CN114563816B - Method and device for establishing seismic interpretation velocity model in oil and gas reservoir evaluation stage - Google Patents
Method and device for establishing seismic interpretation velocity model in oil and gas reservoir evaluation stage Download PDFInfo
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
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Abstract
The invention discloses a method and a device for establishing an earthquake interpretation velocity model in a hydrocarbon reservoir evaluation stage, wherein the method comprises the following steps: determining an earthquake average speed, a well point average speed and a well point layer speed, and sampling into a preset construction grid; calculating the grid earthquake average speed and the grid well point average speed; determining a velocity anisotropy coefficient according to the grid seismic average velocity and the grid well point average velocity, and correcting the grid seismic average velocity; converting the corrected seismic average velocity into a corrected seismic layer velocity; sequentially carrying out trend removal processing, space variogram analysis and trend recovery processing on the corrected seismic layer velocity to obtain a corrected layer velocity model; taking the well point layer speed as hard data, correcting a layer speed model as a data trend, and determining a layer speed model body; and inputting the layer velocity model body into a preset seismic interpretation velocity model frame to obtain a seismic interpretation velocity model. The invention can improve the accuracy and reliability of the speed model.
Description
Technical Field
The invention relates to the technical field of development of geology department hydrocarbon reservoir description, in particular to a method and a device for establishing a seismic interpretation velocity model in a hydrocarbon reservoir evaluation stage.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
At present, three-dimensional seismic data are increasingly applied to the aspects of fine structure research, improvement of inter-well prediction precision of geological models and the like in the oil and gas reservoir description stage, and the commonly used three-dimensional seismic data are time domain data, and to apply the aspects, a high-precision three-dimensional velocity model needs to be established at first.
The establishment of a speed model plays an extremely important role in the aspects of oil and gas field exploration, accurate description of oil and gas reservoirs and the like. Generally, the so-called "velocity modeling" can be divided into two main categories, one is velocity modeling for a seismic data processing stage, and the other is velocity modeling for a hydrocarbon reservoir evaluation stage, which are both different and related: the main data sources of velocity modeling of the method are seismic prestack gathers and the like, the main method comprises a modeling method based on superposition velocity analysis, a modeling method based on migration velocity analysis and a velocity modeling method based on tomographic inversion, and the main application field is seismic imaging; the latter velocity modeling data source mainly comprises more drilling synthetic seismic record velocity data, seismic velocity data acquired in a processing stage, VSP velocity data and the like, the main modeling method comprises methods of weighted interpolation, kriging estimation, random simulation, random inversion and the like, and the application field mainly comprises time-depth conversion of a structural layer, domain conversion of a time domain attribute data body, acquisition of low-frequency components of wave impedance inversion and the like.
In recent years, seismic constraint reservoir modeling and velocity modeling are increasingly widely applied, but how to integrate high quality data types with large differences between two scales, namely a shaft velocity and a seismic velocity, is always a hot problem for geophysicists and geologist research. In the early stage of modeling and exploration and development of the well earthquake combined velocity in practical research, various data such as logging calculation velocity and VSP velocity are generally utilized to correct the seismic processing velocity and then directly form a velocity model. In the current well-seismic joint velocity modeling in the oil-gas reservoir evaluation stage, in the process of establishing a velocity trend body, the velocity trend body is utilized to correct the seismic velocity, the correlation between the velocity anisotropy coefficient involved in correction and the depth is low, the depth trend of data is often less considered in the establishment of the corrected seismic velocity trend model, and the limit condition of geostatistics considered in the establishment process is insufficient, so that the reliability of the established seismic interpretation velocity model is still to be improved.
Disclosure of Invention
The embodiment of the invention provides a method for establishing a seismic interpretation velocity model in a hydrocarbon reservoir evaluation stage, which considers the problem of depth trend existing frequently in velocity data in the process of establishing the seismic interpretation velocity model and is used for improving the precision and reliability of the seismic interpretation velocity model, and the method comprises the following steps:
acquiring logging data and seismic data;
determining an average earthquake speed, an average well point speed and a layer speed according to the logging data and the earthquake data;
Sampling the seismic average speed, the well point average speed and the well point layer speed into a preset construction grid;
Calculating grid seismic average velocity according to the seismic average velocity sampled into the construction grid, and calculating grid well point average velocity according to the well point average velocity sampled into the construction grid;
Determining a velocity anisotropy coefficient according to the grid seismic average velocity and the grid well point average velocity, and correcting the grid seismic average velocity by using the velocity anisotropy coefficient to obtain a corrected seismic average velocity;
converting the corrected seismic average velocity into a corrected seismic layer velocity;
trending the corrected seismic layer velocity to obtain a seismic residual layer velocity;
performing space variation function analysis on the seismic residual layer velocity, and establishing a seismic residual layer velocity model;
performing recovery trend treatment on the seismic residual layer velocity model to obtain a correction layer velocity model;
Taking the well point layer speed as hard data, correcting a layer speed model as a data trend, and determining a layer speed model body;
And inputting the layer velocity model body into a preset seismic interpretation velocity model frame to obtain a seismic interpretation velocity model.
The embodiment of the invention also provides a device for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage, which considers the problem of the depth trend existing frequently in the velocity data in the establishment process of the seismic interpretation velocity model and is used for improving the precision and the reliability of the seismic interpretation velocity model, and the device comprises:
the acquisition module is used for acquiring logging data and seismic data;
the determining module is used for determining the average earthquake speed, the average well point speed and the well point layer speed according to the well logging data and the earthquake data;
the sampling module is used for sampling the average earthquake speed, the average well point speed and the well point layer speed into a preset construction grid;
The determining module is also used for calculating the grid seismic average speed according to the seismic average speed sampled into the construction grid and calculating the grid well point average speed according to the well point average speed sampled into the construction grid;
The correction module is used for determining a velocity anisotropy coefficient according to the grid seismic average velocity and the grid well point average velocity, and correcting the grid seismic average velocity by utilizing the velocity anisotropy coefficient to obtain a corrected seismic average velocity;
the velocity conversion module is used for converting the corrected seismic average velocity into corrected seismic layer velocity;
the processing module is used for carrying out trend removal processing on the corrected seismic layer velocity to obtain a seismic residual layer velocity;
the processing module is also used for carrying out space variation function analysis on the seismic residual layer speed and establishing a seismic residual layer speed model;
the processing module is also used for carrying out recovery trend processing on the seismic residual layer velocity model to obtain a correction layer velocity model;
the determining module is also used for taking the well point layer speed as hard data, correcting the layer speed model as a data trend and determining a layer speed model body;
The model construction module is used for inputting the layer velocity model body into a preset seismic interpretation velocity model frame to obtain a seismic interpretation velocity model.
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 method for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage is realized when the processor executes the computer program.
The embodiment of the invention also provides a computer readable storage medium which stores a computer program for executing the method for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage.
The method and the device for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage provided by the embodiment of the invention utilize the seismic data and the logging data as data sources to determine the average seismic velocity, the average well point velocity and the well point layer velocity, and then sample the velocities into a construction grid to determine the average grid seismic velocity and the average grid well point velocity; then, acquiring a velocity anisotropy coefficient relation according to the grid seismic average velocity and the grid well point average velocity, and correcting the grid seismic average velocity; the corrected seismic average velocity is converted into corrected seismic layer velocity, the situation that the target interval changes in the longitudinal direction is considered, meanwhile, velocity data generally has obvious vertical trend (namely depth trend), a residual layer velocity model is built after vertical trending treatment is carried out on the corrected seismic layer velocity, then the model is subjected to recovery trend treatment to obtain a corrected layer velocity model, then the corrected layer velocity model is used as trend data to restrain well data (namely well point layer velocity) to build a layer velocity model body, and finally the layer velocity model body is used to obtain a final seismic interpretation velocity model, so that integration of well and earthquake different-scale velocity data is realized, the problem that the velocity field in areas with rapid velocity change needs to keep the seismic velocity trend and well point velocity consistent and meanwhile the velocity deviation between wells is as small as possible is well solved, the velocity modeling precision in the oil and gas reservoir evaluation stage is effectively improved, and the reliability of a three-dimensional structure map is guaranteed.
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 flow chart of a method for establishing a seismic interpretation velocity model during a reservoir evaluation phase in accordance with an embodiment of the present invention;
FIG. 2 is a flowchart of a specific method for implementing step 102 in an embodiment of the present invention;
FIG. 3 is a schematic diagram of an average velocity of an earthquake and an average velocity of a well point calculated from well log data and seismic data of a well in a work area according to an embodiment of the present invention;
FIG. 4 is a flowchart of a specific implementation method for determining a velocity anisotropy coefficient according to a grid seismic average velocity and a grid well point average velocity in step 105, and correcting the grid seismic average velocity by using the velocity anisotropy coefficient to obtain a corrected seismic average velocity in the embodiment of the invention;
FIG. 5 (a) is a diagram illustrating the correlation between the root mean square velocity and the average velocity according to the embodiment of the present invention;
FIG. 5 (b) is a diagram illustrating the correlation between layer velocity and average velocity in an embodiment of the present invention;
FIG. 5 (c) is a graph showing the correlation between the root mean square velocity and the layer velocity according to the embodiment of the present invention;
FIG. 6 (a) is a schematic diagram showing the relationship between the average velocity anisotropy coefficient of a DP work area and the two-pass time variation according to the embodiment of the invention;
FIG. 6 (b) is a schematic diagram showing a relationship between the layer velocity anisotropy coefficient of the DP work area and the two-pass time variation according to the embodiment of the invention;
FIG. 7 is a flow chart of a method for establishing a seismic interpretation velocity model during another reservoir evaluation phase in accordance with an embodiment of the present invention;
FIG. 8 is a schematic diagram of an anisotropic correction curve obtained by combining the DBSCAN algorithm with a cubic spline interpolation method to obtain an anisotropic correction function according to an embodiment of the present invention;
FIG. 9 (a) is a diagram showing the correlation between the velocity anisotropy coefficient determined by the linear function fitting method and the two-pass time;
FIG. 9 (b) is a diagram showing the correlation between the velocity anisotropy coefficient determined by the exponential function fitting method and the two-pass time;
FIG. 9 (c) is a diagram showing the correlation of velocity anisotropy coefficients determined using DBSCAN and cubic spline interpolation methods with double pass;
FIG. 10 is a schematic diagram of a device for establishing a seismic interpretation velocity model at a reservoir evaluation stage 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 establishing a seismic interpretation velocity model in an oil and gas reservoir evaluation stage, which is suitable for a working area which can provide seismic migration root mean square velocity, layer velocity or average velocity with more accurate velocity trend and has certain well point synthetic record data, and has better adaptability to research areas in the middle and later stages of exploration and development stages.
As shown in fig. 1, the method includes steps 101 to 111:
Step 101, acquiring logging data and seismic data.
Step 102, determining the average earthquake speed, the average well point speed and the well point layer speed according to the well logging data and the earthquake data.
In the embodiment of the present invention, as shown in fig. 2, step 102 may be performed as steps 1021 to 1024 as follows:
Step 1021, determining a seismic velocity spectrum according to the seismic data.
In one implementation, a seismic velocity spectrum may be determined from the seismic data; in another implementation, the seismic velocity spectrum may also be acquired directly, since the velocity spectrum has been determined prior to entering the reservoir evaluation phase.
And 1022, performing de-coding on the seismic velocity spectrum to obtain the three-dimensional seismic migration root mean square velocity.
In the embodiment of the invention, common seismic velocity spectrum formats such as Paradigm format, omega format and the like can be processed. After the decompression, the root mean square velocity is converted into the seismic layer velocity, the seismic average velocity and the like through a Dix formula.
The formula for converting the root mean square velocity to the seismic layer velocity is as follows:
Wherein V i represents the layer speed of the i-th layer, i=1, 2,3, n; v R,i、VR,i-1 represents the root mean square speed of the i layer and the i-1 layer respectively; t 0,i、t0,i-1 represents the double-pass reflection time of the ith layer and the i-1 th layer respectively.
The formula for converting the seismic layer velocity to the seismic average velocity is as follows:
Wherein V av represents the seismic average velocity of the i-th layer; t i denotes the two-pass time interval of the i-th layer.
The method can also consider that the seismic layer velocity is used for subsequent establishment of a seismic interpretation velocity model at a place with small change of the upper and lower velocities of the stratum, the layer velocity can better retain the detail of the velocity change and is finally converted into the average velocity for domain conversion, but the velocity change of the layer is intense and the regularity is worse than the average velocity under the normal condition, so that the seismic average velocity is utilized in the embodiment of the invention.
The data such as superposition speed, root mean square speed and the like obtained by seismic velocity spectrum de-coding reflect the overall trend of underground velocity field change, and the velocity spectrum can ensure better transverse trend. The grid step length on the velocity spectrum velocity data plane is generally 200m multiplied by 200m to 1000 multiplied by 1000m, and a velocity spectrum with larger data density after interpolation can be provided for a specific work area seismic data processing department. The speed spectrum speed data is manually picked up by taking the speed trend control as a principle, the longitudinal interval of the picked speed data is 50-500 ms or more, the sampling interval of the output seismic speed spectrum data is 20-100 ms, and the vertical resolution of the speed is far lower than the calculation speed of a logging curve.
Step 1023, calculating the seismic average velocity according to the three-dimensional seismic migration root mean square velocity.
Step 1024, performing well synthesis record analysis by using the seismic data and the logging data, and obtaining the average well point speed and the well point layer speed.
Wherein the average well point velocity is the average well point velocity along the well trajectory. The analysis of the synthesis record on the well is a common technical means in the field, and detailed description is omitted here for the specific implementation of this step.
After executing step 102 and before executing step 103, the method can also use the seismic data and the logging data to carry out the analysis of the synthetic record on the well so as to obtain the time-depth relation of the well point; performing time depth relation consistency check on the time depth relation of the well points to obtain check results; if the checking result is that the time-depth relation is inconsistent, correcting the time-depth relation of the well points, and re-determining the average speed of the well points and the layer speed of the well points after the time-depth relation is corrected.
The well point layer velocity obtained by the well synthesis record has high vertical resolution, is a seismic sampling interval, and has the defects of large planar well spacing and extremely uneven distribution. And placing the time-depth relation after the key well is completed to be recorded in the same coordinate system for checking the consistency of the time-depth relation. When the time-depth relation of the wells in the work area is basically coincident, the time-depth relation of the synthetic record analysis is basically reliable, otherwise, the reasons of abnormal speed are required to be searched and analyzed, and abnormal conditions are eliminated.
Further quality control, the consistency of the relation analysis speed between the construction elevation of each construction layer well point and the double-pass time can be extracted, and the principle is similar to that described above, and the description is omitted here.
And 103, sampling the seismic average speed, the well point average speed and the well point layer speed into a preset construction grid.
The point data with discrete space is sampled into a designed construction grid, so that subsequent correction and modeling work are facilitated. E.g., DP work area, a construction grid is built in the time domain, 50m x 4ms in size, and then data sampling is performed.
It should be noted that the discrete points in space are denser, and the number of sampled points is smaller than the number of spatially discrete points, that is, the points sampled into the construction grid are part of the discrete points in space. The design of the construction grid and sampling the speed into the construction grid are common technical means in the art, and detailed description of the specific implementation process of this step is omitted here.
And 104, calculating the grid seismic average speed according to the seismic average speed sampled into the construction grid, and calculating the grid well point average speed according to the well point average speed sampled into the construction grid.
Since the seismic average velocity and some of the well point average velocity are sampled into the formation grid, the grid seismic average velocity and the grid well point average velocity are re-determined for facilitating subsequent calculations.
And 105, determining a velocity anisotropy coefficient according to the grid seismic average velocity and the grid well point average velocity, and correcting the grid seismic average velocity by using the velocity anisotropy coefficient to obtain a corrected seismic average velocity.
In practical application, the average well point velocity calculated by the well point synthesis record of the research target layer is generally smaller than the average earthquake velocity calculated by the earthquake velocity spectrum, as shown in fig. 3, fig. 3 is a schematic diagram of the average earthquake velocity and the average well point velocity calculated according to the well logging data and the earthquake data of a certain well in a certain work area, wherein the dotted line marked 1 is the average earthquake velocity, the dotted line marked 2 is the average well point velocity, and it can be obviously seen that the average well point velocity is smaller than the average earthquake velocity at most times. In addition, T4, T5, tr marks in FIG. 3 are corresponding seismic horizons.
The average velocity of well points is smaller than the average velocity of earthquakes, because the center frequency of acoustic logging is generally 20KHz, which is much higher than the frequency of earthquakes, and the propagation velocity of waves with different frequencies has a dispersion effect and has anisotropic characteristics. Therefore, when using the seismic average velocity, it is generally necessary to perform anisotropic correction of the seismic average velocity first, so as to correct the seismic average velocity to the same level as the average velocity of the well point.
Specifically, as shown in fig. 4, step 105 may be performed as steps 1051 to 1054 as follows:
step 1051, determining the ratio of the grid well point average speed to the grid seismic average speed as a speed anisotropy coefficient.
The formula for calculating the velocity anisotropy coefficient is as follows:
Fang_ani＝vavg_well/vavg_seis
Wherein F ang_ani represents a velocity anisotropy coefficient; v avg_well represents the grid well point average velocity value; v avg_seis denotes the grid seismic average velocity.
Step 1052, calculate the cluster center of the velocity anisotropy coefficient using Density-based noisy spatial clustering (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) algorithm.
And 1053, performing cubic spline curve interpolation on the clustering center to obtain the functional relation between the anisotropic coefficient and the double-pass time.
The functional relationship between the anisotropy coefficient and the double pass F ang_ani(vavg_seis,vavg_well, TWT) may be abbreviated as F ani (TWT), where TWT is the double pass.
There are various ways to calculate F ani (TWT), such as polynomial fitting, piecewise polynomial fitting, etc. In the embodiment of the invention, a DBSCAN algorithm is adopted to calculate the clustering center of the velocity anisotropy coefficient, and then the clustering center is subjected to cubic spline curve interpolation to obtain the F ani (TWT) function relation. Based mainly on the following considerations:
When the stratum has abnormal geologic bodies, lithology mutation and the like, the upper stratum and the lower stratum and different areas of the same stratum have obvious speed difference, the overall polynomial fitting can obtain a smooth curve, but the speed change of the upper stratum and the lower stratum is difficult to accurately express, so that the fitting error is large; the piecewise polynomial function can sometimes reduce local fitting errors, but has the defect that function curves at piecewise nodes are not smooth, artificial difference interfaces can occur, and geological judgment is misled; the embodiment of the invention combines the DBSCAN algorithm with the cubic spline interpolation method to establish the anisotropic correction curve, so that the calculation result has a plurality of excellent properties such as small error, smooth curve, continuous curvature and the like.
The definition of a cubic spline interpolation function is that n points (x i,yi) (i=1, 2, …, n) on a plane are known, where x 1<x2<…<xn, these points are called sample points. If a function S (x) satisfies the following 3 conditions, then S (x) is called a cubic spline function that passes through the n points:
1) S (x i)＝yi (i=1, 2, …, n), i.e. the function passes through the sample points;
2) S (x) is a cubic polynomial over each subinterval [ x i,xi+1 ]
S(x)＝ci1(x-xi)3+ci2(x-xi)2+ci3(x-xi)+ci4
Where c i1、ci2、ci3、ci4 is the coefficient of uncertainty of the cubic spline function.
3) S (x) has successive first and second derivatives over the whole interval.
And 1054, correcting the grid seismic average velocity by utilizing the functional relation between the velocity anisotropy coefficient and the double-pass time to obtain the corrected seismic average velocity.
Specifically, the corrected seismic average velocity V avg_seis_m is calculated using V avg_seis_m＝Vavg_seis×Fani (TWT); wherein V avg_seis represents the grid seismic average velocity; f ani (TWT) represents the velocity anisotropy coefficient as a function of the double pass.
The method in the steps 1051 to 1054 is utilized to correct the grid seismic average velocity, thereby improving the velocity correction precision of the target layer and being more beneficial to the targeted construction research of the target layer in the evaluation stage of the oil and gas reservoir and the domain conversion during the seismic constraint modeling.
Step 106, converting the corrected seismic average velocity into corrected seismic layer velocity.
In this step, the corrected seismic average velocity may be converted to a corrected rms velocity according to a statistical relationship between the average velocity and the rms velocity, and then the corrected seismic layer velocity may be calculated. Because in practical applications, the average speed and the root mean square speed generally have a high correlation, while other speed relationships tend to have a poor correlation. By way of example, fig. 5 (a) shows the correlation between the root mean square velocity and the average velocity, fig. 5 (b) shows the correlation between the layer velocity and the average velocity, and fig. 5 (c) shows the correlation between the root mean square velocity and the layer velocity, as can be seen from fig. 5 (a) to fig. 5 (c), the correlation between the average velocity and the root mean square velocity is higher.
After correcting the grid seismic average velocity, the corrected seismic average velocity is used to calculate the corrected seismic layer velocity, and the correlation between the average velocity and the velocity anisotropy coefficient in the longitudinal direction and the double-pass is far stronger than the correlation between the layer velocity anisotropy coefficient and the double-pass in practical application. By way of example, fig. 6 (a) shows the average speed anisotropy coefficient of the DP work area as a function of time in two passes, and fig. 6 (b) shows the layer speed anisotropy coefficient of the DP work area as a function of time in two passes, and it can be seen from fig. 6 (a) and 6 (b) that the correlation between the average speed anisotropy coefficient and the time in two passes is much stronger than the correlation between the layer speed anisotropy coefficient and the time in two passes.
The method for converting the average velocity into the layer velocity is a mature method in the prior art, so a detailed implementation process for converting the corrected seismic average velocity into the corrected seismic layer velocity will not be described herein.
And 107, performing trend removal processing on the corrected seismic layer velocity to obtain a seismic residual layer velocity.
There are various methods for analyzing the trend of the layer speed along the vertical direction, and the embodiment of the invention adopts a simpler deterministic processing method: a functional relationship V int0 (TWT) between grid seismic velocity and two-pass time is found, from which a vertical trend data volume V int0 can be created. Trending the seismic layer velocity, wherein the formula is as follows:
Vint_seis_res＝Vint_seis_m-Vint0
Where V int_seis_res represents the seismic residual layer velocity (grid data along the well trace); v int0 represents the volume of seismic layer velocity vertical trend data (grid data along the well trajectory).
And 108, performing space variance function analysis on the seismic residual layer velocity, and establishing a seismic residual layer velocity model.
Methods for spatial variogram analysis and establishment of residual layer velocity models are provided in the prior art and are not described in detail herein.
And 109, carrying out recovery trend processing on the seismic residual layer velocity model to obtain a correction layer velocity model.
Specifically, the recovery trend processing is carried out on the seismic residual layer velocity model according to the following method:
Vint_seis_3d＝Vint_seis_res_3d+Vint0_3d
Wherein V int_seis_3d represents a three-dimensional correction layer velocity model; v int_seis_res_3d denotes the seismic residual layer velocity model; v int0_3d three-dimensional seismic layer velocity vertical trend data volume.
And 110, using the well point layer speed as hard data, correcting the layer speed model as a data trend, and determining the layer speed model body.
And 111, inputting the layer velocity model body into a preset seismic interpretation velocity model frame to obtain a seismic interpretation velocity model.
After the execution of step 111, the seismic interpretation velocity model is obtained, as shown in fig. 7, the following steps 701 to 703 may also be executed:
And 701, performing domain conversion on the seismic interpretation velocity model to obtain a domain conversion result.
Step 702, comparing the domain conversion result with the average well point speed, and determining an error between the domain conversion result and the average well point speed.
And 703, if the error is greater than a preset error threshold, reestablishing a seismic residual layer velocity model, carrying out subsequent processing according to the reestablished seismic residual layer velocity model, and reestablishing a seismic interpretation velocity model.
That is, comparing the domain conversion result with the well data, if the error between the domain conversion result and the average speed of the well point is greater than the preset error threshold, determining that the established seismic interpretation speed model does not meet the requirement, checking the seismic residual layer speed model, returning to step 108 to further improve the quality of the seismic residual layer speed model, and repeating the steps 108 to 111 until the obtained seismic interpretation speed model meets the precision requirement.
After steps 701 to 703 are completed, the integration of the speed data of different scales of the well and the earthquake is realized, the difficult problems that the speed field in the area with rapid speed change needs to keep the earthquake speed trend and the well point speed consistent and meanwhile the speed deviation among the wells is as small as possible are well solved, the speed modeling precision in the oil and gas reservoir evaluation stage is effectively improved, the reliability of the three-dimensional structure diagram is ensured, and the domain conversion precision is improved. The method can remarkably improve the precision of simple inter-well velocity interpolation or simple seismic velocity well point correction and other methods in the conventional time-depth conversion work and the inter-well correct velocity trend function, and has stronger overall operability, flow controllability and visual function.
After the domain conversion is finished, the overall accuracy of the seismic interpretation velocity model is improved, the change trend of the seismic interpretation velocity model is not too great when the seismic interpretation velocity model is applied to the domain conversion, but the local well points need to be further finely adjusted in the depth domain when the map and the data volume are finely constructed.
The method for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage provided by the embodiment of the invention utilizes seismic data and logging data as data sources to determine the average seismic velocity, the average well point velocity and the well point layer velocity, and then samples the velocities into a construction grid to determine the average grid seismic velocity and the average grid well point velocity; then, acquiring a velocity anisotropy coefficient relation according to the grid seismic average velocity and the grid well point average velocity, and correcting the grid seismic average velocity; the corrected seismic average velocity is converted into corrected seismic layer velocity, the situation that the target interval changes in the longitudinal direction is considered, meanwhile, velocity data generally has obvious vertical trend (namely depth trend), a residual layer velocity model is built after vertical trending treatment is carried out on the corrected seismic layer velocity, then the model is subjected to recovery trend treatment to obtain a corrected layer velocity model, then the corrected layer velocity model is used as trend data to restrain well data (namely well point layer velocity) to build a layer velocity model body, and finally the layer velocity model body is used to obtain a final seismic interpretation velocity model, so that integration of well and earthquake different-scale velocity data is realized, the problem that the velocity field in areas with rapid velocity change needs to keep the seismic velocity trend and well point velocity consistent and meanwhile the velocity deviation between wells is as small as possible is well solved, the velocity modeling precision in the oil and gas reservoir evaluation stage is effectively improved, and the reliability of a three-dimensional structure map is guaranteed.
In the embodiment of the invention, based on petroleum geologic modeling software Petrel2015 and Matlab language calculation program, a DP work area of a Western Qida basin in China is selected for seismic interpretation speed model modeling, and compared with conventional speed modeling methods. The modeling process is described below.
The DP working area is a three-dimensional earthquake working area deployed in 2012 before the Alarn mountain of the Qidamu basin, the primary coverage area is 452.4km 2, the target layer is bedrock, the top surface burial depth of the bedrock is 1830-4600 m, and the height difference is 2770m.
Sampling the average earthquake velocity obtained by calculating the earthquake velocity spectrum, the average well point velocity and the well point layer velocity obtained by calculating the well synthesis record into a construction grid, and determining the grid average earthquake velocity and the well point average earthquake velocity.
Then, the average velocity and velocity anisotropy coefficients are obtained by using the grid seismic average velocity and the well point seismic average velocity, the obtained velocity anisotropy coefficients are shown in fig. 6 (a), and an anisotropic correction function is obtained by combining a DBSCAN algorithm with a cubic spline interpolation method, so that an anisotropic correction curve is obtained, as shown in fig. 8. The results of fig. 8 show that a total of 5 noise points far from the cluster center are generated when DBSCAN is used for cluster analysis. The effect of the method is compared with that of the conventional fitting method, and is shown in fig. 9 (a) to 9 (c). Fig. 9 (a) is a schematic diagram of the correlation between the velocity anisotropy coefficient determined by the linear function fitting method and the two-way time, fig. 9 (b) is a schematic diagram of the correlation between the velocity anisotropy coefficient determined by the exponential function fitting method and the two-way time, and fig. 9 (c) is a schematic diagram of the correlation between the velocity anisotropy coefficient determined by the DBSCAN and the cubic spline interpolation method and the two-way time.
The error of the correlation between the velocity anisotropy coefficient and the two-pass time determined by using the three methods of fig. 9 (a) to 9 (c) is shown in the following table. In error analysis, the noise points in fig. 8 are considered correspondingly, and the result shows that the error of the residual square sum whether the noise points are considered or not is obviously smaller than that of the linear function fitting method and the exponential function fitting method by the DBSCAN and the cubic spline interpolation method, and the anisotropic curve passes through the clustering center, so that the speed deviation problem caused by factors such as reservoir heterogeneity and the like when the construction height difference is large is more accurately described.
List one
The seismic average velocity data is corrected to the average velocity level of the well point, the corrected seismic average velocity is converted into the seismic corrected root mean square velocity by utilizing the relation between the average velocity and the root mean square velocity, the corrected seismic layer velocity is further calculated, and a corrected layer velocity model is established by a geostatistical modeling method.
After the correction layer velocity model is established, the model is used as the input of velocity modeling, and the seismic interpretation velocity model of the research area is established. The seismic interpretation velocity model can obtain an average velocity model data body, the obtained average velocity model is used for domain conversion, the domain conversion result is compared with a selected typical well, the result is shown in a second table, and the reference table shows that the accuracy of the time-depth domain conversion of the velocity model method adopted by the invention is obviously improved compared with that of the conventional method.
Watch II
The embodiment of the invention also provides a device for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage, as described in the following embodiment. Because the principle of the device for solving the problems is similar to that of the method for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage, the implementation of the device can refer to the implementation of the method for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage, and the repetition is omitted.
As shown in fig. 10, the apparatus 1000 includes an acquisition module 1001, a determination module 1002, a sampling module 1003, a correction module 1004, a speed conversion module 1005, a processing module 1006, and a model construction module 1007.
Wherein, the acquiring module 1001 is configured to acquire logging data and seismic data;
a determining module 1002, configured to determine an average seismic velocity, an average well point velocity, and a layer velocity according to the logging data and the seismic data;
the sampling module 1003 is configured to sample the seismic average velocity, the well point average velocity and the well point layer velocity into a preset construction grid;
the determining module 1002 is further configured to calculate a grid seismic average velocity according to the seismic average velocity sampled into the construction grid, and calculate a grid well point average velocity according to the well point average velocity sampled into the construction grid;
The correction module 1004 is configured to determine a velocity anisotropy coefficient according to the grid seismic average velocity and the grid well point average velocity, and correct the grid seismic average velocity by using the velocity anisotropy coefficient to obtain a corrected seismic average velocity;
A velocity conversion module 1005 for converting the corrected seismic average velocity to a corrected seismic layer velocity;
A processing module 1006, configured to perform trend removal processing on the corrected seismic layer velocity to obtain a seismic residual layer velocity;
The processing module 1006 is further configured to perform a spatial variation function analysis on the seismic residual layer velocity, and establish a seismic residual layer velocity model;
The processing module 1006 is further configured to perform recovery trend processing on the seismic residual layer velocity model to obtain a corrected layer velocity model;
The determining module 1002 is further configured to determine a layer speed model body by using the well point layer speed as hard data and the corrected layer speed model as a data trend;
The model construction module 1007 is configured to input the layer velocity model body into a preset seismic interpretation velocity model frame to obtain a seismic interpretation velocity model.
In one implementation of the embodiment of the present invention, the determining module 1002 is configured to:
determining a seismic velocity spectrum from the seismic data;
Performing de-coding on the seismic velocity spectrum to obtain a three-dimensional seismic migration root mean square velocity;
Calculating the seismic average speed according to the three-dimensional seismic migration root mean square speed;
and (5) carrying out uphole synthetic record analysis by using the seismic data and the logging data, and obtaining the average speed of well points and the layer speed of well points.
In one implementation of the embodiment of the present invention, the determining module 1002 is further configured to:
carrying out uphole synthetic record analysis by utilizing the seismic data and the logging data, and obtaining the time-depth relation of well points;
Performing time depth relation consistency check on the time depth relation of the well points to obtain check results;
if the checking result is that the time-depth relation is inconsistent, correcting the time-depth relation of the well points, and re-determining the average speed of the well points and the layer speed of the well points after the time-depth relation is corrected.
In one implementation of the embodiment of the present invention, the correction module 1004 is configured to:
Determining the ratio of the average speed of the grid well points to the average speed of the grid earthquake as a speed anisotropy coefficient;
Calculating a clustering center of the speed anisotropy coefficient by using a DBSCAN algorithm;
performing cubic spline curve interpolation on the clustering center to obtain a functional relation between each variability coefficient and the double-pass time;
and correcting the grid seismic average velocity by utilizing the functional relation between the velocity anisotropy coefficient and the double-pass time to obtain the corrected seismic average velocity.
In one implementation of the embodiment of the present invention, the correction module 1004 is configured to:
calculating a corrected seismic average velocity V avg_seis_m using V avg_seis_m＝Vavg_seis×Fani (TWT);
Wherein V avg_seis represents the grid seismic average velocity; f ani (TWT) represents the velocity anisotropy coefficient as a function of the double pass.
In one implementation of an embodiment of the present invention, the apparatus 1000 further includes:
The domain conversion module 1008 is configured to perform domain conversion on the seismic interpretation velocity model to obtain a domain conversion result;
A comparison module 1009, configured to compare the domain conversion result with the average well point speed, and determine an error between the domain conversion result and the average well point speed;
The processing module 1006 is further configured to reestablish a seismic residual layer velocity model when the error is greater than a preset error threshold, and call the determining module 1002 and the model constructing module 1007 to perform subsequent processing according to the reestablished seismic residual layer velocity model, and reestablish a seismic interpretation velocity model.
The device for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage provided by the embodiment of the invention utilizes seismic data and logging data as data sources to determine the average seismic velocity, the average well point velocity and the well point layer velocity, and then samples the velocities into a construction grid to determine the average grid seismic velocity and the average grid well point velocity; then, acquiring a velocity anisotropy coefficient relation according to the grid seismic average velocity and the grid well point average velocity, and correcting the grid seismic average velocity; the corrected seismic average velocity is converted into corrected seismic layer velocity, the situation that the target interval changes in the longitudinal direction is considered, meanwhile, velocity data generally has obvious vertical trend (namely depth trend), a residual layer velocity model is built after vertical trending treatment is carried out on the corrected seismic layer velocity, then the model is subjected to recovery trend treatment to obtain a corrected layer velocity model, then the corrected layer velocity model is used as trend data to restrain well data (namely well point layer velocity) to build a layer velocity model body, and finally the layer velocity model body is used to obtain a final seismic interpretation velocity model, so that integration of well and earthquake different-scale velocity data is realized, the problem that the velocity field in areas with rapid velocity change needs to keep the seismic velocity trend and well point velocity consistent and meanwhile the velocity deviation between wells is as small as possible is well solved, the velocity modeling precision in the oil and gas reservoir evaluation stage is effectively improved, and the reliability of a three-dimensional structure map is guaranteed.
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 method for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage is realized when the processor executes the computer program.
The embodiment of the invention also provides a computer readable storage medium which stores a computer program for executing the method for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage.
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.
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