A lithology-compensated sedimentary facies-controlled high-resolution inversion method and system

Abstract

The application provides a lithology compensation-based sedimentary facies-controlled high-resolution inversion method and system, and the inversion method comprises the following steps: S1, acquiring a GR curve, preprocessing the GR curve, and converting the GR curve into a lithology compensation coefficient; S2, establishing a regional geological structure model under the control of a fine interpretation horizon and a fault structure framework, and establishing sedimentary microfacies model data of each sand group of a target layer on the basis of variation function analysis; S3, compensating wave impedance data by using the lithology coefficient to establish a three-dimensional initial wave impedance model, and obtaining a sedimentary facies-controlled wave impedance model through step S2; S4, performing well-controlled waveform slimming high-resolution processing; and S5, performing multiple sedimentary facies-controlled iterative inversion by using the sedimentary facies-controlled wave impedance model as a constraint condition. The method reduces the multi-solution property of inversion and has better operability, innovativeness and practicality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fine prediction of medium-deep thin reservoirs in oil exploration and development, and particularly relates to a sedimentary facies controlled high-resolution inversion method and system based on lithology compensation. BACKGROUND

[0002] The target layer in the research area is complex in fracture, thin in reservoir sand body, and strong in heterogeneity, and the prediction accuracy of the thin sand body reservoir has a great influence on the oilfield productivity. In order to improve the prediction accuracy of the thin sand body reservoir in the research area, the present application comprehensively uses sedimentary, logging and seismic information, and proposes a sedimentary facies controlled high-resolution inversion method based on lithology compensation.

[0003] At present, there are many studies on seismic facies controlled inversion at home and abroad. Huang Jiaodong (2007), Zhang Zhiwei (2011) and Zhang Sheng (2018) respectively proposed nonlinear random inversion under the control of seismic facies for different research blocks, and improved the reliability of inversion. Hang (2009) proposed a statistical inversion method under the control of seismic facies, which was successfully applied to the prediction of sandstone and conglomerate reservoirs. Liu et al. (2018), Wang et al. (2018) and Li et al. (2019) respectively proposed a seismic facies controlled geostatistical inversion method in different research blocks to improve the prediction accuracy of reservoirs. Wang Dongkun (2022) analyzed the direct relationship between seismic facies and sedimentary facies, obtained the sedimentary feature variogram and applied it to geostatistical inversion to quantitatively predict thin sand bodies. There are few studies on geology sedimentary facies constrained inversion. Wang et al. (2009) proposed an inversion method under the control of well data and geology sedimentary facies to improve the resolution of inversion. Yang et al. (2016) proposed a random inversion method under the constraint of geology sedimentary facies. Huang et al. (2020) proposed an iterative facies controlled deterministic inversion method, which added geology sedimentary facies constraint and iteratively improved the inversion accuracy. The seismic facies constrained inversion method has certain limitations, and part of the seismic facies does not match the actual sedimentary characteristics. In addition, under the limitation of low-frequency seismic data, the conventional facies controlled inversion method cannot reliably identify thin sandstone in the area without wells. SUMMARY

[0004] In view of the above problems, the present application is proposed to provide a sedimentary facies controlled high-resolution inversion method and system based on lithology compensation to overcome the above problems or at least partially solve the above problems.

[0005] According to one aspect of the present application, a sedimentary facies controlled high-resolution inversion method based on lithology compensation is provided, which comprises:

[0006] Step S1, obtaining a GR curve, pre-processing the GR curve and converting it into a lithology compensation coefficient;

[0007] Step S2, under the control of the fine interpretation layer and the fault structure framework, a regional geological structure model is established, and on the basis of the variogram analysis, a sedimentary microfacies model data of each sand group of the target layer is established;

[0008] Step S3, a three-dimensional initial wave impedance model is established by using the lithology coefficient to compensate the wave impedance data, and the three-dimensional initial wave impedance model is constrained by the sedimentary facies model in step S2 to obtain a sedimentary facies controlled wave impedance model;

[0009] Step S4, well-controlled waveform slimming high-resolution processing is performed;

[0010] Step S5, on the basis of the high-resolution data body, the sedimentary facies controlled wave impedance model is used as a constraint condition to perform multiple iteration inversions.

[0011] Optionally, the pre-processing of the GR curve specifically includes: low-frequency correction and de-dimension mapping processing of the GR curve.

[0012] Optionally, the step S1 of acquiring the GR curve and pre-processing the GR curve to convert into a lithology compensation coefficient specifically includes:

[0013] On the basis of the analysis of the petrophysical characteristics of the target layer in the study area, the well logging data is optimized, the GR curve is subjected to low-frequency correction processing, and the low-frequency trend of the well logging curve is removed, and the formula is:

[0014]

[0015] In the formula, N is the filter order, f c is the filter cutoff frequency, G is the well logging GR data, and Gl is the data after GR low-frequency correction.

[0016] Optionally, the de-dimension projection mapping processing is performed on the basis of the low-frequency correction to convert into a lithology compensation coefficient, and the formula is:

[0017]

[0018] In the formula, Gl is the data after GR low-frequency correction, a and b are the value range of the constraint coefficients respectively, which are determined according to the relative change amplitude of the maximum and minimum values of the original wave impedance, a is 0.8-0.9, and b is 1.1-1.2.

[0019] Optionally, the step S2 of establishing the regional geological structure model under the control of the fine interpretation layer and the fault structure framework and establishing the sedimentary microfacies model data of each sand group of the target layer specifically includes:

[0020] The regional geological structure model is established under the control of the fine interpretation layer and the fault structure framework, and the constraint condition is established;

[0021] On the basis of variogram analysis, the sedimentary microfacies model data of each sand group of the target layer is established.

[0022] Optionally, the constraint condition specifically includes seismic attribute distribution, sand body thickness distribution and sedimentary paleogeomorphology map.

[0023] Optionally, in the step S2, the regional geological structure model is established under the control of the fine interpretation horizon and the fault structure framework, and the sedimentary microfacies model data of each sand group of the target layer specifically includes:

[0024] Under the control of the fine interpretation horizon and the fault structure framework, the structural modeling is performed, and the regional geological structure model F(x, y, z) is established.

[0025] The seismic attribute distribution, the sand body thickness distribution and the sedimentary paleogeomorphology map are taken as the constraint condition, and on the basis of variogram analysis, the sedimentary microfacies data f i (x, y) of the i-th sand group of the target layer is established, and the formula is:

[0026]

[0027] Wherein, N is the number of sedimentary microfacies, L N is the boundary range of the N-th sedimentary microfacies;

[0028] Under the three-dimensional geological structure model, the sedimentary facies data body F(x, y, z) is obtained:

[0029] F(x, y, z) = f i (x, y), z i <z<z i+1

[0030] Wherein, z i is the time range of the top surface of the i-th sand group.

[0031] Optionally, in the step S3, the lithology coefficient is used to compensate the wave impedance data, the three-dimensional initial wave impedance model is established, and the three-dimensional initial wave impedance model is constrained by the sedimentary facies model in the step S2 to obtain the sedimentary facies controlled wave impedance model, and the step S3 specifically includes:

[0032] The three-dimensional wave impedance model P lit is established by using the wave impedance data compensated by the constraint coefficient lithology.

[0033] The three-dimensional lithology compensated wave impedance model is constrained by the sedimentary facies data F to obtain the facies controlled wave impedance data P facies .

[0034] P facies = P lit + α·β·(F*G)

[0035]

[0036]

[0037] where P lit is the constrained lithology compensated wave impedance data, and α and β are adjustment factors, α ranges from 1 to 3, and β ranges from 2000 to 5000, which are determined by the average velocity of the target layer in the study area;

[0038] F is the obtained sedimentary facies model data volume, G is an adjustment function, and σ is a standard deviation.

[0039] Optionally, the adjustment function adopts a Gaussian function.

[0040] Optionally, the step S4 of performing well-controlled waveform slimming high-resolution processing specifically includes:

[0041] The well-controlled waveform slimming high-resolution processing is performed, and a slimming factor is determined through iterative iteration of well-to-seismic calibration by using deconvolution theory, so as to obtain a high-resolution seismic data volume y(t):

[0042]

[0043]

[0044] where Y(f) is the frequency domain of the slimmed seismic data y(t), X(f) is the original seismic frequency domain data, W(f) is the original seismic wavelet, W'(f) is the slimmed wavelet, s w is the well-controlled slimming factor, R w is the wellpoint reflection coefficient, W w is the given slimmed wavelet, X 0 is the seismic data beside the well, and M is the number of wellpoints in the study area.

[0045] Optionally, the step S5 of performing multiple iterative inversions by using a sedimentary facies-controlled wave impedance model as a constraint condition on the basis of the high-resolution data volume specifically includes:

[0046] On the basis of the well-controlled high-resolution data volume, a minimum objective function E is established by using the facies-controlled wave impedance model as a trend constraint condition:

[0047]

[0048] where r is a reflection coefficient, y is high-resolution seismic data, s is a convolution synthetic seismic data, is facies-controlled wave impedance model data, P i is actual seismic wave impedance data, N is the total number of data samples, and λ and m are weighting factors.

[0049] The application further provides a lithology compensation-based sedimentary facies-controlled high-resolution inversion system, which applies the lithology compensation-based sedimentary facies-controlled high-resolution inversion method.

[0050] A lithology compensation coefficient processing module is configured to acquire a GR curve, pre-process the GR curve, and convert the GR curve into a lithology compensation coefficient.

[0051] A sedimentary facies model data establishing module is configured to establish a regional geological structure model under the control of a fine interpretation horizon and a fault structure framework, and establish sedimentary microfacies model data of each sand group in a target layer.

[0052] A facies-controlled wave impedance model acquiring module is configured to compensate wave impedance data by using the lithology coefficient, establish a three-dimensional initial wave impedance model, constrain the three-dimensional initial wave impedance model by the sedimentary facies model in step S2, and obtain a sedimentary facies-controlled wave impedance model.

[0053] A resolution improvement processing module is configured to perform well-controlled waveform slimming high-resolution processing.

[0054] A sedimentary facies-controlled iterative inversion module is configured to perform multiple iterative inversions by using the sedimentary facies-controlled wave impedance model as a constraint condition on the basis of a high-resolution data body.

[0055] The lithology compensation-based sedimentary facies-controlled high-resolution inversion method and system provided by the application comprises the following steps: S1, acquiring a GR curve, pre-processing the GR curve, and converting the GR curve into a lithology compensation coefficient; S2, establishing a regional geological structure model under the control of a fine interpretation horizon and a fault structure framework, and establishing sedimentary microfacies model data of each sand group in a target layer on the basis of variogram analysis; S3, compensating wave impedance data by using the lithology coefficient, establishing a three-dimensional initial wave impedance model, constraining the three-dimensional initial wave impedance model by the sedimentary facies model in step S2, and obtaining a sedimentary facies-controlled wave impedance model; S4, performing well-controlled waveform slimming high-resolution processing; and S5, performing multiple sedimentary facies-controlled iterative inversions by using the sedimentary facies-controlled wave impedance model as a constraint condition on the basis of a high-resolution data body. More geological and seismic information is added to participate in the inversion, the multi-solution property of the inversion is reduced, and good practical application effects are achieved.

[0056] The application improves the vertical resolution of thin-layer sand bodies in the vertical direction, is consistent with the sedimentary law in the horizontal direction, can reflect the horizontal heterogeneity, more geological and seismic information is added to participate in the inversion, the multi-solution property of the inversion is reduced, and the application has better operability, innovativeness and practicality.

[0057] The above description is only a summary of the technical solutions of the present application, in order to more clearly understand the technical means of the present application, and can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0059] Figure 1 The flow chart of the lithology compensation based sedimentary facies controlled high resolution inversion method provided for the embodiments of the present application;

[0060] Figure 2 The low frequency trend graph of GR curve provided for the embodiments of the present application;

[0061] Figure 3 The compensation coefficient schematic diagram of lithology information provided for the embodiments of the present application;

[0062] Figure 4 The comparison graph of wave impedance curve after lithology coefficient compensation and original wave impedance curve provided for the embodiments of the present application;

[0063] Figure 5 The crossplot of wave impedance and GR before and after compensation of CB151 well provided for the embodiments of the present application;

[0064] Figure 6 The sedimentary facies model graph obtained under the three-dimensional geological structure model provided for the embodiments of the present application;

[0065] Figure 7 The comparison profile graph of north-south direction Inline2275 line facies controlled wave impedance model and conventional wave impedance model provided for the embodiments of the present application;

[0066] Figure 8 The east-west direction seismic high resolution processing effect comparison profile graph of Chengbei 35 well provided for the embodiments of the present application;

[0067] Figure 9 The sedimentary facies controlled high resolution inversion and conventional inversion comparison profile graph provided for the embodiments of the present application;

[0068] Figure 10 The sedimentary facies controlled high resolution inversion and conventional inversion effect plane comparison graph provided for the embodiments of the present application;

[0069] Figure 11 The thin sand flat distribution map of the Dongying Formation is described based on the phase-controlled inversion provided for the implementation examples of the present application. DETAILED DESCRIPTION

[0070] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0071] The terms "include" and "have" and any variations thereof in the specification, claims and drawings of the present application are intended to cover non-exclusive inclusion, for example, inclusion of a series of steps or units.

[0072] The technical solutions of the present application will be described in further detail below in conjunction with the drawings and examples.

[0073] As Figure 1 shown, the purpose of the present application is to provide a new thin reservoir sedimentary facies-controlled inversion method. The purpose of the present application can be achieved by the following technical measures: step 1, low-frequency correction and de-dimension mapping processing are performed on the GR curve, and the lithology compensation coefficient is converted; step 2, a regional geological structure model is established under the control of fine interpretation horizon and fault structure framework, and then the sedimentary facies model data of each sand group of the target layer is established based on the analysis of the variation function, with the seismic attribute distribution, sand body thickness distribution and sedimentary paleogeomorphology map as constraint conditions; step 3, the lithology coefficient compensation wave impedance data is used to establish a three-dimensional initial wave impedance model, and the sedimentary facies-controlled wave impedance model is obtained by constraining the three-dimensional initial wave impedance model with the sedimentary facies model in step 2; step 4, well-controlled waveform slimming high-resolution processing is performed; step 5, based on the high-resolution data body, the sedimentary facies-controlled wave impedance model is used as a constraint condition to perform multiple iterative inversions.

[0074] The present application provides a sedimentary facies-controlled high-resolution inversion method based on lithology compensation, comprising:

[0075] Step 101, based on the analysis of the petrophysical characteristics of the Dongying Formation in the study area, low-frequency correction processing is performed using a Butterworth filter, the low cutoff frequency is selected as 3Hz, and the low-frequency trend of the logging curve is removed (low-frequency correction); Figure 2 The de-dimension mapping processing is performed on the GR curve after low-frequency correction using the formula to map the compensation coefficient that can reflect the lithology information (lithology compensation coefficient); Figure 3), the mapping range requirement is 1 at the mudstone baseline, and in this study area, a is 0.9 and b is 1.2, and Gl in the formula is the GR low-frequency corrected data. The lithology compensation coefficient is multiplied by the wave impedance data for compensation, Figure 4 The blue curve in the left figure is the wave impedance curve after lithology coefficient compensation, and the purple curve in the right figure is the original wave impedance curve without compensation. It can be seen that the compensated data is highly consistent with the GR curve, and low GR corresponds to high impedance value, which can better distinguish sand and mudstone. Figure 5 The left figure is the wave impedance crossplot of CB151 well before and after compensation, and the sandstone is red-yellow. As can be seen from the comparison figure, the impedance data after compensation can better distinguish sand and mudstone.

[0076] Step 102, under the control of fine interpretation horizon and fault structure framework, structural modeling F(x, y, z) is carried out, and then with the seismic attribute distribution, sand body thickness distribution, and sedimentary paleogeomorphology as constraint conditions, the sedimentary microfacies data f i (x, y) of the i-th layer sand group in the target layer is established based on variogram analysis. In this study area, three layers of sedimentary microfacies i = 3 are established, and through comprehensive analysis of well logging facies and lithofacies, it is determined that the nearshore subaqueous fan is the main one in this area, and there are three kinds of sedimentary microfacies L1, L2, and L3:

[0077]

[0078] Under the three-dimensional geological structure model, the sedimentary facies data body F(x, y, z) is obtained:

[0079]

[0080] Among them, z i is the time range of the top surface of the i-th layer sand group.

[0081] Step 103, using the wave impedance data after lithology coefficient compensation, a three-dimensional wave impedance model P lit is established, and the three-dimensional lithology compensation wave impedance model is constrained by the sedimentary facies data F to obtain facies-controlled wave impedance data P facies :

[0082] p facies = P lit + α·β·(F*G)

[0083]

[0084]

[0085] Among them, P lit is the wave impedance data after lithology coefficient compensation, α and β are adjustment factors, α is 2, and β is 3500 in this study area. F is the obtained sedimentary facies model data body, and σ is the standard deviation.

[0086] Figure 7 is the north-south inline 2275 wave impedance model contrast profile, and it can be seen that the wave impedance model after adding the sedimentary facies constraint condition has the concept of sedimentary facies, and the profile feature is more in line with the sedimentary rule.

[0087] In step 104, well control waveform thinning high resolution processing is performed, a thinning factor is determined through repeated iteration of zone 10 well seismic calibration by using deconvolution theory, and a high resolution seismic data body y(t) is obtained:

[0088]

[0089]

[0090] Wherein, Y(f) is the frequency domain of the seismic data y(t) after thinning, X(f) is the original seismic frequency domain data, W(f) is the original seismic wavelet, W'(f) is the wavelet after thinning, s w is a well control thinning factor, R w is a well point reflection coefficient, W w is a given wavelet after thinning, X 0 is the seismic data beside the well, and M is the number of well points in the study area.

[0091] Figure 8 is the east-west seismic high resolution processing effect contrast profile of Chengbei 35 well. From the processing effect, it can be seen that the original seismic data frequency band is obviously widened after processing, the seismic event resolution is higher, the main frequency of the seismic data is widened from 22Hz to 50Hz, the seismic data resolution is obviously improved, and the identification ability of thin sand bodies is increased, laying a foundation for the later phase-controlled inversion.

[0092] In step 105, on the basis of the well control high resolution data body, a phase-controlled wave impedance model is used as a trend constraint condition to establish a minimum target function E:

[0093]

[0094] Wherein, r is a reflection coefficient, y is a seismic high resolution data, s is a convolution synthetic seismic data, is a phase-controlled wave impedance model data, P i is an actual seismic wave impedance data, N is the total number of data samples, and λ and m are weighting factors.

[0095] Figure 9 is a sedimentary facies controlled high resolution inversion and conventional inversion contrast profile, and it can be seen that the inversion data of the present application can identify thin layer sand bodies, and the coincidence degree with the well logging curve is higher. It can be seen from the profile that there is an obvious progradation reflection feature, which is a typical seismic reflection feature of an underwater fan body.

[0096] Figure 10It is a planar comparison chart of the effect of the deposition facies controlled high resolution inversion and the conventional inversion, it can be seen that the distribution characteristics of the conventional inversion plane is not obvious, and the sedimentary distribution characteristics cannot be reliably judged, the inversion adds the deposition facies as a constraint condition, the planar distribution characteristics is clearer, and the sediment source is from the northwest direction, which is consistent with the actual sedimentary rule of the research area. Figure 11 It is a planar distribution chart of the thin sand of the Dongying formation described on the basis of the facies controlled inversion, 28 sets are finely described and quantitatively characterized, and the cumulative area is 31.89km 2 .

[0097] Beneficial effects: the deposition facies controlled high resolution inversion method based on the lithology compensation in the application improves the resolution of the earthquake itself by well-controlled high resolution processing on the conventional seismic data. The GR curve is converted into a lithology compensation coefficient, and the main sedimentation direction is determined through the variogram analysis, the deposition facies model is established by comprehensively considering the well logging lithology, seismic attribute, paleogeomorphology and deposition facies information, and more geological and seismic information is added to participate in the inversion; the deposition facies controlled wave impedance model based on the lithology coefficient compensation is obtained by the fusion of the two, as a constraint condition, the resolution improvement data is used for multiple iteration inversion, the multi-solution of the inversion is reduced, and the inversion accuracy of the well-free area is improved. The application improves the vertical resolution of the thin sand body, realizes the quantitative deposition facies control, the result is consistent with the sedimentary rule in the horizontal direction and can reflect the horizontal heterogeneity, the predicted reservoir distribution characteristics is more consistent with the regional geological rule, has better operability, innovation and practicality, and remarkable application effect is obtained in the thin reservoir prediction of the Chengdao oilfield, and reliable technical support is provided for the shallow sea oilfield reservoir increase.

[0098] The above specific embodiments further specifically describe the purpose, technical scheme and beneficial effects of the application, and it should be understood that the above is only the specific embodiment of the application, and is not used to limit the protection scope of the application, any modification, equivalent replacement, improvement, etc. within the spirit and principle of the application should be included in the protection scope of the application.

Claims

1. A lithology-compensated sedimentary facies-controlled high-resolution inversion method, characterized in that, The inversion method comprises: Step S1, acquiring a GR curve, pre-processing the GR curve, and converting the GR curve into a lithology compensation coefficient; Step S2, establishing a regional geological structure model under the control of fine interpretation horizons and a fault structure framework, and establishing a sedimentary microfacies model of each sand group in the target layer on the basis of variogram analysis; Step S3, establishing a three-dimensional initial wave impedance model by using the lithology compensation coefficient wave impedance data, and obtaining a sedimentary facies controlled wave impedance model by constraining the three-dimensional initial wave impedance model by the sedimentary microfacies model in Step S2; Step S4, performing well-controlled waveform slimming high-resolution processing; Step S5, performing multiple iterative inversions by using the sedimentary facies controlled wave impedance model as a constraint condition on the basis of a high-resolution data body.

2. The method of claim 1, wherein the method is a lithology-compensated, sedimentary facies-constrained, high-resolution inversion method. The pre-processing of the GR curve specifically comprises low-frequency correction and de-dimension mapping processing of the GR curve.

3. The method of claim 1, wherein the method is characterized by, The Step S1 of acquiring a GR curve, pre-processing the GR curve, and converting the GR curve into a lithology compensation coefficient specifically comprises: On the basis of rock physical property analysis of the target layer in the study area, the GR curve is subjected to low-frequency correction processing by optimizing the logging data, and the low-frequency trend of the logging curve is removed, and the formula is: where N is the filter order, f c is the filter cutoff frequency, G is the GR data, and Gl is the GR data after low-frequency correction.

4. The method according to claim 3, wherein, The lithology compensation coefficient has the formula: wherein Gl is the data after GR low-frequency correction, a and b are value range of constraint coefficients respectively, and are determined according to the relative variation amplitude of the maximum and minimum values of the original wave impedance, a is 0.8-0.9, and b is 1.1-1.

2.

5. The method of claim 1, wherein, The Step S2 of establishing a regional geological structure model under the control of fine interpretation horizons and a fault structure framework, and establishing a sedimentary microfacies model of each sand group in the target layer specifically comprises: The regional geological structure model is established under the control of fine interpretation horizons and a fault structure framework, and a constraint condition is established. The sedimentary microfacies model data of each sand group in the target layer are established on the basis of variogram analysis.

6. The method of claim 5, wherein, The constraint condition specifically comprises seismic attribute distribution, sand body thickness distribution, and a sedimentary paleogeomorphology map.

7. The method of claim 1, wherein, The Step S2 of establishing a regional geological structure model under the control of fine interpretation horizons and a fault structure framework, and establishing a sedimentary microfacies model of each sand group in the target layer specifically comprises: The regional geological structure model F(x, y, z) is established by structural modeling under the control of fine interpretation horizons and a fault structure framework; With the seismic attribute distribution, sand thickness distribution, and sedimentary paleogeomorphology map as the constraint conditions, the sedimentary microfacies data f i (x, y) of the i-th sand group of the target layer is established on the basis of the variogram analysis, and the formula is as follows: where N is the number of deposition microfacies, L N is the boundary range of the Nth deposition microfacies; The sedimentary facies data body F(x, y, z) is obtained under the three-dimensional geological structure model: F(x,y,z) = f i (x,y),z i <z<z i+1 where z i is the top surface time range of the i-th sand pack.

8. The method of claim 1, wherein, The Step S3 of establishing a three-dimensional initial wave impedance model by using the lithology compensation coefficient wave impedance data, and obtaining a sedimentary facies controlled wave impedance model by constraining the three-dimensional initial wave impedance model by the sedimentary microfacies model in Step S2 specifically comprises: A three-dimensional wave impedance model P is established by using the constrained coefficient lithology compensated post-wave impedance data lit ; The three-dimensional lithology compensated wave impedance model is constrained by the sedimentary facies data F to obtain facies-controlled wave impedance data P facies : P facies = P lit + a·b·(F*G) where P lit is the constrained lithology compensated wave impedance data, and a and β are adjustment factors, a ranges from 1 to 3, and β ranges from 2000 to 5000, which are determined by the average velocity of the target layer in the study area. F is the obtained sedimentary microfacies model, G is an adjustment function, and σ is a standard deviation.

9. The method according to claim 8, wherein, The adjustment function adopts a Gaussian function.

10. The method of claim 1, wherein the method is a lithology-compensated, sedimentary facies-constrained, high-resolution inversion method. The Step S4 of performing well-controlled waveform slimming high-resolution processing specifically comprises: The well-controlled waveform slimming high-resolution processing is performed, a slimming factor is determined by repeated iteration through well-seismic calibration by using the deconvolution theory, and a high-resolution seismic data body y(t) is obtained: where Y(f) is the frequency domain of the thinned seismic data y(t), X(f) is the original seismic frequency domain data, W(f) is the original seismic wavelet, W'(f) is the thinned wavelet, s w is the well control thinning factor, R w is the well point reflection coefficient, W w is the given thinned wavelet, X 0 is the seismic data beside the well, and M is the number of well points in the study area.

11. The method of claim 1, wherein the method is a lithology-compensated, sedimentary facies-constrained, high-resolution inversion method. The Step S5 of performing multiple iterative inversions by using the sedimentary facies controlled wave impedance model as a constraint condition on the basis of a high-resolution data body specifically comprises: On the basis of well control high resolution data body, using phase controlled wave impedance model as trend constraint condition, minimum target function E is established: where r is the reflection coefficient, y is the high resolution seismic data, s is the convolution synthetic seismic data, is the phase-controlled wave impedance model data, P i is the actual seismic wave impedance data, N is the total number of data samples, and λ and m are weighting factors.

12. A lithology-compensated sedimentary facies-controlled high-resolution inversion system, applying a lithology-compensated sedimentary facies-controlled high-resolution inversion method according to any one of claims 1-11, characterized in that, The inversion system specifically includes: The lithology compensation coefficient processing module is used for obtaining a GR curve, pre-processing the GR curve, and converting the GR curve into a lithology compensation coefficient; The sedimentary microfacies model establishing module is used for establishing a regional geological structure model under the control of the fine interpretation horizon and the fault structure framework, and establishing a sedimentary microfacies model of each sand group of the target layer; The phase controlled wave impedance model obtaining module is used for establishing a three-dimensional initial wave impedance model by using the lithology compensation coefficient wave impedance data, and obtaining a sedimentary phase controlled wave impedance model by using the sedimentary microfacies model in step S2 to constrain the three-dimensional initial wave impedance model; The resolution improvement processing module is used for well control waveform slimming high resolution processing; The sedimentary phase controlled iterative inversion module is used for using the sedimentary phase controlled wave impedance model as a constraint condition to perform multiple iterative inversions on the basis of the high resolution data body.

Images