Method for identifying hidden channel based on pre-stack phase data
By using a method based on pre-stack phase data and employing multi-angle superposition and smoothing techniques, the problem of identifying hidden channels where the channel wave impedance is indistinguishable from the surrounding rock was solved, thus achieving more accurate channel identification.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2022-09-26
- Publication Date
- 2026-07-14
AI Technical Summary
Existing technologies make it difficult to identify hidden river channels where the channel impedance is essentially indistinguishable from the surrounding rock.
Based on pre-stack phase data, seismic data are superimposed at multiple incident angles, and the maximum value of the wave trough is picked to form layer data. After large-scale smoothing, the difference between the two is obtained to obtain layer attribute data. The river channel identification effect is confirmed by combining actual drilling or frequency fusion methods.
It effectively identifies hidden river channels where the channel impedance is essentially indistinguishable from the surrounding rock, avoiding the identification difficulties in existing technologies and improving the accuracy of river channel identification.
Smart Images
Figure CN117761774B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of petroleum exploration technology, and in particular to a method for identifying hidden channels based on pre-stack phase data. Background Technology
[0002] Currently, existing river channel identification methods mainly rely on the fact that variations in the river's physical properties cause differences in its wave impedance compared to the surrounding rock. By fusing different frequencies from pre-stack or post-stack data, the river can be relatively easily identified. However, when the river's wave impedance is essentially indistinguishable from the surrounding rock, existing methods struggle to identify this type of river.
[0003] In other words, existing river identification methods have difficulty identifying rivers where the wave impedance is essentially indistinguishable from the surrounding rock. Summary of the Invention
[0004] This invention provides a method for identifying hidden river channels based on pre-stack phase data, which solves the problem that existing river channel identification methods struggle to identify channels whose wave impedance is essentially indistinguishable from that of the surrounding rock.
[0005] This invention provides a method for identifying hidden river channels based on pre-stack phase data, comprising:
[0006] Step 1: Collect seismic geological data of the work area and determine that wave troughs are characteristic of river channel seismic response.
[0007] Step 2: Stack the pre-stack seismic data of the work area at multiple incident angles;
[0008] Step 3: Obtain the superimposed data for each incident angle;
[0009] Step 4: For the superimposed data of each incident angle at the trough position, according to the preset time window range, pick the time point corresponding to the maximum value of the trough within the time window range to form the layer data of each incident angle.
[0010] Step 5: Perform large-scale smoothing on the layer data for each incident angle to obtain smoothed layer data for each incident angle.
[0011] Step 6: Subtract the smoothed layer data from each incident angle to obtain multiple layer attribute data;
[0012] Step 7: Confirm the effectiveness of the obtained multi-layer attribute data in river channel identification and characterization.
[0013] In one implementation, step three specifically includes: obtaining superimposed data of three incident angles, wherein the three incident angles include a first incident angle, a second incident angle, and a third incident angle, and the three incident angles increase sequentially.
[0014] In one embodiment, the first incident angle is α, and the value of α ranges from 0 to 10°.
[0015] In one embodiment, the second incident angle is β, and the value of β ranges from 21° to 30°.
[0016] In one embodiment, the third incident angle is γ, and the value of γ ranges from 0° to β to 45°.
[0017] In one embodiment, step four specifically includes: at the trough positions of the first incident angle superimposed data, the second incident angle superimposed data, and the third incident angle superimposed data, according to a preset time window range, picking the time point corresponding to the maximum value of the trough within the time window range to form the first incident angle layer data, the second incident angle layer data, and the third incident angle layer data.
[0018] In one embodiment, step five specifically includes: performing large-scale smoothing on the first incident angle layer data, the second incident angle layer data, and the third incident angle layer data to obtain the first incident angle smoothed layer data, the second incident angle smoothed layer data, and the third incident angle smoothed layer data.
[0019] In one embodiment, step six specifically includes: subtracting the first incident angle smoothed layer data from the second incident angle smoothed layer data to obtain first layer attribute data; subtracting the first incident angle smoothed layer data from the third incident angle smoothed layer data to obtain second layer attribute data; and subtracting the second incident angle smoothed layer data from the third incident angle smoothed layer data to obtain third layer attribute data.
[0020] In one implementation, step seven specifically includes: confirming the effect of the obtained multiple layer attribute data on river channel identification and characterization through actual drilling or frequency division fusion methods.
[0021] Compared with existing technologies, the advantages of this invention lie in its ability to identify and characterize channels based on the time difference attributes of the maximum wave trough at different incident angles in the pre-stack channel concentration development area. This avoids the problem in existing technologies that rely on the difference in wave impedance between the channel and the surrounding rock caused by variations in channel physical properties, making it difficult to identify channels with essentially no difference in wave impedance between the channel and the surrounding rock. Attached Figure Description
[0022] The invention will now be described in more detail with reference to embodiments and the accompanying drawings.
[0023] Figure 1 This is a flowchart of the method for identifying hidden channels based on pre-stack phase data in an embodiment of the present invention;
[0024] Figure 2 Showing Figure 1 The specific method flowchart is shown below;
[0025] Figure 3 The diagram shows logging data of a typical well in an embodiment of the present invention;
[0026] Figure 4 This shows a schematic diagram of picking the maximum value of the trough within a certain time window range of small-angle superimposed data in an embodiment of the present invention;
[0027] Figure 5 This diagram illustrates the picking of the maximum value of the trough within a certain time window range for large-angle superimposed data in an embodiment of the present invention.
[0028] Figure 6 This illustrates a schematic diagram of picking the maximum value of the trough within a certain time window range of the fully superimposed data in an embodiment of the present invention;
[0029] Figure 7 The diagram shows a hor_near_s-hor_far_s plan view in an embodiment of the present invention;
[0030] Figure 8 The diagram shows a hor_near_s-hor_s planar view in an embodiment of the present invention;
[0031] Figure 9 The diagram shows a hor_far_s-hor_s plan view in an embodiment of the present invention;
[0032] Figure 10 The diagram shows a plan view of the river channel characterization based on the frequency division fusion attribute in an embodiment of the present invention. Detailed Implementation
[0033] The invention will now be further described with reference to the accompanying drawings.
[0034] like Figure 1 As shown, this invention provides a method for identifying hidden river channels based on pre-stack phase data, comprising:
[0035] Step 1: Collect seismic geological data of the work area and determine that wave troughs are characteristic of river channel seismic response.
[0036] Step 2: Stack the pre-stack seismic data of the work area at multiple incident angles;
[0037] Step 3: Obtain the superimposed data for each incident angle;
[0038] Step 4: For each incident angle superimposed data at the trough position, according to the preset time window range, pick the time point corresponding to the maximum value of the trough within the time window range to form the layer data of each incident angle.
[0039] Step 5: Perform large-scale smoothing on the incident angle layer data for each incident angle to obtain smoothed layer data for each incident angle.
[0040] Step 6: Subtract the smoothed layer data of each incident angle from each other to obtain multiple layer attribute data;
[0041] Step 7: Confirm the effect of the obtained multiple layer attribute data on river channel identification and characterization.
[0042] Based on the above steps, channel identification and characterization are performed using the time difference attributes of the maximum wave trough at different incident angles at the concentrated channel development point before the stack. This avoids the problem in existing technologies that rely on differences in channel wave impedance caused by variations in channel physical properties to identify channels with essentially no difference in wave impedance between the channel and the surrounding rock.
[0043] Specifically, in one embodiment of the present invention, step three specifically includes: obtaining superimposed data of three incident angles. The three incident angles include a first incident angle, a second incident angle, and a third incident angle, with the three incident angles increasing sequentially.
[0044] Specifically, in one embodiment of the present invention, the first incident angle is α, and the value of α is in the range of 0 < α ≤ 10°.
[0045] Specifically, in one embodiment of the present invention, the second incident angle is β, and the value range of β is 21°≤β≤30°.
[0046] Specifically, in one embodiment of the present invention, the third incident angle is γ, and the value of γ ranges from 0° to β to 45°. The third incident angle can also be referred to as the full angle.
[0047] Specifically, in one embodiment of the present invention, step four specifically includes: at the trough position of the first incident angle superimposed data, the second incident angle superimposed data, and the third incident angle superimposed data, according to a preset time window range, picking the time point corresponding to the maximum value of the trough within the time window range to form the first incident angle layer data, the second incident angle layer data, and the third incident angle layer data.
[0048] Specifically, in one embodiment of the present invention, step five specifically includes: performing large-scale smoothing on the first incident angle layer data, the second incident angle layer data, and the third incident angle layer data to obtain the first incident angle smoothed layer data, the second incident angle smoothed layer data, and the third incident angle smoothed layer data.
[0049] Specifically, in one embodiment of the present invention, step six specifically includes: subtracting the first incident angle smoothed layer data from the second incident angle smoothed layer data to obtain first layer attribute data; subtracting the first incident angle smoothed layer data from the third incident angle smoothed layer data to obtain second layer attribute data; and subtracting the second incident angle smoothed layer data from the third incident angle smoothed layer data to obtain third layer attribute data.
[0050] Specifically, in one embodiment of the present invention, step seven specifically includes: confirming the effect of the obtained multiple layer attribute data on river channel identification and characterization by actual drilling or frequency division fusion method.
[0051] The following is for reference. Figures 2 to 10 Here is a more specific embodiment of this application:
[0052] This invention provides a specific process for a method of identifying hidden waterways based on pre-stack phase data, which includes the following steps.
[0053] First, seismic geological data of the work area were collected. Based on previous research results, wave troughs were determined to be a characteristic of river channel seismic response. Synthetic records were used to calibrate the development of the surface river top interface at the wave trough location (see...). Figure 3 The wave impedance of the riverbed sandstone is lower than that of the surrounding rock;
[0054] Secondly, the pre-stack seismic data of the work area were stacked according to the incident angle range of 0-10° (first incident angle), 11-20°, 21-30° (second incident angle), and 0-45° (third incident angle) to obtain small-angle stacked data seis_near (first incident angle stacked data), medium-angle stacked data seis_mid, large-angle stacked data seis_far (second incident angle stacked data), and full-angle stacked data seis (third incident angle stacked data).
[0055] Then, the small-angle, large-angle, and full-angle superimposed data are processed at the trough positions according to a certain time window range (see...). Figures 4 to 6 Pick the time point corresponding to the maximum value of the trough within the range (black layer) to form layer data hor_near (first incident angle layer data), hor_far (second incident angle layer data), and hor (third incident angle layer data).
[0056] Secondly, the stratigraphic data hor_near, hor_far, and hor are smoothed on a large scale to obtain stratigraphic data hor_near_s (smoothed stratigraphic data with the first incident angle), hor_far_s (smoothed stratigraphic data with the second incident angle), and hor_s (smoothed stratigraphic data with the third incident angle).
[0057] Then, perform calculations on the three layers of data, subtracting each pair to obtain three layer attributes: hor_near_s-hor_far_s (first layer attribute data), hor_near_s-hor_s (second layer attribute data), and hor_far_s-hor_s (third layer attribute data).
[0058] It should be noted that the first-level positional attribute data represents the time difference between the maximum value of the trough at the channel development location in the small-angle superimposed data and the large-angle superimposed data (see...). Figure 7 The second-level positional attribute data represents the time difference between the maximum value of the trough at the channel development location in the small-angle superimposed data and the full superimposed data (see...). Figure 8 The third-layer positional attribute data represents the time difference between the maximum value of the trough at the channel development location in the large-angle overlay data and the full overlay data (see...). Figure 9 ).
[0059] Finally, since no actual wells were encountered in the newly identified channels in this area, the frequency-division fusion attributes can be used (see...). Figure 10 The effectiveness of the river characterization attribute was confirmed. A north-south oriented river was identified in the frequency-division fusion attribute data, which effectively characterized the river. However, other attributes failed to effectively characterize the river. In the layer attribute data extracted by this method, the hor_near_s-hor_far_s and hor_near_s-hor_s layer attribute data showed the same effect as the frequency-division fusion attribute river characterization, confirming the reliability of the river's existence.
[0060] Although the invention has been described with reference to preferred embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, the technical features mentioned in the various embodiments can be combined in any manner as long as there is no structural conflict. The invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.
Claims
1. A method for identifying hidden river channels based on pre-stack phase data, characterized in that, include: Step 1: Collect seismic geological data of the work area and determine that wave troughs are characteristic of river channel seismic response. Step 2: Stack the pre-stack seismic data of the work area at multiple incident angles; Step 3: Obtain the superimposed data for each incident angle; Step 4: For each incident angle superimposed data at the trough position, according to the preset time window range, pick the time point corresponding to the maximum value of the trough within the time window range to form the layer data of each incident angle. Step 5: Perform large-scale smoothing on the incident angle layer data for each incident angle to obtain smoothed layer data for each incident angle. Step 6: Subtract the smoothed layer data of each incident angle from each other to obtain multiple layer attribute data; Step 7: Confirm the effect of the obtained multiple layer attribute data on river channel identification and characterization.
2. The method for identifying hidden river channels based on pre-stack phase data as described in claim 1, characterized in that, Step three specifically includes: obtaining superimposed data of three incident angles, wherein the three incident angles include a first incident angle, a second incident angle, and a third incident angle, and the three incident angles increase sequentially.
3. The method for identifying hidden river channels based on pre-stack phase data according to claim 2, characterized in that, The first incident angle is α, and the value of α ranges from 0 to 10°.
4. The method for identifying hidden channels based on pre-stack phase data according to claim 2, characterized in that, The second incident angle is β, and the value of β ranges from 21° to 30°.
5. The method for identifying hidden river channels based on pre-stack phase data according to claim 2, characterized in that, The third incident angle is γ, and the value of γ is in the range of 0°≤γ≤45°.
6. The method for identifying hidden channels based on pre-stack phase data according to claim 2, characterized in that, Step four specifically includes: at the trough positions of the first incident angle superimposed data, the second incident angle superimposed data, and the third incident angle superimposed data, according to a preset time window range, picking the time point corresponding to the maximum value of the trough within the time window range to form the first incident angle layer data, the second incident angle layer data, and the third incident angle layer data.
7. The method for identifying hidden river channels based on pre-stack phase data according to claim 2, characterized in that, Step five specifically includes: performing large-scale smoothing on the first incident angle layer data, the second incident angle layer data, and the third incident angle layer data to obtain the first incident angle smoothed layer data, the second incident angle smoothed layer data, and the third incident angle smoothed layer data.
8. The method for identifying hidden river channels based on pre-stack phase data according to claim 2, characterized in that, Step six specifically includes: subtracting the first incident angle smoothed layer data from the second incident angle smoothed layer data to obtain first layer attribute data; subtracting the first incident angle smoothed layer data from the third incident angle smoothed layer data to obtain second layer attribute data; and subtracting the second incident angle smoothed layer data from the third incident angle smoothed layer data to obtain third layer attribute data.
9. The method for identifying hidden river channels based on pre-stack phase data according to claim 2, characterized in that, Step seven specifically includes: confirming the effect of multiple layer attribute data obtained through actual drilling or frequency fusion methods on river channel identification and characterization.