CN111830562B - Method and device for predicting permeability of oil and gas reservoir - Google Patents
Method and device for predicting permeability of oil and gas reservoir Download PDFInfo
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Abstract
The invention discloses a method and a device for predicting permeability of an oil and gas reservoir, wherein the method comprises the following steps: acquiring logging information and a pre-stack seismic angle trace set, and generating a plurality of elastic impedance curves with different angles according to the logging information and the pre-stack seismic angle trace set; performing various mathematical transformations on each elastic impedance curve; performing angle rotation recombination on every two curves after mathematical transformation to obtain a plurality of new attribute curves; acquiring permeability logging curves, respectively performing curve fitting on each new attribute curve and the permeability logging curves, and taking the new attribute corresponding to the maximum fitting correlation coefficient as a target attribute; and determining the constituent elements of the target attribute, converting the pre-stack seismic elastic impedance inversion data body obtained by pre-stack seismic inversion into a permeability data body according to the constituent elements, and predicting the permeability of the oil and gas reservoir by using the permeability data body. The method can improve the accuracy of the permeability prediction of the oil field reservoir.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration, in particular to a method and a device for predicting permeability of an oil and gas reservoir.
Background
In recent years, the focus of oil and gas exploration in China is gradually oriented to the fields of unconventional oil and gas exploration such as low-hole and low-permeability. Unlike conventional hydrocarbon reservoirs, these new hydrocarbon reservoirs have complex reservoir conditions, strong heterogeneity, large capacity affected by reservoir permeability, and high investment risk, and therefore, it is necessary to provide a high-precision hydrocarbon reservoir permeability prediction method.
Studies have shown that when the permeability of a reservoir changes, its elastic properties also change, resulting in a change in the amplitude of the acquired seismic data. Therefore, in the field of oil and gas seismic exploration technology, seismic amplitude information is often used to directly or indirectly invert to obtain permeability parameters representing reservoir permeability.
In the prior art, permeability prediction is generally performed by using pre-stack seismic data, specifically, for example, sensitivity relationship between a class of elastic parameters in seismic attribute parameters and permeability is used, preferably elastic parameter data sensitive to permeability, and permeability data is reconstructed by using a statistical relationship or a petrophysical plate, so that elastic parameters obtained by seismic inversion are converted into permeability data. Because of the large variety of elastic parameters, conventional empirical screening of sensitive elastic parameters to reconstruct permeability data is inefficient and has a limit to sensitivity to permeability due to the physical limitations of the elastic parameters themselves.
Therefore, the accuracy of the oil field reservoir permeability prediction result obtained by the prior art is low, and the requirements of refined oil and gas exploration at present cannot be met.
Disclosure of Invention
The embodiment of the invention provides a method for predicting permeability of an oil and gas reservoir, which is used for improving the accuracy of oil field reservoir permeability prediction and comprises the following steps:
acquiring logging information and a pre-stack seismic angle trace set, and generating a plurality of elastic impedance curves with different angles according to the logging information and the pre-stack seismic angle trace set;
performing various mathematical transformations on each elastic impedance curve;
performing angle rotation recombination on every two curves after mathematical transformation to obtain a plurality of new attribute curves;
acquiring permeability logging curves, respectively performing curve fitting on each new attribute curve and the permeability logging curves, and taking the new attribute corresponding to the maximum fitting correlation coefficient as a target attribute;
and determining the constituent elements of the target attribute, converting the pre-stack seismic elastic impedance inversion data body obtained by pre-stack seismic inversion into a permeability data body according to the constituent elements, and predicting the permeability of the oil and gas reservoir by using the permeability data body.
Optionally, the logging information includes: a longitudinal wave velocity profile, a transverse wave velocity profile and a density profile;
the range of incidence angles of the seismic waves in the prestack seismic angle trace set is-90 degrees;
generating a plurality of elastic impedance curves of different angles according to logging information, including:
and generating an elastic impedance curve at intervals of preset angles according to the longitudinal wave velocity curve, the transverse wave velocity curve and the density curve in the range of the incidence angle of the seismic waves.
Optionally, the elastic impedance curve X (θ i ) The calculation formula of (2) is as follows:
A＝V p ρ；
wherein θ i The angle of the incident angle of the seismic wave corresponding to the ith elastic impedance curve; v (V) p The longitudinal wave speed is given in m/s; v (V) s The transverse wave speed is expressed in meters per second; ρ is density in kilograms per cubic meter;
the unit is meter/second for the longitudinal wave speed of the target interval;
The unit is meter/second for the transverse wave speed of the target interval; ρ 0 Is the average value of the density in kilograms per cubic meter; k is the ratio of the transverse wave speed to the longitudinal wave speed, and is dimensionless; m is the average longitudinal wave impedance A of the objective interval 0 On the order of magnitude of (2).
Optionally, the mathematical transformation method includes:
the method comprises the following steps of constant logarithmic transformation based on natural indexes, power transformation based on natural indexes, reciprocal transformation, square transformation and evolution transformation.
Optionally, theNew attribute curve T pq The formula of (χ) is as follows:
wherein χ is the rotation angle; recording the set of a plurality of new attribute curves after mathematical transformation as A, A p 、A q The p-th attribute curve and the q-th attribute curve in the curve set A are respectively;
the order of the average value of the p-th attribute curve and the q-th attribute curve in the curve set A are respectively; t (T) pq And (χ) is a new attribute curve obtained by rotating χ degrees of the p-th and q-th attribute curves in the curve set A.
Optionally, a fitting formula adopted for performing curve fitting on each new attribute curve and the permeability log curve respectively is as follows:
P＝aT 2 pq (χ)+bT pq (χ)+c
where P represents a known permeability log and a, b, c represent binomial constant coefficients, respectively.
Optionally, taking the new attribute corresponding to the maximum fitting correlation coefficient as the target attribute includes:
fitting each new attribute curve through a binomial formula to obtain a group of binomial constant coefficients;
bringing each set of constant coefficients and the corresponding new attribute curve into a binomial formula, and calculating to obtain a new permeability prediction curve corresponding to each new attribute curve;
and according to a correlation coefficient formula, calculating the correlation coefficient of each new permeability prediction curve and the known permeability logging curve respectively, and taking the new attribute corresponding to the maximum value of the correlation number as the target attribute.
Optionally, determining the constituent elements of the target attribute, and converting the pre-stack seismic elastic impedance inversion data volume obtained by pre-stack seismic inversion into the permeability data volume according to the constituent elements, including:
screening the target attribute to determine a constituent element of the target attribute, wherein the constituent element comprises: angle information, mathematical transformation information, angle rotation combination information and specific value of binomial fitting constant coefficient of elastic impedance;
determining an angle adopted by inversion of the elastic impedance of the pre-stack earthquake according to the angle information of the elastic impedance;
transforming an elastic impedance inversion data body obtained by pre-stack seismic inversion according to the mathematical transformation information;
performing angle rotation combination on the elastic impedance inversion data body subjected to the data transformation by using the angle rotation combination information;
and (3) taking the binomial fit constant coefficient into the data volume after the angle rotation combination to a binomial formula to obtain the permeability data volume.
The embodiment of the invention also provides a device for predicting the permeability of the oil and gas reservoir, which is used for improving the accuracy of the permeability prediction of the oil field reservoir, and comprises the following steps:
the information acquisition module is used for acquiring logging information and a prestack seismic angle trace set and generating a plurality of elastic impedance curves with different angles according to the logging information and the prestack seismic angle trace set;
the mathematical transformation module is used for performing various mathematical transformations on each elastic impedance curve respectively;
the angle rotation combination module is used for carrying out angle rotation recombination on every two curves after mathematical transformation to obtain a plurality of new attribute curves;
the curve fitting module is used for acquiring permeability logging curves, respectively performing curve fitting on each new attribute curve and the permeability logging curves, and taking the new attribute corresponding to the maximum fitting correlation coefficient as a target attribute;
and the permeability prediction module is used for determining the constituent elements of the target attribute, converting the pre-stack seismic elastic impedance inversion data body obtained by pre-stack seismic inversion into a permeability data body according to the constituent elements, and predicting the permeability of the oil and gas reservoir by using the permeability data body.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method when executing the computer program.
Embodiments of the present invention also provide a computer-readable storage medium storing a computer program for executing the above method.
According to the embodiment of the invention, a plurality of elastic impedance curves with different angles are generated according to logging information and a pre-stack seismic angle trace set, each elastic impedance curve is subjected to various mathematical transformations, and every two curves subjected to the mathematical transformations are subjected to angle rotation recombination to obtain a plurality of new attribute curves, so that the characteristic that sensitivity of the elastic impedance to permeability changes along with the change of angles is well utilized. And constructing a large number of new attribute parameters containing angle information by combining a mathematical transformation means, respectively performing curve fitting on each new attribute curve and the permeability logging curve, taking the new attribute corresponding to the maximum fitting correlation coefficient as a target attribute, determining the constituent elements of the target attribute, and finally obtaining the permeability parameters in a data driving mode according to the permeability data body acquired by the constituent elements, thereby ensuring that the finally acquired permeability of the oil and gas reservoir has higher accuracy.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a schematic flow chart of a method for predicting permeability of a hydrocarbon reservoir according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a device for predicting permeability of a hydrocarbon reservoir according to an embodiment of the present invention;
FIG. 3 is an exemplary plot of a well log for a reservoir interval of a research area in accordance with an embodiment of the present invention;
FIG. 4 is an exemplary graph of different angle elastic impedance curves for a reservoir segment of a research area in accordance with an embodiment of the present invention;
FIG. 5 is a graph showing an example of a mathematical transformation of a reservoir interval elastic impedance curve for a region of interest in accordance with an embodiment of the present invention;
FIG. 6 is a graph illustrating new attribute curves formed by combining angular rotations of reservoir segments of a research area according to an embodiment of the present invention;
FIG. 7 is an exemplary graph of permeability predictions for a reservoir segment of a research area in accordance with an embodiment of the present invention;
FIG. 8 is a cross-sectional view of elastic impedance data for a reservoir segment of a investigation region at an angle of 35℃to the angle of incidence of seismic waves in an embodiment of the present invention;
FIG. 9 is a cross-sectional view of log elastic impedance change data for a reservoir segment of a investigation region at an angle of 40℃to seismic wave incident angle in accordance with an embodiment of the present invention;
FIG. 10 is an exemplary plot of predicted permeability profiles for a reservoir segment of a research area in accordance with an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
The embodiment of the invention provides a method for predicting permeability of an oil and gas reservoir, which is shown in a figure 1, and comprises the following steps:
And 103, carrying out angle rotation recombination on every arbitrary two curves subjected to the logarithmic transformation to obtain a plurality of new attribute curves.
And 105, determining the constituent elements of the target attribute, converting the pre-stack seismic elastic impedance inversion data body obtained by pre-stack seismic inversion into a permeability data body according to the constituent elements, and predicting the permeability of the oil and gas reservoir by using the permeability data body.
According to the oil-gas reservoir permeability prediction method provided by the embodiment of the invention, the logging information and the pre-stack seismic angle trace sets are used for generating a plurality of elastic impedance curves with different angles according to the logging information and the pre-stack seismic angle trace sets, each elastic impedance curve is subjected to various mathematical transformations, and every arbitrary two curves after the mathematical transformations are subjected to angle rotation recombination to obtain a plurality of new attribute curves, so that the characteristic that the sensitivity of the elastic impedance to the permeability changes along with the change of the angle is well utilized (because the sensitivity degree of the elastic parameters with different types to the permeability of the reservoir is also different, the elastic parameter effects with different types can be simulated by the elastic impedance data with different angles, the types of the elastic parameters sensitive to the permeability can be enriched by using the elastic impedance data with different angles. And constructing a large number of new attribute parameters containing angle information by combining a mathematical transformation means, respectively performing curve fitting on each new attribute curve and the permeability logging curve, taking the new attribute corresponding to the maximum fitting correlation coefficient as a target attribute, determining the constituent elements of the target attribute, and finally obtaining the permeability parameters in a data driving mode according to the permeability data body acquired by the constituent elements, thereby ensuring that the finally acquired permeability of the oil and gas reservoir has higher accuracy.
In step 101, the well logging information refers to a well logging curve that needs to be corrected using conventional well logging correction techniques to ensure that all curves accurately reflect the formation information. The pre-stack seismic angle gather is the angle gather after denoising and amplitude preservation treatment.
Optionally, the logging information includes: a longitudinal wave velocity profile, a transverse wave velocity profile and a density profile;
the range of incidence angles of the seismic waves in the prestack seismic angle trace set is-90 degrees;
generating a plurality of elastic impedance curves of different angles according to logging information, including:
and generating an elastic impedance curve at intervals of preset angles according to the longitudinal wave velocity curve, the transverse wave velocity curve and the density curve in the range of the incidence angle of the seismic waves.
The elastic impedance curve X (theta) i ) The calculation formula of (2) is as follows:
A＝V p ρ；
wherein θ i The angle of the incident angle of the seismic wave corresponding to the ith elastic impedance curve; v (V) p The longitudinal wave speed is given in m/s; v (V) s The transverse wave speed is expressed in meters per second; ρ is density in kilograms per cubic meter;
the unit is meter/second for the longitudinal wave speed of the target interval;
The unit is meter/second for the transverse wave speed of the target interval; ρ 0 Is the average value of the density in kilograms per cubic meter; k is the ratio of the transverse wave speed to the longitudinal wave speed, and is dimensionless; m is the average longitudinal wave impedance A of the objective interval 0 On the order of magnitude of (2).
In step 102, the manner of mathematical transformation includes:
the method comprises the following steps of constant logarithmic transformation based on natural indexes, power transformation based on natural indexes, reciprocal transformation, square transformation and evolution transformation.
In step 103, a new attribute curve T pq The formula of (χ) is as follows:
wherein χ is the rotation angle; recording the set of a plurality of new attribute curves after mathematical transformation as A, A p 、A q The p-th attribute curve and the q-th attribute curve in the curve set A are respectively;
the order of the average value of the p-th attribute curve and the q-th attribute curve in the curve set A are respectively; t (T) pq And (χ) is a new attribute curve obtained by rotating χ degrees of the p-th and q-th attribute curves in the curve set A.
In step 104, a fitting formula adopted for performing curve fitting on each new attribute curve and the permeability log curve is as follows:
P＝aT 2 pq (χ)+bT pq (χ)+c
where P represents a known permeability log and a, b, c represent binomial constant coefficients, respectively.
Optionally, taking the new attribute corresponding to the maximum fitting correlation coefficient as the target attribute includes:
fitting each new attribute curve through a binomial formula to obtain a group of binomial constant coefficients;
bringing each set of constant coefficients and the corresponding new attribute curve into a binomial formula, and calculating to obtain a new permeability prediction curve corresponding to each new attribute curve;
and according to a correlation coefficient formula, calculating the correlation coefficient of each new permeability prediction curve and the known permeability logging curve respectively, and taking the new attribute corresponding to the maximum value of the correlation number as the target attribute.
In step 105, determining constituent elements of the target attribute, and converting a pre-stack seismic elastic impedance inversion data volume obtained by pre-stack seismic inversion into a permeability data volume according to the constituent elements, including:
screening the target attribute to determine a constituent element of the target attribute, wherein the constituent element comprises: angle information, mathematical transformation information, angle rotation combination information and specific value of binomial fitting constant coefficient of elastic impedance;
determining an angle adopted by inversion of the elastic impedance of the pre-stack earthquake according to the angle information of the elastic impedance;
transforming an elastic impedance inversion data body obtained by pre-stack seismic inversion according to the mathematical transformation information;
performing angle rotation combination on the elastic impedance inversion data body subjected to the data transformation by using the angle rotation combination information;
and (3) taking the binomial fit constant coefficient into the data volume after the angle rotation combination to a binomial formula to obtain the permeability data volume.
The application is described below using a reservoir section of a study area as an example:
the log of a reservoir section of a region of interest is shown in figure 3, where permeability is higher at the 7340 m to 7370 m section of the reservoir section of the region of interest as can be seen from figure 3.
Acquiring a longitudinal wave velocity curve, a transverse wave velocity curve, a density curve and a prestack seismic angle trace set, wherein the incidence angle range of seismic waves in the prestack seismic angle trace set is-90 degrees;
in the range of the incident angle of the seismic wave, an elastic impedance curve is generated at intervals of 5 degrees according to the longitudinal wave velocity curve, the transverse wave velocity curve and the density curve, and an elastic impedance curve is generated. A total of 36 elastic impedance curves were obtained. Fig. 4 shows the elastic impedance curves of-65 °, 25 °, 40 ° in the examples. It can be seen that there is a large difference in the elastic resistance at different angles between the high permeability reservoir section and the low permeability reservoir section, which represents the difference in sensitivity of the elastic resistance at different angles to reservoir permeability.
The 36 elastic impedance curves were each mathematically transformed as follows: maintain unchanged X (theta) i ) Logarithmic ln (X (θ) i ) Index), index
Reciprocal 1/X (θ) i ) Square X (theta) i ) 2 Prescription->
The set of attribute curves after the record transformation is A, and the total of 216 curves is obtained after the transformation of 36 elastic impedance curves, wherein the nth curve is expressed as A n . Fig. 5 shows a 40 ° elastic impedance curve and a mathematically transformed curve according to an embodiment.
For the 216 curves, the angle rotation combination is carried out according to the following formula:
wherein χ is a rotation angle, and the value is taken from-180 degrees to 180 degrees every 5 degrees, and the total value is 72 angles; a is that p 、A q The p-th attribute curve and the q-th attribute curve in the curve set A are respectively;
the order of the average value of the p-th attribute curve and the q-th attribute curve in the curve set A are respectively; t (T) pq (χ) represents the attribute curve obtained by rotating the χ degree of the p-th and q-th attribute curves in the curve set A.
The 216 curves are rotated by 72 angles in any pair combination, and 1671840 new attribute curves are obtained. The rotation attribute 1 shown in fig. 6 is a new attribute curve obtained by rotating a 40 ° elastic impedance curve X (40 °) and a logarithmic curve ln (X (-65 °)) of 65 ° elastic impedance by 35 ° in the embodiment. The rotation attribute 2 shown in fig. 6 is a new attribute curve obtained by rotating the reciprocal transformation curve 1/(X (-65 °)) of the 40 ° elastic impedance curve X (40 °) and the 65 ° elastic impedance by-60 ° in the example. As can be seen from fig. 6, the new attribute curves obtained with different rotation combinations are more prominent in the high permeability reservoir section.
The 1671840 new attribute curves obtained above are respectively fitted with known permeability log, and a binomial fitting formula is adopted as a fitting formula:
P＝aT 2 pq (χ)+bT pq (χ)+c
where P represents a known permeability log and a, b, c represent binomial constant coefficients, respectively.
1671840 new attribute curves are fitted by binomial terms to obtain 1671840 groups of binomial constant coefficients; bringing each set of constant coefficients and the corresponding new attribute curve into a binomial formula, and calculating to obtain a permeability prediction curve corresponding to each new attribute curve; according to a conventional correlation coefficient formula, calculating correlation coefficients of each permeability prediction curve and a known permeability logging curve respectively to obtain 1671840 correlation coefficients in total, and taking a new attribute corresponding to the maximum value of the phase relation number as a target attribute.
By comparing 1671840 correlation coefficient calculation results, a correlation coefficient maximum value of 0.8555 is obtained, and the corresponding new attribute is a new attribute obtained by combining 35-degree elastic impedance X (35 degrees) and 40-degree elastic impedance logarithm ln (X (40 degrees)) and rotating for-10 degrees. FIG. 7 shows a comparison of the predicted permeability curve and the true permeability log of the new property 1, the new property 2, and the target property of FIG. 6 using the embodiment of the present invention. The correlation coefficient of the permeability curve predicted by the new attribute 1 and the real permeability log is 0.4468, the correlation coefficient of the permeability curve predicted by the new attribute 2 and the real permeability log is 0.6905, and the correlation coefficient of the permeability curve predicted by the target number attribute and the real permeability log reaches 0.8555.
In summary, according to the construction process of the target attribute, the construction element of the target attribute is determined retrospectively, which specifically includes the elastic impedance X (θ) corresponding to the target attribute in the construction process i ) Angle information, mathematical transformation information, angle rotation combination attribute T pq (χ) information, constant coefficients a, b, c of binomial fitThe specific value is taken. The constituent elements of the target attribute determined in fig. 7 in the example of the present invention are: the angle information is 35 degrees and 40 degrees; mathematical transformation information is a constant X (35 °) and a logarithmic transformation ln (X (40 °)); the rotation angle χ is-10 degrees; the fitting constant coefficients a, b and c are respectively 0.001, 87.60 and 65.81.
And (3) carrying out elastic impedance inversion according to the obtained target attribute constituent elements to obtain elastic impedance data of 35 degrees and 45 degrees, as shown in fig. 8 and 9.
Respectively carrying out constant and logarithmic transformation on the obtained elastic impedance data X (35 degrees) and X (40 degrees) of 35 degrees and 45 degrees to obtain X (35 degrees) and ln (X (40 degrees)) data volumes, and then calculating according to a rotation combination formula to obtain a target attribute data volume:
T(-10°)＝X(35°)/F X(35°) sin(-10°)+ln(X(40°))/F ln(X(40°)) cos(-10°)
and then obtaining a final permeability prediction result (namely a permeability data volume) according to a binomial constant coefficient fitting:
P＝0.001T 2 (-10°)+87.60T(-10°)-65.81
and evaluating the permeability of the oil and gas reservoir according to the obtained permeability data body. The method is mainly used for evaluating the permeability and the spatial distribution of the oil and gas reservoir according to the size and the spatial distribution characteristics of permeability data values, the oil and gas reservoir with good permeability corresponds to high permeability data values, and the larger the distribution range of the area with good permeability is, the larger the drilling and oil and gas exploitation values are. FIG. 10 is a graph of predicted permeability data for an embodiment of the present invention, where the area indicated by the dashed oval line in FIG. 10 is a high permeability area, representing a region of better reservoir permeability, and having value in drilling and hydrocarbon recovery.
Based on the same inventive concept, the embodiment of the invention also provides an oil and gas reservoir permeability prediction device, as described in the following embodiment. Because the principle of solving the problem of the oil and gas reservoir permeability prediction device is similar to that of the oil and gas reservoir permeability prediction method, the implementation of the oil and gas reservoir permeability prediction device can be referred to the implementation of the oil and gas reservoir permeability prediction method, and repeated parts are not repeated. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The embodiment of the invention provides an oil and gas reservoir permeability prediction device, which is shown in a figure 2 and comprises the following components:
the information acquisition module 201 is configured to acquire logging information and a pre-stack seismic angle gather, and generate a plurality of elastic impedance curves with different angles according to the logging information and the pre-stack seismic angle gather;
a mathematical transformation module 202, configured to perform multiple mathematical transformations on each elastic impedance curve;
the angle rotation combination module 203 is configured to perform angle rotation recombination on every two curves after mathematical transformation to obtain a plurality of new attribute curves;
the curve fitting module 204 is configured to obtain permeability log curves, respectively perform curve fitting on each new attribute curve and the permeability log curve, and take the new attribute corresponding to the maximum fitting correlation coefficient as the target attribute;
the permeability prediction module 205 is configured to determine constituent elements of a target attribute, convert a pre-stack seismic elastic impedance inversion data body obtained by pre-stack seismic inversion into a permeability data body according to the constituent elements, and predict permeability of the hydrocarbon reservoir using the permeability data body.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium stores a computer program for executing the method.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (11)
1. A method of hydrocarbon reservoir permeability prediction comprising:
acquiring logging information and a pre-stack seismic angle trace set, and generating a plurality of elastic impedance curves with different angles according to the logging information and the pre-stack seismic angle trace set;
performing various mathematical transformations on each elastic impedance curve;
performing angle rotation recombination on every two curves after mathematical transformation to obtain a plurality of new attribute curves;
acquiring permeability logging curves, respectively performing curve fitting on each new attribute curve and the permeability logging curves, and taking the new attribute corresponding to the maximum fitting correlation coefficient as a target attribute;
and determining the constituent elements of the target attribute, converting the pre-stack seismic elastic impedance inversion data body obtained by pre-stack seismic inversion into a permeability data body according to the constituent elements, and predicting the permeability of the oil and gas reservoir by using the permeability data body.
2. The method of hydrocarbon reservoir permeability prediction as set forth in claim 1, wherein the logging information comprises: a longitudinal wave velocity profile, a transverse wave velocity profile and a density profile;
the range of incidence angles of the seismic waves in the prestack seismic angle trace set is-90 degrees;
generating a plurality of elastic impedance curves of different angles according to logging information, including:
and generating an elastic impedance curve at intervals of preset angles according to the longitudinal wave velocity curve, the transverse wave velocity curve and the density curve in the range of the incidence angle of the seismic waves.
3. The method of hydrocarbon reservoir permeability prediction as claimed in claim 2, which comprisesCharacterized in that the elastic impedance curve X (theta i ) The calculation formula of (2) is as follows:
A＝V p ρ；
A 0 ＝V p0 ρ 0 ；
wherein θ i The angle of the incident angle of the seismic wave corresponding to the ith elastic impedance curve; v (V) p The longitudinal wave speed is given in m/s; v (V) s The transverse wave speed is expressed in meters per second; ρ is density in kilograms per cubic meter; v (V) p0 The unit is meter/second for the longitudinal wave speed of the target interval; v (V) s0 The unit is meter/second for the transverse wave speed of the target interval; ρ 0 Is the average value of the density in kilograms per cubic meter; k is the ratio of the transverse wave speed to the longitudinal wave speed, and is dimensionless; m is the average longitudinal wave impedance A of the objective interval 0 On the order of magnitude of (2).
4. The method of hydrocarbon reservoir permeability prediction as set forth in claim 3, wherein said means for mathematically transforming comprises:
the method comprises the following steps of constant logarithmic transformation based on natural indexes, power transformation based on natural indexes, reciprocal transformation, square transformation and evolution transformation.
5. The method of hydrocarbon reservoir permeability prediction as claimed in claim 1, wherein said new attribute curve T pq The formula of (χ) is as follows:
wherein χ is the rotation angle; recording the collection of a plurality of new attribute curves after mathematical transformation as cut-A, A p 、A q The p-th attribute curve and the q-th attribute curve in the curve set curve-A are respectively;
the order of the average value of the p-th attribute curve and the q-th attribute curve in the curve set curve-A are respectively; t (T) pq And (χ) is a new attribute curve obtained by rotating χ degrees of the p and q attribute curves in the curve set curve-A. />
6. The method for predicting permeability of an oil and gas reservoir according to claim 5, wherein a fitting formula adopted for respectively performing curve fitting on each new attribute curve and the permeability log is as follows:
P＝aT 2 pq (χ)+bT pq (χ)+c
where P represents a known permeability log and a, b, c represent binomial fit constant coefficients, respectively.
7. The method of hydrocarbon reservoir permeability prediction as set forth in claim 6, wherein taking as the target attribute the new attribute corresponding to the maximum fitting correlation coefficient, comprising:
each new attribute curve is fitted through a binomial formula to obtain a group of binomial formula fitting constant coefficients;
bringing each set of constant coefficients and the corresponding new attribute curve into a binomial formula, and calculating to obtain a new permeability prediction curve corresponding to each new attribute curve;
and according to a correlation coefficient formula, calculating the correlation coefficient of each new permeability prediction curve and the known permeability logging curve respectively, and taking the new attribute corresponding to the maximum value of the correlation number as the target attribute.
8. The method of hydrocarbon reservoir permeability prediction as set forth in claim 7, wherein determining constituent elements of the target attribute and converting a pre-stack seismic elastance inversion data volume resulting from a pre-stack seismic inversion into a permeability data volume based on the constituent elements, comprises:
screening the target attribute to determine a constituent element of the target attribute, wherein the constituent element comprises: angle information, mathematical transformation information, angle rotation combination information and specific value of binomial fitting constant coefficient of elastic impedance;
determining an angle adopted by inversion of the elastic impedance of the pre-stack earthquake according to the angle information of the elastic impedance;
transforming an elastic impedance inversion data body obtained by pre-stack seismic inversion according to the mathematical transformation information;
performing angle rotation combination on the elastic impedance inversion data body subjected to the data transformation by using the angle rotation combination information;
and (3) taking the data body obtained by combining the binomial fit constant coefficient and the angle rotation into a binomial formula to obtain the permeability data body.
9. An oil and gas reservoir permeability prediction apparatus, comprising:
the information acquisition module is used for acquiring logging information and a prestack seismic angle trace set and generating a plurality of elastic impedance curves with different angles according to the logging information and the prestack seismic angle trace set;
the mathematical transformation module is used for performing various mathematical transformations on each elastic impedance curve respectively;
the angle rotation combination module is used for carrying out angle rotation recombination on every two curves after mathematical transformation to obtain a plurality of new attribute curves;
the curve fitting module is used for acquiring permeability logging curves, respectively performing curve fitting on each new attribute curve and the permeability logging curves, and taking the new attribute corresponding to the maximum fitting correlation coefficient as a target attribute;
and the permeability prediction module is used for determining the constituent elements of the target attribute, converting the pre-stack seismic elastic impedance inversion data body obtained by pre-stack seismic inversion into a permeability data body according to the constituent elements, and predicting the permeability of the oil and gas reservoir by using the permeability data body.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 8 when executing the computer program.
11. 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 8.
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