Method and device for predicting thin interbedded sand and shale reservoirs based on pre-stack waveform indicator inversion

Abstract

This invention relates to the field of oil and gas geological exploration technology, and particularly to a method and apparatus for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion. The method includes: processing pre-stack CRP gathers to obtain corner gather data; performing AVO attribute analysis on the corner gathers; stacking them at different angles to generate an offset stacked data volume; obtaining shear wave curves for all wells in the work area based on logging curves and geological parameters; determining rock elastic parameters that reflect reservoir and fluid characteristics based on these curves; establishing a rock physical quantity scale and selecting sensitive parameters that can distinguish between sandstone and mudstone and fluids; setting key inversion parameters; performing inversion on the offset stacked data volume based on the sensitive parameters and key inversion parameters; and predicting thin interbedded mud-sand reservoirs based on the obtained inversion results. This invention achieves high-precision inversion results, effectively improving the accurate prediction of thin interbedded layers, meeting the well accuracy requirements for development, and providing support for the efficient development of tight oil and gas.

Description

Technical Field

[0001] This invention relates to the field of oil and gas geological exploration technology, and in particular to a method and apparatus for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion. Background Technology

[0002] Currently, oil and gas exploration and development targets are becoming increasingly complex, with deteriorating quality and significant resource degradation. Exploration faces more complex geological objects, including deeper, smaller, thinner, and less permeable reservoir sand bodies. These sand bodies exhibit strong heterogeneity both vertically and horizontally, are discontinuous, and feature overlapping sand and mud layers, along with natural fractures and faults, making the characterization of sweet spot sand bodies extremely difficult. Seismic data has achieved "high-quality" processing, with richer data ranging from post-stack to pre-stack. Reservoir prediction techniques have gradually transitioned from well logging attribute inversion and model inversion to high-resolution inversions such as geostatistical inversion and waveform indicator inversion. The integration of well and seismic data is becoming tighter, with increasing vertical and horizontal separation rates and drilled well consistency, leading to higher accuracy in reservoir identification.

[0003] Based on a survey of patents applied for and related papers published at home and abroad, the main methods for predicting thin interbedded reservoirs are as follows: (1) The pseudo-impedance seismic lithology inversion method using the fusion of natural gamma and wave impedance. The curve frequency fusion is carried out using "low frequency compensation and high frequency recovery" to better identify sandstones of different thicknesses and break through the limitations of single factors. According to the characteristics of different geological and sedimentary features reflected by different frequency scales of logging curves, the low frequency component of the wave impedance curve is fused with the high frequency component of the natural gamma curve to generate a pseudo-wave impedance curve. The pseudo-wave impedance curve after fusion has greatly improved the distinction between sandstone and mudstone compared with the previous one. (2) Thin layer prediction method based on waveform indication simulation. Well seismic calibration and synthetic records are carried out for drilled wells. The singular value decomposition method is used to realize the dynamic clustering of seismic waveforms. The initial model is obtained by weighted averaging the correlation of GR curves. Then, iterative inversion is carried out under the guidance of Bayesian theory to obtain the final inversion result. (3) Prediction method for high-quality tight sandstone reservoirs. This method combines core observation, thin section identification, pore structure analysis, physical property analysis, and well logging data to conduct sedimentary microfacies analysis based on well-seismic co-location, select the preferred sedimentary microfacies types, predict the preferred lithofacies based on sedimentary microfacies constraints, predict the preferred diagenetic facies based on sedimentary microfacies-lithofacies constraints, and perform three-phase coupling analysis to predict favorable reservoirs in tight sandstone.

[0004] The three representative methods mentioned above combine post-stack seismic data and sensitive logging parameters for reservoir prediction. These are conventional methods, but the inversion results are highly variable and limited by the resolution of seismic data, resulting in poor prediction results. Currently, there is no effective method to improve the prediction accuracy for thin interbedded mud-sand layers. Summary of the Invention

[0005] This invention proposes a method and apparatus for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion. This addresses the problem that existing thin interbedded reservoir prediction methods do not consider the quality of seismic data, rely solely on conventional logging curves, and employ conventional inversion methods that do not fully consider the influence of sedimentary phases on seismic prediction, resulting in low prediction accuracy.

[0006] According to one aspect of the present invention, a method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion is provided, comprising:

[0007] Acquire pre-stack CRP gathers, logging curves, and geological parameters for the work area;

[0008] The pre-stack CRP gathers are processed to obtain corner gather data. AVO attribute analysis is performed on the corner gathers to determine the seismic response characteristics of thin interlayered structures. Based on the seismic response characteristics, the corner gathers are stacked to generate an offset stacked data volume.

[0009] Based on the logging curves and geological parameters, the shear wave curves of all wells in the work area are obtained, and the rock elastic parameters that can reflect the reservoir and fluid are determined based on the shear wave curves.

[0010] Establish a rock physical quantity model and select the sensitive parameters among the various rock elastic parameters that can distinguish between sandstone, mudstone and fluid;

[0011] Key inversion parameters are set, and the offset stacked data volume is inverted based on the sensitive parameters and key inversion parameters. Based on the obtained inversion results, the mud-sand thin interbedded reservoir is predicted.

[0012] Preferably, the method for processing the pre-stack CRP gather to obtain corner gather data includes:

[0013] The pre-stack CRP gather is processed by removing unwanted reflections, denoising, flattening, amplitude compensation, and gather conversion to obtain corner gather data.

[0014] Preferably, the method for stacking the corner gathers based on the seismic response characteristics to generate a migration stacking data volume includes:

[0015] The corner gathers of each well in the work area are divided into different angles, and the corner gathers are superimposed according to the divided angles to obtain the corresponding offset superimposed data volume;

[0016] The migration stacking data volumes of each well are analyzed based on the seismic response characteristics. The angle division result corresponding to the migration stacking data volume of the well with the highest signal-to-noise ratio that is consistent with the seismic response characteristics is selected as the final angle division result.

[0017] Based on the final angle division results, partial angle gathers are superimposed on the work area to obtain the final offset superimposed data volume.

[0018] Preferably, before obtaining the shear wave curves of all wells in the work area based on the logging curves and geological parameters, the logging curves are preprocessed, the method of which includes:

[0019] The well logging curves are subjected to outlier processing, standardization, environmental correction, and / or density curve fitting.

[0020] Preferably, the geological parameters include: clay content, total porosity, and / or water saturation.

[0021] Preferably, before determining the rock elastic parameters reflecting the reservoir and fluid based on the shear wave curve, the obtained shear wave curve is subjected to quality control, the method comprising:

[0022] The obtained shear wave curve is correlated with the measured shear wave curve in the work area. If the correlation reaches a predetermined percentage or more, the quality control of the obtained shear wave curve is passed. Otherwise, the type of the geological parameters is readjusted and the corresponding shear wave curve is obtained until the correlation between the obtained shear wave curve and the measured shear wave curve reaches a predetermined percentage or more and passes the quality control.

[0023] Preferably, the method for determining rock elastic parameters that reflect reservoir and fluid based on the shear wave curve includes:

[0024] Obtain the longitudinal wave curve of the work area;

[0025] Based on the longitudinal wave curve and the transverse wave curve, the rock elastic parameters of the fluid are determined, wherein the rock elastic parameters include: longitudinal wave impedance, transverse wave impedance, longitudinal wave velocity ratio, Poisson's ratio, bulk modulus, shear modulus, Young's modulus, Lamé constant, Lamé constant multiplied by density, and shear modulus multiplied by density.

[0026] Preferably, the method for determining the rock elastic parameters of the fluid based on the longitudinal wave curve and the transverse wave curve includes:

[0027] The longitudinal wave impedance is calculated using equation (1);

[0028] AI = ρV p (1);

[0029] In the formula: ρ is the density of the rock; V p For longitudinal wave velocity;

[0030] The transverse wave impedance is calculated using equation (2);

[0031] SI = ρV S (2);

[0032] In the formula: ρ is the density of the rock; V S For longitudinal wave velocity;

[0033] The P-wave to S-wave velocity ratio is calculated using equation (3);

[0034] V P V S =V P / V S (3);

[0035] In the formula: V p V is the longitudinal wave velocity; S For longitudinal wave velocity;

[0036] Poisson's ratio is calculated using equation (4);

[0037]

[0038] In the formula: ρ is the density of the rock; V p V is the longitudinal wave velocity; s The transverse wave velocity;

[0039] The bulk modulus is calculated using equation (5);

[0040]

[0041] In the formula: ρ is the density of the rock; V s The transverse wave velocity;

[0042] The shear modulus is calculated using equation (6);

[0043]

[0044] In the formula: λ is Poisson's ratio; μ is bulk modulus;

[0045] The Young's modulus is calculated using equation (7);

[0046]

[0047] In the formula: λ is Poisson's ratio; μ is bulk modulus;

[0048] The Lamé constant is calculated using equation (8);

[0049]

[0050] In the formula: λ is Poisson's ratio; E is Young's modulus.

[0051] Preferably, the method further includes: during the inversion process, quality control is performed on the obtained results; if the results do not meet the quality control requirements, the key inversion parameters are adjusted, and the inversion is performed again based on the adjusted key inversion parameters until the obtained inversion results pass the quality control.

[0052] The key inversion parameters include: number of samples, optimal cutoff frequency, smoothing radius, and / or sampling rate.

[0053] According to one aspect of the present invention, a device for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion is provided, comprising:

[0054] The acquisition unit is used to acquire pre-stack CRP gathers, logging curves, and geological parameters of the work area.

[0055] The offset stacking data volume generation unit is used to process the pre-stack CRP gathers to obtain corner gather data, perform AVO attribute analysis on the corner gathers to determine the seismic response characteristics of thin interlayered structures, and stack the corner gathers according to the seismic response characteristics to generate an offset stacking data volume.

[0056] The rock elastic parameter determination unit is used to obtain the shear wave curves of all wells in the work area based on the logging curves and geological parameters, and to determine the rock elastic parameters that can reflect the reservoir and fluid based on the shear wave curves.

[0057] A sensitive parameter determination unit is used to establish a rock physical quantity scale, preferably among the various rock elastic parameters, the sensitive parameters that can distinguish between sandstone, mudstone and fluid;

[0058] The reservoir prediction unit is used to set key inversion parameters, perform inversion on the offset stacked data volume based on the sensitive parameters and key inversion parameters, and predict the mud-sand interbedded reservoir based on the obtained inversion results.

[0059] The present invention has at least the following beneficial effects:

[0060] This invention proposes a method and apparatus for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion. By stacking pre-stack CRP gathers at different angles, the differences in AVO response are highlighted. Combined with well logging curves and geological parameters, rock elastic parameters that can reflect reservoirs and fluids are determined. Sensitive parameters that can distinguish between sandstone and mudstone and fluids are selected for inversion, thereby obtaining inversion results that conform to regional geological sedimentary patterns, have well connection trends consistent with seismic trends, and match well point conditions with actual drilling conditions, with a high well-to-well consistency rate. This effectively improves the accuracy of predicting thin interbedded layers, meets the well-laying accuracy requirements for development, and provides support for the efficient development of tight oil and gas. Attached Figure Description

[0061] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present invention and, together with the specification, serve to explain the technical solutions of the present invention.

[0062] Figure 1 A flowchart is shown for a method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion according to an embodiment of the present invention.

[0063] Figure 2 The following diagrams show near-, intermediate-, and far-offset seismic profiles of pre-stack gather processing according to an embodiment of the present invention; wherein, (a) is a near-offset seismic profile, (b) is an intermediate-offset seismic profile, and (c) is a far-offset seismic profile.

[0064] Figure 3 This diagram shows a comparison between the calculated and measured shear waves according to an embodiment of the present invention.

[0065] Figure 4 This diagram illustrates the correlation between calculated and measured shear waves according to an embodiment of the present invention.

[0066] Figure 5 This diagram illustrates the calculated rock elastic parameters according to an embodiment of the present invention.

[0067] Figure 6 This shows a cross-plot of the P-wave impedance and P-wave / S-wave velocity ratio according to an embodiment of the present invention;

[0068] Figure 7 This diagram shows the intersection of Young's modulus and P-wave / S-wave velocity ratio according to an embodiment of the present invention.

[0069] Figure 8 A well-connected inversion profile diagram according to an embodiment of the present invention is shown. Detailed Implementation

[0070] Various exemplary embodiments, features, and aspects of the present invention will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0071] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0072] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0073] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without certain specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order to highlight the spirit of the invention.

[0074] Figure 1 A flowchart is shown for a method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion according to an embodiment of the present invention. Figure 2 This diagram shows near-, mid-, and far-offset seismic profiles of pre-stack gather processing according to an embodiment of the present invention. Figure 3 This diagram shows a comparison between the calculated and measured shear waves according to an embodiment of the present invention. Figure 4 This diagram illustrates the correlation between calculated and measured shear waves according to an embodiment of the present invention. Figure 5 This diagram illustrates the calculated rock elastic parameters according to an embodiment of the present invention. Figure 6 This shows a cross-plot of the P-wave impedance and P-wave / S-wave velocity ratio according to an embodiment of the present invention; Figure 7 This diagram shows the intersection of Young's modulus and P-wave / S-wave velocity ratio according to an embodiment of the present invention. Figure 8 A well-connected inversion profile diagram according to an embodiment of the present invention is shown. Figure 1-8 As shown, a method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion includes: Step S01: acquiring pre-stack CRP gathers, logging curves, and geological parameters of the work area; Step S02: processing the pre-stack CRP gathers to obtain corner gather data, performing AVO attribute analysis on the corner gathers to determine the seismic response characteristics of the thin interbedded layers, and stacking the corner gathers according to the seismic response characteristics to generate a migration stacked data volume; Step S03: obtaining the shear wave curves of all wells in the work area according to the logging curves and geological parameters, and determining the rock elastic parameters that can reflect the reservoir and fluids according to the shear wave curves; Step S04: establishing a rock physical quantity model, and selecting the sensitive parameters among the various rock elastic parameters that can distinguish between sandstone and mudstone and fluids; Step S05: setting inversion key parameters, performing inversion on the migration stacked data volume according to the sensitive parameters and inversion key parameters, and predicting the thin interbedded mud-sand reservoirs according to the obtained inversion results.

[0075] The present invention provides a method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion, which specifically includes the following steps:

[0076] Step S01: Obtain pre-stack CRP gathers, logging curves, and geological parameters for the work area.

[0077] Step S02: Process the pre-stack CRP gathers to obtain corner gather data, perform AVO attribute analysis on the corner gathers to determine the seismic response characteristics of thin interlayered structures, and perform stacking processing on the corner gathers according to the seismic response characteristics to generate an offset stacking data volume.

[0078] In this invention, the method for processing the pre-stack CRP gather to obtain corner gather data includes: removing unwanted reflections, denoising, flattening the gather, compensating for amplitude, and converting the gather to obtain corner gather data.

[0079] In this embodiment of the invention, the pre-stack CRP gather is optimized and quality-controlled. The optimized near-mid-far migration results are selected to set the AVO analysis method and analysis angle, and the intercept, gradient, and their combined parameters are calculated. AVO attribute analysis and quality control are then performed. Specifically, this includes the following:

[0080] The pre-stack CRP gathers are processed by removing unwanted reflections, FX denoising, gather flattening, amplitude compensation, and gather conversion. The processing parameters are tested and optimized to obtain the required high-quality angle gather data.

[0081] AVO attribute analysis was performed using angle gather data. The AVO analysis method and analysis angle were set to clarify the seismic response characteristics of thin interlayered structures.

[0082] In this invention, the method for superimposing the corner gathers based on the seismic response characteristics to generate a migration superimposed data volume includes: dividing the corner gathers of each well in the work area into different angles; superimposing partial corner gathers according to the divided angles to obtain corresponding migration superimposed data volumes; analyzing the obtained migration superimposed data volumes of each well based on the seismic response characteristics; selecting the angle division result corresponding to the migration superimposed data volume of the well that is consistent with the seismic response characteristics and has the highest signal-to-noise ratio as the final angle division result; and superimposing partial corner gathers in the work area according to the final angle division result to obtain the final migration superimposed data volume.

[0083] In this embodiment of the invention, the high-quality angle gather data obtained after pre-stack processing optimization of CRP gathers is partially stacked to form three types of inversion migration stacked data volumes: near-field, mid-field, and far-field. Figure 2As shown, (a), (b), and (c) represent near, mid, and far offset stacked data volumes, respectively. Specifically, they include:

[0084] Pre-stack inversion is performed based on the stacking of partial angle gathers, therefore angle division is necessary to determine the stacking profile of partial angle gathers. In actual processing, in order to improve the signal-to-noise ratio of AVO gather records and increase the lateral resolution to a certain extent, records with three or more coverages in each angle gather can be selected for stacking.

[0085] Taking a certain work area as an example, the analysis of CRP gathers shows that the effective offset distance of the reservoir in the work area is 100-650m. By using the pre-stack time migration velocity to establish the layer velocity field, the CRP gathers are transformed from the offset domain to the angle domain (angle gather data). The effective incident angle range of the corresponding target layer is 0°-36°.

[0086] The AVO response characteristics (thin interbedded seismic response characteristics) are used to monitor the division results (near, middle and far angle division) to ensure that the divided corner gather stacked profiles can better reflect the AVO response consistent with the well points.

[0087] Comparative analysis revealed that the near, middle, and far-field angle stacking profiles corresponding to Well A exhibited high signal-to-noise ratios, preserving the reservoir's AVO response characteristics. This determined angle segmentation ensures that the pre-stack inversion results reflect variations in reservoir physical properties and fluidity. Therefore, the final decision was to use the three angle gathers from Well A—0°–12°, 12°–24°, and 24°–36°—for stacking and application in the pre-stack inversion.

[0088] Step S03: Based on the logging curves and geological parameters, obtain the shear wave curves of all wells in the work area, and determine the rock elastic parameters that can reflect the reservoir and fluid based on the shear wave curves.

[0089] In this invention, before obtaining the shear wave curves of all wells in the work area based on the logging curves and geological parameters, the logging curves are preprocessed. The method includes: processing outliers, standardizing, performing environmental correction, and / or density curve fitting on the logging curves.

[0090] In this invention, the geological parameters include: clay content, total porosity, and / or water saturation.

[0091] In this embodiment of the invention, the logging curves are processed through outlier handling, standardization, environmental correction, and density curve fitting. A rock volume model is established to estimate shear waves, and the elastic parameters of each rock are calculated using the P-wave, S-wave velocity, and density curves. Specifically, this includes:

[0092] In order to obtain the shear wave curve, the obtained logging curves undergo preprocessing such as outlier handling, standardization, environmental correction, and density curve fitting to ensure the quality of the logging curves.

[0093] The pre-processed logging curves are used to calculate the shear wave curves using empirical parameters from the model work area, namely clay content, total porosity, and / or water saturation.

[0094] By comparing various rock physics models, the Pride model, suitable for the reservoir characteristics of the study area, was selected for rock physics modeling and prediction of shear wave curves. Taking Yangdachengzi in the study area as an example, well B, with measured shear wave velocity, was used as a standard for parameter testing. Rock physics (elastic) parameters such as the P-wave velocity, S-wave velocity, and density of the oil reservoir skeleton and clay, as well as the consolidation coefficient of the rock, were determined. These parameters were then input into the model to calculate clay content, total porosity, and / or water saturation, ultimately obtaining the shear wave curves. When calculating clay content, the single-curve GRS calculation method was used, with parameters including a logging value of 85 API for pure sandstone sections, a logging value of 100 API for pure mudstone sections, and a regional empirical coefficient of 2. Parameters for calculating total porosity included: density curve, clay content curve, and a fluid density value of 1 g / cm³. 3 The skeletal density is 2.65 g / cm³. 3 The density of the clay is 2.38 g / cm³. 3 The parameters used to calculate saturation include: porosity curve, resistivity curve, formation water resistivity of 0.16, cementation index of 2, saturation index of 2, correction factor of 1, and core coefficient of 1.

[0095] The rock physics modeling parameters used in the shear wave calculations include: fluid type (oil-water mixture), seawater depth (0 m), surface temperature (20℃), temperature gradient (1.82℃ / 100m), seawater pressure gradient (9500 Pa / m), pore pressure gradient (10180 Pa / m), overlying formation pressure gradient (22620 Pa / m), and crude oil density (0.8 g / cm³). 3 The natural gas specific gravity is 0.56, the gas-oil ratio is 200, the NaCl salinity is 20000 ppm, the gas-water ratio is 0.6, the calculation method is the Bries formula, the Bries parameter is 3, the mineral types are quartz and clay, the calculation formula is the Wyllie average, the quartz volume content is sand, and the density is 2.65 g / cm³. 3 The longitudinal wave velocity is 6040 m / s, the transverse wave velocity is 4120 m / s, the clay volume content (VSH) is 2.6 g / cm³, and the density is 2.6 g / cm³. 3 The longitudinal wave velocity is 3410 m / s, the transverse wave velocity is 1630 m / s, the total porosity curve is PHIT, the water saturation is SW, the rock model is the dry rock fluid saturation model, the selected model is the Xu Huai Te model, the clay content curve is VSH, and the aspect ratio of pore 1 (clay) is 0.03.

[0096] In this invention, before determining the rock elastic parameters that reflect the reservoir and fluid based on the shear wave curve, the obtained shear wave curve is subjected to quality control. The method includes: performing a correlation analysis between the obtained shear wave curve and the measured shear wave curve in the work area; if the correlation reaches a predetermined percentage or more, the obtained shear wave curve passes quality control; otherwise, the type of the geological parameter is readjusted, and the corresponding shear wave curve is obtained, until the correlation between the obtained shear wave curve and the measured shear wave curve reaches a predetermined percentage or more and passes quality control.

[0097] In this embodiment of the invention, the quality control of the obtained shear wave curve includes: performing a correlation analysis based on the obtained shear wave curve analysis results (calculated shear wave curve) and the measured shear wave curves obtained from shear wave logging in certain work areas. If the correlation between the two reaches 80% or more, the quality control is passed. If the correlation does not reach 80% or more, the selected empirical parameter type is adjusted, and the corresponding shear wave curve is recalculated until the correlation between the calculated shear wave curve and the measured shear wave curve reaches 80% or more.

[0098] like Figure 3 , Figure 4 As shown, by comparison, the predicted shear wave velocity curve is very close to the measured shear wave curve, with a correlation coefficient greater than 0.89, that is, a correlation of 89%. The curve shapes are basically the same, which can meet the accuracy requirements of pre-stack inversion.

[0099] Based on the established empirical parameters, namely clay content, total porosity, and water saturation, the shear wave curves of all wells in the block are obtained.

[0100] In this invention, the method for determining rock elastic parameters that reflect the reservoir and fluid based on the shear wave curve includes: obtaining the longitudinal wave curve of the working area; determining the rock elastic parameters of the fluid based on the longitudinal wave curve and the shear wave curve, wherein the rock elastic parameters include: longitudinal wave impedance, shear wave impedance, longitudinal wave to shear wave velocity ratio, Poisson's ratio, bulk modulus, shear modulus, Young's modulus, Lamé constant, Lamé constant multiplied by density, and shear modulus multiplied by density.

[0101] The longitudinal wave impedance is calculated using equation (1);

[0102] AI = ρV p (1);

[0103] In the formula: ρ is the density of the rock; V p For longitudinal wave velocity;

[0104] The transverse wave impedance is calculated using equation (2);

[0105] SI = ρV S(2);

[0106] In the formula: ρ is the density of the rock; V S For longitudinal wave velocity;

[0107] The P-wave to S-wave velocity ratio is calculated using equation (3);

[0108] V P V S =V P / V S (3);

[0109] In the formula: V p V is the longitudinal wave velocity; S For longitudinal wave velocity;

[0110] Poisson's ratio is calculated using equation (4);

[0111]

[0112] In the formula: ρ is the density of the rock; V p V is the longitudinal wave velocity; s The transverse wave velocity;

[0113] The bulk modulus is calculated using equation (5);

[0114]

[0115] In the formula: ρ is the density of the rock; V s The transverse wave velocity;

[0116] The shear modulus is calculated using equation (6);

[0117]

[0118] In the formula: λ is Poisson's ratio; μ is bulk modulus;

[0119] The Young's modulus is calculated using equation (7);

[0120]

[0121] In the formula: λ is Poisson's ratio; μ is bulk modulus;

[0122] The Lamé constant is calculated using equation (8);

[0123]

[0124] In the formula: λ is Poisson's ratio; E is Young's modulus.

[0125] Step S04: Establish a rock physical quantity model, and select the sensitive parameters among the various rock elastic parameters that can distinguish between sandstone, mudstone and fluid.

[0126] In this embodiment of the invention, based on the calculated shear wave curve, Poisson's ratio, Young's modulus, bulk modulus, and other rock mechanical (elastic) parameters reflecting reservoir properties and fluids are calculated using formulas, such as... Figure 5 As shown; a rock physical analysis template was established to quantitatively analyze the identification ability of various rock elastic parameters for reservoirs and fluids, and to select curves that are effective in identification and sensitive to fluids. Specifically, this includes:

[0127] Establish a rock physical quantity model, and select the parameters that can better distinguish sandstone and mudstone and fluid-sensitive parameters from the rock elastic parameters calculated in step S03 using cross plots or histograms.

[0128] like Figure 6 and Figure 7 As shown, cross-plot analysis was used to select the most sensitive parameters that can effectively distinguish between sandstone, mudstone, and fluids. Ultimately, the P-wave velocity ratio (VPVS) and Young's modulus (E) were selected as the most sensitive parameters.

[0129] Step S05: Set the key inversion parameters, perform inversion on the offset stacked data volume according to the sensitive parameters and the key inversion parameters, and predict the mud-sand interbedded reservoir based on the obtained inversion results.

[0130] In this invention, the method further includes: during the inversion process, quality control is performed on the obtained results; if the results do not meet the quality control requirements, the key inversion parameters are adjusted, and the inversion is performed again based on the adjusted key inversion parameters until the obtained inversion results pass the quality control.

[0131] The key inversion parameters include: number of samples, optimal cutoff frequency, smoothing radius, and / or sampling rate.

[0132] In this embodiment of the invention, sensitive parameters and key inversion parameters are used to perform pre-stack seismic waveform indication inversion on the migrated data volume. When faced with the characteristics of thin, scattered reservoir sand bodies, complex vertical stacking relationships, and strong lateral heterogeneity, coupled with the increased difficulty in qualitative and quantitative reservoir prediction due to the limitations of seismic data resolution, traditional statistical inversion—a method combining stochastic simulation theory and seismic inversion—is prone to multiple solutions. Furthermore, the variability function is difficult to accurately establish under conditions of uneven well locations, resulting in low prediction accuracy in areas with rapid lateral reservoir variations.

[0133] Seismic waveform indication inversion is a new technique developed based on traditional statistics. This method fully utilizes the lateral variations of seismic waveforms. Since seismic waveform characteristics are closely related to the sedimentary environment, using the lateral variation characteristics of seismic waveforms instead of the variogram function to characterize the spatial variation patterns of reservoirs can fully reflect the influence of sedimentary elements, achieving stochastic inversion under facies-controlled conditions and improving the determinism of high-frequency components.

[0134] Pre-stack waveform indication inversion utilizes gather waveforms and AVO features. Based on gather waveform similarity, AVO features, and spatial distance as indicator variables, wells with similar gather characteristics are extracted as spatial estimation samples. Statistical elastic impedance is used as prior information, and high-precision pre-stack elastic parameter inversion results are obtained by applying seismic waveform indication inversion. This method shows good results in predicting the spatial distribution of sand bodies.

[0135] During the inversion process, various methods are used to continuously test and control the quality, optimize key parameters of waveform indication inversion, such as the number of samples, the optimal cutoff frequency, the smoothing radius, and the sampling rate, and perform waveform indication inversion. The inversion results are then quality controlled to obtain the inversion data volume. The obtained inversion data volume is used to effectively identify thin interlayers, porosity, and fluids.

[0136] like Figure 8 The image shown is a cross-sectional view of the interconnected wells obtained through inversion. Figure 8 It can be seen that the predicted lateral extension length obtained by applying the pre-stack waveform indicator inversion is consistent with the trend of seismic waveform changes, with a high degree of correspondence. The longitudinal trend matches the well logging interpretation results well, and the inversion slices on the plane can well depict the river channel distribution morphology, with a high well matching rate.

[0137] Post-drilling analysis showed that the effective thickness of the sandstone actually drilled was greater than the predicted thickness, and the actual sandstone thickness of the entire well in the well layout area was greater than the predicted thickness in the plan. The average length of sandstone encountered in horizontal wells was 848m, with a sandstone encounter rate of 99.2%. The length of oil-bearing sandstone encountered was 846m, with an oil-bearing sandstone encounter rate of 99%. High production was also achieved during fracturing and production, realizing the effective prediction of thin interbedded layers.

[0138] It is understood that the various method embodiments mentioned above in this invention can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this invention will not elaborate further.

[0139] The execution entity of the method for predicting thin, mud-bearing sand-bearing reservoirs based on pre-stack waveform indication inversion can be an image processing device. For example, the method can be executed by a terminal device, a server, or other processing devices. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, this method can be implemented by a processor calling computer-readable instructions stored in memory.

[0140] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0141] This invention also proposes a mud-and-sand thin interbedded reservoir prediction device based on pre-stack waveform indication inversion, comprising: an acquisition unit for acquiring pre-stack CRP gathers, logging curves, and geological parameters of the work area; a migration stacking data volume generation unit for processing the pre-stack CRP gathers to obtain corner gather data, performing AVO attribute analysis on the corner gathers to determine the seismic response characteristics of the thin interbedded layers, and performing stacking processing on the corner gathers according to the seismic response characteristics to generate a migration stacking data volume; a rock elastic parameter determination unit for obtaining shear wave curves of all wells in the work area according to the logging curves and geological parameters, and determining rock elastic parameters that can reflect reservoirs and fluids according to the shear wave curves; a sensitive parameter determination unit for establishing a rock physical quantity scale, and preferentially selecting sensitive parameters among the various rock elastic parameters that can distinguish between sandstone and mudstone and fluids; and a reservoir prediction unit for setting inversion key parameters, performing inversion on the migration stacking data volume according to the sensitive parameters and inversion key parameters, and predicting mud-and-sand thin interbedded reservoirs according to the obtained inversion results.

[0142] In some embodiments, the functions or modules and units included in the apparatus provided in this disclosure can be used to execute the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0143] Low accuracy in reservoir prediction directly impacts well deployment and the selection of optimal oil and water well strategies. This invention optimizes key parameters such as inversion sampling rate and maximum cutoff frequency, and employs multiple inversion quality control methods to ensure the quality of inversion results. Planar analysis shows that the predicted sandstone thickness and effective thickness are consistent with the channel development trend, conforming to regional geological sedimentary patterns. Profile analysis shows that the well connection trend is consistent with the seismic trend, and the well point results closely match the actual drilling conditions, resulting in a high well-to-well accuracy rate. This effectively improves the accuracy of predicting thin interbedded layers, meets the well deployment accuracy requirements for development, and provides support for the efficient development of tight oil and gas.

[0144] This invention presents a lithology and reservoir parameter prediction method based on rock physics analysis and pre-stack high-resolution inversion. By using the waveform characteristics of well-side gathers and performing angular stacking, the differences in AVO response can be highlighted. Based on the results of angular stacking, the elastic parameters calculated from the predicted well logging shear waves are combined with cross-analysis. The key parameters of the inversion are optimized by selecting the sensitive curves to carry out pre-stack inversion, thereby achieving the goal of accurate prediction of thin interbedded layers.

[0145] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion, characterized in that, include: Acquire pre-stack CRP gathers, logging curves, and geological parameters for the work area; The pre-stack CRP gathers are processed to obtain corner gather data. AVO attribute analysis is performed on the corner gathers to determine the seismic response characteristics of thin interlayered structures. Based on the seismic response characteristics, the corner gathers are stacked to generate an offset stacked data volume. Based on the logging curves and geological parameters, the shear wave curves of all wells in the work area are obtained, and the rock elastic parameters that can reflect the reservoir and fluid are determined based on the shear wave curves. Establish a rock physical quantity model and select the sensitive parameters among the various rock elastic parameters that can distinguish between sandstone, mudstone and fluid; Key inversion parameters are set, and the offset stacked data volume is inverted based on the sensitive parameters and key inversion parameters. Based on the obtained inversion results, the mud-sand thin interbedded reservoir is predicted.

2. The method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion according to claim 1, characterized in that, The method for processing the pre-stack CRP gather to obtain corner gather data includes: The pre-stack CRP gather is processed by removing unwanted reflections, denoising, flattening, amplitude compensation, and gather conversion to obtain corner gather data.

3. The method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion according to claim 1, characterized in that, The method for stacking the corner gathers based on the seismic response characteristics to generate a migration stacked data volume includes: The corner gathers of each well in the work area are divided into different angles, and the corner gathers are superimposed according to the divided angles to obtain the corresponding offset superimposed data volume; The migration stacking data volumes of each well are analyzed based on the seismic response characteristics. The angle division result corresponding to the migration stacking data volume of the well with the highest signal-to-noise ratio that is consistent with the seismic response characteristics is selected as the final angle division result. Based on the final angle division results, partial angle gathers are superimposed on the work area to obtain the final offset superimposed data volume.

4. The method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion according to claim 1, characterized in that, Before obtaining the shear wave curves of all wells in the work area based on the logging curves and geological parameters, the logging curves are preprocessed. The method includes: The well logging curves are subjected to outlier processing, standardization, environmental correction, and / or density curve fitting.

5. The method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion according to claim 1, characterized in that: The geological parameters include: clay content, total porosity, and / or water saturation.

6. The method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion according to claim 1, characterized in that, Before determining the rock elastic parameters reflecting the reservoir and fluid based on the shear wave curve, the method for quality control of the obtained shear wave curve includes: The obtained shear wave curve is correlated with the measured shear wave curve in the work area. If the correlation reaches a predetermined percentage or more, the quality control of the obtained shear wave curve is passed. Otherwise, the type of the geological parameters is readjusted and the corresponding shear wave curve is obtained until the correlation between the obtained shear wave curve and the measured shear wave curve reaches a predetermined percentage or more and passes the quality control.

7. The method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion according to claim 1, characterized in that, The method for determining rock elastic parameters that reflect reservoir and fluid based on the shear wave curve includes: Obtain the longitudinal wave curve of the work area; Based on the longitudinal wave curve and the transverse wave curve, the rock elastic parameters of the fluid are determined, wherein the rock elastic parameters include: longitudinal wave impedance, transverse wave impedance, longitudinal wave velocity ratio, Poisson's ratio, bulk modulus, shear modulus, Young's modulus, Lamé constant, Lamé constant multiplied by density, and shear modulus multiplied by density.

8. The method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion according to claim 7, characterized in that, The method for determining the rock elastic parameters of the fluid based on the longitudinal wave curve and the transverse wave curve includes: The longitudinal wave impedance is calculated using equation (1); AI=ρV p (1); In the formula: ρ is the density of the rock; V p For longitudinal wave velocity; The transverse wave impedance is calculated using equation (2); SI=ρV S (2); In the formula: ρ is the density of the rock; V S For longitudinal wave velocity; The P-wave to S-wave velocity ratio is calculated using equation (3); V P V S =V P / V S (3); In the formula: V p V is the longitudinal wave velocity; S For longitudinal wave velocity; Poisson's ratio is calculated using equation (4); λ=ρV p 2 -2pV s 2 (4); In the formula: ρ is the density of the rock; V p V is the longitudinal wave velocity; s The transverse wave velocity; The bulk modulus is calculated using equation (5); μ=ρV s 2 (5); In the formula: ρ is the density of the rock; V s The transverse wave velocity; The shear modulus is calculated using equation (6); In the formula: λ is Poisson's ratio; μ is bulk modulus; The Young's modulus is calculated using equation (7); In the formula: λ is Poisson's ratio; μ is bulk modulus; The Lamé constant is calculated using equation (8); In the formula: λ is Poisson's ratio; E is Young's modulus.

9. The method for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion according to any one of claims 1-8, characterized in that, Also includes: During the inversion process, the obtained results are subject to quality control. If the results do not meet the quality control requirements, the key inversion parameters are adjusted, and the inversion is performed again based on the adjusted key inversion parameters until the obtained inversion results pass the quality control. The key inversion parameters include: number of samples, optimal cutoff frequency, smoothing radius, and / or sampling rate.

10. A device for predicting thin interbedded mud-sand reservoirs based on pre-stack waveform indication inversion, characterized in that, include: The acquisition unit is used to acquire pre-stack CRP gathers, logging curves, and geological parameters of the work area. The offset stacking data volume generation unit is used to process the pre-stack CRP gathers to obtain corner gather data, perform AVO attribute analysis on the corner gathers to determine the seismic response characteristics of thin interlayered structures, and stack the corner gathers according to the seismic response characteristics to generate an offset stacking data volume. The rock elastic parameter determination unit is used to obtain the shear wave curves of all wells in the work area based on the logging curves and geological parameters, and to determine the rock elastic parameters that can reflect the reservoir and fluid based on the shear wave curves. A sensitive parameter determination unit is used to establish a rock physical quantity scale, preferably among the various rock elastic parameters, the sensitive parameters that can distinguish between sandstone, mudstone and fluid; The reservoir prediction unit is used to set key inversion parameters, perform inversion on the offset stacked data volume based on the sensitive parameters and key inversion parameters, and predict the mud-sand interbedded reservoir based on the obtained inversion results.

Images