CN111856575A - Method for improving prediction precision of thin sandstone reservoir - Google Patents
Method for improving prediction precision of thin sandstone reservoir Download PDFInfo
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- CN111856575A CN111856575A CN202010699135.7A CN202010699135A CN111856575A CN 111856575 A CN111856575 A CN 111856575A CN 202010699135 A CN202010699135 A CN 202010699135A CN 111856575 A CN111856575 A CN 111856575A
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- 239000011435 rock Substances 0.000 claims description 4
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- 238000005315 distribution function Methods 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
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- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/62—Physical property of subsurface
- G01V2210/622—Velocity, density or impedance
- G01V2210/6226—Impedance
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
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- G01V2210/624—Reservoir parameters
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Abstract
The invention belongs to the technical field of petroleum exploration and development, and particularly relates to a method for improving prediction precision of a thin sandstone reservoir; adopting a geostatistical inversion method with higher resolution ratio for a thin sandstone reservoir, and adopting a reservoir characteristic curve to replace a conventional wave impedance curve for inversion, wherein the wave impedance is difficult to effectively distinguish sand mudstones for the thin sandstone reservoir; the wave impedance data body is used as a co-simulation body to perform co-simulation on the characteristic curve of the reservoir, and the formed characteristic curve data body can well identify the thin reservoir because the resolution of the characteristic curve is high and the curve value has good correspondence with the sand shale; the prediction of the thin sandstone reservoir based on the post-stack seismic data volume has universality and applicability, the method focuses on the prediction of the post-stack seismic reservoir, and the prediction precision of the thin sandstone reservoir is effectively improved by adopting a geostatistical inversion method with higher resolution.
Description
Technical Field
The invention belongs to the technical field of petroleum exploration and development, and particularly relates to a method for improving prediction precision of a thin sandstone reservoir.
Background
With the further improvement of the oil and gas exploration and development degree, the geological requirement on the reservoir prediction precision is higher and higher. And the wave impedance is difficult to effectively distinguish sand mudstones, so that reservoir prediction is difficult. At present, the research field of predicting the thin sandstone reservoir mainly focuses on the research of rock geophysical aspects by utilizing prestack data, and the prediction of the post-stack earthquake thin sandstone reservoir is slow in progress. Pre-stack seismic reservoir prediction has two constraints: firstly, the workload is very large; the second is a prestack data volume that needs to be specially processed. Therefore, a method capable of effectively improving the prediction accuracy of the thin sandstone reservoir is needed to be designed.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a method for improving the prediction precision of a thin sandstone reservoir, the thin sandstone reservoir prediction based on a post-stack seismic data body has universality and applicability, the method focuses on the post-stack seismic reservoir prediction, and the prediction precision of the thin sandstone reservoir is effectively improved by adopting a geostatistical inversion method with higher resolution.
The invention relates to a method for improving prediction accuracy of a thin sandstone reservoir, which comprises the following steps:
(1) inverting the conventional wave impedance to form a wave impedance data volume;
because the wave impedance has poor sensitivity to the thin reservoir and low resolution, the thin reservoir cannot be directly predicted, and the data body can be used as a trend body in the subsequent collaborative simulation process;
(2) dividing a single-well sand-shale profile by utilizing a reservoir characteristic curve, selecting a curve sensitive to a reservoir, such as SP, GR, COND and the like, and dividing the sand-shale by setting a cut-off value of the curve to distinguish the sand-shale to form a discrete sand-shale curve, wherein the sand-shale is set to be 0, and the sandstone is set to be 1;
(3) standardizing the reservoir characteristic curve to an interval of 0-1;
(4) analyzing a probability density function, a variation function and cloud transformation;
(5) and performing co-simulation on the reservoir characteristic curve by taking the wave impedance data body as a co-simulation body.
Further, in the step (3), the standardization is divided into three steps:
a. manually generating a mudstone baseline curve;
b. subtracting the manually drawn mudstone baseline from the original curve, and further straightening the baseline of the original curve;
c. and finally, standardizing the range of the characteristic curve to be in the range of 0-1.
Further, in the step (4), the probability density function is performed according to the reservoir characteristic curve, and the sand separation rock and the mudstone are respectively analyzed; the variation function mainly controls the distribution range of the characteristic curve and the sand shale in the longitudinal direction and the transverse direction, and mainly comprises a longitudinal variation range, a transverse variation range, a function type (exponential type or logarithmic type) and anisotropy; cloud transformation, namely a relation function between a wave impedance data volume and a reservoir characteristic curve, is a nonlinear relation function, and cloud transformation analysis is respectively carried out on sandstone and mudstone.
Further, in the step (4), the probability density function is:
and a and b are respectively the maximum value and the minimum value of the wave impedance of the well section of the target layer, the function is close to normal distribution, and the P value approaches to 1.
Further, in the step (4), the cloud transformation function:
wherein a is an amplitude coefficient, n is the number of normal functions, Ex is an expected value, En is entropy, and He is super entropy.
Further, in the step (4), the cloud transformation calculating step:
a. calculating a frequency distribution function f (x) of the data set;
b. decomposing f (x) into the sum of n normal functions;
c. calculating the expectation of the cloud model according to the normal function;
d. and calculating the entropy and the super entropy of the n cloud models.
Compared with the prior art, the invention has the following advantages: firstly, a geostatistical inversion method with higher resolution is adopted for a thin sandstone reservoir, and for the thin sandstone reservoir, the wave impedance is difficult to effectively distinguish sand mudstones, and a reservoir characteristic curve is adopted to replace a conventional wave impedance curve for inversion; the wave impedance data body is used as a co-simulation body to perform co-simulation on the characteristic curve of the reservoir, and the characteristic curve data body can well identify the thin reservoir because the resolution of the characteristic curve is high and the curve value has good correspondence with the sand shale.
Drawings
FIG. 1 is a sand mudstone curve calculated by using SP curves according to the present invention;
FIG. 2 is a graph of the results of the natural potential curve normalization calculation of the present invention;
FIG. 3 is a comparison of the natural potential curves of the present invention before and after normalization;
FIG. 4 is a graph of a comparison of Gr curves before and after normalization in accordance with the present invention;
FIG. 5 is a graphical representation of a variogram;
FIG. 6 is a graph of reservoir prediction results in accordance with the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The invention is further illustrated by the following specific examples in combination with the accompanying drawings.
Example 1:
the method for improving the prediction accuracy of the thin sandstone reservoir comprises the following steps:
(1) inverting the conventional wave impedance to form a wave impedance data volume;
because the wave impedance has poor sensitivity to the thin reservoir and low resolution, the thin reservoir cannot be directly predicted, and the data body can be used for a trend body in a subsequent collaborative simulation process.
(2) The method comprises the steps of dividing a single-well sand-shale profile by utilizing a reservoir characteristic curve, selecting a curve which is sensitive to a reservoir, such as SP, GR, COND and the like, dividing the sand-shale by setting a cut-off value of the curve to form a discrete sand-shale curve, setting the sand-shale curve to be 0, setting the sandstone to be 1, and as shown in figure 1, setting the deep color to be the sand-shale and setting the light color to be the sandstone.
(3) Standardizing the reservoir characteristic curve to an interval of 0-1;
because the curve value ranges of different wells are different, and the mudstone base line of the curve is not straight, the method cannot be directly used for inversion, and the standardization is divided into three steps:
a. manually generating a mudstone baseline curve;
b. subtracting the manually drawn mudstone baseline from the original curve, and further straightening the baseline of the original curve;
c. And finally, standardizing the range of the characteristic curve to be in the range of 0-1.
The curve normalization results are shown in figure 2, the mudstone base line is close to 1, the sandstone is far less than 1 between 0 and 1, and the amplitude value of the deviation base line represents the probability value of the sandstone. After the curves are standardized, the curves can effectively distinguish sand mudstones, as shown in attached figures 3 and 4, the curves are respectively sand mudstone distribution histograms before and after the SP curves and the GR curves are standardized, and the light color is sandstone and the dark color is mudstone.
(4) Analyzing a probability density function, a variation function and cloud transformation;
the probability density function is carried out according to the reservoir characteristic curve, and sand rock and mudstone are respectively analyzed;
the variation function mainly controls the distribution range of the characteristic curve and the sand shale in the longitudinal direction and the transverse direction, and mainly comprises a longitudinal variation range, a transverse variation range, a function type (exponential type or logarithmic type) and anisotropy;
cloud transformation, namely a relation function between a wave impedance data volume and a reservoir characteristic curve, is a nonlinear relation function, and cloud transformation analysis is respectively carried out on sandstone and mudstone.
Probability density function:
and a and b are respectively the maximum value and the minimum value of the wave impedance of the well section of the target layer, the function is close to normal distribution, and the P value approaches to 1.
Function of variation: the variation function is shown in figure 5, wherein the variation range a means that a curve has better correlation with sampling points in a range a, the longitudinal variation range is a sand thickness parameter, the transverse variation range is a sand range parameter, C0 is a lump value, and Sill (C + C0) is a base value and represents the variation range of the variable.
Cloud transformation function:
wherein a is an amplitude coefficient, n is the number of normal functions, Ex is an expected value, En is entropy, and He is super entropy.
Cloud transformation calculation:
a. calculating a frequency distribution function f (x) of the data set;
b. decomposing f (x) into the sum of n normal functions;
c. calculating the expectation of the cloud model according to the normal function;
d. and calculating the entropy and the super entropy of the n cloud models.
(5) Carrying out co-simulation on the reservoir characteristic curve by taking a wave impedance data body as a co-simulation body;
because the resolution ratio of the characteristic curve is high and the correspondence between the curve value and the sand shale is good, the formed characteristic curve data body can well identify the thin reservoir, and if the wave impedance data body needs to be obtained, the characteristic curve data body and the wave impedance data body can be operated to obtain the wave impedance data body capable of identifying the thin reservoir. Fig. 6 shows the conventional inversion result and the reservoir characteristic curve inversion result, where the upper graph is the conventional inversion result and the lower graph is the reservoir characteristic curve inversion result, and it is obvious that the latter is obviously due to the former in terms of resolution and accuracy.
The above embodiments are only specific examples of the present invention, and the protection scope of the present invention includes but is not limited to the product forms and styles of the above embodiments, and any suitable changes or modifications made by those skilled in the art according to the claims of the present invention shall fall within the protection scope of the present invention.
Claims (6)
1. A method for improving prediction accuracy of a thin sandstone reservoir is characterized by comprising the following steps: the method comprises the following steps:
(1) inverting the conventional wave impedance to form a wave impedance data volume;
(2) dividing a single-well sand-shale profile by utilizing a reservoir characteristic curve, selecting a curve sensitive to a reservoir, and dividing the sand-shale by setting a cut-off value of the curve for distinguishing the sand-shale to form a discrete sand-shale curve, wherein the sand-shale is set to be 0, and the sandstone is set to be 1;
(3) standardizing the reservoir characteristic curve to an interval of 0-1;
(4) analyzing a probability density function, a variation function and cloud transformation;
(5) and performing co-simulation on the reservoir characteristic curve by taking the wave impedance data body as a co-simulation body.
2. The method for improving the prediction accuracy of the thin sandstone reservoir of claim 1, wherein the method comprises the following steps: in the step (3), the standardization is divided into three steps:
a. manually generating a mudstone baseline curve;
b. subtracting the manually drawn mudstone baseline from the original curve, and further straightening the baseline of the original curve;
c. and finally, standardizing the range of the characteristic curve to be in the range of 0-1.
3. The method for improving the prediction accuracy of the thin sandstone reservoir of claim 1, wherein the method comprises the following steps: in the step (4), the probability density function is carried out according to the reservoir characteristic curve, and the sand separation rock and the mudstone are respectively analyzed; the variation function controls the distribution range of the characteristic curve and the sand shale in the longitudinal direction and the transverse direction, and comprises a longitudinal variation range, a transverse variation range, a function type and anisotropy; cloud transformation, namely a relation function between a wave impedance data volume and a reservoir characteristic curve, is a nonlinear relation function, and cloud transformation analysis is respectively carried out on sandstone and mudstone.
4. The method for improving the prediction accuracy of the thin sandstone reservoir of claim 1, wherein the method comprises the following steps: the probability density function:
and a and b are respectively the maximum value and the minimum value of the wave impedance of the well section of the target layer, the function is close to normal distribution, and the P value approaches to 1.
5. The method for improving the prediction accuracy of the thin sandstone reservoir of claim 1, wherein the method comprises the following steps: in the step (4), the cloud transformation function:
wherein a is an amplitude coefficient, n is the number of normal functions, Ex is an expected value, En is entropy, and He is super entropy.
6. The method for improving the prediction accuracy of the thin sandstone reservoir of claim 1, wherein the method comprises the following steps: in the step (4), the cloud transformation calculation step:
a. calculating a frequency distribution function f (x) of the data set;
b. decomposing f (x) into the sum of n normal functions;
c. calculating the expectation of the cloud model according to the normal function;
d. and calculating the entropy and the super entropy of the n cloud models.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090093963A1 (en) * | 2005-11-14 | 2009-04-09 | Patrick Rasolofosaon | Method for quantitative evaluation of fluid pressures and detection of overpressures in an underground medium |
| CN104516018A (en) * | 2013-09-30 | 2015-04-15 | 中国石油化工股份有限公司 | Porosity inversion method under lithological constraint in geophysical exploration |
| CN105044770A (en) * | 2015-07-06 | 2015-11-11 | 成都理工大学 | Compact glutenite gas reservoir quantificational prediction method |
| US20190179049A1 (en) * | 2017-12-07 | 2019-06-13 | Saudi Arabian Oil Company | Mapping chemostratigraphic signatures of a reservoir with rock physics and seismic inversion |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090093963A1 (en) * | 2005-11-14 | 2009-04-09 | Patrick Rasolofosaon | Method for quantitative evaluation of fluid pressures and detection of overpressures in an underground medium |
| CN104516018A (en) * | 2013-09-30 | 2015-04-15 | 中国石油化工股份有限公司 | Porosity inversion method under lithological constraint in geophysical exploration |
| CN105044770A (en) * | 2015-07-06 | 2015-11-11 | 成都理工大学 | Compact glutenite gas reservoir quantificational prediction method |
| US20190179049A1 (en) * | 2017-12-07 | 2019-06-13 | Saudi Arabian Oil Company | Mapping chemostratigraphic signatures of a reservoir with rock physics and seismic inversion |
Non-Patent Citations (3)
| Title |
|---|
| 代劲: "云模型在文本挖掘应用中的关键问题研究", 《中国优秀博硕士学位论文全文数据库（博士） 信息科技辑》 * |
| 张飞飞等: "密井网地质统计学反演在河道砂体预测中的应用", 《断块油气田》 * |
| 黄广谭: "浅薄层油藏储层预测技术研究与应用", 《中国优秀博硕士学位论文全文数据库（硕士） 基础科学辑》 * |
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