Horizon interpretation method of velocity field constraint
By acquiring and preprocessing seismic data, calculating the three-dimensional velocity field, and combining seismic waveforms and formation velocity constraints, the problem of multiple solutions in seismic horizon interpretation was solved, and accurate automatic horizon interpretation was achieved in areas with poor seismic data quality.
Patent Information
- Application Number
- CN202410695432.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-05-31
- Publication Date
- 2025-12-02
AI Technical Summary
In seismic horizon interpretation, existing technologies are not ideal for areas with poor seismic quality and poor continuity of seismic phase axes. In particular, when the correlation of seismic traces is low or there are multiple high correlations within the sliding time window, it is difficult to achieve accurate automatic horizon interpretation.
By collecting various seismic and geological data, preprocessing them, calculating the three-dimensional velocity field, and further constraining the automatic stratigraphic tracking by utilizing the velocity variation law of adjacent traces, and combining the constraints of seismic waveforms and stratigraphic velocities, the connection between seismic traces is optimized, reducing ambiguity and obtaining more reasonable and accurate stratigraphic interpretation results.
In areas with poor seismic data quality, the automatic horizon tracking process is optimized by introducing three-dimensional velocity variation patterns as constraints, reducing multiple interpretations and improving the accuracy and rationality of horizon interpretation.
Smart Images
Figure CN121049971A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of oil and gas reservoir exploration and development, and in particular to a velocity field-constrained stratigraphic interpretation method. Background Technology
[0002] Seismic horizon interpretation is a crucial aspect of seismic interpretation, currently primarily conducted using specialized software. First, a synthetic seismic record is obtained by combining well logging curves from drilled wells with seismic wavelet convolution. By comparing this record with adjacent seismic traces from drilled wells, a connection is established between depth-domain and time-domain seismic data. Seismic reflection characteristics of the target layer are determined through well-seismic calibration. Then, seismic horizon interpretation is performed on the interconnected well profiles, constructing an interpretation framework. While ensuring the framework's horizon interpretation closure, finer-grained interpretation is conducted. Finally, the horizon interpretation results are obtained.
[0003] Currently, seismic stratigraphic interpretation in oil and gas exploration and development research largely relies on manual interpretation. Due to the large volume of data and the need to consider factors such as stratigraphic closure, manual interpretation is extremely labor-intensive, consuming a significant amount of researchers' time and energy. To address this issue, existing seismic interpretation software offers automatic stratigraphic interpretation functions, primarily through automatic tracking based on the similarity of seismic phase axes. This automatic stratigraphic interpretation has achieved good results in areas with high-quality seismic data, continuous seismic phase axes, and underdeveloped faults.
[0004] Yin Wen (2017) et al. introduced Support Vector Machine (SVM) technology to classify seismic data and improve the ant colony tracking strategy, realizing automatic seismic horizon tracking and providing a technical approach for fine reservoir description. Ding Liangbo (2018) et al. used global optimization seismic automatic interpretation technology based on cost function to realize the rapid construction of three-dimensional seismic stratigraphic interpretation models. Wang Xiaopin (2021) et al. used the optimal matching equation set of horizons as an analogy to Huygens' propagation principle of wave propagation to realize the automatic construction of stratigraphic framework models with complex stratigraphic contact relationships. Yu Yue (2022) et al. carried out intelligent seismic horizon interpretation based on sample selection and multiple quality control methods, realizing the simultaneous picking of multiple sets of target horizons while ensuring the reliability and rationality of horizon picking. Zhu Zhenyu (2023) et al. applied the U-net network based on image segmentation technology to seismic horizon interpretation, improving the accuracy and efficiency of automatic interpretation in the identification of small and weakly reflective layers.
[0005] The development of automatic horizon interpretation technology has made the tedious process of horizon interpretation faster and more efficient, reducing the workload of interpreters. However, in areas with poor seismic quality and poor continuity of phase axes, the correlation of seismic traces within the sliding time window is low or there are multiple areas of high correlation, resulting in less than ideal results from automatic horizon interpretation. Summary of the Invention
[0006] In view of the above problems, the present invention is proposed to provide a method for interpreting velocity field constraints in a layered manner to overcome or at least partially solve the above problems.
[0007] According to one aspect of the present invention, a method for interpreting the stratigraphic level of velocity field constraints is provided, the method comprising:
[0008] Step S1: Collect various types of seismic geological data to obtain a seismic data volume;
[0009] Step S2: Preprocess the seismic data volume to obtain preprocessed seismic data;
[0010] Step S3: Calculate the three-dimensional velocity field based on the preprocessed seismic data;
[0011] Step S4: Perform automatic spatial tracking;
[0012] Step S5: Further constrain automatic stratum tracking based on the velocity variation pattern of adjacent channels;
[0013] Step S6: Obtain the three-dimensional interpretation results of the target stratum.
[0014] Optionally, step S1: acquiring various seismic geological data to obtain a seismic data body specifically includes:
[0015] Three-dimensional seismic data and well logging data; the well logging data includes acoustic and density curves.
[0016] Optionally, step S2: preprocessing the seismic data volume specifically includes:
[0017] The seismic data volume is filtered and denoised, and the well logging curves are standardized.
[0018] Optionally, step S3: calculating the three-dimensional velocity field based on the preprocessed seismic data specifically includes:
[0019] Synthetic well logging records are generated and compared with wellside seismic traces for calibration to obtain the wellpoint time-depth relationship, i.e., wellpoint velocity. The three-dimensional velocity field is obtained using the Kriging interpolation method.
[0020] Optionally, step S4: performing automatic spatial tracking specifically includes:
[0021] Using the interpretation of the artificial sample line as input, the seismic trace where the sample point is located and the adjacent seismic trace are correlated within the sliding time window. The maximum value of the correlation is used as a new input sample point for automatic spatial tracking.
[0022] Optionally, step S5: further constraining automatic stratum tracking based on the velocity variation law of adjacent channels specifically includes:
[0023] To address situations where the correlation is poor or there are multiple high correlations within the sliding time window, a three-dimensional velocity field is introduced into automatic layer tracking, and automatic layer tracking is further constrained based on the velocity variation law of adjacent traces.
[0024] Optionally, the step of creating a synthetic well logging record, comparing and calibrating it with the well-side seismic trace to obtain the wellpoint time-depth relationship, i.e., the wellpoint velocity, and using the Kriging interpolation method to obtain the three-dimensional velocity field specifically includes:
[0025] Using well logging curves from existing wells and seismic wavelets, convolution calculations are performed to create synthetic seismic records;
[0026] By comparing the synthetic seismic records with the seismic traces near drilled wells, the relationship between depth domain well data and time domain seismic data was established, and the time-depth relationship and well velocity of the well points were obtained.
[0027] The Kriging interpolation method is used to interpolate the velocity between wells, and the three-dimensional velocity distribution, i.e., the three-dimensional velocity field, is obtained.
[0028] Optionally, the step of using the interpretation of the artificial sample line as input, performing correlation calculations between the seismic trace where the sample point is located and adjacent seismic traces within a sliding time window, and using the maximum correlation value as a new input sample point for automatic spatial tracking specifically includes:
[0029] Assume x i It is the amplitude value at a certain point on the current seismic trace, y j It is the amplitude value of a point within a certain time window T of an adjacent trace, j∈[1,n], and the amplitude value of a point x on the current seismic trace. i The formula for the maximum similarity between a point and all points within a certain time window T of an adjacent channel is as follows:
[0030]
[0031] Where k is half the sampling length used to calculate the similarity of a point on the current seismic trace.
[0032] Optionally, for cases where the correlation is poor or there are multiple high correlations within the sliding time window, the introduction of a three-dimensional velocity field into automatic layer tracking, and further constraint of automatic layer tracking based on the velocity variation law of adjacent traces, specifically includes:
[0033] Assume x i It is the ground velocity value at a certain point on the current seismic trace, y j It is the velocity value of a point within a certain time window T of an adjacent trace, j∈[1,n], and the velocity value of a point x on the current seismic trace. i The formula for determining the minimum similarity between points and all points within a certain time window T of adjacent channels is as follows:
[0034]
[0035] Where k is half the sampling length used to calculate the similarity of a point on the current seismic trace.
[0036] Optionally, step S6: obtaining the three-dimensional interpretation results of the target stratum specifically includes:
[0037] Seismic waveforms and formation velocities are used together to constrain automatic stratigraphic tracking, resulting in three-dimensional interpretation of the target stratigraphic level.
[0038] This invention provides a velocity field-constrained stratigraphic interpretation method, comprising: Step S1: acquiring various seismic geological data to obtain a seismic data volume; Step S2: preprocessing the seismic data volume to obtain preprocessed seismic data; Step S3: calculating a three-dimensional velocity field based on the preprocessed seismic data; Step S4: performing automatic spatial tracking; Step S5: further constraining automatic stratigraphic tracking based on the velocity variation law of adjacent traces; Step S6: obtaining the three-dimensional interpretation result of the target stratigraphic level. This method optimizes the connection between seismic traces, reduces the ambiguity of automatic stratigraphic tracking, and obtains more reasonable and accurate automatic stratigraphic interpretation results.
[0039] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description
[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0041] Figure 1 A detailed flowchart of a novel method for interpreting velocity field constraints at different levels, provided in an embodiment of the present invention;
[0042] Figure 2 This is a diagram illustrating the effect of filtering on actual seismic data in a specific embodiment of the present invention.
[0043] Figure 3 This is a comparison of the histograms of the sound wave curves before and after standardization processing in a specific embodiment of the present invention;
[0044] Figure 4 This is a calibration map of the synthesized seismic record from a drilled well, as shown in a specific embodiment of the present invention.
[0045] Figure 5This is a three-dimensional velocity field spatial distribution diagram in a specific embodiment of the present invention;
[0046] Figure 6 This is a cross-sectional view of automatic stratigraphic tracking in a specific embodiment of the present invention. Detailed Implementation
[0047] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
[0048] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.
[0049] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.
[0050] like Figure 1 As shown, Figure 1 This is a flowchart illustrating a novel method for interpreting velocity field constraints according to the present invention.
[0051] In step 1, input various seismic and geological data, including 3D seismic data and well logging data, with well logging data mainly consisting of sonic and density curves. The process then proceeds to step 2.
[0052] In step 2, the seismic data volume is filtered and the well logging curves are standardized.
[0053] ① Seismic data filtering
[0054] By filtering the raw seismic data, random noise is suppressed, thereby improving the signal-to-noise ratio of the seismic data and providing a better data foundation for automatic horizon tracking. The results of processing actual seismic data using filtering are shown below. Figure 2 .
[0055] ② Standardization of well logging curves
[0056] For the target layer, the original logging curves were standardized, and the results are as follows: Figure 3 The left figure shows the original sonic curves of the three wells (different colors represent different wells), and the right figure shows the sonic curves after standardization. This eliminates the baseline differences of sonic curves in stable sedimentary strata, laying the foundation for establishing a more accurate velocity field. The process then proceeds to step 3.
[0057] In step 3, a well logging synthetic seismic record is generated and compared with the wellside seismic trace for calibration to obtain the wellpoint time-depth relationship, i.e., the wellpoint velocity. The three-dimensional velocity field is obtained using the Kriging interpolation method.
[0058] First, convolution calculations are performed using well logging curves from drilled wells and seismic wavelets to create synthetic seismic records. By comparing the synthetic seismic records with the seismic traces near drilled wells, the relationship between depth-domain well data and time-domain seismic data is established, thus obtaining the time-depth relationship of well points, i.e., well point velocities. Based on this, inter-well velocity interpolation is performed using the Kriging interpolation method to obtain the three-dimensional velocity distribution, i.e., the three-dimensional velocity field. Figure 4 This is a calibration diagram of the drilled well composite record, where (a) is the negative polarity seismic wavelet, (b) is the seismic trace near the drilled well, (c) is the composite seismic trace obtained by convolving the drilled well logging curve with the seismic wavelet, and (d) is the spontaneous potential curve and lithological indication. Red represents sandstone and light blue represents mudstone. Figure 5 It shows the trend of velocity change in three-dimensional space.
[0059] The process proceeds to step 4.
[0060] In step 4, using the interpretation of the artificial sample line as input, the correlation between the seismic trace where the sample point is located and the adjacent seismic trace is calculated within the sliding time window. The maximum value of the correlation is used as a new input sample point for automatic spatial tracking.
[0061] Assume x i It is the amplitude value at a certain point on the current seismic trace, y j It is the amplitude value of a point within a certain time window T of an adjacent trace, j∈[1,n], and the amplitude value of a point x on the current seismic trace. i The formula for the maximum similarity between a point and all points within a certain time window T of an adjacent channel is as follows:
[0062]
[0063] Where k is half the sampling length used to calculate the similarity of a point on the current seismic trace.
[0064] The process proceeds to step 5.
[0065] In step 5, for cases where the correlation is poor or there are multiple high correlations within the sliding time window, the three-dimensional velocity field is introduced into the automatic stratigraphic tracking, and the automatic stratigraphic tracking is further constrained based on the minimum velocity change of adjacent strata.
[0066] Assume x i It is the ground velocity value at a certain point on the current seismic trace, y j It is the velocity value of a point within a certain time window T of an adjacent trace, j∈[1,n], and the velocity value of a point x on the current seismic trace. iThe formula for determining the minimum similarity between points and all points within a certain time window T of adjacent channels is as follows:
[0067]
[0068] Where k is half the sampling length used to calculate the similarity of a point on the current seismic trace.
[0069] The results of automatic target horizon tracking under the combined constraints of seismic waveform and formation velocity are as follows: Figure 6 As shown, the yellow line on the seismic profile represents the tracking result of this patented method, while the blue line represents the tracking result of professional software. In areas where the seismic waveform is relatively continuous, the tracking results of the two methods are not significantly different; however, in areas with weak seismic reflection intensity and more complex seismic waveforms (within the green circle), this patented method performs better than the professional software.
[0070] The process proceeds to step 6.
[0071] In step 6, the seismic waveform and formation velocity are used together to automatically track the stratigraphic horizon, outputting the three-dimensional interpretation results of the target horizon. The process ends.
[0072] Beneficial effects: The novel method for stratigraphic interpretation with velocity field constraints in this invention introduces the three-dimensional velocity variation law as a constraint condition into the automatic stratigraphic tracking process. By setting the maximum similarity of waveforms between adjacent seismic traces and the minimum variation of stratigraphic velocity, the connection between seismic traces is further optimized, the ambiguity of automatic stratigraphic tracking is reduced, and more reasonable and accurate automatic stratigraphic interpretation results are obtained.
[0073] Introducing the three-dimensional velocity variation law as a constraint into the automatic horizon tracking process reduces the ambiguity of automatic horizon interpretation, provides a solution to the horizon interpretation problem in areas with poor seismic data quality, and makes the horizon interpretation results more reasonable and accurate.
[0074] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for interpreting the stratigraphic level of velocity field constraints, characterized in that, The interpretation method includes: Step S1: Collect various types of seismic geological data to obtain a seismic data volume; Step S2: Preprocess the seismic data volume to obtain preprocessed seismic data; Step S3: Calculate the three-dimensional velocity field based on the preprocessed seismic data; Step S4: Perform automatic spatial tracking; Step S5: Further constrain automatic stratum tracking based on the velocity variation pattern of adjacent channels; Step S6: Obtain the three-dimensional interpretation results of the target stratum.
2. The method for interpreting the layer of velocity field constraints according to claim 1, characterized in that, Step S1: Acquiring various types of seismic geological data to obtain the seismic data volume specifically includes: Three-dimensional seismic data and well logging data; the well logging data includes acoustic and density curves.
3. The method for interpreting the layer of velocity field constraints according to claim 2, characterized in that, Step S2: Preprocessing the seismic data volume specifically includes: The seismic data volume is filtered and denoised, and the well logging curves are standardized.
4. The method for interpreting the layer of velocity field constraints according to claim 1, characterized in that, Step S3, calculating the three-dimensional velocity field based on the preprocessed seismic data, specifically includes: Synthetic well logging records are generated and compared with wellside seismic traces for calibration to obtain the wellpoint time-depth relationship, i.e., wellpoint velocity. The three-dimensional velocity field is obtained using the Kriging interpolation method.
5. The method for interpreting the layer of velocity field constraints according to claim 1, characterized in that, Step S4: Performing automatic spatial tracking specifically includes: Using the interpretation of the artificial sample line as input, the seismic trace where the sample point is located and the adjacent seismic trace are correlated within the sliding time window. The maximum value of the correlation is used as a new input sample point for automatic spatial tracking.
6. The method for interpreting the layer of velocity field constraints according to claim 1, characterized in that, Step S5: Further constraining automatic stratum tracking based on the velocity variation law of adjacent channels specifically includes: To address situations where the correlation is poor or there are multiple high correlations within the sliding time window, a three-dimensional velocity field is introduced into automatic layer tracking, and automatic layer tracking is further constrained based on the velocity variation law of adjacent traces.
7. The method for interpreting the layer of velocity field constraints according to claim 4, characterized in that, The process of creating synthetic well logging records, comparing and calibrating them with well-side seismic traces to obtain the wellpoint time-depth relationship (i.e., wellpoint velocity), and using Kriging interpolation to obtain the three-dimensional velocity field specifically includes: Using well logging curves from existing wells and seismic wavelets, convolution calculations are performed to create synthetic seismic records; By comparing the synthetic seismic records with the seismic traces near drilled wells, the relationship between depth domain well data and time domain seismic data was established, and the time-depth relationship and well velocity of the well points were obtained. The Kriging interpolation method is used to interpolate the velocity between wells, and the three-dimensional velocity distribution, i.e., the three-dimensional velocity field, is obtained.
8. The method for interpreting the layer of velocity field constraints according to claim 5, characterized in that, The process of using the interpretation of artificial sample lines as input, performing correlation calculations between the seismic trace where the sample point is located and adjacent seismic traces within a sliding time window, and using the maximum correlation value as a new input sample point for automatic spatial tracking specifically includes: Assume x i It is the amplitude value at a certain point on the current seismic trace, y j It is the amplitude value of a point within a certain time window T of an adjacent trace, j∈[1,n], and the amplitude value of a point x on the current seismic trace. i The formula for the maximum similarity between a point and all points within a certain time window T of an adjacent channel is as follows: Where k is half the sampling length used to calculate the similarity of a point on the current seismic trace.
9. The method for interpreting the layer of velocity field constraints according to claim 6, characterized in that, For cases where the correlation is poor or there are multiple high correlations within the sliding time window, the three-dimensional velocity field is introduced into automatic layer tracking. Further constraints on automatic layer tracking based on the velocity variation patterns of adjacent traces specifically include: Assume x i It is the ground velocity value at a certain point on the current seismic trace, y j It is the velocity value of a point within a certain time window T of an adjacent trace, j∈[1,n], and the velocity value of a point x on the current seismic trace. i The formula for determining the minimum similarity between points and all points within a certain time window T of adjacent channels is as follows: Where k is half the sampling length used to calculate the similarity of a point on the current seismic trace.
10. The method for interpreting the layer of velocity field constraints according to claim 1, characterized in that, Step S6: Obtaining the three-dimensional interpretation results of the target stratum specifically includes: Seismic waveforms and formation velocities are used together to constrain automatic stratigraphic tracking, resulting in three-dimensional interpretation of the target stratigraphic level.