CN104850897A - Prediction method for coal and gas outburst based on seismic information - Google Patents
Prediction method for coal and gas outburst based on seismic information Download PDFInfo
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Abstract
The invention discloses a prediction method for coal and gas outburst based on seismic information. The prediction method includes following steps: determining main geological factors for affecting coal seam gas occurrence and outburst according to the mine geological condition of a coal mining area; classifying the geological factors according to the classification criterion of the factors for affecting the coal seam gas occurrence and outburst; determining the weight coefficient Omegai value of the main geological factors according to the contribution of various geological factors to the gas outburst; calculating the gas outburst danger coefficient G value and the gas outburst hazard index R value; and predicting and evaluating the coal seam gas outburst regarding the gas outburst hazard index R value as the evaluation index. According to the prediction method, the main geological factors for affecting the coal seam gas occurrence and outburst are quantified by the adoption of various lithological seismic methods, the prediction and evaluation method for the coal and gas outburst based on the seismic information is proposed, deformed coal development areas and gas enrichment areas related to gas outburst are evaluated and predicted, and important technical support is provided for the prevention of the coal mine gas outburst.
Description
Technical field
The present invention relates to a kind of Forecasting Methodology of the coal and gas prominent based on earthquake information, belong to applied geophysics and the cross one another technical field of mining engineering.
Background technology
Gas is the mixed gas that the tax formed by incoalation is stored in based on methane in coal seam.Coal and gas prominent refers in coal production process, from coal seam, the enrichment gushing out on workplace of rock stratum and the goaf various harmful gases of releasing, thus causes the Coal Mine Disasters of gas explosion.
The colliery of China 46% belongs to gassy mine, and gas bearing capacity is large, and gas permeability of coal seam is low, and the gas drainage difficulty before exploitation is large, and China's coalfield structure is very complicated in addition, terrestrial stress large, during digging, Gas Outburst phenomenon very easily occurs.Along with the increase gradually of the pit mining degree of depth, number of times and the degree of China's generation coal and gas prominent disaster remain high always, have become the number one killer causing the especially big serious accident in colliery.In the face of the severe situation of Safety of Coal Mine Production, further investigation mine and coalbed gas geology hosting pattern, for mine safety production provides reliable Geological ensuring very urgent with scientific basis.
Existing Coal and Gas Outbursts Prediction method comprises the Forecasting Methodology of base electromagnetic radiation, the Forecasting Methodology based on artificial intelligence and the Forecasting Methodology based on seismic survey.Its subject matter existed is: in the forecasting process of coal and gas prominent, (1) only considers single influence factor, and does not consider multiple influence factor simultaneously; (2) only carry out qualitative evaluation, do not carry out quantitative evaluation.Therefore, utilize current exploration means cannot obtain the regularity of distribution of coal-bed gas enrichment, cause colliery in process of production, measure targetedly cannot be formulated according to Gas Distribution situation.
Summary of the invention
In order to overcome above-mentioned defect, the invention provides a kind of Forecasting Methodology of the coal and gas prominent based on earthquake information, this Forecasting Methodology utilizes earthquake information to carry out quantitative evaluation to coal and gas prominent, accurately and fast.
The present invention in order to the technical scheme solving its technical matters and adopt is: a kind of Forecasting Methodology of the coal and gas prominent based on earthquake information, comprises following key step:
(1) according to the geological condition of coal mine of mine district, determine that affecting coal-bed gas composes the main geologic factor of depositing with outstanding;
(2) compose each main geologic factor classification criterion of depositing with outstanding according to affecting coal-bed gas, quantized result classification is carried out to the main geologic factor that step (1) has been determined;
(3) according to the contribution of each geologic agent to Gas Outburst, the weight coefficient ω of main geologic factor is determined
ivalue;
(4) composite index law is utilized to introduce the concept of Gas Outburst danger coefficient G value, then
i is the number of categories value of geologic agent, and namely affect i-th kind of geologic agent of Gas Outburst, n is integer, and n>=1; G
ifor the hazard level of this geologic agent in Gas Outburst, ω
irefer to the contribution rate of this geologic agent to Gas Outburst;
(5) concept of Gas Outburst hazard index R value is introduced, then
(6) using Gas Outburst hazard index R value as evaluation index, make prediction and evaluation to coal-bed gas is outstanding.
As a further improvement on the present invention, in step (4),
and n=11.
As a further improvement on the present invention, the described main geologic factor affecting coal and gas prominent comprises: tomography and other structure distribution; Coal seam depth of burial; Seam inclination and fold; Coal Seam Thickness Change; Seam Roof And Floor lithology; Coal seam factor of porosity; The distribution of deformation coal and thickness; The growth direction in crack and density in coal seam and roof and floor; Magmatic Rock coal seam; Hydrogeological condition and stress field are extremely; For any one survey region, there will be one or more than one above-mentioned factor.
As a further improvement on the present invention, set up an appraisement system and evaluate described 11 kinds of main geologic agents, the method for building up of this system is:
A () first according to earthquake information, i.e. structure and lithologic interpretation result, is divided into three classes by each factor: be unfavorable for gas bearing with outstanding be I class; Be conducive to gas bearing with outstanding be III class; Be II class on gas bearing and outstanding impact between I class, III class;
B () is secondly classified to single factor test, is quantized;
C () is then comprehensively analyzed multiple factor;
D () final comprehensive evaluation method carries out Forecast and evaluation to coal-bed gas outburst hazard.
As a further improvement on the present invention, the weight coefficient sum of described each main geologic factor
As a further improvement on the present invention,
(1) Discussion of Earthquake Attribute Technology is utilized, extract the seismic properties value of coal seam reflection wave, and the seismic attributes slice generated along coal seam, combined structure interpretation results, choose the seismic properties value to structure significant reaction, wherein based on energy class, phase Ganlei seismic properties, as the evaluation index of " tomography and other structure distribution " factor;
(2) T of digitized borehole columnar section and coal seam reflection wave is utilized
0time, carry out high-precision time and depth transfer and can obtain seat earth depth value, the evaluation as " coal seam depth of burial " factor refers to;
(3) utilize Discussion of Earthquake Attribute Technology, generate multiple earthquake attribute volume, then calculate along the inclination angle in coal seam from earthquake attribute volume, curvature attributes value, as the judging quota of " seam inclination and fold " factor;
(4) utilize sound impedance inversion technique, obtain Wave Impedance Data Volume; According to the change of wave impedance value, poor according to the hourage of seismic horizon interpretation procedure calculating coal seam reflection wave crest, base plate; The thickness of coal seam information utilizing well-log information to obtain and coal seam reflection wave crest, base plate hourage poor, calculating coal seam in seismic wave propagation speed, i.e. interval velocity; Time depth conversion formula h=vt/2 is utilized to calculate thickness of coal seam, as the evaluation index of " Coal Seam Thickness Change " factor;
(5) utilize sound impedance inversion technique, obtain Wave Impedance Data Volume, the wave impedance value utilizing wave impedance inversion to cut into slices changes, as the evaluation index of " Seam Roof And Floor lithology " factor;
(6) utilize probability neural network inversion technology, according to acoustic velocity logging and density logging curve, obtain porosity data body, utilize the factor of porosity in coal seam to cut into slices, as the evaluation index of " coal seam factor of porosity " factor;
(7) sound impedance inverting (AI) and elastic impedance inversion (EI) technology is utilized, the AI invert data body of acquisition and EI invert data body; Utilize AI inverting section and the EI inverting section in coal seam, calculating Coal Pore Structure scale factor P value, evaluates " distribution of deformation coal and thickness " factor, the soft layering in so-called deformation coal and coal seam;
(8) utilize anisotropy technology, the multiple seismic properties according to different azimuth calculate fracture extension zone density (B/A value) and direction
the evaluation index of " in coal seam and roof and floor the growth direction in crack and density " factor as evaluating;
(9) utilize Discussion of Earthquake Attribute Technology, obtain spectral factorization section and the seismic facies classification figure in coal seam; Utilize sound impedance inversion technique, obtain the sound impedance section in coal seam; Utilize the integrated interpretation result that three kinds are cut into slices, as the evaluation index of " Magmatic Rock coal seam " factor of evaluation;
(10) utilize probability neural network inversion technology, according to apparent resistivity logging curve, obtain apparent resistivity data body, utilize the apparent resistivity in coal seam to cut into slices, as the evaluation index of " hydrogeological condition " factor;
(11), after utilizing the mechanics parameter such as interval transit time and density logging material computation stratum Poisson ratio, elastic modulus, indirect predictions terrestrial stress size, as the evaluation index of " stress field is abnormal " factor.
The invention has the beneficial effects as follows: the present invention will utilize different kinds of rocks seismic method, comprise seismotectonics and lithological information that Discussion of Earthquake Attribute Technology, elastic impedance inversion, probability neural network inversion and anisotropy technology obtain, compose the main geologic factor of depositing and giving prominence to quantize affecting coal-bed gas, propose the Coal and Gas Outbursts Prediction based on earthquake information and evaluation method; Evaluate and the prediction deformation coal development area relevant to Gas Outburst and gas enrichment region, give prominence to prevention for coal-mine gas and important technical support is provided.
Accompanying drawing explanation
Fig. 1 is that situation is deposited in the tax in the present invention 3 coal seam;
Fig. 2 is thickness of coal seam situation of the present invention;
Fig. 3 is the wave impedance section schematic diagram of the present invention 3 roof;
Fig. 4 is 3 coal seam factor of porosity sections;
Fig. 5 is 3 coal seam seismic properties;
Fig. 6 is earthquake curvature attributes and contacting between seam inclination and fold, and wherein (a) is positive curvature value; B () is negative curvature values; (c) intermediate value curvature; D () is inclination value;
Fig. 7 is the schematic diagram of coal seam depth of burial;
Fig. 8 is coal seam stress field situation;
Fig. 9 is 3 coal-bed gas outburst danger coefficient situations;
Figure 10 is the result figure of the specific embodiment of the invention.
Embodiment
The present invention will be further elaborated below.
Based on a Forecasting Methodology for the coal and gas prominent of earthquake information, comprise following key step:
(1) according to the geological condition of coal mine of mine district, determine that affecting coal-bed gas composes the main geologic factor of depositing with outstanding;
(2) compose each main geologic factor classification criterion of depositing with outstanding according to affecting coal-bed gas, quantized result classification is carried out to the main geologic factor that step (1) has been determined;
(3) according to the contribution of each geologic agent to Gas Outburst, the weight coefficient ω of main geologic factor is determined
ivalue;
(4) composite index law is utilized to introduce the concept of Gas Outburst danger coefficient G value, then
i is the number of categories value of geologic agent, and namely affect i-th kind of geologic agent of Gas Outburst, n is integer, and n>=1, we select n=11 in the present embodiment; G
ifor the hazard level of this geologic agent in Gas Outburst, ω
irefer to the contribution rate of this geologic agent to Gas Outburst;
(5) concept of Gas Outburst hazard index R value is introduced, then
(6) using Gas Outburst hazard index R value as evaluation index, make prediction and evaluation to coal-bed gas is outstanding.
The described main geologic factor affecting coal and gas prominent comprises: tomography and other structure distribution; Coal seam depth of burial; Seam inclination and fold; Coal Seam Thickness Change; Seam Roof And Floor lithology; Coal seam factor of porosity; The distribution of deformation coal and thickness; The growth direction in crack and density in coal seam and roof and floor; Magmatic Rock coal seam; Hydrogeological condition and stress field are extremely; For any one survey region, there will be one or more than one above-mentioned factor.
Set up an appraisement system to evaluate described 11 kinds of main geologic agents, the method for building up of this system is:
A () first according to earthquake information, i.e. structure and lithologic interpretation result, is divided into three classes by each factor: be unfavorable for gas bearing with outstanding be I class; Be conducive to gas bearing with outstanding be III class; Be II class on gas bearing and outstanding impact between I class, III class;
B () is secondly classified to single factor test, is quantized;
C () is then comprehensively analyzed multiple factor;
D () final comprehensive evaluation method carries out Forecast and evaluation to coal-bed gas outburst hazard.
The weight coefficient sum of described each main geologic factor
(1) Discussion of Earthquake Attribute Technology is utilized, extract the seismic properties value of coal seam reflection wave, and the seismic attributes slice generated along coal seam, combined structure interpretation results, choose the seismic properties value to structure significant reaction, wherein based on energy class, phase Ganlei seismic properties, as the evaluation index of " tomography and other structure distribution " factor;
(2) T of digitized borehole columnar section and coal seam reflection wave is utilized
0time, carry out high-precision time and depth transfer and can obtain seat earth depth value, the evaluation as " coal seam depth of burial " factor refers to;
(3) utilize Discussion of Earthquake Attribute Technology, generate multiple earthquake attribute volume, then calculate along the inclination angle in coal seam from earthquake attribute volume, curvature attributes value, as the judging quota of " seam inclination and fold " factor;
(4) utilize sound impedance inversion technique, obtain Wave Impedance Data Volume; According to the change of wave impedance value, poor according to the hourage of seismic horizon interpretation procedure calculating coal seam reflection wave crest, base plate; The thickness of coal seam information utilizing well-log information to obtain and coal seam reflection wave crest, base plate hourage poor, calculating coal seam in seismic wave propagation speed, i.e. interval velocity; Time depth conversion formula h=vt/2 is utilized to calculate thickness of coal seam, as the evaluation index of " Coal Seam Thickness Change " factor;
(5) utilize sound impedance inversion technique, obtain Wave Impedance Data Volume, the wave impedance value utilizing wave impedance inversion to cut into slices changes, as the evaluation index of " Seam Roof And Floor lithology " factor;
(6) utilize probability neural network inversion technology, according to acoustic velocity logging and density logging curve, obtain porosity data body, utilize the factor of porosity in coal seam to cut into slices, as the evaluation index of " coal seam factor of porosity " factor;
(7) sound impedance inverting (AI) and elastic impedance inversion (EI) technology is utilized, the AI invert data body of acquisition and EI invert data body; Utilize AI inverting section and the EI inverting section in coal seam, calculating Coal Pore Structure scale factor P value, evaluates " distribution of deformation coal and thickness " factor, the soft layering in so-called deformation coal and coal seam;
(8) utilize anisotropy technology, the multiple seismic properties according to different azimuth calculate fracture extension zone density (B/A value) and direction
the evaluation index of " in coal seam and roof and floor the growth direction in crack and density " factor as evaluating;
(9) utilize Discussion of Earthquake Attribute Technology, obtain spectral factorization section and the seismic facies classification figure in coal seam; Utilize sound impedance inversion technique, obtain the sound impedance section in coal seam; Utilize the integrated interpretation result that three kinds are cut into slices, as the evaluation index of " Magmatic Rock coal seam " factor of evaluation;
(10) utilize probability neural network inversion technology, according to apparent resistivity logging curve, obtain apparent resistivity data body, utilize the apparent resistivity in coal seam to cut into slices, as the evaluation index of " hydrogeological condition " factor;
(11), after utilizing the mechanics parameter such as interval transit time and density logging material computation stratum Poisson ratio, elastic modulus, indirect predictions terrestrial stress size, as the evaluation index of " stress field is abnormal " factor.
What table one reacted is affect coal-bed gas to compose each factor classification criterion of depositing with outstanding
Table one affects coal-bed gas and composes each factor classification criterion of depositing with outstanding
Foregoing is the Forecasting Methodology of the coal and gas prominent based on earthquake information, and the Forecasting Methodology of concrete condition to this patent now for the Xin Jing colliery, subordinate colliery of Yangquan Shanxi coal industry (group) company limited is further analyzed:
There is coal and gas prominent accident time and again in Lu Nan bis-district, badly influence the safety in production in colliery in driving and exploitation process.
Lu Nan bis-district, new scape colliery has comparatively complete three dimensional seismic data and probing, well-log information, simultaneously in driving back production engineering, have collected a large amount of gas monitor data and relevant geologic information, provide basic data support and verification condition to the prediction work of gas outburst risk.
Lu Nan bis-district has following features:
(1) local area goal in research coal seam is 3 coal seams, buries shallow, deformation coal agensis, can not consider " deformation coal " factor in forecasting process;
(2) 3 cracks in coal seam agensis, can not consider " in coal seam and roof and floor the growth direction in crack and density " factor in forecasting process;
(3) there is no Magmatic Rock phenomenon, " magmatite " factor can not be considered in forecasting process;
(4) basal water of local area is Ordovician limestone, far apart with 3 coal seams, can not consider " hydrogeological condition " factor in forecasting process;
(5) 3 coal roof lithologic are comparatively complicated;
(6) be subject to the impact of wash zone, 3 Coal Seam Thickness Change are violent;
(7) district's internal drilling is intensive, is evenly distributed;
(8) having a large amount of gas monitor data, can test to finally predicting the outcome;
According to actual geologic information and working experience, the prediction work in Lu Nan bis-district chooses following influence factor:
(1) thickness of coal seam;
(2) Seam Roof And Floor lithological change;
(3) coal seam factor of porosity;
(4) tomography and other structure distribution;
(5) fold and seam inclination;
(6) coal seam depth of burial;
(7) stress field is abnormal.
One, sound impedance inversion prediction thickness of coal seam is utilized:
Before carrying out seismic inversion, first existing borehole data is added up, to understand the geologic aspects of local area.Statistics is as shown in table 2:
Table 23 coal seam statistical form
Disclose known according to boring, coal seam, Lu Nan bis-district 3 is subject to washing away impact, and variation in thickness is larger; The relative country rock in coal seam or roof and floor, density and velocity of longitudinal wave are all low values, and therefore on Wave Impedance Data Volume, coal seam presents low value reaction, and colour code represents the change from low to high of wave impedance value by dark color to light change.
With 3 coal seam reflection wave T3 ripples for benchmark, window when upwards 2ms opens, carries out calculating to the wave impedance value of inside, coal seam and asks for root-mean-square value, forms wave impedance section, as shown in Figure 1.
As can be seen from Figure 1 the tax in 3 coal seams deposits situation all relatively well, and coal seam general morphologictrend is relatively milder, and distribution is relatively more even, and it is thinning that regional area exists washout, represents in figure with shade of white.
Based on wave impedance section, the statistics of associative list 1, the actual thickness of coal seam utilizing borehole data to disclose carries out interpolation, predicts 3 thickness of coal seam, sees Fig. 2.
The thickness of coal seam that borehole data discloses is contrasted with the thickness of coal seam utilizing earthquake information to predict, and calculates predicated error, as shown in table 3: in table, thickness of coal seam is the gross thickness of coal seam and dirt band.
Table 33 coal seam actual (real) thickness and thickness prediction contrast table
| Boring | Actual (real) thickness (m) | Thickness prediction (m) | Predicated error (m) |
| 3-133 | 2.15 | 2 | 0.15 |
| 3-137 | 2.6 | 2.7 | 0.1 |
| 3-144 | 2.6 | 2.52 | 0.08 |
| 3-46 | 2.7 | 2.9 | 0.2 |
| 3-50 | 1.87 | 1.5 | 0.37 |
| 3-62 | 2.61 | 2.7 | 0.09 |
| 3-66 | 3.18 | 3.12 | 0.06 |
| 3-67 | 2.94 | 2.83 | 0.11 |
| 3-73 | 2.7 | 2.4 | 0.3 |
| 3-122 | 2.83 | 2.73 | 0.1 |
| 3-124 | 2.37 | 2.28 | 0.09 |
| 3-129 | 2.25 | 2.04 | 0.21 |
| 3-130 | 2.55 | 2.52 | 0.03 |
| 3-134 | 2.9 | 2.76 | 0.14 |
| 3-140 | 2.5 | 2.4 | 0.1 |
Be not difficult to find out from table 3: the whole district 3 thickness of coal seam is comparatively stable, major part is between 2 ~ 3m, and within the scope of the wash zone of northeast, coal is thick thinning, is even all washed away (in figure darker regions), thickness of coal seam predicated error is less than 0.5m, and the confidence level that predicts the outcome is higher.
Two, sound impedance inversion prediction coal roof lithologic is utilized:
Disclose known according to boring in table 2, the immediate roof in local area 3 coal seam is mostly mud stone or Sandy Silt, is sandstone or sand-mud interbed always at most, and 3 coal seam immediate roofs of subregion are sandstone.Predicting the outcome in conjunction with thickness of coal seam known, there is top board and washes away phenomenon in 3 coal seams, and local thickness of coal seam is thinning, easily Gas Outburst phenomenon occurs.
Only utilize densimetric curve to carry out work requirements that seismic inversion can not meet prediction 3 seam roof lithology, therefore selects suitable logging trace to participate in seismic inversion as constraint condition and to be very important element task.By finding the research and analysis of logging trace, for 3 coal seams and top board thereof, the response characteristic of natural gamma curve is obvious, and its magnitude relationship is mud stone > sandstone > coal seam.Therefore, merge the exception of natural gamma curve and densimetric curve, the plan densimetric curve of acquisition as the constraint condition of sandstone inverting, can carry out seismic inversion and obtains plan Wave Impedance Data Volume.
Fig. 3 is the wave impedance section of 3 roofs, and it presents 3 seam roof lithology distributions.
Can understand the plane distribution overview of Roof rock feature intuitively from 3 roof wave impedance sections, known in conjunction with borehole data, 3 coal seams mainly using mud stone and Sandy Silt as immediate roof, and are mainly sandstone and sand-mud interbed above immediate roof.Sandstone distribution range can be clearly identified from the relative size of wave impedance value,
In figure 3, colour code is grayish high wave impedance value region is sandstone distribution.
Three, probabilistic neural network technological prediction coal seam factor of porosity is utilized:
Utilize probabilistic neural network technology, carry out porosity inversion to coal seam, Lu Nan bis-district 3, made 3 coal seam factor of porosity sections, as shown in Figure 4, colour code change from bottom to top represents increasing progressively of porosity value.Local area 3 thickness of coal seam is stablized, and most of region is primary coal, and factor of porosity changes between 16 ~ 18%, only reaches more than 24% washing away region apertures porosity.
Four, Discussion of Earthquake Attribute Technology Coalbed Interpretation is utilized to construct:
Local area structural feature is fairly simple, and the major constituents in 3 coal seams is karst collapse col umn and minor fault.Centered by 3 coal seam reflection wave T3, definition window length is 20ms, extracts seismic properties, and optimizes effect and merged preferably, see Fig. 5.In figure, the comprehensive earthquake property value in normal coal seam is low value, below 0.6, is dark color; The comprehensive earthquake property value in structural anormaly district is higher, and more than 0.7, colour code is light color, and difference is obvious.In figure, solid black lines is structure elucidation achievement, higher with the exceptions area scope goodness of fit in section, and therefore, comprehensive seismic attributes slice can as the distinguishing rule of " tomography and other structure distribution ".
Five, earthquake curvature attributes is utilized to come forecasting coal inclination layer and fold:
Centered by the reflection wave T3 ripple in 3 coal seams, definition window length is 20ms, extracts positive curvature, negative curvature, intermediate value curvature and inclination angle seismic properties, and superimposed with layer bit time isoline, sees Fig. 6.In figure, the solid line of black bands mark is time isoline.
Local area 3 coal seam deposited stabilizer, can be found out by the superimposed result of seismic attributes slice and layer bit time isoline, and in the region that the change of isoline density is relatively violent, curvature value and dip angle attribute value have abnormal response.
Six, the explanation of coal seam depth of burial:
First, the base plate reflection wave layer bit time in 3 coal seams is explained, on this basis, draws out time isogram.Again according to the velocity field obtained during inversion procedure, calculate coal-seam floor contour map.In order to give prominence to the relation of time and absolute altitude more intuitively, the two be superimposed in same figure, as Fig. 7, wherein background colour is layer bit time, and solid black lines is base plate level line.
Seven, well-log information is utilized to calculate stress field abnormal:
Utilize the density logging data in district to carry out inversion of Density, obtain density data body, centered by 3 coal seam reflection wave T3 ripples, obtain the interval of a 30ms, therefrom try to achieve vertical stress field, see Fig. 8.
Eight, gas outburst risk is predicted:
Classified by the quantized value of every geologic agent, sorting criterion is with reference to table 4.
According to geologic condition and the working experience in the past in Lu Nan bis-district, the weight coefficient ω i of each influence factor is defined: (1) " coal roof lithologic " Factor right modulus is maximum; (2) " Coal Seam Thickness Change " factor is taken second place with the weight coefficient of " seat earth factor of porosity " factor; (3) other factors are less owing to affecting, and weight coefficient suitably reduces, and concrete numerical value is as shown in table 5:
Utilize and calculate Gas Outburst danger coefficient G value.Then, utilize and calculate Gas Outburst hazard index R value, as shown in Figure 9.
In Fig. 9, background colour is 3 coal-bed gas outburst danger index R values, and solid black lines is seat earth level line, and solid white line is structure.According to R value colour code, the R value of black region is less than 35%, is Gas Outburst Di Wei district; The R value of dark gray areas, between 35 ~ 50%, is Gas Outburst poor risk district; The R value of light gray areas is greater than 50%, is the high-risk district of Gas Outburst.
As can be known from Fig. 9, the western gas outburst risk in Lu Nan bis-district is greater than east; The wash zone of northeast is an explosive area, place.By the comparison of colour code, predict Gas Outburst hazardous location everywhere altogether, draw a circle to approve its scope with white wire frame in the drawings.
(1) No. 1 explosive area
Be positioned at western part, exploiting field, area is comparatively large, and in region, roof is sandstone, and wave impedance is higher, and buries comparatively dark, and have two factor classifications to be in III class district, local R value is greater than 60%, is highly dangerous district.
(2) No. 2 explosive areas
Be positioned at the north, exploiting field by east, the wash zone region in former structure elucidation scheme.In district, coal seam is replaced by sandstone, and Coal Seam Thickness Change is violent; Roof is sandstone, and wave impedance is comparatively large, and factor of porosity is high, in multiple factor classification, be all high-risk district, therefore final R value is higher, is highly dangerous district.
(3) No. 3 explosive areas
Be positioned in the middle part of exploiting field, roof wave impedance is high level, and factor of porosity is comparatively large, buries dark, and R value is within the scope of high-risk, poor risk.
(4) No. 4 explosive areas
Be positioned at south, exploiting field, roof wave impedance is high level; Be subject to structure impact, comprehensive earthquake property value is also high level; Coal seam is buried comparatively dark, and R value is in middle and high degree risk range.
Nine, achievement checking is predicted:
According to the 3 coal-bed gas projecting point distribution plans that new scape colliery provides, there are 9 gas outburst points (1 ~ No. 9) and 2 gas blow-off points (10, No. 11) in Lu Nan bis-district.Therefore, Gas Outburst or ejection data is utilized to carry out contrast verification to integrated forecasting result.Coordinate and the relevant information of 11 check posts are as shown in table 6, and the result as shown in Figure 10.
Table 6 Gas Outburst dot information
As can be seen from Figure 10, No. 1, No. 2, No. 3, No. 4, No. 5 gas outburst points are positioned at No. 2 explosive areas, and R value is all more than 50%; No. 7 gas outburst points are in No. 1 explosive area, and R value is 58%; No. 8, No. 9 gas outburst points are positioned at prediction No. 4 explosive areas, and R value is respectively 48% and 53%; No. 10, No. 11 gas blow-off points are positioned at No. 4 anti-explosive areas of prediction, and R value is respectively 47% and 49%.No. 6 gas outburst points are not in explosive area.From the whole result of checking, predictablity rate is 90%, and verification the verifying results is better.
Claims (6)
1. based on a Forecasting Methodology for the coal and gas prominent of earthquake information, it is characterized in that: comprise following key step:
(1) according to the geological condition of coal mine of mine district, determine that affecting coal-bed gas composes the main geologic factor of depositing with outstanding;
(2) compose each main geologic factor classification criterion of depositing with outstanding according to affecting coal-bed gas, quantized result classification is carried out to the main geologic factor that step (1) has been determined;
(3) according to the contribution of each geologic agent to Gas Outburst, the weight coefficient ω of main geologic factor is determined
ivalue;
(4) composite index law is utilized to introduce the concept of Gas Outburst danger coefficient G value, then
i is the number of categories value of geologic agent, and namely affect i-th kind of geologic agent of Gas Outburst, n is integer, and n>=1; G
ifor the hazard level of this geologic agent in Gas Outburst, ω
irefer to the contribution rate of this geologic agent to Gas Outburst;
(5) concept of Gas Outburst hazard index R value is introduced, then
(6) using Gas Outburst hazard index R value as evaluation index, make prediction and evaluation to coal-bed gas is outstanding.
2. the Forecasting Methodology of the coal and gas prominent based on earthquake information according to claim 1, is characterized in that: in step (4),
and n=11.
3. the Forecasting Methodology of the coal and gas prominent based on earthquake information according to claim 1, is characterized in that: the described main geologic factor affecting coal and gas prominent comprises: tomography and other structure distribution; Coal seam depth of burial; Seam inclination and fold; Coal Seam Thickness Change; Seam Roof And Floor lithology; Coal seam factor of porosity; The distribution of deformation coal and thickness; The growth direction in crack and density in coal seam and roof and floor; Magmatic Rock coal seam; Hydrogeological condition and stress field are extremely; For any one survey region, there will be one or more than one above-mentioned factor.
4. the Forecasting Methodology of the coal and gas prominent based on earthquake information according to claim 3, is characterized in that: set up an appraisement system and evaluate described 11 kinds of main geologic agents, the method for building up of this system is:
A () first according to earthquake information, i.e. structure and lithologic interpretation result, is divided into three classes by each factor: be unfavorable for gas bearing with outstanding be I class; Be conducive to gas bearing with outstanding be III class; Be II class on gas bearing and outstanding impact between I class, III class;
B () is secondly classified to single factor test, is quantized;
C () is then comprehensively analyzed multiple factor;
D () final comprehensive evaluation method carries out Forecast and evaluation to coal-bed gas outburst hazard.
5. the Forecasting Methodology of the coal and gas prominent based on earthquake information according to any one of Claims 1-4, is characterized in that: the weight coefficient sum ∑ ω of described each main geologic factor
i=1.
6. the Forecasting Methodology of the coal and gas prominent based on earthquake information according to claim 3, is characterized in that:
(1) Discussion of Earthquake Attribute Technology is utilized, extract the seismic properties value of coal seam reflection wave, and the seismic attributes slice generated along coal seam, combined structure interpretation results, choose the seismic properties value to structure significant reaction, wherein based on energy class, phase Ganlei seismic properties, as the evaluation index of " tomography and other structure distribution " factor;
(2) T of digitized borehole columnar section and coal seam reflection wave is utilized
0time, carry out high-precision time and depth transfer and can obtain seat earth depth value, the evaluation as " coal seam depth of burial " factor refers to;
(3) utilize Discussion of Earthquake Attribute Technology, generate multiple earthquake attribute volume, then calculate along the inclination angle in coal seam from earthquake attribute volume, curvature attributes value, as the judging quota of " seam inclination and fold " factor;
(4) utilize sound impedance inversion technique, obtain Wave Impedance Data Volume; According to the change of wave impedance value, poor according to the hourage of seismic horizon interpretation procedure calculating coal seam reflection wave crest, base plate; The thickness of coal seam information utilizing well-log information to obtain and coal seam reflection wave crest, base plate hourage poor, calculating coal seam in seismic wave propagation speed, i.e. interval velocity; Time depth conversion formula h=vt/2 is utilized to calculate thickness of coal seam, as the evaluation index of " Coal Seam Thickness Change " factor;
(5) utilize sound impedance inversion technique, obtain Wave Impedance Data Volume, the wave impedance value utilizing wave impedance inversion to cut into slices changes, as the evaluation index of " Seam Roof And Floor lithology " factor;
(6) utilize probability neural network inversion technology, according to acoustic velocity logging and density logging curve, obtain porosity data body, utilize the factor of porosity in coal seam to cut into slices, as the evaluation index of " coal seam factor of porosity " factor;
(7) sound impedance inverting (AI) and elastic impedance inversion (EI) technology is utilized, the AI invert data body of acquisition and EI invert data body; Utilize AI inverting section and the EI inverting section in coal seam, calculate Coal Pore Structure scale factor P value, evaluate " distribution of deformation coal and thickness " factor, described deformation coal is the soft layering in coal seam;
(8) utilize anisotropy technology, calculate fracture extension zone density and B/A value and direction according to the multiple seismic properties of different azimuth
the evaluation index of " in coal seam and roof and floor the growth direction in crack and density " factor as evaluating;
(9) utilize Discussion of Earthquake Attribute Technology, obtain spectral factorization section and the seismic facies classification figure in coal seam; Utilize sound impedance inversion technique, obtain the sound impedance section in coal seam; Utilize the integrated interpretation result that three kinds are cut into slices, as the evaluation index of " Magmatic Rock coal seam " factor of evaluation;
(10) utilize probability neural network inversion technology, according to apparent resistivity logging curve, obtain apparent resistivity data body, utilize the apparent resistivity in coal seam to cut into slices, as the evaluation index of " hydrogeological condition " factor;
(11), after utilizing the mechanics parameter such as interval transit time and density logging material computation stratum Poisson ratio, elastic modulus, indirect predictions terrestrial stress size, as the evaluation index of " stress field is abnormal " factor.
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