CN113945970B - Compact sandstone reservoir prediction method - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 56
- 235000021185 dessert Nutrition 0.000 claims abstract description 53
- 238000012545 processing Methods 0.000 claims abstract description 11
- 238000010606 normalization Methods 0.000 claims description 22
- 238000004458 analytical method Methods 0.000 claims description 18
- 238000011161 development Methods 0.000 claims description 17
- 238000011160 research Methods 0.000 claims description 16
- 238000005070 sampling Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 238000010183 spectrum analysis Methods 0.000 claims description 4
- 230000001419 dependent effect Effects 0.000 claims description 2
- 238000011835 investigation Methods 0.000 claims description 2
- 239000003208 petroleum Substances 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 description 8
- 239000010410 layer Substances 0.000 description 7
- 235000011850 desserts Nutrition 0.000 description 5
- 239000011148 porous material Substances 0.000 description 5
- 238000001514 detection method Methods 0.000 description 4
- 238000001228 spectrum Methods 0.000 description 4
- 239000004215 Carbon black (E152) Substances 0.000 description 2
- 238000005553 drilling Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 239000012530 fluid Substances 0.000 description 2
- 229930195733 hydrocarbon Natural products 0.000 description 2
- 239000004576 sand Substances 0.000 description 2
- 239000002356 single layer Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000000151 deposition Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 150000002430 hydrocarbons Chemical class 0.000 description 1
- 125000001183 hydrocarbyl group Chemical group 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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Abstract
The invention relates to a tight sandstone reservoir prediction method, which belongs to the technical field of petroleum exploration seismic reservoir prediction, and comprises the steps of performing a series of processing by utilizing post-stack seismic data to respectively obtain a wave impedance inversion body, an instantaneous frequency attribute body and a difference body of a dominant frequency single frequency body, and determining a tight sandstone dessert attribute body by utilizing the performance characteristics of the seismic attribute bodies in tight sandstone, namely the characteristics of high wave impedance, high difference body and low instantaneous frequency, so that the tight sandstone dessert is reliably predicted finally, and the dessert prediction precision is improved. Moreover, the method does not need to utilize pre-stack seismic data, so that the method is more suitable for reservoir dessert prediction in areas without the pre-stack seismic data.
Description
Technical Field
The invention belongs to the technical field of petroleum exploration seismic reservoir prediction, and particularly relates to a tight sandstone reservoir prediction method.
Background
A tight sandstone reservoir is generally referred to as a sandstone reservoir with a porosity of less than 10%, and a tight sandstone "dessert" is referred to as a reservoir that has some pore space (the lower limit of porosity is also different according to the development of the oil and gas testing process, and currently east is a porosity of greater than 7%) and contains oil and gas in the overall low-porosity, low-permeability sandstone. At present, methods for predicting desserts at home and abroad mainly comprise logging methods, petrophysical methods and earthquake methods, wherein the technical methods for predicting the desserts of tight sandstone reservoirs mainly comprise the following three types:
The first category is a seismic phase analysis method, for example, in the paper published by authors Zhang Xihua, chen Hongde, et al and entitled "research on a method for predicting tight sandstone gas reservoirs in the new field of the West of Sichuan basin", which uses seismology as an idea, performs well control enhancement resolution treatment by taking a signal-to-noise ratio spectrum as a reference, makes a section zero-phased while widening the spectrum, and determines the geological meaning of a phase axis of seismic reflection by forward modeling and real drilling contrast analysis, and on the basis, performs seismic phase interpretation by using a seismic slicing technology to predict a favorable phase band of a tight sandstone reservoir.
The second type is a seismic attribute analysis method, as indicated in the paper entitled "dense oil reservoir dessert seismic prediction" issued by authors Zhu Chao, xia Zhiyuan, et al, the method uses petrophysical analysis in combination with forward modeling analysis to determine that desserts in a research area have seismic response characteristics of medium amplitude and medium frequency, and further uses techniques such as frequency division imaging, 90-degree phase conversion, frequency division attribute optimization, and the like to predict dense oil desserts in red Liu Quan area by using dessert attributes (amplitude-to-frequency ratio).
The method for predicting the dense sandstone dessert mainly utilizes earthquake phases and post-stack earthquake attribute analysis, and has a certain polynosicity for predicting the porosity and the oil-gas content of the dense sandstone.
The third category is seismic inversion methods, for example, as reported by authors Zhang Qi, liu Juntian, et al in the paper entitled "pre-stack inversion-based tight sandstone reservoir prediction and oil-gas detection," which indicates that the methods are based on pre-stack seismic data, preferably elastic parameters, and perform pre-stack inversion to predict the physical properties and fluid-containing properties of the reservoir, and can effectively solve the problem of the above-mentioned polynomials, and can be used as an important method for tight sandstone reservoir prediction and oil-gas detection research, but the method is limited by pre-stack data, and the prediction method fails or has poor prediction accuracy when the pre-stack seismic data is not available in the research area or when the quality of the pre-stack seismic data is poor.
Besides the three types of dessert earthquake prediction technical methods, other oil reservoir prediction methods in the prior art are not suitable for performing dense sandstone reservoir dessert prediction, for example, chinese patent literature with patent authority publication number of CN104142519B discloses a mudstone fissure oil reservoir prediction method, the root mean square amplitude attribute of post-stack earthquake data is utilized to firstly identify a mudstone development area, then the attribute such as frequency division coherence, curvature and the like is utilized to identify a fissure development zone in mudstone, finally the oiliness of the mudstone fissure development zone is detected through the frequency-dependent attribute difference method based on a matching pursuit time-frequency analysis technology, so that the distribution range of the mudstone fissure oil reservoir is finely carved, the gradual depth of crack oil-bearing detection is realized from the fissure reservoir prediction, the prediction precision of the mudstone fissure oil reservoir is improved, but the technology for predicting the mudstone fissure by utilizing the post-stack earthquake data cannot meet the prediction requirement of the dense sandstone dessert, the reservoir is different from the mudstone reservoir, the sand reservoir is mostly deposited by the mudstone, the depth and the single-layer thickness is small, the transverse variation is rapid, the frequency division coherence and the curvature is utilized to identify the reservoir boundary is difficult to develop.
Therefore, the technology for predicting the dense sandstone dessert by the prior technical means is not perfect enough, and is difficult to meet the prediction requirement of the dense sandstone dessert, and mainly has the following problems:
(1) The porosity and oil-gas content prediction technology of compact sandstone used in the current mainstream method are all based on prestack seismic data, so that the prediction requirement of compact sandstone dessert in areas without prestack seismic data or with poor prestack data quality in old areas is difficult to meet;
(2) The tight sandstone reservoir is mostly formed by depositing sand shale thin interbed layers, the buried depth is deep, the single-layer thickness is small, the transverse change is quick, and the accuracy of directly predicting the tight sandstone dessert by adopting a seismic attribute and seismic phase analysis method is low.
Disclosure of Invention
The invention aims to provide a tight sandstone reservoir prediction method which is used for solving the problem that the existing method is low in prediction precision and is not applicable to reservoir dessert prediction in areas without pre-stack seismic data.
Based on the purposes, the technical scheme of the tight sandstone reservoir prediction method is as follows:
1) Acquiring post-stack seismic data of a research area, and obtaining a wave impedance inversion body by using a sparse pulse inversion method;
2) Acquiring post-stack seismic data of a research area, and extracting an instantaneous frequency attribute body of the post-stack seismic data;
3) Obtaining a difference value body of the two dominant frequency single frequency bodies by using a frequency variation attribute difference value method based on matching pursuit time-frequency analysis;
4) Respectively carrying out normalization treatment on the wave impedance inversion body, the instantaneous frequency attribute body and the difference value body of the dominant frequency single frequency body obtained in the steps 1), 2) and 3), and calculating according to the following formula to obtain a dense sandstone dessert attribute body;
T＝IMP*△AMP/FREQ
wherein T is a dense sandstone dessert attribute body; IMP is normalized wave resistance antibody; delta AMP is the difference of normalized dominant frequency single frequency body; FREQ is the normalized instantaneous frequency body;
5) And (3) extracting the dessert attribute average value from the dense sandstone dessert attribute body obtained in the step (4) along the top-bottom time window of the target interval, and identifying the area of which the dessert attribute average value is larger than a set value as a dense sandstone dessert development area.
The beneficial effects of the technical scheme are as follows:
According to the method, a series of processing is carried out by utilizing post-stack seismic data, a wave impedance inversion body, an instantaneous frequency attribute body and a difference body of a dominant frequency single frequency body are respectively obtained, and then the characteristics of the three seismic attribute bodies in tight sandstone, namely the characteristics of high wave impedance, high difference body and low instantaneous frequency are utilized, so that the attribute body of the tight sandstone dessert is determined, the tight sandstone dessert is finally and reliably predicted, and the dessert prediction precision is improved. Moreover, the method does not need to utilize pre-stack seismic data, so that the method is more suitable for reservoir dessert prediction in areas without the pre-stack seismic data.
Further, the step 3) of obtaining the difference between the two dominant frequency single frequency bodies by using a frequency-variant attribute difference method based on matching pursuit time-frequency analysis includes:
(1) Carrying out oil-water well spectrum analysis on a research area, and determining two dominant frequencies;
(2) Converting post-stack seismic data of the investigation region into single frequency volumes of the two dominant frequencies;
(3) And carrying out difference on the single frequency bodies of the two dominant frequencies to obtain a difference value body of the single frequency bodies of the two dominant frequencies.
Further, the normalization processing formula of the wave impedance inversion body is as follows:
IMP＝(Ii-Imin)/(Imax-Imin)
In the above formula, IMP is an inversion wave resistance antibody after normalization; i i is an inversion wave resistance antibody; i min is the minimum value in the inverted wave resistance body; i max is the maximum value in the inverted wave impedance body.
Further, the normalization processing formula of the transient frequency attribute body is as follows:
FREQ＝(Fi-Fmin)/(Fmax-Fmin)
In the above formula, FREQ is the instantaneous frequency body after normalization; f i is the instantaneous frequency volume; f min is the minimum in the instantaneous frequency bin; f max is the maximum in the instantaneous frequency bin.
Further, the normalization processing formula of the difference value of the dominant frequency single frequency body is as follows:
△AMP＝(△Ai-△Amin)/(△Amax-△Amin)
In the above formula, delta AMP is the difference of the normalized dominant frequency single frequency body; delta A i is the difference of dominant frequency single frequency body; deltaA min is the minimum value in the difference volume of the dominant frequency single frequency volume; Δa max is the maximum value in the difference volume of the dominant frequency single frequency volume.
Further, the calculation formula of the dessert attribute average value is as follows:
M＝(X1+X2+...+Xn)/n
In the above formula, M is a dessert attribute average value, X1, X2, …, and Xn are dessert attribute values of sampling points, and n is a sampling point number.
Drawings
FIG. 1 is a flow chart of a tight sandstone reservoir prediction method in an embodiment of the present invention;
FIG. 2 is a graph of a predicted development zone of a dense sandstone dessert for an interval of interest, in an embodiment of the present invention.
Detailed Description
The embodiment provides a method for predicting a tight sandstone reservoir, taking a certain sand group (namely a target interval) of a certain research area as an example, wherein the sand group mainly develops the tight sandstone, the research area is provided with a three-dimensional post-stack seismic data body and top and bottom interpretation layers of the certain sand group of the target layer, more than 20 wells are drilled into the certain sand group of the target layer, wherein near 10 wells have logging curves such as natural gamma, acoustic time difference, density, resistivity and the like, and wave impedance curves and porosity result interpretation data are calculated by the product of the reciprocal of the acoustic time difference and the density. According to analysis of the wave impedance of the drilled well in the research area and the natural gamma intersection diagram, determining the characteristic of high wave impedance of the tight sandstone; determining that the seismic frequency is characterized by being reduced along with the increase of the porosity of the compact sandstone through forward modeling analysis of geologic models with different compact sandstone contents and different pore conditions established by taking sound waves and density values obtained by real drilling as model parameters; and determining that the oil and gas-containing reservoir has the characteristic of poor high-frequency variable attribute through the seismic spectrum analysis at the side of the well where the well is drilled and the seismic attribute extraction analysis at different frequencies.
With the above consideration, a specific embodiment of the present invention will be further described below by taking a certain sand group in a certain research area as an example, with reference to the accompanying drawings, and as shown in fig. 1, the specific implementation steps are as follows:
step one, post-stack seismic data of a research area are obtained, a longitudinal wave impedance inversion body is obtained by using a sparse pulse inversion method, and a high-value area with inversion wave impedance larger than 1.165 x 10 x 7 g/cubic centimeter x meter/second is identified as a tight sandstone development area.
The implementation of this embodiment is achieved by using a INVERTRACE-Plus module in Jason software from CGG, france, using a constrained sparse pulse inversion technique. In this embodiment, the constrained sparse pulse inversion technique is one of mature wave impedance inversion techniques, see "post-stack constrained sparse pulse inversion chinese training course" pages 45-50 "constrained sparse pulse inversion (CSSI)" by the hui solid earth technologies (beijing) limited.
And step two, utilizing an instantaneous attribute extraction instant Attribute Window module in geological magnifier Geoscope software of Beijing Nox Petroleum science and technology Co, and converting the post-stack seismic data volume into an instantaneous frequency volume.
And (3) identifying a low-value region with the instantaneous frequency smaller than 22 Hz as a compact sandstone development region with a certain pore space on the basis of the compact sandstone development region determined in the step (A) by utilizing the instantaneous frequency body.
And thirdly, carrying out oil gas detection on the tight sandstone reservoir by using a frequency-varying attribute difference method based on matching pursuit time-frequency analysis, and identifying a high-value region in the dominant frequency single-frequency body difference body as an oil-gas-containing region. Specifically, the method comprises the following substeps:
3.1 oil-water well spectrum analysis: seismic spectrums at oil-containing well points and water-containing well points of a target layer section (i.e. a research area) are respectively extracted, spectrum characteristics at the well points containing different fluids are analyzed, effective frequency bands of the target layer section at the oil-containing well points and the water-containing well points are determined, and dominant frequencies of high-frequency attenuation and low-frequency change are optimized according to the characteristics of high-frequency attenuation and low-frequency increase of the reservoir after oil and gas are contained. Therefore, the dominant frequencies in this study area were 30HZ and 15HZ, respectively;
3.2 generating dominant frequency single frequency body: converting the post-stack seismic data into single frequency bodies with dominant frequencies of 30HZ and 15HZ respectively by using a matching pursuit time-frequency analysis MP TIME FREQENCE module in geological magnifier Geoscope software of Beijing Nox Petroleum technology Co;
3.3 dominant frequency single frequency body difference analysis: and (3) carrying out difference calculation on the dominant frequency single frequency body generated in the step (3.2) according to the following formula, wherein the calculation formula is as follows:
△A＝Afl-Afh
Wherein, delta A is the difference of dominant frequency single frequency volume (namely the seismic amplitude attribute difference data volume); a fl is a single frequency body with low-frequency dominant frequency, namely 15 hz; a fh is a single frequency body with a high-frequency dominant frequency, namely 30 hz.
After the difference value body of the dominant frequency single frequency body is obtained, determining that a high-value area which is larger than 250 in the difference value body of the dominant frequency single frequency body is an oil-gas-containing area in the dense sandstone development area which is predicted in the step two and has a certain pore.
Step four, normalization processing:
And (3) respectively carrying out normalization treatment on the inversion wave resistance body, the seismic frequency body and the difference body of the dominant frequency single frequency body obtained in the step (I), the step (II) and the step (III) to obtain normalized wave resistance body, frequency body and difference body of the dominant frequency single frequency body. The specific normalization process is as follows:
4.1, inversion wave resistance antibody normalization treatment: and (3) carrying out normalization processing on the inversion wave resistance antibody obtained in the step (I) according to the following formula to obtain the normalized inversion wave resistance antibody.
IMP＝(Ii-Imin)/(Imax-Imin)
In the above formula, IMP is an inversion wave resistance antibody after normalization; i i is an inversion wave resistance antibody; i min is the minimum value in the inverted wave resistance body; i max is the maximum value in the inverted wave impedance body.
4.2, Instantaneous frequency body normalization processing: and (3) normalizing the instantaneous frequency body obtained in the step (II) according to the following formula to obtain a normalized instantaneous frequency body.
FREQ＝(Fi-Fmin)/(Fmax-Fmin)
In the above formula, FREQ is the instantaneous frequency body after normalization; f i is the instantaneous frequency volume; f min is the minimum in the instantaneous frequency bin; f max is the maximum in the instantaneous frequency bin.
4.3 Differential value body normalization processing of dominant frequency single frequency body: and (3) carrying out normalization processing on the dominant frequency difference body delta A obtained in the step (III) according to the following formula to obtain a difference body of the normalized dominant frequency single frequency body.
△AMP＝(△Ai-△Amin)/(△Amax-△Amin)
In the above formula, delta AMP is the difference of the normalized dominant frequency single frequency body; delta A i is the difference of dominant frequency single frequency body; deltaA min is the minimum value in the difference volume of the dominant frequency single frequency volume; Δa max is the maximum value in the difference volume of the dominant frequency single frequency volume.
Step five, determining a dense sandstone dessert attribute body:
And (3) carrying out mathematical operation on the normalized wave resistance body IMP, the instantaneous frequency body FREQ and the difference body delta AMP of the dominant frequency single frequency body obtained in the step (IV) according to the following formula to obtain the dense sandstone dessert attribute body. The calculation formula is as follows:
T＝IMP*△AMP/FREQ
Wherein T is a dense sandstone dessert attribute body; IMP is normalized wave resistance antibody; delta AMP is the difference of normalized dominant frequency single frequency body; FREQ is the normalized instantaneous frequency bin.
Step six, predicting the distribution of dense sandstone desserts of a target layer:
And (3) extracting the dessert attribute average value of the target interval according to the following formula on the dense sandstone dessert attribute body T obtained in the step five:
M＝(X1+X2+...+Xn)/n
In the above formula, M is the average value of the dessert attribute of the target interval, X is the dessert attribute value of the sampling point, and n is the sampling point.
After determining the dense sandstone dessert attribute body T, extracting a dessert attribute average value along a top-bottom time window of a target interval, and predicting a development area of the dense sandstone reservoir dessert to obtain a dense sandstone reservoir dessert distribution prediction plan as shown in fig. 2, wherein a region with the dessert attribute average value larger than 1.1 is the dense sandstone dessert development area, and symbols in fig. 2 represent all well number positions.
In this embodiment, the dense sandstone development zone determined in step one, the dense sandstone development zone with a certain pore space determined in step two, and the hydrocarbon-bearing zone determined in step three are not necessary in this embodiment, but are used to mutually verify the predicted development zone of the dense sandstone reservoir dessert; in other embodiments, in the practical application of the present embodiment, the difference between the inverted wave resistance body, the seismic frequency body and the dominant frequency single frequency body may be directly determined without the development area and the hydrocarbon-containing area in the first, second and third steps. And, the three seismic attribute volume determination processes may be performed separately, not necessarily in the order of the present embodiment.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (6)
1. A tight sandstone reservoir prediction method, comprising the steps of:
1) Acquiring post-stack seismic data of a research area, and obtaining a wave impedance inversion body by using a sparse pulse inversion method;
2) Acquiring post-stack seismic data of a research area, and extracting an instantaneous frequency attribute body of the post-stack seismic data;
3) Obtaining a difference value body of the two dominant frequency single frequency bodies by using a frequency variation attribute difference value method based on matching pursuit time-frequency analysis;
4) Respectively carrying out normalization treatment on the wave impedance inversion body, the instantaneous frequency attribute body and the difference value body of the dominant frequency single frequency body obtained in the steps 1), 2) and 3), and calculating according to the following formula to obtain a dense sandstone dessert attribute body;
T＝IMP*△AMP/FREQ
wherein T is a dense sandstone dessert attribute body; IMP is normalized wave resistance antibody; delta AMP is the difference of normalized dominant frequency single frequency body; FREQ is the normalized instantaneous frequency body;
5) And (3) extracting the dessert attribute average value from the dense sandstone dessert attribute body obtained in the step (4) along the top-bottom time window of the target interval, and identifying the area of which the dessert attribute average value is larger than a set value as a dense sandstone dessert development area.
2. The tight sandstone reservoir prediction method according to claim 1, wherein in step 3) using a frequency-dependent attribute difference method based on matching pursuit time-frequency analysis, obtaining a difference between two dominant frequency single frequency volumes comprises:
(1) Carrying out oil-water well spectrum analysis on a research area, and determining two dominant frequencies;
(2) Converting post-stack seismic data of the investigation region into single frequency volumes of the two dominant frequencies;
(3) And carrying out difference on the single frequency bodies of the two dominant frequencies to obtain a difference value body of the single frequency bodies of the two dominant frequencies.
3. The tight sandstone reservoir prediction method according to claim 1, wherein the normalization process formula of the wave impedance inversion body is as follows:
IMP＝(Ii-Imin)/(Imax-Imin)
In the above formula, IMP is an inversion wave resistance antibody after normalization; i i is an inversion wave resistance antibody; i min is the minimum value in the inverted wave resistance body; i max is the maximum value in the inverted wave impedance body.
4. The tight sandstone reservoir prediction method according to claim 1, wherein the normalization process formula of the instantaneous frequency attribute is as follows:
FREQ＝(Fi-Fmin)/(Fmax-Fmin)
In the above formula, FREQ is the instantaneous frequency body after normalization; f i is the instantaneous frequency volume; f min is the minimum in the instantaneous frequency bin; f max is the maximum in the instantaneous frequency bin.
5. The tight sandstone reservoir prediction method according to claim 1, wherein the normalization processing formula of the difference volume of the dominant frequency single frequency volume is as follows:
△AMP＝(△Ai-△Amin)/(△Amax-△Amin)
In the above formula, delta AMP is the difference of the normalized dominant frequency single frequency body; delta A i is the difference of dominant frequency single frequency body; deltaA min is the minimum value in the difference volume of the dominant frequency single frequency volume; Δa max is the maximum value in the difference volume of the dominant frequency single frequency volume.
6. The tight sandstone reservoir prediction method of claim 1, wherein the calculation of the mean value of the dessert property is as follows:
M＝(X1+X2+...+Xn)/n
In the above formula, M is a dessert attribute average value, X1, X2, …, and Xn are dessert attribute values of sampling points, and n is a sampling point number.
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