CN117950017A - Reservoir prediction parameter fusion method and device - Google Patents
Reservoir prediction parameter fusion method and device Download PDFInfo
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Abstract
The application relates to a reservoir prediction parameter fusion method and device, wherein the method comprises the following steps: obtaining geological data of a reservoir; establishing a response characteristic model of the reservoir through the geological data; extracting geophysical information for the reservoir; calculating the vibration arc attribute of the reservoir according to the geophysical information; and generating a seismic attribute map of the reservoir according to the vibration arc attribute. The reservoir prediction parameter fusion method and device provided by the application are suitable for various lithologic reservoirs, have wide application prospects, and have very important significance for oil reservoir exploration and development; the method can improve the reservoir prediction precision and effectively improve the reservoir prediction precision and the success rate of the new well drilling in the reservoir.
Description
Technical Field
The application relates to the field of petroleum and natural gas drilling exploration and development, in particular to a reservoir prediction parameter fusion method and device.
Background
Improving reservoir prediction accuracy has been an important geological awareness requirement and goal for oil and gas exploration and development, and seismic attribute calculation can be used as a main means for solving the problems. In general, the extracted seismic attributes reflect geometric, kinematic, dynamic and statistical characteristics of the seismic waveform, and in practical situations, a single attribute cannot accurately describe geological problems and target information, and multiple attribute images of the problems and targets need to be fused so as to more comprehensively and accurately describe the problems and target information.
In the research of the existing reservoir prediction technology, common seismic attribute fusion methods comprise RGB fusion, cluster analysis, multiple linear regression and other seismic attribute fusion, but the existing methods have the problems of complex operation process, low reservoir prediction precision and the like.
Disclosure of Invention
In order to solve the technical problems or at least partially solve the technical problems, the application provides a reservoir prediction parameter fusion method and device.
In a first aspect, the present application provides a reservoir prediction parameter fusion method, the method comprising the steps of:
Obtaining geological data of a reservoir;
establishing a response characteristic model of the reservoir through the geological data;
Extracting geophysical information for the reservoir;
calculating the vibration arc attribute of the reservoir according to the geophysical information;
And generating a seismic attribute map of the reservoir according to the vibration arc attribute.
Preferably, said establishing a response characteristic model of said reservoir from said geological data comprises the steps of:
Acquiring logging data and seismic data of the reservoir;
Establishing a seismic wave event of the reservoir according to the logging data and the seismic data;
performing horizon calibration on the reservoir in the seismic wave homophase shaft;
And obtaining a response characteristic model generated after horizon calibration.
Preferably, the extracting geophysical information for the reservoir comprises the steps of:
comparing each stratum of the reservoir;
Tracking and explaining the sequence of the reservoir;
constructing a three-dimensional data volume of the reservoir according to the tracking interpretation result;
Determining a time window for the reservoir;
Cutting out sub-volumes from the three-dimensional data volume according to the time window;
and acquiring geophysical information of the reservoir through the sub-body.
Preferably, the expression of the time window is:
Wherein t represents a time window, H represents the thickness of a reservoir, V represents the propagation speed of seismic waves in a medium, delta represents error time, H is less than 10m, and 0 is less than or equal to delta is less than 1ms.
Preferably, the expression of the time window is:
Wherein t represents a time window, H represents the thickness of a reservoir, V represents the propagation speed of seismic waves in a medium, delta represents error time, H is more than or equal to 10m, and 0 is less than or equal to delta < 1ms.
Preferably, said calculating the arc attribute of the reservoir from the geophysical information comprises the steps of:
Determining an effective frequency band for the reservoir;
Extracting root mean square amplitude attribute and arc length attribute of the reservoir;
Acquiring a vibration arc attribute calculation formula;
And substituting the root mean square amplitude attribute and the arc length attribute into the arc vibration attribute calculation formula to obtain the arc vibration attribute.
Preferably, the expression of the vibration arc attribute calculation formula is:
AWY＝A*AL+B*ARMS；
wherein AWY denotes an arc vibration attribute, AL denotes an arc length attribute, ARMS denotes a root mean square amplitude attribute, and a and B denote scaling factors.
Preferably, the arc length attribute is expressed as:
Wherein x (i+1) represents the amplitude value of the (i+1) th sample in the time window, x (i) represents the amplitude value of the i th sample in the time window, N represents the number of samples in the time window, and T represents the sampling interval.
Preferably, the expression of the root mean square amplitude attribute is:
wherein x (i) represents the amplitude value of the ith sample point in the time window, and N represents the number of sample points in the time window.
In a second aspect, the present application further provides a reservoir prediction parameter fusion device, including:
the geological data acquisition module is used for acquiring geological data of the reservoir;
the response characteristic model building module is used for building a response characteristic model of the reservoir through the geological data;
A geophysical information extraction module for extracting geophysical information for the reservoir;
The vibration arc attribute calculation module is used for calculating the vibration arc attribute of the reservoir according to the geophysical information;
and the seismic attribute map generation module is used for generating a seismic attribute map of the reservoir according to the vibration arc attribute.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
The reservoir prediction parameter fusion method and device provided by the invention are suitable for various lithologic reservoirs, have wide application prospects, and have very important significance for oil reservoir exploration and development; the method can improve the reservoir prediction precision and effectively improve the reservoir prediction precision and the success rate of the new well drilling in the reservoir.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a reservoir prediction parameter fusion method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of seismic synthetic record calibration in a reservoir prediction parameter fusion method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a reservoir time window in a reservoir prediction parameter fusion method according to an embodiment of the present application;
Fig. 4 is a schematic diagram of specific calculation of vibration arc attribute in a reservoir prediction parameter fusion method according to an embodiment of the present application;
FIG. 5 is a schematic diagram of vibration arc attribute in a reservoir prediction parameter fusion method according to an embodiment of the present application;
Fig. 6 is a schematic structural diagram of a reservoir prediction parameter fusion device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Fig. 1 is a schematic flow chart of a reservoir prediction parameter fusion method according to an embodiment of the present application.
The application provides a reservoir prediction parameter fusion method, which comprises the following steps:
S1: obtaining geological data of a reservoir;
Specifically, the geological data of the reservoir includes: logging data and seismic data of a reservoir, in particular, logging data and seismic data, may be obtained by various dedicated detection devices, and the detection devices and detection methods are fundamental knowledge to those skilled in the art, and thus are not described in detail herein.
S2: establishing a response characteristic model of the reservoir through the geological data;
in an embodiment of the present application, the establishing a response characteristic model of the reservoir through the geological data includes the steps of:
Acquiring logging data and seismic data of the reservoir;
Establishing a seismic wave event of the reservoir according to the logging data and the seismic data;
performing horizon calibration on the reservoir in the seismic wave homophase shaft;
And obtaining a response characteristic model generated after horizon calibration.
Specifically, fine horizon calibration is a key method for endowing seismic waves with same phase axes to specific geological significance, is a key step in the comprehensive interpretation process of oil and gas reservoirs, and is also a foundation for determining seismic reflection characteristics of each target layer and oil production points and carrying out reservoir layer attribute extraction and transverse prediction. That is, horizon calibration is the first step in reservoir lateral prediction, which can be tracked along the in-phase axis only if the reservoir is accurately identified. At present, 3 methods are generally adopted at home and abroad to calibrate the horizon, and the method is as follows: (1) Cross-well Profile method: and (3) calibrating the seismic horizon by selecting the seismic section of the well and utilizing the stratum layering interface of the well. This is the most widely used method in early stages, and is convenient to use without additional treatment. However, the accuracy of calibration is not very high due to the difficulty in time-depth conversion. (2) synthetic seismic recording: synthetic seismic records are made from acoustic (or density) logs and then the locations of the reservoirs are calibrated on the through-the-well seismic profile. The key point of the method is the quality of the synthetic seismic record, which is often required to be corrected for multiple times. This is the most widely used method at present. (3) VSP method: horizon calibration is the most accurate method by using VSP (vertical seismic profile) data with zero offset. However, VSP data is not currently very common.
Specifically, through the logging data and the seismic data of the reservoir obtained in the step S1, the logging data and the seismic data can be represented in a coordinate axis according to time sequence, seismic wave phase axes of the reservoir can be established according to geological knowledge in the coordinate axis, and horizon calibration operation of the reservoir is performed in the seismic wave phase axes. In the method, a synthetic seismic recording method can be adopted to perform horizon calibration operation on the reservoir in the seismic wave event. And after the horizon calibration is completed in the coordinate graph, obtaining a response characteristic model of the reservoir.
S3: extracting geophysical information for the reservoir;
In an embodiment of the present application, the extracting geophysical information for the reservoir includes the steps of:
comparing each stratum of the reservoir;
Tracking and explaining the sequence of the reservoir;
constructing a three-dimensional data volume of the reservoir according to the tracking interpretation result;
Determining a time window for the reservoir;
Cutting out sub-volumes from the three-dimensional data volume according to the time window;
and acquiring geophysical information of the reservoir through the sub-body.
In particular, to extract geophysical information for a reservoir, a three-dimensional data volume for the reservoir needs to be created. Specifically, when comparing each stratum, the stratum is compared under the control of the isochronous stratum lattice frame by taking the stratum sequence as the boundary, and the stratum sequence interface is tracked and explained. The data of each stratum can be obtained by tracking and explaining the sequence, and a three-dimensional data volume of the reservoir can be created according to the data. Further, to develop an hour window seismic attribute analysis study for a target layer, it is necessary to determine the time window of the reservoir. And cutting the sub-body conforming to the time window from the three-dimensional data body, and acquiring the geophysical information of the reservoir from the sub-body obtained by cutting. The determination of the reservoir time window can better constrain the three-dimensional data volume to reflect the true information of the subsurface reservoir conditions.
In the embodiment of the present application, the expression of the time window is:
Wherein t represents a time window, H represents the thickness of a reservoir, V represents the propagation speed of seismic waves in a medium, delta represents error time, H is less than 10m, and 0 is less than or equal to delta is less than 1ms.
In the embodiment of the present application, the expression of the time window is:
Wherein t represents a time window, H represents the thickness of a reservoir, V represents the propagation speed of seismic waves in a medium, delta represents error time, H is more than or equal to 10m, and 0 is less than or equal to delta < 1ms.
S4: calculating the vibration arc attribute of the reservoir according to the geophysical information;
in an embodiment of the present application, the calculating the arc attribute of the reservoir according to the geophysical information includes the steps of:
Determining an effective frequency band for the reservoir;
Extracting root mean square amplitude attribute and arc length attribute of the reservoir;
Acquiring a vibration arc attribute calculation formula;
And substituting the root mean square amplitude attribute and the arc length attribute into the arc vibration attribute calculation formula to obtain the arc vibration attribute.
Specifically, the vibration arc attribute is to integrate root mean square amplitude attribute and arc length attribute, perform mathematical operation transformation on the two attributes, consider the influence factors of each attribute on the reservoir, amplify the dominant characteristics of the reservoir, combine the influence factors, calculate the fusion specific gravity of the two seismic attributes by using well positions, and calculate the vibration arc attribute according to the calculation formula of the vibration arc attribute.
Further, the root mean square amplitude Attribute (ARMS) and the arc length Attribute (AL) are fused according to a proportion relationship to generate an arc vibration Attribute (AWY), the minimum value and the maximum value of the arc length attribute AL are respectively AL min、ALmax, data among the data AL (AL 1,AL2) are selected to participate in fusion, and AL min≤AL1＜AL2≤ALmax; the minimum and maximum values of the root mean square amplitude attribute ARMS are ARMS min、ARMSmax respectively, the data between the ARMS (ARMS 1,ARMS2) are selected to participate in fusion, and the minimum and maximum values of the ARMS min≤ARMS1＜ARMS2≤ARMSmax and the data AWY after fusion are AWY min、AWYmax respectively.
In the present application, the minimum and maximum values of the data AWY are designed to be equal to the minimum and maximum values of the attribute data ARMS, that is, AWY min＝ARMSmin,AWYmax＝ARMSmax in the program. To blend attribute data AL and ARMS proportionally into data AWY, an appropriate value is selected between the minimum and maximum values of data AWY as a demarcation point AWY C and AWY min<AWYC<AWYmax.
In the embodiment of the application, the expression of the vibration arc attribute calculation formula is as follows:
AWY＝A*AL+B*ARMS； (3)
wherein AWY denotes an arc vibration attribute, AL denotes an arc length attribute, ARMS denotes a root mean square amplitude attribute, and a and B denote scaling factors.
Specifically, when the known wells exist in the predicted target area, A represents the proportionality coefficient of the number of wells of the drilling and encountering reservoir in the arc length attribute diagram to the total number of wells, and B represents the proportionality coefficient of the number of wells of the drilling and encountering reservoir in the root mean square amplitude attribute diagram to the total number of wells; when no known well exists in the predicted target area, A represents the proportionality coefficient of the arc length attribute predicted reservoir area occupied total area, and B represents the proportionality coefficient of the root mean square amplitude attribute predicted reservoir area occupied total area.
In the embodiment of the present application, the expression of the arc length attribute is:
Wherein x (i+1) represents the amplitude value of the (i+1) th sample in the time window, x (i) represents the amplitude value of the i th sample in the time window, N represents the number of samples in the time window, and T represents the sampling interval.
In the embodiment of the present application, the expression of the root mean square amplitude attribute is:
wherein x (i) represents the amplitude value of the ith sample point in the time window, and N represents the number of sample points in the time window.
S5: and generating a seismic attribute map of the reservoir according to the vibration arc attribute.
Specifically, in order to predict the development position favorable for the reservoir and finally realize attribute fusion, an optimized result is obtained, and after the vibration arc attribute is obtained in step S4, a seismic attribute map may be output according to the vibration arc attribute. When the reservoir prediction accuracy is verified, the reservoir prediction accuracy can be verified according to the proportion of the number of completed wells of the reservoir drilled in the reservoir prediction result map to the total number of wells in the target area.
The invention is described in detail below with reference to the drawings and the specific embodiments.
Taking GX oilfield XX well region reservoir NmII-8-3 as an example, the reservoir prediction parameter fusion method provided by the application specifically comprises the following steps:
the first step: establishing a response characteristic model of a reservoir in a seismic event:
The target area-clear ballasted river sand body is thin and is not compacted, the propagation speed of the target area-clear ballasted river sand body is smaller than that of the upper mudstone and the lower mudstone, and particularly, when the speed of the river sand body is obviously reduced after oil gas is contained in the river sand body, a stronger wave impedance interface is generated between the target area-clear ballasted river sand body and the upper and lower surrounding rocks, so that the river sand shows strong amplitude and low frequency on earthquake response, and the characteristics of bright spots are displayed along with polarity inversion, diffraction and other phenomena.
Based on the NmII-8-3 oil-containing sand body of the XX well, after the sand bodies of the upper and lower mudstone clamps contain oil, the speed is reduced, and two stronger reflection peaks which are symmetrical up and down are shown on the seismic section, so that a reservoir response characteristic model (figure 2) is built, the propagation time of the seismic wave in the NmII-8-3 oil-containing sand body is 1016ms, the depth is 1043m, and therefore the propagation speed of the seismic wave in the reservoir can be calculated to be V=2X104 3/1016=2.05m/ms.
And a second step of: extracting geophysical information of the reservoir:
well earthquake is developed in combination with fine stratum comparison, geological earthquake horizons are unified, objective intervals are leveled, influences of construction factors are removed, sedimentary phenomena are explained layer by layer, and various geologic bodies and lithology changes are drawn out.
For a target zone, the reservoir thickness is 6m, the time window t= 2*6/2.05+0.15 (the error time is 0.15ms from the known well back-thrust) =6 ms is calculated by formula (1), thus moving up 3ms from the interpretation horizon, moving down 3ms, opening a 6ms time window, using this time window to cut a sub-body from the three-dimensional data volume (fig. 3), and obtaining the geophysical information of the reservoir through the sub-body.
And a third step of: calculating the vibration arc attribute according to the vibration arc attribute formula, and outputting a graph:
The method utilizes the local discontinuity between the adjacent channels of seismic signals to describe the transverse heterogeneity of stratum, lithology and the like, and has good effect in identifying hidden geologic bodies. Taking a target area as an example, the main frequency of the seismic data of a target interval is 22Hz, and the effective frequency band is 20-50Hz, so that the amplitude attribute and the arc length attribute of the sand body are respectively extracted in the effective frequency band, the proportion coefficient A is 2/3 and the proportion coefficient B is 1/3 obtained through the condition of a known well, and a new vibration arc attribute diagram (figure 5) is formed by fusing the two values through a formula (3).
Fourth step: reservoir prediction accuracy verification:
the method is characterized in that the vibration arc attribute is used for predicting the matching of the XX well region reservoir condition and the 10 old well drilling reservoir condition (figure 4), the matching effect is good, and the reservoir prediction accuracy reaches 100%.
As shown in fig. 6, the present application further provides a reservoir prediction parameter fusion device, which includes:
A geological data acquisition module 10 for acquiring geological data of a reservoir;
a response feature model building module 20 for building a response feature model of the reservoir from the geological data;
A geophysical information extraction module 30 for extracting geophysical information for the reservoir;
an arc attribute calculation module 40 for calculating arc attributes of the reservoir based on the geophysical information;
a seismic attribute map generation module 50 for generating a seismic attribute map of the reservoir based on the arc of vibration attributes.
The reservoir prediction parameter fusion device provided by the application can execute the reservoir prediction parameter fusion method.
The reservoir prediction parameter fusion method and device provided by the invention are suitable for various lithologic reservoirs, have wide application prospects, and have very important significance for oil reservoir exploration and development; the method can improve the reservoir prediction precision and effectively improve the reservoir prediction precision and the success rate of the new well drilling in the reservoir.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of reservoir prediction parameter fusion, the method comprising the steps of:
Obtaining geological data of a reservoir;
establishing a response characteristic model of the reservoir through the geological data;
Extracting geophysical information for the reservoir;
calculating the vibration arc attribute of the reservoir according to the geophysical information;
And generating a seismic attribute map of the reservoir according to the vibration arc attribute.
2. The reservoir prediction parameter fusion method of claim 1, wherein the modeling of response characteristics of the reservoir from the geological data comprises the steps of:
Acquiring logging data and seismic data of the reservoir;
Establishing a seismic wave event of the reservoir according to the logging data and the seismic data;
performing horizon calibration on the reservoir in the seismic wave homophase shaft;
And obtaining a response characteristic model generated after horizon calibration.
3. The reservoir prediction parameter fusion method of claim 1, wherein the extracting the geophysical information for the reservoir comprises the steps of:
comparing each stratum of the reservoir;
Tracking and explaining the sequence of the reservoir;
constructing a three-dimensional data volume of the reservoir according to the tracking interpretation result;
Determining a time window for the reservoir;
Cutting out sub-volumes from the three-dimensional data volume according to the time window;
and acquiring geophysical information of the reservoir through the sub-body.
4. A reservoir prediction parameter fusion method as defined in claim 3, wherein the expression of the time window is:
Wherein t represents a time window, H represents the thickness of a reservoir, V represents the propagation speed of seismic waves in a medium, delta represents error time, H is less than 10m, and 0 is less than or equal to delta is less than 1ms.
5. A reservoir prediction parameter fusion method as defined in claim 3, wherein the expression of the time window is:
Wherein t represents a time window, H represents the thickness of a reservoir, V represents the propagation speed of seismic waves in a medium, delta represents error time, H is more than or equal to 10m, and 0 is less than or equal to delta < 1ms.
6. The reservoir prediction parameter fusion method of claim 1, wherein the calculating the arc attribute of the reservoir from the geophysical information comprises the steps of:
Determining an effective frequency band for the reservoir;
Extracting root mean square amplitude attribute and arc length attribute of the reservoir;
Acquiring a vibration arc attribute calculation formula;
And substituting the root mean square amplitude attribute and the arc length attribute into the arc vibration attribute calculation formula to obtain the arc vibration attribute.
7. The reservoir prediction parameter fusion method of claim 6, wherein the expression of the vibration arc attribute calculation formula is:
AWY＝A*AL+B*ARMS；
wherein AWY denotes an arc vibration attribute, AL denotes an arc length attribute, ARMS denotes a root mean square amplitude attribute, and a and B denote scaling factors.
8. The reservoir prediction parameter fusion method of claim 7, wherein the arc length attribute is expressed as:
Wherein x (i+1) represents the amplitude value of the (i+1) th sample in the time window, x (i) represents the amplitude value of the i th sample in the time window, N represents the number of samples in the time window, and T represents the sampling interval.
9. The reservoir prediction parameter fusion method of claim 7, wherein the expression of the root mean square amplitude attribute is:
wherein x (i) represents the amplitude value of the ith sample point in the time window, and N represents the number of sample points in the time window.
10. A reservoir prediction parameter fusion device, comprising:
the geological data acquisition module is used for acquiring geological data of the reservoir;
the response characteristic model building module is used for building a response characteristic model of the reservoir through the geological data;
A geophysical information extraction module for extracting geophysical information for the reservoir;
The vibration arc attribute calculation module is used for calculating the vibration arc attribute of the reservoir according to the geophysical information;
and the seismic attribute map generation module is used for generating a seismic attribute map of the reservoir according to the vibration arc attribute.
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