Prediction method for high-sand-to-ground-ratio stratum river channel in down-col period

Abstract

The invention discloses a method for predicting a river channel in a high sand-to-ground ratio stratum in a down-down period, and belongs to the technical field of oil-gas exploration. In a method for predicting a river channel sand body in a deep stratum in the down-down period, a lithologic model is established by utilizing an earthquake forward modeling technology to identify a river channel sand earthquake response characteristic; the method comprises the following steps: determining a river channel sand body distribution range and a sand body thickness distribution trend by using seismic attributes and a stratigraphic slicing technology, then determining a river channel sand body space distribution form by using a waveform indication inversion technology, and finally carrying out hydrocarbon detection on the basis of a fluid replacement forward modeling technology. And completing depiction of a favorable area with sand body distribution by combining attribute fusion clustering analysis with a seismic facies method. According to the method, seismic sedimentology and geomorphology are used as theoretical guidance, seismic forward modeling, attribute analysis and inversion technologies are used as technical supports, qualitative description of river channel sand body distribution is converted into specific quantitative analysis, the multiplicity of solutions of conventional river channel sand reservoir prediction is theoretically reduced, the error between the predicted thickness and the actual thickness is reduced, and the prediction accuracy of the river channel sand reservoir is improved. The problem of low prediction precision of the deep riverway type reservoir is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas exploration, and particularly relates to a method for predicting a river channel in a high sand-to-ground ratio formation in a fault depression period. Background Art

[0002] As one of the important reservoirs, river channel sand bodies have been widely studied in the industry. After previous research, shallow river channel sand bodies have formed a complete set of technical processes dominated by seismic attribute analysis. However, in recent years, with the continuous deepening of exploration and development technology, the development of deep river channel sand bodies has become a new hot research field. Unlike shallow strata, deep strata are in the fault-depression transition period, buried deeper, with thinner single-layer thickness of sandstone and high sand-to-formation ratio. It is difficult to characterize the distribution form of a single river channel sand by relying solely on seismic attributes. Therefore, it is necessary to form technical means for identifying and characterizing deep river channel sand bodies.

[0003] It is quite difficult to predict gas layers in deep river channel sand bodies. The main problems to be faced are: (1) The deep strata are in the fault-depression transition period, buried deep and with thin single-layer sandstone thickness, with a high sand-to-ground ratio, which is far less than the resolution of seismic data; (2) There are multiple lithological combinations, so seismic waves will be affected by the frequent changes of sandstone and mudstone, making the reservoir prediction results likely to have multiple solutions, which makes sand body identification more difficult. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes: a method for predicting a river channel in a high sand-to-ground ratio stratum during a fault depression period, comprising the following steps:

[0005] S1. After obtaining the seismic data and well seismic data of the target area, extract the sub-waves according to the main frequency of the seismic data, perform forward simulation on the target layer, obtain the seismic reflection model, and obtain the seismic response characteristics of the river channel sand body;

[0006] S2. According to the seismic response characteristics of the channel sand body, combined with the well seismic data, the target layer section is calibrated and the structure is interpreted. According to the structural interpretation results, the amplitude attributes are extracted and the vertical segmentation is performed along the stratigraphic direction. The channel information is obtained on the slices to reflect the distribution characteristics of the sand body and to characterize the distribution of the channel sand body;

[0007] S3, standardizing and normalizing the logging curve, selecting sensitive parameters and reconstructing the logging curve by using a method of merging multiple curve frequencies and features, performing waveform indication inversion on the newly obtained sensitive parameter curve, optimizing parameters, obtaining an inversion body of the target layer, and determining the quantitative relationship of the thickness of the channel sand body through the difference in lithological sensitive parameters;

[0008] S4. Carry out fluid replacement forward modeling based on the gas content of the target layer and use different amplitude changes to identify areas with abnormal hydrocarbons;

[0009] S5. Cluster analysis is performed on the various attributes of the target layer to more accurately characterize the distribution characteristics of the river channel and compare the location correspondence of the wells and reservoirs.

[0010] Furthermore, in step S1, the well seismic data of the target layer section is obtained, and the adapted sub-wave is extracted according to the main frequency of the seismic data to establish a single sand body model, and then the established geological model is forward simulated to obtain the corresponding forward seismic profile and seismic reflection model, determine the seismic response characteristics of the river channel sand body, and verify it using the well logging curve;

[0011] First, establish a geological model. First, use the well logging acoustic time difference and density curve to analyze the lithology characteristics of the target layer and use the acoustic time difference curve and density curve to calculate the acoustic velocity and density of the sandstone and mudstone in the target layer to establish a seismic geological model. According to the actual stratigraphic development characteristics and the resolution ability of seismic data, establish a horizontal layered model.

[0012] Then, forward simulation: using the convolution model:

[0013] S(t)=w(t)*r(t)

[0014] The convolution model regards the seismic reflection signal S(t) as the convolution of the seismic wavelet w(t) and the underground reflection coefficient r(t).

[0015] Furthermore, in step S2, well-seismic calibration is performed in combination with logging and seismic data, structural interpretation of the target layer segment is performed, a suitable time window is selected, and multiple amplitude attributes are extracted to determine the sensitive attributes of sandstone. The basic relationship between seismic attributes and the position and thickness of river sand bodies is preliminarily determined based on the lithological interpretation of the exploration well, and multiple attributes are optimized and combined to obtain an attribute combination that is relatively sensitive to the river phase reservoir. At the same time, the seismic tuning frequency is determined to obtain the minimum sandstone thickness that can be characterized.

[0016] Furthermore, in step S3, the logging curve is standardized and normalized to make the value range basically consistent, the sensitive parameters of sandstone and mudstone are optimized in the form of a cross plot, and the identification threshold of sandstone and mudstone is statistically calculated. The logging curve is reconstructed by the method of "multi-curve frequency domain feature merging", and the high-frequency band of the lithology sensitivity curve and the low-frequency band of the wave impedance curve are reconstructed to generate a new lithology sensitivity curve for waveform indication inversion, adding the control of geological prior information; after the well is calibrated, a model is established according to the layer position, and then repeated iterative tests are carried out to optimize the inversion parameters and time window parameters to obtain an inversion body that can truly reflect the spatial distribution morphology of the target layer sand body; the difference in lithology sensitivity parameters is used to characterize the target layer sand body of the inversion body through the combination of plane and section; according to the obtained inversion body, the target layer sand body is characterized by the difference in lithology sensitivity parameters, the corresponding thickness is extracted for the target layer, and the quantitative relationship of the thickness of the river channel sand body is determined.

[0017] Furthermore, in step S4, based on pre-stack seismic data and logging data, after the well is calibrated using pre-stack data, a geological model is established according to the shear wave velocity, compressional wave velocity and formation thickness parameters obtained from rock physics analysis, and then different fluids are replaced in the geological model, and the reflection coefficient is recalculated from the replaced geological model, and the AVO response is converted into physical parameters of the gas-bearing reservoir according to the replaced gather analysis and AVO response law.

[0018] Furthermore, in step S5, the sandstone thickness of the well in the target layer is first counted, and an intersection analysis is performed with different seismic attributes at the well point to calculate the correlation coefficient, and then screening is performed, the screened attributes are combined and clustered, and the clustering results are compared to reflect the distribution pattern and gas content of the reservoir thickness in the target layer.

[0019] The beneficial effects of the present invention are as follows: in the prediction method of channel sand bodies in deep strata during the fault-depression period, the present invention uses seismic forward modeling technology to establish a lithology model to identify the seismic response characteristics of channel sand bodies, uses seismic attributes and stratigraphic slicing technology to determine the distribution range of channel sand bodies and the distribution trend of sand body thickness, and then uses waveform indication inversion technology to determine the morphology of the spatial distribution of channel sand bodies. Finally, hydrocarbon detection is performed on the basis of fluid replacement forward modeling technology, and the characterization of favorable areas with sand body distribution is completed by combining attribute fusion cluster analysis with seismic phases. This mainly solves the problem of low prediction accuracy of deep channel reservoirs and provides a solution to the difficult development of deep natural gas.

[0020] The present invention is guided by the theory of seismic sedimentology and seismic geomorphology, and supported by seismic forward modeling, attribute analysis and inversion technology. It explores a set of prediction and characterization technologies for deep-layer high-sand-to-ground ratio river channel sand reservoirs, transforming the distribution of river channel sand bodies from qualitative description to specific quantitative analysis, theoretically reducing the multi-solution of conventional river channel sand reservoir prediction, and reducing the error between the predicted thickness and the actual thickness. The present invention is first applied to the Fu 14 block in the Fushuang area, and the predicted river channel sand body thickness and the drilling level well have a consistency rate of up to 80.3%. At the same time, it has been widely used in the prediction of deep river channel sand reservoirs in Changling Depression and Dehui Depression, and the drilling rate of deployed wells in actual production has been significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a technical flow chart of a method for predicting a river channel in a high sand-to-ground ratio formation during a fault depression period provided by an embodiment of the present invention;

[0022] Figure 2 It is a diagram of lithology combination and seismic forward model in an embodiment of the present invention;

[0023] Figure 3 It is an amplitude attribute and a curvature attribute map extracted based on the interpretation result in the embodiment of the present invention;

[0024] Figure 4 It is a schematic diagram of the sand body in the inversion section obtained by waveform indication inversion in the embodiment of the present invention and a quantitative reservoir prediction thickness diagram of the sand body according to the inversion result;

[0025] Figure 5 is a forward model diagram obtained based on fluid replacement in an embodiment of the present invention;

[0026] Figure 6 This is the comprehensive evaluation diagram for implementing effective closure finally obtained in the embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the technical means and objectives of the present invention easy to understand, the present invention is further described below in combination with specific embodiments. A method for predicting river channels in high sand-to-ground ratio strata during the fault depression period is as follows: Figure 1 As shown, the following steps are included:

[0028] S1. After obtaining the seismic data and well seismic data of the target area, extract the sub-waves according to the main frequency of the seismic data, perform forward simulation on the target layer, obtain the seismic reflection model, and obtain the seismic response characteristics of the river channel sand body;

[0029] S2. According to the seismic response characteristics of the channel sand body, combined with the well seismic data, the target layer section is calibrated and the structure is interpreted. According to the structural interpretation results, the amplitude attributes are extracted and the vertical segmentation is performed along the stratigraphic direction. The channel information is obtained on the slices to reflect the distribution characteristics of the sand body and to characterize the distribution of the channel sand body;

[0030] S3, standardizing and normalizing the logging curve, selecting sensitive parameters and reconstructing the logging curve by using a method of merging multiple curve frequencies and features, performing waveform indication inversion on the newly obtained sensitive parameter curve, optimizing parameters, obtaining an inversion body of the target layer, and determining the quantitative relationship of the thickness of the channel sand body through the difference in lithological sensitive parameters;

[0031] S4. Carry out fluid replacement forward modeling technology based on the gas content of the target layer, and use different amplitude changes to identify areas with abnormal hydrocarbons;

[0032] S5. Cluster analysis is performed on the various attributes of the target layer to more accurately characterize the distribution characteristics of the river channel and compare the location correspondence of the wells and reservoirs.

[0033] Step S1: Obtain seismic data and well seismic data of the target area, extract wavelets according to the main frequency of the seismic data, perform forward modeling on the target layer segment, and obtain a seismic reflection model.

[0034] Specifically, the well seismic data of the target layer is obtained, and the adapted sub-wave is extracted according to the main frequency of the seismic data to establish a single sand body model, and then the established geological model is forward simulated to obtain the corresponding forward seismic profile and seismic reflection model, and the seismic response characteristics of the river channel sand body are determined, and verified by the well logging curve, which provides a basis for river channel identification and has guiding significance for seismic interpretation. The basic process of the forward simulation method used in this step of the patent of the present invention is: 1. Establish a geological model, 2. Forward simulation.

[0035] To establish a geological model, in this example, we first use the well logging acoustic time difference and density curve to analyze the lithology characteristics of the target layer, and then use the acoustic time difference curve and density curve to calculate the acoustic velocity and density of the sandstone and mudstone in the target layer to establish a seismic geological model. According to the actual stratigraphic development characteristics and the resolution of seismic data, this example establishes a horizontal layered model.

[0036] (2) Forward modeling: This forward modeling uses the convolution model, which is the basis of geophysical exploration technology. The model formula is:

[0037] S(t)=w(t)*r(t)

[0038] The convolution model actually treats the seismic reflection signal S(t) as the convolution of the seismic wavelet w(t) and the underground reflection coefficient r(t).

[0039] In this example, four sandstone and mudstone geological models are established according to the main frequency of seismic data and the stratigraphic characteristics of the target layer. Ricker wavelets with a length of 100ms and a main frequency of 35Hz are selected as the seismic wavelets of the convolution model for forward simulation. Finally, according to the forward response results, the forward models corresponding to different sand body thicknesses are identified, the reflection characteristics of the target earthquake on the sandstone and mudstone in the example are clarified, and the effective resolution of the seismic data is determined. The seismic response characteristics corresponding to the sandstone and mudstone are also of guiding significance for the subsequent well-seismic calibration, such as Figure 2 shown.

[0040] Step S2: According to the seismic response characteristics of the channel sand body, the well seismic data is combined for calibration, and the target layer segment is structurally interpreted. According to the structural interpretation results, the amplitude attributes are extracted and the vertical segmentation is performed along the stratigraphic direction, and the channel information is obtained on the slices to reflect the distribution characteristics of the sand body and to qualitatively characterize the distribution of the channel sand body.

[0041] Specifically, we combine logging and seismic data to carry out fine well-seismic calibration, interpret the structure of the target layer, select the appropriate time window, extract multiple amplitude attributes to determine the sensitive attributes of sandstone, and preliminarily determine the basic relationship between seismic attributes and the location and thickness of river sand bodies based on the lithological interpretation of the exploration wells. We optimize the combination of multiple attributes to obtain the attribute combination that is more sensitive to river-facies reservoirs and determine the seismic tuning frequency at the same time to obtain the minimum sandstone thickness that can be characterized. According to previous experience, it is more effective to use amplitude attributes to judge the distribution and contour of sand bodies. Most of the well-developed river channels have thick value areas with continuous strip-shaped distribution in amplitude attributes. However, in deep strata, due to the limitations of amplitude attributes themselves, a single amplitude attribute can only extract part of the information of the geological body. In order to improve the prediction accuracy, other attributes need to be extracted as a basis for supplementary characterization of sedimentary facies.

[0042] The attributes extracted in this example are root mean square amplitude, maximum energy, average energy, variance, and curvature. Different attribute maps show the planar distribution of the target layer channel sand bodies and the spatial superposition relationship of the channel, such as Figure 3 shown.

[0043] In order to reflect the real underground structure and roughly grasp the development of the river channel, the extracted attribute body is segmented vertically along the direction of the layer on the basis of the layer interpretation. The specific method is: interpolate the target layer in equal proportion, establish the layer with the generated stratigraphic slice, open a one-hour window with the layer as the center, and extract various seismic attributes within the time window. According to the change of the sequence, the multi-stage river channel in the target layer is identified vertically. The river channel information displayed on the slice can determine the real morphology of the reservoir in different geological periods, the change and distribution law of the sedimentary phase, identify the target and clarify the development period of the river sand body, and further reflect the lithological distribution characteristics of the sand body.

[0044] Step S3: Standardize and normalize the logging curve, select sensitive parameters and reconstruct the logging curve using the method of merging multiple curve frequencies and features. Perform waveform indication inversion on the newly obtained sensitive parameter curve, optimize the parameters, and obtain the inversion volume of the target layer.

[0045] Specifically, the logging curves are standardized and normalized to make the value range basically consistent. The sensitive parameters of sandstone and mudstone are optimized in the form of cross plots, and the identification threshold of sandstone and mudstone is statistically calculated. Sandstone in deep formations is affected by compaction, and it is difficult to distinguish between sandstone and mudstone. Therefore, the method of "multi-curve frequency domain feature merging" is used to reconstruct the logging curves. The specific method is: the high frequency band of the lithology sensitivity curve and the low frequency band of the wave impedance curve are reconstructed to generate a new lithology sensitivity curve for waveform indication inversion, and the control of geological prior information is added. After the calibration of the well, a model is established according to the layer position, and then repeated iterative tests are carried out to optimize the inversion parameters and time window parameters to obtain an inversion body that can truly reflect the spatial distribution morphology of the target layer sand body. Using the difference in lithology sensitivity parameters, the target layer sand body of the inversion body is characterized by combining plane and section. According to the obtained inversion body, the target layer sand body is characterized by the difference in lithology sensitivity parameters, and the corresponding thickness is extracted for the target layer to determine the quantitative relationship of the thickness of the river channel sand body.

[0046] The inversion method used in this example is the waveform indication inversion method. After completing the calibration of the well, the prediction method can further determine the direction, distribution and combination connectivity of the channel sand body for the target layer segment of the example, such as Figure 4 shown.

[0047] Step S4: Perform fluid replacement forward modeling technology based on the gas content of the target layer, and use different amplitude changes to identify areas with abnormal hydrocarbons.

[0048] Specifically, based on pre-stack seismic data and logging data, after calibrating the well using pre-stack seismic data, a geological model is established based on parameters such as shear wave velocity, longitudinal wave velocity, and formation thickness obtained from rock physics analysis. Then, different fluids are replaced in the geological model, and the reflection coefficient is recalculated from the replaced geological model. Based on the replaced track set analysis and AVO response law, the AVO response can be converted into the physical parameters of the gas-bearing reservoir. Different fluids have different physical parameters, and the fluid type of the gas-bearing reservoir can be determined by comparing the physical parameters of different fluids, such as Figure 5 shown.

[0049] Step S5: Cluster analysis is performed on the various attributes of the target layer segment to more accurately characterize the channel distribution characteristics and compare the location correspondence of the well and the reservoir.

[0050] Specifically, seismic attributes contain geological information and also indicate amplitude changes caused by lithology, fluid, etc. First, the sandstone thickness of the well in the target layer is counted, and the intersection analysis is performed with different seismic attributes at the well point, and the correlation coefficient is calculated, and then screened to ensure accuracy. The screened attributes are combined and clustered. Commonly used clustering algorithms include K-means, hierarchical clustering, etc. By comparing the clustering results, it can be found that the distribution pattern and gas content of the reservoir thickness in the target layer can be reflected, and favorable oil and gas areas can be identified, such as Figure 6 shown.

[0051] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for predicting river channels in high sand-to-ground ratio strata during the fault depression period, characterized in that: The steps include: S1. After obtaining the seismic data and well seismic data of the target area, extract the sub-waves according to the main frequency of the seismic data, perform forward simulation on the target layer, obtain the seismic reflection model, and obtain the seismic response characteristics of the river channel sand body; S2. According to the seismic response characteristics of the channel sand body, combined with the well seismic data, the target layer section is calibrated and the structure is interpreted. According to the structural interpretation results, the amplitude attributes are extracted and the vertical segmentation is performed along the stratigraphic direction. The channel information is obtained on the slices to reflect the distribution characteristics of the sand body and to characterize the distribution of the channel sand body; S3, standardizing and normalizing the logging curve, selecting sensitive parameters and reconstructing the logging curve by using a method of merging multiple curve frequencies and features, performing waveform indication inversion on the newly obtained sensitive parameter curve, optimizing parameters, obtaining an inversion body of the target layer, and determining the quantitative relationship of the thickness of the channel sand body through the difference in lithological sensitive parameters; S4. Carry out fluid replacement forward modeling based on the gas content of the target layer and use different amplitude changes to identify areas with abnormal hydrocarbons; S5. Cluster analysis is performed on the various attributes of the target layer to more accurately characterize the distribution characteristics of the river channel and compare the location correspondence of the wells and reservoirs.

2. The method for predicting river channels in high sand-to-ground ratio strata during the fault depression period according to claim 1, characterized in that: In step S1, the well seismic data of the target layer section are obtained, and the adapted sub-wave is extracted according to the main frequency of the seismic data to establish a single sand body model. Then, the established geological model is forward simulated to obtain the corresponding forward seismic profile and seismic reflection model, determine the seismic response characteristics of the river channel sand body, and verify it using the logging curve.

3. The method for predicting river channels in high sand-to-ground ratio strata during the fault depression period as claimed in claim 2, characterized in that: In step S1, first, a geological model is established. First, the lithological characteristics of the target layer are analyzed by using the logging sonic time difference and density curve, and the sonic velocity and density of the sandstone and mudstone in the target layer are statistically calculated through the sonic time difference curve and the density curve to establish a seismic geological model. According to the actual stratigraphic development characteristics and the resolution ability of the seismic data, a horizontal layered model is established.

4. The method for predicting river channels in high sand-to-ground ratio strata during the fault depression period as claimed in claim 3, characterized in that: In step S1, then, the forward simulation is performed using the convolution model formula: S(t)=w(t)*r(t) The convolution model regards the seismic reflection signal S(t) as the convolution of the seismic wavelet w(t) and the underground reflection coefficient r(t).

5. The method for predicting river channels in high sand-to-ground ratio strata during fault depression period according to claim 1, characterized in that: In step S2, well-seismic calibration is performed in combination with logging and seismic data, structural interpretation is performed on the target layer segment, a suitable time window is selected, and multiple amplitude attributes are extracted to determine the sensitive attributes of sandstone. The basic relationship between seismic attributes and the location and thickness of river sand bodies is preliminarily determined based on the lithological interpretation of the exploration well, and multiple attributes are optimized and combined to obtain an attribute combination that is relatively sensitive to the river phase reservoir. At the same time, the seismic tuning frequency is determined to obtain the minimum sandstone thickness that can be characterized.

6. The method for predicting river channels in high sand-to-ground ratio strata during the fault depression period according to claim 1, characterized in that: In the step S3, the logging curve is standardized and normalized to make the value range basically consistent, the sensitive parameters of sandstone and mudstone are optimized in the form of cross plots, and the identification threshold of sandstone and mudstone is statistically calculated. The logging curve is reconstructed by the method of "multi-curve frequency domain feature merging", and the high frequency band of the lithology sensitivity curve and the low frequency band of the wave impedance curve are reconstructed to generate a new lithology sensitivity curve for waveform indication inversion, and the control of geological prior information is added; after the well is calibrated, a model is established according to the layer position, and then repeated iterative tests are performed to optimize the inversion parameters and time window parameters to obtain an inversion body that can truly reflect the spatial distribution morphology of the target layer sand body; the difference in lithology sensitivity parameters is used to characterize the target layer sand body of the inversion body through the combination of plane and section; according to the obtained inversion body, the target layer sand body is characterized by the difference in lithology sensitivity parameters, the corresponding thickness is extracted for the target layer, and the quantitative relationship of the thickness of the river channel sand body is determined.

7. The method for predicting river channels in high sand-to-ground ratio strata during the fault depression period according to claim 1, characterized in that: In step S4, based on the pre-stack seismic data and the logging data, after the well is calibrated using the pre-stack data, a geological model is established according to the shear wave velocity, compressional wave velocity and formation thickness parameters obtained by the rock physics analysis, and then different fluids are replaced in the geological model, and the reflection coefficient is recalculated based on the replaced geological model. According to the replaced gather analysis and AVO response law, the AVO response is converted into the physical parameters of the gas-bearing reservoir.

8. The method for predicting river channels in high sand-to-ground ratio strata during the fault depression period as claimed in claim 1, characterized in that: In step S5, the sandstone thickness of the well in the target layer is first counted, and an intersection analysis is performed with different seismic attributes at the well point to calculate the correlation coefficient, and then screening is performed, the screened attributes are combined and clustered, and the clustering results are compared to reflect the distribution pattern and gas content of the reservoir thickness in the target layer.

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