamma ray curve.
It should be noted that the present embodiment, based on the analysis to tectonic soft coal Crack cause, the distribution that tomography is utilized comes
Reflect tectonic soft coal, still, if predicted only with ant data volume, if do not plan a successor at a certain position
The position is then unable to judge accurately with the presence or absence of tectonic soft coal, even and if there are also can not accurately conclude structure when tomography for a certain position
Which region that cherry coal is located within the scope of tomography made, it is seen that based on the prediction technique of single attribute there are certain shortcoming, because
The present embodiment provides the schemes predicted after a kind of more attribute fusions for this, while utilizing the distribution situation of tomography, density and natural gal
The variation of horse reflects the development of tectonic soft coal, also, since fusion value unified dimension can not carry out pair between tectonic soft coal
Than, therefore the present embodiment is also matched using the situation of change of fusion value with the gas outburst point taken off in fact, so that it is determined that structure out
Make the development region of cherry coal.
It should also be noted that, due to applying 3D seismic data in above scheme, compared to coal sample, drilling, well logging
Etc. data be only capable of representing a point or a line, three-dimensional seismic data cube can be improved the accuracy of lateral prediction, also,
Accurate Prediction is also able to achieve in the distribution of the tectonic soft coal far from drilling certain area (or coal seam does not disclose area).
Based on the same inventive concept, a kind of prediction meanss of tectonic soft coal development area are also provided in the embodiment of the present application, are joined
According to Fig. 3, which includes:
Data acquisition module 201, for obtaining 3D seismic data and log in survey region；
Data processing module 202 obtains ant number for analyzing using ant colony tracing algorithm 3D seismic data
According to body, and, it carries out with log being the seismic inversion constrained according to 3D seismic data and log, be logged well
Data volume；Wherein, ant data volume is the set of the ant attribute value in survey region stratum at each position, ant attribute value
Indicate that, with the presence or absence of tomography at the position, log data body is the collection of the well log attributes value in survey region stratum at each position
It closes；
Data fusion module 203, for extracting each mesh in target coal seam from ant data volume and log data body respectively
The ant attribute value and well log attributes value of punctuate, and the corresponding ant attribute value of each target point and well log attributes value are carried out
Fusion obtains the fusion value of each target point of target coal seam；
Prediction module 204, for according to the relationship of the fusion value of each target point and given threshold target coal seam plane
On mark off at least one region, will be obtained ahead of time and carried out pair there are the position of the position of tectonic soft coal and at least one region
Than, and according to the development region of comparing result determining tectonic soft coal from least one region.
Optionally, log includes density curve and gamma ray curve, and data processing module 202 is specifically used for:
It is carried out taking density curve as the seismic inversion constrained according to 3D seismic data and density curve, obtains density data body, with
And carried out taking gamma ray curve as the seismic inversion constrained according to 3D seismic data and gamma ray curve, obtained from
Right gamma data body, density data body and natural gamma data volume are respectively the density value in survey region stratum at each position
Set and natural gamma value set；Data fusion module 203 is specifically used for: respectively from ant data volume, density data body
And ant attribute value, density value and the natural gamma value of each target point in target coal seam are extracted in natural gamma data volume,
And the corresponding ant attribute value of each target point, density value and natural gamma value are merged.
Optionally, data processing module 202 is also used to: being smoothed to gamma ray curve.
Optionally, data processing module 202 is specifically used for: using probabilistic neural network model to 3D seismic data and
Log carries out seismic inversion.
The prediction meanss of the tectonic soft coal development area of above-mentioned offer and the basic principle of former approach embodiment and generation
Technical effect is identical, and to briefly describe, the present embodiment part does not refer to place, can refer to corresponding in above-mentioned embodiment of the method
Content, this will not be repeated here.
Referring to Fig. 4, the present embodiment provides a kind of electronic equipment 300, including processor 301 and memory 302, memory
At least one instruction, at least a Duan Chengxu, code set or instruction set, at least one instruction, an at least Duan Cheng are stored in 302
Sequence, code set or instruction set are loaded by processor 301 and are executed to realize tectonic soft coal development area provided by the above embodiment
Prediction technique.Electronic equipment 300 can also include communication interface 303, communication bus 304, wherein processor 301, memory
302 and communication interface 303 mutual communication completed by communication bus 304.
Memory 302 may include high-speed random access memory (as caching), can also include non-volatile memories
Device, for example, at least a disk memory, flush memory device or other volatile solid-state parts.Communication bus 304 is to connect
It connects the circuit of described element and realizes transmission between these elements.For example, processor 301 passes through communication bus 304
Order is received from other elements, decodes the order received, calculating or data processing are executed according to decoded order.Communication connects
The electronic equipment 300 is attached by mouth 303 with other network equipments, user equipment, network.For example, communication interface 303 can be with
By being wired or wirelessly connected to network to be connected to external other network equipments or user equipment.Wireless communication may include
Following at least one: WIFI, bluetooth, cellular communication and global system for mobile communications (Global System for Mobile
Communication, GSM) etc., wire communication may include following at least one: universal serial bus (Universal
Serial Bus, USB), high-definition multimedia interface (High Definition Multimedia Interface,
HDMI), asynchronous transmission standard interface (Recommended Standard232, RS-232) etc..
In embodiment provided herein, it should be understood that disclosed device and method, it can be by others side
Formula is realized.The apparatus embodiments described above are merely exemplary, for example, the division of the unit, only one kind are patrolled
Function division is collected, there may be another division manner in actual implementation, in another example, multiple units or components can combine or can
To be integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Coupling, direct-coupling or communication connection can be INDIRECT COUPLING or communication link by some communication interfaces, device or unit
It connects, can be electrical property, mechanical or other forms.
In addition, unit may or may not be physically separated as illustrated by the separation member, as unit
The component of display may or may not be physical unit, it can and it is in one place, or may be distributed over more
In a network unit.Some or all of unit therein can be selected to realize this embodiment scheme according to the actual needs
Purpose.
Furthermore each functional module in each embodiment of the application can integrate one independent portion of formation together
Point, it is also possible to modules individualism, an independent part can also be integrated to form with two or more modules.
If it should be noted that function is realized in the form of software function module and sells or make as independent product
Used time can store in a computer readable storage medium.Based on this understanding, the technical solution of the application or
The part of the technical solution can be embodied in the form of software products, which is stored in a storage medium,
It uses including some instructions so that a computer equipment (can be personal computer, server or the network equipment etc.) is held
The all or part of the steps of each embodiment the method for row the application.And storage medium above-mentioned include: USB flash disk, mobile hard disk,
Read-only memory (Read-Only Memory, ROM) random access memory (Random Access Memory, RAM), magnetic disk
Or the various media that can store program code such as CD.
Herein, relational terms such as first and second and the like be used merely to by an entity or operation with it is another
One entity or operation distinguish, and without necessarily requiring or implying between these entities or operation, there are any this reality
Relationship or sequence.
The above description is only an example of the present application, the protection scope being not intended to limit this application, for ability
For the technical staff in domain, various changes and changes are possible in this application.Within the spirit and principles of this application, made
Any modification, equivalent substitution, improvement and etc. should be included within the scope of protection of this application.
Claims (10)
1. a kind of prediction technique of tectonic soft coal development area characterized by comprising
Obtain the 3D seismic data and log in survey region；
The 3D seismic data is analyzed using ant colony tracing algorithm, obtains ant data volume, the ant data volume
For the set of the ant attribute value at each position in survey region stratum, the ant attribute value indicates whether deposit at the position
In tomography；
It carries out with log being the seismic inversion constrained according to the 3D seismic data and the log, be surveyed
Well data volume, the log data body are the set of the well log attributes value in survey region stratum at each position；
Respectively from the ant attribute value of each target point and well logging in extraction target coal seam in ant data volume and log data body
Attribute value, and the corresponding ant attribute value of each target point is merged with well log attributes value, obtain each target of target coal seam
The fusion value of point；
At least one is marked off in the plane of the target coal seam according to the relationship of the fusion value of each target point and given threshold
Region, by be obtained ahead of time there are the positions of tectonic soft coal and the position at least one region to compare, and according to right
The development region of tectonic soft coal is determined from least one described region than result.
2. the method according to claim 1, wherein the log includes density curve and natural gamma
Curve is carried out taking log as the seismic inversion constrained according to the 3D seismic data and the log, be obtained
Log data body, comprising:
It is carried out taking density curve as the seismic inversion constrained according to the 3D seismic data and the density curve, be obtained close
Data volume is spent, and, it carries out with gamma ray curve being about according to the 3D seismic data and the gamma ray curve
The seismic inversion of beam, obtains nature gamma data body, and the density data body and the natural gamma data volume are respectively to study
The set of density value in regional stratum at each position and the set of natural gamma value；
It is described extracted from ant data volume and log data body in target coal seam respectively the ant attribute value of each target point and
Well log attributes value, and the corresponding ant attribute value of each target point is merged with well log attributes value, comprising:
Respectively from the ant for extracting each target point in target coal seam in ant data volume, density data body and natural gamma data volume
Ant attribute value, density value and natural gamma value, and by the corresponding ant attribute value of each target point, density value and natural gal
Horse value is merged.
3. according to the method described in claim 2, it is characterized in that, according to the 3D seismic data and the natural gal
Before the progress of horse curve is the seismic inversion constrained with gamma ray curve, the method also includes:
The gamma ray curve is smoothed.
4. according to the method described in claim 2, it is characterized in that, according to the 3D seismic data and the log
It carries out taking log as the seismic inversion constrained, comprising:
Seismic inversion is carried out to the 3D seismic data and log using probabilistic neural network model.
5. according to the method described in claim 4, it is characterized in that, in utilization probabilistic neural network model to the 3-D seismics
Before data and log carry out seismic inversion, the method also includes:
The density curve and gamma ray curve respectively to drill in research on utilization region to probabilistic neural network model be trained with
And cross validation, with the parameter of the determination probabilistic neural network model.
6. according to the method described in claim 5, it is characterized in that, the density curve and nature that respectively drill in research on utilization region
Gamma curve is trained probabilistic neural network model, comprising:
The density curve for meeting preset condition and gamma ray curve respectively to drill in research on utilization region is to the probabilistic neural
Network model is trained, wherein the preset condition refers to that the variation degree of curve is greater than predeterminable level.
7. a kind of prediction meanss of tectonic soft coal development area characterized by comprising
Data acquisition module, for obtaining 3D seismic data and log in survey region；
Data processing module obtains ant data for analyzing using ant colony tracing algorithm the 3D seismic data
Body, and, it carries out with log being the seismic inversion constrained according to the 3D seismic data and the log, obtain
Obtaining log data body, wherein the ant data volume is the set of the ant attribute value in survey region stratum at each position,
The ant attribute value indicates that, with the presence or absence of tomography at the position, the log data body is each position in survey region stratum
The set of the well log attributes value at place；
Data fusion module, for respectively from the ant for extracting each target point in target coal seam in ant data volume and log data body
Ant attribute value and well log attributes value, and the corresponding ant attribute value of each target point is merged with well log attributes value, it obtains
Obtain the fusion value of each target point of target coal seam；
Prediction module, for being drawn in the plane of the target coal seam according to the fusion value of each target point and the relationship of given threshold
At least one region is separated, will be obtained ahead of time and carried out pair there are the position of the position of tectonic soft coal and at least one region
Than, and according to the development region of comparing result determining tectonic soft coal from least one described region.
8. device according to claim 7, which is characterized in that the log includes density curve and natural gamma
Curve, the data processing module are specifically used for:
It is carried out taking density curve as the seismic inversion constrained according to the 3D seismic data and the density curve, be obtained close
Data volume is spent, and, it carries out with gamma ray curve being about according to the 3D seismic data and the gamma ray curve
The seismic inversion of beam, obtains nature gamma data body, and the density data body and the natural gamma data volume are respectively to study
The set of density value in regional stratum at each position and the set of natural gamma value；
The data fusion module is specifically used for: respectively from ant data volume, density data body and natural gamma data volume
Ant attribute value, density value and the natural gamma value of each target point in target coal seam are extracted, and each target point is corresponding
Ant attribute value, density value and natural gamma value are merged.
9. device according to claim 8, which is characterized in that the data processing module is also used to: to the natural gal
Horse curve is smoothed.
10. device according to claim 8, which is characterized in that the data processing module is specifically used for: utilizing probability mind
Seismic inversion is carried out to the 3D seismic data and log through network model.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111983690A (en) * | 2020-08-12 | 2020-11-24 | 阳泉煤业（集团）股份有限公司 | Coal seam roof sandstone distribution geophysical prediction method |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009038886A9 (en) * | 2007-08-02 | 2009-10-01 | Schlumberger Canada Limited | Method and system for cleat characterization in coal bed methane wells for completion optimization |
| CN105510964A (en) * | 2015-11-27 | 2016-04-20 | 中国石油大学(华东) | Seismic recognition method of low-order strike-slip faults in complex structural areas |
| CN106599377A (en) * | 2016-11-22 | 2017-04-26 | 长江大学 | Method for quantitative division of coal body structure based on logging data and coal body structure parameters |
| CN107367757A (en) * | 2017-07-20 | 2017-11-21 | 中国石油化工股份有限公司 | The depicting method of the disconnected solution of carbonate rock |
| CN107765301A (en) * | 2017-10-13 | 2018-03-06 | 中国煤炭地质总局地球物理勘探研究院 | The method for quickly identifying and device of coal seam craven fault |
| CN108802812A (en) * | 2017-04-28 | 2018-11-13 | 中国石油天然气股份有限公司 | Well-seismic fusion stratum lithology inversion method |
| US10168442B2 (en) * | 2015-05-19 | 2019-01-01 | Schlumberger Technology Corporation | Differential energy analysis for dipole acoustic measurement |
| CN109839663A (en) * | 2019-03-20 | 2019-06-04 | 山西山地物探技术有限公司 | A kind of seismic identification and device of hidden karst pillar |
-
2019
- 2019-07-02 CN CN201910592958.7A patent/CN110208861B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009038886A9 (en) * | 2007-08-02 | 2009-10-01 | Schlumberger Canada Limited | Method and system for cleat characterization in coal bed methane wells for completion optimization |
| US10168442B2 (en) * | 2015-05-19 | 2019-01-01 | Schlumberger Technology Corporation | Differential energy analysis for dipole acoustic measurement |
| CN105510964A (en) * | 2015-11-27 | 2016-04-20 | 中国石油大学(华东) | Seismic recognition method of low-order strike-slip faults in complex structural areas |
| CN106599377A (en) * | 2016-11-22 | 2017-04-26 | 长江大学 | Method for quantitative division of coal body structure based on logging data and coal body structure parameters |
| CN108802812A (en) * | 2017-04-28 | 2018-11-13 | 中国石油天然气股份有限公司 | Well-seismic fusion stratum lithology inversion method |
| CN107367757A (en) * | 2017-07-20 | 2017-11-21 | 中国石油化工股份有限公司 | The depicting method of the disconnected solution of carbonate rock |
| CN107765301A (en) * | 2017-10-13 | 2018-03-06 | 中国煤炭地质总局地球物理勘探研究院 | The method for quickly identifying and device of coal seam craven fault |
| CN109839663A (en) * | 2019-03-20 | 2019-06-04 | 山西山地物探技术有限公司 | A kind of seismic identification and device of hidden karst pillar |
Non-Patent Citations (1)
| Title |
|---|
| 刘明举 等: ""潘三矿C_13_1_煤层构造软煤分布特征及其主控因素分析"", 《煤炭工程》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111983690A (en) * | 2020-08-12 | 2020-11-24 | 阳泉煤业（集团）股份有限公司 | Coal seam roof sandstone distribution geophysical prediction method |
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