CN112346118A - Reservoir characteristic prediction method and device based on seismic attribute optimization - Google Patents
Reservoir characteristic prediction method and device based on seismic attribute optimization Download PDFInfo
- Publication number
- CN112346118A CN112346118A CN201910733046.7A CN201910733046A CN112346118A CN 112346118 A CN112346118 A CN 112346118A CN 201910733046 A CN201910733046 A CN 201910733046A CN 112346118 A CN112346118 A CN 112346118A
- Authority
- CN
- China
- Prior art keywords
- value
- seismic attribute
- well
- attribute
- value range
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000005457 optimization Methods 0.000 title claims abstract description 27
- 238000012216 screening Methods 0.000 claims abstract description 17
- 230000035945 sensitivity Effects 0.000 claims abstract description 10
- 238000012360 testing method Methods 0.000 claims abstract description 10
- 238000004364 calculation method Methods 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 13
- 238000003860 storage Methods 0.000 claims description 7
- 238000013459 approach Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 14
- 230000004927 fusion Effects 0.000 description 8
- 239000004576 sand Substances 0.000 description 6
- 230000006870 function Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000009826 distribution Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 238000004836 empirical method Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 229930195733 hydrocarbon Natural products 0.000 description 1
- 150000002430 hydrocarbons Chemical class 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Images
Classifications
-
- 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. analysis, for interpretation, for correction
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
-
- 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
- G01V2210/624—Reservoir parameters
Abstract
The invention provides a reservoir characteristic prediction method and device based on seismic attribute optimization, wherein the method comprises the following steps: acquiring a plurality of seismic attribute values and reservoir characteristic values at each well point of a logging well; calculating the relevant slope value of any seismic attribute by well points according to the plurality of seismic attribute values and the reservoir characteristic value; dividing the value range of the relevant slope value of any seismic attribute at each well point at equal intervals, and determining the number of well points in each value range interval; determining a threshold value of the value range interval, screening the main value range interval and acquiring the number of wells in the main value range interval; calculating a correlation coefficient of any seismic attribute value and a reservoir characteristic value at a well point in the main value range interval; judging whether any seismic attribute is a sensitive attribute or not according to the correlation coefficient and the number of well points in the main value domain interval; and predicting the reservoir characteristic value of the well testing area according to the sensitivity attribute. According to the scheme, reservoir characteristic prediction is carried out on the basis of the sensitive attribute after attribute optimization, and the reliability and accuracy of reservoir prediction can be improved.
Description
Technical Field
The invention relates to the technical field of geophysical exploration, in particular to a reservoir characteristic prediction method and device based on seismic attribute optimization.
Background
In recent years, with the increasing depth of exploration difficulty, seismic attributes have been widely used in various stages of exploration and development. With the continuous progress of the technology, more and more attributes are extracted, and how to select the attribute which can best reflect the reservoir change characteristics of the research area from the plurality of attributes is an inevitable research problem for geophysicists.
The current common attribute optimization method can be summarized into an empirical method and a mathematical theory method, wherein the empirical method is a method for optimizing the attribute by means of artificial experience on the basis of mastering a large amount of reservoir information of a research area, but the method is poor in applicability and long in experience accumulation; the mathematical theory method is to use some mathematical methods to optimize the attributes, common methods include rough set theory, genetic BP neural network method and correlation method, but these methods are easily affected by input data, and the correction of the input data by human factors directly affects the result of seismic attribute optimization, so that the accuracy of reservoir prediction is low.
Disclosure of Invention
The embodiment of the invention provides a reservoir characteristic prediction method based on seismic attribute optimization, which solves the technical problem of low accuracy of reservoir prediction caused by the influence of input data and human factors on a seismic attribute optimization result in the existing attribute optimization method, and comprises the following steps:
acquiring a plurality of seismic attribute values and reservoir characteristic values at each well point of a logging area;
calculating the relevant slope value of any seismic attribute by well points according to the plurality of seismic attribute values and the reservoir characteristic value;
dividing the value range of the relevant slope value of any seismic attribute at each well point at equal intervals, and determining the number of well points in each value range interval;
determining a threshold value of the value range interval according to the number of well points in each value range interval, screening a main value range interval and obtaining the number of well points in the main value range interval, wherein the main value range interval refers to all value range intervals of which the lower limit is greater than or equal to the threshold value;
calculating a correlation coefficient of any seismic attribute value and a reservoir characteristic value at a well point in the main value range interval;
judging whether any seismic attribute is a sensitive attribute or not according to the correlation coefficient and the number of well points in the main value domain interval;
and predicting the reservoir characteristic value of the well testing area according to the sensitivity attribute.
The embodiment of the invention also provides a reservoir characteristic prediction device based on seismic attribute optimization, which solves the technical problem of lower accuracy of reservoir prediction caused by the influence of input data and human factors on the seismic attribute optimization result in the existing attribute optimization method, and comprises the following steps:
the data acquisition module is used for acquiring a plurality of seismic attribute values and reservoir characteristic values at each well point of a logging area;
the relevant slope value calculation module is used for calculating the relevant slope value of any seismic attribute from well point to well point according to the plurality of seismic attribute values and the reservoir characteristic value;
the related slope value interval division module is used for dividing the value range of the related slope value of any seismic attribute at each well point at equal intervals and determining the number of the well points in each value range interval;
the main value range interval screening module is used for determining a threshold value of a value range interval according to the number of well points in each value range interval, screening the main value range interval and acquiring the number of the well points in the main value range interval, wherein the main value range interval refers to all value range intervals of which the lower limit is greater than or equal to the threshold value;
the correlation coefficient calculation module is used for calculating the correlation coefficient of any seismic attribute value and reservoir characteristic value at a well point in the main value domain interval;
the earthquake attribute judging module is used for judging whether any earthquake attribute is a sensitive attribute according to the correlation coefficient and the number of the well points in the main value domain interval;
and the reservoir characteristic value prediction module is used for predicting the reservoir characteristic value of the well testing area according to the sensitivity attribute.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the following method is implemented:
acquiring a plurality of seismic attribute values and reservoir characteristic values at each well point of a logging area;
calculating the relevant slope value of any seismic attribute by well points according to the plurality of seismic attribute values and the reservoir characteristic value;
dividing the value range of the relevant slope value of any seismic attribute at each well point at equal intervals, and determining the number of well points in each value range interval;
determining a threshold value of the value range interval according to the number of well points in each value range interval, screening a main value range interval and obtaining the number of well points in the main value range interval, wherein the main value range interval refers to all value range intervals of which the lower limit is greater than or equal to the threshold value;
calculating a correlation coefficient of any seismic attribute value and a reservoir characteristic value at a well point in the main value range interval;
judging whether any seismic attribute is a sensitive attribute or not according to the correlation coefficient and the number of well points in the main value domain interval;
and predicting the reservoir characteristic value of the well testing area according to the sensitivity attribute.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program for executing the following method is stored in the computer-readable storage medium:
acquiring a plurality of seismic attribute values and reservoir characteristic values at each well point of a logging area;
calculating the relevant slope value of any seismic attribute by well points according to the plurality of seismic attribute values and the reservoir characteristic value;
dividing the value range of the relevant slope value of any seismic attribute at each well point at equal intervals, and determining the number of well points in each value range interval;
determining a threshold value of the value range interval according to the number of well points in each value range interval, screening a main value range interval and obtaining the number of well points in the main value range interval, wherein the main value range interval refers to all value range intervals of which the lower limit is greater than or equal to the threshold value;
calculating a correlation coefficient of any seismic attribute value and a reservoir characteristic value at a well point in the main value range interval;
judging whether any seismic attribute is a sensitive attribute or not according to the correlation coefficient and the number of well points in the main value domain interval;
and predicting the reservoir characteristic value of the well testing area according to the sensitivity attribute.
The embodiment of the invention comprises the following steps: acquiring a plurality of seismic attribute values and reservoir characteristic values at each well point of a logging well; calculating the relevant slope value of any seismic attribute by well points according to the plurality of seismic attribute values and the reservoir characteristic value; dividing the value range of the relevant slope value of any seismic attribute at each well point at equal intervals, and determining the number of well points in each value range interval; determining a threshold value of the value range interval according to the well point number in each value range interval, screening the main value range interval and acquiring the well point number in the main value range interval; calculating a correlation coefficient of any seismic attribute value and a reservoir characteristic value at a well point in the main value range interval; judging whether any seismic attribute is a sensitive attribute or not according to the correlation coefficient and the number of well points in the main value domain interval; reservoir characteristic values of the logging zones are predicted according to the sensitive attributes, well point seismic attribute data with the most representative property are screened out, and reliable input data are provided for attribute optimization, so that reservoir characteristic prediction is performed based on the sensitive attributes after attribute optimization, and reliability and accuracy of reservoir prediction can be improved.
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. In the drawings:
FIG. 1 is a schematic diagram of a reservoir characteristic prediction method based on seismic attribute optimization in an embodiment of the invention.
FIG. 2 is a scatter plot of seismic attribute values and reservoir eigenvalues prior to seismic attribute optimization in an embodiment of the present invention;
FIG. 3 is a schematic diagram of the calculation of the associated slope value for any seismic attribute in an embodiment of the invention;
FIG. 4 is a histogram of the associated slope values for any of the seismic attributes in an embodiment of the present invention;
FIG. 5 is a graph of the intersection of seismic attribute values with reservoir eigenvalues after seismic attribute optimization in an embodiment of the present invention;
fig. 6 is a structural diagram of a reservoir characteristic prediction device based on seismic attribute optimization in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
In order to eliminate the influence of human factors when well point seismic attribute data are selected by a conventional method, screen out the most representative well point seismic attribute data, provide input data with preferred and reliable seismic attributes, and improve the reliability and accuracy of reservoir prediction, an embodiment of the present invention provides a reservoir characteristic prediction method based on seismic attribute preference, as shown in fig. 1, the method may include:
step 101: acquiring a plurality of seismic attribute values and reservoir characteristic values at each well point of a logging area;
step 102: calculating the relevant slope value of any seismic attribute by well points according to the plurality of seismic attribute values and the reservoir characteristic value;
step 103: dividing the value range of the relevant slope value of any seismic attribute at each well point at equal intervals, and determining the number of well points in each value range interval;
step 104: determining a threshold value of the value range interval according to the number of well points in each value range interval, screening a main value range interval and obtaining the number of well points in the main value range interval, wherein the main value range interval refers to all value range intervals of which the lower limit is greater than or equal to the threshold value;
step 105: calculating a correlation coefficient of any seismic attribute value and a reservoir characteristic value at a well point in the main value range interval;
step 106: judging whether any seismic attribute is a sensitive attribute or not according to the correlation coefficient and the number of well points in the main value domain interval;
step 107: and predicting the reservoir characteristic value of the well testing area according to the sensitivity attribute.
In specific implementation, step 101 is to collect conventional logging data for each well point in the logging area, the seismic attribute refers to geometrical, kinematic, dynamic or statistical characteristics of seismic waves obtained by mathematically transforming pre-stack or post-stack seismic data, and the seismic attribute may be amplitude, frequency, phase or other characteristics; reservoir characteristics refer to certain reservoir characteristics of the rock formation having interconnected pores that allow hydrocarbons to be stored and percolated therein, which may be reservoir thickness, reservoir porosity, reservoir permeability, or other characteristics.
In specific implementation, in step 102, a schematic diagram of a calculation formula of the relevant slope values is shown in fig. 3, in a plane coordinate, an X axis is a reservoir characteristic value, a Y axis is a seismic attribute value, both a solid dot and a hollow dot are well points, a hollow dot is taken as a center, slopes from the hollow dot to each solid dot are respectively calculated by a two-point method, and the slopes are averaged to obtain the relevant slope values of the seismic attribute at the hollow dot.
In an embodiment, the associated slope value for any seismic attribute at any well point is calculated according to the following formula:
wherein: n is the number of well points, M is the number of seismic attributes, SijIs the seismic attribute value, S, of the ith seismic attribute th j well pointikIs the seismic attribute value, R, of the ith seismic attribute pointjReservoir characteristic value, R, for the jth well pointkReservoir characteristic value, Q, for the k well pointikThe correlation slope value of the ith seismic attribute k well point is shown, and Num is the number of the ith seismic attribute k well point and other well points actually participating in calculation.
In step 103, in order to visually obtain the distribution state and the overall distribution condition of the related slope values, the number of wells in each range of the related slope values is determined by a histogram statistical method, as shown in fig. 4, the sum of all ranges of the related slope value ranges is the distribution range of the related slope values.
In specific implementation, the threshold value of the value range interval is determined according to the well point number in each value range interval, the main value range interval is screened, and the well point number in the main value range interval is obtained, wherein the main value range interval refers to all the value range intervals of which the lower limit is greater than or equal to the threshold value.
In an embodiment, the method for setting the threshold value of the range interval includes, when the number of well points in any range interval is greater than or equal to half of the maximum number of well points in all range intervals, acquiring a lower limit of the range interval, and taking the minimum value of the acquired lower limits of all range intervals as the threshold value.
In step 105, the correlation coefficient may represent the degree of correlation between the seismic attribute value and the reservoir characteristic value, and the calculation formula of the correlation coefficient is as follows:
wherein X, Y are two variables, r (X, Y) is the correlation coefficient of X and Y, Cov (X, Y) is the covariance of X and Y, Var [ X ] is the variance of X, Var [ Y ] is the variance of Y. The quantitative characterization of the correlation coefficient shows the degree of correlation between X and Y, i.e. the greater r (X, Y), the greater the degree of correlation, and when r (X, Y) is equal to 1, the highest degree of correlation is corresponded; the smaller r (X, Y) is, the smaller the correlation degree is, and when r (X, Y) is equal to 0, the corresponding correlation degree is the lowest.
In step 105, the correlation degree between the seismic attribute value and the reservoir characteristic value is visually obtained through the scatter diagram, as shown in fig. 2, in a plane coordinate, the abscissa is the reservoir characteristic sand body thickness value, the ordinate is the seismic attribute value, the solid dots are scatter points, the black straight line is a fitting straight line of each scatter point, and the fitting straight line means that when one straight line is used, the sum of squares of distances from all scatter points to the straight line is minimum, and then the straight line becomes the fitting straight line of the scatter points. The more scattered points are gathered on the fitting straight line, the higher the correlation degree of the seismic attribute value and the sand body thickness value is, and the more scattered points are scattered on the fitting straight line, the lower the correlation degree of the seismic attribute value and the sand body thickness value is.
In step 106, the method for determining whether any seismic attribute is a sensitive attribute is that, for any seismic attribute, when the number of well points in the acquired main value domain interval is greater than or equal to half of the number of all well points in the logging area and the correlation coefficient is greater than or equal to a given value, the seismic attribute is considered as a sensitive attribute, and the reservoir characteristics can be better reflected.
In step 107, in a specific implementation, a plurality of sensitive attributes are fused into a fusion attribute by using mathematical operation methods such as RGB attribute fusion, cluster analysis attribute fusion, multiple linear regression attribute fusion, well attribute fusion, and fuzzy logic-based attribute fusion, and then an equivalent relationship between the fusion attribute value and a reservoir characteristic value is established, all the fusion attribute values are converted into reservoir characteristic values, and finally all the reservoir characteristic values of the predicted well logging area are obtained.
A specific example is given below to illustrate the specific application of the preferred reservoir characteristic prediction method based on seismic attributes in the embodiment of the present invention. In this specific embodiment, a plurality of seismic attribute values and reservoir characteristic values at each well point of the logging area are obtained, as shown in fig. 2, the number of well points of the logging area is 62, there are 5 seismic attributes, in this embodiment, the seismic attribute 1 is taken as an example, and the reservoir characteristic value is the sand thickness; calculating a correlation slope value of any seismic attribute from well point to well point according to the plurality of seismic attribute values and the reservoir characteristic values, as shown in fig. 3; dividing the value range of the relevant slope value of any seismic attribute at each well point at equal intervals, determining the number of wells in each value range interval, counting by using a line graph as shown in FIG. 4, taking 0.05 as the interval of the relevant slope value range and-0.2 as the lower limit of the value range, and determining the number of wells in each value range interval; determining a threshold value of the value domain interval according to the number of well points in each value domain interval, screening the main value domain interval and obtaining the number of well points in the main value domain interval, as shown in fig. 4, wherein the number of well points in the value domain interval from 0.1 to 0.15 is greater than half of the maximum number of well points in all the value domain intervals, and 0.1 is the minimum value of all interval lower limits meeting the conditions, so 0.1 is taken as the threshold value, all the value domain intervals with the screening lower limit greater than or equal to 0.1 are taken as the main value domain interval and the number of well points in the main value domain interval is obtained, as shown in fig. 5, the number of well points in the main value domain interval is 45; calculating a correlation coefficient of any seismic attribute value and reservoir characteristic value at a well point in the main value range interval, wherein the calculated correlation coefficient is 0.69 as shown in fig. 5; judging whether any seismic attribute is a sensitive attribute or not according to the correlation coefficient and the number of well points in the main value domain interval, wherein as shown in fig. 5, the given value is 0.5, the correlation coefficient 0.69 is greater than 0.5, the number of well points 45 in the main value domain interval is greater than half of the number 62 of all well points in the logging area, and the seismic attribute 1 can be judged as the sensitive attribute; and predicting the reservoir characteristic value of the well testing area according to the sensitivity attribute. Comparing fig. 2 and fig. 5, it can be seen that the correlation coefficient between seismic attribute 1 and sand thickness is only 0.28 before the attribute is optimized, and the correlation coefficient between seismic attribute 1 and sand thickness is 0.68 after the attribute is optimized according to the invention, which shows that the invention provides reliable input data for the attribute optimization, and can improve the reliability and accuracy of reservoir prediction.
Based on the same inventive concept, the embodiment of the invention also provides a reservoir characteristic prediction device based on seismic attribute optimization, and the device is described in the following embodiment. Because the principles of solving the problems are similar to the reservoir characteristic prediction method based on seismic attribute optimization, the implementation of the device can be referred to the implementation of the method, and repeated details are not repeated.
Fig. 6 is a structural diagram of reservoir characteristic prediction based on seismic attribute optimization in the embodiment of the present invention, and as shown in fig. 6, the apparatus includes:
the data acquisition module 110 is configured to acquire a plurality of seismic attribute values and reservoir characteristic values at each well point of the logging zone;
a correlation slope value calculation module 210, configured to calculate a correlation slope value of any seismic attribute from well point to well point according to the plurality of seismic attribute values and the reservoir characteristic value;
a related slope value interval division module 310, configured to divide the value range of the related slope value of any seismic attribute at each well point at equal intervals, and determine the number of wells in each value range interval;
a main value range interval screening module 410, configured to determine a threshold value of a value range interval according to the number of well points in each value range interval, screen the main value range interval, and obtain the number of well points in the main value range interval, where the main value range interval is all value range intervals in which the lower limit of the value range interval is greater than or equal to the threshold value;
a correlation coefficient calculation module 510, configured to calculate a correlation coefficient between any seismic attribute value and a reservoir characteristic value at a well point within the main value range interval;
the seismic attribute judging module 610 is configured to judge whether any seismic attribute is a sensitive attribute according to the correlation coefficient and the number of well points in the main value domain interval;
and the reservoir characteristic value prediction module 710 is used for predicting the reservoir characteristic value of the logging zone according to the sensitivity attribute.
In an embodiment, the correlation slope value calculation module 210 is further configured to calculate the correlation slope value of any seismic attribute at any of the points of approach according to the following formula:
wherein: n is the number of well points, M is the number of seismic attributes, SijIs the ith groundSeismic attribute value of jth well point, Sik is seismic attribute value of ith seismic attribute kth well point, RjReservoir characteristic value, R, for the jth well pointkReservoir characteristic value, Q, for the k well pointikThe correlation slope value of the ith seismic attribute k well point is shown, and Num is the number of the ith seismic attribute k well point and other well points actually participating in calculation.
In an embodiment, the main value range interval screening module 410 is further configured to set a threshold value according to a method that, when the number of well points in any value range interval is greater than or equal to half of the maximum number of well points in all value range intervals, the lower limit of the value range interval is obtained, and the minimum value of the obtained lower limits of all value range intervals is used as the threshold value.
In an embodiment, the seismic attribute determining module 610 is further configured to determine whether any seismic attribute is a sensitive attribute according to the following method, and regarding any seismic attribute, when the number of well points in the obtained main value domain interval is greater than or equal to half of all the well points of the logging area, and the correlation coefficient is greater than or equal to a given value, the seismic attribute is considered as the sensitive attribute, and the reservoir characteristics can be reflected.
In summary, the embodiment of the present invention provides: acquiring a plurality of seismic attribute values and reservoir characteristic values at each well point of a logging well; calculating the relevant slope value of any seismic attribute by well points according to the plurality of seismic attribute values and the reservoir characteristic value; dividing the value range of the relevant slope value of any seismic attribute at each well point at equal intervals, and determining the number of well points in each value range interval; determining a threshold value of the value range interval according to the well point number in each value range interval, screening the main value range interval and acquiring the well point number in the main value range interval; calculating a correlation coefficient of any seismic attribute value and a reservoir characteristic value at a well point in the main value range interval; judging whether any seismic attribute is a sensitive attribute or not according to the correlation coefficient and the number of well points in the main value domain interval; reservoir characteristic values of the well testing area are predicted according to the sensitive attributes, well point seismic attribute data with the most representativeness are screened out, reliable input data are provided for attribute optimization, attribute optimization is achieved, reservoir characteristic prediction is conducted based on the sensitive attributes after the attribute optimization, and reliability and accuracy of reservoir prediction can be improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A reservoir characteristic prediction method based on seismic attribute optimization is characterized by comprising the following steps:
acquiring a plurality of seismic attribute values and reservoir characteristic values at each well point of a logging area;
calculating the relevant slope value of any seismic attribute by well points according to the plurality of seismic attribute values and the reservoir characteristic value;
dividing the value range of the relevant slope value of any seismic attribute at each well point at equal intervals, and determining the number of well points in each value range interval;
determining a threshold value of the value range interval according to the number of well points in each value range interval, screening a main value range interval and obtaining the number of well points in the main value range interval, wherein the main value range interval refers to all value range intervals of which the lower limit is greater than or equal to the threshold value;
calculating a correlation coefficient of any seismic attribute value and a reservoir characteristic value at a well point in the main value range interval;
judging whether any seismic attribute is a sensitive attribute or not according to the correlation coefficient and the number of well points in the main value domain interval;
and predicting the reservoir characteristic value of the well testing area according to the sensitivity attribute.
2. The method of claim 1, wherein the associated slope value for any seismic attribute at any well point is calculated according to the formula:
wherein: n is the number of well points, M is the number of seismic attributes, SijIs the seismic attribute value, S, of the ith seismic attribute th j well pointikIs the seismic attribute value, R, of the ith seismic attribute pointjReservoir characteristic value, R, for the jth well pointkReservoir characteristic value, Q, for the k well pointikThe correlation slope value of the ith seismic attribute k well point is shown, and Num is the number of the ith seismic attribute k well point and other well points actually participating in calculation.
3. The method of claim 1, wherein the threshold value is set in such a manner that when the number of well points in any one of the value range intervals is greater than or equal to half the maximum number of well points in all the value range intervals, the lower limit of the value range interval is obtained, and the minimum value of the obtained lower limits of all the value range intervals is used as the threshold value.
4. The method as claimed in claim 1, wherein whether any seismic attribute is sensitive is judged according to a method that, for any seismic attribute, when the number of the acquired well points in the main value range interval is greater than or equal to half of all the well points of the logging area and the correlation coefficient is greater than or equal to a given value, the seismic attribute is considered as sensitive and can reflect the reservoir characteristics.
5. A seismic attribute-based preferred reservoir characteristic prediction apparatus, comprising:
the data acquisition module is used for acquiring a plurality of seismic attribute values and reservoir characteristic values at each well point of a logging area;
the relevant slope value calculation module is used for calculating the relevant slope value of any seismic attribute from well point to well point according to the plurality of seismic attribute values and the reservoir characteristic value;
the related slope value interval division module is used for dividing the value range of the related slope value of any seismic attribute at each well point at equal intervals and determining the number of the well points in each value range interval;
the main value range interval screening module is used for determining a threshold value of a value range interval according to the number of well points in each value range interval, screening the main value range interval and acquiring the number of the well points in the main value range interval, wherein the main value range interval refers to all value range intervals of which the lower limit is greater than or equal to the threshold value;
the correlation coefficient calculation module is used for calculating the correlation coefficient of any seismic attribute value and reservoir characteristic value at a well point in the main value domain interval;
the seismic attribute judging module is used for judging whether any seismic attribute is a sensitive attribute according to the correlation coefficient and the number of the well points in the main value domain interval;
and the reservoir characteristic value prediction module is used for predicting the reservoir characteristic value of the well testing area according to the sensitivity attribute.
6. The apparatus of claim 5, wherein the correlation slope value calculation module is further configured to calculate the correlation slope value for any seismic attribute at any of the points of approach according to the following formula:
wherein: n is the number of well points, M is the number of seismic attributes, SijIs the seismic attribute value, S, of the ith seismic attribute th j well pointikIs the seismic attribute value, R, of the ith seismic attribute pointjReservoir characteristic value, R, for the jth well pointkReservoir characteristic value, Q, for the k well pointikThe correlation slope value of the ith seismic attribute k well point is shown, and Num is the number of the ith seismic attribute k well point and other well points actually participating in calculation.
7. The apparatus of claim 5, wherein the primary interval screening module is further configured to set a threshold value according to a method that obtains a lower limit of any one of the interval when the number of well points in the interval is greater than or equal to half of the maximum number of well points in all the intervals, and uses the minimum value of the obtained lower limits of all the intervals as the threshold value.
8. The apparatus of claim 5, wherein the seismic attribute determination module is further configured to determine whether any seismic attribute is a sensitive attribute, and for any seismic attribute, when the number of acquired well points in the main value range interval is greater than or equal to half of all well points of the logging zone and the correlation coefficient is greater than or equal to a given value, the seismic attribute is considered as a sensitive attribute and can reflect the reservoir characteristics.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910733046.7A CN112346118A (en) | 2019-08-09 | 2019-08-09 | Reservoir characteristic prediction method and device based on seismic attribute optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910733046.7A CN112346118A (en) | 2019-08-09 | 2019-08-09 | Reservoir characteristic prediction method and device based on seismic attribute optimization |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112346118A true CN112346118A (en) | 2021-02-09 |
Family
ID=74367573
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910733046.7A Pending CN112346118A (en) | 2019-08-09 | 2019-08-09 | Reservoir characteristic prediction method and device based on seismic attribute optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112346118A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5940778A (en) * | 1997-07-31 | 1999-08-17 | Bp Amoco Corporation | Method of seismic attribute generation and seismic exploration |
| US20130080066A1 (en) * | 2011-09-28 | 2013-03-28 | Saudi Arabian Oil Company | Reservoir properties prediction with least square support vector machine |
| CN106353811A (en) * | 2016-08-12 | 2017-01-25 | 北京天工新源科技有限公司 | Method for identifying fluid of multi-attribute fusion reservoirs on basis of well information attribute optimization |
| CN106842317A (en) * | 2016-12-22 | 2017-06-13 | 中国石油天然气集团公司 | A kind of method and device for predicting oil sand body distribution |
| CN107894615A (en) * | 2017-11-13 | 2018-04-10 | 中国石油化工股份有限公司华北油气分公司勘探开发研究院 | A kind of method of quantitative evaluation 3-D seismics attribute forecast reservoir parameter validity |
-
2019
- 2019-08-09 CN CN201910733046.7A patent/CN112346118A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5940778A (en) * | 1997-07-31 | 1999-08-17 | Bp Amoco Corporation | Method of seismic attribute generation and seismic exploration |
| US20130080066A1 (en) * | 2011-09-28 | 2013-03-28 | Saudi Arabian Oil Company | Reservoir properties prediction with least square support vector machine |
| CN106353811A (en) * | 2016-08-12 | 2017-01-25 | 北京天工新源科技有限公司 | Method for identifying fluid of multi-attribute fusion reservoirs on basis of well information attribute optimization |
| CN106842317A (en) * | 2016-12-22 | 2017-06-13 | 中国石油天然气集团公司 | A kind of method and device for predicting oil sand body distribution |
| CN107894615A (en) * | 2017-11-13 | 2018-04-10 | 中国石油化工股份有限公司华北油气分公司勘探开发研究院 | A kind of method of quantitative evaluation 3-D seismics attribute forecast reservoir parameter validity |
Non-Patent Citations (3)
| Title |
|---|
| 刘江;姚云霞;: "综合利用地震属性进行快速储层预测", 中国石油和化工标准与质量, no. 07, 1 July 2011 (2011-07-01), pages 172 * |
| 张兆辉;李胜军;张忠义;蔡刚;苏明军;: "目标砂体厚度精细预测技术在歧南地区的应用", 地球物理学进展, no. 05, 15 October 2014 (2014-10-15), pages 2317 - 2318 * |
| 鲍祥生;尹成;赵伟;张金淼;刘志斌;: "储层预测的地震属性优选技术研究", 石油物探, no. 01, 28 February 2006 (2006-02-28), pages 28 - 33 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US7415401B2 (en) | Method for constructing 3-D geologic models by combining multiple frequency passbands | |
| AU2001288441A1 (en) | Method for constructing 3-D geologic models by combining multiple frequency passbands | |
| AU2012253529B2 (en) | System and method for characterizing reservoir formation evaluation uncertainty | |
| CN104101902B (en) | Seismic properties clustering method and device | |
| CN109492775B (en) | Geological structure interpretation detection method, geological structure interpretation detection device and readable storage medium | |
| CN109184677A (en) | Reservoir evaluation methods for heterogeneous alternating layers sand body | |
| CN111025384A (en) | Reservoir stratum prediction method and device based on waveform classification intersection fusion | |
| CN105700016A (en) | Organic bank reservoir earthquake prediction method | |
| CN104656140A (en) | Median filtering method and system based on vertical earthquake attenuation laws | |
| KR20200058258A (en) | System and method for predicting ground layer information, and a recording medium having computer readable program for executing the method | |
| CN116201535B (en) | Automatic dividing method, device and equipment for oil and gas reservoir target well sign stratum | |
| CN111239809B (en) | Dominant reservoir determination method and system based on attribute fusion principle | |
| CN112346118A (en) | Reservoir characteristic prediction method and device based on seismic attribute optimization | |
| CN112327357A (en) | Method and device for predicting maturity of hydrocarbon source rock based on three-dimensional seismic data | |
| AU2002312550B2 (en) | Method for performing object-based connectivity analysis in 3-D seismic data volumes | |
| CN111781661A (en) | Method and device for predicting sedimentary microphase plane spread in small-well area | |
| CN113050191A (en) | Shale oil TOC prediction method and device based on double parameters | |
| CN112649853A (en) | Fracture parameter prediction method and system based on post-stack data | |
| CN112147679A (en) | Lithology prediction method and device based on elastic parameters under fuzzy logic framework | |
| CN112346117B (en) | Reservoir characteristic prediction method and device based on seismic attribute fusion | |
| CN111983677B (en) | Reservoir porosity prediction method and device based on Russell attribute factors | |
| AU2021379607B2 (en) | Well correlation through intermediary well | |
| CN110888163B (en) | Method and device for determining development position of micro-fracture | |
| US20220291408A1 (en) | System and method for quantitative quality assessment of seismic surfaces | |
| CN111856569B (en) | Stratum sand body prediction method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |