A multi-azimuth multi-frequency reservoir fluid prediction method based on five-dimensional data

Abstract

The application discloses a multi-azimuth multi-frequency reservoir fluid prediction method based on five-dimensional data, and the prediction method comprises the following steps: finding the influence of azimuth anisotropy on reservoir fluid by using the azimuth angle and offset distance information of pre-stack OVT domain five-dimensional data, selecting a dominant azimuth, and establishing a split-azimuth stacking template; on the basis of split-azimuth seismic stacking data, ideal frequency domain data volume is obtained by using a time-frequency analysis method; the frequency sensitive section of the study area is determined by combining the frequency gradient response characteristics of actual well points with different oil contents; the frequency gradient attribute map of the whole study area is calculated, and the reservoir fluid is predicted. The reservoir fluid is effectively identified, and the drilling success rate of lithologic oil and gas reservoirs is improved.

Description

Technical Field

[0001] This invention relates to the field of reservoir fluid prediction, and in particular to a multi-directional, multi-frequency reservoir fluid prediction method based on five-dimensional data. Background Technology

[0002] With the development of oil and gas exploration and development, there is a growing demand for more precise and detailed research and analysis of exploration and development targets. Seismic technology must not only accurately image the target geological body but also effectively identify reservoir fluids to improve the drilling success rate of lithologic oil and gas reservoirs. However, due to the differences in amplitude response between oil-bearing and water-bearing reservoirs at different frequencies, and the influence of anisotropy such as fractures and reservoir heterogeneity within the geological body, fluid prediction errors can be significant.

[0003] The development of wide-azimuth, high-density exploration has driven the application of OVT domain five-dimensional data in exploration and development. OVT domain five-dimensional data gathers contain spatial three-dimensional coordinates as well as rich azimuth and shot-receiver distance information. They have wide bandwidth, rich information content, and high fidelity, enabling better analysis of the changes in properties such as travel time, velocity, amplitude, frequency, and phase difference of seismic waves propagating in anisotropic media with azimuth. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a multi-directional, multi-frequency reservoir fluid prediction method based on five-dimensional data to overcome or at least partially solve the above problems.

[0005] According to one aspect of the present invention, a multi-directional, multi-frequency reservoir fluid prediction method based on five-dimensional data is provided, the prediction method comprising:

[0006] Using the azimuth and offset information of the pre-stack OVT domain five-dimensional data, the influence of azimuth anisotropy on reservoir fluids is identified, the dominant azimuth is selected, and a azimuth-based stacking template is established.

[0007] Based on the azimuth-based seismic overlay data, an ideal frequency domain data volume is obtained using time-frequency analysis methods;

[0008] By combining the frequency gradient response characteristics of actual well points with different oil-bearing properties, the frequency-sensitive segment of the study area was determined;

[0009] Calculate the frequency gradient property map of the entire study area to predict reservoir fluids.

[0010] Optionally, the step of using the azimuth and offset information of the pre-stack OVT domain five-dimensional data to find the influence of azimuth anisotropy on reservoir fluids and select the dominant azimuth specifically includes:

[0011] The OVT pre-stack gathers were stacked with full offset, azimuth stacks of 0-10°, 0-20°, 0-30°, and 0-40° to obtain azimuth seismic data volumes of 4 different stacking sectors;

[0012] Stratigraphic slices were extracted from the four seismic bodies, and the slices that could reflect reservoir information were selected to determine the azimuth stacking sector.

[0013] Optionally, establishing the azimuth overlay template specifically includes:

[0014] Set the overlay template for the OVT gather, select all-around overlay, and divide the offset distance into 3 groups according to near, medium and far: 100-500m, 500-900m, and 900-1500m;

[0015] Using formula The root mean square attribute of the three sets of offset parameter data volumes was extracted respectively, and the OVT offset difference attribute was constructed using the following formula:

[0016]

[0017] Where B(i) represents the offset difference attribute, A(i) represents the attribute value at the calculation point, and A min Indicates the minimum attribute value, A max Indicates the maximum attribute value;

[0018] The offset difference attribute was used to further evaluate and analyze the data volumes of the three sets of different offset distance parameters. The large offset difference attribute of OVT indicates obvious anisotropy information. The offset distance corresponding to the obvious response of OVT offset difference attribute was selected as the effective offset distance superposition range.

[0019] Optionally, the step of using the azimuth and offset information of the pre-stack OVT domain five-dimensional data to find the influence of azimuth anisotropy on reservoir fluids and select the dominant azimuth further includes:

[0020] Wavelet data extracted from the P-wave velocity curve, density curve, and S-wave velocity curve of a single well;

[0021] Using the Aki & Richards approximate equation method, we obtained the forward seismic gathers of AVO from a single well and extracted the AVO curve along the top of the reservoir.

[0022] By analyzing the AVO characteristics of a single well under various fluid conditions, the amplitude of the AVO curve gradually decreases as the incident angle increases. Based on Castagna's AVO classification chart, the location of the oil layer in the single well is identified as having Type I AVO characteristics.

[0023] Optionally, the establishment of the azimuth overlay template further includes:

[0024] Based on the azimuth stacking sector and the effective range of offset, AVA analysis is carried out by continuously adjusting the pre-stack gather stacking template of the OVT domain. The optimal azimuth and offset range that reflect the characteristics of Class I AVO of typical oil layers are found. A stacking template with fluid orientation significance is established, and azimuth stacking is performed to obtain azimuth stacked seismic bodies.

[0025] Optionally, obtaining the ideal frequency domain data volume based on the azimuth-based seismic overlay data using time-frequency analysis methods specifically includes:

[0026] Based on the aforementioned azimuth-stacked seismic body, time-frequency analysis is performed using the generalized S-transform algorithm.

[0027] Generalized S-transform algorithm formula:

[0028] By setting adjustment factors λ=2 and p=1.1, time-frequency decomposition was performed on single-channel seismic data to obtain a series of seismic records with multiple frequencies.

[0029] Optionally, determining the frequency-sensitive segment of the study area by combining the frequency gradient response characteristics of actual well points with different oil content specifically includes:

[0030] Analyzing the frequency gradient curves at well points with various oil and gas content revealed differences in the frequency gradient curves under different fluid conditions.

[0031] By adjusting multiple frequency ranges, the frequency-sensitive segment corresponding to the frequency gradient curve showing low-frequency enhancement in oil wells can be obtained.

[0032] Optionally, calculating the frequency gradient attribute map of the entire study area and predicting reservoir fluids specifically includes:

[0033] Based on the frequency-sensitive segment, the dominant frequency range is determined. Under the dominant frequency, the azimuth frequency gradient attribute calculation is performed across the entire region to obtain the planar distribution map of the reservoir intercept gradient attribute under the four dominant azimuths.

[0034] Under the dominant frequency, the azimuth gradient fusion attribute is calculated to obtain the fusion attribute map. Then, based on the statistical analysis of actual drilling wells, areas with strong intercept gradient attribute response have good oil and gas content in actual drilling wells, while areas with weak intercept gradient attribute response have poor oil and gas content in actual drilling wells.

[0035] By utilizing the obtained intercept gradient property plane strength display, the reservoir fluid distribution pattern can be predicted.

[0036] Optionally, the advantageous frequency range is 15-49 Hz.

[0037] Optionally, the azimuth gradient fusion attribute under the calculated dominant frequency specifically includes:

[0038] Reservoir fluids exhibit anisotropy in different orientations, and an attribute fusion factor is constructed.

[0039]

[0040] Among them, A i represents the frequency gradient attribute in each direction, and fi represents the weight represented by each attribute in each direction.

[0041] This invention provides a multi-azimuth, multi-frequency reservoir fluid prediction method based on five-dimensional data. The method includes: utilizing azimuth and offset information from pre-stack OVT domain five-dimensional data to identify the influence of azimuth anisotropy on reservoir fluids, selecting the dominant azimuth, and establishing a sub-azimuth stacking template; based on the sub-azimuth seismic stacking data, using time-frequency analysis to obtain an ideal frequency domain data volume; combining the frequency gradient response characteristics of actual well points with different oil-bearing properties to determine the frequency-sensitive segment of the study area; calculating the frequency gradient attribute map of the entire study area to predict reservoir fluids. This effectively identifies reservoir fluids, thereby improving the drilling success rate of lithologic oil and gas reservoirs.

[0042] 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

[0043] 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.

[0044] Figure 1 A flowchart illustrating a multi-directional, multi-frequency reservoir fluid prediction method based on five-dimensional data, provided in an embodiment of the present invention;

[0045] Figure 2 This is a slice image of a seismic body along different superimposed sectors provided in an embodiment of the present invention;

[0046] Figure 3 Offset difference attribute map provided for embodiments of the present invention;

[0047] Figure 4 A schematic diagram of the AVO curve extracted along the top of the reservoir provided in an embodiment of the present invention;

[0048] Figure 5This is a schematic diagram of the preferred orientation of the pre-stack amplitude slice provided in an embodiment of the present invention;

[0049] Figure 6 This is a schematic diagram of the AVO characteristic curve extracted along the top surface of the reservoir from an actual drilled well, provided in an embodiment of the present invention.

[0050] Figure 7 This invention provides frequency gradient curve feature maps of different hydrocarbon-bearing properties in the study area, as shown in the embodiments of the present invention.

[0051] Figure 8 This is a gradient attribute map of the azimuth intercept under the dominant frequency of the study area provided in an embodiment of the present invention;

[0052] Figure 9 This is a fusion map of the intercept gradient attributes of the study area provided in an embodiment of the present invention. Detailed Implementation

[0053] 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.

[0054] 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.

[0055] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0056] like Figure 1 As shown, a multi-directional, multi-frequency reservoir fluid prediction method based on five-dimensional data includes:

[0057] Step 1: Perform full-offset stacking of the OVT pre-stack gathers at azimuth intervals of 0-15°, 0-35°, 0-45°, and 0-55° to obtain four azimuth seismic data volumes for different stacking sectors. Extract stratigraphic slices along the layers of each of the four seismic volumes. The stratigraphic slice images of the azimuth seismic volumes for different stacking sectors are shown below. Figure 2 As shown, the slices that can reflect reservoir information are selected through analysis, and the azimuth superposition sector is determined.

[0058] Step 2: Set the overlay template for the OVT gather. First, select omnidirectional overlay, and divide the offset distance into three groups according to near, medium, and far distances: 100-500m, 500-900m, and 900-1500m. Use the formula... The root mean square attribute of the three sets of offset parameter data volumes was extracted respectively, and the OVT offset difference attribute was constructed using the following formula: Where B(i) represents the offset difference attribute, A(i) represents the attribute value at the calculation point, and A min Indicates the minimum attribute value, A max This represents the maximum attribute value. The offset difference attribute is used to further evaluate and analyze the three sets of data with different offset parameters. A large OVT offset difference attribute indicates significant anisotropy, and the offset corresponding to the most significant OVT offset difference attribute response is selected as the effective offset stacking range. For example... Figure 3 As shown, this is a graph illustrating the offset difference attributes.

[0059] Step 3: Based on the single-well P-wave velocity curve, density curve, and S-wave velocity curve (using empirical formulas) The extracted wavelet data was used to obtain the single-well AVO forward modeling seismic gather using the Aki & Richards approximation equation method. The AVO curve was then extracted along the top of the reservoir, as illustrated in the diagram below. Figure 4 As shown. By analyzing the AVO characteristics of a single well under different fluid conditions, the amplitude of the AVO curve gradually decreases with the increase of the incident angle. According to Castagna's (1998) AVO classification diagram, the location of the oil layer in the single well is identified as having Type I AVO characteristics.

[0060] Step 4: Based on the azimuth stacking sector and effective offset range obtained in Steps 1 and 2, conduct AVA analysis by continuously adjusting the pre-stack gather stacking template in the OVT domain (i.e., adjusting the azimuth and offset parameters) to find the optimal azimuth and offset range that can reflect the characteristics of a typical oil layer's Class I AVO. Establish a stacking template with fluid directionality and perform azimuth-based stacking. Pre-stack amplitude slices are selected based on the preferred azimuth. Figure 5 As shown. The AVO characteristic curve extracted along the top surface of the reservoir from the actual drilled well is as follows. Figure 6 As shown.

[0061] Step 5: For the azimuth-stacked seismic bodies obtained in Step 4, perform time-frequency analysis using the generalized S-transform algorithm. The formula for the generalized S-transform algorithm is as follows: Set the adjustment factor λ ＝2 With p=1.1, time-frequency decomposition was performed on the single-channel seismic data to obtain a series of seismic records of different frequencies.

[0062] Step 6: By analyzing the frequency gradient curves at well points with different hydrocarbon content, the characteristic diagrams of frequency gradient curves under different hydrocarbon content in the study area are obtained, such as... Figure 7As shown, the frequency gradient curves differ under different fluids. By adjusting different frequency ranges, the frequency sensitive segment corresponding to the frequency gradient curve in which low-frequency enhancement of oil wells occurs can be found. The principle is based on (1) the formation's absorption and attenuation capacity for high-frequency components is greater than its absorption and attenuation capacity for low-frequency components, and (2) the (longitudinal wave) velocity of the formation is different when it contains different fluids, resulting in different absorption and attenuation capacities. When the reservoir contains oil and gas, the phenomenon of low-frequency enhancement of oil wells occurs.

[0063] Step 7: Based on the frequency sensitive segment obtained in Step 6, determine the dominant frequency range (15-49 Hz). The azimuth intercept gradient attribute map of the dominant frequency in the study area is shown below. Figure 8 As shown, under the dominant frequency, the azimuth frequency gradient attribute calculation is performed across the entire region, resulting in planar distribution maps of the reservoir intercept gradient attributes under the four dominant azimuths. Due to the anisotropy of reservoir fluids under different azimuths, an attribute fusion factor is constructed. Among them, A i Let represent the frequency gradient attribute under each orientation, and fi represent the weight of each orientation attribute (value between 0 and 1). Using this formula, the azimuth-specific gradient fusion attribute under the dominant frequency is calculated, resulting in a fusion attribute map. The frequency gradient fusion attribute map of the study area is shown below. Figure 9 As shown, based on statistical analysis of actual drilled wells, areas with strong intercept gradient attribute response (red area) indicate good oil and gas content, while areas with weak intercept gradient attribute response (blue area) indicate poor oil and gas content. The distribution pattern of reservoir fluids can be predicted using the obtained intercept gradient attribute plane strength display.

[0064] Beneficial effects: By utilizing the azimuth and offset information of the pre-stack OVT domain five-dimensional data, the influence of azimuth anisotropy on reservoir fluids is identified, the dominant azimuth is selected, and a azimuth-based stacking template is established. Based on the azimuth-based seismic stacking data, an ideal frequency domain data volume is first obtained using appropriate time-frequency analysis methods. Then, combined with the frequency gradient response characteristics of actual well points with different oil-bearing properties, the frequency-sensitive segment of the study area is determined, thereby calculating the frequency gradient attribute map of the entire study area, thus realizing reservoir fluid prediction.

[0065] 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 predicting reservoir fluids from multiple directions and frequencies based on five-dimensional data, characterized in that, The prediction method includes: Using the azimuth and offset information of the pre-stack OVT domain five-dimensional data, the influence of azimuth anisotropy on reservoir fluids is identified, the dominant azimuth is selected, and a azimuth-based stacking template is established. Based on the azimuth-based seismic overlay data, an ideal frequency domain data volume is obtained using time-frequency analysis methods; By combining the frequency gradient response characteristics of actual well points with different oil-bearing properties, the frequency-sensitive segment of the study area was determined; Calculate the frequency gradient property map of the entire study area to predict reservoir fluids.

2. The method for predicting reservoir fluids based on five-dimensional data in multiple directions and frequencies according to claim 1, characterized in that, The process of utilizing the azimuth and offset information from the pre-stack OVT domain five-dimensional data to identify the influence of azimuth anisotropy on reservoir fluids and select the dominant azimuth specifically includes: The OVT pre-stack gathers were stacked with full offset, azimuth stacks of 0-10°, 0-20°, 0-30°, and 0-40° to obtain azimuth seismic data volumes of 4 different stacking sectors; Stratigraphic slices were extracted from the four seismic bodies, and the slices that could reflect reservoir information were selected to determine the azimuth stacking sector.

3. The method for predicting reservoir fluids based on five-dimensional data in multiple directions and frequencies according to claim 1, characterized in that, The establishment of the azimuth overlay template specifically includes: Set the overlay template for the OVT gather, select all-around overlay, and divide the offset distance into 3 groups according to near, medium and far: 100-500m, 500-900m, and 900-1500m; Using formula The root mean square attribute of the three sets of offset parameter data volumes was extracted respectively, and the OVT offset difference attribute was constructed using the following formula: Where B(i) represents the offset difference attribute, A(i) represents the attribute value at the calculation point, and A min Indicates the minimum attribute value, A max Indicates the maximum attribute value; The offset difference attribute was used to further evaluate and analyze the data volumes of the three sets of different offset distance parameters. The large offset difference attribute of OVT indicates obvious anisotropy information. The offset distance corresponding to the obvious response of OVT offset difference attribute was selected as the effective offset distance superposition range.

4. The method for multi-directional, multi-frequency reservoir fluid prediction based on five-dimensional data according to claim 1, characterized in that, The process of using azimuth and offset information from pre-stack OVT domain five-dimensional data to identify the influence of azimuth anisotropy on reservoir fluids and select the dominant azimuth also includes: Wavelet data extracted from the P-wave velocity curve, density curve, and S-wave velocity curve of a single well; Using the Aki & Richards approximate equation method, we obtained the forward seismic gathers of AVO from a single well and extracted the AVO curve along the top of the reservoir. By analyzing the AVO characteristics of a single well under various fluid conditions, the amplitude of the AVO curve gradually decreases as the incident angle increases. Based on Castagna's AVO classification chart, the location of the oil layer in the single well is identified as having Type I AVO characteristics.

5. The method for predicting reservoir fluids based on five-dimensional data in multiple directions and frequencies according to claim 1, characterized in that, The method for establishing the azimuth overlay template also includes: Based on the azimuth stacking sector and the effective range of offset, AVA analysis is carried out by continuously adjusting the pre-stack gather stacking template of the OVT domain. The optimal azimuth and offset range that reflect the characteristics of Class I AVO of typical oil layers are found. A stacking template with fluid orientation significance is established, and azimuth stacking is performed to obtain azimuth stacked seismic bodies.

6. The method for predicting reservoir fluids based on five-dimensional data in multiple directions and frequencies according to claim 5, characterized in that, The process of obtaining an ideal frequency domain data volume based on azimuth-based seismic overlay data using time-frequency analysis methods specifically includes: Based on the aforementioned azimuth-stacked seismic body, time-frequency analysis is performed using the generalized S-transform algorithm. Generalized S-transform algorithm formula: By setting adjustment factors λ=2 and p=1.1, time-frequency decomposition was performed on single-channel seismic data to obtain a series of seismic records with multiple frequencies.

7. The method for predicting reservoir fluids based on five-dimensional data from multiple directions and frequencies according to claim 1, characterized in that, The determination of the frequency-sensitive segment of the study area by combining the frequency gradient response characteristics of actual well points with different oil content specifically includes: Analyzing the frequency gradient curves at well points with various oil and gas content revealed differences in the frequency gradient curves under different fluid conditions. By adjusting multiple frequency ranges, the frequency-sensitive segment corresponding to the frequency gradient curve showing low-frequency enhancement in oil wells can be obtained.

8. The method for predicting reservoir fluids based on five-dimensional data in multiple directions and frequencies according to claim 1, characterized in that, The calculation of the frequency gradient attribute map of the entire study area and the prediction of reservoir fluids specifically include: Based on the frequency-sensitive segment, the dominant frequency range is determined. Under the dominant frequency, the azimuth frequency gradient attribute calculation is performed across the entire region to obtain the planar distribution map of the reservoir intercept gradient attribute under the four dominant azimuths. Under the dominant frequency, the azimuth gradient fusion attribute is calculated to obtain the fusion attribute map. Then, based on the statistical analysis of actual drilling wells, areas with strong intercept gradient attribute response have good oil and gas content in actual drilling wells, while areas with weak intercept gradient attribute response have poor oil and gas content in actual drilling wells. By utilizing the obtained intercept gradient property plane strength display, the reservoir fluid distribution pattern can be predicted.

9. The method for predicting reservoir fluids based on five-dimensional data in multiple directions and frequencies according to claim 8, characterized in that, The preferred frequency range is 15-49 Hz.

10. The method for predicting reservoir fluids based on five-dimensional data in multiple directions and frequencies according to claim 8, characterized in that, The specific azimuth gradient fusion attributes under the calculated dominant frequency include: Reservoir fluids exhibit anisotropy in different orientations, and an attribute fusion factor is constructed. Among them, A i represents the frequency gradient attribute in each direction, and fi represents the weight represented by each attribute in each direction.

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