A method for establishing an initial wave impedance model of a deposition microfacies fusion pseudo-acoustic wave inversion

Abstract

The application discloses a method for establishing a deposition microfacies fusion pseudo-acoustic wave inversion initial wave impedance model, adopts an information statistical weighting method to construct a pseudo-acoustic wave curve, and adopts a collaborative Kriging interpolation algorithm with a deposition microfacies result plane constraint to realize a low-frequency component composition of 'logging + sedimentary facies + earthquake', and improve the precision of the establishment of the initial wave impedance model of the continental oil reservoir, and provide an effective technical means and field implementation method for the reservoir prediction of the continental oil reservoir. The method can make the inversion result obtain higher longitudinal and lateral resolution, realize the quantitative prediction of a single sand body, reduce the uncertainty of the reservoir prediction result from the perspective of geological genesis, and improve the precision of lithologic trap identification.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas extraction, and in particular, it is a method for establishing an initial wave impedance model for sedimentary microfacies fusion pseudo-acoustic inversion. Background Technology

[0002] Terrestrial sedimentary reservoirs are controlled by a variety of factors, resulting in diverse reservoir types, rapid facies transitions, poor lateral continuity, and significant vertical thickness variations. Conventional initial wave impedance models, constrained by vertical interpretation horizons, rely solely on well logging data interpolation. Under complex geological conditions, they cannot accurately reflect geological phenomena, exhibiting low precision and poor consistency with subsequent drilling, making it difficult to meet production requirements.

[0003] Currently, the establishment of initial acoustic impedance models for continental oil reservoirs faces the following technical challenges: (1) The original acoustic curves have poor ability to distinguish lithology, resulting in unsatisfactory inversion results; (2) Relying solely on the low-frequency components of "well logging + seismic data" results in low accuracy due to the lack of low-frequency components in seismic data; (3) Interpolation algorithms are generally based on inverse distance weighted interpolation, which has poor relevance to geological understanding. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method for establishing an initial acoustic impedance model by fusion of sedimentary microfacies and pseudo-acoustic inversion. The method uses the information statistical weighting method to construct pseudo-acoustic curves and adopts a collaborative kriging interpolation algorithm with plane constraints of sedimentary microfacies results to realize the low-frequency component composition of "well logging + sedimentary facies + seismic". This improves the accuracy of establishing the initial acoustic impedance model of continental oil reservoirs and provides technical support for the exploration and development of continental reservoirs.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for establishing an initial wave impedance model for deposition microphase fusion pseudo-acoustic inversion, comprising the following steps: (1) Perform singular value correction processing on the original AC, GR and RT curves of each well in the target layer; (2) Normalize the RT, GR and AC curves after singular value correction, and use the information statistical weighting method to assign different weight values ​​to synthesize normalized pseudo-acoustic curves; (3) Statistically analyze the distribution range of the original acoustic transit time curve values ​​of each target layer in each well, and perform value range regression on the normalized pseudo-acoustic curve to return to the range of the original acoustic transit time curve values ​​in each layer. (4) Calculate the longitudinal wave impedance value of a single well. Based on the original AC curve and density DEN curve of the single well, use the original wave impedance calculation formula to calculate the change of the longitudinal wave impedance value of the single well and determine the wave impedance range of each microphase. (5) Statistically analyze the main range of wave impedance values ​​of the corresponding sedimentary microfacies of each sand body, correct the pseudo-acoustic curves that differ significantly from the wave impedance value range, and ensure that the value range of each sedimentary microfacies is consistent. By obtaining the wave impedance range of each microfacies in each well and comparing it with the wave impedance range of each microfacies statistically obtained in step (4), correct the pseudo-acoustic curves that differ significantly from the wave impedance value range after value range regression, and ensure that the value range of each sedimentary microfacies is consistent. (6) Calculate the width and length of the river channel at each stratum and obtain the variation function of the relevant data; (7) The layered approach adopts the co-kriging interpolation algorithm and uses the deposition microphase plane diagram to co-constrain extrapolate the initial wave impedance model.

[0006] In step (1), AC curve values ​​with a time difference (AC) curve distribution range outside 160-600 are eliminated; GR curve values ​​with a distribution range outside 30-150 are eliminated; and RT curve values ​​with a resistivity (RT) curve distribution range outside 1-30 are eliminated.

[0007] In step (2), the normalization formulas for the GR, RT, and AC curves are: GR p =(GR-GR min ) / (GR max -GR min ) In the formula, GR p —Normalized gamma values ​​of drilled wells in the sub-layers; GR—The original gamma value of the drilled well in the sub-layer; GR max —The maximum gamma value of the drilled well in the small layer; GR min —The minimum gamma value of the drilled well in the small layer; RT p =(RT-RT min ) / (RT) max -RT min ) In the formula, RT p —Normalized resistivity values ​​of drilled wells in the sub-layers; RT—The original resistivity value of the drilled well in the sub-layer; RT max —The maximum resistivity value of the drilled well in the small layer; RT min —The minimum resistivity value of the drilled well in the small layer; AC p =(AC-AC min ) / (AC max -ACmin ) In the formula, AC p —Normalized acoustic transit time of drilled wells in small layers; AC – The original acoustic transit time value of the drilled well in the small layer; AC max —The maximum sonic transit time value of the drilled well in the small layer; AC min —The minimum acoustic transit time value of the drilled well in the small layer; Ensure that the curve values ​​are all within the range of 0-1, and guarantee that the data of each curve is consistent.

[0008] In step (2), the normalized pseudo-acoustic curve is synthesized using an information statistical weighting method, which integrates the information of other curves into the acoustic curve with certain weights, so that the reconstructed curve can highlight the reservoir differences. The normalized pseudo-acoustic curve reconstruction formula is as follows: AC N =a*AC p + b*RT p + c*(1-GR p ) In the formula, AC N —The normalized value of the pseudo-acoustic curve in the small layer; GR p —Normalized gamma values ​​of drilled wells in the sub-layers; RT p —Normalized resistivity values ​​of drilled wells in the sub-layers; AC p —Normalized acoustic transit time of drilled wells in small layers; a, b, and c are weighting coefficients. The GR curve has the strongest ability to identify lithology, with a c value range of 0.5-0.7. The RT curve has a slightly stronger ability to identify lithology than the AC curve, with a b value range of 0.3-0.2 and an a value range of 0.2-0.1. The sum of a, b, and c is 1.

[0009] In step (3), the range regression formula for the pseudo-acoustic curve is: AC Ny = AC min + AC N *(AC max -AC min ) In the formula, AC Ny AC is the regressed sound wave value. min AC is the minimum sound wave value within this layer. N AC is the normalized value of the acoustic wave within this layer. max This represents the maximum sound wave value within this layer.

[0010] In step (4), the formula for calculating the original wave impedance is: IMP i =ρ i v i In the formula, IMP i —The original wave impedance of the bottom interface of the i-th layer, ρ i —Density of the i-th layer, v i —The velocity of the i-th layer.

[0011] In step (6), based on the sedimentary microfacies planar map of each layer, considering the correlation of data, the width and length of the river channel in the study area are statistically analyzed to obtain the variation function of the relevant data. The variation function analysis results reflect the magnitude of the regional variable of the single sand body layer in a certain direction and the average scale of the carrier of the regional variable in this direction. The range of variation is used to reflect the influence range of reservoir parameters, so as to predict the distribution law of reservoir properties in this layer.

[0012] In step (7), the wave impedance prediction formula of the co-kriging interpolation algorithm is adopted. Based on the correlation of variable data, the variation function of relevant data is obtained. Considering the data correlation based on the width and length of the channel, the interlayer interpolation mode is adopted. The initial wave impedance model is calculated by co-constrained extrapolation using the sedimentary microfacies plane map. The co-kriging interpolation formula is as follows:

[0013] In the formula, ----The estimated value of the random variable at position 0; ----n sample data for the initial variable; ----m sample data for the second-order variable; , ----Co-Kriging weight coefficient.

[0014] The beneficial effects of this invention are: it provides an effective technical means and field implementation method for the prediction of continental oil reservoirs. According to the method of this invention, the inversion results can obtain higher vertical and horizontal resolution, realize the quantitative prediction of single sand bodies, reduce the uncertainty of reservoir prediction results from the perspective of geological genesis, and improve the accuracy of lithological trap identification. Attached Figure Description

[0015] Figure 1 This is the histogram of the AC curve; Figure 2 This is a seismic profile of the wells. Figure 3A diagram showing the seismic interpretation results of the top and bottom interfaces of an unconformity hydrocarbon reservoir; Figure 4 Histogram of wave impedance values ​​in underwater branch channels; Figure 5 This is a graph showing the analysis of the variation function. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0017] The method for establishing the initial acoustic impedance model for depositional microphase fusion pseudo-acoustic inversion according to the present invention includes the following steps: (1) Perform singular value correction processing on the original AC, GR and RT curves of each well in the target layer; (2) Normalize the RT, GR and AC curves after singular value correction, and use the information statistical weighting method to assign different weight values ​​to synthesize normalized pseudo-acoustic curves; (3) Statistically analyze the distribution range of the original acoustic transit time curve values ​​of each target layer in each well, and perform value range regression on the normalized pseudo-acoustic curve to return to the range of the original acoustic transit time curve values ​​in each layer. (4) Calculate the longitudinal wave impedance value of a single well. Based on the original AC curve and density DEN curve of the single well, use the original wave impedance calculation formula to calculate the change of the longitudinal wave impedance value of the single well and determine the wave impedance range of each microphase. (5) Statistically analyze the main range of wave impedance values ​​of the corresponding sedimentary microfacies of each sand body, correct the pseudo-acoustic curves that differ significantly from the wave impedance value range, and ensure that the value range of each sedimentary microfacies is consistent. By obtaining the wave impedance range of each microfacies in each well and comparing it with the wave impedance range of each microfacies statistically obtained in step (4), correct the pseudo-acoustic curves that differ significantly from the wave impedance value range after value range regression, and ensure that the value range of each sedimentary microfacies is consistent. (6) Calculate the width and length of the river channel at each stratum and obtain the variation function of the relevant data; (7) The layered approach adopts the co-kriging interpolation algorithm and uses the deposition microphase plane diagram to co-constrain extrapolate the initial wave impedance model.

[0018] In step (1), AC curve values ​​with a time difference (AC) curve distribution range outside 160-600 are eliminated; GR curve values ​​with a distribution range outside 30-150 are eliminated; and RT curve values ​​with a resistivity (RT) curve distribution range outside 1-30 are eliminated.

[0019] In step (2), the normalization formulas for the GR, RT, and AC curves are: GR p =(GR-GR min ) / (GR max -GR min (1-1) In the formula, GR p —Normalized gamma values ​​of drilled wells in the sub-layers; GR—The original gamma value of the drilled well in the sub-layer; GR max —The maximum gamma value of the drilled well in the small layer; GR min —The minimum gamma value of the drilled well in the small layer; RT p =(RT-RT min ) / (RT) max -RT min (1-2) In the formula, RT p —Normalized resistivity values ​​of drilled wells in the sub-layers; RT—The original resistivity value of the drilled well in the sub-layer; RT max —The maximum resistivity value of the drilled well in the small layer; RT min —The minimum resistivity value of the drilled well in the small layer; AC p =(AC-AC min ) / (AC max -AC min (1-3) In the formula, AC p —Normalized acoustic transit time of drilled wells in small layers; AC – The original acoustic transit time value of the drilled well in the small layer; AC max —The maximum sonic transit time value of the drilled well in the small layer; AC min —The minimum acoustic transit time value of the drilled well in the small layer; Ensure that the curve values ​​are all within the range of 0-1, and guarantee that the data of each curve is consistent.

[0020] In step (2), the normalized pseudo-acoustic curve is synthesized using an information statistical weighting method, which integrates the information of other curves into the acoustic curve with certain weights, so that the reconstructed curve can highlight the reservoir differences. The normalized pseudo-acoustic curve reconstruction formula is as follows: ACN =a*AC p + b*RT p + c*(1-GR p (2) In the formula, AC N —The normalized value of the pseudo-acoustic curve in the small layer; GR p —Normalized gamma values ​​of drilled wells in the sub-layers; RT p —Normalized resistivity values ​​of drilled wells in the sub-layers; AC p —Normalized acoustic transit time of drilled wells in small layers; a, b, and c are weighting coefficients. The GR curve has the strongest ability to identify lithology, with a c value range of 0.5-0.7. The RT curve has a slightly stronger ability to identify lithology than the AC curve, with a b value range of 0.3-0.2 and an a value range of 0.2-0.1. The sum of a, b, and c is 1.

[0021] In step (3), the range regression formula for the pseudo-acoustic curve is: AC Ny = AC min + AC N *(AC max -AC min (3) In the formula, AC Ny AC is the regressed sound wave value. min AC is the minimum sound wave value within this layer. N AC is the normalized value of the acoustic wave within this layer. max This represents the maximum sound wave value within this layer.

[0022] In step (4), the formula for calculating the original wave impedance is: IMP i =ρ i v i (4) In the formula, IMP i —The original wave impedance of the bottom interface of the i-th layer, ρ i —Density of the i-th layer, v i —The velocity of the i-th layer.

[0023] In step (6), based on the sedimentary microfacies planar map of each layer, considering the correlation of data, the width and length of the river channel in the study area are statistically analyzed to obtain the variation function of the relevant data. The variation function analysis results reflect the magnitude of the regional variable of the single sand body layer in a certain direction and the average scale of the carrier of the regional variable in this direction. The range of variation is used to reflect the influence range of reservoir parameters, so as to predict the distribution law of reservoir properties in this layer.

[0024] In step (7), the wave impedance prediction formula of the co-kriging interpolation algorithm is adopted. Based on the correlation of variable data, the variation function of relevant data is obtained. Considering the correlation of data based on the width and length of the channel, the interlayer interpolation mode is adopted. The initial wave impedance model is calculated by co-constrained extrapolation using the sedimentary microfacies plane map. Co-kriging interpolation formula (5):

[0025] In the formula, ----The estimated value of the random variable at position 0; ----n sample data for the initial variable; ----m sample data for the second-order variable; , ----Co-Kriging weight coefficient.

[0026] Co-kriging interpolation utilizes the spatial correlation between several variables to spatially estimate one or more of them, making it particularly suitable for situations where there is limited observational data for the variable being estimated.

[0027] The method for establishing the initial acoustic impedance model of sedimentary microfacies fusion pseudo-acoustic inversion according to the present invention, for establishing a scale evaluation system for continental oil reservoirs, can be regarded as an effective technical application method. The specific implementation method is as follows: (1) Perform singular value correction processing on the original AC, GR and RT curves of each well in the target layer.

[0028] The logging environment, such as well diameter, mud density, wellbore diameter, mud intrusion zone, surrounding rock, and differences in measuring instruments, can all significantly affect the logging curve and easily generate outliers. While ensuring the logging curve's ability to identify lithology, outliers must be removed to ensure the curve values ​​remain within a reasonable range. For example, the main distribution range of the acoustic transit time (AC) curve is within 160-600 (…). Figure 1 Therefore, AC curve values ​​outside the specified range need to be removed. The main distribution range of the GR curve is between 30 and 150, so GR curve values ​​outside this range also need to be removed. The main distribution range of the resistivity RT curve is between 1 and 30, so RT curve values ​​outside this range also need to be removed.

[0029] (2) Normalize the RT, GR and AC curves and assign different permission values ​​to synthesize normalized pseudo-acoustic curves.

[0030] The RT, GR, and AC curves are normalized. The formulas for the RT and AC curves are consistent with the normalization formula for the GR curve, ensuring that the curve values ​​are all within the range of 0-1, guaranteeing data consistency for each curve, and preparing for the subsequent curve fitting. The method used in this invention to synthesize the normalized pseudo-acoustic curve is the information statistical weighting method. This method essentially integrates information from other curves into the acoustic curve according to certain weights, enabling the reconstructed curve to highlight reservoir differences (Formula 2).

[0031] (3) Statistical analysis of the distribution range of the original sonic transit time curve values ​​of the target layer in each well by sub-layer and well.

[0032] The distribution range of the original sonic transit time curve values ​​for each target layer in well W1 was statistically analyzed by sub-layer and well by well. For example, the range of sonic transit time curve values ​​between layers T1 and T2 in well W1 was analyzed separately, and then the range of sonic transit time curve values ​​between layers T2 and T3 was analyzed to complete the layered statistics of well W1. Then, the range of layered curve values ​​for well W2 was analyzed. Figure 2 ).

[0033] The normalized pseudo-acoustic curves are subjected to range regression, and the regression is performed layer by layer to the range of the original acoustic transit time curve values. For example, if the range of the acoustic transit time curves between layers T1 and T2 in well W1 is between 200 and 400, then the regression range is also between 200 and 400. The range regression adopts formula 3.

[0034] (4) Calculate the longitudinal wave impedance value of a single well.

[0035] Based on the original AC curve and density DEN curve of a single well, the change in longitudinal wave impedance value of a single well is calculated using the original wave impedance calculation formula (Formula 4).

[0036] (5) Statistically determine the main regional range of the wave impedance values ​​of the corresponding sedimentary microfacies of each sand body.

[0037] Statistical analysis of the impedance values ​​of each deposited microphase ( Figure 3 For example, the value of underwater distributary channels is mainly distributed in the range of 7000-9000. Figure 4 The average value is 8000. The wave impedance value of the estuary dam is mainly distributed in the range of 8000-10000, with an average value of 9000. The wave impedance value of the mudstone underwater distributary channel is mainly distributed in the range of 5000-8000, with an average value of 6500.

[0038] The pseudo-acoustic curves, which deviate significantly from the acoustic impedance range, are corrected to ensure consistency in the value ranges of each sedimentary microfacies. By calculating the acoustic impedance range of each microfacies in each well and comparing it with the previously statistically calculated ranges, the pseudo-acoustic curves, after value regression to the original range, are corrected to ensure consistency in the value ranges of each sedimentary microfacies. For example, the acoustic impedance range of the underwater distributary channel at 3000 meters in well W1 is 6000-8000, with an average of 7000. The range of the pseudo-acoustic curve needs adjustment. Formula 4 is used to ensure that the corrected average acoustic impedance is 8500. Based on this method, the correction of the pseudo-acoustic curves for all microfacies in all wells is completed.

[0039] (6) Calculate the width and length of the river channel at each stratum and obtain the variation function of the relevant data. Based on the sedimentary microfacies planar maps of each layer, and considering data correlation, the variogram of the relevant data is obtained by statistically analyzing the width and length of the channel in the study area. The variogram analysis results can reflect the magnitude of the regional variables of the single sand body layer in a certain direction, and can also roughly reflect the average scale of the carrier of the regional variables (such as reservoir sand bodies) in this direction. Therefore, the range of influence of reservoir parameters can be reflected by using the range, thereby achieving the prediction of the distribution law of reservoir properties in this layer.

[0040] (7) The layered approach adopts the co-kriging interpolation algorithm and uses the deposition microphase plane diagram to co-constrain extrapolate the initial wave impedance model.

[0041] The wave impedance prediction formula (Formula 5) using the co-kriging interpolation algorithm is used to obtain the variation function of the relevant data based on the correlation of the variable data. Figure 5 This patent mainly considers data correlation based on the width and length of the river channel, adopts a layer-by-layer interpolation mode, and uses sedimentary microfacies planar maps to collaboratively constrain extrapolate and calculate the initial wave impedance model.

[0042] In summary, the content of this invention is not limited to the above-described embodiments. Those skilled in the art can easily propose other embodiments within the technical guiding principles of this invention, but such embodiments are all included within the scope of this invention.

Claims

1. A method for establishing an initial acoustic impedance model for sedimentary microfacies fusion pseudo-acoustic inversion, characterized in that, Includes the following steps: (1) Perform singular value correction processing on the original AC, GR and RT curves of each well in the target layer; (2) Normalize the RT, GR and AC curves after singular value correction, and use the information statistical weighting method to assign different weight values ​​to synthesize normalized pseudo-acoustic curves; (3) Statistically analyze the distribution range of the original acoustic transit time curve values ​​of each target layer in each well, and perform value range regression on the normalized pseudo-acoustic curve to return to the range of the original acoustic transit time curve values ​​in each layer. (4) Calculate the longitudinal wave impedance value of a single well. Based on the original AC curve and density DEN curve of the single well, use the original wave impedance calculation formula to calculate the change of the longitudinal wave impedance value of the single well and determine the wave impedance range of each microphase. (5) Statistically analyze the main range of wave impedance values ​​of the corresponding sedimentary microfacies of each sand body, correct the pseudo-acoustic curves that differ significantly from the wave impedance value range, and ensure that the value range of each sedimentary microfacies is consistent. By obtaining the wave impedance range of each microfacies in each well and comparing it with the wave impedance range of each microfacies calculated in step (4), correct the pseudo-acoustic curves that differ significantly from the wave impedance value range after value range regression, and ensure that the value range of each sedimentary microfacies is consistent. (6) Calculate the width and length of the river channel at each stratum and obtain the variation function of the relevant data; (7) The layered approach adopts the co-kriging interpolation algorithm and uses the deposition microphase plane diagram to co-constrain extrapolate the initial wave impedance model.

2. The method for establishing the initial acoustic impedance model for sedimentary microfacies fusion pseudo-acoustic inversion according to claim 1, characterized in that, In step (1), AC curve values ​​with a time difference (AC) curve distribution range outside 160-600 are eliminated; GR curve values ​​with a distribution range outside 30-150 are eliminated; and RT curve values ​​with a resistivity (RT) curve distribution range outside 1-30 are eliminated.

3. The method for establishing the initial acoustic impedance model for sedimentary microfacies fusion pseudo-acoustic inversion according to claim 1, characterized in that, In step (2), the normalization formulas for the GR, RT, and AC curves are: GR p =(GR-GR) min ) / (GR max -GR min ) In the formula, GR p —Normalized gamma values ​​of drilled wells in the sub-layers; GR—The original gamma value of the drilled well in the sub-layer; GR max —The maximum gamma value of the drilled well in the small layer; GR min —The minimum gamma value of the drilled well in the small layer; RT p =(RT-RT min ) / (RT max -RT min ) In the formula, RT p —Normalized resistivity values ​​of drilled wells in the sub-layers; RT—The original resistivity value of the drilled well in the sub-layer; RT max —The maximum resistivity value of the drilled well in the small layer; RT min —The minimum resistivity value of the drilled well in the small layer; AC p = (AC-AC min ) / (AC max -AC min ) In the formula, AC p —Normalized acoustic transit time of drilled wells in small layers; AC – The original acoustic transit time value of the drilled well in the small layer; AC max —The maximum sonic transit time value of the drilled well in the small layer; AC min —The minimum acoustic transit time value of the drilled well in the small layer; Ensure that the curve values ​​are all within the range of 0-1, and guarantee that the data of each curve is consistent.

4. The method for establishing the initial acoustic impedance model for sedimentary microfacies fusion pseudo-acoustic inversion according to claim 3, characterized in that, In step (2), the normalized pseudo-acoustic curve is synthesized using an information statistical weighting method, which integrates the information of other curves into the acoustic curve with certain weights, so that the reconstructed curve can highlight the reservoir differences. The normalized pseudo-acoustic curve reconstruction formula is as follows: AC N =a*AC p + b*RT p + c*(1-GR p ) In the formula, AC N —The normalized value of the pseudo-acoustic curve in the small layer; GR p —Normalized gamma values ​​of drilled wells in the sub-layers; RT p —Normalized resistivity values ​​of drilled wells in the sub-layers; AC p —Normalized acoustic transit time of drilled wells in small layers; a, b, and c are weighting coefficients. The GR curve has the strongest ability to identify lithology, with a c value range of 0.5-0.

7. The RT curve has a slightly stronger ability to identify lithology than the AC curve, with a b value range of 0.3-0.2 and an a value range of 0.2-0.

1. The sum of a, b, and c is 1.

5. The method for establishing the initial acoustic impedance model for sedimentary microfacies fusion pseudo-acoustic inversion according to claim 1, characterized in that, In step (3), the range regression formula for the pseudo-acoustic curve is: AC Ny = AC min + AC N *(AC max -AC min ) In the formula, AC Ny AC represents the regressed acoustic wave value. min AC is the minimum sound wave value within this layer. N AC is the normalized value of the acoustic wave within this layer. max This represents the maximum sound wave value within this layer.

6. The method for establishing the initial acoustic impedance model for sedimentary microfacies fusion pseudo-acoustic inversion according to claim 1, characterized in that, In step (4), the formula for calculating the original wave impedance is: IMP i =ρ i v i In the formula, IMP i —The original wave impedance of the bottom interface of the i-th layer, ρ i —Density of the i-th layer, v i —The velocity of the i-th layer.

7. The method for establishing the initial acoustic impedance model for sedimentary microfacies fusion pseudo-acoustic inversion according to claim 1, characterized in that, In step (6), based on the sedimentary microfacies planar map of each layer, considering the correlation of data, the width and length of the river channel in the study area are statistically analyzed to obtain the variation function of the relevant data. The variation function analysis results reflect the magnitude of the regional variable of a single sand body layer in a certain direction and the average scale of the carrier of the regional variable in this direction. The range of variation is used to reflect the influence range of reservoir parameters, so as to predict the distribution law of reservoir properties in this layer.

8. The method for establishing the initial acoustic impedance model for sedimentary microfacies fusion pseudo-acoustic inversion according to claim 1, characterized in that, In step (7), the wave impedance prediction formula of the co-kriging interpolation algorithm is adopted. Based on the correlation of variable data, the variation function of relevant data is obtained. Considering the data correlation based on the width and length of the channel, the interlayer interpolation mode is adopted. The initial wave impedance model is calculated by co-constrained extrapolation using the sedimentary microfacies plane map. The co-kriging interpolation formula is as follows: In the formula, ----The estimated value of the random variable at position 0; ----n sample data for the initial variable; ----m sample data for the second-order variable; , ----Co-Kriging weight coefficient.

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