Method and system for constructing velocity field model

Abstract

The invention relates to the technical field of oil and gas exploration and development, and discloses a velocity field model construction method and system, and the method comprises the steps: obtaining a higher-precision anisotropic depth migration layer velocity model through step-by-step iteration of an initial depth domain low-frequency velocity model on the basis of adding geological information, and obtaining a final depth migration result data body. The problems that in the prior art, modeling precision is low, pre-stack depth migration imaging is difficult, and complex structure exploration and development scenes are difficult to adapt are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and in particular to a velocity field model construction method and system. Background Art

[0002] It is well known that high-precision velocity models are the key to deep migration imaging. How to obtain accurate velocity models is an important factor in guiding the fine imaging of underground microstructures and small faults in shale gas exploration and development in complex mountainous areas.

[0003] The conventional depth migration processing method is based on Kirchhoff integral, which generates the reflection travel time of each channel in the CMP gather more accurately through ray tracing, and forms a diffraction hyperbola superposition to generate the phase axis reflecting the underground medium. This method strongly depends on the accuracy of the velocity model. An inaccurate velocity model will cause inaccurate migration homing, amplitude fidelity, poor waveform characteristics and enhanced migration noise.

[0004] The establishment of conventional velocity models is mainly divided into two steps: initial velocity model establishment and model update. The initial layer velocity in the depth domain is mainly converted by the root mean square velocity field in the time domain through the Dix formula, but outliers and large lateral velocity fluctuations often occur, affecting the accuracy of the initial model. Model update is currently commonly used in actual production based on data-driven grid tomography inversion, which is completely dependent on seismic track information. For high-steep and complex structures, especially for velocity traps caused by geological factors such as reverse faults and sedimentation, the velocity traps with lateral velocity mutations cannot adapt, resulting in a single depth domain velocity model, small lateral velocity changes, and cannot truly reflect the underground medium.

[0005] The exploration and development of shale gas in complex mountainous areas requires high-quality seismic imaging data support. In the face of fine imaging of microstructures and small faults in the underground target layer, high-precision velocity modeling is the key. Conventional depth domain velocity modeling methods need to pick up residual delays based on the CIP gathers of the previous round of migration, but the signal-to-noise ratio is low in complex structures, making it difficult to accurately pick up residual delays on CIP gathers, and the velocity model cannot be effectively established, making pre-stack depth migration imaging difficult. Summary of the invention

[0006] In order to overcome the shortcomings of the prior art, the present invention provides a velocity field model construction method and system to solve the problems of low modeling accuracy, difficulty in prestack depth migration imaging, and difficulty in adapting to complex structural exploration and development scenarios in the prior art.

[0007] The technical solution adopted by the present invention to solve the above problems is:

[0008] A velocity field model construction method, based on the addition of geological information, obtains a higher-precision anisotropic depth migration layer velocity model by gradually iterating the initial depth domain low-frequency velocity model, and then obtains the final depth migration result data body.

[0009] As a preferred technical solution, the following steps are included:

[0010] S1, initial depth domain low-frequency velocity model construction: construct an initial depth domain low-frequency velocity model;

[0011] S2, depth domain low frequency layer velocity model construction: obtain the depth domain low frequency layer velocity model based on the initial depth domain low frequency layer velocity model;

[0012] S3, construction of isotropic depth domain full-band layer velocity model: based on the depth domain low-frequency layer velocity model, the isotropic depth domain full-band layer velocity model is obtained;

[0013] S4, anisotropic depth migration velocity model construction: anisotropic depth migration velocity model is obtained based on isotropic depth domain full-band layer velocity model;

[0014] S5, depth migration data acquisition: obtain the final depth migration result data volume based on the anisotropic depth migration velocity model.

[0015] As a preferred technical solution, step S1 includes the following steps:

[0016] S11, completing prestack time migration, and performing detailed interpretation on each marker layer of the time migration result data to obtain geological information; wherein the geological information includes stratigraphic information;

[0017] S12, using the time-migrated RMS velocity field, the Dix formula is used to perform layer velocity inversion to obtain the initial depth-domain low-frequency velocity model.

[0018] As a preferred technical solution, step S1 further includes the following steps:

[0019] S13, using grid tomography technology to iterate the initial deep domain low-frequency velocity model, using quantized optimal residual delay technology to obtain delay information, and using sparse matrix technology to increase the update grid density of the initial deep domain low-frequency velocity model to obtain an updated high-density deep domain low-frequency velocity model.

[0020] As a preferred technical solution, in step S2, layer constraint information is added to the updated high-density depth domain low-frequency velocity model to make the formation dip and azimuth information picked up by the grid tomography technology more accurate, and a layer-constrained tomography inversion algorithm is used to obtain a more reliable depth domain low-frequency layer velocity model.

[0021] As a preferred technical solution, in step S3, the value of the velocity mutation layer is adjusted using VSP and well velocity information to increase the frequency of the high-density depth domain low-frequency velocity model and obtain an isotropic depth domain full-band layer velocity model.

[0022] As a preferred technical solution, step S4 includes the following steps:

[0023] S41, performing isotropic depth migration processing according to the full-band layer velocity model in the depth domain, performing layer interpretation on the isotropic depth migration data volume, and obtaining more accurate layer information in the depth domain;

[0024] S42, the depth domain layer information is added again to obtain the anisotropic parameters, and the grid tomography technology is used to invert and obtain the final anisotropic depth migration layer velocity model.

[0025] As a preferred technical solution, in step S42, the layer-constrained minimum curvature method is used to obtain the anisotropic parameters δ and ε, wherein δ represents the Thomsen stress component and ε represents the Thomsen strain component.

[0026] As a preferred technical solution, in step S5, a reverse time depth migration process based on a TTI medium is carried out on the basis of an anisotropic depth migration layer velocity model to obtain a final depth migration result data volume.

[0027] A velocity field model construction system, used to implement the velocity field model construction method, comprises the following modules connected in sequence:

[0028] Initial depth domain low-frequency velocity model construction module: used to construct an initial depth domain low-frequency velocity model;

[0029] A depth domain low-frequency layer velocity model construction module is used to obtain a depth domain low-frequency layer velocity model based on an initial depth domain low-frequency layer velocity model;

[0030] Isotropic depth domain full-band layer velocity model construction module: used to obtain an isotropic depth domain full-band layer velocity model based on the depth domain low-frequency layer velocity model;

[0031] Anisotropic depth migration velocity model building module: used to obtain anisotropic depth migration velocity model based on isotropic depth domain full-band layer velocity model;

[0032] Depth migration data acquisition module: used to obtain the final depth migration result data volume based on the anisotropic depth migration velocity model.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention proposes a velocity modeling method suitable for complex mountainous areas, forming an anisotropic depth domain high-precision velocity model optimization technical process with high-density grid tomography technology, quantitative optimization of residual delay technology, layer constraint velocity inversion technology, and well control velocity fine adjustment technology as the core, improving the imaging accuracy of deep seismic data, improving the reliability of deep seismic data, guiding drilling and fracturing construction, increasing the drilling rate of shale gas platinum targets, and increasing the single shale gas well production. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flow chart of a velocity field model construction method according to the present invention;

[0036] Figure 2 It is an initial picked time domain geological horizon map based on prestack time migration section;

[0037] Figure 3 It is the gather map before and after the residual delay is picked up by using the quantitative optimal residual delay technology;

[0038] Figure 4 It is a velocity field map established by fusing multiple information such as well logging, VSP and seismic interpretation;

[0039] Figure 5 It is a comparison diagram of the velocity model established by conventional methods and the velocity model established by the present invention;

[0040] Figure 6 It is a comparison diagram of the conventional velocity model migration effect and the velocity model migration profile effect of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0042] Example 1

[0043] like Figures 1 to 6 As shown, the present invention relates to the technical field of shale gas seismic exploration and development in mountainous complex structural areas, and in particular to a depth domain velocity modeling method suitable for seismic imaging in mountainous complex areas.

[0044] The present invention proposes a high-precision velocity field modeling processing method and system, which is used to solve the problem of high-precision imaging of underground formations, improve the prediction accuracy of micro-structures and small faults, guide the drilling of shale gas horizontal wells, increase the drilling rate of shale gas platinum targets, and increase the production of single wells. Compared with conventional velocity field modeling, the imaging effect is higher in accuracy, more realistically reflects the underground structural morphology, and more accurately depicts the imaging of micro-structures and small faults, which plays an important role in the drilling process of shale gas horizontal wells in complex mountainous areas.

[0045] The present invention forms a processing method and system for high-precision velocity field modeling of pre-stack depth migration, which provides high-quality basic seismic data for seismic data interpretation in the field of shale gas exploration and development.

[0046] The present invention is mainly achieved through the following scheme: Figure 1 ):

[0047] (1) Detailed structural interpretation:

[0048] The positions of each marker layer are carefully interpreted based on the pre-stack time migration data to obtain accurate initial layer information.

[0049] (2) Multi-information fusion grid tomography technology:

[0050] Based on the data body, the layer information, well location stratification, velocity and other information were imported. After adding geological knowledge, directional constraint modeling was carried out.

[0051] (3) Gather residual delay picking:

[0052] The residual delay is picked up for the event axes of each marker layer of the gather, and a method for quantitatively evaluating the residual delay is constructed to accurately eliminate invalid residual delays, improve the accuracy of delay information, and thus improve the inversion accuracy.

[0053] (4) Anisotropic parameter constraint modeling:

[0054] The anisotropic parameters of the well are obtained according to the VSP data, and plane interpolation is performed through extrapolation and interpolation. The anisotropic parameters are introduced to optimize and update the velocity field model.

[0055] (5) Reverse time depth migration processing.

[0056] The processing method of anisotropic prestack depth migration based on TTI media can more truly reflect the actual situation of the underlying strata in high-steep and complex structural belts.

[0057] The present invention can improve the accuracy of prestack depth migration velocity model, improve the imaging accuracy of deep seismic data, ensure the imaging quality of data, improve the reliability of deep seismic data, guide drilling and fracturing operations, increase the drilling rate of shale gas platinum targets, and increase the production of shale gas single wells. This technology has been applied on a large scale in the imaging processing of seismic data in the shale gas block of the Longmaxi Formation of the Silurian System in the Sichuan Basin.

[0058] Example 2

[0059] like Figures 1 to 6 As shown, as a further optimization of Example 1, based on Example 1, this embodiment also includes the following technical features:

[0060] The present invention proposes a velocity modeling method suitable for complex mountainous areas, forming an anisotropic depth domain high-precision velocity model optimization technical process with high-density grid tomography technology, quantitative optimization of residual delay technology, layer constraint velocity inversion technology, and well control velocity fine adjustment technology as the core, improving the imaging accuracy of deep seismic data, improving the reliability of deep seismic data, guiding drilling and fracturing construction, increasing the drilling rate of shale gas platinum targets, and increasing the single shale gas well production.

[0061] With the gradual deepening of shale gas development in the Longmaxi Formation of the Silurian System in Sichuan and Chongqing, the target high-quality reservoir has shifted from large targets to platinum targets with a thickness of 2-3m. The drilling rate of platinum targets is closely related to natural gas production. The accuracy of seismic data is a key factor restricting the increase in reserves and production of shale gas in Sichuan and Chongqing. The challenges of fine processing of seismic data in blocks with large surface fluctuations, high and steep and complex overburden structures, developed faults, and active micro-structures in southern Sichuan are becoming increasingly greater.

[0062] The present invention comprehensively applies various technological innovations to form an anisotropic depth domain high-precision velocity model optimization technical process with high-density grid tomography technology, quantitative optimization of residual delay technology, layer constraint velocity inversion technology, and well control velocity fine adjustment technology as the core.

[0063] The specific implementation of the present invention is as follows: Figure 1 ):

[0064] Step 1: Complete pre-stack time migration, conduct detailed interpretation of each marker layer of the time migration result data, and obtain geological information such as stratigraphic position.

[0065] Step 2: Use the time-migrated RMS velocity field to constrain the layer velocity inversion using the Dix formula to obtain the initial depth-domain low-frequency velocity model.

[0066] Step 3: Use data-driven grid tomography technology to gradually update and iterate the initial deep domain low-frequency velocity model. In the process, use the quantitative optimization of residual delay technology to obtain high-precision delay information, and use sparse matrix technology to increase the model update grid density to obtain an updated high-density deep domain low-frequency velocity model.

[0067] Step 4: Add layer constraint information to the updated high-density low-frequency velocity model in the depth domain to make the formation dip and azimuth information picked up by the grid tomography technology more accurate. Use layer constraint tomography inversion calculation to obtain a more reliable low-frequency layer velocity model in the depth domain.

[0068] Step 5: Use VSP and well velocity information to fine-tune the value of the velocity mutation layer, increase the frequency of the velocity model, make up for the shortcoming that grid tomography can only obtain the low-frequency background velocity field, and obtain an isotropic depth domain full-band layer velocity model.

[0069] Step 6: Perform isotropic depth migration processing based on the full-band layer velocity model in the depth domain, interpret the stratigraphic information of the isotropic depth migration data volume, and obtain more accurate stratigraphic information in the depth domain.

[0070] Step 7: Add the depth domain layer information again, and use the layer-constrained minimum curvature method to obtain the anisotropic parameters δ and ε (δ represents the Thomsen stress component, and ε represents the Thomsen strain component). Use the model constraint and well-seismic combined high-density grid tomography technology to invert and obtain the final anisotropic depth migration layer velocity model.

[0071] Step 8: Based on the high-precision velocity model, reverse time depth migration processing based on TTI medium is carried out to obtain the final depth deviation data (depth migration result data body).

[0072] Figure 2 In the process of pre-stack time migration, a detailed horizon interpretation is performed on the data of pre-stack time migration results to obtain the time domain horizon information of each set of seismic reflection marker layers. This information will be used as the boundary control condition for the subsequent grid tomography velocity inversion. The grid tomography will consider the extension of the stratum fluctuation along the horizon, rather than simply the horizontal or vertical extension.

[0073] Figure 3 In the process, the phase axis residual delay evaluation technology is used on the common reflection point gathers to flatten the motion of each set of seismic reflection marker layers and accurately eliminate the invalid residual delay. It can be seen that the marker layer gathers without residual delay evaluation have uneven near and far offset traces. After the residual delay evaluation, the gathers are flattened and the residual time difference of the near and far offset traces is eliminated.

[0074] Figure 4 In the process of grid tomography velocity inversion with layer information constraints, it can be seen that the depth domain low-frequency layer velocity model obtained after inversion has a more reasonable vertical and horizontal velocity structure compared with the unconstrained low-frequency velocity model, and can better reflect the real underground velocity structure.

[0075] Figure 5 In the above, after adding the layer and anisotropy parameters, compared with the conventional velocity model, the velocity model of the anisotropic depth migration layer obtained by using the layer constraint and high-density grid tomography technology has richer information, more reasonable lateral velocity changes, and more accurate representation of the formation velocity mutation;

[0076] Figure 6 In the comparison with the conventional depth migration results data, based on the high-precision velocity model, the depth migration results data obtained by reverse time depth migration processing based on TTI medium more truly reflects the underground micro-structural morphology, and the breakpoints are clearer.

[0077] As described above, the present invention can be preferably implemented.

[0078] All features disclosed in all embodiments in this specification, or steps in all methods or processes implicitly disclosed, except for mutually exclusive features and / or steps, can be combined and / or expanded or replaced in any manner.

[0079] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. According to the technical essence of the present invention, within the spirit and principles of the present invention, any simple modification, equivalent replacement and improvement made to the above embodiment still falls within the protection scope of the technical solution of the present invention.

Claims

1. A velocity field model construction method, characterized in that: On the basis of adding geological information, by gradually iterating the initial depth domain low-frequency velocity model, a higher-precision anisotropic depth migration layer velocity model is obtained, and then the final depth migration result data volume is obtained.

2. A velocity field model construction method according to claim 1, characterized in that: The following steps are involved: S1, initial depth domain low-frequency velocity model construction: construct an initial depth domain low-frequency velocity model; S2, depth domain low frequency layer velocity model construction: obtain the depth domain low frequency layer velocity model based on the initial depth domain low frequency layer velocity model; S3, construction of isotropic depth domain full-band layer velocity model: based on the depth domain low-frequency layer velocity model, the isotropic depth domain full-band layer velocity model is obtained; S4, anisotropic depth migration velocity model construction: anisotropic depth migration velocity model is obtained based on isotropic depth domain full-band layer velocity model; S5, depth migration data acquisition: obtain the final depth migration result data volume based on the anisotropic depth migration velocity model.

3. A velocity field model construction method according to claim 2, characterized in that: Step S1 includes the following steps: S11, completing prestack time migration, and performing detailed interpretation on each marker layer of the time migration result data to obtain geological information; wherein the geological information includes stratigraphic information; S12, using the time-migrated RMS velocity field, the Dix formula is used to perform layer velocity inversion to obtain the initial depth-domain low-frequency velocity model.

4. A velocity field model construction method according to claim 3, characterized in that: Step S1 also includes the following steps: S13, using grid tomography technology to iterate the initial deep domain low-frequency velocity model, using quantized optimal residual delay technology to obtain delay information, and using sparse matrix technology to increase the update grid density of the initial deep domain low-frequency velocity model to obtain an updated high-density deep domain low-frequency velocity model.

5. A velocity field model construction method according to claim 2, characterized in that: In step S2, the layer constraint information is added to the updated high-density low-frequency velocity model in the depth domain, so that the formation dip and azimuth information picked up by the grid tomography technology is more accurate, and a layer-constrained tomography inversion algorithm is used to obtain a more reliable low-frequency layer velocity model in the depth domain.

6. A velocity field model construction method according to claim 2, characterized in that: In step S3, the value of the velocity mutation layer is adjusted using the VSP and well velocity information to increase the frequency of the high-density depth domain low-frequency velocity model and obtain an isotropic depth domain full-band layer velocity model.

7. A velocity field model construction method according to claim 2, characterized in that: Step S4 includes the following steps: S41, performing isotropic depth migration processing according to the full-band layer velocity model in the depth domain, performing layer interpretation on the isotropic depth migration data volume, and obtaining more accurate layer information in the depth domain; S42, the depth domain layer information is added again to obtain the anisotropic parameters, and the grid tomography technology is used to invert and obtain the final anisotropic depth migration layer velocity model.

8. A velocity field model construction method according to claim 7, characterized in that: In step S42, the anisotropic parameters δ and ε are obtained by using the layer-constrained minimum curvature method, wherein δ represents the stress component and ε represents the strain component.

9. A velocity field model construction method according to any one of claims 2 to 8, characterized in that: In step S5, a reverse time depth migration process based on the TTI medium is carried out on the basis of the anisotropic depth migration layer velocity model to obtain a final depth migration result data volume.

10. A velocity field model construction system, characterized in that: A velocity field model construction method for implementing any one of claims 1 to 9, comprising the following modules connected in sequence: Initial depth domain low-frequency velocity model construction module: used to construct an initial depth domain low-frequency velocity model; A depth domain low-frequency layer velocity model construction module is used to obtain a depth domain low-frequency layer velocity model based on an initial depth domain low-frequency layer velocity model; Isotropic depth domain full-band layer velocity model construction module: used to obtain an isotropic depth domain full-band layer velocity model based on the depth domain low-frequency layer velocity model; Anisotropic depth migration velocity model building module: used to obtain anisotropic depth migration velocity model based on isotropic depth domain full-band layer velocity model; Depth migration data acquisition module: used to obtain the final depth migration result data volume based on the anisotropic depth migration velocity model.

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