CN114563816A - 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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Abstract
The invention discloses a method and a device for establishing a seismic interpretation velocity model in an oil and gas reservoir evaluation stage, wherein the method comprises the following steps: determining the average seismic velocity, the average well point velocity and the well point interval velocity, and sampling the average seismic velocity, the average well point velocity and the well point interval velocity into a preset construction grid; calculating the average grid seismic velocity and the average grid well point velocity; 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 interval velocity; carrying out trend removing processing, spatial variation function analysis and trend restoring processing on the corrected seismic interval velocity in sequence to obtain a corrected interval velocity model; determining a layer velocity model body by taking the well point layer velocity as hard data and a correction layer velocity model as a data trend; 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 precision and reliability of the speed model.
Description
Technical Field
The invention relates to the technical field of development of geological subject oil and gas reservoir description, in particular to a method and a device for establishing a seismic interpretation velocity model in an oil and gas 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 in a description stage of an oil and gas reservoir, improvement of prediction precision among wells of a geological model and the like, and the common three-dimensional seismic data are time domain data, and a high-precision three-dimensional velocity model needs to be established for the application of the aspects.
The establishment of the velocity 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. The so-called "velocity modeling" can be generally divided into two categories, one is velocity modeling for the seismic data processing phase, and the other is velocity modeling for the reservoir evaluation phase, which are different and related: the main data sources of the velocity modeling of the former are seismic prestack gathers and the like, the main methods comprise a modeling method based on stack velocity analysis, a modeling method based on migration velocity analysis and a velocity modeling method based on chromatography inversion, and the main application field is seismic imaging; the velocity modeling data source of the latter mainly comprises more well drilling synthetic seismic recording 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 constrained reservoir modeling and velocity modeling are increasingly widely applied, but how to fuse the two data types with large scale difference of wellbore velocity and seismic velocity in a high quality mode is a hot problem for research of geophysicists and development geologists. In practical research, the well-seismic velocity modeling exploration and development in the early stage generally utilizes various data, such as logging calculation velocity and VSP velocity, to correct seismic processing velocity and then directly form a velocity model. In the current well-seismic combined velocity modeling in the evaluation stage of an oil and gas reservoir, the seismic velocity is corrected by using the well velocity in the establishing process of a velocity trend body, the correlation between a velocity anisotropy coefficient and the depth involved in the correction is often low, the depth trend of data is often less considered in the establishing process of a corrected seismic velocity trend model, the limit condition of geostatistics considered in the establishing process is insufficient, and therefore 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 an oil and gas reservoir evaluation stage, which considers the problem of the depth trend of 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 comprises the following steps:
acquiring logging data and seismic data;
determining the average seismic velocity, the average well point velocity and the interval well point velocity according to the logging data and the seismic data;
sampling the seismic average velocity, the well point average velocity and the well point interval velocity into a preset construction grid;
calculating the average seismic speed of the grid according to the average seismic speed sampled into the construction grid, and calculating the average well point speed of the grid according to the average well point speed 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 interval velocity;
trend removing processing is carried out on the corrected seismic interval velocity to obtain the seismic residual interval velocity;
carrying out space variation function analysis on the seismic residual layer velocity, and establishing a seismic residual layer velocity model;
performing recovery trend processing on the seismic residual layer velocity model to obtain a corrected layer velocity model;
determining a layer velocity model body by taking the well point layer velocity as hard data and a correction layer velocity model as a data trend;
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 of 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 seismic average velocity, the well point average velocity and the well point interval velocity according to the logging data and the seismic data;
the sampling module is used for sampling the seismic average velocity, the well point average velocity and the well point interval velocity into a preset construction grid;
the determining module is further 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 using 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 a corrected seismic interval velocity;
the processing module is used for performing trend removing processing on the corrected seismic interval velocity to obtain a seismic residual interval velocity;
the processing module is also used for carrying out space variation function analysis on the seismic residual layer velocity and establishing a seismic residual layer velocity model;
the processing module is also used for carrying out recovery trend processing on the seismic residual interval velocity model to obtain a corrected interval velocity model;
the determining module is also used for determining a layer velocity model body by taking the well point layer velocity as hard data and the corrected layer velocity model as a data trend;
and the model construction module is used for inputting the interval velocity model body into a preset seismic interpretation velocity model frame to obtain the seismic interpretation velocity model.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the method for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage when executing the computer program.
Embodiments of the present invention also provide a computer-readable storage medium storing a computer program for executing the method for establishing a seismic interpretation velocity model in the hydrocarbon reservoir evaluation phase.
The method and the device for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation phase provided by the embodiment of the invention use seismic data and logging data as data sources to determine the seismic average velocity, the well point average velocity and the well point interval velocity, then sample the velocities into a construction grid, and determine the grid seismic average velocity and the grid well point average velocity; then obtaining 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 the corrected seismic interval velocity, the velocity data generally has obvious vertical trend (namely depth trend) in consideration of the change condition of a target interval in the longitudinal direction, the corrected seismic interval velocity is subjected to vertical detrending treatment to establish a residual interval velocity model, the model is subjected to trend recovery treatment to obtain a corrected interval velocity model, the corrected interval velocity model is used as trend data to constrain well data (namely well point interval velocity) to establish an interval velocity model body, the interval velocity model body is used for obtaining a final seismic interpretation velocity model, the integration of different scales of velocity data of wells and earthquakes is realized, the problems that the velocity field in an area with rapid velocity change needs to keep the well point velocity consistent and the well velocity deviation as small as possible are well solved, the velocity modeling precision in the oil and gas reservoir evaluation stage is effectively improved, ensuring the reliability of the three-dimensional structure chart.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart of a method for establishing a seismic interpretation velocity model during a reservoir evaluation phase in an embodiment of the invention;
FIG. 2 is a flowchart illustrating a method for implementing step 102 according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the average seismic velocity and the average well point velocity calculated from the well log data and seismic data of a well in a work area in an embodiment of the invention;
FIG. 4 is a flowchart of a specific implementation method in which step 105 determines a velocity anisotropy coefficient according to the grid seismic average velocity and the grid well point average velocity, and corrects the grid seismic average velocity using the velocity anisotropy coefficient to obtain a corrected seismic average velocity according to the embodiment of the present invention;
FIG. 5(a) is a graphical representation of the correlation between RMS velocity and average velocity in an embodiment of the invention;
FIG. 5(b) is a schematic illustration of the correlation between layer velocity and average velocity in an embodiment of the present invention;
FIG. 5(c) is a graphical representation of a correlation between RMS velocity and layer velocity in an embodiment of the invention;
FIG. 6(a) is a schematic diagram illustrating the relationship between the average velocity anisotropy coefficient of a DP work area and the time variation of two passes according to an embodiment of the present invention;
FIG. 6(b) is a schematic diagram illustrating the relationship between the velocity anisotropy coefficients of a DP work area layer and two-way time variation according to an embodiment of the present disclosure;
FIG. 7 is a flow chart of another method of establishing a seismic interpretation velocity model during a reservoir evaluation phase in an embodiment of the invention;
FIG. 8 is a schematic diagram of an anisotropy correction curve obtained by applying a DBSCAN algorithm in combination with a cubic spline interpolation method to obtain an anisotropy correction function in the embodiment of the present invention;
FIG. 9(a) is a graphical illustration of the correlation of velocity anisotropy coefficients determined using a linear function fitting method with two passes;
FIG. 9(b) is a schematic representation of the correlation of velocity anisotropy coefficients determined using an exponential function fitting method with two passes;
FIG. 9(c) is a schematic diagram of the correlation of velocity anisotropy coefficients determined using DBSCAN and cubic spline interpolation methods with two passes;
fig. 10 is a schematic structural diagram of an apparatus for establishing a seismic interpretation velocity model in a reservoir evaluation phase in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
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 work area which can provide seismic migration root-mean-square velocity, interval 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 exploration and development stages.
As shown in fig. 1, the method includes steps 101 to 111:
And 102, determining the seismic average velocity, the well point average velocity and the well point interval velocity according to the logging data and the seismic data.
In the embodiment of the present invention, as shown in fig. 2, step 102 may be performed as following steps 1021 to 1024:
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. And after the solution is compiled, the root mean square velocity is converted into seismic interval velocity, seismic average velocity and the like through a Dix formula.
The equation for converting the root mean square velocity to seismic interval velocity is as follows:
wherein, ViRepresents the layer speed of the i-th layer, i ═ 1,2, 3.., n; vR,i、VR,i-1Respectively representing the root mean square velocity of the ith layer and the ith-1 layer; t is t0,i、t0,i-1Respectively representing the double-pass reflection time of the ith layer and the ith-1 layer.
The equation for converting seismic interval velocities to seismic mean velocities is as follows:
wherein, VavRepresenting the seismic average velocity of the ith layer; t is tiIndicating a two-pass time interval for layer i.
In places with small change of the upper and lower velocities of the stratum, the seismic interval velocity can be considered for subsequently establishing a seismic interpretation velocity model, the interval velocity can better keep the velocity change details and is finally converted into the average velocity for domain conversion, but the interval velocity is changed violently under the common condition and has poor regularity compared with the average velocity, so the seismic average velocity is utilized in the embodiment of the invention.
The data such as stacking velocity, root mean square velocity and the like obtained by the seismic velocity spectrum decoding reflect the whole trend of the change of the underground velocity field, and the velocity spectrum can ensure better transverse trend. The grid step length on the speed spectrum speed data plane is generally 200m multiplied by 200 m-1000 m, a speed spectrum with higher data density after interpolation is possibly provided for a specific work area seismic data processing department, the speed spectrum speed data plane is characterized in that the data covers the whole work area range and is far larger than the plane range of well data, and the defects that longitudinal data are sparse and the resolution ratio is difficult to guarantee are overcome. The velocity spectrum velocity data is generally picked up manually on the basis of the principle of controlling the velocity trend, the longitudinal interval of the picked-up velocity data is 50-500 ms or more, the sampling interval of the output seismic velocity spectrum data is generally 20-100 ms, and the sampling interval is far lower than the vertical resolution of the calculated velocity of the logging curve.
And step 1023, calculating the earthquake average velocity according to the three-dimensional earthquake migration root-mean-square velocity.
And step 1024, performing synthetic record analysis on the well by using the seismic data and the logging data to obtain the average well point speed and the interval well point speed.
Wherein the average speed of the well point is the average speed of the well point along the well track. The analysis of synthetic records on the well is a common technical means in the field, and the detailed implementation of the step is not described herein.
After step 102 is executed and before step 103 is executed, the time-depth relation of well points can be obtained by performing synthetic record analysis on the well by using the seismic data and the logging data; carrying out time-depth relationship consistency check on the time-depth relationship of the well points to obtain a check result; and if 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 speed of the well point layer after the time-depth relation is corrected.
The vertical resolution of the well point layer velocity obtained by the synthetic record on the well is high, and the well point layer velocity is at the interval of seismic sampling, and the defects are that the plane well spacing is large and the distribution is often extremely uneven. And (4) placing the time-depth relationship after the key well is subjected to synthesis recording in the same coordinate system for checking the time-depth relationship consistency. When the time-depth relationship of the wells in the work area basically coincides, the time-depth relationship is basically reliable when the synthetic records are analyzed, otherwise, the reason of speed abnormity needs to be searched and analyzed, and the abnormal condition is eliminated.
Further quality control can also be carried out, the relationship between the construction elevation of each construction layer well point and the two-way time can be extracted, the speed consistency is analyzed, the principle is similar to the above description, and the description is omitted here.
And 103, sampling the seismic average velocity, the well point average velocity and the well point layer velocity into a preset construction grid.
And the point data with discrete space is sampled into a designed structural grid, so that the subsequent correction and modeling work is facilitated. E.g., DP work area, builds a structuring grid in the time domain, taking the size 50m x 4ms, and then data samples are taken.
It should be noted that, discrete points in space are dense, and the number of sampled points is less than that of 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 the sampling of the speed into the construction grid are common technical means in the field, and the specific implementation process of the step is not described herein again.
And 104, calculating the average grid seismic speed according to the average seismic speed sampled into the construction grid, and calculating the average grid well point speed according to the average well point speed sampled into the construction grid.
Because the part of the seismic average velocity and the well point average velocity is sampled into the construction grid, the grid seismic average velocity and the grid well point average velocity are re-determined for the convenience of subsequent calculation.
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 applications, the average velocity of the well points calculated by the well point synthetic record of the research target layer is generally smaller than the average velocity of the seismic calculated by the seismic velocity spectrum, as shown in fig. 3, fig. 3 is a schematic diagram of the average velocity of the seismic and the average velocity of the well points calculated according to the logging data and the seismic data of a certain well in a certain work area, wherein the dashed line marked as 1 is the average velocity of the seismic, and the dashed line marked as 2 is the average velocity of the well points, it can be clearly seen that the average velocity of the well points is smaller than the average velocity of the seismic at most moments. In addition, the T4, T5 and Tr marked lines in FIG. 3 are corresponding seismic horizons.
The average speed of the well point is less than the average speed of the earthquake, because the central frequency of the acoustic logging is generally 20KHz, which is much higher than the frequency of the earthquake speed, and the propagation speeds of the waves with different frequencies have frequency dispersion effect and anisotropic characteristics. Therefore, when the seismic average velocity is used, anisotropic correction of the seismic average velocity is generally required, so that the seismic average velocity is corrected to the same level as the well point average velocity.
Specifically, as shown in fig. 4, step 105 may be executed as the following steps 1051 to 1054:
The formula for calculating the velocity anisotropy coefficient is shown below:
Fang_ani＝vavg_well/vavg_seis
in the formula, Fang_aniRepresents a velocity anisotropy coefficient; v. ofavg_wellRepresenting the average speed value of grid well points;vavg_seisrepresenting the grid seismic mean velocity.
And 1053, carrying out cubic spline curve interpolation on the clustering center to obtain the functional relation between the anisotropy coefficient and the double-pass time.
Functional relationship F between anisotropy coefficient and double passang_ani(vavg_seis,vavg_wellTWT), can be abbreviated as Fani(TWT), wherein TWT is two-way.
FaniThere are various ways to find (TWT), such as polynomial fitting, piecewise polynomial fitting, etc. The embodiment of the invention adopts a DBSCAN algorithm to obtain the clustering center of the velocity anisotropy coefficient, and then carries out cubic spline curve interpolation on the clustering center to obtain Fani(TWT) functional relationship. Mainly based on the following considerations:
when the stratum has abnormal geologic bodies, lithologic mutation and other conditions, the different areas of the upper stratum and the lower stratum and the same stratum often have obvious speed difference, and 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 reduce local fitting errors sometimes, but the function curve at the piecewise node is not smooth, so that an artificial difference interface can appear, and geological judgment is misled; the embodiment of the invention adopts the DBSCAN algorithm and the cubic spline interpolation method to establish the anisotropic correction curve, so that the calculation result has a plurality of excellent properties of small error, smooth curve, continuous curvature and the like.
The cubic spline interpolation function is defined by knowing n points (x) on a planei,yi) (i ═ 1,2, …, n), where x1<x2<…<xnThese points are referred to as sample points. If some function S (x) satisfies the following 3 conditions, S (x) is called a cubic spline function passing through the n points:
1)S(xi)＝yi(i＝1,2,…,n) I.e. the function passes through the sample points;
2) s (x) in each subinterval [ x ]i,xi+1]Polynomial of degree three
S(x)＝ci1(x-xi)3+ci2(x-xi)2+ci3(x-xi)+ci4
In the formula ci1、ci2、ci3、ci4And determining coefficients for the cubic spline function.
3) S (x) has continuous first and second derivatives over the entire interval.
And 1054, correcting the grid seismic average velocity by using the velocity anisotropy coefficient and the function relation of the two-way time to obtain the corrected seismic average velocity.
In particular, using Vavg_seis_m＝Vavg_seis×Fani(TWT) calculating corrected seismic mean velocity Vavg_seis_m(ii) a Wherein, Vavg_seisRepresenting the average velocity of the grid earthquake; fani(TWT) represents the velocity anisotropy coefficient as a function of two-way.
The method in the steps 1051 to 1054 is used for correcting the grid seismic average velocity, so that the velocity correction precision of the target layer is improved, and the method is more beneficial to performing targeted structural research on the target layer and performing domain conversion during seismic constraint modeling in the oil and gas reservoir evaluation stage.
In this step, the corrected seismic average velocity may be converted to a corrected root mean square velocity according to a statistical relationship between the average velocity and the root mean square velocity, and then the corrected seismic interval velocity may be calculated. Because in practical application, the average speed and the root mean square speed generally have high correlation, and other speed relations tend to have poor correlation. Illustratively, 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, fig. 5(c) shows the correlation between the root mean square velocity and the layer velocity, and as can be seen from fig. 5(a) to 5(c), the correlation between the average velocity and the root mean square velocity is high.
After the grid seismic average velocity is corrected, the corrected seismic average velocity is used for solving the corrected seismic interval velocity, and the correlation between the average velocity anisotropy coefficient in the longitudinal direction and the double-pass time is considered to be far stronger than the correlation between the interval velocity anisotropy coefficient and the double-pass time in practical application. For example, fig. 6(a) shows the average velocity anisotropy coefficient of the DP work area as a function of two-way time, fig. 6(b) shows the layer velocity anisotropy coefficient of the DP work area as a function of two-way time, and as can be seen from fig. 6(a) and fig. 6(b), 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 method for converting the average velocity into the interval velocity is a mature method in the prior art, and therefore, the detailed implementation process for converting the corrected seismic average velocity into the corrected seismic interval velocity is not repeated herein.
And 107, performing trend removing processing on the corrected seismic interval velocity to obtain the seismic residual interval velocity.
There are many methods for analyzing the vertical trend of the layer velocity, and the embodiment of the invention adopts a simpler deterministic processing method: calculating the functional relation V between the grid seismic velocity and the two-way timeint0(TWT) by means of which a vertical trend data volume V can be createdint0. Detrending is performed on seismic interval velocities using the formula:
Vint_seis_res＝Vint_seis_m-Vint0
in the formula, Vint_seis_resRepresenting seismic residual interval velocities (grid data along well trajectories); vint0Representing a seismic interval velocity vertical trend data volume (grid data along well trajectory).
And 108, carrying out spatial variation function analysis on the seismic residual layer velocity to establish a seismic residual layer velocity model.
Methods for analyzing the spatial variation function and establishing the velocity model of the residual layer are provided in the prior art, and are not described herein again.
And step 109, performing recovery trend processing on the seismic residual interval velocity model to obtain a corrected interval velocity model.
Specifically, the seismic residual interval velocity model is subjected to recovery trend processing according to the following method:
Vint_seis_3d＝Vint_seis_res_3d+Vint0_3d
in the formula, Vint_seis_3dA corrected layer velocity model representing three dimensions; vint_seis_res_3dRepresenting a seismic residual interval velocity model; vint0_3dAnd (3) a three-dimensional seismic interval velocity vertical trend data volume.
And step 110, determining a stratum velocity model body by taking the well point stratum velocity as hard data and the corrected stratum velocity model as a data trend.
And step 111, inputting the interval velocity model body into a preset seismic interpretation velocity model frame to obtain a seismic interpretation velocity model.
After step 111 is executed to obtain the seismic interpretation velocity model, as shown in fig. 7, the following steps 701 to 703 may be further executed:
and 701, performing domain conversion on the seismic interpretation velocity model to obtain a domain conversion result.
And 703, if the error is larger than the preset error threshold value, reestablishing the seismic residual layer velocity model, carrying out subsequent processing according to the reestablished seismic residual layer velocity model, and reestablishing the 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 well point average velocity is greater than the preset error threshold, determining that the established seismic interpretation velocity model does not meet the requirement, checking the seismic residual interval velocity model, returning to step 108 to further improve the quality of the seismic residual interval velocity model, and repeating the steps 108 to 111 until the obtained seismic interpretation velocity model meets the precision requirement.
After the steps 701 to 703 are completed, integration of speed data of different scales of wells and earthquakes is realized, the problems that the speed field of an area with rapid speed change needs to keep the trend of the earthquake speed and the speed of well points consistent, and the speed deviation between wells is small as much as possible are well solved, the speed modeling precision of an oil and gas reservoir evaluation stage is effectively improved, the reliability of a three-dimensional structure image is ensured, and the precision of domain conversion is improved. The method can obviously improve the precision of simple inter-well velocity interpolation or simple seismic velocity well point correction and other methods in conventional time-depth conversion work and the inter-well correct velocity trend function, and has stronger overall operability, flow controllability and visualization function.
After the domain conversion is completed, 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 domain conversion is carried out by applying the seismic interpretation velocity model, but the local well point needs to be further finely adjusted in a depth domain when a map and a data body are finely constructed and converted.
The method for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation phase provided by the embodiment of the invention comprises the steps of determining the seismic average velocity, the well point average velocity and the well point interval velocity by using seismic data and logging data as data sources, sampling the velocities into a construction grid, and determining the grid seismic average velocity and the grid well point average velocity; then obtaining 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 the corrected seismic interval velocity, the velocity data generally has obvious vertical trend (namely depth trend) in consideration of the change condition of a target interval in the longitudinal direction, the corrected seismic interval velocity is subjected to vertical detrending treatment to establish a residual interval velocity model, the model is subjected to trend recovery treatment to obtain a corrected interval velocity model, the corrected interval velocity model is used as trend data to constrain well data (namely well point interval velocity) to establish an interval velocity model body, the interval velocity model body is used for obtaining a final seismic interpretation velocity model, the integration of different scales of velocity data of wells and earthquakes is realized, the problems that the velocity field in an area with rapid velocity change needs to keep the well point velocity consistent and the well velocity deviation as small as possible are well solved, the velocity modeling precision in the oil and gas reservoir evaluation stage is effectively improved, ensuring the reliability of the three-dimensional structure chart.
In the embodiment of the invention, a DP work area of a Kaida wood basin in the west of China is selected to carry out seismic interpretation velocity model modeling based on petroleum geological modeling software Petrel2015 and a Matlab language calculation program, and compared with a plurality of conventional velocity modeling methods. The modeling process is described later.
The DP work area is a three-dimensional seismic work area deployed 2012 before the Aljinshan of the Chaodia basin, and the primary coverage area is 452.4km2The target layer is bedrock, the burial depth of the top surface of the bedrock is 1830-4600 m, and the height difference reaches 2770 m.
Sampling the seismic average velocity obtained by calculating the seismic velocity spectrum, the well point average velocity obtained by calculating the synthetic record on the well and the well point layer velocity into a structural grid, and determining the grid seismic average velocity and the well point seismic average velocity.
Then, the average velocity and velocity anisotropy coefficient is obtained by using the grid seismic average velocity and the well point seismic average velocity, the obtained velocity anisotropy coefficient is shown as fig. 6(a), and an anisotropy correction function is obtained by combining a DBSCAN algorithm and a cubic spline interpolation method, so that an anisotropy correction curve is obtained, as shown in fig. 8. The results in fig. 8 show that a total of 5 noise points far from the cluster center are generated when the DBSCAN is used for cluster analysis. The effect of this method is compared with the conventional fitting method as shown in FIGS. 9(a) to 9 (c). Fig. 9(a) is a schematic diagram of a correlation between a velocity anisotropy coefficient determined by a linear function fitting method and a double pass, fig. 9(b) is a schematic diagram of a correlation between a velocity anisotropy coefficient determined by an exponential function fitting method and a double pass, and fig. 9(c) is a schematic diagram of a correlation between a velocity anisotropy coefficient determined by a DBSCAN and a cubic spline interpolation method and a double pass.
The error of the velocity anisotropy coefficient determined by the three methods of fig. 9(a) to 9(c) and the correlation in two passes, and the error analysis data are shown in the following table one. In error analysis, noise points in the graph 8 are considered correspondingly, and the result shows that the error of the DBSCAN and cubic spline interpolation method is obviously smaller than that of a linear function fitting method and that of an exponential function fitting method no matter whether the noise points are considered or not, and the speed deviation problem caused by factors such as reservoir heterogeneity and the like when the construction height difference is large is accurately described by an anisotropic curve passing through a clustering center.
The seismic average velocity data is corrected to the well point average velocity level, 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 interval velocity is further obtained, and a corrected interval velocity model is established by a geostatistical modeling method.
After the calibration interval velocity model is established, the model is used as the input of velocity modeling, and a seismic interpretation velocity model of the research area is established. The average velocity model data volume can be obtained through the earthquake interpretation velocity model, the obtained average velocity model is used for domain conversion, the domain conversion result is compared with the selected typical well, the result is shown in a table two, and the reference table two shows that the precision of the time-depth domain conversion of the velocity model method adopted by the invention is obviously improved compared with the conventional method.
Watch two
The embodiment of the invention also provides a device for establishing the seismic interpretation velocity model in the evaluation stage of the oil and gas reservoir, which is described in the following embodiment. The principle of the device for solving the problems is similar to the method for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage, so 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 repeated parts are not repeated.
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 building module 1007.
The acquiring module 1001 is used for acquiring logging data and seismic data;
a determining module 1002, configured to determine an average seismic velocity, an average well point velocity, and a well point interval velocity according to the logging data and the seismic data;
the sampling module 1003 is used for sampling the seismic average velocity, the well point average velocity and the well point interval 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 structural grid, and calculate a grid well point average velocity according to the well point average velocity sampled into the structural grid;
the correction module 1004 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 using the velocity anisotropy coefficient to obtain a corrected seismic average velocity;
a velocity conversion module 1005 for converting the corrected seismic mean velocity to a corrected seismic interval velocity;
a processing module 1006, configured to perform detrending processing on the corrected seismic interval velocity to obtain a seismic residual interval velocity;
the processing module 1006 is further configured to perform spatial variation function analysis on the seismic residual interval velocity, and establish a seismic residual interval velocity model;
the processing module 1006 is further configured to perform trend recovery processing on the seismic residual interval velocity model to obtain a corrected interval velocity model;
the determining module 1002 is further configured to determine a interval velocity model body by using the well point interval velocity as hard data and the corrected interval velocity model as a data trend;
the model building module 1007 is configured to input the interval velocity model into a preset seismic interpretation velocity model frame to obtain a seismic interpretation velocity model.
In an implementation manner 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 earthquake average speed according to the three-dimensional earthquake migration root-mean-square speed;
and performing synthetic record analysis on the well by using the seismic data and the logging data to obtain the average speed of the well point and the layer speed of the well point.
In an implementation manner of the embodiment of the present invention, the determining module 1002 is further configured to:
performing synthetic record analysis on the well by using the seismic data and the logging data to obtain a time-depth relation of well points;
carrying out time-depth relationship consistency check on the time-depth relationship of the well points to obtain a check result;
and if 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 speed of the well point layer after the time-depth relation is corrected.
In an implementation manner of the embodiment of the present invention, the correcting module 1004 is configured to:
determining the ratio of the grid well point average velocity to the grid seismic average velocity as a velocity anisotropy coefficient;
calculating the clustering center of the velocity anisotropy coefficient by using a DBSCAN algorithm;
carrying out cubic spline curve interpolation on the clustering center to obtain the functional relation between the anisotropy coefficient and the double-pass time;
and correcting the grid seismic average velocity by using the velocity anisotropy coefficient and the function relation of the two-way time to obtain the corrected seismic average velocity.
In an implementation manner of the embodiment of the present invention, the correcting module 1004 is configured to:
using Vavg_seis_m＝Vavg_seis×Fani(TWT) calculating corrected seismic mean velocity Vavg_seis_m；
Wherein, Vavg_seisRepresenting grid seismic mean velocity；Fani(TWT) represents the velocity anisotropy coefficient as a function of two-way.
In an implementation manner of the embodiment of the present invention, the apparatus 1000 further includes:
the domain conversion module 1008 is used for performing 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 well point average speed, and determine an error between the domain conversion result and the well point average speed;
the processing module 1006 is further configured to reestablish the seismic residual layer velocity model when the error is greater than the preset error threshold, and call the determining module 1002 and the model building module 1007 to perform subsequent processing according to the reestablished seismic residual layer velocity model, so as to reestablish the seismic interpretation velocity model.
The device for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation phase provided by the embodiment of the invention determines the seismic average velocity, the well point average velocity and the well point interval velocity by using seismic data and logging data as data sources, then samples the velocities into a construction grid, and determines the grid seismic average velocity and the grid well point average velocity; then obtaining 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 the corrected seismic interval velocity, the velocity data generally has obvious vertical trend (namely depth trend) in consideration of the change condition of a target interval in the longitudinal direction, the corrected seismic interval velocity is subjected to vertical detrending treatment to establish a residual interval velocity model, the model is subjected to trend recovery treatment to obtain a corrected interval velocity model, the corrected interval velocity model is used as trend data to constrain well data (namely well point interval velocity) to establish an interval velocity model body, the interval velocity model body is used for obtaining a final seismic interpretation velocity model, the integration of different scales of velocity data of wells and earthquakes is realized, the problems that the velocity field in an area with rapid velocity change needs to keep the well point velocity consistent and the well velocity deviation as small as possible are well solved, the velocity modeling precision in the oil and gas reservoir evaluation stage is effectively improved, ensuring the reliability of the three-dimensional structure chart.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the method for establishing the seismic interpretation velocity model in the oil and gas reservoir evaluation stage when executing the computer program.
Embodiments of the present invention further provide a computer-readable storage medium storing a computer program for executing the method for establishing a seismic interpretation velocity model in the evaluation stage of the hydrocarbon reservoir.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (14)
1. A method of establishing a seismic interpretation velocity model during a reservoir evaluation phase, the method comprising:
acquiring logging data and seismic data;
determining the average seismic velocity, the average well point velocity and the interval well point velocity according to the logging data and the seismic data;
sampling the seismic average velocity, the well point average velocity and the well point interval velocity into a preset construction grid;
calculating the average seismic speed of the grid according to the average seismic speed sampled into the construction grid, and calculating the average well point speed of the grid according to the average well point speed 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 interval velocity;
trend removing processing is carried out on the corrected seismic interval velocity to obtain the seismic residual interval velocity;
carrying out space variation function analysis on the seismic residual layer velocity, and establishing a seismic residual layer velocity model;
performing recovery trend processing on the seismic residual layer velocity model to obtain a corrected layer velocity model;
determining a layer velocity model body by taking the well point layer velocity as hard data and a correction layer velocity model as a data trend;
and inputting the layer velocity model body into a preset seismic interpretation velocity model frame to obtain a seismic interpretation velocity model.
2. The method of claim 1, wherein determining the seismic mean velocity, the well point mean velocity, and the well point interval velocity from the log data and the seismic data comprises:
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 earthquake average speed according to the three-dimensional earthquake migration root-mean-square speed;
and performing synthetic record analysis on the well by using the seismic data and the logging data to obtain the average speed of the well point and the layer speed of the well point.
3. The method of claim 1 or 2, wherein prior to sampling the seismic average velocity, the well point average velocity, and the well point interval velocity into a preset formation grid, the method further comprises:
performing synthetic record analysis on the well by using the seismic data and the logging data to obtain a time-depth relation of well points;
carrying out time-depth relationship consistency check on the time-depth relationship of the well points to obtain a check result;
and if 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 speed of the well point layer after the time-depth relation is corrected.
4. The method of claim 1, wherein determining a velocity anisotropy coefficient from the grid seismic mean velocity and the grid well point mean velocity, and using the velocity anisotropy coefficient to correct the grid seismic mean velocity to obtain a corrected seismic mean velocity comprises:
determining the ratio of the grid well point average velocity to the grid seismic average velocity as a velocity anisotropy coefficient;
calculating the clustering center of the velocity anisotropy coefficient by using a DBSCAN algorithm;
carrying out cubic spline curve interpolation on the clustering center to obtain the functional relation between the anisotropy coefficient and the double-pass time;
and correcting the grid seismic average velocity by using the velocity anisotropy coefficient and the function relation of the two-way time to obtain the corrected seismic average velocity.
5. The method of claim 4, wherein the step of correcting the grid seismic mean velocity using the velocity anisotropy coefficient and a two-way time functional relationship to obtain a corrected seismic mean velocity comprises:
using Vavg_seis_m＝Vavg_seis×Fani(TWT) calculating corrected seismic mean velocity Vavg_seis_m；
Wherein, Vavg_seisRepresenting the grid seismic average velocity; fani(TWT) represents the velocity anisotropy coefficient as a function of two-way.
6. The method of claim 1, wherein after inputting the interval velocity model into a preset seismic interpretation velocity model framework to obtain a seismic interpretation velocity model, the method further comprises:
performing domain conversion on the seismic interpretation velocity model to obtain a domain conversion result;
comparing the domain conversion result with the average speed of the well points, and determining the error between the domain conversion result and the average speed of the well points;
and if the error is larger than the preset error threshold value, correcting the seismic residual layer velocity model, performing subsequent processing according to the re-corrected seismic residual layer velocity model, and re-establishing the seismic interpretation velocity model.
7. An apparatus for creating a seismic interpretive velocity model during a reservoir evaluation phase, the apparatus comprising:
the acquisition module is used for acquiring logging data and seismic data;
the determining module is used for determining the seismic average velocity, the well point average velocity and the well point interval velocity according to the logging data and the seismic data;
the sampling module is used for sampling the seismic average velocity, the well point average velocity and the well point interval velocity into a preset construction grid;
the determining module is further 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 using 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 a corrected seismic interval velocity;
the processing module is used for performing trend removing processing on the corrected seismic interval velocity to obtain a seismic residual interval velocity;
the processing module is also used for carrying out space variation function analysis on the seismic residual layer velocity and establishing a seismic residual layer velocity model;
the processing module is also used for carrying out recovery trend processing on the seismic residual interval velocity model to obtain a corrected interval velocity model;
the determining module is also used for determining a layer velocity model body by taking the well point layer velocity as hard data and the corrected layer velocity model as a data trend;
and the model construction module is used for inputting the interval velocity model body into a preset seismic interpretation velocity model frame to obtain the seismic interpretation velocity model.
8. The apparatus of claim 7, wherein the means for determining 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 earthquake average speed according to the three-dimensional earthquake migration root-mean-square speed;
and performing synthetic record analysis on the well by using the seismic data and the logging data to obtain the average speed of the well point and the layer speed of the well point.
9. The apparatus of claim 7 or 8, wherein the determining module is further configured to:
performing synthetic record analysis on the well by using the seismic data and the logging data to obtain a time-depth relation of well points;
carrying out time-depth relationship consistency check on the time-depth relationship of the well points to obtain a check result;
and if the time-depth relation is inconsistent, correcting the time-depth relation of the well point, and re-determining the average speed of the well point and the layer speed of the well point after the time-depth relation is corrected.
10. The apparatus of claim 7, wherein the correction module is configured to:
determining the ratio of the grid well point average velocity to the grid seismic average velocity as a velocity anisotropy coefficient;
calculating the clustering center of the velocity anisotropy coefficient by using a DBSCAN algorithm;
performing cubic spline curve interpolation on the clustering center to obtain the function relationship between the anisotropy coefficient and the double-pass time;
and correcting the grid seismic average velocity by using the velocity anisotropy coefficient and the function relation of the two-way time to obtain the corrected seismic average velocity.
11. The apparatus of claim 10, wherein the correction module is configured to:
using Vavg_seis_m＝Vavg_seis×Fani(TWT) calculating corrected seismic mean velocity Vavg_seis_m；
Wherein, Vavg_seisRepresenting the grid seismic average velocity; fani(TWT) represents the velocity anisotropy coefficient as a function of two-way.
12. The apparatus of claim 7, further comprising:
the domain conversion module is used for carrying out domain conversion on the seismic interpretation velocity model to obtain a domain conversion result;
the comparison module is used for comparing the domain conversion result with the well point average speed and determining the error between the domain conversion result and the well point average speed;
and the processing module is also used for reestablishing the seismic residual layer velocity model when the error is larger than the preset error threshold value, calling the determining module and the model building module to perform subsequent processing according to the reestablished seismic residual layer velocity model, and reestablishing the seismic interpretation velocity model.
13. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 6 when executing the computer program.
14. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 6.
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