CN110259442A - A kind of coal measure strata hydraulic fracturing disrupted beds position recognition methods - Google Patents
A kind of coal measure strata hydraulic fracturing disrupted beds position recognition methods Download PDFInfo
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- CN110259442A CN110259442A CN201910571709.XA CN201910571709A CN110259442A CN 110259442 A CN110259442 A CN 110259442A CN 201910571709 A CN201910571709 A CN 201910571709A CN 110259442 A CN110259442 A CN 110259442A
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
- E21B43/25—Methods for stimulating production
- E21B43/26—Methods for stimulating production by forming crevices or fractures
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
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Abstract
The invention discloses a kind of coal measure strata hydraulic fracturing disrupted beds position recognition methods, it includes step 1, selected needs pressure break coal measure strata, using core drilling rig drilled formation core, hydraulic fracturing rupture elastic wave test experiment is carried out to each stratum, obtains each stratum hydraulic fracturing rupture Characteristics of Elastic Wave；Step 2 ruptures Characteristics of Elastic Wave and coal seam corresponding relationship using neural metwork training, establishes rupture elastic wave layer position identification model；Step 3 ruptures elastic wave by rupture elastic wave layer position identification model according to field hydraulic pressure break, identifies the layer position actually ruptured.The solution have the advantages that: the real-time monitoring of hydraulic fracturing rupture process is realized, judges whether rock stratum ruptures by the fracturing features of rock stratum and coal seam, to adjust fracturing technology in time, improves hydraulic fracturing efficiency；Suitable for coal bed gas hydraulic fracturing monitoring and evaluation.
Description
Technical field
The invention belongs to cbm development and utilize technical field, and in particular to a kind of coal measure strata hydraulic fracturing disrupted beds
Position recognition methods, the evaluation for coal bed gas hydraulic fracturing.
Background technique
Hydraulic fracturing technology is an important technical in cbm development with its strong effective anti-reflection ability.?
Underground coal mine application hydraulic fracturing technology, not only effectively eliminates fire damp outburst hazard, further promotes the efficient of gas
Extraction.
Currently, the judgement of pressure break range and the monitoring of rupture process are the difficult points of underground coal mine hydraulic fracturing research.Pressure break
Range not only includes coal seam, also includes Seam Roof And Floor；While hydraulic fracturing causes coal seam to rupture, Seam Roof And Floor also occurs
Rupture.A large amount of coal seam rupture is conducive to the gas drainage in later period, but excessive rock breakdown will lead to a large amount of frac water leakages
Reduce coal seam fracturing effect.
Existing hydraulic fracturing, which also lacks, effectively judges the technological means of pressure break range, therefore, it is necessary to find a kind of waterpower
Fracturing process coal rock layer ruptures recognition methods, which layer position of real-time judge is rupturing, to adjust hydraulic fracturing in time
Technique improves hydraulic fracturing efficiency.
Summary of the invention
For the problem that being difficult to determine coal seam rupture, or rock breakdown when existing hydraulic fracturing, the present invention to be solved
The technical issues of be just to provide a kind of coal measure strata hydraulic fracturing disrupted beds position recognition methods, its real-time monitoring hydraulic fracturing rupture
Process, and hydraulic fracturing process is adjusted in time, improve hydraulic fracturing efficiency.
The technical problem to be solved by the present invention is in this way technical solution realize, it the following steps are included:
Step 1, it is selected need pressure break coal measure strata, using core drilling rig drilled formation core, hydraulic fracturing is carried out to each stratum
Elastic wave test experiment is ruptured, each stratum hydraulic fracturing rupture Characteristics of Elastic Wave is obtained；
Step 2 ruptures Characteristics of Elastic Wave and coal seam corresponding relationship using neural metwork training, establishes the identification of rupture elastic wave layer position
Model；
Step 3, according to field hydraulic pressure break rupture elastic wave by rupture elastic wave layer position identification model, identify and actually rupture
Layer position.
The solution have the advantages that:
1, the real-time monitoring for realizing hydraulic fracturing rupture process judges whether rock stratum breaks by the fracturing features of rock stratum and coal seam
It splits, to adjust fracturing technology in time, improves hydraulic fracturing efficiency.
2, belonging to lossless detection method, project amount is small, and it is easy to implement, it is suitable for coal bed gas hydraulic fracturing monitoring and evaluation.
Detailed description of the invention
Detailed description of the invention of the invention is as follows:
Fig. 1 is neural network operation figure；
Fig. 2 is Matlab neural network tool and data management window figure；
Fig. 3 is embodiment recognition effect figure.
In Fig. 2, Input Data is input block；Target Data is target data area；Input Delay
States is input delay turntable area；Networks is network area；Output Data is output data area；Error Data is
Error information area；Layer Delay States is floor delaying state area；It Imprt... is to be imported from space or data file
The button for needing to operate when data variable；The button for needing to operate when being New... New-deployed Network or variable；It Open... is opening
The button for needing to operate when variable or neural network；It is needed when Export... for export data to working space or data file
The button of operation；Delete is the button for needing to operate when deleting variable or network；Help is display Matlab neural network work
The button for needing to operate when tool and data management window help information；It needs to operate when Close closes data management window
Button.
In Fig. 3, a is the signal identification result only having under the rupture event of coal seam；B is the signal only having under the rupture event of coal seam
Identification error；C is the signal identification result only having under sandstone layer rupture event；D is the signal only having under sandstone layer rupture event
Identification error；D is that existing coal seam has the signal identification result under sandstone layer rupture event again；F is that existing coal seam has sandstone layer again
Signal identification error under rupture event.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples:
The present invention the following steps are included:
Step 1, it is selected need pressure break coal measure strata, using core drilling rig drilled formation core, hydraulic fracturing is carried out to each stratum
Elastic wave test experiment is ruptured, each stratum hydraulic fracturing rupture Characteristics of Elastic Wave is obtained.
This step specifically includes the following steps:
11), pre- pressure break coal measure strata core is taken out using core drilling rig, 100 millimeters of core diameter；
12), the core of taking-up is layered according to layer position relationship, by each layer, successively number is from shallow to deepC(C=1,2, 3 ..., k).Have in the present embodiment two layers, one layer is coal, number 1；Another layer is rock, number 2.Then each layer is separated,
Separated layer position is successively subjected to compression experiment using uni-axial press, while being pressed using elastic wave test instrument acquisition layer position
Contracting experimental elasticity wave signal, when no less than 50 rupture elastic waves when then extracting each layer of position compression failure spectrum as to
Handle data.Spectrum is used as pending data when the present embodiment obtains the elastic wave that 100 rock stratum and coal seam rupture.
13), the resulting pending data of step 12) is subjected to FFT operation one by one, obtains frequency spectrum data.Then, frequency spectrum is taken
The corresponding frequency of maximum amplitude is as dominant frequency in data；Divided by the resulting quotient of frequency bandwidth as flat after taking amplitude upon frequency to integrate
Equal frequency.Secondly, spectrum signal amplitude crosses the threshold of elastic wave Acquisition Instrument setting to maximum for the first time when taking step 12) resulting
Amplitude time interval experienced is as the rise time；Amplitude is taken to cross threshold for the first time experienced to threshold to finally coming
Time interval is the duration；Take in duration Amplitude-squared and as energy.Finally, by the knot after each data processing
Fruit is assigned to rupture Characteristics of Elastic Wave sequence Pa=[dominant frequency, average frequency, rise time, duration, energy].
Step 2 ruptures Characteristics of Elastic Wave and rock stratum corresponding relationship using neural metwork training, establishes rupture elastic wave layer position
Identification model.
As shown in Figure 1, neural network includes input layer, hidden layer and output layer, input node is rupture Characteristics of Elastic Wave
Element in sequence Pa exports result as coal rock layer numberC.It is following to establish specifically including for rupture elastic wave layer position identification model
Step:
21) Matlab software, is opened, nntool is inputted in command window and presses "enter" key", opens Neural Network/
Data Manager window, as shown in Figure 2.
22) input data and target data, are created.It is hit in Neural Network/Data Manager window point
New... button selects data tab card, input variable name P selects types of variables for Inputs, and will obtain in step 1
All characteristic sequence Pa copy to Value window, click Create button and complete creation.Same method creates target data T,
The value of T is the corresponding rock stratum number of Pa.P contains the destruction signals characteristic sequence of coal and each 50 groups of sandstone in the present embodiment.
23) network, is created.New... button is hit in Neural Network/Data Manager window point, is selected
Network option card exports network name and network type.Network name uses default value, network type choosing in the present embodiment
Select stringent radial basis function network (Radial basis (exat fif)).P is selected in Input data combobox,
T is selected in Target data combobox.Spread takes default value.
24) network training.The present embodiment uses radial basis function network, without training.For needing the network of training,
The network that creation is chosen in Neural Network/Data Manager window, clicks Open... button, in pop-up
Choose Train tabs in network dialog box, specify input data and target data, then click Train Network by
Button.
Step 3 ruptures elastic wave according to field hydraulic pressure break by rupture elastic wave layer position identification model, identifies practical broken
The layer position split.
This step specifically includes the following steps:
31), live elastic wave acquisition.Hydraulic fracturing is carried out in selected coal measure strata construction drill, while away from pressure break hole hole
The disturbance lesser place of focus is selected in 100 meters of bottom, construction diameter is that 50mm drills to hard and steady rock stratum, is packed into drilling
Solid metal is in close contact metallic rod and rock mass using seccotine sealing of hole.In the fixed elastic wave detection of metallic rod exposed junction
Device, and elastic wave Acquisition Instrument is connected, acquire the elastic wave signal in hydraulic fracturing process；
32), on-site signal feature extraction.By the step 13) method in step 1, scene rupture Characteristics of Elastic Wave sequence is extracted
Pt=[dominant frequency, average frequency, rise time, duration, energy]；
33) on-site signal inputs.Pt is created as input data according to the step 22) in step 2.Known to the present embodiment uses
Destruction signals and disrupted beds position data test to model.Pt comes from three kinds of situations in the present embodiment, as shown in Fig. 2, first
Signal Ptestcoal when kind for only coal seam rupture be for second signal Ptestrock when only sandstone ruptures, third
Kind is the signal Ptestcoalrock existing coal has sandstone rupture again when.In order to investigate the accuracy of model, need to calculate identification
As a result with the error of practical disrupted beds position, the present embodiment in the case where knowing corresponding disrupted beds position in advance, creation three with
The corresponding output data of Ptestcoal, Ptestrock and Ptestcoalrock, respectively Ttestcoal, Ttestrock and
Ttestcoalrock。
34), disrupted beds position identifies.The network of creation is chosen in Neural Network/Data Manager window, it is single
Open... button is hit, Simulate tabs is chosen in the network dialog box of pop-up, then in Inputs combobox
Input data Ptestcoal is selected, Supply Targets check box, the Targets combobox selection output number of lower section are chosen
According to Ttestcoal, Simulate Network button is then clicked.Same method, identification Ptestrock and
Ptestcoalrock。
35) recognition result is observed.In Neural Network/Data Manager window, click Export... by
Button selects network1_outputscoal, network1_outputsrock, network1_ in the window of pop-up
Outputscoalrock, network1_errorscoal, network1_errorsrock and network1_
Errorscoalrock clicks Export button.Drawing tool is utilized in Matlab, draws recognition result, as shown in Figure 3:
The present embodiment obtains network model using each 50 groups of training datas of coal petrography, then carries out practical disrupted beds position identification.In Fig. 3 a
In, 10 test sample signals are inputted, recognition result 1 belongs to coal seam rupture；In figure 3 c, 10 test sample letters are inputted
Number, recognition result 2 belongs to rock breakdown；In Fig. 3 e, 1-5 test sample signal is inputted, recognition result 1 inputs 6-
10 test sample signals, recognition result 2, that is, 1-5 test sample signal are coal seam destruction signals, 6-10 test sample letter
Number be rock breakdown signal.
It can be seen that from the recognition result of 30 test samples, recognition accuracy has reached 100%, and identification error is almost
0, demonstrate the validity of present method invention.
The problem of can not differentiating which layer rupture the present invention overcomes existing hydraulic fracturing process, it is broken to realize hydraulic fracturing
The real-time monitoring of process is split, provides effective technical solution for the monitoring and evaluation of hydraulic fracturing.
Claims (4)
1. a kind of coal measure strata hydraulic fracturing disrupted beds position recognition methods, it is characterized in that:
Step 1, it is selected need pressure break coal measure strata, using core drilling rig drilled formation core, hydraulic fracturing is carried out to each stratum
Elastic wave test experiment is ruptured, each stratum hydraulic fracturing rupture Characteristics of Elastic Wave is obtained；
Step 2 ruptures Characteristics of Elastic Wave and coal seam corresponding relationship using neural metwork training, establishes the identification of rupture elastic wave layer position
Model；
Step 3, according to field hydraulic pressure break rupture elastic wave by rupture elastic wave layer position identification model, identify and actually rupture
Layer position.
2. a kind of coal measure strata hydraulic fracturing disrupted beds position recognition methods according to claim 1, characterized in that in step 1
In, specifically according to the following steps:
11), pre- pressure break coal measure strata core is taken out using core drilling rig, 100 millimeters of core diameter；
12), the core of taking-up is layered according to layer position relationship, each layer is successively numbered from shallow to deep for C, C=1,2,
Then 3 ..., k separates each layer, separated layer position is successively carried out compression experiment using uni-axial press, is utilized simultaneously
Then elastic wave wave detector detection layers position compression experiment elastic wave signal extracts no less than 50 when each layer of position compression failure
Rupture elastic wave signal as pending data；
13), the resulting pending data of step 12) is handled one by one, acquisition rupture Characteristics of Elastic Wave sequence Pa=[dominant frequency,
Average frequency, rise time, duration, energy].
3. a kind of coal measure strata hydraulic fracturing disrupted beds position recognition methods according to claim 2, characterized in that in step 2
In, network design, training and identification are carried out using Matlab neural network tool.
4. a kind of coal measure strata hydraulic fracturing disrupted beds position recognition methods according to Claims 2 or 3, characterized in that step 3
In, comprising the following steps:
31) hydraulic fracturing, is carried out in selected coal measure strata construction drill, while selecting and disturbing in 100 meters away from pressure break hole bottom hole
The dynamic lesser place of focus is installed by the elastic wave signal in elastic wave sensor acquisition hydraulic fracturing process；
32), by the method for step 13) in step 1, extract scene rupture Characteristics of Elastic Wave sequence Pt=[dominant frequency, average frequency,
Rise time, duration, energy]；
33), the disrupted beds position identification model obtained using step 2 inputs live elastic data Pt, obtains live pressure break rupture
Layer positionC。
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021092698A1 (en) * | 2019-11-15 | 2021-05-20 | Peck Tech Consulting Ltd. | Systems, apparatuses, and methods for determining rock-coal transition with a drilling machine |
| US11834943B2 (en) | 2019-11-15 | 2023-12-05 | Peck Tech Consulting Ltd. | Systems, apparatuses, and methods for determining rock-coal transition with a drilling machine |
| CN111636859A (en) * | 2020-07-09 | 2020-09-08 | 中煤科工集团重庆研究院有限公司 | Coal rock while-drilling self-identification method based on micro-fracture wave detection |
| CN112906595A (en) * | 2021-03-03 | 2021-06-04 | 中国矿业大学(北京) | Landslide prediction method and system based on elastic waves |
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