A method and system for seismic prediction of biological limestone reservoirs

Abstract

The application discloses a kind of biological limestone reservoir seismic prediction methods, comprising the following steps: according to the possibility of whether the research target has biological limestone reservoir development of geological understanding of research area judgment;Rock physics analysis is carried out to the biological limestone reservoir section and its surrounding rock of well logging data of research area, the applicability of longitudinal wave impedance, P-S wave velocity ratio in biological limestone reservoir prediction is studied;The velocity spectrum and velocity profile characteristics of biological limestone reservoir are analyzed, to determine whether the possible biological limestone reservoir development area is low-speed characteristic compared with surrounding rock, to further reduce the multiple solutions of reservoir prediction;Forward modeling is carried out, the corresponding relationship between the distribution characteristics of biological limestone reservoir in forward model and seismic profile is discussed, and the possibility of biological limestone reservoir development is verified;Using the seismic data collected in the research area, prestack inversion and bayesian discriminant analysis based thereon are carried out, and the planar and profile distribution of biological limestone reservoir is predicted by longitudinal wave impedance and P-S wave velocity ratio double parameters.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, specifically to a seismic prediction method and system for biogenic limestone reservoirs in the exploration stage. Background Technology

[0002] The climate was hot and humid during the depositional period of the Sha-1 and Sha-2 members in the Bohai Bay Basin, resulting in the development of lacustrine carbonate rocks. Carbonate reservoirs are less affected by burial depth and exhibit strong dissolution, making them an important type of deep reservoir in the Bohai Bay Basin. Among them, bioclastic limestone (hereinafter referred to as bioclastic limestone) is generally associated with the abundant reproduction of organisms (especially snails), often exhibiting well-developed porosity and high permeability, making it a good reservoir. In recent years, bioclastic limestone reservoirs in lacustrine carbonate rocks have become an important new exploration area in the Sha-1 and Sha-2 members of the Bohai Bay Basin.

[0003] The formation of biogenic limestone is controlled by factors such as paleotopography, provenance, water depth, and substrate. Environments with relatively flat terrain, few terrigenous clastic rocks, shallow water, and hard substrate are conducive to biogenic limestone formation. Due to the harsh growth conditions, narrow facies bands, rapid facies transitions, and unclear seismic reflection characteristics, conventional methods often characterize the distribution range of biogenic limestone anomalies through seismic attributes such as waveform classification and frequency-division RGB fusion. However, seismic attributes are insufficient for analyzing reservoir parameters, making it difficult to guarantee a detailed characterization of its thickness and distribution. Inversion methods can typically provide a detailed characterization of reservoir thickness; however, when using inversion methods for reservoir prediction, seismic data, being a limited bandwidth signal (approximately 6-60 Hz), lacks sufficient information for low-frequency components below 6 Hz. Therefore, well logging interpolation is often used to supplement low-frequency information. However, well logging interpolation methods are greatly affected by well data, and in the exploration phase with limited well data, there is uncertainty in the low-frequency model.

[0004] Overall, there is currently limited research in the industry on geophysical prediction of biogenic limestone reservoirs, and there is no mature geophysical method that can predict the distribution of biogenic limestone reservoirs during the exploration phase. Summary of the Invention

[0005] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention provides a seismic prediction method for biogenic limestone reservoirs, which aims to solve the problem of multiple solutions in existing single carbonate reservoir prediction methods by comprehensively applying multiple geophysical methods such as rock physics analysis, velocity data analysis, forward modeling, and pre-stack inversion. The final reservoir prediction results provide technical support for comprehensive target evaluation and well location deployment.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a seismic prediction method for biogenic limestone reservoirs, comprising the following steps:

[0008] Based on the geological understanding of the study area, determine whether the research target has the potential for the development of biogenic limestone reservoirs. If it is possible for biogenic limestone reservoirs to develop, proceed to the next step.

[0009] Rock physics analysis was conducted on the biogenic limestone reservoir section and its surrounding rocks based on well logging data in the study area. The applicability of P-wave impedance and P-wave-to-S-wave velocity ratio in biogenic limestone reservoir prediction was studied. If applicable, the next step was taken.

[0010] Analyze the velocity spectrum and velocity profile characteristics of the biogenic limestone reservoir to determine whether the possible biogenic limestone reservoir development zone has low velocity characteristics compared with the surrounding rock, so as to further reduce the ambiguity of reservoir prediction. If it has low velocity characteristics, proceed to the next step.

[0011] Conduct forward modeling, discuss the correspondence between the distribution characteristics of biogenic limestone reservoirs in the forward model and the seismic profile, and verify the possibility of biogenic limestone reservoir development. If the possibility of biogenic limestone reservoir development is verified, proceed to the next step.

[0012] Using seismic data collected in the study area, pre-stack inversion and Bayesian discriminant analysis were carried out. The planar and profile distribution of biogenic limestone reservoirs was predicted by the two parameters of P-wave impedance and P-wave-S-wave velocity ratio.

[0013] As a preferred option: the geological understanding refers to paleogeographic reconstruction and the collection and analysis of drilled data, specifically including:

[0014] Paleomorphological restoration: Paleomorphological restoration is performed on the target layer of the research object. The commonly used method is layer flattening. The layer flattening method first selects the top and bottom surfaces of the target layer, and then calculates the time difference between the top and bottom surfaces. The flattened top surface is the sea / lake level during the original deposition period, and the bottom surface morphology is the relative paleomorphology of the target layer during the deposition period.

[0015] Data collection and analysis of drilled wells: All logging data in the study area will be collected, including logging curve data, logging data and core thin section data. Based on the logging data and core thin section data, it will be determined whether any wells have encountered biogenic limestone in the target layer. If so, it proves that the target layer has the potential to develop biogenic limestone reservoirs. If not, the method ends.

[0016] Preferably, the rock physical analysis specifically includes:

[0017] The collected logging curve data were used to interpret the logging curve data within the study area to obtain the clay content, porosity, water saturation and logging interpretation conclusions, which were used to interpret the lithology, physical properties and hydrocarbon content of reservoir parameters.

[0018] The collected logging data within the study area were analyzed to obtain the measurement depth ranges of biogenic limestone reservoirs and other lithological distributions, which were used for lithological interpretation.

[0019] By examining the P-wave velocity, density, P-wave impedance, clay content, porosity, water saturation, well logging interpretation conclusions, and well logging lithology, the measurement depth ranges of the biogenic limestone reservoir and its surrounding rocks are identified. Furthermore, the velocity and density parameters of the biogenic limestone reservoir and its surrounding rocks are extracted to prepare data for subsequent forward modeling.

[0020] The P-wave impedance data and P-wave velocity ratio data of the biogenic limestone reservoir section and its surrounding rocks are interpolated to obtain the P-wave impedance and P-wave velocity ratio interpolation diagram in the well logging curve. It is determined whether the interpolation of the above two parameters can distinguish between other lithologies in the biogenic limestone reservoir and the surrounding rocks. If they can be distinguished, the subsequent pre-stack inversion can be carried out; otherwise, the method ends.

[0021] Preferably, the velocity spectrum and velocity profile of the biogenic limestone reservoir are obtained through multiple rounds of tomographic velocity update iterations. The specific steps for these multiple rounds of tomographic velocity update iterations are as follows:

[0022] S301. Perform time-domain noise suppression, multiple suppression, and common-depth point gather velocity analysis on the shot gather data obtained from seismic acquisition to obtain the time-domain velocity spectrum. By picking up the energy clusters of the velocity spectrum, obtain the velocity value at each time sampling point on the selected common-depth point.

[0023] S302. Convert the velocity values ​​at each time sampling point of the common depth point selected in the time domain to the depth domain to obtain the velocity values ​​in the depth domain. By interpolation, convert the velocity values ​​in the depth domain into a velocity profile in the depth domain to obtain the initial depth domain velocity profile data.

[0024] S303. Using the initial depth domain velocity profile data, perform pre-stack depth migration to obtain common imaging point gathers;

[0025] Pre-stack depth migration can relocate seismic information received on the ground to the actual depth location underground, and obtain common imaging point gathers that reflect the actual depth location underground.

[0026] S304. Determine if the common imaging point gather is flattened: If it is not flattened, pick the remaining depth difference of the common imaging point gather, add the structural layer constraint information, and then perform tomographic velocity inversion to update the initial depth domain velocity profile data.

[0027] The chromatography velocity inversion method is as follows:

[0028] L·Δs=ΔZ

[0029] In the formula, s is the slowness; Δs is the slowness correction; L is the wave ray propagation path; ΔZ is the remaining depth difference; the initial depth domain velocity data is updated by calculating the slowness correction Δs.

[0030] S305. Using the updated initial depth domain velocity profile, perform pre-stack depth migration. The accuracy of the depth domain velocity profile is judged by the flattening of the common imaging point gathers: if the gathers are flattened, the velocity profile is accurate and can be used for biogenic limestone reservoir prediction; if the gathers are not flattened, return to step S304, iteratively update the depth domain velocity profile, and finally flatten the common imaging point gathers to obtain an accurate depth domain velocity profile.

[0031] Preferably, the forward modeling is achieved by first analyzing and simulating the propagation process and laws of seismic waves in the subsurface medium within a two-dimensional geological body, and then implementing it through forward modeling of the vertical propagation of seismic waves. Specific steps include:

[0032] Observe typical seismic profiles that may contain biogenic limestone, and construct a preliminary two-dimensional geological model by manually outlining the distribution range of biogenic limestone and surrounding rocks;

[0033] Based on the velocity and density of typical biogenic limestone reservoir sections and surrounding rocks encountered in wells within the study area, the longitudinal wave velocity and density parameters of different layers in the two-dimensional geological body model were filled.

[0034] The dominant frequency of a typical seismic profile that may contain biogenic limestone was analyzed. Based on this dominant frequency, a Ricker wavelet was designed. The vertical propagation of the seismic wave was simulated using a two-dimensional geological body model to obtain the forward-modeled seismic profile.

[0035] The forward-modeled seismic profile is compared with a typical seismic profile that may contain biogenic limestone. If they are similar, it indicates that the two-dimensional geological model is reasonably designed and the parameters used to fill the model are reasonable and reliable, further verifying the possibility of biogenic limestone reservoir development. If they are not similar, the method ends.

[0036] Preferably, the pre-stack inversion is a pre-stack constrained sparse pulse inversion technique commonly used in industry, which calculates the P-wave impedance and P-wave / S-wave velocity ratio data.

[0037] As a preferred embodiment, the Bayesian discriminant analysis specifically includes: determining the probability density functions of the P-wave impedance and P-wave velocity ratio corresponding to biogenic limestone reservoirs and other lithologies, as well as the proportion of biogenic limestone reservoirs and other lithologies in the total data, based on the scatter distribution range of the P-wave impedance and P-wave velocity ratio cross plot in the well logging curves. Using this as input, the P-wave impedance and P-wave velocity ratio data volume calculated by pre-stack inversion is used to obtain the posterior probability through Bayesian discriminant analysis, calculate the most likely lithofacies type of a certain point in three-dimensional space, and finally determine the most likely distribution range of biogenic limestone reservoirs.

[0038] Secondly, the present invention provides a seismic prediction system for biogenic limestone reservoirs, comprising:

[0039] The first processing unit is used to determine, based on the geological understanding of the study area, whether the research target has the potential for the development of biogenic limestone reservoirs.

[0040] The second processing unit is used to conduct rock physical analysis on the biogenic limestone reservoir section and its surrounding rocks in the well logging data of the study area, and to study the applicability of P-wave impedance and P-wave-S-wave velocity ratio in biogenic limestone reservoir prediction.

[0041] The third processing unit is used to analyze the velocity spectrum and velocity profile characteristics of biogenic limestone reservoirs, determine whether the possible biogenic limestone reservoir development zone is low-velocity compared with the surrounding rock, and further reduce the ambiguity of reservoir prediction.

[0042] The fourth processing unit is used to conduct forward modeling, discuss the correspondence between the distribution characteristics of biogenic limestone reservoirs in the forward model and the seismic profile, and verify the possibility of biogenic limestone reservoir development.

[0043] The fifth processing unit is used to conduct pre-stack inversion and Bayesian discriminant analysis based on the seismic data collected in the study area, and to predict the planar and profile distribution of biogenic limestone reservoirs using the two parameters of P-wave impedance and P-wave-S-wave velocity ratio.

[0044] Thirdly, the present invention provides a computer storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the seismic prediction method for biogenic limestone reservoirs described in the first aspect of the present invention.

[0045] Fourthly, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the seismic prediction method for biogenic limestone reservoirs described in the first aspect of the present invention.

[0046] The present invention has the following advantages due to the adoption of the above technical solutions:

[0047] 1. The seismic prediction method for biogenic limestone reservoirs provided by this invention can calculate the reservoir parameters of biogenic limestone and achieve a fine characterization of the thickness and distribution of biogenic limestone.

[0048] 2. This invention solves the problem of multiple solutions in existing single biogenic limestone reservoir prediction methods by comprehensively applying multiple geophysical methods such as rock physical analysis, forward modeling, velocity data analysis, pre-stack inversion, and Bayesian discriminant analysis.

[0049] 3. This invention, through meticulous work, can provide effective technical support for comprehensive target evaluation and well site deployment. Attached Figure Description

[0050] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:

[0051] Figure 1 This is a flowchart of a seismic prediction method for biogenic limestone reservoirs provided in an embodiment of the present invention;

[0052] Figure 2 This is a logging curve of a typical well that encountered a biogenic limestone reservoir;

[0053] Figure 3 It is a cross-plot of the P-wave impedance and the P-wave / S-wave velocity ratio in the well logging curve;

[0054] Figure 4 This is a velocity spectrum profile of biogenic limestone;

[0055] Figure 5 This is a velocity profile of biogenic limestone;

[0056] Figure 6 This is a comparison between forward-modeled seismic profiles and seismic profiles that may contain biogenic limestone reservoirs;

[0057] Figure 7 This is a profile of biolithic limestone obtained from Bayesian discriminant analysis. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0059] The seismic prediction method for biogenic limestone reservoirs provided by this invention includes the following steps: determining whether the research target has the potential for biogenic limestone reservoir development based on geological understanding of the study area; conducting rock physical analysis on the biogenic limestone reservoir section and its surrounding rocks based on well logging data of the study area, and studying the applicability of P-wave impedance and P-wave / S-wave velocity ratio in biogenic limestone reservoir prediction; analyzing the velocity spectrum and velocity profile characteristics of the biogenic limestone reservoir, determining whether the possible biogenic limestone reservoir development zone has low velocity characteristics compared with the surrounding rocks, and further reducing the ambiguity of reservoir prediction; conducting forward modeling, discussing the correspondence between the distribution characteristics of biogenic limestone reservoirs and seismic profiles in the forward model, and verifying the possibility of biogenic limestone reservoir development; using the seismic data collected in the study area, conducting pre-stack inversion and Bayesian discriminant analysis based on it, and predicting the planar and profile distribution of biogenic limestone reservoirs through the dual parameters of P-wave impedance and P-wave / S-wave velocity ratio. This invention solves the problem of multiple solutions in existing single carbonate reservoir prediction methods by comprehensively applying various geophysical methods such as rock physics analysis, velocity data analysis, forward modeling, and pre-stack inversion. The final reservoir prediction results provide technical support for comprehensive target evaluation and well location deployment.

[0060] The seismic prediction method and system for biogenic limestone reservoirs provided in the embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0061] Example 1:

[0062] Please see Figure 1 This embodiment provides a seismic prediction method for biogenic limestone reservoirs, comprising the following steps:

[0063] S100. Based on the geological understanding of the study area, determine whether the research target has the potential for the development of biogenic limestone reservoirs. If biogenic limestone reservoirs are likely to develop, proceed to the next step.

[0064] S200. Conduct rock physics analysis on the biogenic limestone reservoir section and its surrounding rocks based on the well logging data of the study area, and study the applicability of elastic parameters such as P-wave impedance and P-wave velocity ratio in biogenic limestone reservoir prediction. If applicable, proceed to the next step.

[0065] S300. Analyze the velocity spectrum and velocity profile characteristics of the biogenic limestone reservoir to determine whether the possible biogenic limestone reservoir development zone has low velocity characteristics compared with the surrounding rock, further reducing the ambiguity of reservoir prediction. If it has low velocity characteristics, proceed to the next step.

[0066] S400. Conduct forward modeling, discuss the correspondence between the distribution characteristics of biogenic limestone reservoirs in the forward model and the seismic profile, and verify the possibility of biogenic limestone reservoir development. If it is verified that biogenic limestone reservoirs may develop, proceed to the next step.

[0067] S500. Using seismic data collected in the study area, pre-stack inversion and Bayesian discriminant analysis based on it were carried out. The planar and profile distribution of biogenic limestone reservoirs was predicted by the dual parameters of P-wave impedance and P-wave-S-wave velocity ratio.

[0068] In the above embodiments, preferably, the geological understanding in step S100 refers to paleogeographic reconstruction and the collection and analysis of drilled well data, specifically including:

[0069] S101. Paleogeomorphological Restoration: Paleogeomorphological restoration is performed on the target stratum of the research objective. This method commonly uses stratum flattening. First, the top and bottom surfaces of the target stratum are selected. Then, the time difference between the top and bottom surfaces is calculated. The flattened top surface represents the sea (lake) level during the original depositional period, and the bottom surface morphology represents the relative paleogeomorphology of the target stratum during its depositional period. Paleogeomorphology has a significant impact on the distribution of biogenic limestone reservoirs. Underwater highlands are the most favorable areas for the development of biogenic limestone reservoirs, and their location on a specific paleogeomorphological map is a relatively high point.

[0070] S102. Collection and Analysis of Drilled Well Data: Collect all logging data in the study area, including logging curve data, well logging data, and core thin section data. Based on the well logging data and core thin section data, determine whether any wells have encountered biogenic limestone in the target layer. If so, it proves that the target layer has the potential to develop biogenic limestone reservoirs. If not, the method ends.

[0071] In the above embodiments, preferably, the rock physical analysis in step S200 specifically includes:

[0072] S201. Using the logging curve data collected in step S102, the logging curve data within the study area are interpreted to obtain the clay content, porosity, water saturation, and logging interpretation conclusions (including interpretation conclusions for water layers, dry layers, oil layers, gas layers, etc.), which are used to interpret the lithology, physical properties, and hydrocarbon content of reservoir parameters.

[0073] S202. Analyze the logging data collected in step S102 within the study area to obtain the measurement depth range of the distribution of biogenic limestone reservoirs and other lithologies (such as sandstone, mudstone, limestone, igneous rocks, etc.), which are used for lithological interpretation;

[0074] S203. Examine the P-wave velocity, density, P-wave impedance, clay content, porosity, water saturation, well logging interpretation conclusions, and well logging lithology to find the measurement depth range of the biogenic limestone reservoir and its surrounding rock. Extract the velocity and density parameters of the biogenic limestone reservoir and its surrounding rock to prepare data for subsequent forward modeling.

[0075] S204. Cross-plot the P-wave impedance data and P-wave / S-wave velocity ratio data of the biogenic limestone reservoir section and its surrounding rocks to obtain the P-wave impedance and P-wave / S-wave velocity ratio cross-plot in the well logging curves (see [link]). Figure 3 The method involves determining whether the intersection of the two parameters can distinguish between the biogenic limestone reservoir and other lithologies in the surrounding rocks. If they can be distinguished, the subsequent pre-stack inversion can be performed; otherwise, the method ends. If the pre-stack inversion can be performed, the intersection plot can provide prior probability data for Bayesian discriminant analysis.

[0076] In the above embodiments, preferably, the P-wave to S-wave velocity ratio in step S200 is obtained from the S-wave velocity logging curve. If there is no S-wave velocity logging curve, S-wave prediction is required. The S-wave prediction method can adopt the classic GC formula method, that is, Greenberg and Castagna (1992) gave an expression for estimating the S-wave velocity using the P-wave velocity of multiple minerals and composite lithologies. In the formula, the S-wave velocity can be obtained by simply averaging the arithmetic mean and harmonic mean of the S-wave velocity of each constituent lithology:

[0077]

[0078]

[0079] In the formula, X i N represents the volumetric mineral composition of the rock; L represents the number of minerals constituting the rock; N represents the total mineral volume of the rock. i a is the multinomial index of component i; ij V represents the experimental regression coefficient; p V represents the longitudinal wave velocity of the rock. s The transverse wave velocity of the rock.

[0080] Castagna provides a representative P-wave / S-wave relationship for a single pure lithology, namely:

[0081]

[0082] Table 1 shows the regression coefficients for different lithologies. Based on the different lithology data obtained from the logging data analysis in step S202, different lithology regression coefficients are assigned to different logging curve depth segments to obtain the shear wave velocity logging curves.

[0083] Table 1 Regression coefficients for different lithologies

[0084]

[0085] In the above embodiments, preferably, the velocity spectrum and velocity profile of the biogenic limestone reservoir in step S300 are obtained through multiple rounds of tomographic velocity updates and iterations, resulting in high accuracy and thus providing evidence supporting the existence of biogenic limestone reservoirs. According to previous research, the P-wave velocity of the biogenic limestone framework is much higher than that of sandstone and mudstone. However, the P-wave velocities of the high-porosity biogenic limestone in the Sha-1 and Sha-2 sections of the Bohai Bay Basin, especially the high-porosity gas-bearing biogenic limestone reservoirs, are slightly lower than those of oil-bearing sandstone and mudstone, and even lower than those of other types of carbonate rocks and igneous rocks. Therefore, high-precision velocity data can provide a valid reference for predicting biogenic limestone reservoirs. Since seismic data is a signal with a limited bandwidth, ranging from approximately 6-60 Hz, information on low-frequency components below 6 Hz is insufficient. Therefore, inversion methods in reservoir prediction often use well logging interpolation to supplement low-frequency information for biogenic limestone reservoir prediction. However, well logging interpolation methods are greatly affected by well data and suffer from uncertainty in low-frequency models. By verifying velocity data, the problem of missing the 0-6Hz frequency range in seismic data can be compensated for, providing velocity information in the 0-2Hz range and providing more sufficient evidence to support reservoir prediction results.

[0086] The specific steps for the multi-round tomography rate update iteration are as follows:

[0087] S301. Perform time-domain noise suppression, multiple suppression, and common depth point (CDP) gather velocity analysis on the shot gather data obtained from seismic acquisition to obtain the time-domain velocity spectrum. By picking up the energy clusters of the velocity spectrum, obtain the velocity value at each time sampling point on the selected common depth point.

[0088] S302. Convert the velocity values ​​at each time sampling point of the common depth point selected in the time domain to the depth domain to obtain the velocity values ​​in the depth domain. By interpolation, convert the velocity values ​​in the depth domain into a velocity profile in the depth domain to obtain the initial depth domain velocity profile data.

[0089] S303. Using the initial depth domain velocity profile data, perform pre-stack depth migration to obtain common imaging point (CIG) gathers;

[0090] Pre-stack depth migration can relocate seismic information received on the ground to the actual depth location underground, and obtain common imaging point gathers that reflect the actual depth location underground.

[0091] S304. Determine if the common imaging point gather is flattened: If it is not flattened, pick the remaining depth difference of the common imaging point gather (the remaining depth from flattening), add the structural stratigraphic constraint information (obtain stratigraphic data through structural interpretation, add well logging velocity data for correction to obtain structural stratigraphic constraint information), and then perform tomographic velocity inversion to update the initial depth domain velocity profile data.

[0092] The chromatography velocity inversion method is as follows:

[0093] L·Δs=ΔZ

[0094] In the formula, s is the slowness (the reciprocal of the P-wave velocity), Δs is the slowness correction amount; L is the wave ray propagation path; ΔZ is the remaining depth difference; the initial depth domain velocity data is updated by calculating the slowness correction amount Δs.

[0095] S305. Using the updated initial depth domain velocity profile, perform pre-stack depth migration. The accuracy of the depth domain velocity profile is judged by the flattening of the common imaging point gathers: if the gathers are flattened, the velocity profile is accurate and can be used for biogenic limestone reservoir prediction; if the gathers are not flattened, return to step S304, iteratively update the depth domain velocity profile, and finally flatten the common imaging point gathers to obtain an accurate depth domain velocity profile.

[0096] Compared to the initial depth domain velocity profile data, the velocity after multiple rounds of tomographic velocity updates provides higher accuracy in characterizing the subsurface medium and can meet the needs of predicting biogenic limestone reservoirs.

[0097] In the above embodiments, preferably, the forward modeling in step S400 is achieved by first analyzing and simulating the propagation process and laws of seismic waves in the subsurface medium in a two-dimensional geological body, and then by performing forward modeling of the vertical propagation of seismic waves. Specific steps include:

[0098] S401. Observe typical seismic profiles that may contain biogenic limestone, and construct a preliminary two-dimensional geological model by manually outlining the distribution range of biogenic limestone and surrounding rocks.

[0099] S402. Based on the velocity and density of biogenic limestone reservoirs and surrounding rocks in typical wells of biogenic limestone encountered in the study area in step S203, fill the P-wave velocity and density parameters between different layers of the two-dimensional geological body model.

[0100] S403. Analyze the dominant frequency of a typical seismic profile that may contain biogenic limestone, design the Ricker wavelet based on the dominant frequency, and perform forward modeling of the vertical propagation of seismic waves using a two-dimensional geological body model to obtain the forward modeled seismic profile.

[0101] S404. Compare the forward-modeled seismic profile with typical seismic profiles that may contain biogenic limestone. If they are similar, it indicates that the two-dimensional geological model is reasonably designed and the parameters used to fill the model are reasonable and reliable, further verifying the possibility of biogenic limestone reservoir development; if they are not similar, the method ends.

[0102] In the above embodiments, preferably, the pre-stack inversion in step S500 is the pre-stack constrained sparse pulse inversion technique commonly used in industry, and the longitudinal wave impedance and longitudinal and transverse wave velocity ratio data are obtained by pre-stack inversion calculation.

[0103] In the above embodiments, preferably, the Bayesian discriminant analysis in step S500 specifically includes: determining the probability density function of the P-wave impedance and P-wave velocity ratio corresponding to biogenic limestone reservoirs and other lithologies based on the scatter distribution range of the P-wave impedance and P-wave velocity ratio cross plot in the logging curves in step S204 (locations with more scatter points have higher probability, and locations with fewer scatter points have lower probability, which can be described by a normal distribution function), as well as the proportion of biogenic limestone reservoirs and other lithologies in the total data. Using this as input, the P-wave impedance and P-wave velocity ratio data volume calculated by pre-stack inversion is used to obtain the posterior probability through Bayesian discriminant analysis, calculate the most likely lithofacies type of a certain point in three-dimensional space, and finally determine the most likely distribution range of biogenic limestone reservoirs.

[0104] Pre-stack inversion and Bayesian discriminant analysis based on it can provide a detailed characterization of the thickness and distribution range of biogenic limestone reservoirs.

[0105] The following example, using a specific block in the Bohai Bay Basin, further illustrates the practical application effect of the seismic prediction method for biogenic limestone reservoirs of the present invention.

[0106] Figure 2 This is a logging curve of a typical well that encountered a biogenic limestone reservoir. The biogenic limestone gas layer and the mudstone development section can be identified in the lithofacies diagram on the far right of the figure. The biogenic limestone gas layer has the characteristics of low P-wave velocity, low density, low P-wave impedance, low clay content, high porosity, low water saturation, and low P-wave / S-wave velocity ratio compared to the surrounding rock (mudstone).

[0107] Figure 3 This is a cross-plot of the P-wave impedance and P-wave velocity ratio from the logging curves of two wells that encountered biogenic limestone reservoirs within the work area. As can be seen from the plot, the P-wave impedance and P-wave velocity ratio of the biogenic limestone gas layer are lower than those of mudstone, limestone, and dolomitic limestone. Based on these parameter characteristics, pre-stack inversion and Bayesian discriminant analysis can be carried out to predict the planar distribution and thickness of the biogenic limestone reservoir.

[0108] Figure 4 This is a velocity spectrum profile of biogenic limestone. The left image is a common depth point (CDP) gather, and the right image is the velocity spectrum obtained by velocity analysis using this gather. The dashed line in the image represents the conventional velocity spectrum trend, and the solid line represents the velocity spectrum trend of this block. As can be seen from the image, a significant low-velocity anomaly appears in the region between 2.5s and 2.75s. This low-velocity anomaly trend can provide evidence to support the existence of biogenic limestone reservoirs.

[0109] Figure 5The images show velocity profiles of biogenic limestone. The top image is a typical seismic profile that may contain biogenic limestone, while the bottom image is the corresponding velocity profile of biogenic limestone. This velocity profile was obtained through multiple rounds of tomographic velocity updates. The velocity profiles show that the predicted biogenic limestone development zone has a low P-wave velocity relative to the surrounding rock, which verifies the possibility of biogenic limestone development.

[0110] Figure 6 This is a comparison between a forward-modeled seismic profile and a seismic profile that may contain biogenic limestone reservoirs. Based on the dominant frequency analysis of the seismic profile potentially containing biogenic limestone reservoirs, the Ricker wavelet frequency was determined to be 20Hz. A geological model was designed, primarily including biogenic limestone reservoirs and mudstone. The model length and depth are 1000m and 3000m respectively, with a grid step size of 1m*1m and a trace spacing of 10m. The morphology of the biogenic limestone and mudstone was delineated based on the seismic profile. P-wave velocity and density parameters of the biogenic limestone reservoir and surrounding rock were selected based on typical wells that encountered biogenic limestone. According to the statistical results of drilled wells, the P-wave velocity range of the biogenic limestone reservoir is 2290-2670m / s, and the density range is 2.26-2.42g / cm³. 3 The longitudinal wave velocity of mudstone ranges from 2500 to 3100 m / s, and its density ranges from 2.3 to 2.44 g / cm³. 3 The selected biogenic limestone reservoir has a P-wave velocity of 2400 m / s and a density of 2.3 g / cm³. 3 The longitudinal wave velocity of the mudstone background was selected as 2800 m / s and the density as 2.4 g / cm³. 3 The velocities and densities of the two potentially altered mudstone strata are 3100 m / s and 2.4 g / cm³, respectively. 3 And 2600m / s, 2.3g / cm 3 Using 20Hz Ricker wavelet, forward modeling was performed based on the vertical propagation law of waves (i.e., self-excitation and self-reception model). The forward modeling profile was compared with the actual seismic profile. The waveform morphology was found to be similar, which can confirm that the reservoir and surrounding rock parameters were reasonably selected and can verify the possibility of developing biogenic limestone reservoirs.

[0111] Figure 7 This is a biogenic limestone profile obtained from Bayesian discriminant analysis. Figure 3 The rock physics analysis diagrams shown indicate that the P-wave impedance and P-wave / S-wave velocity ratio can distinguish biogenic limestone reservoirs from other lithologies. Based on this, pre-stack inversion is performed to calculate the elastic parameters of P-wave impedance and P-wave / S-wave velocity ratio. Figure 3 The rock physics analysis diagram shown is used to set a prior probability density function, perform Bayesian discriminant analysis, calculate the posterior probability of the biogenic limestone reservoir, and obtain the predicted distribution data of the biogenic limestone reservoir. Figure 7The top image shows a seismic profile of the biogenic limestone reservoir distribution, and the bottom image shows a predicted biogenic limestone profile. The reservoir thickness measured from the profile is approximately 60m when converted to the depth domain.

[0112] Example 2:

[0113] Embodiment 1 above provides a seismic prediction method for biogenic limestone reservoirs. Correspondingly, this embodiment provides a seismic prediction system for biogenic limestone reservoirs. The seismic prediction system provided in this embodiment can implement the seismic prediction method of Embodiment 1. This seismic prediction system can be implemented through software, hardware, or a combination of both. For example, the seismic prediction system may include integrated or separate functional modules or units to perform the corresponding steps in the seismic prediction methods of Embodiment 1. Since the seismic prediction system in this embodiment is basically similar to the method embodiment, the description process in this embodiment is relatively simple. Relevant details can be found in the description of Embodiment 1. The seismic prediction system in this embodiment is merely illustrative.

[0114] The seismic prediction system for biogenic limestone reservoirs provided in this embodiment includes:

[0115] The first processing unit is used to determine, based on the geological understanding of the study area, whether the research target has the potential for the development of biogenic limestone reservoirs.

[0116] The second processing unit is used to conduct rock physical analysis on the biogenic limestone reservoir section and its surrounding rocks in the well logging data of the study area, and to study the applicability of elastic parameters such as P-wave impedance and P-wave velocity ratio in biogenic limestone reservoir prediction.

[0117] The third processing unit is used to analyze the velocity spectrum and velocity profile characteristics of biogenic limestone reservoirs, determine whether the possible biogenic limestone reservoir development zone is low-velocity compared with the surrounding rock, and further reduce the ambiguity of reservoir prediction.

[0118] The fourth processing unit is used to conduct forward modeling, discuss the correspondence between the distribution characteristics of biogenic limestone reservoirs in the forward model and the seismic profile, and verify the possibility of biogenic limestone reservoir development.

[0119] The fifth processing unit is used to conduct pre-stack inversion and Bayesian discriminant analysis based on the seismic data collected in the study area, and to predict the planar and profile distribution of biogenic limestone reservoirs using the two parameters of P-wave impedance and P-wave-S-wave velocity ratio.

[0120] Example 3:

[0121] This embodiment provides a processing device for implementing the seismic prediction method for biogenic limestone reservoirs provided in Embodiment 1. The processing device can be a client-side processing device, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the seismic prediction method of Embodiment 1.

[0122] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to communicate with each other. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the earthquake prediction method provided in Embodiment 1.

[0123] Preferably, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0124] Preferably, the processor can be any type of general-purpose processor such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation herein.

[0125] Example 4:

[0126] The seismic prediction method for biogenic limestone reservoirs in Embodiment 1 can be specifically implemented as a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the seismic prediction method described in Embodiment 1 are loaded.

[0127] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A seismic prediction method for biogenic limestone reservoirs, characterized in that, Includes the following steps: Based on the geological understanding of the study area, determine whether the research target has the potential for the development of biogenic limestone reservoirs. If it is possible for biogenic limestone reservoirs to develop, proceed to the next step. Rock physics analysis was conducted on the biogenic limestone reservoir section and its surrounding rocks based on well logging data in the study area. The applicability of P-wave impedance and P-wave-to-S-wave velocity ratio in biogenic limestone reservoir prediction was studied. If applicable, the next step was taken. Analyze the velocity spectrum and velocity profile characteristics of the biogenic limestone reservoir to determine whether the possible biogenic limestone reservoir development zone has low velocity characteristics compared with the surrounding rock, so as to further reduce the ambiguity of reservoir prediction. If it has low velocity characteristics, proceed to the next step. Conduct forward modeling, discuss the correspondence between the distribution characteristics of biogenic limestone reservoirs in the forward model and the seismic profile, and verify the possibility of biogenic limestone reservoir development. If the possibility of biogenic limestone reservoir development is verified, proceed to the next step. Using seismic data collected in the study area, pre-stack inversion and Bayesian discriminant analysis based on the pre-stack inversion results were carried out. The planar and profile distribution of biogenic limestone reservoirs was predicted by the two parameters of P-wave impedance and P-wave / S-wave velocity ratio.

2. The earthquake prediction method according to claim 1, characterized in that, The geological understanding referred to here is the reconstruction of paleogeography and the collection and analysis of drilled well data, specifically including: Paleomorphological restoration: Paleomorphological restoration is performed on the target layer of the research object. The commonly used method is layer flattening. The layer flattening method first selects the top and bottom surfaces of the target layer, and then calculates the time difference between the top and bottom surfaces. The flattened top surface is the sea / lake level during the original deposition period, and the bottom surface morphology is the relative paleomorphology of the target layer during the deposition period. Data collection and analysis of drilled wells: All logging data in the study area will be collected, including logging curve data, logging data and core thin section data. Based on the logging data and core thin section data, it will be determined whether any wells have encountered biogenic limestone in the target layer. If so, it proves that the target layer has the potential to develop biogenic limestone reservoirs. If not, the method ends.

3. The earthquake prediction method according to claim 2, characterized in that, The rock physical analysis specifically includes: The collected logging curve data were used to interpret the logging curve data within the study area to obtain the clay content, porosity, water saturation and logging interpretation conclusions, which were used to interpret the lithology, physical properties and hydrocarbon content of reservoir parameters. The collected logging data within the study area were analyzed to obtain the measurement depth ranges of biogenic limestone reservoirs and other lithological distributions, which were used for lithological interpretation. By examining the P-wave velocity, density, P-wave impedance, clay content, porosity, water saturation, well logging interpretation conclusions, and well logging lithology, the measurement depth ranges of the biogenic limestone reservoir and its surrounding rocks are identified. Furthermore, the velocity and density parameters of the biogenic limestone reservoir and its surrounding rocks are extracted to prepare data for subsequent forward modeling. The P-wave impedance data and P-wave velocity ratio data of the biogenic limestone reservoir section and its surrounding rocks are interpolated to obtain the P-wave impedance and P-wave velocity ratio interpolation diagram in the well logging curve. It is determined whether the interpolation of the above two parameters can distinguish between other lithologies in the biogenic limestone reservoir and the surrounding rocks. If they can be distinguished, the subsequent pre-stack inversion can be carried out; otherwise, the method ends.

4. The earthquake prediction method according to claim 3, characterized in that, The velocity spectrum and velocity profile of the biogenic limestone reservoir were obtained through multiple rounds of tomographic velocity update iterations. The specific steps of the multiple rounds of tomographic velocity update iterations are as follows: S301. Perform time-domain noise suppression, multiple suppression, and common-depth point gather velocity analysis on the shot gather data obtained from seismic acquisition to obtain the time-domain velocity spectrum. By picking up the energy clusters of the velocity spectrum, obtain the velocity value at each time sampling point on the selected common-depth point. S302. Convert the velocity values ​​at each time sampling point on the common depth point selected in the time domain to the depth domain to obtain the velocity values ​​in the depth domain. Then, through interpolation, convert the velocity values ​​in the depth domain into a velocity profile in the depth domain to obtain the initial depth domain velocity profile data. S303. Using the initial depth domain velocity profile data, perform pre-stack depth migration to obtain common imaging point gathers; Pre-stack depth migration can relocate seismic information received on the ground to the actual depth location underground, and obtain common imaging point gathers that reflect the actual depth location underground. S304. Determine if the common imaging point gather is flattened: If it is not flattened, pick the remaining depth difference of the common imaging point gather, add the structural layer constraint information, and then perform tomographic velocity inversion to update the initial depth domain velocity profile data. The chromatography velocity inversion method is as follows: L·Δs=ΔZ In the formula, s is the slowness; Δs is the slowness correction; L is the wave ray propagation path; ΔZ is the remaining depth difference; the initial depth domain velocity data is updated by calculating the slowness correction Δs. S305. Using the updated initial depth domain velocity profile, perform pre-stack depth migration. The accuracy of the depth domain velocity profile is judged by the flattening of the common imaging point gathers: if the gathers are flattened, the velocity profile is accurate and can be used for biogenic limestone reservoir prediction; if the gathers are not flattened, return to step S304, iteratively update the depth domain velocity profile, and finally flatten the common imaging point gathers to obtain an accurate depth domain velocity profile.

5. The earthquake prediction method according to claim 4, characterized in that, The forward modeling simulation first analyzes and simulates the propagation process and laws of seismic waves in the subsurface medium within a two-dimensional geological body, and then implements it through forward modeling of the vertical propagation of seismic waves. Specific steps include: Observe typical seismic profiles that may contain biogenic limestone, and construct a preliminary two-dimensional geological model by manually outlining the distribution range of biogenic limestone and surrounding rocks; Based on the velocity and density of typical biogenic limestone reservoir sections and surrounding rocks encountered in wells within the study area, the longitudinal wave velocity and density parameters of different layers in the two-dimensional geological body model were filled. The dominant frequency of a typical seismic profile that may contain biogenic limestone is analyzed. Based on this dominant frequency, a Ricker wavelet is designed. The vertical propagation of the seismic wave is simulated using a two-dimensional geological body model to obtain the forward-modeled seismic profile. The forward-modeled seismic profile is compared with a typical seismic profile that may contain biogenic limestone. If they are similar, it indicates that the two-dimensional geological model is reasonably designed and the parameters used to fill the model are reasonable and reliable, further verifying the possibility of biogenic limestone reservoir development. If they are not similar, the method ends.

6. The earthquake prediction method according to claim 5, characterized in that, The aforementioned pre-stack inversion is a commonly used pre-stack constrained sparse pulse inversion technique in industry, which calculates the P-wave impedance and P-wave / S-wave velocity ratio data.

7. The earthquake prediction method according to claim 6, characterized in that, The Bayesian discriminant analysis specifically includes: determining the probability density functions of P-wave impedance and P-wave velocity ratio corresponding to biogenic limestone reservoirs and other lithologies, as well as the proportion of biogenic limestone reservoirs and other lithologies in the total data, based on the scatter distribution range of the P-wave impedance and P-wave velocity ratio cross plot in the well logging curves. Using this as input, the P-wave impedance and P-wave velocity ratio data volume calculated by pre-stack inversion is used to obtain the posterior probability through Bayesian discriminant analysis, calculate the most likely lithofacies type of a certain point in three-dimensional space, and finally determine the most likely distribution range of biogenic limestone reservoirs.

8. A seismic prediction system for biogenic limestone reservoirs, characterized in that, include: The first processing unit is used to determine, based on the geological understanding of the study area, whether the research target has the potential for the development of biogenic limestone reservoirs. The second processing unit is used to conduct rock physical analysis on the biogenic limestone reservoir section and its surrounding rocks in the well logging data of the study area, and to study the applicability of P-wave impedance and P-wave-S-wave velocity ratio in biogenic limestone reservoir prediction. The third processing unit is used to analyze the velocity spectrum and velocity profile characteristics of biogenic limestone reservoirs, determine whether the possible biogenic limestone reservoir development zone is low-velocity compared with the surrounding rock, and further reduce the ambiguity of reservoir prediction. The fourth processing unit is used to conduct forward modeling, discuss the correspondence between the distribution characteristics of biogenic limestone reservoirs in the forward model and the seismic profile, and verify the possibility of biogenic limestone reservoir development. The fifth processing unit is used to conduct pre-stack inversion and Bayesian discriminant analysis based on the pre-stack inversion results using seismic data collected in the study area. It predicts the planar and profile distribution of biogenic limestone reservoirs using two parameters: P-wave impedance and P-wave / S-wave velocity ratio.

9. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the seismic prediction method for biogenic limestone reservoirs as described in any one of claims 1-7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the seismic prediction method for biogenic limestone reservoirs according to any one of claims 1-7.

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