CN113848593A - Method for quantitatively predicting rock slurry erosion area in coal-bearing stratum - Google Patents
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Abstract
The invention relates to the field of coal field geological exploration, in particular to a method for quantitatively predicting the distribution of magma rocks based on joint inversion of mining area high-density three-dimensional seismic exploration data and logging data. The accuracy of the rock pulp rock prediction distribution range in the existing coal field geological exploration can be improved, the reconstructed logging curve and the high-density three-dimensional seismic data can be used for carrying out joint inversion, and a wave impedance data body reflecting the formation lithology information is obtained, so that the rock pulp rock erosion area range and the thickness in the coal-bearing formation can be quantitatively predicted.
Description
Technical Field
The invention relates to the field of coal field geological exploration, in particular to a method for quantitatively predicting the distribution of magma rocks based on joint inversion of mining area high-density three-dimensional seismic exploration data and logging data.
Background
The prediction of the rock-magma distribution area is always a big problem in the energy exploration and development, because the rock-magma belongs to a crack-pore multi-medium, the storage performance of the rock-magma is not only controlled by lithology and a rock structure, but also influenced by a plurality of factors such as later erosion transformation, structural movement damage and the like. Mine geological data show that the invasion of the rock pulp rock has great destructive effect on the coal bed, and the coal bed has poor mining performance and stability, improved deterioration degree and reduced roof strength, thereby seriously influencing the safe mining and roadway layout of the coal mine. The rock pulp rock prediction problem belongs to the lithology exploration category, and at present, in the lithology seismic exploration field of coal fields, the main detection means comprises a seismic attribute technology and a seismic inversion technology. With the increasing requirement of coal mine development on geological exploration precision, the attribute technology cannot meet the requirement of actual production to some extent, and for a seismic attribute analysis method, the method mainly has the following two defects: (1) the extracted attributes are increasing, but there are few attributes that can be provided to the user for interpretation applications. (2) The geological significance of the interpretation of various attributes is ambiguous in the absence of a suitable method. It can be said that the conventional seismic attributes lose two fundamental pieces of information, namely the overall variation of the seismic signal and the distribution law of such variations. Thus, it is difficult to provide a reliable estimate of the seismic signal variation at the well site, and hence difficult to extrapolate reliably. And under the conditions of less drilling data and faster transverse change, the multi-solution is stronger, and the accurate prediction is difficult. The seismic inversion technology is one of important measures for lithology seismic exploration, and fully utilizes the favorable conditions that the longitudinal resolution of borehole logging data (acoustic velocity, density, resistivity and the like) is very high to carry out constraint inversion on the seismic data beside a well, and carries out recursion on the seismic data among the wells on the basis of the constraint inversion, so that the seismic information is converted into a lithology data body capable of reflecting the stratum characteristics. The well logging curve is the basis of seismic inversion, but conventional well logging curves such as sound wave and density cannot obviously distinguish the rock pulp, and the space distribution rule of the rock pulp cannot be accurately drawn by inverting the well logging curve. A great deal of analysis is carried out on the logging curve characteristics of the rock pulp by a plurality of scholars, and the sensitivity of each attribute to the rock pulp is different, wherein the natural gamma is most sensitive, and the porosity and the resistivity are second. The pseudo lithology logging curve is constructed through logging lithology and sensitive logging curves, so that the characteristic difference of the logging curves with different lithologies is more obvious, the logging curves are used as an inversion target curve, the inversion precision can be improved, and the prediction result is more reliable.
Disclosure of Invention
The invention mainly aims to solve the technical problems and provides a method for quantitatively predicting a magma rock erosion area in a coal-bearing stratum, which is used for solving the problem of lower accuracy of a magma rock prediction distribution range in the existing coal field geological exploration;
the scheme of the invention is as follows:
a method of quantitatively predicting a magma erosion zone in a coal-bearing formation, comprising:
carrying out amplitude preservation processing on high-density three-dimensional seismic exploration data of a coal mining area to obtain a three-dimensional seismic data volume;
through comprehensive comparative analysis of logging curves of a coal mining area, a first logging curve sensitive to rock reflection is selected preferably;
carrying out geological horizon division according to the first logging curve, and determining the depth and thickness of the magma rock at the drilling position;
constructing a lithology logging curve for representing lithology on the basis of the result of geological horizon division, and reconstructing the acoustic wave and density logging curve by taking the lithology logging curve as constraint to obtain a second logging curve sensitive to the reaction of the rock pulp and rock;
performing model-based seismic wave impedance inversion by using the second well log and the high-density three-dimensional seismic data volume to obtain an inverted data volume;
and setting a wave impedance threshold value of the rock pulp on the inversion data body, carving a rock pulp erosion area, and determining the erosion range and the thickness distribution rule of the rock pulp.
Preferably, the three-dimensional seismic data volume comprises structural and lithological information.
Preferably, in the method for quantitatively predicting the rock-magma erosion area in the coal-bearing stratum, the second log is a sonic density log.
Preferably, the method for quantitatively predicting the magmatic rock erosion area in the coal-bearing formation, the constructing the lithology log based on the horizon division result of the first log, includes:
identifying the magma rock by using the resistivity and natural gamma curve, dividing the geological layer to obtain the starting and stopping depth of the magma rock layer, the average speed of the magma rock and the average density information of the magma rock, and constructing a lithology logging curve according to the starting and stopping depth of the magma rock layer, the average speed of the magma rock and the average density information of the magma rock.
Preferably, in the method for quantitatively predicting the magma erosion area in the coal-bearing formation, the lithology log is constructed by setting the value of the magma to be 1 and setting other lithology values to be 0.
Preferably, the method for quantitatively predicting the magma rock erosion area in the coal-bearing stratum reconstructs the acoustic wave and density well logging curves by taking the lithology well logging curve as a constraint on the basis of the following formula:
z＝a*x*y+x
in the formula, z is a reconstructed second logging curve, x is a sample logging curve, y is a lithology logging curve, and a is a scaling factor for logging fusion of a magma section.
Preferably, in the method for quantitatively predicting the magma erosion area in the coal-bearing stratum, the value range of the proportional factor a of the logging fusion of the magma section is between 0.5 and 2.
Preferably, the above method for quantitatively predicting a magma erosion area in a coal-bearing formation, the model-based seismic wave impedance inversion to obtain an inverted data volume includes:
performing a joint inversion of wave impedance through the seismic data and the well log data, wherein the model-based inversion is achieved by objective function minimization:
J＝Weight_1×(T-W*r)+Weight_2×(M-H*r)
in the formula, J is an inversion result, namely an obtained inversion data volume, T is a seismic trace, W is a wavelet, r is a final reflection coefficient, M is an initial guess model impedance, H is an integral operator which generates final impedance by convolution with the final reflection coefficient, and Weight1 and Weight2 are weights.
Preferably, in the method for quantitatively predicting the rock pulp erosion area in the coal-bearing formation, the rock pulp carving is performed by setting a wave impedance threshold value of the rock pulp in the inverted data volume, wherein the time thickness of the rock pulp on the inverted data volume is calculated as:
when a certain sample point value in the wave impedance body is between the maximum wave impedance value and the minimum wave impedance value, setting the value of the sample point as the sampling interval of the impedance body, otherwise, setting the value as 0;
the sums are accumulated point-by-point within a given horizon interval to obtain a planar distribution of the time thickness of the magma.
Preferably, the method for quantitatively predicting the magma erosion area in the coal-bearing formation quantitatively predicts the thickness of the magma based on the time-thickness inversion, and specifically comprises the following steps:
starting from a well point on a wave impedance inversion section, selecting a proper wave impedance threshold value according to a seismic synthetic record calibration result, and calculating the number of sample points and time thickness of each path in a target layer;
calculating the average speed of the rock pulp rock by using a rock pulp rock acoustic curve encountered by the drilling in the work area;
and (4) calculating the thickness of the magma by using the formula h-1/2 vt, wherein the predicted thickness is the time thickness of the magma, and the velocity of the magma is the predicted thickness.
Therefore, compared with the prior art, the invention has the advantages that: the accuracy of the rock pulp rock prediction distribution range in the existing coal field geological exploration can be improved, the reconstructed logging curve and the high-density three-dimensional seismic data can be used for carrying out joint inversion, and a wave impedance data body reflecting the formation lithology information is obtained, so that the rock pulp rock erosion area range and the thickness in the coal-bearing formation can be quantitatively predicted.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for quantitatively predicting a magma erosion area in a coal-bearing formation according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a well log reconstruction structure process provided by an embodiment of the invention;
FIG. 3 is a flow chart of wave impedance inversion provided by an embodiment of the present invention;
FIG. 4 is a diagram illustrating the effect of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic implementation flow diagram of a quantitative prediction method for a magma rock according to an embodiment of the present invention, and as shown in fig. 1, the method mainly includes the following steps:
102, determining a first logging curve sensitive to the reaction of the rock pulp through comprehensive comparison and analysis of logging curves of a coal mining area;
103, dividing geological layers according to the sensitive logging curves, and determining the distribution thickness and depth of the rock pulp at the drill holes;
105, performing seismic wave impedance inversion based on the model by using the reconstructed second logging curve and the high-density three-dimensional seismic data volume, and determining a lithologic inversion data volume;
and 106, setting a wave impedance threshold value of the rock pulp on the inversion data body, finely carving a rock pulp erosion area, and determining the distribution range and thickness of the rock pulp.
In step 101, the problem mainly solved by this step is to provide the basic seismic data for the rock-magma inversion. The coal field high-density three-dimensional seismic exploration can obtain seismic data with wide frequency band, wide azimuth and high sampling density, and a data body containing structural information and lithology information is obtained through the fine processing of the seismic data, so that the most important data basis is provided for the rock-magma inversion. The method comprises the steps of firstly, conducting horizon interpretation on a seismic data body to obtain structural characteristics of a main target layer, and then building a structural framework by using interpreted horizons.
In step 102, the primary purpose of this step is to prefer a first log sensitive to the reflection of the rock. The well logging interpretation results already provide depth and thickness information of the rock, and well logging curve characteristics can be reversely deduced. Through comprehensive comparison and analysis of logging curves at the drill hole, the characteristics of the rock pulp on various logging curves can be obtained, and then the first logging curve sensitive to rock pulp reflection is preferably selected. Because the sensitivity of each logging curve to the rock is different, the resistivity is most sensitive generally, the gamma is natural, the acoustic time difference curve is more sensitive, and the density is least sensitive. And the sensitive logging curve can be used for carrying out rock-magma identification and geological horizon division. The logging curves reflect different lithology by comparing relative differences, and the amplitude change of different work areas is large.
In step 103, this step is primarily to solve the problem of geological stratification of the magma. And (3) obtaining a sensitive logging curve through the optimization of the magma logging curve in the step (2), and then comparing the logging curves to perform geological identification and horizon division of the magma so as to obtain depth and thickness data of the magma at the drill hole. The comprehensive comparison of well logs may be performed by means common in the art and will not be described in further detail herein.
In step 104, the primary objective is to obtain a log for the seismic inversion of the shale through log reconstruction. Firstly, a lithology logging curve is constructed by using the depth and thickness data of the rock pulp obtained in the step 3. Lithology logs represent different lithologies (e.g., mag-rock, sandstone, coal seam, etc.).
In order to simplify the log and improve the resolution of the magma, when constructing the lithology log, the value of the magma section is set to 1 and the values of the other lithology sections are set to 0. Because the input of the conventional seismic wave impedance inversion needs the acoustic wave and density logging curve, but the acoustic wave and density logging curve is insensitive to the reaction of the rock and cannot meet the requirement of lithology inversion on the resolution of the logging curve, the logging curve meeting the rock and the rock inversion is obtained through the reconstruction of the logging curve.
Wherein the sensitive log (first log) of step 102 is obtained by preferably obtaining one or more logs reflecting the magma, by observing characteristics of the logs. The sensitive log (second log) in 104 is a new log obtained by reconstructing two curves with originally poor effects of sound wave and density. The two curves are different, the first logging curve range is wide, the second logging curve refers to a curve reconstructed through sound waves and density specifically, and the second logging curve can also be regarded as a sound wave density logging curve (a reconstructed sound wave and density curve).
The basic structure and process of well logging curve reconstruction are shown in figure 2. Firstly, carrying out rock-magma geological layer division and thickness calibration according to a sensitive logging curve through comprehensive comparison and analysis of the logging curve to obtain rock-magma depth and thickness data at a drill hole; then, constructing a lithology logging curve according to the data; and finally, reconstructing a new acoustic wave and density logging curve by using the acoustic wave, density and lithology logging curve, wherein the curve reconstruction formula is as follows:
z＝a*x*y+x
in the formula, z is a well logging curve determined by reconstruction, x is a sample well logging curve (original sound wave and density well logging curve), y is a lithology well logging curve, and a is a scaling factor fused with the well logging curve of the rock-pulp section. Through test research, the a value is too small, the reconstruction effect of the logging curve is not obvious, namely the magma rock can not be well distinguished; the value a is too large, although the reconstructed logging curve can obviously reflect the rock pulp, the inversion result has stronger shielding effect on other layers due to larger value range, and the value range is preferably between 0.5 and 2.
In step 105, a pulp wave impedance inversion is performed. In step 104, a logging curve meeting the inversion accuracy requirement is obtained through logging curve reconstruction, the reconstructed logging curve is input in step 105, then, a precise time-depth relation between logging and seismic data is established through wavelet extraction and single-well seismic synthetic record making, a low-frequency model is established by using seismic explained horizons, faults and seismic data, and inversion attribute space variation functions counted by the logging curve are used for guiding model interpolation in the inversion process. And (3) taking the low-frequency geological model as an input constraint condition, and selecting a proper interpolation algorithm to perform interpolation to obtain an inversion result. The seismic inversion process is shown in figure 3. The method mainly comprises the following steps: (1) processing earthquake and geological data; (2) processing logging data; (3) extracting seismic wavelets and making synthetic records; (4) calibrating and finely explaining a horizon; (5) establishing a geological model; (6) estimating a model; (7) and (4) inversion.
Performing a joint inversion of the wave impedance from the seismic data and the well log data, wherein the model-based inversion is performed by minimizing an objective function:
J＝Weight1×(T-W*r)+Weight2×(M-H*r)
in the formula, J is an inversion result, T is a seismic trace, W is a wavelet, r is a final reflection coefficient, M is an initial guess model impedance, H is an integral operator which is convoluted with the final reflection coefficient to generate final impedance, Weight1 and Weight2 are weights, and the value interval is [0,1 ].
In the process of seismic inversion, the establishment of an initial model is a man-machine interaction processing process, and the establishment has a direct influence on the quality of an inversion result. Firstly, performing horizon interpretation on a three-dimensional seismic data body; then, performing horizon calibration on each well and seismic channels beside the well through synthetic recording; and finally, using the horizon interpretation as control, starting from the well points, extrapolating and interpolating the logging data, and establishing an initial model at each point of the three-dimensional space. This process is actually a process that combines seismic interface information that varies continuously in the lateral direction with logging information that has high resolution in the vertical direction. The accuracy of the initial model depends on the following aspects: one is horizon picking. Selecting a reflecting interface which has stronger reflection and can be continuously tracked in the transverse direction from the layer position; fault interpretation needs to be accurate and meticulous; above and below the destination level, as many levels as possible are picked. And secondly, the reliability of the logging data and the accuracy of the calibration of the logging data and the horizon of the seismic channel beside the well. The accuracy of the initial model can be further improved by adding other geological information in the inversion.
In step 106, the primary objective is to quantitatively predict the distribution range and thickness of the rock slurry by inverting the data volume. And performing rock-magma seismic inversion, wherein how to effectively describe the thickness and the spread range of the rock-magma seismic inversion is the key of the inversion work. According to the embodiment of the invention, the time thickness of the rock pulp is obtained on the wave impedance data body by mutually contrasting and carefully analyzing the thickness of the rock pulp encountered by the mining area and the wave impedance inversion section. And then, obtaining the average speed of the rock pulp based on drilling statistics, and performing time-depth conversion to finally obtain the depth domain thickness value of the rock pulp space distribution. The formula for finely carving the thickness of the rock pulp on the inverted data body is as follows:
Thickness＝sum((Low＜Pimp＜Hight)？sample:0)
wherein, Thickness represents the time Thickness of the rock, the type is time (s or ms), Pimp represents the wave impedance attribute body, and Low and High represent the minimum and maximum attribute values respectively; sample represents the sampling interval of the seismic or inversion data. The above formula is explained as follows: the time thickness of the magma is that when a certain sample point value in the wave impedance body is between the maximum wave impedance value and the minimum wave impedance value, the value of the sample point is set as the sampling interval (0.001s or 1ms) of the impedance body, (it should be noted here that the unit and the numerical value of the sampling interval should be consistent with the unit and the numerical value of the sampling interval of the inversion data body), the rest is 0, and then the point-by-point cumulative summation is carried out in the given horizon interval; the planar distribution of the time thickness of the rock pulp can be obtained.
The result obtained by the calculation is the time thickness of the magma, and the true thickness of the magma is obtained by time-depth conversion. In conclusion, the calculation steps for quantitatively predicting the thickness of the magma rock based on the wave impedance inversion of the high-density three-dimensional seismic data are as follows:
firstly, starting from a well point on a wave impedance inversion section, calculating a wave impedance value range of the rock pulp according to sound waves and density values, then setting a wave impedance threshold value, and calculating the number of sample points and time thickness of each path in a target layer;
secondly, calculating the average speed of the rock pulp by using a rock pulp acoustic curve encountered by the drilling in the work area;
finally, using the formula
And calculating the thickness of the rock pulp, wherein h is the explanation thickness, t is the time thickness of the rock pulp, and v is the speed of the rock pulp.
Because the inversion section explains the reservoir stratum by using a color code, a system error is often generated, and the explained thickness and the well drilling thickness have a certain difference due to the speed influence in time-depth conversion, the explained thickness needs to be reasonably corrected, and finally the predicted thickness of the rock pulp is obtained.
FIG. 4 is a schematic diagram illustrating an application test effect according to an embodiment of the present invention; by reconstructing a logging curve and carrying out traveling wave impedance inversion based on a high-density three-dimensional seismic data body, the inversion section clearly describes the thickness distribution rule of the rock pulp (the deep color represents the rock pulp), it can be seen that the rock pulp amplitude on the time section has large variation, the interface type seismic section is converted into a lithologic wave impedance section by seismic inversion, the corresponding relation between the inversion result and the logging curve of each well is good, and the spatial distribution characteristic of the rock pulp is clearly described.
Claims (10)
1. A method for quantitatively predicting a magma erosion zone in a coal-bearing formation, comprising:
carrying out amplitude preservation processing on high-density three-dimensional seismic exploration data of a coal mining area to obtain a three-dimensional seismic data volume;
through comprehensive comparative analysis of logging curves of a coal mining area, a first logging curve sensitive to rock reflection is selected preferably;
carrying out geological horizon division according to the first logging curve, and determining the depth and thickness of the magma rock at the drilling position;
constructing a lithology logging curve for representing lithology on the basis of the result of geological horizon division, and reconstructing the acoustic wave and density logging curve by taking the lithology logging curve as constraint to obtain a second logging curve sensitive to the reaction of the rock pulp and rock;
performing model-based seismic wave impedance inversion by using the second well log and the high-density three-dimensional seismic data volume to obtain an inverted data volume;
and setting a wave impedance threshold value of the rock pulp on the inversion data body, carving a rock pulp erosion area, and determining the erosion range and the thickness distribution rule of the rock pulp.
2. The method of claim 1, wherein the three-dimensional seismic data volume contains structural and lithology information.
3. The method of claim 1, wherein the second log is a sonic density log.
4. The method of claim 1, wherein constructing a lithology log based on the horizon partitioning results of the first log comprises:
identifying the magma rock by using the resistivity and natural gamma curve, dividing the geological layer to obtain the starting and stopping depth of the magma rock layer, the average speed of the magma rock and the average density information of the magma rock, and constructing a lithology logging curve according to the starting and stopping depth of the magma rock layer, the average speed of the magma rock and the average density information of the magma rock.
5. The method of claim 4, wherein the lithology log is constructed by setting a mudstone value to 1 and other lithology values to 0.
6. The method of claim 1, wherein the acoustic and density logs are reconstructed with lithology log constraints based on the following equation:
z＝a*x*y+x
in the formula, z is a reconstructed second logging curve, x is a sample logging curve, y is a lithology logging curve, and a is a scaling factor for logging fusion of a magma section.
7. The method of claim 1, wherein the scaling factor a for the log fusion of the shale section ranges from 0.5 to 2.
8. The method of claim 1, wherein model-based seismic wave impedance inversion to obtain an inverted data volume comprises:
performing a joint inversion of wave impedance through the seismic data and the well log data, wherein the model-based inversion is achieved by objective function minimization:
J＝Weight1×(T-W*r)+Weight2×(M-H*r)
in the formula, J is an inversion result, namely an obtained inversion data volume, T is a seismic trace, W is a wavelet, r is a final reflection coefficient, M is an initial guess model impedance, H is an integral operator which generates final impedance by convolution with the final reflection coefficient, and Weight1 and Weight2 are weights.
9. The method of claim 1, wherein the rock carving is performed by setting a wave impedance threshold of the rock in the inverted data volume, wherein a time thickness of the rock on the inverted data volume is calculated as:
when a certain sample point value in the wave impedance body is between the maximum wave impedance value and the minimum wave impedance value, setting the value of the sample point as the sampling interval of the impedance body, otherwise, setting the value as 0;
the sums are accumulated point-by-point within a given horizon interval to obtain a planar distribution of the time thickness of the magma.
10. The method of claim 8, wherein quantitatively predicting the thickness of the magmatic rock based on the time-thickness inversion comprises:
starting from a well point on a wave impedance inversion section, selecting a proper wave impedance threshold value according to a seismic synthetic record calibration result, and calculating the number of sample points and time thickness of each path in a target layer;
calculating the average speed of the rock pulp rock by using a rock pulp rock acoustic curve encountered by the drilling in the work area;
by the formula
And calculating the thickness of the rock pulp, wherein h is the predicted thickness, t is the time thickness of the rock pulp, and v is the speed of the rock pulp.
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| CN114861428A (en) * | 2022-04-26 | 2022-08-05 | 淮北矿业（集团）有限责任公司 | Deep soft rock identification method and system and electronic equipment |
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