Pre-stack high-resolution inversion tight oil and gas reservoir sweet spot prediction method and application thereof

Abstract

The invention provides a pre-stack high-resolution inversion compact oil and gas reservoir sweet spot prediction method and application thereof, particularly provides a pre-stack high-resolution inversion compact oil and gas reservoir sweet spot prediction method under multi-dimensional information constraint and application thereof, and relates to the field of geophysical exploration. According to the method, a plurality of high-resolution elastic attribute models fully fusing geological, logging and seismic information are obtained through a pre-stack high-resolution inversion technology under multi-dimensional information constraint; and performing high-resolution pre-stack inversion by using angle-divided superposition seismic data and participation weight thereof in the angle-divided superposition scheme and geostatistical parameters of high-resolution inversion under multi-dimensional information constraint to obtain an elastic attribute body representing geological desserts and engineering desserts, and performing multi-scale description on a target layer of a research area. The thin reservoir stratum of the target stratum can be clearly identified, the resolution ratio is 3-4 times higher than that of conventional inversion, the method is popularized and applied to unconventional oil and gas reservoir blocks, and the exploration and development efficiency is greatly improved.

Description

Technical Field

[0001] The present invention belongs to the field of geophysical exploration, and relates to a pre-stack high-resolution inversion tight oil and gas reservoir sweet spot prediction method and application thereof, and specifically relates to a pre-stack high-resolution inversion tight oil and gas reservoir sweet spot prediction method and application thereof under multi-dimensional information constraints. Background Art

[0002] The global demand for oil and gas is growing rapidly, and the direction of oil and gas exploration has gradually shifted from conventional oil and gas reservoirs to unconventional oil and gas reservoirs. In the process of oil and gas exploration in different regions, the lithology, formation, geology and other properties of different oil fields are different. The detection of unconventional oil and gas reservoirs is achieved through "sweet spot" prediction. Researchers have focused on sweet spot prediction.

[0003] Chinese invention patent 202111236175.9 provides a shale gas sweet spot prediction method and equipment by multi-parameter fusion inversion, the method comprising: based on well logging data, vertically dividing the target layer into small layers to obtain multiple small layers; based on seismic data, obtaining predicted reservoir segment boundaries in multiple small layers; based on the well logging data of the predicted reservoir segment boundaries, intersecting multiple well logging attribute parameters to obtain multi-parameter fitting formulas for carbon content, gas content and porosity respectively; applying the prestack parameter inversion results, combined with the multi-parameter fitting formula, obtaining the profile results and plane results of carbon content, gas content and porosity; combining the profile results and plane results of carbon content, gas content and porosity to predict shale gas sweet spots. This invention weakens the influence of the instability of a single parameter and overcomes the instability of the single parameter results caused by the inversion results of parameters such as density.

[0004] In addition, Chinese invention patent 202011153335.9 provides a method and system for predicting sweet spots in fracture-cavity carbonate micro-reservoirs. The method includes: obtaining seismic pre-stack migration data in the study area; pre-processing the seismic pre-stack migration data with weighted tracks of adjacent seismic tracks to obtain a pre-processed seismic data body; performing gridded formation dip and azimuth scanning based on the pre-processed seismic data body; using the parameters obtained from the formation dip and azimuth scanning to analyze the pre-processed seismic data body, performing weighted average calculations of adjacent seismic tracks guided by structure, and obtaining corresponding formation background data; calculating the residual between the formation background data and the original seismic track in the grid to obtain the sweet spot data of the target reservoir in the study area. This method can effectively identify the secondary reflection intensity and medium-small-scale beaded reflections based on the current existing seismic data, thereby identifying the sweet spot area of ​​the carbonate reservoir. It provides a strong basis for the development of carbonate oil reservoirs and the optimization and deployment of well sites.

[0005] Although there are more and more methods for predicting sweet spots, it is found in the process of exploring oil fields of different properties that the Carboniferous bottom conglomerate strata in some oil fields are thin and dense in lithology, showing the characteristics of unconventional tight sandstone reservoirs with low porosity and low permeability. Drilling data show that there are no structural traps around most low-yield oil flows and thicker oil layers, indicating that the oil and gas accumulation in this layer is not only affected by the structure, but also controlled by lithology or other complex factors. Structural analysis alone cannot identify the distribution range of oil reservoirs and the development pattern of sweet spots. Therefore, the exploration and development of this layer urgently needs to introduce the idea of ​​unconventional oil and gas exploration and development, and increase efforts to improve the ability to identify favorable areas of geological sweet spots and the prediction level of engineering sweet spots.

[0006] Therefore, the present invention is based on the traditional prestack inversion technology. On the one hand, it fully considers the influence of geological factors and builds an initial attribute model that reflects the geological macro-trend. On the other hand, it introduces geostatistical parameters as input data to improve the thin reservoir identification accuracy of conventional seismic data. Its research and development purpose is to digitize multi-dimensional information sources such as geology, seismology, and well logging in the prestack inversion calculation engine. After multiple rounds of iterative simulation operations, a set of high-resolution elastic attribute bodies that highly restore geological-geological information is finally obtained. This set of high-resolution attribute bodies and their derived attribute bodies can also indirectly predict favorable engineering sweet spots. Summary of the invention

[0007] In view of the problems existing in the prior art, the present invention provides a method for predicting sweet spots in tight oil and gas reservoirs by pre-stack high-resolution inversion and its application, and specifically provides a method for predicting sweet spots in tight oil and gas reservoirs by pre-stack high-resolution inversion and its application under multi-dimensional information constraints. The high-resolution pre-stack high-resolution inversion technology under multi-dimensional information constraints is used to obtain multiple elastic attribute models that fully integrate geological, logging, and seismic information at high resolution. Multiple elastic attribute models can be converted into key attributes required to characterize and evaluate geological sweet spots and engineering sweet spots, such as physical properties, oil and gas content, and brittleness. The application of the prediction method described in the present invention will greatly improve the comprehensive prediction capabilities of unconventional oil and gas reservoir geology and engineering sweet spots, thereby providing efficient and accurate data support for promoting the exploration and development of unconventional oil and gas reservoirs.

[0008] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0009] First, the present invention provides a method for predicting sweet spots in tight oil and gas reservoirs by prestack high-resolution inversion, comprising the following steps:

[0010] S1. Establishing an elastic attribute model and a geological model according to the attribute curve and using a spatial interpolation algorithm, and correcting the model according to geological and sedimentary characteristics;

[0011] S2. Use the corrected velocity field to convert the angle gathers, test the stacking schemes at different angles, and perform pre-stack inversion tests on the well line to determine the stacking schemes at different angles.

[0012] S3. Conduct geostatistical simulation for the target strata in the study area and determine the geostatistical parameters for high-resolution inversion under multi-dimensional information constraints;

[0013] S4, performing high-resolution prestack inversion according to the model described in step S1, the angle stacking scheme described in step S2, and the inverted geostatistical parameters obtained in step S3, to obtain an elastic property body characterizing the geological sweet spot and the engineering sweet spot;

[0014] S5. Based on the elastic properties of the above-mentioned geological sweet spots and engineering sweet spots, the target layer in the study area is characterized at multiple scales.

[0015] Preferably, step S1 is specifically as follows: make full use of key wells in the work area, start from logging data, and use spatial interpolation algorithm to establish an elastic attribute model under layer control; establish a geological model associated with lithology, physical properties, and oil and gas properties under the condition of well logging data support, and extract the plane attributes of the geological model to establish its correlation with geological trends and sedimentary characteristics; use this type of geological model to correct the elastic attribute model, add pseudo wells when necessary, and correct until the plane characteristics of the elastic attributes roughly match the plane characteristics of the geological attributes; input the corrected elastic attribute model into the inversion engine as the data basis for pre-stack inversion.

[0016] Preferably, in step S1, the attribute curve includes logging data, geology, lithology, physical properties and oil and gas properties.

[0017] Further preferably, the logging data includes logging curves directly or indirectly related to geological sweet spots and engineering sweet spots.

[0018] More preferably, the logging curve includes longitudinal wave velocity, shear wave velocity, longitudinal and shear wave velocity ratio, longitudinal wave impedance, shear wave impedance, shear modulus, Poisson's ratio, Young's modulus and Lame coefficient.

[0019] More preferably, the logging curve includes longitudinal wave impedance-transverse wave impedance, transverse wave velocity-ratio of longitudinal and transverse wave velocity, Young's modulus-Poisson's ratio, longitudinal wave velocity-ratio of longitudinal and transverse wave velocity, Lame coefficient and shear modulus.

[0020] In the present invention, the longitudinal wave impedance is the product of the longitudinal wave velocity and the density; the longitudinal-to-straight wave velocity ratio is the ratio of the longitudinal wave velocity to the shear wave velocity; the Young's modulus is calculated by the longitudinal-to-straight wave velocity ratio and the longitudinal wave velocity; and the Poisson's ratio is calculated by the longitudinal-to-straight wave velocity ratio.

[0021] Preferably, in step S1, the interpolation algorithm includes inverse distance weighted method, triangle interpolation method and Kriging interpolation method.

[0022] Preferably, in step S1, using a spatial interpolation algorithm means: interpolating corresponding logging curves between layers under the control constraints of geological layers.

[0023] Preferably, in step S1, the elastic property model includes an interpolation model of the logging curves of P-wave velocity, S-wave velocity, P-wave velocity ratio, P-wave impedance, S-wave impedance, shear modulus, Poisson's ratio, Young's modulus and Lame coefficient.

[0024] In the present invention, the pseudo well is mainly used in the case where the known wells are sparsely distributed or skewed in the study area, or the well location distribution cannot reflect the spatial distribution trend of the target geological body, reservoir, and oil and gas content.

[0025] Preferably, step S2 specifically comprises: strictly quality controlling the pre-stack time migration gathers; converting the seismic stacking / or migration velocity field into the layer velocity field, and performing well control and layer control correction to ensure the accuracy of the layer velocity field; converting the offset distance gathers into angle gathers using the corrected layer velocity field, and testing stacking schemes at different angles, and performing pre-stack inversion tests for key well-connected lines; and obtaining angle-divided stacking schemes by testing the stability of the pre-stack inversion of key well-connected lines.

[0026] Further preferably, in step S2, the pre-stack time migration gathers are seismic data processing data, which are seismic gathers obtained after being processed by a pre-stack time migration module.

[0027] Further preferably, in step S2, the quality control is specifically as follows: detecting whether the flattening degree of the target layer meets the requirements of AVO analysis, and whether the amplitude energy compensation of the near-well seismic gathers under well control can accurately reflect the true relationship between the formation and lithology interface changes represented by the logging reflection coefficient; if the above conditions are met, the quality control is qualified and the next step is carried out; if the above conditions are not met, it is necessary to return to the relevant processing steps of the seismic gathers for gather optimization processing.

[0028] In the present invention, the gather flattening degree refers to: theoretically, after the prestack time migration gather is subjected to dynamic correction, the seismic amplitude tends to increase or decrease horizontally with the increase of the offset distance.

[0029] In the present invention, the AVO analysis is a simplified formula of the Zeoppritz formula, which is a method technology for performing full-angle analysis on seismic data and is a seismic analysis method of amplitude variation with offset.

[0030] In the present invention, the reflection coefficient is obtained through the longitudinal wave impedance of well logging. The reflection coefficient is the ratio of the difference and the sum of the longitudinal wave impedances above and below the formation interface, which can reflect the lithology change amplitude of the formation contact surface.

[0031] Further preferably, in step S2, the stability of the pre-stack inversion is the stability of the inverted elastic properties under the angle-divided scheme.

[0032] Preferably, in step S3, the geostatistical parameters of the geostatistical simulation include probability density functions and variograms of various elastic properties of different lithologies.

[0033] Preferably, step S3 is specifically as follows: obtaining geostatistical parameters through logging curves of known wells, mastering geological laws by analyzing geostatistical parameters of various elastic properties under different lithologies, importing the set of parameters as input data into a geostatistical simulation engine to simulate reservoir lithology and elastic properties, and performing quality control on the sections and planes of the simulated property body.

[0034] Further preferably, in step S3, the quality control is a process of simulating the lithology and elastic properties to roughly invert the scale, size and thickness of the target geological body and reservoir in the study area; the above process is an iterative process, and ultimately the geostatistical parameters with the best simulation effect are involved in the inversion.

[0035] Preferably, in step S4, the angle-divided stacking scheme refers to the angle-divided stacking seismic data and its participation weight in the angle-divided stacking scheme; the participation weight refers to the weight of the angle-divided pre-stack seismic data body participating in the inversion, which can be input as numerical data through an absolute / relative signal-to-noise ratio value, and the signal-to-noise ratio is the ratio of the seismic effective signal to the noise.

[0036] Preferably, in step S4, the elastic property body includes a longitudinal wave velocity body, a longitudinal wave impedance body, a transverse wave velocity body, a transverse wave impedance body, a longitudinal and transverse wave velocity ratio body, a Poisson's ratio body and a Young's modulus body.

[0037] Preferably, step S4 is specifically as follows: on the basis of the input numerical geological information, the geostatistical parameters reflecting the lithology ratio, the spatial development scale and the scale of the geological body are combined, and finally, under the constraints of the pre-stack angle stacking seismic data body and its participation weights, a pre-stack inversion test is performed and the various types of input data participating in the inversion are adjusted, and finally the input data is inverted to obtain multiple elastic property bodies that can characterize geological sweet spots and engineering sweet spots.

[0038] Further preferably, the geological information refers to various corrected elastic property models that can reflect geological laws.

[0039] Preferably, step S5 specifically includes: performing multi-scale characterization of the sweet spot development pattern, development scale, and spatial distribution of the target layer in the study area according to the elastic property body and its derived mechanical properties that characterize the geological sweet spots and engineering sweet spots.

[0040] Furthermore, the present invention provides the application of the above prediction method in the prediction of favorable geological and engineering sweet spots.

[0041] Then, the present invention provides an elastic property model and / or an elastic property body established by the above prediction method.

[0042] Preferably, the elastic property model includes geological, well logging, lithology, physical properties, hydrocarbon content and seismic information.

[0043] Finally, the present invention provides the application of the above elastic property model and / or elastic property body in the prediction of favorable geological and engineering sweet spots.

[0044] In the present invention, the high-resolution prestack inversion technology under the constraints of multi-dimensional information targets the characteristics of low porosity and low permeability of the Carboniferous system and rapid lateral changes in sedimentary subphases. On the one hand, the geological, sedimentary and lithological information is digitized and input into the inversion engine as prior information for prestack inversion. On the other hand, a fine stratigraphic grid is built according to the thickness of the thinnest reservoir in the target formation, and the elastic parameter seed point is calculated at each grid point and a prestack synthetic record is formed through wavelets. The optimal elastic parameter seed point is determined by continuous optimization to achieve a high degree of match with the prestack seismic data.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The pre-stack high-resolution inversion method for predicting sweet spots in tight oil and gas reservoirs under multi-dimensional information constraints provided by the present invention can obtain a high-resolution elastic model that fully integrates multiple attributes of geological, logging, and seismic information; the multi-attribute elastic parameter model can be converted into key attributes required for evaluating geological sweet spots and engineering sweet spots, such as physical properties, oil and gas content, and brittleness.

[0047] 2. The prediction method provided by the present invention can clearly identify thin reservoirs of the target layer, and the resolution is 3-4 times higher than that of conventional inversion.

[0048] 3. The prediction method provided by the present invention integrates geological and lithological information into the elastic model, uses data related to geological information to calibrate the elastic model, and obtains an elastic model whose plane laws are consistent with geological laws. Only by carrying out inversion on the basis of this model can the inversion results of composite geological laws be obtained.

[0049] 4. The prediction method provided by the present invention can obtain multiple elastic properties. On the basis of calculating the geological sweet spot, it can also meet the calculation of engineering sweet spot parameters. It can provide data support for the exploration and development process of unconventional oil and gas reservoirs to a great extent. If it is promoted and applied in unconventional oil and gas reservoir blocks, it can greatly improve the efficiency of exploration and development and provide hard-core support for the contribution to increasing reserves and production. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of the prediction method of the present invention.

[0051] Figure 2a It is a curve diagram of geological, lithological, physical and oil and gas properties of the present invention.

[0052] Figure 2b It is a statistical analysis diagram of the probability density function of elastic properties of different lithologies according to the present invention.

[0053] Figure 2c It is a cross-plot of elastic properties of different lithologies according to the present invention.

[0054] Figure 3 It is a comparison chart showing that the conventional interpolation model cannot reflect the changes in geological trends in the study area.

[0055] Figure 4 It is a model diagram of elastic properties assisted by the spatial interpolation model of lithology and physical properties in the study area in the prediction method described in the present invention.

[0056] Figure 5 It is a comparison diagram of geological characteristics of increasing elastic properties by adding geological constraints in the prediction method of the present invention.

[0057] Figure 6 It is a flow chart of geostatistical inversion parameter testing.

[0058] Figure 7 This is a comparison chart of how high-resolution multi-dimensional information-constrained inversion technology improves reservoir identification accuracy.

[0059] Figure 8 This is a demonstration result diagram showing how the elastic model is optimized to make the inversion results more consistent with geological laws.

[0060] Figure 9a It is a well profile result diagram of high-resolution multi-dimensional information-constrained inversion results used to predict favorable geological sweet spots.

[0061] Figure 9b It is a well profile result diagram that uses high-resolution multi-dimensional information-constrained inversion results to predict favorable engineering sweet spots.

[0062] Fig.10a It is a high-resolution multi-dimensional information-constrained inversion result used for the attribute result map of the prediction of favorable geological and engineering sweet spots.

[0063] Fig.10b It is a high-resolution multi-dimensional information-constrained inversion result used for the preliminary distribution result map of comprehensive rating indicators of geological and engineering sweet spots.

[0064] Fig.10c It is a high-resolution multi-dimensional information-constrained inversion result used for the comprehensive prediction of geological and engineering sweet spots and planar distribution result map. DETAILED DESCRIPTION

[0065] The following non-limiting examples can make those of ordinary skill in the art understand the present invention more comprehensively, but do not limit the present invention in any way. The following content is merely an exemplary description of the scope of the present invention, and those skilled in the art can make various changes and modifications to the present invention according to the disclosed content, and it should also belong to the scope of the present invention. When the embodiment gives a numerical range, it should be understood that, unless otherwise specified in the present invention, the two endpoints of each numerical range and any numerical value between the two endpoints can be selected. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meanings as those of ordinary skill in the art to which the present invention belongs. The present invention will be further described below in the form of specific embodiments.

[0066] Example 1

[0067] A method for predicting sweet spots in tight oil and gas reservoirs using prestack high-resolution inversion under multi-dimensional information constraints. Figure 1 The flowchart comprises the steps of:

[0068] Step S1: Establish an elastic attribute model based on the elastic attribute curve of the seismic interpretation layer and the logging data, and calibrate the elastic attribute model based on the geological and sedimentary characteristics.

[0069] Starting from the well logging data, make full use of the key wells in the work area, use spatial interpolation, and establish an elastic property model. Among them, the well logging data used for model interpolation are elastic well logging curves directly or indirectly related to geological sweet spots and engineering sweet spots, including P-wave velocity-P-wave impedance, S-wave velocity-P-S wave velocity ratio, Young's modulus-Poisson's ratio and other logging curve data information. Key wells are representative wells in the study area with complete logging curves. When the known wells are sparsely distributed in the study area or cannot reflect the spatial distribution trend of the target geological body, reservoir, and oil and gas content due to the skewed distribution of well locations, pseudo wells are added. The pseudo wells should meet the requirements that the interpolation attribute model involved in the establishment conforms to the plane distribution law of the geological characteristics of the study area.

[0070] Under the support of logging data, a geological model is established that associates lithology, physical properties, and hydrocarbon content. The plane and profile laws of the model are extracted, and the functional relationship between elastic properties and geological trends and sedimentary characteristics is established. Repeated iterations are performed, and pseudo wells are added when necessary until the plane characteristics of the elastic properties match the geological characteristics. The model is input into the inversion engine as the data basis for prestack inversion. Figure 2a The present invention provides the geological, lithological, physical and hydrocarbon attribute curves.

[0071] Figure 2b The present invention provides a probability density function statistical analysis diagram of elastic properties of different lithologies; the ordinates in the nine figures are Frequency (probability), and each row of the abscissas is, from left to right: P-Veloctiy (P-wave velocity), S-Veloctiy (S-wave velocity), Vp / Vs (P-wave velocity ratio), P-Impedance (P-wave impedance), S-Impedance (S-wave impedance), Murho (shear modulus), Poisson's ratio, Youngs modulus (Young's modulus), and Lambda rho (Lame coefficient).

[0072] Figure 2c The elastic property cross-plots of different lithologies of the present invention are as follows: I. P-wave impedance and S-wave impedance cross-plots, with the ordinate being S-Impedance, g / cm 3* m / s, the horizontal axis is the longitudinal wave impedance (P-Impedance, g / cm 3* m / s); Ⅱ P-wave impedance and P-S wave velocity ratio cross plot, the ordinate is the P-S wave velocity ratio (Vp / Vs), the abscissa is the P-wave impedance (P-Impedance, g / cm 3* m / s); ⅢPoisson's ratio intersects with Young's modulus, and the ordinate is Young's modulus (Youngsmodulus, N / m 2 ), the horizontal axis is Posson's ratio; IV The Lame coefficient intersects with the shear modulus, the vertical axis is the Lame coefficient, and the horizontal axis is the shear modulus.

[0073] Step S2: Use the corrected velocity field to convert the angle gathers, test stacking schemes at different angles, and perform pre-stack inversion tests on the well line to determine the angle-based stacking schemes.

[0074] On the one hand, strict quality control is performed on the pre-stack time migration gathers, and at the same time, the velocity of the pre-stack time migration is input, and the velocity field is corrected for well control to ensure the accuracy of the velocity field. Among them, strict quality control is performed on the pre-stack seismic gathers by checking whether the flattening degree of the pre-stack seismic gathers meets the requirements of AVO analysis, and whether the amplitude energy compensation of the wellside seismic gathers can accurately reflect the true relationship between the formation and lithology interface changes represented by the logging reflection coefficient. If the above conditions are not met, it is necessary to return to the relevant processing steps of the seismic gathers for gather optimization. The optimization process is to optimize the amplitude attributes of the actual seismic gathers with the wellside forward modeling gathers as the target, until the seismic amplitude of the actual gather target layer is comparable to the amplitude of the wellside forward modeling gathers with the offset distance. The specific processing and quality control steps include the following steps:

[0075] (1) Quality control of amplitude-energy relationship in time domain and processing of amplitude-energy gain of spherical diffusion in time domain;

[0076] (2) Spatial domain amplitude-energy relationship quality control and spatial domain filtering, multiple interference removal and other processing;

[0077] (3) Quality control of the relative relationship of the offset domain amplitude and processing of the offset domain amplitude energy gain;

[0078] (4) Quality control and consistency processing of AVO amplitude attributes.

[0079] On the other hand, the seismic stacking / or offset velocity field is converted into the layer velocity field, and corrected under well control and layer control to ensure the accuracy of the layer velocity field. The corrected velocity field is used to convert the angle gather to test the stacking schemes of different angles (under the conditions of wide azimuth acquisition and long enough offset distance (wide azimuth acquisition mainly refers to the offset distance and coverage times of acquisition, both of which are larger than conventional acquisition, and the underground structure imaging accuracy obtained is higher. The offset distance is the distance between the shot point and the receiver point collected in the field), the maximum incident angle of the target layer reflection wave reaches more than 40°, which exceeds the lower limit of the angle of 30° incident angle required for pre-stack inversion to obtain a stable inversion body. Under this condition, more than 3 partial angle stacking schemes can be tested), and the best angle stacking scheme is determined by testing the stability of the pre-stack inversion results of the key well-connected lines.

[0080] Step S3: Conduct geostatistical simulation for the target strata in the study area, mainly to determine the geostatistical parameters of high-resolution inversion under the constraints of multi-dimensional information.

[0081] Geostatistical parameters mainly include probability density functions and variograms of various elastic properties of different lithologies, which are mainly obtained through logging curves of known wells. By analyzing the geostatistical parameters of various elastic properties under different lithologies, the geological laws are mastered, and the set of parameters is imported into the geostatistical simulation engine as input data to simulate the reservoir lithology and elastic properties, and the profiles and planes of the simulated attribute bodies are quality controlled. The key element of quality control is that the simulated lithology body and elastic attribute body can roughly invert the scale, size and thickness of the target geological body and reservoir in the study area. This process is an iterative process, and finally the geostatistical parameters with the best simulation effect are involved in the inversion.

[0082] Step S4: Based on the above-mentioned various elastic attribute models, the optimized angle-based stacking seismic data and their participation weights (the angle-based pre-stack seismic data body participation in inversion weights can be input as numerical data through absolute / relative signal-to-noise ratio values), and the optimized geostatistical parameters, high-resolution pre-stack inversion is performed to ultimately obtain multiple elastic attribute bodies that can directly or indirectly characterize geological sweet spots and engineering sweet spots.

[0083] On the basis of inputting numerical geological information (various corrected elastic property models that can reflect geological laws), geostatistical parameters that reflect lithology proportion, spatial development scale and scale of geological bodies are combined, and finally, under the constraints of pre-stack angle stacking seismic data body and its participation weights, pre-stack inversion test is carried out and the above types of input data participating in the inversion are further adjusted. Finally, the input data is inverted to obtain multiple elastic property bodies that can characterize geological sweet spots and engineering sweet spots.

[0084] Step S5: Based on the above-mentioned multiple inversion elastic attribute bodies (and their derived mechanical properties) that directly or indirectly characterize geological sweet spots and engineering sweet spots, the sweet spot development pattern, development scale, and spatial distribution of the target layer in the study area are characterized at multiple scales to provide detailed and accurate data and model support for exploration and development.

[0085] Application Example 1

[0086] The prediction method of the present invention (Example 1) is applied to the prediction of favorable areas of geological and engineering sweet spots:

[0087] Compared with the elastic attribute model in the prediction method of this application, the conventional inversion elastic model interpolation is more accurate, especially for large study areas (greater than 1000km 2 ), the geological and sedimentary laws change. Due to the limited number of wells or the skewed distribution of well locations, the changes in the overall geological trend cannot be reflected. This makes the plane law of the interpolation model usually inconsistent with the geological law, such as Figure 3 Comparison chart shown.

[0088] In order to make the elastic attribute model more consistent with geological laws and integrate geological and lithological information into the elastic attribute model, it is necessary to use geological information, such as Figure 4 As shown in the figure, the elastic attribute model is corrected based on the interpolation model of the lithology and physical properties of the well logging data, and an elastic attribute model consistent with the plane law and the geological law is obtained, as shown in the figure. Figure 5 shown.

[0089] High-resolution prestack inversion technology under multi-dimensional information constraints, targeting the characteristics of low porosity and low permeability in the target area and rapid lateral changes in sedimentary subfacies, on the one hand, digitizes the geological, sedimentary, and lithological information as prior information for prestack inversion and inputs it into the inversion engine. On the other hand, a fine stratigraphic grid is built according to the thickness of the thinnest reservoir in the target layer, and elastic parameter seed points are calculated at each grid point and prestack synthetic records are formed through wavelets. The optimal elastic parameter seed points are determined by continuous optimization to achieve a high degree of match with prestack seismic data. This technology can obtain a high-resolution elastic model that fully integrates multiple attributes of geological, logging, and seismic information. The multi-attribute elastic parameter model can be converted into key attributes required to evaluate geological sweet spots and engineering sweet spots, such as physical properties, oil and gas content, and brittleness.

[0090] The prediction method and application described in the embodiments and application examples of the present invention are as follows:

[0091] (1) It can clearly identify thin reservoirs of the target layer, with a resolution 3-4 times higher than conventional inversion;

[0092] It is mainly achieved through geostatistical algorithms. The most common type of algorithm on the market is the conjugate gradient global error minimization optimization method. The Gaussian global optimization method, Monte Carlo method, etc. have been used in the field of geology and have worked well. With the support of the algorithm, geostatistical parameters need to be obtained. Geostatistical parameters mainly include probability density functions and variation functions of various elastic properties of different lithologies. Such parameters are mainly obtained through logging curves of known wells. By analyzing the geostatistical parameters of various elastic properties under different lithologies and mastering their geological laws, this set of parameters is imported as input data into the geostatistical simulation engine to simulate the reservoir lithology and elastic properties, and to perform quality control on the profiles and planes of the simulated attribute bodies. The process is as follows: Figure 6 As shown. The key element of quality control is that the simulated lithology and elastic properties can roughly reflect the scale, size and thickness of the target geological body and reservoir in the study area. This process is an iterative process, and the geostatistical parameters with the best simulation effect will eventually be used for inversion. Geostatistical inversion can improve the resolution of target geological bodies by 3-4 times based on conventional inversion, such as Figure 7 shown.

[0093] (2) The inverted reservoir spatial distribution law is combined with geological laws;

[0094] Conventional inversion elastic model interpolation, especially for large study areas (greater than 1000km 2 ), the geological and sedimentary laws change. Due to the limited number of wells or the skewed distribution of well locations, the plane law of the interpolation model is usually inconsistent with the geological law. In order to make the initial elastic model more consistent with the geological law, the geological and lithological information is integrated into the elastic model. It is necessary to use the data related to the geological information to correct the initial elastic model and obtain an elastic model with consistent plane law and geological law, such as Figure 8 Only by carrying out inversion based on such an initial model can we obtain the inversion results of composite geological laws.

[0095] (3) Multiple elastic properties can be obtained. On the basis of calculating the geological sweet spot, the engineering sweet spot parameters can also be calculated. The geological sweet spot is a high-quality area of ​​tight sandstone gas resources, and the engineering sweet spot is an area where complex fractures are easily formed by fracturing. Only by combining the "geological-engineering" double sweet spots and fully considering the strong heterogeneity of the reservoir can the wellbore trajectory, fracturing location and process design be effectively guided to extract tight sandstone gas in an efficient and low-cost manner.

[0096] From the perspective of geophysical research, the key is to obtain as many attributes as possible that can directly and indirectly accurately reflect geological sweet spots and engineering sweet spots, including lithological attributes, physical attributes, elastic attributes, etc. These attributes can be obtained by the prediction method described in the present invention, as shown in FIG9 . Figure 9a The geological sweet spots include structural sedimentation, lithology, physical properties and hydrocarbon content; Figure 9b The engineering sweet spots include brittleness index, stress pressure and fracture cracks. By integrating multiple attributes, a comprehensive analysis of geological and engineering sweet spots can be conducted to ultimately determine favorable exploration and development areas, as shown in Figure 10, and to achieve a graded evaluation of them. Fig.10a It is a high-resolution multi-attribute inversion result used for the attribute result map of the prediction of favorable geological and engineering sweet spots; Fig.10b It is a high-resolution multi-attribute inversion result for the preliminary distribution result map of comprehensive rating indicators of geological and engineering sweet spots; Fig.10c It is a high-resolution multi-attribute inversion result used for comprehensive prediction of geological and engineering sweet spots and plane distribution result map.

[0097] The results of the present invention can provide data support for the exploration and development process of unconventional oil and gas reservoirs to a great extent. If promoted and applied in unconventional oil and gas reservoir blocks, it can greatly improve the efficiency of exploration and development and contribute hard-core support from the geophysical field to increasing reserves and production.

[0098] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A method for predicting sweet spots in tight oil and gas reservoirs using prestack high-resolution inversion. It is characterized in that Includes steps: S1. Establishing an elastic attribute model and a geological model according to the attribute curve and using a spatial interpolation algorithm, and correcting the model according to geological and sedimentary characteristics; S2. Use the corrected velocity field to convert the angle gathers, test the stacking schemes at different angles, and perform pre-stack inversion tests on the well line to determine the stacking schemes at different angles. S3. Conduct geostatistical simulation for the target strata in the study area and determine the geostatistical parameters for high-resolution inversion under multi-dimensional information constraints; S4, performing high-resolution prestack inversion according to the model described in step S1, the angle stacking scheme described in step S2, and the inverted geostatistical parameters obtained in step S3, to obtain an elastic property body characterizing the geological sweet spot and the engineering sweet spot; S5. Based on the elastic properties of the above-mentioned geological sweet spots and engineering sweet spots, the target layer in the study area is characterized at multiple scales.

2. The prediction method according to claim 1, It is characterized in that Step S1 is specifically as follows: make full use of key wells in the work area, start from logging data, and use spatial interpolation algorithm to establish an elastic attribute model under layer control; establish a geological model associated with lithology, physical properties, and oil and gas properties under the condition of well logging data support, and extract the plane attributes of the geological model to establish its correlation with geological trends and sedimentary characteristics; use this type of geological model to correct the elastic attribute model, add pseudo wells when necessary, and correct until the plane characteristics of the elastic attributes roughly match the plane characteristics of the geological attributes; input the corrected elastic attribute model into the inversion engine as the data basis for pre-stack inversion.

3. The prediction method according to claim 1, It is characterized in that In step S1, the attribute curve includes logging data, geology, lithology, physical properties and oil and gas properties; the logging data includes logging curves directly or indirectly related to geological sweet spots and engineering sweet spots.

4. The prediction method according to claim 3, It is characterized in that The logging curve includes longitudinal wave velocity, shear wave velocity, longitudinal and shear wave velocity ratio, longitudinal wave impedance, shear wave impedance, shear modulus, Poisson's ratio, Young's modulus and Lame coefficient.

5. The prediction method according to claim 4, It is characterized in that The logging curve includes longitudinal wave impedance-transverse wave impedance, transverse wave velocity-ratio of longitudinal and transverse wave velocity, Young's modulus-Poisson's ratio, longitudinal wave velocity-ratio of longitudinal and transverse wave velocity, Lame coefficient and shear modulus.

6. The prediction method according to claim 1, It is characterized in that In step S1, the interpolation algorithm includes inverse distance weighted method, triangle interpolation method and Kriging interpolation method; in step S1, the use of the interpolation algorithm means: under the control constraint of the geological layer, the corresponding logging curve is interpolated between layers.

7. The prediction method according to claim 1, It is characterized in that In step S1, the elastic property model includes an interpolation model of the logging curves of P-wave velocity, S-wave velocity, P-wave velocity ratio, P-wave impedance, S-wave impedance, shear modulus, Poisson's ratio, Young's modulus, and Lame coefficient.

8. The prediction method according to claim 1, It is characterized in that Step S2 is specifically as follows: strictly quality control the pre-stack time migration gather; convert the seismic stacking / or migration velocity field into the layer velocity field, and perform well control and layer control correction to ensure the accuracy of the layer velocity field; use the corrected layer velocity field to convert the offset gather into the angle gather, test the stacking schemes at different angles, and perform pre-stack inversion test for key well-connected lines; and obtain the angle-divided stacking scheme by testing the stability of the pre-stack inversion of key well-connected lines.

9. The prediction method according to claim 8, It is characterized in that In step S2, the prestack time migration gather is seismic data processing data, which is a seismic gather obtained after being processed by a prestack time migration module; in step S2, the stability of the prestack inversion is the stability of the inverted elastic properties under the angle-dividing scheme.

10. The prediction method according to claim 8, It is characterized in that In step S2, the quality control is specifically as follows: detecting whether the flattening degree of the target layer meets the requirements of AVO analysis, and whether the amplitude energy compensation of the near-well seismic gathers under well control can accurately reflect the true relationship between the formation and lithology interface changes represented by the logging reflection coefficient; if the above conditions are met, the quality control is qualified and the next step is carried out; if the above conditions are not met, it is necessary to return to the relevant processing steps of the seismic gathers for gather optimization processing.

11. The prediction method according to claim 1, It is characterized in that In step S3, the geostatistical parameters of the geostatistical simulation include probability density functions and variograms of various elastic properties of different lithologies.

12. The prediction method according to claim 1, It is characterized in that Step S3 is specifically as follows: obtaining geostatistical parameters through logging curves of known wells, mastering geological laws by analyzing geostatistical parameters of various elastic properties under different lithologies, importing this set of parameters as input data into the geostatistical simulation engine to simulate reservoir lithology and elastic properties, and performing quality control on the profiles and planes of the simulated property body.

13. The prediction method according to claim 12, It is characterized in that In step S3, the quality control is the process of simulating the lithology and elastic properties to roughly invert the scale, size and thickness of the target geological body and reservoir in the study area; the above process is an iterative process, and ultimately the geostatistical parameters with the best simulation effect are involved in the inversion.

14. The prediction method according to claim 1, It is characterized in that In step S4, the angle-divided stacking scheme refers to the angle-divided stacking seismic data and its participation weight in the angle-divided stacking scheme; the participation weight refers to the weight of the angle-divided pre-stack seismic data body participating in the inversion, which can be input as numerical data through the absolute / relative signal-to-noise ratio value, and the signal-to-noise ratio is the ratio of the seismic effective signal to the noise.

15. The prediction method according to claim 1, It is characterized in that In step S4, the elastic property body includes a longitudinal wave velocity body, a longitudinal wave impedance body, a transverse wave velocity body, a transverse wave impedance body, a longitudinal and transverse wave velocity ratio body, a Poisson's ratio body and a Young's modulus body.

16. The prediction method according to claim 1, It is characterized in that Step S4 is specifically as follows: based on the input numerical geological information, the geostatistical parameters that reflect the lithology ratio, spatial development scale and scale of the geological body are combined, and finally, under the constraints of the pre-stack angle stacking seismic data body and its participation weights, a pre-stack inversion test is performed and the various types of input data involved in the inversion are adjusted, and finally the input data is inverted to obtain multiple elastic property bodies that can characterize geological sweet spots and engineering sweet spots.

17. The prediction method according to claim 16, It is characterized in that In step S4, the geological information refers to various corrected elastic property models that can reflect geological laws.

18. The prediction method according to claim 1, It is characterized in that Step S5 is specifically as follows: based on the elastic properties of the geological sweet spots and engineering sweet spots and their derived mechanical properties, a multi-scale characterization of the sweet spot development pattern, development scale, and spatial distribution of the target layer in the study area is performed.

19. Application of the prediction method according to any one of claims 1 to 18 in the prediction of favorable geological and engineering sweet spots.

20. The elastic property model and / or elastic property body established by the prediction method according to any one of claims 1 to 18.

21. Application of the elastic property model and / or elastic property body according to claim 20 in the prediction of favorable geological and engineering sweet spots.

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