CN114563816B - Method and device for establishing earthquake interpretation velocity model in oil and gas reservoir evaluation stage - Google Patents

Abstract

The present invention discloses a method and device for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage, wherein the method comprises: determining the seismic average velocity, the well point average velocity and the well point layer velocity, and sampling them into a preset structural grid; calculating the grid seismic average velocity and the grid well point average velocity; determining the 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 the corrected seismic layer velocity; sequentially performing detrending processing, spatial variogram analysis and trend recovery processing on the corrected seismic layer velocity to obtain a corrected layer velocity model; taking the well point layer velocity as hard data and the corrected layer velocity model as data trend to determine the layer velocity model body; inputting the layer velocity model body into a preset seismic interpretation velocity model framework to obtain a seismic interpretation velocity model. The present invention can improve the accuracy and reliability of the velocity model.

Description

Method and device for establishing seismic interpretation velocity model in oil and gas reservoir evaluation stage

Technical Field

The invention relates to the technical field of oil and gas reservoir description in the development of geological disciplines, and in particular to a method and device for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage.

Background technique

This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No description herein is admitted to be prior art by inclusion in this section.

At present, 3D seismic data is increasingly used in the detailed structural research in the oil and gas reservoir description stage and to improve the inter-well prediction accuracy of geological models. The commonly used 3D seismic data is time domain data. To carry out these applications, it is first necessary to establish a high-precision 3D velocity model.

The establishment of velocity model plays an extremely important role in oil and gas field exploration and accurate description of oil and gas reservoirs. Usually, the so-called "velocity modeling" can be divided into two categories, one is the velocity modeling for the seismic data processing stage, and the other is the velocity modeling for the oil and gas reservoir evaluation stage. The two are both different and related: the former's velocity modeling mainly comes from seismic pre-stack gathers, etc. The main methods include modeling methods based on stacking velocity analysis, modeling methods based on offset velocity analysis, and velocity modeling methods based on tomography inversion. The main application field is seismic imaging; the latter's velocity modeling mainly comes from a large number of drilling synthetic seismic record velocity data, seismic velocity data obtained in the processing stage, VSP velocity data, etc. The main modeling methods include weighted interpolation, Kriging estimation, random simulation and random inversion, etc. The application fields mainly include time-depth conversion at the structural level, domain conversion of time domain attribute data bodies, and obtaining low-frequency components of wave impedance inversion.

In recent years, seismic constrained reservoir modeling and velocity modeling have been increasingly widely used. However, how to integrate the two data types with large scale differences, wellbore velocity and seismic velocity, with high quality has always been a hot topic for geophysicists and development geologists. In actual research, the early stages of exploration and development of this well-seismic combined velocity modeling generally use various data, such as logging velocity calculation and VSP velocity, to directly form a velocity model after correcting the seismic processing velocity. At present, in the well-seismic combined velocity modeling in the oil and gas reservoir evaluation stage, the well velocity is used to correct the seismic velocity in the process of establishing the velocity trend body. The correlation between the velocity anisotropy coefficient involved in the correction and the depth is often low. The establishment of the corrected seismic velocity trend model often rarely considers the depth trend of the data. The geostatistical constraints considered in its establishment process are also insufficient. Therefore, the reliability of the constructed seismic interpretation velocity model needs to be improved.

Summary of the invention

The embodiment of the present invention provides a method for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage. In the process of establishing the seismic interpretation velocity model, the problem of depth trend often existing in velocity data is taken into consideration to improve the accuracy and reliability of the seismic interpretation velocity model. The method includes:

Acquisition of well logging and seismic data;

Determine the average seismic velocity, the average wellpoint velocity and the wellpoint layer velocity based on the well logging data and seismic data;

The seismic average velocity, the well point average velocity and the well point layer velocity are sampled into a preset structural grid;

Calculate the grid seismic average velocity according to the seismic average velocity sampled in the structural grid, and calculate the grid well point average velocity according to the well point average velocity sampled in the structural grid;

Determine the velocity anisotropy coefficient according to the grid seismic average velocity and the grid well point average velocity, and use the velocity anisotropy coefficient to correct the grid seismic average velocity to obtain the corrected seismic average velocity;

Convert the corrected seismic mean velocity into corrected seismic layer velocity;

Detrending the corrected seismic layer velocity, the seismic residual layer velocity is obtained;

Conduct spatial variogram analysis on seismic residual layer velocity and establish seismic residual layer velocity model;

Perform recovery trend processing on the seismic residual layer velocity model to obtain the correction layer velocity model;

Taking the well point layer velocity as hard data and the corrected layer velocity model as data trend, the layer velocity model body is determined;

The layer velocity model body is input into the preset seismic interpretation velocity model framework to obtain the seismic interpretation velocity model.

The embodiment of the present invention also provides a device for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage. In the process of establishing the seismic interpretation velocity model, the problem of depth trend often existing in velocity data is taken into account to improve the accuracy and reliability of the seismic interpretation velocity model. The device includes:

An acquisition module, used to acquire well logging data and seismic data;

A determination module, for determining the average seismic velocity, the average well point velocity and the well point layer velocity according to the well logging data and the seismic data;

A sampling module is used to sample the seismic average velocity, the well point average velocity and the well point layer velocity into a preset structural grid;

The determination module is further used to calculate the grid seismic average velocity according to the seismic average velocity sampled into the structural grid, and calculate the grid well point average velocity according to the well point average velocity sampled into the structural grid;

A correction module is used to determine a velocity anisotropy coefficient according to a grid seismic average velocity and a grid well point average velocity, and to correct the grid seismic average velocity using the velocity anisotropy coefficient to obtain a corrected seismic average velocity;

A velocity conversion module, used for converting the corrected seismic average velocity into a corrected seismic layer velocity;

A processing module is used to perform detrending processing on the corrected seismic layer velocity to obtain the seismic residual layer velocity;

The processing module is also used to perform spatial variogram analysis on the seismic residual layer velocity and establish a seismic residual layer velocity model;

The processing module is also used to perform recovery trend processing on the seismic residual layer velocity model to obtain a correction layer velocity model;

The determination module is also used to determine the layer velocity model body by taking the well point layer velocity as hard data and the corrected layer velocity model as data trend;

The model building module is used to input the layer velocity model body into the preset seismic interpretation velocity model framework to obtain the seismic interpretation velocity model.

An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage is implemented.

An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for executing the method for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage.

The method and device for establishing a seismic interpretation velocity model in the reservoir evaluation stage provided in the embodiment of the present invention use seismic data and well logging data as data sources to determine the seismic average velocity, the well point average velocity and the well point layer velocity, then sample the above velocities into the structural grid to determine the grid seismic average velocity and the grid well point average velocity; then obtain the velocity anisotropy coefficient relationship based on the grid seismic average velocity and the grid well point average velocity, and correct the grid seismic average velocity; convert the corrected seismic average velocity into the corrected seismic layer velocity, taking into account the longitudinal changes of the target layer segment, and the velocity data generally has a very obvious vertical trend (i.e., depth The corrected seismic layer velocity is processed vertically to remove the trend, and then the residual layer velocity model is established. The model is then processed to restore the trend to obtain the corrected layer velocity model. The corrected layer velocity model is then used as trend data to constrain the well data (i.e., well-point layer velocity) to establish a layer velocity model body. The layer velocity model body is then used to obtain the final seismic interpretation velocity model, and the integration of well and seismic velocity data of different scales is achieved. This solves the problem that the velocity field in areas with rapid velocity changes must maintain both the seismic velocity trend and the well-point velocity consistency, while minimizing the inter-well velocity deviation. This effectively improves the velocity modeling accuracy in the reservoir evaluation stage and ensures the reliability of 3D structural mapping.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

FIG1 is a flow chart of a method for establishing a seismic interpretation velocity model in an oil and gas reservoir evaluation stage according to 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;

3 is a schematic diagram of the average seismic velocity and the average well point velocity calculated based on the well logging data and seismic data of a well in a certain work area in an embodiment of the present invention;

4 is a flowchart of a specific execution method of step 105 in an embodiment of the present invention, in which a velocity anisotropy coefficient is determined according to the grid seismic average velocity and the grid well point average velocity, and the grid seismic average velocity is corrected using the velocity anisotropy coefficient to obtain the corrected seismic average velocity;

FIG5( a ) is a schematic diagram of a correlation between a root mean square velocity and an average velocity in an embodiment of the present invention;

FIG5( b ) is a schematic diagram of the correlation between a layer velocity and an average velocity in an embodiment of the present invention;

FIG5(c) is a schematic diagram of a correlation between a root mean square velocity and a layer velocity in an embodiment of the present invention;

FIG6( a ) is a schematic diagram showing the relationship between the average velocity anisotropy coefficient of a DP zone and the two-way time according to an embodiment of the present invention;

FIG6( b ) is a schematic diagram of the relationship between the anisotropy coefficient of the layer velocity in a DP work area and the two-way time according to an embodiment of the present invention;

7 is a flow chart of another method for establishing a seismic interpretation velocity model in the reservoir evaluation stage according to an embodiment of the present invention;

8 is a schematic diagram of an anisotropic correction curve obtained by applying the DBSCAN algorithm in combination with the cubic spline interpolation method to obtain an anisotropic correction function in an embodiment of the present invention;

FIG9( a ) is a schematic diagram showing the correlation between the velocity anisotropy coefficient and the two-pass time determined by the linear function fitting method;

FIG9( b ) is a schematic diagram showing the correlation between the velocity anisotropy coefficient and the two-pass time determined by the exponential function fitting method;

FIG9( c ) is a schematic diagram showing the correlation between the velocity anisotropy coefficient and the two-way time determined using DBSCAN and the cubic spline interpolation method;

FIG. 10 is a schematic diagram of the structure of a device for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage in an embodiment of the present invention.

Detailed ways

To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

An embodiment of the present invention provides a method for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage. The method is suitable for work areas that can provide seismic offset root mean square velocity, layer velocity or average velocity with relatively accurate velocity trends and have certain well point synthetic record data. The method has good adaptability to research areas in the middle and late stages of exploration and the development stage.

As shown in FIG1 , the method includes steps 101 to 111:

Step 101: Acquire well logging data and seismic data.

Step 102: Determine the average seismic velocity, the average wellpoint velocity and the wellpoint layer velocity based on the well logging data and the seismic data.

In the embodiment of the present invention, as shown in FIG. 2 , step 102 may be performed as follows: step 1021 to step 1024:

Step 1021: Determine the seismic velocity spectrum based on the seismic data.

In one implementation, the seismic velocity spectrum may be determined based on seismic data; in another implementation, since the velocity spectrum has been determined before entering the reservoir evaluation stage, the seismic velocity spectrum may also be directly acquired.

Step 1022: decode the seismic velocity spectrum to obtain the three-dimensional seismic migration root mean square velocity.

In the embodiment of the present invention, common seismic velocity spectrum formats can be processed, such as Paradigm format, Omega format, etc. After decompilation, the root mean square velocity is converted into seismic layer velocity, seismic average velocity, etc. by Dix formula.

The formula for converting RMS velocity to seismic layer velocity is as follows:

Wherein, V _i represents the layer velocity of the i-th layer, i=1,2,3,...,n; _VR,i and _VR,i-1 represent the root mean square velocities of the i-th layer and the i-1-th layer respectively; _t0,i and _t0,i-1 represent the two-way reflection times of the i-th layer and the i-1-th layer respectively.

The formula for converting seismic layer velocity to seismic average velocity is as follows:

Where _Vav represents the average earthquake velocity of the i-th layer; _ti represents the two-way time interval of the i-th layer.

In places where the velocity variation between the upper and lower strata is small, the seismic layer velocity may be used to subsequently establish a seismic interpretation velocity model. The layer velocity can better retain the details of the velocity variation and is finally converted into the average velocity for domain conversion. However, the layer velocity usually varies dramatically and its regularity is worse than that of the average velocity. Therefore, in the embodiment of the present invention, the seismic average velocity is used.

The stacking velocity, root mean square velocity and other data obtained by the seismic velocity spectrum decoding reflect the overall trend of the underground velocity field change, and the velocity spectrum can guarantee a good lateral trend. The grid step size on the velocity spectrum velocity data plane is generally 200m×200m～1000×1000m. For a specific work area, the seismic data processing department may provide a velocity spectrum with a large data density after interpolation. Its characteristics are that the data covers the entire work area, which is much larger than the plane range of the well data. The disadvantage is that the vertical data is sparse and the resolution is difficult to guarantee. Generally, the velocity spectrum velocity data is manually picked up based on the principle of being able to control the velocity trend. The vertical interval of the picked velocity data is 50～500ms or even larger. The sampling interval of the output seismic velocity spectrum data is generally 20ms～100ms, which is much lower than the vertical resolution of the velocity calculated by the logging curve.

Step 1023: Calculate the seismic average velocity based on the 3D seismic migration root mean square velocity.

Step 1024: Use seismic data and logging data to perform on-well synthetic record analysis to obtain the well point average velocity and well point layer velocity.

The average velocity of the well point is the average velocity of the well point along the well trajectory. The well synthetic record analysis is a common technical means in this field, and the specific implementation of this step is not described here.

After executing step 102 and before executing step 103, seismic data and logging data can also be used to perform on-well synthetic record analysis to obtain the time-depth relationship of the well point; the time-depth relationship of the well point is checked for consistency to obtain an inspection result; if the inspection result is that the time-depth relationship is inconsistent, the time-depth relationship of the well point is corrected, and the average velocity and layer velocity of the well point are re-determined after the time-depth relationship is modified.

The vertical resolution of the well point layer velocity obtained by the synthetic record on the well is high, which is the seismic sampling interval. The disadvantage is that the plane well spacing is large and the distribution is often extremely uneven. The time-depth relationship after the synthetic record of the key well is placed in the same coordinate system to check the consistency of the time-depth relationship. When the time-depth relationship of the wells in the work area basically coincides, it means that the time-depth relationship analyzed by the synthetic record is basically reliable. Otherwise, it is necessary to find the cause of the velocity anomaly and analyze it to eliminate the abnormal situation.

For further quality control, the relationship between the well point structural elevation and the two-way time at each structural level can be extracted to analyze the velocity consistency. The principle is similar to the above description and will not be repeated here.

Step 103: Sample the seismic average velocity, the well point average velocity and the well point layer velocity into a preset structural grid.

The spatially discrete point data is sampled into the designed structural grid to facilitate subsequent correction and modeling. For example, in the DP work area, a structural grid is established in the time domain with a size of 50m×50m×4ms, and then data sampling is performed.

It should be noted that the discrete points in space are relatively dense, and the number of sampled points is less than the discrete points in space, 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 velocity into the construction grid are commonly used technical means in this field, and the specific implementation process of this step will not be described here.

Step 104: Calculate the grid average seismic velocity based on the average seismic velocity sampled in the structural grid, and calculate the grid average well point velocity based on the average well point velocity sampled in the structural grid.

Since part of the seismic average velocity and the well point average velocity are sampled into the structural grid, in order to facilitate subsequent calculations, the grid seismic average velocity and the grid well point average velocity are re-determined.

Step 105: Determine the velocity anisotropy coefficient according to the grid seismic average velocity and the grid well point average velocity, and use the velocity anisotropy coefficient to correct the grid seismic average velocity to obtain the corrected seismic average velocity.

In practical applications, the average wellpoint velocity calculated by synthetic wellpoint records of the target layer is generally smaller than the average seismic velocity calculated by the seismic velocity spectrum, as shown in Figure 3, which is a schematic diagram of the average seismic velocity and the average wellpoint velocity calculated based on the logging data and seismic data of a well in a certain work area, where the dotted line marked as 1 is the average seismic velocity, and the dotted line marked as 2 is the average wellpoint velocity. It can be clearly seen that the average wellpoint velocity is smaller than the average seismic velocity at most times. In addition, the T4, T5, and Tr lines in Figure 3 are the corresponding seismic horizons.

The average wellpoint velocity is lower than the average seismic velocity. This is because the center frequency of acoustic logging is generally 20KHz, which is much higher than the seismic velocity frequency. The propagation speed of waves of different frequencies has a dispersion effect and anisotropic characteristics. Therefore, when using the average seismic velocity, it is generally necessary to first perform an anisotropy correction on the average seismic velocity, so as to correct the average seismic velocity to the same level as the average wellpoint velocity.

Specifically, as shown in FIG. 4 , step 105 may be performed as follows: step 1051 to step 1054:

Step 1051: determine the ratio of the grid well point average velocity to the grid seismic average velocity as the velocity anisotropy coefficient.

The formula for calculating the velocity anisotropy coefficient is as follows:

F _{ang_ani} = v _{avg_well} / v _{avg_seis}

Where _{Fang_ani} represents the velocity anisotropy coefficient; _{vavg_well} represents the average velocity value of the grid well points; and _{vavg_seis} represents the average grid seismic velocity.

Step 1052: Calculate the cluster center of the velocity anisotropy coefficient using the density-based spatial clustering of applications with noise (DBSCAN) algorithm.

Step 1053: perform cubic spline interpolation on the cluster centers to obtain the functional relationship between the anisotropy coefficients and the two-way time.

The functional relationship between the anisotropy coefficient and the two-way time is _{Fang_ani} (v _{avg_seis} ,v _{avg_well} ,TWT), which can be abbreviated as _Fani (TWT), where TWT is the two-way time.

There are many ways to obtain F _ani (TWT), such as polynomial fitting, piecewise polynomial fitting, etc. The embodiment of the present invention uses the DBSCAN algorithm to obtain the cluster center of the velocity anisotropy coefficient, and then performs cubic spline curve interpolation on the cluster center to obtain the F _ani (TWT) function relationship. It is mainly based on the following considerations:

When there are abnormal geological bodies, lithology mutations, etc. in the strata, there are often obvious velocity differences between the upper and lower strata and between different areas of the same stratum. The overall polynomial fitting can obtain a smooth curve, but it is difficult to accurately express the velocity changes of the upper and lower strata, resulting in large fitting errors. The piecewise polynomial function can sometimes reduce the local fitting error, but the function curve is not smooth at the segmented nodes, and an artificial difference interface will appear, misleading geological judgment. The embodiment of the present invention adopts the DBSCAN algorithm combined with the cubic spline interpolation method to establish an anisotropic correction curve, so that the calculation result has many excellent properties such as small error, smooth curve, and continuous curvature.

The definition of the cubic spline interpolation function is that, given n points (x _i , y _i ) (i = 1, 2, ..., n) on a known plane, where x ₁ <x ₂ < ... <x _n , these points are called sample points. If a function S(x) satisfies the following three conditions, then S(x) is called a cubic spline function passing through these n points:

1) S( _xi ) = _yi (i = 1, 2, ..., n), that is, the function passes through these sample points;

2) S(x) is a cubic polynomial on each subinterval [ _xi , _xi+1 ]

S(x)＝ _ci1 ( _xxi ) ³ + _ci2 ( _xxi ) ² + _ci3 ( _xxi )+ _ci4

In the formula, c _i1 , c _i2 , c _i3 , and c _i4 are the unknown coefficients of the cubic spline function.

3) S(x) has continuous first and second order derivatives over the entire interval.

Step 1054: Correct the grid seismic average velocity using the functional relationship between the velocity anisotropy coefficient and the two-way 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} =V _{avg_seis} ×F _ani (TWT); wherein V _{avg_seis} represents the grid seismic average velocity; and F _ani (TWT) represents the functional relationship between the velocity anisotropy coefficient and the two-way time.

The grid seismic average velocity is corrected using the method in steps 1051 to 1054, thereby improving the velocity correction accuracy of the target layer, and being more conducive to targeted structural research on the target layer and domain conversion during seismic constraint modeling during the oil and gas reservoir evaluation stage.

Step 106: Convert the corrected seismic average velocity into the corrected seismic layer velocity.

In this step, the corrected seismic average velocity can be converted into the corrected root mean square velocity according to the statistical relationship between the average velocity and the root mean square velocity, and then the corrected seismic layer velocity is calculated. Because in practical applications, the average velocity and the root mean square velocity are generally highly correlated, while other velocity relationships are often poorly correlated. For 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. It can be seen from FIG. 5(a) to FIG. 5(c) that the correlation between the average velocity and the root mean square velocity is high.

After correcting the grid seismic average velocity, the corrected seismic average velocity is used to calculate the corrected seismic layer velocity, mainly because in practical applications, the average velocity anisotropy coefficient is generally much more correlated with the two-way time in the vertical direction than the layer velocity anisotropy coefficient is with the two-way time. For example, Figure 6(a) shows the relationship between the average velocity anisotropy coefficient of the DP work area and the two-way time, and Figure 6(b) shows the relationship between the layer velocity anisotropy coefficient of the DP work area and the two-way time. It can be seen from Figure 6(a) and Figure 6(b) that the correlation between the average velocity anisotropy coefficient and the two-way time is much stronger than the correlation between the layer velocity anisotropy coefficient and the two-way time.

The above method of converting the average velocity into the layer velocity is a mature method in the prior art. Therefore, the specific implementation process of converting the corrected seismic average velocity into the corrected seismic layer velocity will not be described in detail here.

Step 107: Detrend the corrected seismic layer velocity to obtain the seismic residual layer velocity.

There are many methods for analyzing the trend of layer velocity along the vertical direction. The embodiment of the present invention adopts a relatively simple deterministic processing method: obtain the functional relationship between the grid seismic velocity and the two-way time _Vint0 (TWT), and create a vertical trend data volume _Vint0 through the functional relationship. Detrending the seismic layer velocity, the formula used is as follows:

V _{int_seis_res} =V _{int_seis_m} -V _int0

Wherein, _{Vint_seis_res} represents the seismic residual layer velocity (grid data along the well trajectory); _Vint0 represents the seismic layer velocity vertical trend data volume (grid data along the well trajectory).

Step 108: Perform spatial variogram analysis on the seismic residual layer velocity and establish a seismic residual layer velocity model.

The methods for analyzing the spatial variogram and establishing the residual layer velocity model have been provided in the prior art and will not be described in detail here.

Step 109: Perform recovery trend processing on the seismic residual layer velocity model to obtain a correction layer velocity model.

Specifically, the seismic residual layer velocity model is processed to restore the trend according to the following method:

V _{int_seis_3d} =V _{int_seis_res_3d} +V _{int0_3d}

Wherein, _{Vint_seis_3d} represents the three-dimensional correction layer velocity model; _{Vint_seis_res_3d} represents the seismic residual layer velocity model; _{Vint0_3d} represents the three-dimensional seismic layer velocity vertical trend data body.

Step 110: Taking the well point layer velocity as hard data and the corrected layer velocity model as the data trend, the layer velocity model body is determined.

Step 111: input the layer velocity model body into a preset seismic interpretation velocity model framework to obtain a seismic interpretation velocity model.

After executing step 111 and obtaining the seismic interpretation velocity model, as shown in FIG7 , the following steps 701 to 703 may also be executed:

Step 701: Perform domain conversion on the seismic interpretation velocity model to obtain a domain conversion result.

Step 702: Compare the domain conversion result with the average velocity of the well point, and determine the error between the domain conversion result and the average velocity of the well point.

Step 703: If the error is greater than a preset error threshold, the seismic residual layer velocity model is re-established, and subsequent processing is performed based on the re-established seismic residual layer velocity model to re-establish the seismic interpretation velocity model.

That is to say, by comparing the domain conversion result with the well data, if the error between the domain conversion result and the average velocity of the well point is greater than the preset error threshold, it is determined that the established seismic interpretation velocity model does not meet the requirements, and it is necessary to check the seismic residual layer velocity model, return to step 108 to further improve the quality of the seismic residual layer velocity model, and repeat steps 108 to 111 until the obtained seismic interpretation velocity model meets the accuracy requirements.

After the completion of steps 701 to 703, the integration of well and seismic velocity data of different scales is realized, which solves the problem that the velocity field in the area with rapid velocity changes must maintain both the seismic velocity trend and the well point velocity consistency, and the inter-well velocity deviation is as small as possible, effectively improving the velocity modeling accuracy in the reservoir evaluation stage, ensuring the reliability of three-dimensional structural mapping, and improving the accuracy of domain conversion. It can significantly improve the accuracy of simple inter-well velocity interpolation or simple seismic velocity well point correction in conventional time-depth conversion work and the correct velocity trend function between wells, and has stronger overall operability, process controllability, and visualization functions.

After the domain conversion is completed, the overall accuracy of the seismic interpretation velocity model is improved. When the seismic interpretation velocity model is applied for domain conversion, its change trend is no longer a big problem. However, when fine-tuning the map and converting the data body, it is necessary to further fine-tune the local well points in the depth domain.

The method for establishing a seismic interpretation velocity model in the reservoir evaluation stage provided in the embodiment of the present invention uses seismic data and well logging data as data sources to determine the seismic average velocity, the well point average velocity and the well point layer velocity, and then samples the above velocities into a structural grid to determine the grid seismic average velocity and the grid well point average velocity; then obtains the velocity anisotropy coefficient relationship based on the grid seismic average velocity and the grid well point average velocity, and corrects the grid seismic average velocity; converts the corrected seismic average velocity into a corrected seismic layer velocity, taking into account the longitudinal changes of the target layer segment, and the velocity data generally has a very obvious vertical trend (i.e., depth trend). The corrected seismic layer velocity is vertically detrended to establish a residual layer velocity model, and then the model is restored to a trend to obtain a corrected layer velocity model. The corrected layer velocity model is then used as trend data to constrain well data (i.e., well-point layer velocity) to establish a layer velocity model body. The layer velocity model body is then used to obtain the final seismic interpretation velocity model, and the integration of well and seismic velocity data of different scales is achieved. This solves the problem of maintaining both the seismic velocity trend and the well-point velocity consistency in the velocity field in areas with rapid velocity changes, and minimizing the inter-well velocity deviation. This effectively improves the velocity modeling accuracy in the reservoir evaluation stage and ensures the reliability of three-dimensional structural mapping.

In the embodiment of the present invention, based on the petroleum geological modeling software Petrel2015 and the Matlab language calculation program, the DP work area in the Qaidam Basin in western my country was selected for seismic interpretation velocity modeling, and compared with several conventional velocity modeling methods. The modeling process is described below.

The DP work area is a 3D seismic work area deployed in 2012 in front of the Altun Mountains in the Qaidam Basin. It covers an area of ^452.4km2 at one time. The target layer is bedrock, with a top surface burial depth of 1830 to 4600m and a height difference of 2770m.

The seismic average velocity calculated from the seismic velocity spectrum, and the well point average velocity and well point layer velocity calculated from the well synthetic record are sampled into the structural grid, and the grid seismic average velocity and the well point seismic average velocity are determined.

Afterwards, the average velocity anisotropy coefficient was obtained using the grid seismic average velocity and the well point seismic average velocity. The obtained velocity anisotropy coefficient is shown in Figure 6(a). The DBSCAN algorithm was combined with the cubic spline interpolation method to obtain the anisotropy correction function, and the anisotropy correction curve was obtained, as shown in Figure 8. The results in Figure 8 show that when DBSCAN was used for cluster analysis, a total of 5 noise points far away from the cluster center were generated. The effect comparison of this method with the conventional fitting method is shown in Figures 9(a) to 9(c). Among them, Figure 9(a) is a schematic diagram of the correlation between the velocity anisotropy coefficient and the two-way time determined by the linear function fitting method, Figure 9(b) is a schematic diagram of the correlation between the velocity anisotropy coefficient and the two-way time determined by the exponential function fitting method, and Figure 9(c) is a schematic diagram of the correlation between the velocity anisotropy coefficient and the two-way time determined by the DBSCAN and cubic spline interpolation method.

The error of the correlation between the velocity anisotropy coefficient and the two-way time determined by the three methods in Figures 9(a) to 9(c) is shown in Table 1 below. In the error analysis, the noise points in Figure 8 were considered accordingly. The results show that the residual sum of squares, whether the noise points are considered or not, the errors of DBSCAN and cubic spline interpolation methods are significantly smaller than those of linear function fitting method and exponential function fitting method, and the anisotropy curve passes through the cluster center, which more accurately describes the velocity deviation problem caused by factors such as reservoir heterogeneity when the structural height difference is large.

Table I

By correcting the seismic average velocity data to the well point average velocity level, and using the relationship between the average velocity and the root mean square velocity, the corrected seismic average velocity is converted into the seismic corrected root mean square velocity, and the corrected seismic layer velocity is further obtained. Through the geostatistical modeling method, a corrected layer velocity model is established.

After the correction layer velocity model is established, the model is used as the input of velocity modeling to establish the seismic interpretation velocity model of the study area. Through the seismic interpretation velocity model, the average velocity model data body can be obtained, and the obtained average velocity model is used for domain conversion. The domain conversion results are compared with the selected typical wells. The results are shown in Table 2. Referring to Table 2, it can be seen that the velocity model method used in the present invention has significantly improved the accuracy of time-depth domain conversion compared with conventional methods.

Table II

The present invention also provides a device for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage, as described in the following embodiments. Since the principle of the device to solve the problem is similar to the method for establishing a 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 a seismic interpretation velocity model in the oil and gas reservoir evaluation stage, and the repeated parts will not be repeated.

As shown in FIG. 10 , the device 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 building module 1007 .

Wherein, the acquisition module 1001 is used to acquire well logging data and seismic data;

A determination module 1002 is used to determine the average seismic velocity, the average well point velocity and the well point layer velocity according to the well logging data and the seismic data;

The sampling module 1003 is used to sample the seismic average velocity, the well point average velocity and the well point layer velocity into a preset structural grid;

The determination module 1002 is further used to calculate the grid seismic average velocity according to the seismic average velocity sampled in the structural grid, and calculate the grid well point average velocity according to the well point average velocity sampled in the structural grid;

The correction module 1004 is used to determine the velocity anisotropy coefficient according to the grid seismic average velocity and the grid well point average velocity, and to correct the grid seismic average velocity using the velocity anisotropy coefficient to obtain a corrected seismic average velocity;

Velocity conversion module 1005, used to convert the corrected seismic average velocity into a corrected seismic layer velocity;

Processing module 1006, for performing detrending processing on the corrected seismic layer velocity to obtain seismic residual layer velocity;

The processing module 1006 is also used to perform spatial variogram analysis on the seismic residual layer velocity and establish a seismic residual layer velocity model;

The processing module 1006 is also used to perform recovery trend processing on the seismic residual layer velocity model to obtain a correction layer velocity model;

The determination module 1002 is further used to determine the layer velocity model body by taking the well point layer velocity as hard data and the corrected layer velocity model as data trend;

The model building module 1007 is used to input the layer velocity model body into the preset seismic interpretation velocity model framework to obtain the seismic interpretation velocity model.

In an implementation of the embodiment of the present invention, the determination module 1002 is used to:

Determine seismic velocity spectrum from seismic data;

Decode the seismic velocity spectrum to obtain the 3D seismic migration root mean square velocity;

Calculate the average seismic velocity based on the 3D seismic migration RMS velocity;

The seismic data and well logging data are used to perform synthetic record analysis on the well to obtain the average wellpoint velocity and wellpoint layer velocity.

In an implementation of the embodiment of the present invention, the determining module 1002 is further configured to:

Use seismic data and well logging data to perform synthetic log analysis on the well to obtain the time-depth relationship of the well point;

Conduct a consistency check on the time-depth relationship of the well point to obtain the check result;

If the inspection result shows that the time-depth relationship is inconsistent, the time-depth relationship of the well point is corrected, and the well point average velocity and the well point layer velocity are re-determined after the time-depth relationship is modified.

In one implementation of the embodiment of the present invention, the correction module 1004 is used to:

The ratio of the grid well point average velocity to the grid seismic average velocity is determined as the velocity anisotropy coefficient;

The cluster center of velocity anisotropy coefficient is calculated using DBSCAN algorithm;

The cluster centers are interpolated with cubic spline curves to obtain the functional relationship between the anisotropy coefficient and the two-way time.

The grid seismic average velocity is corrected using the functional relationship between velocity anisotropy coefficient and two-way time to obtain the corrected seismic average velocity.

In one implementation of the embodiment of the present invention, the correction module 1004 is used to:

Calculate the corrected seismic average velocity V _{avg_seis_m} using V _{avg_seis_m} = V _{avg_seis} × F _ani (TWT);

Among them, V _{avg_seis} represents the grid average seismic velocity; _Fani (TWT) represents the functional relationship between velocity anisotropy coefficient and two-way time.

In an implementation manner of the embodiment of the present invention, the apparatus 1000 further includes:

A domain conversion module 1008 is used to perform domain conversion on the seismic interpretation velocity model to obtain a domain conversion result;

A comparison module 1009 is used to compare the domain conversion result with the average velocity of the well point and determine the error between the domain conversion result and the average velocity of the well point;

The processing module 1006 is also used to re-establish the seismic residual layer velocity model when the error is greater than a preset error threshold, and call the determination module 1002 and the model construction module 1007 to perform subsequent processing based on the re-established seismic residual layer velocity model to re-establish the seismic interpretation velocity model.

The device for establishing a seismic interpretation velocity model in the reservoir evaluation stage provided in the embodiment of the present invention uses seismic data and well logging data as data sources to determine the seismic average velocity, the well point average velocity and the well point layer velocity, and then samples the above velocities into the structural grid to determine the grid seismic average velocity and the grid well point average velocity; then obtains the velocity anisotropy coefficient relationship according to the grid seismic average velocity and the grid well point average velocity, and corrects the grid seismic average velocity; converts the corrected seismic average velocity into the corrected seismic layer velocity, taking into account the longitudinal changes of the target layer segment, and the velocity data generally has a very obvious vertical trend (i.e., depth trend). The corrected seismic layer velocity is processed vertically to remove the trend, and then the residual layer velocity model is established. The model is then processed to restore the trend to obtain the corrected layer velocity model. The corrected layer velocity model is then used as trend data to constrain the well data (i.e., well-point layer velocity) to establish a layer velocity model body. The layer velocity model body is then used to obtain the final seismic interpretation velocity model, and the integration of well and seismic velocity data of different scales is achieved. This solves the problem that the velocity field in areas with rapid velocity changes must maintain both the seismic velocity trend and the well-point velocity consistency, while minimizing the inter-well velocity deviation. This effectively improves the velocity modeling accuracy in the reservoir evaluation stage and ensures the reliability of three-dimensional structural mapping.

An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage is implemented.

An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for executing the method for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage.

Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

The present invention is described with reference to the flowchart and/or block diagram of the method, device (system), and computer program product according to the embodiment of the present invention. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the process and/or box in the flowchart and/or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims (10)

1. A method for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage, characterized in that the method comprises: Acquisition of well logging and seismic data; Determine the average seismic velocity, the average wellpoint velocity and the wellpoint layer velocity based on the well logging data and seismic data; The seismic average velocity, the well point average velocity and the well point layer velocity are sampled into a preset structural grid; Calculate the grid seismic average velocity according to the seismic average velocity sampled in the structural grid, and calculate the grid well point average velocity according to the well point average velocity sampled in the structural grid; Determine the velocity anisotropy coefficient according to the grid seismic average velocity and the grid well point average velocity, and use the velocity anisotropy coefficient to correct the grid seismic average velocity to obtain the corrected seismic average velocity; Convert the corrected seismic mean velocity into corrected seismic layer velocity; Detrending the corrected seismic layer velocity, the seismic residual layer velocity is obtained; Conduct spatial variogram analysis on seismic residual layer velocity and establish seismic residual layer velocity model; Perform recovery trend processing on the seismic residual layer velocity model to obtain the correction layer velocity model; Taking the well point layer velocity as hard data and the corrected layer velocity model as data trend, the layer velocity model body is determined; Inputting the layer velocity model body into a preset seismic interpretation velocity model framework to obtain a seismic interpretation velocity model; The velocity anisotropy coefficient is determined according to the grid seismic average velocity and the grid well point average velocity, and the grid seismic average velocity is corrected by the velocity anisotropy coefficient to obtain the corrected seismic average velocity, including: The ratio of the grid well point average velocity to the grid seismic average velocity is determined as the velocity anisotropy coefficient; The cluster center of velocity anisotropy coefficient is calculated using DBSCAN algorithm; The cluster centers are interpolated with cubic spline curves to obtain the functional relationship between the anisotropy coefficient and the two-way time. The grid seismic average velocity is corrected using the functional relationship between velocity anisotropy coefficient and two-way time to obtain the corrected seismic average velocity. Among them, the grid seismic average velocity is corrected using the functional relationship between the velocity anisotropy coefficient and the two-way time to obtain the corrected seismic average velocity, including: Calculate the corrected seismic average velocity V _{avg_seis_m} using V _{avg_seis_m} = V _{avg_seis} × F _ani (TWT); Where V _{avg_seis} represents the grid average seismic velocity; _Fani (TWT) represents the functional relationship between velocity anisotropy coefficient and two-way time. 2. The method according to claim 1, characterized in that determining the seismic average velocity, the well point average velocity and the well point layer velocity according to the well logging data and the seismic data comprises: Determine seismic velocity spectrum from seismic data; Decode the seismic velocity spectrum to obtain the 3D seismic migration root mean square velocity; Calculate the average seismic velocity based on the 3D seismic migration RMS velocity; The seismic data and well logging data are used to perform synthetic record analysis on the well to obtain the average wellpoint velocity and wellpoint layer velocity. 3. The method according to claim 1 or 2, characterized in that before sampling the seismic average velocity, the well point average velocity and the well point layer velocity into a preset structural grid, the method further comprises: Use seismic data and well logging data to perform synthetic log analysis on the well to obtain the time-depth relationship of the well point; Conduct a consistency check on the time-depth relationship of the well point to obtain the check result; If the inspection result shows that the time-depth relationship is inconsistent, the time-depth relationship of the well point is corrected, and the well point average velocity and the well point layer velocity are re-determined after the time-depth relationship is modified. 4. The method according to claim 1, characterized in that after inputting the layer velocity model body into a preset seismic interpretation velocity model framework to obtain the seismic interpretation velocity model, the method further comprises: Perform domain conversion on the seismic interpretation velocity model to obtain domain conversion results; Compare the domain conversion result with the average velocity of the well point, and determine the error between the domain conversion result and the average velocity of the well point; If the error is greater than a preset error threshold, the seismic residual layer velocity model is corrected, and subsequent processing is performed based on the re-corrected seismic residual layer velocity model to re-establish the seismic interpretation velocity model. 5. A device for establishing a seismic interpretation velocity model in the oil and gas reservoir evaluation stage, characterized in that the device comprises: An acquisition module, used to acquire well logging data and seismic data; A determination module, for determining the average seismic velocity, the average well point velocity and the well point layer velocity according to the well logging data and the seismic data; A sampling module is used to sample the seismic average velocity, the well point average velocity and the well point layer velocity into a preset structural grid; The determination module is further used to calculate the grid seismic average velocity according to the seismic average velocity sampled into the structural grid, and calculate the grid well point average velocity according to the well point average velocity sampled into the structural grid; A correction module is used to determine a velocity anisotropy coefficient according to a grid seismic average velocity and a grid well point average velocity, and to correct the grid seismic average velocity using the velocity anisotropy coefficient to obtain a corrected seismic average velocity; A velocity conversion module, used for converting the corrected seismic average velocity into a corrected seismic layer velocity; A processing module is used to perform detrending processing on the corrected seismic layer velocity to obtain the seismic residual layer velocity; The processing module is also used to perform spatial variogram analysis on the seismic residual layer velocity and establish a seismic residual layer velocity model; The processing module is also used to perform recovery trend processing on the seismic residual layer velocity model to obtain a correction layer velocity model; The determination module is also used to determine the layer velocity model body by taking the well point layer velocity as hard data and the corrected layer velocity model as data trend; A model building module is used to input the layer velocity model body into a preset seismic interpretation velocity model framework to obtain a seismic interpretation velocity model; The correction module is specifically used for: The ratio of the grid well point average velocity to the grid seismic average velocity is determined as the velocity anisotropy coefficient; The cluster center of velocity anisotropy coefficient is calculated using DBSCAN algorithm; The cluster centers are interpolated with cubic spline curves to obtain the functional relationship between the anisotropy coefficient and the two-way time. The grid seismic average velocity is corrected using the functional relationship between velocity anisotropy coefficient and two-way time to obtain the corrected seismic average velocity. Among them, the grid seismic average velocity is corrected using the functional relationship between the velocity anisotropy coefficient and the two-way time to obtain the corrected seismic average velocity, including: Calculate the corrected seismic average velocity V _{avg_seis_m} using V _{avg_seis_m} = V _{avg_seis} × F _ani (TWT); Where V _{avg_seis} represents the grid average seismic velocity; _Fani (TWT) represents the functional relationship between velocity anisotropy coefficient and two-way time. 6. The device according to claim 5, characterized in that the determination module is used to: Determine seismic velocity spectrum from seismic data; Decode the seismic velocity spectrum to obtain the 3D seismic migration root mean square velocity; Calculate the average seismic velocity based on the 3D seismic migration RMS velocity; The seismic data and well logging data are used to perform synthetic record analysis on the well to obtain the average wellpoint velocity and wellpoint layer velocity. 7. The device according to claim 5 or 6, characterized in that the determination module is further used for: Use seismic data and well logging data to perform synthetic log analysis on the well to obtain the time-depth relationship of the well point; Conduct a consistency check on the time-depth relationship of the well point to obtain the check result; If the inspection result shows that the time-depth relationship is inconsistent, the time-depth relationship of the well point is corrected, and the well point average velocity and the well point layer velocity are re-determined after the time-depth relationship is modified. 8. The device according to claim 5, characterized in that the device further comprises: A domain conversion module is used to perform domain conversion on the seismic interpretation velocity model to obtain domain conversion results; A comparison module, used for comparing the domain conversion result with the average velocity of the well point, and determining the error between the domain conversion result and the average velocity of the well point; The processing module is also used to re-establish the seismic residual layer velocity model when the error is greater than a preset error threshold, and call the determination module and the model construction module to perform subsequent processing according to the re-established seismic residual layer velocity model to re-establish the seismic interpretation velocity model. 9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the methods of claims 1 to 4 when executing the computer program. 10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method according to any one of claims 1 to 4.