CN111897008B - Fracture grading prediction method based on seismic frequency division technology - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 30
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- 206010017076 Fracture Diseases 0.000 claims abstract description 127
- 208000010392 Bone Fractures Diseases 0.000 claims abstract description 107
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Abstract
The invention discloses a fracture grading prediction method based on a seismic frequency division technology, which comprises the following steps: collecting basic data; making a single-well synthetic record, and calibrating a research target layer; analyzing the original post-stack amplitude data frequency spectrum of the target layer to determine the effective signal range of the target layer data; defining the approximate scale of the target horizon fracture development; establishing a forward modeling for simulation; performing frequency division processing on a simulation result, and preferably selecting fracture response frequencies of different orders; performing targeted frequency division processing on the original amplitude data; analyzing each level of fracture properties and optimizing an algorithm; analyzing fracture horizontal splitting and three-dimensional space prediction effects at all levels; fusing the fractures of all levels into a graph, and performing well drilling data matching analysis; and eliminating the fracture detection abnormal area, and completing the fracture grading prediction. The invention has low requirement on data processing conditions, small operand and high processing speed; the problem of multiple solution of a large number of fracture interpretations is solved, the complex problem is simplified, the fracture release efficiency is improved, the workload of seismic interpretation is reduced, and the accuracy of fracture prediction is increased.
Description
Technical Field
The invention relates to the technical field of petroleum exploration and development, in particular to a fracture grading prediction method based on a seismic frequency division technology.
Background
At present, fracture (crack) prediction methods popular at home and abroad mainly include three types of recognition prediction methods of geology, well logging and earthquake, and from the recognition scale, the traditional geology and well logging recognition method mainly recognizes and predicts cracks with a certain distribution range and a smaller scale by means of data such as actual drilling, outcrop, rock core, well logging and the like, and the prediction range is limited.
Due to the influence of factors such as regional tectonic events, fracture development stages, strength and the like, the fractures in the research region are generally divided into main-stem (large-scale) fractures, secondary (medium-scale) fractures and micro (small-scale) fractures, and the prominent manifestations of the fractures in seismic data are that the fracture pitches are different in size and form. With the rapid development of three-dimensional seismic technology, crack identification methods based on earthquake are increasing and can be divided into prediction methods based on pre-stack data and post-stack data, and the former has relatively harsh processing conditions on data and relatively high cost, so that various crack earthquake detection attributes based on post-stack data are the main means for crack prediction. At present, the mainstream post-stack fracture prediction methods are all used for carrying out prediction work on full-frequency-band seismic data. The basic method comprises the following steps: firstly, seismic data filtering (structure-oriented filtering), then carrying out optimization work of methods such as waveform difference, coherence, edge detection, dip angle, curvature property, ant body, maximum likelihood property and the like, and finally completing fracture prediction. That is to say, for fractures with different geological scales, a certain preferred prediction method is uniformly used, so that the differences of the response of seismic data to fractures with different grades under different frequency conditions and the differences of the response of different algorithms to fractures with different grades are ignored, the research on fracture grades is weakened, and the deviation is not avoided.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a fracture grading prediction method based on the seismic frequency division technology, which perfects the problems in the technology.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
a fracture grading prediction method based on a seismic frequency division technology comprises the following steps:
s1, basic data collection: collecting data from the area of interest, the data comprising: data such as zone adjustment data, post-stack migration seismic body, conventional logging curve, drilling and logging data, well deviation data, coring data, layering data, seismic interpretation horizon, single well testing productivity and the like;
s2, making a single-well synthetic record, and calibrating a research target layer and an upper layer and a lower layer;
s3, analyzing the frequency spectrum of the original post-stack amplitude data of the target layer to determine the effective signal range of the data of the target layer;
s4, directly obtaining the approximate scale of the fracture development of the target horizon according to relevant geological data such as actual drilling, well logging, exposure and zone adjustment;
s5, designing a theoretical forward model according to the fault development scale, and setting forward model parameters according to original amplitude data volume parameters;
s6, frequency division processing is carried out on the forward modeling earthquake result, section identifiability of different-grade fracture is observed under different frequency data, and optimal response frequency of different fracture grades is selected;
s7, carrying out frequency division processing of the preferred frequency in S6 on the data body within the effective signal range of the original amplitude data body to obtain data bodies of a plurality of frequency bands;
s8, analyzing each level of fracture property and optimizing an algorithm;
s9, analyzing the fracture bisection and three-dimensional space prediction effect of each level;
and S10, fusing all levels of fractures into a graph, and matching and performing coincidence analysis on data related to fractures (fractures) such as actual drilling fracture and drilling conditions, fracture development conditions, reservoir development conditions, single well testing productivity and the like.
S11, eliminating the detection abnormality caused by the data, and completing the classification prediction of the fracture.
Further, the slice acquisition parameter in S4 is preceded by a fault distance.
Further, in S5, the forward modeling is to ensure that the parameters of the obtained seismic forward data and the original amplitude data are consistent as much as possible, and mainly includes: dominant frequency, bandwidth, etc.
Further, in said S7, the inventor proposes to perform frequency division processing on the original amplitude data volume by a frequency division technique (frequency magnifying glass) based on fast matching pursuit.
Further, in the S8, in the crack property and algorithm optimization, the inventor proposes that the low frequency volume is used for the identification and prediction calculation of the large-scale fracture, the medium frequency volume is used for the identification and prediction calculation of the medium-scale fracture, and the high frequency volume is used for the identification and prediction calculation of the small-scale or micro fracture (crack). Waveform difference attribute computation may be used for large scale fractures, coherence, similarity, etc. for mesoscale fractures, curvature analysis, maximum likelihood, ant tracking, etc. for small scale or microfractures (fractures)
Furthermore, in the S9 plano-section effect and the three-dimensional spatial prediction analysis, it is noted that a background is formed by combining the basic structural features and fractures of the actual research area, and the comprehensive analysis of the original amplitude data volume is combined, so that the prediction misalignment caused by the abnormality generated in the frequency division processing is avoided.
Further, the S10 matches and fits the data related to fracture (fracture) such as actual drilling fracture situation, fracture development situation, reservoir development situation, single well testing productivity, etc., and needs to specifically analyze the influence of the fracture cause, reservoir type and fracture on oil and gas migration and accumulation in the actual research area.
Further, the S11 eliminates detection abnormality caused by the data, and fracture detection is often abnormal for a non-full coverage area of the three-dimensional data.
Compared with the prior art, the invention has the advantages that:
(1) the method is a comprehensive application and innovation of the prior art, the process can be realized under the existing seismic exploration technical condition, and the time period from development to application of the crack prediction attribute or algorithm is saved.
(2) The method adopts post-stack migration data as basic seismic data, and has smaller calculated amount and higher calculating speed compared with a pre-stack fracture (crack) prediction means.
(3) The overall thought of fracture-fracture system frequency division hierarchical prediction is adopted, fractures are hierarchically depicted, the primary and secondary relations of the fractures are clarified, for seismic interpreters, the problem of multiple resolutions of a large number of fracture interpretations is eliminated, the complex problem is simplified, the fracture splitting release efficiency is improved, and fracture flat-cutting and space depiction are clearer.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram illustrating a spectrum analysis of a target horizon for original post-stack data according to an embodiment of the present invention;
FIG. 3 is a simulation section of the size of the fracture development of the target zone and the earthquake forward modeling of the embodiment of the invention;
FIG. 4 is a forward analog frequency division profile according to an embodiment of the present invention;
FIG. 5a is a schematic diagram of the spatial distribution of stem fracture in accordance with an embodiment of the present invention;
FIG. 5b is a graphical representation of a stem fracture profile of an embodiment of the present invention;
FIG. 5c is a secondary fracture space spread characteristic diagram according to an embodiment of the present invention;
FIG. 5d is a graphical representation of the secondary fracture profile spread characteristics of an embodiment of the present invention;
FIG. 5e is a graph of the spatial distribution of cracks in accordance with an embodiment of the present invention;
FIG. 5f is a graphical representation of the fracture profile spread characteristics of an embodiment of the present invention;
FIG. 6 is a graph of fracture plane spread characteristics and single well productivity matching for an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings by way of examples.
As shown in fig. 1, a fracture stage prediction method based on seismic frequency division technology includes the following steps:
s1, basic data collection: collecting data from the area of interest, the data comprising: zone adjustment data, a post-stack migration seismic body, a conventional logging curve, drilling and logging data, well deviation data, coring data, layering data, seismic interpretation horizon and single well testing productivity data;
s2, making a single-well synthetic record by using the acoustic wave and the density logging curve, and calibrating a top-bottom interface of a target layer and interfaces of upper and lower layers of the target layer;
s3, analyzing the original superposed amplitude data spectrum of the target layer, and determining the effective signal range of the target layer data, the dominant frequency of the target layer data is 37.5Hz, the effective bandwidth is 17-60 Hz, and the absolute bandwidth is 43 Hz;
as shown in fig. 2, it is clear that the effective signal range of the original post-stack seismic data is the basic condition for determining the frequency division range.
S4, directly obtaining the approximate scale of the fracture development of the target horizon according to the regional adjustment data and the related geological data such as actual drilling, well logging and the like;
s5, respectively designing a theoretical forward modeling with the fault distance from small to large according to the development scale of the zonal adjustment fault, acquiring the speed value and the thickness value of the theoretical stratigraphic unit from the actual drilling data, and selecting an excitation receiving mode of self excitation and self collection in the forward modeling mode. Setting the forward modeling parameters according to the original amplitude data volume parameters;
as shown in FIG. 3, the size of the fracture development in the area is determined and forward simulation is performed, so that the seismic response characteristics of the fractures of different levels can be effectively determined.
S6, frequency division processing is carried out on the forward modeling earthquake result, section identifiability of different-grade fracture is observed under different frequency data, and optimal response frequency of different fracture grades is selected;
s7, carrying out frequency division processing of the preferred frequency in S6 on the data body within the effective signal range of the original amplitude data body to obtain data bodies of a plurality of frequency bands; distributing and acquiring narrow-band frequency dividers with main frequencies of 25Hz, 40Hz and 60Hz by using a frequency division technology of a rapid matching pursuit algorithm (MPD);
as shown in fig. 4, the frequency division processing is performed by using the theoretical forward modeling data, so that the fracture response frequencies of different orders can be better screened, the frequency division processing frequency of the original post-stack data is determined, and the data processing time is reduced.
S8, fracture attribute analysis and algorithm optimization; the large-scale fracture is identified by optimizing waveform difference attributes to the low and narrow frequency with the main frequency of 25Hz, the medium and narrow frequency with the main frequency of 40Hz adopts high eigenvalue coherence to identify the medium-scale fracture, and the high and narrow frequency with the main frequency of 60Hz adopts maximum likelihood attributes to identify the small-scale fracture.
S9, fracture bisection and three-dimensional space prediction effect analysis; the main stem fracture distribution area is small, the wave group dislocation section is obvious, the secondary fracture distance is small, the wave group dislocation section is often derived beside the main stem fracture, cracks are developed at local twisting, flexing and micro dislocation positions of the same phase axis, and the cracks are relatively widely developed in the whole area.
And S10, matching and matching the data related to fracture (fracture) such as actual drilling fracture and drilling encounter condition, fracture development condition, reservoir development condition, single well testing productivity and the like. The regional fracture development degree of the developing crack-hole type reservoir body is in a relative positive correlation with the reservoir and the single-well test yield.
As shown in fig. 5a, 5b, 5c, 5d, 5e, and 5f, the horizontal section, spatial distribution characteristics, and rationality of each stage of fracture were analyzed in conjunction with the background formed by the region fracture.
S11, eliminating the detection abnormality caused by the data, and completing the classification prediction of the fracture.
As shown in fig. 6, the multi-stage fracture is fused into a graph, and is matched with relevant data of actual drilling well in an analysis manner, so that abnormal fracture detection caused by data reasons is eliminated, and the graded prediction of fracture is completed.
It will be appreciated by those of ordinary skill in the art that the examples described herein are intended to assist the reader in understanding the manner in which the invention is practiced, and it is to be understood that the scope of the invention is not limited to such specifically recited statements and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (7)
1. A fracture grading prediction method based on a seismic frequency division technology is characterized by comprising the following steps:
s1, basic data collection: collecting data from the area of interest, the data comprising: zone adjustment data, a post-stack migration seismic body, a conventional logging curve, drilling and logging data, well deviation data, coring data, layering data, seismic interpretation horizon and single well testing productivity data;
s2, making a single-well synthetic record, and calibrating a research target layer and an upper layer and a lower layer;
s3, analyzing the frequency spectrum of the original post-stack amplitude data of the target layer to determine the effective signal range of the data of the target layer;
s4, directly obtaining the approximate scale of the target horizon fracture development according to the relevant geological data of actual drilling, well logging, head exposure and regional phase modulation;
s5, designing a theoretical forward model according to the fault development scale, and setting forward model parameters according to original amplitude data volume parameters;
s6, frequency division processing is carried out on the forward modeling earthquake result, section identifiability of different-grade fracture is observed under different frequency data, and optimal response frequency of different fracture grades is selected;
s7, carrying out frequency division processing of the preferred frequency in S6 on the data body within the effective signal range of the original amplitude data body to obtain data bodies of a plurality of frequency bands;
s8, analyzing each level of fracture property and optimizing an algorithm;
in S8, the low frequency body is used for the identification, prediction and calculation of large-scale fracture, the medium frequency body is used for the identification, prediction and calculation of medium-scale fracture, and the high frequency body is used for the identification, prediction and calculation of small-scale fracture or crack; calculating using waveform difference attributes for large-scale fracture, calculating using coherence and similarity attributes for medium-scale fracture, and calculating using curvature analysis, maximum likelihood attributes and ant tracking attributes for small-scale fracture or crack;
s9, analyzing the fracture bisection and three-dimensional space prediction effect of each level;
s10, fusing all levels of fractures into a graph, and matching and performing coincidence analysis on data related to fracture or fracture of actual drilling fracture and drilling conditions, fracture development conditions, reservoir development conditions and single well testing productivity;
s11, eliminating the detection abnormality caused by the data, and completing the classification prediction of the fracture.
2. The fracture grading prediction method based on the seismic frequency division technology as claimed in claim 1, wherein: and in the step S4, acquiring the approximate scale of the fracture development of the target horizon, wherein the fault acquisition parameters are with the fault distance as the first factor.
3. The fracture grading prediction method based on the seismic frequency division technology as claimed in claim 1, wherein: in S5, the forward modeling of the forward model is performed to obtain seismic forward data that is consistent with the original amplitude data parameters, and the method mainly includes: main frequency, bandwidth information.
4. The fracture grading prediction method based on the seismic frequency division technology as claimed in claim 1, wherein: in S7, the original amplitude data volume is divided by a frequency division technique based on fast matching pursuit.
5. The fracture grading prediction method based on the seismic frequency division technology as claimed in claim 1, wherein: in the S9 plano-section effect and three-dimensional space prediction analysis, a background is formed by combining basic structural characteristics and fracture of an actual research area, and the prediction misalignment caused by abnormity generated by frequency division processing is avoided by combining the comprehensive analysis of an original amplitude data volume.
6. The fracture grading prediction method based on the seismic frequency division technology as claimed in claim 1, wherein: and S10, matching and matching the data of actual drilling fracture and drilling encounter condition, fracture development condition, reservoir development condition and single well test productivity about fracture or fracture, and specifically analyzing the influence of the actual research area fracture cause, reservoir type and fracture on oil and gas transportation and gathering.
7. The fracture grading prediction method based on the seismic frequency division technology as claimed in claim 1, wherein: and S11, eliminating detection abnormality caused by data reasons, wherein the fracture detection abnormality often occurs aiming at the non-full coverage area of the three-dimensional data.
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