Device and method for predicting azimuth angle of main stream line of narrow channel type reservoir

Abstract

The present application relates to narrow small river type reservoir main flow line azimuth prediction device and method. The top layer and the bottom layer of the narrow small river type reservoir are explained; the seismic attribute of the narrow small river type reservoir is extracted, and the sensitive seismic attribute reflecting the plane distribution characteristics of the river channel is selected; the inner boundary and the outer boundary of the narrow small river type reservoir are prepared according to the sensitive seismic attribute; the sensitive seismic attribute is converted into a binary image in combination with the inner boundary and the outer boundary of the narrow small river type reservoir; the skeleton image of the binary image is obtained by using the image morphology skeleton extraction algorithm, the curve segment is gradually extracted from the skeleton image, and each node on the curve segment is subtracted from the skeleton image until the skeleton image no longer contains the node; and the azimuth of each curve segment and the azimuth of the inner boundary and the outer boundary of the narrow small river type reservoir are calculated.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oilfield development, in particular to a narrow channel type reservoir main flow line azimuth prediction device and method. BACKGROUND

[0002] Fluvial facies reservoir is one of the most important oil and gas reservoirs in China's offshore, accounting for more than 40% of the total oil and gas reserves. Developing fine description of fluvial facies reservoir and establishing high-precision reservoir geological model are of great significance to implement the development of fluvial facies reservoir. However, fluvial facies reservoir has the characteristics of severe lateral migration swing and strong heterogeneity, and the well spacing of offshore well pattern is sparse, the azimuth of fluvial facies reservoir main flow line changes dramatically, so it is challenging to realize fine geological modeling of fluvial facies reservoir.

[0003] At present, sequential indicator simulation method is generally used to describe sand body physical properties in geological modeling, which needs to determine the sandstone variable range direction as a constraint to constrain the planar distribution of channel reservoir in geological modeling. At present, the macro qualitative sediment source direction is usually used as the main variable range direction to constrain geological modeling. However, fluvial facies reservoir has the characteristics of severe lateral migration swing, and the macro qualitative sediment source direction has low accuracy in constraining channel distribution, which often causes the problem that the modeling result does not conform to the actual geological condition. SUMMARY

[0004] In view of the above problems, the purpose of the present application is to provide a narrow channel type reservoir main flow line azimuth prediction device and method, which can obtain the azimuth attribute of the main flow line of the channel and guide the geological modeling of the fluvial facies reservoir.

[0005] To achieve the above purpose, the present application adopts the following technical scheme:

[0006] A narrow channel type reservoir main flow line azimuth prediction method, comprising the following steps:

[0007] Interpret the top surface and the bottom surface of the narrow channel type reservoir;

[0008] Extract the seismic attribute of the narrow channel type reservoir, and select the sensitive seismic attribute reflecting the planar distribution characteristics of the channel;

[0009] According to the sensitive seismic attribute, the inner boundary and the outer boundary of the narrow channel type reservoir are prepared;

[0010] The sensitive seismic attribute is converted into a binary image by combining the inner boundary and the outer boundary of the narrow channel type reservoir, the area between the inner boundary and the outer boundary of the narrow channel type reservoir is assigned as 1, the area outside the outer boundary of the narrow channel type reservoir is assigned as 0, and the area inside the inner boundary of the narrow channel type reservoir is assigned as 0;

[0011] extracting curve segments from the skeleton image step by step, and subtracting each node on the curve segments from the skeleton image until the skeleton image no longer contains nodes; and

[0012] calculating the azimuth angle of each extracted curve segment, and the azimuth angle of the inner boundary and the outer boundary of the narrow and small channel type reservoir.

[0013] The present application has the following advantages: the fluvial facies reservoir fine geological modeling is realized. BRIEF DESCRIPTION OF DRAWINGS

[0014] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a better understanding of the preferred embodiments, and are not to be considered limitations of the present application. Throughout the drawings, like reference numerals will be used to denote like components. In the drawings:

[0015] Figure 1 a flow chart of the narrow and small channel type reservoir main flow line azimuth angle prediction method provided for the implementation of the present application;

[0016] Figure 2 a reservoir sensitive seismic attribute plan view of the extracted Bohai A oilfield sand body 1;

[0017] Figure 3 the reservoir inner and outer boundaries of the prepared sand body 1;

[0018] Figure 4 a binary image obtained by converting the reservoir sensitive seismic attribute of the sand body 1;

[0019] Figure 5 a skeleton image of the sand body 1 obtained by using the skeleton extraction algorithm; and

[0020] Figure 6 a sand body 1 main flow line azimuth angle prediction attribute map finally obtained.

[0021] In the drawings, each mark represents the following:

[0022] 1. Sand body. DETAILED DESCRIPTION

[0023] Exemplary embodiments of the present application will be described herein below with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present application can be more thoroughly and completely conveyed to those skilled in the art.

[0024] A narrow channel type reservoir main flow line azimuth prediction method, comprising the following steps:

[0025] 1) complete the narrow channel type reservoir top and bottom surface interpretation;

[0026] 2) extract the narrow channel type reservoir seismic attribute, preferably the sensitive seismic attribute which can better reflect the channel plane distribution characteristics;

[0027] 3) according to the sensitive seismic attribute, complete the narrow channel type reservoir inner and outer boundary preparation;

[0028] 4) combine the reservoir inner and outer boundaries to convert the sensitive seismic attribute into a binary image, the region between the inner and outer boundaries is assigned a value of 1, and the region outside the outer boundary and inside the inner boundary is assigned a value of 0;

[0029] 5) obtain the skeleton image of the above-mentioned binary image by using the image morphology skeleton extraction algorithm;

[0030] 6) extract the curve segment from the skeleton image, and subtract each node of the curve segment from the skeleton image until the skeleton extraction result no longer contains the node;

[0031] 7) calculate the azimuth of each curve segment and the azimuth of the reservoir inner and outer boundaries;

[0032] 8) obtain the azimuth of each sample point on the plane by using the interpolation algorithm;

[0033] 9) retain the azimuth result between the reservoir inner and outer boundaries, and assign a value of 0 to the azimuth of the remaining positions, that is, obtain the narrow channel reservoir main flow line azimuth prediction attribute.

[0034] As shown in Figure 1 , taking the fluvial facies reservoir of Bohai A oilfield as an example, the method realizes the main flow line azimuth attribute prediction of the narrow channel type reservoir sand body 1, which specifically comprises the following steps:

[0035] 1) trace the top and bottom horizons of the sand body 1 in the seismic data to form a top and bottom horizon time grid;

[0036] 2) extract the reservoir seismic attribute of the sand body 1 from the seismic data along the top and bottom horizon time grid, a total of 4 attributes including minimum amplitude, root mean square amplitude, average amplitude and instantaneous frequency are extracted. It is found that the minimum amplitude attribute can better reflect the channel plane distribution characteristics of the sand body 1, and the smaller the minimum amplitude attribute value, the better the reservoir development degree, as shown in Figure 2 , the minimum amplitude attribute is used as the sensitive seismic attribute;

[0037] 3) as shown in Figure 3As shown in FIG. 1, the inner boundary of the reservoir is the boundary of the sensitive seismic attribute inside the reservoir (the location where the value becomes large), and the outer boundary of the reservoir is the boundary where the sensitive seismic attribute outside the reservoir gradually pinches out (the location where the value becomes large).

[0038] 4) As shown in FIG. 4, the sensitive seismic attribute map is converted into a binary image according to the inner and outer boundaries of the sand body 1. The region between the inner and outer boundaries is assigned a value of 1 by logical operation, and the region outside the outer boundary and inside the inner boundary is assigned a value of 0. Figure 4

[0039] 5) As shown in FIG. 5, the skeleton of the binary image of the sand body 1 is extracted by using the image morphological skeleton extraction algorithm to form a skeleton image. The location with a value of 1 in the skeleton image is the skeleton of the image, representing the main flow line of the sand body 1, and the location with a value of 0 is the non-skeleton information of the image. Figure 5

[0040] 6) The curve segments are extracted from the skeleton image in a loop. As shown in FIG. 6, any point on the curve segment satisfies that the value of the corresponding position of the point on the skeleton image is 1. The end points of the extracted curve segments are marked as star points in FIG. 6. Further, each point on the curve segment is subtracted from the skeleton image in sequence, that is, the corresponding position of each point on the curve segment in the skeleton image is assigned a value of 0, until the skeleton image no longer contains points with a value of 1. Figure 5

[0041] 7) All points on the curve segments, all points on the inner boundary of the reservoir, and all points on the outer boundary of the reservoir are recorded as known points. The azimuth angle of each known point is calculated, and the calculated azimuth angle value of each known point is placed at the corresponding position of the known point in the plan view.

[0042] 8) The coordinates and corresponding azimuth angle values of the known points obtained above are used as input data to calculate the azimuth angle value of each sample point on the sensitive attribute plane of the sand body 1 by using the planar irregular interpolation algorithm.

[0043] 9) As shown in FIG. 9, the azimuth angle value interpolation result between the inner and outer boundaries of the sand body 1 reservoir is retained, and the azimuth angle values of the regions inside the inner boundary and outside the outer boundary are assigned a value of 0, thereby obtaining the main flow line azimuth angle prediction attribute of the sand body 1. The value of each point in the attribute represents the main flow line azimuth of the sand body 1 reservoir at the point. Figure 6

[0044] In some embodiments, a main flow line azimuth angle prediction method for a narrow and small channel type reservoir includes the following steps:

[0045] (1) The top and bottom layer surfaces of the narrow and small channel type reservoir are interpreted;​​​​

[0046] (2) Extract the seismic attribute of narrow channel type reservoir, and optimize the sensitive seismic attribute which can better reflect the planar distribution characteristics of the channel;

[0047] (3) According to the sensitive seismic attribute, the inner and outer boundaries of the narrow channel type reservoir are compiled;

[0048] (4) The sensitive seismic attribute is converted into a binary image combined with the inner and outer boundaries of the reservoir, the area between the inner and outer boundaries is assigned a value of 1, and the area outside the outer boundary and inside the inner boundary is assigned a value of 0;

[0049] (5) The skeleton image of the above binary image is obtained by using the image morphological skeleton extraction algorithm;

[0050] (6) The curve segments are extracted from the skeleton image step by step, and each node on the curve segment is subtracted from the skeleton image until the skeleton image no longer contains nodes;

[0051] (7) The azimuth angle of each curve segment extracted and the azimuth angle of the inner and outer boundaries of the reservoir are calculated;

[0052] (8) The azimuth angle of each sample point on the plane is obtained by using the interpolation algorithm;

[0053] (9) The azimuth angle result between the inner and outer boundaries of the reservoir is retained, and the azimuth angle of the remaining positions is assigned a value of 0 to obtain the main flow line azimuth angle prediction attribute of the narrow channel type reservoir.

[0054] The specific method for gradually extracting the curve segment from the skeleton image and subtracting each node on the curve segment from the skeleton image is as follows:

[0055] ① First, record the x, y coordinates of all sample points with a value of 1 in the skeleton image, and sort the above sample points in ascending order of x coordinate value. If there are two sample points with the same x coordinate, arrange them in ascending order of y coordinate data. The final recorded sample point set is called a sample library;

[0056] ② Divide the points in the sample library into nodes and endpoints. When determining whether a point P1 is a node or an endpoint, use a 3*3 filter centered on P1, and the eight points around P1 are respectively marked as P2-P9. If at most two of the eight points have a value of 1 at the corresponding position in the skeleton image, P1 is a node, otherwise P1 is an endpoint. Label the endpoints in the sample library.

[0057] ③From the first node P0 in the sample library, find the nearest point P1 to the node and connect the node P0 with P1, then delete P0 and P1 from the sample library, and then find the nearest point to the previous point in the sample library and delete it from the sample library, until the found point is an end point, end the finding of new points, and save the curve segment formed by the connection as L1. Repeat the above operation to start the construction of curve segment L2, until there is no point in the sample library, complete the extraction of multiple curve segments L1 to Ln from the skeleton image.

[0058] The azimuth of each extracted curve segment and the azimuth of the inner and outer boundaries of the reservoir are calculated as follows:

[0059] ①For each extracted curve segment, the azimuth of all points on the curve segment is calculated. For the inner boundary of the reservoir, which is usually a plurality of closed polygons, the azimuth of all points on each polygon is calculated. For the outer boundary of the reservoir, which is a closed polygon, the azimuth of all points on the polygon is calculated.

[0060] ②The formula used to calculate the azimuth of any point P is:

[0061] θ = tan -1 ((y1-y2) / (x1-x2)), where θ is the azimuth value (unit: degree) of point P on each extracted curve segment, or the inner boundary of the reservoir, or the outer boundary of the reservoir, tan -1 represents the inverse tangent function, x1, y1 are the x, y coordinate values of any point P, and x2, y2 are the x, y coordinate values of the next point adjacent to point P.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not limited thereto; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent replacements to some technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting the azimuth of the main flow line in a narrow channel type reservoir, characterized in that, The method comprises the following steps: Interpret the top and bottom surfaces of the narrow and small channel type reservoir; Extract seismic attributes of the narrow and small channel type reservoir, and select sensitive seismic attributes reflecting the planar distribution characteristics of the channel; Draw the inner boundary and outer boundary of the narrow and small channel type reservoir according to the sensitive seismic attributes; Combine the inner boundary and outer boundary of the narrow and small channel type reservoir, convert the sensitive seismic attributes into a binary image, assign a value of 1 to the area between the inner boundary and the outer boundary of the narrow and small channel type reservoir, assign a value of 0 to the area outside the outer boundary of the narrow and small channel type reservoir, and assign a value of 0 to the area inside the inner boundary of the narrow and small channel type reservoir; Obtain a skeleton image of the binary image by using an image morphological skeleton extraction algorithm, gradually extract curve segments from the skeleton image, and subtract each node on the curve segment from the skeleton image until the skeleton image no longer contains nodes; and Calculate the azimuth angles of each curve segment and the inner boundary and outer boundary of the narrow and small channel type reservoir.

2. The method of claim 1, wherein, The method further comprises the step of obtaining the azimuth angle of each sample point on the channel plane by using an interpolation algorithm.

3. The method of claim 2, wherein, The method further comprises the steps of retaining the azimuth angle results between the inner boundary and the outer boundary of the narrow and small channel type reservoir, assigning a value of 0 to the azimuth angle at other positions, and obtaining the main flow line azimuth angle prediction attribute of the narrow and small channel type reservoir.

4. The method of claim 2, wherein, Record the x and y coordinates of all sample points with a value of 1 in the skeleton image, and sort the sample points in ascending order of the x coordinate value, and if the x coordinates of two sample points are the same, the two sample points are arranged in ascending order of the y coordinate value, and the finally recorded sample point set is used as a sample point library.

5. The method of claim 4, wherein, Divide the sample points in the sample point library into nodes and endpoints, wherein the following method is used to determine whether the sample point is a node or an endpoint: set a 3*3 filter centered on the sample point, and record the eight sample points around the sample point as P2-P9, if at most two sample points in the eight sample points have a value of 1 at the corresponding position in the skeleton image, the sample point is a node, otherwise the sample point is an endpoint.

6. The method of claim 5, wherein, Starting from the first node in the sample point library, find the sample point closest to the first node and connect it to the node, then delete the first node and the sample point closest to the first node from the sample point library, and then find the sample point closest to the previous sample point in the sample point library and delete it from the sample point library, until the found sample point is an endpoint, end the search for new sample points, and save the connected curve segment as the first curve segment.

7. The method of claim 6, wherein, Start the construction of the second curve segment until there is no sample point in the sample point library, and extract multiple curve segments from the skeleton image.

8. The method of claim 7, wherein, For each curve segment, calculate the azimuth angle of all sample points on each curve segment, for the inner boundary of the reservoir which is a plurality of closed polygons, calculate the azimuth angle of all sample points on each polygon, and for the outer boundary of the reservoir which is a closed polygon, calculate the azimuth angle of all sample points on the polygon.

9. The method of claim 8, wherein, The formula used to calculate the azimuth angle of any point is: θ = tan -1 ((y1-y2) / (x1-x2)), where θ is the azimuth angle value of a sample point on each extracted curve segment or an intra- or extra-reservoir boundary, tan -1 represents the arctangent function, x1, y1 are the x, y coordinate values of the sample point, and x2, y2 are the x, y coordinate values of the next point immediately adjacent to the sample point.

10. A device for predicting the azimuth of the main flow line in a narrow channel type reservoir, characterized in that, The method comprises: an interpretation unit configured to interpret the top and bottom surfaces of the narrow and small channel type reservoir; a sensitive seismic attribute unit configured to extract seismic attributes of the narrow and small channel type reservoir, and select sensitive seismic attributes reflecting the planar distribution characteristics of the channel; The boundary unit is configured to compile the inner boundary and the outer boundary of the narrow and small channel type reservoir according to the sensitive seismic attribute; The assignment unit is configured to convert the sensitive seismic attribute into a binary image by combining the inner boundary and the outer boundary of the narrow and small channel type reservoir, assign a value of 1 to the area between the inner boundary and the outer boundary of the narrow and small channel type reservoir, assign a value of 0 to the area outside the outer boundary of the narrow and small channel type reservoir, and assign a value of 0 to the area inside the inner boundary of the narrow and small channel type reservoir; The skeleton extraction unit is configured to obtain a skeleton image of the binary image by using an image morphological skeleton extraction algorithm, gradually extract a curve segment from the skeleton image, and subtract each node on the curve segment from the skeleton image until the skeleton image no longer contains the node; and The azimuth calculation unit is configured to calculate the azimuth of each extracted curve segment and the azimuth of the inner boundary and the outer boundary of the narrow and small channel type reservoir.

Images