A method for removing gypsolayer based on model forward
By using forward modeling technology, drilling and logging data are used to calculate strata parameters, design geological models, and perform forward modeling of wave equations. Seismic reflection characteristics are matched and compared to eliminate gypsum-salt layers, solving the problem of difficult identification of gypsum-salt layers in existing technologies and improving reservoir prediction accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- PETROCHINA CO LTD
- Filing Date
- 2022-06-08
- Publication Date
- 2026-06-12
AI Technical Summary
Existing technologies cannot effectively identify gypsum-salt layers and reservoirs, affecting the accuracy of reservoir prediction.
Using a model-based forward modeling approach, we calculate the thickness, velocity, and density of rock formations using drilling and logging data, design a geological model, perform forward modeling of wave equations, match and compare seismic reflection characteristics, analyze amplitude properties, and eliminate gypsum-salt layers.
It improves the accuracy of identifying gypsum-salt layers and reservoirs, reduces the ambiguity of information in seismic data, and achieves more accurate reservoir prediction.
Smart Images
Figure CN117233830B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of seismic exploration technology for oil and gas, and more specifically to a method for removing gypsum-salt layers based on model forward modeling. Background Technology
[0002] The Lower Triassic Jialingjiang Formation in the Sichuan Basin is an important natural gas producing formation, containing well-developed reservoirs. The reservoirs are mainly located in the Jialingjiang No. 2 Formation. 2 The upper part of the subsection is dominated by dolomite. (Jia Er) 2 The sub-section has a stratum thickness of approximately 50 meters. From bottom to top, the lithology is mainly limestone, gypsum-salt rock, and dolomite, with varying thicknesses for each type of lithology. (Jia Er) 1 The sub-section also features thin reservoirs, approximately 25 meters thick, with a suite of gypsum rocks above and dolomite as the main component below. Figure 2 As shown.
[0003] The lithology of the target formation is complex, containing interbedded layers of gypsum-salt, dolomite, and thin layers of gypsum-salt and dolomite, while the reservoir is mainly developed within the dolomite. Because the velocity of the gypsum-salt layers is not significantly different from the velocity of the reservoir (the propagation speed of artificially induced seismic waves in the strata, hereinafter referred to as velocity), it greatly interferes with researchers' predictions, thus affecting the accuracy of reservoir prediction. Accurately summarizing the seismic reflection characteristics of the reservoir and gypsum-salt layers on seismic profiles, thereby effectively identifying the gypsum-salt layers and reservoirs, is a key focus of reservoir prediction in the Jialingjiang Formation.
[0004] In the prior art, patent CN106353808B discloses a method and apparatus for analyzing the pull-up law of seismic reflection time of underlying strata. The method includes: establishing a generalized pull-up model to calculate the change in the pull-up amplitude of the underlying seismic reflection strata caused by anomaly velocity interlayers in gypsum-salt rock; and analyzing the influence of the gypsum-salt rock anomaly velocity layer on the pull-up law of the underlying seismic reflection time based on the generalized pull-up model. This technical solution establishes a typical model that conforms to actual structures through forward modeling of a simple geological model. It studies the influence of different thickness combinations of salt rock and gypsum rock on the imaging of the underlying target layer, and investigates the seismic response characteristics corresponding to different salt rock contents in the gypsum layer on the right bank of the Amu Darya River. This facilitates pre-stack velocity analysis and makes it easier to understand the influence of post-stack corrected gypsum-salt rock velocity changes on the seismic reflection time of the underlying carbonate target layer.
[0005] However, the method provided by the aforementioned patent is not applicable to identifying and removing normal gypsum-salt layers, meaning it cannot effectively identify gypsum-salt layers and reservoirs to improve the accuracy of reservoir prediction. Summary of the Invention
[0006] In order to overcome the defects in the prior art, the present invention discloses a method for removing gypsum-salt layers based on model forward modeling. The purpose of the present invention is to solve the problem that the prior art cannot effectively identify gypsum-salt layers and reservoirs.
[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0008] A method for removing gypsum-salt layers based on model forward modeling includes data preparation, lithological calculation, model design, model forward modeling, matching and comparison, and planar lithological distribution analysis, as detailed below:
[0009] S1. Data Preparation
[0010] The data preparation steps include preparing basic data such as drilling data, lithological data, well logging data, and pre-stack time migration data.
[0011] In the above steps, drilling data is used for subsequent lithology calculations and model design, lithology data is used for subsequent S2 lithology calculations and S3 model design, well logging data is used for subsequent S2 steps to calculate the thickness, velocity, and density values of different lithologies, and pre-stack time migration data is used for subsequent S5 steps to determine the comparative analysis of seismic reflection characteristics of different lithologies or lithology combinations.
[0012] Specifically, the drilling data includes drilling coordinates, core elevation, well inclination data, and well location stratification; the lithological data includes lithological data of the main target strata; and the logging data includes sonic transit time, density, porosity, and gamma ray.
[0013] In the above steps, based on the drilling and logging data, completion geological reports, or logging (well logging) reports obtained from wells already drilled in the study area and adjacent areas, such as drilling coordinates, core filling elevation, geology (well logging stratification), cuttings data, well inclination data, and comprehensive interpretation results of well logging of the main target layer (sonic transit time, gamma curve, density curve, porosity curve, etc.), drilling data, lithological data, and well logging data are obtained.
[0014] Preferably, in the data preparation step, seismic data of the study area is obtained through geophysical exploration, and the pre-stack time migration data is obtained by observing and analyzing the response of various underground strata to artificially induced seismic waves by utilizing the differences in elasticity and density of the underground medium, and by processing the data with professional indoor computer software.
[0015] In this invention, pre-stack time migration data is obtained by acquiring seismic data of the study area through geophysical exploration, and then processing the data using specialized indoor software. Specifically, by utilizing the differences in elasticity and density of the subsurface medium, and by observing and analyzing the Earth's response to artificially induced seismic waves, pre-stack time migration data is obtained. Indoor pre-stack time migration processing of field seismic data is one of the most effective methods for imaging complex subsurface structures. It is suitable for situations where the propagation velocities of longitudinal and transverse seismic waves vary significantly across different strata, and is applicable to migration and repositioning imaging of complex structures.
[0016] S2, Lithological Calculation
[0017] The lithology calculation step uses drilling and logging data to calculate the thickness, velocity, and density values of different lithological layers.
[0018] The method for calculating the thickness of the rock strata in the above steps is as follows:
[0019] S211. Using the prepared drilling and logging data, the thickness of each section and subsection of the formation in each well is statistically analyzed, and the average thickness of each section and subsection of the formation is obtained as the thickness of each section and subsection of the formation.
[0020] In the above method, the thickness of the Jialingjiang Formation in the second and first (sub-) sections is statistically analyzed using the collected drilling and logging data, and the average value of each well is used to design the initial model. Since the Jialingjiang Formation is a marine carbonate rock formation, the thickness of each (sub-) section is relatively stable in the lateral direction, so the thickness of each (sub-) section in the initial model is consistent.
[0021] S212. Statistically calculate the thickness of different lithologies within each section and subsection of each well, and obtain the average thickness of different lithologies within each section and subsection of the strata. Use this average thickness as the lithology filling value for the model.
[0022] In the above method, due to subtle differences in sedimentary environment, the thickness of different lithologies will vary laterally. By statistically analyzing the thickness of different lithologies within each well in the Jia-2 and Jia-1 sections of the area, the thickness of different lithologies within each (sub)section was designed and calculated.
[0023] In the above steps, the method for calculating the rock layer velocity is as follows: In the logging data, analyze the wellbore curve, remove the part of the wellbore curve with collapsed sections, and then in the logging data after removing the collapsed sections, take the sonic curves of different lithological sections of each well, calculate the average sonic velocity of different lithologies of each well, and use it as the velocity of different lithological rock layers.
[0024] In the above method, the collected well logging data is used to obtain the sonic curves (velocities) of different lithologies (gypsum, limestone and dolomite) in each well, and the average velocity of different lithologies in each well is calculated.
[0025] In the above steps, the method for calculating the density of the rock layer is as follows: In the logging data, analyze the wellbore curve, remove the part of the wellbore curve with collapsed sections, and then in the logging data after removing the collapsed sections, take the density curve of different lithological sections of each well, calculate the average density of different lithologies of each well, and use it as the density of rock layers of different lithologies.
[0026] In the above method, the density curves of different lithologies (gypsum, limestone and dolomite) in each well are obtained using the collected well logging data, and the average density of different lithologies in each well is calculated.
[0027] In this invention, the lithology of the Jia-2 section is complex both longitudinally and transversely, with differences in velocity and density among various lithologies. By analyzing well logging curves, the average values of velocity and density for different lithological sections are obtained. Note that the wellbore curve must first be analyzed, and sections with collapsed sections should be removed to ensure the accuracy of the velocity and density values.
[0028] In this invention, by analyzing the rock electrical characteristics of the core well, it was found that the lithology of the second member of the Jialingjiang Formation is relatively complex in the vertical direction, with gypsum, dolomite and limestone interbedded. The electrical characteristics of different lithological sections are quite different, and the interbedded gypsum-salt and reservoir dolomite show the most obvious distinction in density curve response.
[0029] In this invention, due to the good correlation between rock and electrical properties in the core wells, logging curves corresponding to different lithologies can be marked in the software, and their average values can be automatically calculated. Specifically, gypsum rock has a velocity of approximately 5500 m / s, a density of 2.88 g / cc, and an impedance of 16700 m / s*g / cc; limestone has a velocity of approximately 6000 m / s, a density of 2.78 g / cc, and an impedance of 16680 m / s*g / cc; while dolomite has a velocity of approximately 6300 m / s, a density of 2.82 g / cc, and an impedance of 17200 m / s*g / cc.
[0030] In open-hole wells, due to the formation being flushed and soaked by drilling fluid, phenomena such as drilling fluid intrusion or dissolution of the formation can easily occur in different lithologies. These phenomena can be delineated and verified using wellbore curves. For denser limestone and dolomite, which have poor permeability and are relatively hard, the wellbore is less affected, and the wellbore curve shows almost no change. However, for gypsum-salt rock, which is easily dissolved and collapses, the wellbore will significantly enlarge, resulting in abrupt changes in the wellbore curve and a high value. This allows for a precise depiction of the boundary between gypsum-salt rock and limestone-dolomite.
[0031] S3, Model Design
[0032] The model design steps utilize information on the thickness, velocity, and density of different lithological strata, as well as the drilling data, to design a geological model.
[0033] Preferably, in the model design step, the thickness information of different lithological strata is used to draw a model of the distribution characteristics of the strata; and the lithology of the model is determined by the lithological changes in the longitudinal and transverse directions of each section and subsection of the actual drilled well; then the velocity and density of each lithology and reservoir section are set on the model to unify the model into a geological-physical model.
[0034] In the above steps, the geological model is designed based on the geological analysis results of the well data. First, the formation thickness and reservoir thickness of the Jia-2 and Jia-1 (sub-sections) are statistically determined through actual drilling in the area. Wells with abnormal formation thickness encountered due to faults are not selected. Second, the lithology of the model is determined based on the longitudinal and transverse lithological variations of each section (sub-section) of the actual drilled wells. If the lithological variations match those of the drilled wells, the forward modeling results will be closer to the reflection characteristics of the seismic traces near the well. After the lithology and thickness are determined, the velocity and density (velocity * density = wave impedance value) of each lithology and reservoir section are set using software, thereby obtaining the wave impedance value of each lithological section within each section (sub-section).
[0035] In this invention, the geological model is crucial. It is designed based on the thickness, velocity, and density information of actual formation reservoir sections and gypsum-salt layers obtained from well logging data. The thickness, velocity, and density information near the well is set according to actual drilling and logging data. Lithologies with varying thickness between wells are set in a wedge shape, while those without thickness variations are set in a horizontal layered shape. Because the lithological thickness varies between different blocks and sedimentary facies, the lithological thickness in the model must be determined according to the actual drilling and logging results.
[0036] The above steps utilize the thickness, velocity, and density information obtained in step S2, as well as the drilling data from step S1, to design a geological model by combining the thickness and lithological changes of each segment (sub-segment) of the Jialingjiang Formation obtained from actual drilling.
[0037] S4, Model Forward Modeling
[0038] The model forward modeling step involves performing forward modeling on the geological model using the wave equation forward modeling method to simulate the seismic wave propagation characteristics under different lithologies, velocities, and densities.
[0039] Preferably, in the forward modeling step, the geological model is forward modeled using a wave equation to simulate seismic waves, and a forward seismic profile is calculated. The forward seismic profile is obtained using a synthetic seismic record, which is equal to the convolution of the seismic wavelet and the reflection coefficient. The reflection coefficient is equal to the wave impedance value obtained by multiplying the velocities and densities of different rock layers, and is converted into a reflection coefficient using a formula.
[0040] In this invention, after the geological model is designed, forward modeling is mainly performed using specialized software, and seismic forward modeling is achieved through physical simulation. Based on the propagation principle of seismic waves in the subsurface medium, the seismic record of the established geological model is calculated using the wave equation forward modeling method. The foundation of geological model forward modeling lies in the fact that different rock strata have different velocities and densities, and their product is the wave impedance. The difference in wave impedance between adjacent strata will produce the reflection coefficient R. Assuming that the seismic wave is incident perpendicularly, the reflection coefficient of the normal incident wave can be calculated. In geological model forward modeling, selecting a suitable seismic wavelet is also crucial in determining whether the final forward modeling results match the actual seismic record. Generally, the Ricker wavelet (25Hz), which is close to the dominant frequency of the actual seismic data, is selected for forward modeling.
[0041] Preferably, in the model forward modeling step, the model forward modeling uses the Rick 25Hz wavelet. In this invention, the wave equation forward modeling method is adopted, and the Rick 25Hz wavelet, which is close to the dominant frequency of the seismic data, is selected for model forward modeling to achieve the purpose of accurately simulating the seismic wave propagation characteristics under different lithologies, velocities, and densities.
[0042] Wave equation forward modeling is a method that simulates seismic reflection characteristics by establishing a geological model, transforming geological facies into seismic facies. By obtaining the thickness and location (depth) of the reservoir, a model of the stratigraphic distribution characteristics is drawn on software. Then, given the velocity and density values of different lithologies calculated in the previous step, a unified geological-physical model is established. Seismic waves are then simulated using a 25Hz Ricker wavelet with a dominant frequency similar to that of the seismic data, and numerical simulation calculations are performed (automatic calculation by the software; the theoretical method is: the synthetic seismic record equals the convolution of the seismic wavelet and the reflection coefficient, where the reflection coefficient equals the wave impedance value obtained by multiplying the velocities and densities of adjacent different rock layers, and is converted to the reflection coefficient using a formula). This allows for the calculation of the corresponding forward-modeled seismic profile.
[0043] S5, Matching and Comparison
[0044] The matching and comparison step involves matching and comparing the forward modeling results with actual seismic data and making corrections to determine the seismic reflection characteristics of different lithologies or combinations of lithologies.
[0045] In the above steps, the geological model is designed based on the strata, thickness, velocity, and density of the actual drilled well. The selected Ricker wavelet is also close to the dominant frequency of the actual seismic data. Therefore, the forward modeling results of each well are basically consistent with the seismic reflection characteristics, amplitude, and wave group relationships of the pre-stack time migration profile near the well. If there are significant differences between the forward modeling results and the actual seismic reflection characteristics and wave group relationships near the well, the reasons should be analyzed, and the thickness of each stratum or lithological section in the model should be appropriately modified, or the velocity and density values should be slightly modified until the forward modeling results are in good agreement with the seismic traces near the well. Finally, it is necessary to analyze whether the reflection characteristics of the top and bottom boundaries of the reservoir section in the forward modeling results of the actual drilled well are consistent with the characteristics of the top and bottom boundaries of the reservoir in the actual traces near the well, thereby obtaining the ideal forward modeling results.
[0046] Preferably, in the matching and comparison step, the forward modeling results and the actual seismic migration profile are analyzed on the pre-stack time migration data volume. The wave group characteristics and reflection energy strength of the pyrolithic and gypsum-salt sections within the target layer are compared to determine the principles and characteristics of lateral comparison.
[0047] In this invention, the principle of lateral comparison refers to comparing the seismic reflection phase axis along a fixed geological interface. The characteristics refer to its waveform, amplitude, and energy. The purpose of determining the principle and characteristics of lateral comparison is to accurately identify a geological reflection interface on the pre-stack time migration profile that characterizes the underground geological structure, thereby confirming its structure and carrying out reservoir prediction work.
[0048] In the above steps, the waveform characteristics of each reflection interface obtained from forward modeling are applied to the comparison and tracking of actual seismic profile layers. This invention mainly targets marine carbonate rock strata, where the thickness of marine sediments is relatively stable. Therefore, we referenced the reflection at the bottom of Feisi Formation (peak reflection, continuous phase axis, easy to compare and track) which is close to the Jialingjiang Formation, and combined the model's forward modeling results with the Jialingjiang Formation's reflection at the bottom of Feisi Formation (peak reflection, continuous phase axis, easy to compare and track). 3 Bottom and Jia Er 2 The reliability is high when compared with the bottom.
[0049] S6, Planar Lithological Distribution Analysis
[0050] The planar lithological distribution analysis step involves extracting and analyzing the amplitude attributes of the seismic reflection characteristics of a certain interface to obtain a planar distribution map of the reservoir development zone and gypsum-salt distribution zone in the entire area. The gypsum-salt distribution zone is then removed to obtain the reservoir development zone.
[0051] Preferably, in the planar lithological distribution analysis step, the attribute is an attribute that characterizes the amplitude information of the seismic data; in the planar distribution map, the strong amplitude area is the gypsum-salt development area, and the weak amplitude area is the reservoir development area.
[0052] In this invention, the strong amplitude region corresponds to the gypsum-salt development region, and the thicker the gypsum-salt, the stronger the amplitude; the weak amplitude region is the gypsum-salt thinner (undeveloped) region, which is the relatively developed reservoir region.
[0053] Seismic attributes characterize the morphology, kinematics, dynamics, and statistical characteristics of seismic waves. According to the forward modeling results, the reflection characteristics of gypsum-salt layers and reservoirs differ significantly in amplitude. By selecting attributes that characterize the amplitude information of seismic data for analysis, a planar distribution map of reservoir development areas and gypsum-salt distribution areas in the entire region is obtained, thereby effectively identifying reservoir development areas.
[0054] In this invention, forward modeling results show that the reflection characteristics of gypsum-salt layers and reservoirs differ significantly in amplitude. Therefore, attributes characterizing the amplitude information of seismic data are selected for analysis. The waveform characteristics obtained from forward modeling can be used for layer correlation of actual seismic profiles, and the obtained layer data forms the basis for creating seismic attribute maps.
[0055] In this invention, a time-domain small-layer stratigraphic framework model is established using well stratification data and seismic horizon data. Amplitude attributes are extracted using software to generate an amplitude seismic facies plane map (i.e., a planar distribution map). Further analysis and verification of the seismic facies interpretation plane map are performed. First, the azimuth delineated by the seismic facies is cross-validated with the gypsum-salt distribution from the core wells. If inconsistencies arise, the seismic horizon is traced and compared for further verification. This determines that the strong amplitude areas on the seismic facies map are gypsum-salt development zones, and the distribution of gypsum-salt on the plane is determined.
[0056] The beneficial effects of this invention are:
[0057] This invention utilizes the velocity and density of actual drilling and logging data to create a forward-modeled geological model. By continuously adjusting the model's parameters, different forward-modeling results are obtained. These results are then compared with actual seismic data to determine the seismic reflection characteristics of different lithologies or lithology combinations. Finally, combined with amplitude attribute analysis, the distribution patterns of different lithologies are predicted. This method, by fully utilizing actual drilling and logging data to simulate the reflection characteristics of different lithologies, yields results that are more consistent with geological laws.
[0058] This invention utilizes forward modeling to simulate the seismic reflection characteristics of different lithologies and lithology combinations, which can reduce the ambiguity of information obtained from seismic data under complex media conditions and effectively identify reservoirs. Attached Figure Description
[0059] Figure 1 This is a schematic diagram of the method of the present invention;
[0060] Figure 2 A comprehensive columnar section of the Jialingjiang Formation in eastern Sichuan;
[0061] Figure 3 Well logging curves and lithology diagrams for the Jialingjiang Formation;
[0062] Figure 4 The model and forward modeling results of the Jialingjiang Formation;
[0063] Figure 5 This is a well offset profile;
[0064] Figure 6 This is a plan view of the gypsum-salt development zone and the reservoir development zone. Detailed Implementation
[0065] The following will provide a clear and complete description of the concept, specific structure, and technical effects of the present invention in conjunction with the embodiments and accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention.
[0066] Example 1
[0067] A method for removing salt deposits based on model forward modeling, such as Figure 1 The steps include data preparation, lithology calculation, model design, model forward modeling, matching and comparison, and planar lithology distribution analysis.
[0068] The data preparation steps include preparing basic data such as drilling data, lithological data, logging data, and pre-stack time migration data.
[0069] The lithology calculation step uses drilling and logging data to calculate the thickness, velocity, and density values of different lithological layers;
[0070] The model design steps utilize information on the thickness, velocity, and density of different lithological strata, as well as the drilling data, to design a geological model.
[0071] The model forward modeling step involves performing forward modeling on the geological model using the wave equation forward modeling method to simulate the seismic wave propagation characteristics under different lithologies, velocities, and densities.
[0072] The matching and comparison step involves matching and comparing the forward modeling results with actual seismic data and making corrections to determine the seismic reflection characteristics of different lithologies or combinations of lithologies.
[0073] The planar lithological distribution analysis step extracts and analyzes the amplitude attributes of seismic reflection characteristics to obtain a planar distribution map of the reservoir development zone and gypsum-salt distribution zone in the whole area. The gypsum-salt distribution zone is then removed to obtain the reservoir development zone.
[0074] The above method utilizes the velocity and density of actual drilling and logging data to create a forward-modeled geological model. By continuously adjusting the relevant parameters of the model, different forward-modeling results are obtained. These results are then matched and compared with actual seismic data to determine the seismic reflection characteristics of different lithologies or lithology combinations. Finally, combined with amplitude attribute analysis, the distribution patterns of different lithologies are predicted. This method, by fully utilizing actual drilling and logging data to simulate the reflection characteristics of different lithologies, yields results that are more consistent with geological laws. Furthermore, using forward modeling to simulate the seismic reflection characteristics of different lithologies and lithology combinations can compensate for the lack of understanding of the propagation laws of seismic waves in complex media.
[0075] Example 2
[0076] This embodiment further elaborates on the data preparation steps based on Embodiment 1. In the data preparation steps, drilling data is used for subsequent lithology calculations and model design; lithology data is used for subsequent S2 lithology calculations and S3 model design; well logging data is used in the subsequent S2 step to calculate the thickness, velocity, and density values of different lithologies; and pre-stack time migration data is used in the subsequent S5 step to determine the comparative analysis of seismic reflection characteristics of different lithologies or lithology combinations.
[0077] Specifically, the drilling data includes drilling coordinates, core elevation, well inclination data, and well location stratification; the lithological data includes lithological data of the main target strata; and the logging data includes sonic transit time, density, porosity, and gamma ray.
[0078] In the above steps, based on the drilling and logging data, completion geological reports, or logging (well logging) reports obtained from wells already drilled in the study area and adjacent areas, such as drilling coordinates, core filling elevation, geology (well logging stratification), cuttings data, well inclination data, and comprehensive interpretation results of well logging of the main target layer (sonic transit time, gamma curve, density curve, porosity curve, etc.), drilling data, lithological data, and well logging data are obtained.
[0079] In the data preparation step, seismic data of the study area are obtained through geophysical exploration, and the response of various underground strata to artificially induced seismic waves is observed and analyzed by utilizing the differences in elasticity and density of the underground medium. The pre-stack time migration data is then obtained by processing the data using professional indoor computer software.
[0080] In this embodiment, pre-stack time migration data is obtained by acquiring seismic data of the study area through geophysical exploration, and then processed using specialized indoor software. Specifically, by utilizing the differences in elasticity and density of the subsurface medium, and observing and analyzing the Earth's response to artificially induced seismic waves, pre-stack time migration data is obtained. Indoor pre-stack time migration processing of field seismic data is one of the most effective methods for imaging complex subsurface structures. It is suitable for situations where the propagation velocities of longitudinal and transverse seismic waves vary significantly across different strata, and is applicable to migration and repositioning imaging of complex structures.
[0081] Example 3
[0082] This embodiment further elaborates on the lithology calculation steps based on Embodiment 2.
[0083] 1. Calculation of rock stratum thickness
[0084] S211. Using the prepared drilling and logging data, the thickness of each section and subsection of the formation in each well is statistically analyzed, and the average thickness of each section and subsection of the formation is obtained as the thickness of each section and subsection of the rock strata.
[0085] In the above method, the thickness of the Jialingjiang Formation in the second and first (sub-) sections is statistically analyzed using the collected drilling and logging data, and the average value of each well is used to design the initial model. Since the Jialingjiang Formation is a marine carbonate rock formation, the thickness of each (sub-) section is relatively stable in the lateral direction, so the thickness of each (sub-) section in the initial model is consistent.
[0086] S212. Statistically analyze the thickness of different lithologies within each section and subsection of each well, and obtain the average thickness of different lithologies within each section and subsection of the strata as the thickness of different lithologies within each section and subsection of the strata.
[0087] In the above method, due to subtle differences in sedimentary environment, the thickness of different lithologies will vary laterally. By statistically analyzing the thickness of different lithologies within each well in the Jia-2 and Jia-1 sections of the area, the thickness of different lithologies within each (sub)section was designed and calculated.
[0088] 2. Calculation of rock strata velocity
[0089] In the logging data, the wellbore curve is analyzed, and the part of the wellbore curve with collapsed section is removed. Then, in the logging data after removing the collapsed section, the sonic curve of different lithological sections of each well is taken, and the average sonic velocity of different lithologies of each well is calculated as the velocity of different lithological strata.
[0090] In the above method, the collected well logging data is used to obtain the sonic curves (velocities) of different lithologies (gypsum, limestone and dolomite) in each well, and the average velocity of different lithologies in each well is calculated.
[0091] 3. Calculation of rock layer density
[0092] In the logging data, the wellbore curve is analyzed, and the portion of the wellbore curve with collapsed sections is removed. Then, in the logging data after removing the collapsed sections, the density curves of different lithological sections of each well are taken, and the average density of different lithologies in each well is calculated as the density of different lithological layers.
[0093] In the above method, the density curves of different lithologies (gypsum, limestone and dolomite) in each well are obtained using the collected well logging data, and the average density of different lithologies in each well is calculated.
[0094] In this embodiment, the lithology of the Jia-2 section is complex both longitudinally and transversely, with differences in velocity and density among various lithologies. By analyzing the well logging curves, the average values of velocity and density for different lithological sections are obtained. Note that the wellbore curve must first be analyzed, and sections with collapsed sections should be removed to ensure the accuracy of the velocity and density values. Figure 3 As shown.
[0095] In this embodiment, by analyzing the rock electrical characteristics of the core well, it was found that the lithology of the second section of the Jialingjiang Formation is relatively complex in the vertical direction, with gypsum, dolomite and limestone interbedded. The electrical characteristics of different lithological sections are quite different, and the interbedded gypsum-salt and reservoir dolomite show the most obvious distinction in density curve response.
[0096] In this embodiment, since the rock-electric correlation of the core well is good, the logging curves corresponding to different lithologies can be marked in the software, and their average values can be automatically calculated. Among them, gypsum rock has a velocity of about 5500 m / s, a density of 2.88 g / cc, and an impedance value of 16700 m / s*g / cc; limestone has a velocity of about 6000 m / s, a density of 2.78 g / cc, and an impedance value of 16680 m / s*g / cc; while dolomite has a velocity of about 6300 m / s, a density of 2.82 g / cc, and an impedance value of 17200 m / s*g / cc.
[0097] In open-hole wells, due to the formation being flushed and soaked by drilling fluid, phenomena such as drilling fluid intrusion or dissolution of the formation can easily occur in different lithologies. These phenomena can be delineated and verified using wellbore curves. For denser limestone and dolomite, which have poor permeability and are relatively hard, the wellbore is less affected, and the wellbore curve shows almost no change. However, for gypsum-salt rock, which is easily dissolved and collapses, the wellbore will significantly enlarge, resulting in abrupt changes in the wellbore curve and a high value. This allows for a precise depiction of the boundary between gypsum-salt rock and limestone-dolomite.
[0098] Example 4
[0099] This embodiment further elaborates on the model design steps based on Embodiment 3. In the model design steps, the thickness information of different lithological strata is used to draw a model of the distribution characteristics of the strata; the lithology of the model is determined by the lithological changes in the longitudinal and transverse directions of each section and subsection of the actual drilled well; and the velocity and density of each lithology and reservoir section are set on the model to unify the model into a geological-physical model.
[0100] In the above steps, the geological model is designed based on the geological analysis results of the well data. First, the formation thickness and reservoir thickness of the Jia-2 and Jia-1 (sub-sections) are statistically determined through actual drilling in the area. Wells with abnormal formation thickness encountered due to faults are not selected. Second, the lithology of the model is determined based on the longitudinal and transverse lithological variations of each section (sub-section) of the actual drilled wells. If the lithological variations match those of the drilled wells, the forward modeling results will be closer to the reflection characteristics of the seismic traces near the well. After the lithology and thickness are determined, the velocity and density (velocity * density = wave impedance value) of each lithology and reservoir section are set using software, thereby obtaining the wave impedance value of each lithological section within each section (sub-section).
[0101] In this embodiment, the geological model is crucial. It is designed based on the thickness, velocity, and density information of the actual formation reservoir sections and gypsum-salt layers obtained from well logging data. The thickness, velocity, and density information near the well is set according to actual drilling and logging data. Lithologies with varying thickness between wells are set in a wedge shape, while those without thickness variations are set in a horizontal layered shape. Because the lithological thickness varies between different blocks and facies zones, the lithological thickness in the model must be determined according to the actual drilling and logging results. Figure 4 As shown.
[0102] The above steps utilize the thickness, velocity, and density information obtained in step S2, as well as the drilling data from step S1, to design a geological model by combining the thickness and lithological changes of each segment (sub-segment) of the Jialingjiang Formation obtained from actual drilling.
[0103] Example 5
[0104] This embodiment further elaborates on the forward modeling steps based on Embodiment 4. In the forward modeling steps, the geological model is forward modeled using wave equations to simulate seismic waves, and a forward seismic profile is calculated. The forward seismic profile is obtained using synthetic seismic records, which are equal to the convolution of the seismic wavelet and the reflection coefficient. The reflection coefficient is equal to the wave impedance value obtained by multiplying the velocities and densities of different rock layers, and is converted into the reflection coefficient using a formula.
[0105] In this embodiment, after the geological model is designed, forward modeling is mainly performed using specialized software, and seismic forward modeling is achieved through physical simulation. Based on the propagation principle of seismic waves in the subsurface medium, the seismic record of the established geological model is calculated using the wave equation forward modeling method. The foundation of geological model forward modeling lies in the fact that different rock layers have different velocities and densities, and their product is the wave impedance. The difference in wave impedance between adjacent strata will produce the reflection coefficient R. Assuming that the seismic wave is incident perpendicularly, the reflection coefficient of the normal incident wave can be calculated. In geological model forward modeling, selecting a suitable seismic wavelet is also crucial in determining whether the final forward modeling results match the actual seismic record. Generally, the Ricker wavelet (25Hz), which is close to the dominant frequency of the actual seismic data, is selected for forward modeling.
[0106] In the model forward modeling step, the model forward modeling uses the Ricker 25Hz wavelet. In this embodiment, the wave equation forward modeling method is used, and the Ricker 25Hz wavelet, which is close to the dominant frequency of the seismic data, is selected for model forward modeling to achieve the purpose of accurately simulating the seismic wave propagation characteristics under different lithologies, velocities, and densities. Figure 4 As shown.
[0107] Wave equation forward modeling is a method that simulates seismic reflection characteristics by establishing a geological model, transforming geological facies into seismic facies. By obtaining the thickness and location (depth) of the reservoir, a model of the stratigraphic distribution characteristics is drawn on software. Then, given the velocity and density values of different lithologies calculated in the previous step, a unified geological-physical model is established. Seismic waves are then simulated using a 25Hz Ricker wavelet with a dominant frequency similar to that of the seismic data, and numerical simulation calculations are performed (automatic calculation by the software; the theoretical method is: the synthetic seismic record equals the convolution of the seismic wavelet and the reflection coefficient, where the reflection coefficient equals the wave impedance value obtained by multiplying the velocities and densities of adjacent different rock layers, and is converted to the reflection coefficient using a formula). This allows for the calculation of the corresponding forward-modeled seismic profile.
[0108] The results show that: Jia Er 3 For a wave peak near zero-point reflection; Jia Er 2 The bottom reflection is weak, consisting of a weak peak or a weak trough reflection.
[0109] Example 6
[0110] This embodiment further elaborates on the matching and comparison steps based on Embodiment 5. In this matching and comparison step, the geological model is designed based on the strata, thickness, velocity, and density of the actual drilled well. The selected Ricker wavelet is also close to the dominant frequency of the actual seismic data. Therefore, the forward modeling results of each well are basically consistent with the seismic reflection characteristics, amplitude, and wave group relationships of the pre-stack time migration profile near the well. If there are significant differences between the forward modeling results and the actual seismic reflection characteristics and wave group relationships near the well, the reasons should be analyzed, and the thickness of each stratum or lithological section in the model should be appropriately modified, or the velocity and density values should be slightly modified until the forward modeling results closely match the seismic traces near the well. Finally, the reflection characteristics of the top and bottom boundaries of the reservoir section in the forward modeling results of the actual drilled well are analyzed to see if they are consistent with the characteristics of the top and bottom boundaries of the reservoir in the actual well traces, thereby obtaining the ideal forward modeling results.
[0111] In the matching and comparison step, the forward modeling results are used to guide the fine correlation of the bottom boundary strata of each sub-section within the Jia-2 seismic stratum. On the pre-stack time migration data volume, the forward modeling results are analyzed against the actual seismic migration profiles, mainly comparing the wave group characteristics and reflection energy intensity relationships of the dolomite and gypsum-salt sections within the target stratum to determine the principles and characteristics of lateral correlation, such as... Figure 5 As shown.
[0112] In this embodiment, the lateral comparison principle refers to comparing the seismic reflection phase axis along a fixed geological interface. The characteristics refer to its waveform, amplitude, and energy. The purpose of determining the lateral comparison principle and characteristics is to accurately identify a geological reflection interface on the pre-stack time migration profile characterizing the subsurface geological structure, thereby confirming its structure and carrying out reservoir prediction work. In the above steps, the waveform characteristic results of each reflection interface obtained from forward modeling are applied to the comparison and tracking of actual seismic profile layers. This embodiment mainly targets marine carbonate rock strata. Marine sedimentary thickness is relatively stable, so we refer to the Feisi bottom reflection (peak reflection, continuous phase axis, easy to compare and track) which is close to the Jialingjiang Formation. Simultaneously, we combine the model forward modeling results with the Jialingjiang Formation's... 3 Bottom and Jia Er 2 The reliability is high when compared with the bottom.
[0113] Example 7
[0114] This embodiment further elaborates on the planar lithological distribution analysis steps based on Embodiment 6. In the planar lithological distribution analysis steps, the attribute is an attribute characterizing the amplitude information of seismic data; in the planar distribution map, areas with strong amplitude are gypsum-salt development areas, and areas with weak amplitude are reservoir development areas.
[0115] In this embodiment, the strong amplitude region corresponds to the gypsum-salt development region. The thicker the gypsum-salt, the stronger the amplitude. The weak amplitude region is the gypsum-salt thinner (undeveloped) region, which is the relatively developed reservoir region.
[0116] Seismic attributes characterize the morphology, kinematics, dynamics, and statistical features of seismic waves. Based on forward modeling results, the reflection characteristics of gypsum-salt layers and reservoirs show significant differences in amplitude. Attributes characterizing the amplitude information of seismic data are selected for analysis to obtain planar distribution maps of reservoir development zones and gypsum-salt distribution zones across the entire area, thereby effectively identifying reservoir development zones. Figure 6 As shown.
[0117] In this embodiment, the forward modeling results show that the reflection characteristics of the gypsum-salt layer and the reservoir differ significantly in amplitude. Therefore, attributes characterizing the amplitude information of seismic data are selected for analysis. The waveform characteristics obtained from the forward modeling can be used for layer correlation of actual seismic profiles, and the obtained layer data forms the basis for creating seismic attribute maps.
[0118] Using well stratigraphic data and seismic horizon data, a time-domain small-layer stratigraphic framework model was established, and amplitude attributes were extracted using software to create an amplitude seismic facies plane map (i.e., a planar distribution map). Further analysis and verification of the seismic facies interpretation plane map were conducted. First, the azimuth delineation of the seismic facies was cross-validated with the gypsum-salt distribution from the core wells. If inconsistencies were found, the seismic horizon was traced and compared for further verification. This determined that the strong amplitude areas on the seismic facies map were gypsum-salt development zones, and the distribution of gypsum-salt on the plane was determined.
[0119] The embodiments of the present invention have been described in detail above, but the present invention is not limited to the described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalents or substitutions are all included within the scope defined by the claims of the present invention.
Claims
1. A method for removing salt-oil layers based on model forward modeling, characterized in that, This includes steps such as data preparation, lithology calculation, model design, model forward modeling, matching and comparison, and planar lithology distribution analysis. The data preparation steps include preparing basic data such as drilling data, lithological data, logging data, and pre-stack time migration data. The lithology calculation step utilizes drilling and logging data to calculate the thickness, velocity, and density values of different lithological layers; wherein: The method for calculating the thickness of the rock strata is as follows: using the prepared drilling and logging data, the thickness of each section and subsection of the strata in each well is statistically analyzed, and the average thickness of each section and subsection of the strata is obtained as the thickness of each section and subsection of the rock strata; then, the thickness of the rock strata of different lithologies within each section and subsection of the strata in each well is statistically analyzed, and the average thickness of the rock strata of different lithologies within each section and subsection of the strata is obtained as the thickness of the rock strata of different lithologies within each section and subsection of the strata. The method for calculating the rock layer velocity is as follows: In the logging data, analyze the wellbore curve, remove the part of the wellbore curve with collapsed section, and then in the logging data after removing the collapsed section, take the sonic curve of different lithological sections of each well, calculate the average sonic velocity of different lithologies of each well, and use it as the velocity of different lithological rock layers. The method for calculating the density of the rock layer is as follows: In the logging data, analyze the well diameter curve, remove the part of the well diameter curve with a collapsed section, and then in the logging data after removing the collapsed section, take the density curve of different lithological sections of each well, calculate the average density of different lithologies of each well, and use it as the density of different rock layers. The model design steps utilize information on the thickness, velocity, and density of different lithological strata, as well as the drilling data, to design a geological model. The forward modeling step involves performing forward modeling on the geological model using the wave equation forward modeling method to simulate the seismic wave propagation characteristics under different lithologies, velocities, and densities. The forward seismic profile is calculated by simulating seismic waves on the geological model using the wave equation method. The forward seismic profile is obtained using synthetic seismic records, which are equal to the convolution of the seismic wavelet and the reflection coefficient. The reflection coefficient is equal to the wave impedance value obtained by multiplying the velocities and densities of different rock layers, and is converted into a reflection coefficient using a formula. The matching and comparison step involves matching and comparing the forward modeling results with actual seismic data and making corrections to determine the seismic reflection characteristics of different lithologies or combinations of lithologies. The planar lithological distribution analysis step extracts and analyzes the amplitude attributes of seismic reflection characteristics to obtain a planar distribution map of the reservoir development zone and gypsum-salt distribution zone in the whole area. The gypsum-salt distribution zone is then removed to obtain the reservoir development zone.
2. The method for removing salt layers based on model forward modeling as described in claim 1, characterized in that, In the data preparation step, seismic data of the study area are obtained through geophysical exploration, and the response of various underground strata to artificially induced seismic waves is observed and analyzed by utilizing the differences in elasticity and density of the underground medium. The pre-stack time migration data is then obtained by processing the data using professional indoor computer software.
3. The method for removing the salt layer based on model forward modeling as described in claim 1, characterized in that, In the model design steps, the thickness information of different lithological strata is used to draw a model of the distribution characteristics of the strata; the lithology of the model is determined by the lithological changes in the longitudinal and transverse directions of each section and subsection of the actual drilled well; and the velocity and density of each lithology and reservoir section are set on the model to unify the model into a geological-physical model.
4. The method for removing the salt layer based on model forward modeling as described in claim 1, characterized in that, In the model forward modeling step, the model forward modeling is performed using the Lake 25Hz wavelet.
5. The method for removing salt layers based on model forward modeling as described in claim 1, characterized in that, In the matching and comparison step, the forward modeling results and the actual seismic migration profiles are analyzed on the pre-stack time migration data volume. The wave group characteristics and reflection energy strength of the pyrolith and gypsum-salt sections within the target layer are compared to determine the principles and characteristics of lateral comparison.
6. The method for removing salt layers based on model forward modeling as described in claim 1, characterized in that, In the planar lithological distribution analysis step, the attribute is an attribute that characterizes the amplitude information of the seismic data; in the planar distribution map, the strong amplitude area is the gypsum-salt development area, and the weak amplitude area is the reservoir development area.