Method for identifying small scale faults and fault displacement by using coarse and fine combined seismic compound wave

Abstract

The present application relates to a method for identifying small-scale faults and fault throw by using coarse and fine combined seismic compound waves, which comprises the following steps: determining the lithology composition of the target layer in the study area, the velocity and density of different lithologies, and the main frequency of the 3D seismic data; based on the actual data of the study area, establishing forward models of lithology layers with different thicknesses, observing the forward simulation results, and determining the minimum thickness of the lithology layer that can be identified by the seismic data in the study area; based on the minimum thickness, establishing forward models of different fault throws, determining the fault throw characteristics and fault throw of the minimum fault that can be identified according to the fault throw characteristics of the coarse and fine combined seismic compound waves formed by the forward, determining the profile position and fault throw of the small-scale fault according to the actual fault throw characteristics of the coarse and fine combined seismic compound waves and referring to the forward results in the 3D seismic profile, and identifying the planar distribution of the fault. The present application uses the fault throw characteristics of the coarse and fine combined seismic compound waves to identify the small-scale faults and fault throw, and has high identification accuracy.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration of oil and gas, specifically to a method for identifying small-scale faults and their displacements using coarse and fine combined seismic composite waves, which can be applied to guide the exploration and development of oil and gas reservoirs. Background Technology

[0002] Seismic data consists of seismic waves generated at the Earth's surface that propagate downwards, reflect off interfaces (impedance interfaces) of underground geological bodies, and are recorded by receivers. The seismic waveforms reflect the characteristics of these interfaces. Planar tracing of the boundaries of geological bodies can be performed using in-phase axes formed by multiple adjacent in-phase seismic waves.

[0003] Due to the interference of adjacent impedance interfaces, seismic reflected waves will superimpose to form composite waves. Their morphology is quite complex, manifesting on seismic phase axes as combinations of phase axes with varying thicknesses or amplitudes. This combination form also serves as an important basis for identifying special geological bodies.

[0004] The presence of faults can lead to vertical displacement of strata, which manifests as displacement of the same phase axis on seismic profiles. Therefore, seismic data is crucial for fault identification. However, due to the limitations of vertical resolution in seismic data, small-scale faults, with their small displacements, do not exhibit clear displacement characteristics on seismic profiles and are easily confused with lithological abrupt changes, making identification difficult. In recent years, with the increasing sophistication of oil and gas exploration and development, attention has been paid to the crucial role of small-scale faults in sand body distribution, trap formation, and the conduction or obstruction of oil and gas. Describing their displacements also contributes to the quantification of related research. Therefore, conducting research on the identification of small-scale faults and their displacements is of great significance.

[0005] Previous researchers have proposed various methods for identifying small-scale faults and their displacements. Generally, these methods focus on improving the imaging quality of seismic data and optimizing the identification of sensitive seismic attributes. Regarding improving the imaging quality of seismic data, Chinese invention patent document CN202311233441.1 discloses a seismic identification method system for the internal structure of small-scale, low-order fault zones. This method uses the seismic frequency division volume of the target segment to determine the tuning frequency division volume of the small fault principal component and the background frequency division volume of the small fault, and performs fault point sharpening. The processed seismic amplitude data are then merged to obtain a fused data volume. Finally, the internal structure of the fault zone is determined based on the fused data volume, improving the identification accuracy of small faults in seismic data. Wu Chenxiao, in his paper "Application of Tectonic Steering Filtering Technology in the Identification of Small Carboniferous Faults in the Southern Belt of the Di'nan Uplift in the Junggar Basin," proposed using tectonic steering filtering technology to process seismic data, improving the identifiability of faults on the profile and increasing the signal-to-noise ratio for identifying micro-faults. In 2021, the journal *Petroleum Geophysical Exploration* published Xiao Xi et al.'s "Fault Identification Method and Application Based on Diffraction Information Extraction Technology." This method improves seismic data resolution through compressed sensing processing based on L0-norm sparse inversion, then extracts diffraction information from amplitude-preserving migrated seismic data volumes. Combining the spatial discontinuities of diffraction wave amplitude and phase, faults and fractures are characterized by ant-like tracking, ultimately achieving small-scale fault identification. Regarding the optimization and identification of sensitive seismic attributes, Chinese invention patent document CN202311296456.2 discloses a multi-domain small-scale fault identification method based on probabilistic neural networks. This method fully mines fault information in amplitude, frequency, and phase attributes by performing edge detection on the "three instantaneous" attributes of seismic records, and then uses probabilistic neural network technology for multi-domain small-scale fault optimization and detection, ultimately achieving accurate identification of small-scale faults. In their paper, "Application of Unsupervised Pattern Recognition Technology for Faults under Dominant Frequency Conditions," Yin Ning et al. proposed optimizing frequencies reflecting faults at different scales. Based on this optimization, they extracted various types of geometric fault attributes and used unsupervised pattern recognition algorithms to characterize the distribution features of single-scale and full-scale faults, thereby improving the lateral resolution of faults and effectively enhancing the recognition accuracy of small faults. Sun Zhiyuan, in his paper, "Research on Strike-Slip Fault Recognition Based on Dominant Frequency Band Seismic Attribute Fusion—Taking the Moxizhuang Oilfield in the Junggar Basin as an Example," pointed out that by screening and fusing "dominant frequency band attributes" that are more sensitive to strike-slip fault recognition, the fusion method enabled the identification of NW-trending small-displacement faults that could not be identified by conventional methods.

[0006] The methods described above for improving the imaging quality of seismic data can make fault features more obvious to some extent, but the improvement effect is still limited. Furthermore, the methods for optimizing sensitive attributes mainly focus on planar identification of faults, and ultimately, the presence of faults still needs to be confirmed by combining seismic profile features. A more reliable method that can identify small-scale faults and their displacements on seismic profiles is needed. Summary of the Invention

[0007] The purpose of this invention is to provide a method for identifying small-scale faults and their displacements using coarse and fine combined seismic composite waves. This method addresses the problem that existing methods for identifying small-scale faults and their displacements on seismic profiles are not reliable enough.

[0008] The technical solution adopted by this invention to solve its technical problem is as follows: This method for identifying small-scale faults and their displacements using coarse and fine combined seismic composite waves includes the following steps: Step 1: Determine the lithological composition of the target layer in the study area, as well as the velocity and density of different lithologies, and determine the dominant frequency of the 3D seismic data; Step 2: Based on the actual data of the study area, establish forward modeling models for lithological layers of different thicknesses. By observing the seismic waveform characteristics of the lithological interfaces at different thicknesses in the forward modeling results profile, determine the minimum thickness of the lithological layers that can be identified by the seismic data of the study area. Step 3: Based on the minimum thickness of the lithological layer that can be identified from the seismic data determined in Step 2, establish forward modeling models with different fault displacements. Based on the fault characteristics of the coarse and fine combined seismic composite waves formed by forward modeling, determine the fault characteristics and fault displacement of the smallest identifiable fault. Step 4: In the three-dimensional seismic profile of the study area, based on the actual fault characteristics of the coarse and fine combined seismic composite waves, and referring to the forward modeling results in Step 3 above, determine the profile location and fault displacement of small-scale faults. Step 5: Based on the cross-sectional location of the small-scale fracture determined in Step 4, and the time domain depth corresponding to the thin coherent axis with the most obvious faulting, extract the coherent volume attribute time slice at that depth using an hour window to further identify the planar distribution of the fracture.

[0009] The method for determining the velocity and density of different lithologies in step one of the above scheme is as follows: the velocity of the corresponding lithology is determined using the sonic transit time curve, and the density of the corresponding lithology is determined using the density curve. If there is no density curve, the Gardner formula is used to calculate the velocity.

[0010] The method for establishing forward modeling models of different lithological layers in step two of the above scheme is as follows: the strata used for simulation are interbedded layers of different lithological layers, and the velocity and density parameters of different lithologies are obtained from step one; the thickness of each lithological layer in the same model is the same; multiple comparison models are set up, each model has a different lithological layer thickness, arranged in ascending order, and forward modeling simulation is performed on each model using the seismic dominant frequency determined in step one.

[0011] The standard for identifying the minimum thickness of lithological layers in step two of the above scheme is as follows: observe the response of each rock layer interface in the forward modeling result profile. If each rock layer interface has a seismic reflection response, it means that the stratum of that thickness can be identified in the seismic data. When the rock layer thickness is less than a certain threshold, some rock layer interfaces do not have seismic reflection axes. This thickness is the minimum identifiable thickness. At this thickness, the seismic reflection is a composite wave with a combination of coarse and fine phases. That is, the top (or bottom) surface of a certain rock layer shows a coarser phase axis, and the bottom (or top) surface shows a finer phase axis.

[0012] The method for establishing forward modeling models with different fault displacements in step three of the above scheme is as follows: the strata used for simulation are interbedded layers of different lithologies, and the velocity and density parameters of different lithologies are obtained from step one; the thickness of the lithology is the minimum thickness that can be identified by seismic data, which is obtained from step two; the structural style of the forward model is set as a normal fault; multiple comparison models are set, each with a different fault displacement, arranged in ascending order, and forward simulation is performed on each model using the dominant seismic frequency determined in step one.

[0013] The criteria for identifying the minimum fault and its displacement in step three of the above scheme are as follows: Based on the fault characteristics of the coarse-fine combined seismic composite waves formed by forward modeling, observe the faulting and deformation of coarse and fine phase axes (seismic composite waves) on the forward modeling simulation profile with different displacements. Since the fine phase axis responds more significantly to the fault than the coarse phase axis, the minimum fault characteristic that can be identified is: in a set of coarse and fine phase axis combinations, the fine phase axis just fails to break, while the coarse phase axis only undergoes bending deformation; when the minimum fault characteristic appears in the actual seismic data, it indicates the development of a small fault, and its displacement is the displacement set in the forward modeling simulation.

[0014] When determining the profile location and displacement of small-scale faults in step four of the above scheme, if the fault characteristics of earthquake composite waves in the original seismic profile are not obvious, the original seismic data is subjected to an overall 90-degree phase transformation before identification. That is, the positive phase of the original data is converted to zero phase, the zero phase is converted to negative phase, and so on, in order to highlight the fault characteristics of zero phase in the original seismic data.

[0015] In the above scheme, step five uses an hourly window to extract the coherent volume attribute time slice at that depth, with the hourly window selected as 20ms or 10ms. Beneficial effects

[0016] 1. This invention proposes to identify small-scale faults and their displacements by utilizing the fault characteristics of "coarse-fine combined seismic composite waves" in seismic profiles. In this combination, the response of fine in-phase axes to the fault is more obvious, which can improve the profile identification accuracy of small faults.

[0017] 2. In actual seismic data, interference at rock strata interfaces is prevalent, and this "coarse-fine combined seismic composite wave" is also common. This method is simple to operate, highly reliable, and has strong applicability and promotional value. Attached Figure Description

[0018] Figure 1 These are lithology and logging curves of typical wells in the example area; Figure 2 This is a dominant frequency analysis diagram of 3D seismic data; Figure 3 These are forward model diagrams of lithological layers of different thicknesses; Figure 4 These are the results of forward modeling of lithological layers of different thicknesses; Figure 5 It is a forward model diagram of different fault displacements for a rock layer with a thickness of 20m; Figure 6 This is a graph showing the results of forward modeling of different fault displacements for rock layers with a thickness of 20m. Figure 7 This is a diagram showing the original seismic profile and minor fault annotations in the example area; Figure 8 This is a 90-degree phase-shift seismic profile and small fault annotation map of the example area; Figure 9 It is a slice image of the coherent body at the small fracture point (2390ms) at the same time. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings: This method for identifying small-scale faults and their displacements using coarse-fine combined seismic waves includes the following steps: Step 1: Determine the lithological composition of the target layer in the study area, as well as the velocity and density of different lithologies, and determine the dominant frequency of the 3D seismic data; Methods for determining velocity and density for different lithologies: Determine the velocity of the corresponding lithology using the sonic transit time curve, and determine the density of the corresponding lithology using the density curve. If there is no density curve, calculate the velocity using the Gardner formula.

[0020] Step 2: Based on the actual data of the study area, establish forward modeling models for lithological layers of different thicknesses. By observing the seismic waveform characteristics of the lithological interfaces at different thicknesses, determine the minimum thickness of the lithological layers that can be identified by the seismic data of the study area. Method for establishing forward modeling models of lithological layers with different thicknesses: The strata used for simulation are interbedded layers of different lithological layers, and the velocity and density parameters of different lithologies are obtained from step one; the thickness of each lithological layer in the same model is the same; multiple comparison models are set up, each model has a different lithological layer thickness, arranged in ascending order, and forward simulation is performed on each model using the seismic dominant frequency determined in step one.

[0021] The standard for minimum identifiable thickness is as follows: Observe the response of each rock layer interface in the forward modeling results profile. If each rock layer interface has a seismic reflection response, it indicates that the stratum of that thickness is identifiable in the seismic data. When the rock layer thickness is less than a certain threshold, some rock layer interfaces do not have seismic reflection axes, and this thickness is the minimum identifiable thickness. At this thickness, the seismic reflection exhibits a composite wave with a combination of coarse and fine phase axes, that is, the top (or bottom) surface of a certain rock layer exhibits a coarser phase axis, and the bottom (or top) surface exhibits a finer phase axis.

[0022] Step 3: Based on the minimum thickness of the lithological layer that can be identified from the above seismic data, establish forward modeling models with different fault displacements. Based on the fault characteristics of the coarse and fine combined seismic composite waves formed by forward modeling, determine the fault characteristics and fault displacement of the smallest identifiable fault. Method for establishing forward modeling models with different fault displacements: The strata used for simulation are interbedded layers of different lithologies, and the velocity and density parameters of different lithologies are obtained from step one; the thickness of the lithology is the minimum thickness that can be identified by seismic data (obtained from step two); the structural style of the forward modeling model is set as a normal fault; multiple comparison models are set, each with a different fault displacement, arranged in ascending order, and forward simulation is performed on each model using the dominant seismic frequency determined in step one.

[0023] The criteria for identifying the minimum fault and its displacement are as follows: Observe the faulting and deformation of coarse and fine phase axes (seismic composite waves) on forward modeling profiles with different displacements. Since the fine phase axis responds more significantly to faulting than the coarse phase axis, the minimum identifiable fault is characterized by the fine phase axis just being faulted in a set of coarse and fine phase axes, while the coarse phase axis only undergoes bending deformation. When the same characteristics appear in actual seismic data, it indicates the development of a small fault, and its displacement is the displacement set in the forward modeling.

[0024] Step 4: In the 3D seismic profile of the study area, based on the actual fault characteristics of the coarse-to-fine combined seismic composite waves, and referring to the forward modeling results in Step 3 above, determine the profile location and displacement of small-scale faults. If the fault characteristics of the seismic composite waves in the original seismic profile are not obvious, a 90-degree phase transformation can be performed on the original seismic data before identification. That is, the positive phase of the original data is converted to (negative / positive) zero phase, (negative / positive) zero phase is converted to negative phase, and so on, which can highlight the zero-phase fault characteristics in the original seismic data.

[0025] Step 5: Based on the cross-sectional location of the small-scale fracture determined in Step 4, and the time domain depth corresponding to the thin coherent axis with the most obvious faulting, extract the coherent volume attribute time slice at this depth using hourly windows (such as 20ms or 10ms) to further identify the planar distribution of the fracture. Example

[0026] This method of identifying small-scale faults and their displacements using a combination of coarse and fine seismic composite waves includes the following steps: Step 1, such as Figure 1 As shown, the target stratum in the example well consists of mudstone and siltstone. The lithological velocities of the formation can be derived from the reciprocal of the sonic transit time curve, with average values ​​of 4000 m / s and 4500 m / s respectively. The densities of the formation lithology can be directly read from the density curve in the figure, with average values ​​of 2.47 g / m³. 3 2.54g / m 3 .like Figure 2 As shown, spectral analysis of the seismic data determined that the dominant frequency of the 3D seismic data is approximately 26Hz.

[0027] Step 2: Based on actual data from the study area, establish forward modeling models for lithological layers of different thicknesses. For example... Figure 3 As shown, the strata used for simulation consist of alternating layers of mudstone and siltstone, with the lithological velocity and density determined according to step one. The thickness of each lithological layer is the same within the same model. Multiple comparison models are set, with lithological layer thicknesses set in a sequence of 5m, 10m, 15m, 20m, 25m, 30m, 35m, and 40m. Then, forward modeling is performed based on the seismic dominant frequency analyzed in step one. The simulation results are shown below. Figure 4 As shown, the 5m, 10m, and 15m thickness models only show response at the top and bottom interfaces, with blank reflections at the interfaces of each sub-layer. Starting from a thickness of 20m, seismic reflections appear at the interfaces of each sub-layer. The reflection axes at the interfaces of models with thicknesses of 25m and above are of uniform thickness, while the seismic reflections of the 20m thickness model exhibit a composite wave pattern of coarse and fine reflections, meaning that the in-phase axes at some interfaces are coarser than those at others. Therefore, it can be determined that the minimum thickness for identifying lithological layers in this seismic data is 20m.

[0028] Step 3: Establish forward modeling for different fault displacements. For example... Figure 5 As shown, the strata used for simulation consist of alternating layers of mudstone and siltstone. The velocity and density parameters for different lithologies were obtained in step one. The thickness of the lithological layers is the minimum thickness identifiable by seismic data (obtained in step two, 20m). The structural style of the forward model is set as a normal fault. Multiple comparison models are set with fault displacements of 3m, 5m, 8m, 10m, 15m, 20m, and 30m. Forward simulations are performed on each model using the dominant seismic frequency determined in step one. Figure 6As shown in the simulation results, the coarse reflection axis only exhibits significant torsion starting from a break distance of 8m, while the thin reflection axis shows obvious faulting starting from a break distance of 5m. This indicates that considering only the characteristics of the coarse reflection axis, only a break distance of 8m can be identified; however, considering the characteristics of the thin reflection axis allows for the identification of smaller breaks at a break distance of 5m, thus improving the accuracy of break identification.

[0029] Step four: In the three-dimensional seismic profile of the study area, based on the actual fault characteristics of the coarse-fine combined seismic composite waves and referring to the forward modeling results in step three above, determine the profile location and displacement of small-scale faults. For example... Figure 7 As shown in the figure, at fracture 1, the coarse phase axis is clearly twisted, and the thin phase axis below it is clearly dislocated. Therefore, it can be determined that a fracture has developed at this location, and its fault displacement is based on the forward modeling results (…). Figure 6 (Approximately 8m). Next, to highlight the discontinuity characteristics of the zero-phase in-phase axis in the original data volume, a 90-degree phase transformation was performed on the original seismic data before identification, such as... Figure 8 As shown, compared to the original seismic profile, the faulting characteristics of the identified fault 1 are more pronounced. Meanwhile, a new fault 2 is identified to the right of fault 1. Fault 2 exhibits slight distortion of the coarse phase axis and obvious faulting of the fine phase axis. Its fault displacement is referenced from the forward modeling results (…). Figure 6 (), approximately 5m.

[0030] Step 5: Based on the cross-sectional location of the small-scale fracture determined in Step 4, and the time-domain depth corresponding to the most obvious fault in the thin coherent axis (approximately 2390 ms), a coherent volume attribute time slice at this depth is extracted using a 20 ms hourly window to further identify the planar distribution of the fracture. Figure 9 As shown, this combination of planar and cross-sectional views depicts the three-dimensional morphology of small fractures.

Claims

1. A method for identifying small-scale faults and their displacements using coarse-fine combined seismic composite waves, characterized in that... The steps include the following: Step 1: Determine the lithological composition of the target layer in the study area, as well as the velocity and density of different lithologies, and determine the dominant frequency of the 3D seismic data; Step 2: Based on the actual data of the study area, establish forward modeling models for lithological layers of different thicknesses. By observing the seismic waveform characteristics of the lithological interfaces at different thicknesses in the forward modeling results profile, determine the minimum thickness of the lithological layers that can be identified by the seismic data of the study area. The standard for identifying the minimum thickness of lithological layers is as follows: Observe the response of each lithological interface in the forward modeling results profile. If each lithological interface has a seismic reflection response, it means that the stratum of that thickness is identifiable in the seismic data. When the lithological layer thickness is less than a certain threshold, some lithological interfaces do not have seismic reflection axes. This thickness is the minimum identifiable thickness. At this thickness, the seismic reflection is a composite wave with a combination of coarse and fine phases. That is, the top or bottom surface of a certain lithological layer shows a coarser phase axis, and the bottom or top surface shows a finer phase axis. Step 3: Based on the minimum thickness of the lithological layer that can be identified from the seismic data determined in Step 2, establish forward modeling models with different fault displacements. Based on the fault characteristics of the coarse and fine combined seismic composite waves formed by forward modeling, determine the fault characteristics and fault displacement of the smallest identifiable fault. Method for establishing forward modeling models with different fault displacements: The strata used for simulation are interbedded layers of different lithologies, and the velocity and density parameters of different lithologies are obtained from step one; the thickness of the lithology is the minimum thickness that can be identified by seismic data, which is obtained from step two; the structural style of the forward modeling model is set as a normal fault; multiple comparison models are set, each with a different fault displacement, arranged in ascending order, and forward simulation is performed on each model using the dominant seismic frequency determined in step one; The criteria for identifying the minimum fault and its displacement are as follows: Based on the fault characteristics of coarse and fine phase axis combined seismic composite waves generated by forward modeling, observe the faulting and deformation of coarse and fine phase axis combined seismic composite waves on forward modeling simulation profiles with different displacements. Since the fine phase axis responds more significantly to faults than the coarse phase axis, the minimum fault characteristic that can be identified is: in a set of coarse and fine phase axis combinations, the fine phase axis just fails to break, while the coarse phase axis only undergoes bending deformation; when the minimum fault characteristic appears in actual seismic data, it indicates the development of a small fault, and its displacement is the displacement set in the forward modeling simulation. Step 4: In the three-dimensional seismic profile of the study area, based on the actual fault characteristics of the coarse and fine combined seismic composite waves, and referring to the forward modeling results in Step 3 above, determine the profile location and fault displacement of small-scale faults. Step 5: Based on the cross-sectional location of the small-scale fracture determined in Step 4, and the time domain depth corresponding to the thin coherent axis with the most obvious faulting, extract the coherent volume attribute time slice at that depth using an hour window to further identify the planar distribution of the fracture.

2. The method for identifying small-scale faults and their displacements using coarse-fine combined seismic composite waves as described in claim 1, characterized in that: The method for determining the velocity and density of different lithologies in step one is as follows: the velocity of the corresponding lithology is determined using the sonic transit time curve, and the density of the corresponding lithology is determined using the density curve. If there is no density curve, the Gardner formula is used to calculate the velocity.

3. The method for identifying small-scale faults and their displacements using coarse and fine combined seismic composite waves according to claim 2, characterized in that: The method for establishing forward modeling models of different lithological layers in step two is as follows: the strata used for simulation are interbedded layers of different lithological layers, and the velocity and density parameters of different lithologies are obtained from step one; the thickness of each lithological layer in the same model is the same; multiple comparison models are set up, each model has a different lithological layer thickness, arranged in ascending order, and forward modeling simulation is performed on each model using the seismic dominant frequency determined in step one.

4. The method for identifying small-scale faults and their displacements using coarse and fine combined seismic composite waves according to claim 3, characterized in that: When determining the profile location and displacement of small-scale faults in step four, if the fault characteristics of earthquake composite waves in the original seismic profile are not obvious, the original seismic data is subjected to an overall 90-degree phase transformation before identification. That is, the positive phase of the original data is converted to zero phase, the zero phase is converted to negative phase, and so on, in order to highlight the fault characteristics of zero phase in the original seismic data.

5. The method for identifying small-scale faults and their displacements using coarse and fine combined seismic composite waves according to claim 4, characterized in that: Step five involves extracting a time slice of the coherent volume attributes at that depth using an hourly window, with the hourly window selected as 20ms or 10ms.

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