Method and device for predicting cave type reservoir bodies in carbonate rock stratum

Abstract

The invention provides a prediction method and device for a cave type reservoir body in a carbonate rock stratum. The prediction method for the cave type reservoir body in the carbonate rock stratum comprises the steps that a straight plate low-frequency model of a target work area is generated according to logging data of the target work area; performing constrained sparse pulse inversion on the straight plate low-frequency model according to the seismic data of the target work area to generate a cave low-frequency model; in the cave low-frequency model, endowing a wave impedance value of carbonate rock as a wave impedance value of mudstone to generate a cave type reservoir body prediction model so as to identify a cave type reservoir body of the target work area; wherein the cave type reservoir body is not filled with sand shale. According to the method, the limitation of the prior art is overcome, the wave impedance difference between the sand shale and the oil-gas-water is amplified, and accurate prediction of the effective cave type reservoir body is realized.

Description

Technical Field

[0001] The present application belongs to the technical field of oil and gas exploration, in particular to the technical field of oil and gas exploration using geophysics and geology, and specifically to a method and device for predicting cave-type reservoirs in carbonate formations. Background Art

[0002] Cavernous reservoirs, formed by fluid dissolution and alteration of carbonate formations, typically reach tens of meters in vertical depth and offer promising exploration prospects. However, cavernous reservoirs developed within carbonate formations are often filled by the collapse of overlying sandstone and mudstone strata. Predicting caverns not filled by sandstone and mudstone has become a geophysical challenge in recent years.

[0003] In the existing technology, Li Haiying et al. (2020) proposed to use the structural tensor attributes to determine the boundary threshold value to characterize the boundary of the fault-karst body, and established a low-frequency model with the structural tensor body as a constraint, performed constrained sparse pulse inversion, determined the cave contour characteristics in the fault-karst body, used the AFE attribute to characterize the distribution of fractures, and combined the weak attribute fusion technology to carve the spatial structure of the fault-karst body. This method only characterizes the boundary of the cave-type reservoir body, and does not distinguish whether the cave is filled (Li Haiying, Liu Jun, Gong Wei, et al. Identification and description technology of strike-slip faults and fault-karst body closures in Shunbei area [J]. China Petroleum Exploration, 2020, 25(03):107-120.). Ma Yuchun et al. (2022) mainly established a straight low-frequency model of carbonate rocks to obtain beaded seismic phases, and based on the low-frequency model and beaded seismic phases, obtained a phase-controlled low-frequency model, carried out constrained sparse pulse inversion, obtained a longitudinal wave impedance body, and characterized the vug-fracture reservoirs in the fault-karst body. This method only qualitatively characterized the vug-fracture reservoirs and did not distinguish the effectiveness of cave-type reservoirs (Ma Yuchun. Method and electronic equipment for characterizing the internal structure of carbonate fault-karst bodies by phase-controlled inversion [P]. China: CN114428356A, 2022.). Zhang Wenbiao et al. (2022) established the configuration of the fault-karst carbonate reservoir to be evaluated based on different dissolution stages by analyzing the geological genesis process of the fault-karst body and combining it with the dissolution and filling formation mechanism; based on the three-dimensional seismic model and well logging interpretation data, the fault-karst body characterization analysis was carried out according to the stage of the configuration. However, the accuracy of this method in characterizing the fault-karst reservoir based on the dissolution and filling stage is somewhat insufficient (Zhang Wenbiao. A characterization method and system for fault-karst carbonate reservoirs [P]. China: CN114114452A, 2022.).

[0004] The above-mentioned existing carbonate rock reservoir prediction technologies all have limitations. That is, the prediction methods for cave-type reservoirs can mainly depict the cave outline, but there are few prediction methods that can distinguish whether the cave is filled with sandstone or mudstone. In summary, there is an urgent need to develop a method for quantitatively predicting effective cave-type reservoirs. Summary of the Invention

[0005] One objective of the present invention is to provide a method for predicting cavernous reservoirs in carbonate formations. This method overcomes the limitations of existing technologies, amplifies the wave impedance difference between sandstone and mudstone, and oil, gas, and water, and enables accurate prediction of effective cavernous reservoirs.

[0006] Another object of the present invention is to provide a device for predicting cavernous reservoirs in carbonate formations. A further object of the present invention is to provide an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the aforementioned method for predicting cavernous reservoirs in carbonate formations are implemented. A further object of the present invention is to provide a readable medium having a computer program stored thereon, and when the processor executes the computer program, the steps of the aforementioned method for predicting cavernous reservoirs in carbonate formations are implemented.

[0007] In order to solve the technical problems in the background technology of this application, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for predicting cave-type reservoirs in carbonate formations, comprising:

[0009] generating a straight plate low-frequency model of the target work area according to the well logging data of the target work area;

[0010] Performing constrained sparse pulse inversion on the straight plate low-frequency model according to the seismic data of the target work area to generate a cave low-frequency model;

[0011] In the cave low-frequency model, the wave impedance value of carbonate rock is assigned as the wave impedance value of mudstone to generate a cave-type reservoir prediction model to predict the cave-type reservoir in the target work area; wherein the cave-type reservoir is not filled with sandstone and mudstone.

[0012] In some embodiments of the present invention, a method for predicting cave-type reservoirs in carbonate formations further includes:

[0013] The wave impedance value of the mudstone is determined based on the core data of the target work area and the well logging data.

[0014] In some embodiments of the present invention, determining the wave impedance value of the mudstone according to the core data of the target work area and the well logging data includes:

[0015] determining the depth of the mudstone-filled cave based on the core data;

[0016] The wave impedance value of the mudstone is determined according to the acoustic transit time logging curve and the density logging curve corresponding to the depth.

[0017] In some embodiments of the present invention, a method for predicting cave-type reservoirs in carbonate formations further includes:

[0018] The wave impedance value of the carbonate rock is determined according to the cave low-frequency model.

[0019] In some embodiments of the present invention, determining the wave impedance value of the carbonate rock according to the cave low-frequency model includes:

[0020] An iterative constrained sparse pulse inversion is performed on the cave low-frequency model to determine the wave impedance value of the carbonate rock.

[0021] In some embodiments of the present invention, a method for predicting cave-type reservoirs in carbonate formations further includes:

[0022] The approximate range of the wave impedance of the cave-type reservoir is determined based on the cave low-frequency model.

[0023] In some embodiments of the present invention, in the cave low-frequency model, the wave impedance value of carbonate rock is assigned as the wave impedance value of mudstone to generate a cave-type reservoir prediction model, and the cave-type reservoir in the target work area is predicted, including:

[0024] Within the approximate range of the wave impedance of the cave-type reservoir, the accurate range of the wave impedance of the cave-type reservoir is determined according to the cave-type reservoir prediction model to predict the cave-type reservoir in the target work area.

[0025] In a second aspect, the present invention provides a device for predicting cave-type reservoirs in carbonate formations, the device comprising:

[0026] A straight plate low-frequency model generation module is used to generate a straight plate low-frequency model of the target work area according to the well logging data of the target work area;

[0027] a cave low-frequency model generation module, configured to perform constrained sparse pulse inversion on the straight plate low-frequency model according to the seismic data of the target work area to generate a cave low-frequency model;

[0028] The cave-type reservoir prediction module is used to assign the wave impedance value of carbonate rock to the wave impedance value of mudstone in the cave low-frequency model to generate a cave-type reservoir prediction model and predict the cave-type reservoir in the target work area; wherein the cave-type reservoir is not filled with sandstone and mudstone.

[0029] In some embodiments of the present invention, a device for predicting cave-type reservoirs in carbonate formations further includes:

[0030] The mudstone wave impedance value determination module is used to determine the wave impedance value of the mudstone according to the core data of the target work area and the well logging data.

[0031] In some embodiments of the present invention, the mudstone wave impedance value determination module includes:

[0032] a mudstone cave depth determination unit, configured to determine the depth of the mudstone-filled cave based on the core data;

[0033] The mudstone wave impedance value determining unit is used to determine the wave impedance value of the mudstone according to the acoustic time difference logging curve and the density logging curve corresponding to the depth.

[0034] In some embodiments of the present invention, a device for predicting cave-type reservoirs in carbonate formations further includes:

[0035] The carbonate rock wave impedance value determination module is used to determine the wave impedance value of the carbonate rock according to the cave low-frequency model.

[0036] In some embodiments of the present invention, the carbonate rock wave impedance value determination module includes:

[0037] The carbonate rock wave impedance value determination unit is used to perform iterative constrained sparse pulse inversion on the cave low-frequency model to determine the wave impedance value of the carbonate rock.

[0038] In some embodiments of the present invention, a device for predicting cave-type reservoirs in carbonate formations further includes:

[0039] The approximate range determination module is used to determine the approximate range of the wave impedance of the cave-type reservoir based on the cave low-frequency model.

[0040] In some embodiments of the present invention, the cavernous reservoir prediction module includes:

[0041] The cave type reservoir prediction unit is used to determine the accurate range of the wave impedance of the cave type reservoir according to the cave type reservoir prediction model within the approximate range of the wave impedance of the cave type reservoir, so as to predict the cave type reservoir in the target work area.

[0042] In a third aspect, the present invention provides a computer program product comprising a computer program / instruction, which, when executed by a processor, implements the steps of a method for predicting cave-type reservoirs in carbonate formations.

[0043] In a fourth aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of a method for predicting cave-type reservoirs in carbonate formations are implemented.

[0044] In a fifth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for predicting cave-type reservoirs in carbonate formations.

[0045] From the above description, it can be seen that an embodiment of the present invention provides a method and device for predicting cave-type reservoirs in carbonate rock formations. The corresponding method for predicting cave-type reservoirs in carbonate rock formations includes: first, generating a straight low-frequency model of the target work area based on the well logging data of the target work area; then, performing constrained sparse pulse inversion on the straight low-frequency model based on the seismic data of the target work area to generate a cave low-frequency model; finally, in the cave low-frequency model, the wave impedance value of the carbonate rock is assigned the wave impedance value of the mudstone to generate a cave-type reservoir prediction model to identify the cave-type reservoirs in the target work area; wherein the cave-type reservoirs are not filled with sandstone and mudstone.

[0046] The corresponding device for predicting cave-type reservoirs in carbonate rock formations includes: a straight low-frequency model generation module, which is used to generate a straight low-frequency model of the target work area based on the well logging data of the target work area; a cave low-frequency model generation module, which is used to perform constrained sparse pulse inversion on the straight low-frequency model based on the seismic data of the target work area to generate a cave low-frequency model; and a cave-type reservoir prediction module, which is used to assign the wave impedance value of carbonate rock to the wave impedance value of mudstone in the cave low-frequency model to generate a cave-type reservoir prediction model to predict the cave-type reservoirs in the target work area; wherein the cave-type reservoirs are not filled with sandstone or mudstone.

[0047] The method for predicting cave-type reservoirs in carbonate formations provided by the embodiment of the present invention overcomes the limitations of existing technologies, amplifies the wave impedance difference between sandstone and mudstone and oil, gas and water, and realizes the prediction of effective cave-type reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A schematic flow chart of a method for predicting cave-type reservoirs in carbonate formations according to an embodiment of the present invention;

[0050] Figure 2 1 is another flow chart of a method for predicting cave-type reservoirs in carbonate formations according to an embodiment of the present invention;

[0051] Figure 3This is a flow chart of step 400 of a method for predicting cave-type reservoirs in carbonate formations according to an embodiment of the present invention;

[0052] Figure 4 This is a third flow chart of a method for predicting cave-type reservoirs in carbonate formations according to an embodiment of the present invention;

[0053] Figure 5 This is a fourth flow chart of a method for predicting cave-type reservoirs in carbonate formations according to an embodiment of the present invention;

[0054] Figure 6 This is a flow chart of step 300 of a method for predicting cave-type reservoirs in carbonate formations according to an embodiment of the present invention;

[0055] Figure 7 This is a schematic flow chart of a method for predicting cave-type reservoirs in carbonate formations in a specific embodiment of the present invention;

[0056] Figure 8 A mind map of a method for predicting cave-type reservoirs in carbonate formations in a specific embodiment of the present invention;

[0057] Figure 9 Schematic diagram of a cross section of a straight plate low-frequency model passing through well A in a specific embodiment of the present invention;

[0058] Figure 10 This is a schematic diagram of a low-frequency model cross section through Well A in a specific embodiment of the present invention;

[0059] Figure 11 Schematic diagram of the inversion section through Well A in a specific embodiment of the present invention;

[0060] Figure 12 This is a schematic diagram of the inversion assignment section through Well A in a specific embodiment of the present invention;

[0061] Figure 13 Schematic diagram of the inversion section through Well B in a specific embodiment of the present invention;

[0062] Figure 14 A schematic diagram of a cross section of a mudstone wave impedance value assigned to the wave impedance value of the surrounding rock of Well B in a specific embodiment of the present invention;

[0063] Figure 15 This is a block diagram of a device for predicting cave-type reservoirs in carbonate formations according to an embodiment of the present invention;

[0064] Figure 16 Schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0066] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices. The embodiments in this application and the features described in the embodiments may be combined with each other unless there is a conflict. The present application will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0068] The acquisition, storage, use, and processing of data in the technical solution of this application comply with relevant laws and regulations.

[0069] Example 1:

[0070] As exploration progresses, carbonate oil and gas reservoirs are increasingly becoming dominant. Cavernous reservoirs primarily develop within carbonate formations, but they are often filled by the collapse of overlying sandstone and mudstone formations. Predicting cavernous reservoirs unfilled by sandstone and mudstone is crucial for future well placement. Carbonate rocks exhibit significant differences in wave impedance from clastic rocks, oil, gas, and water. While there are also some differences in wave impedance between clastic rocks and oil, gas, and water, these differences are much smaller than those with carbonate rocks. Therefore, conventional reservoir inversion prediction methods cannot accurately distinguish unfilled caverns.

[0071] Based on the above reasons, the embodiment of the present invention provides a specific implementation method of a method for predicting cave-type reservoirs in carbonate formations, see Figure 1 , specifically including the following contents:

[0072] Step 100: generating a straight plate low-frequency model of the target work area according to the well logging data of the target work area;

[0073] Step 200: performing constrained sparse pulse inversion on the straight plate low-frequency model according to the seismic data of the target work area to generate a cave low-frequency model;

[0074] Step 300: In the cave low-frequency model, the wave impedance value of carbonate rock is assigned as the wave impedance value of mudstone to generate a cave-type reservoir prediction model to predict the cave-type reservoir in the target work area; wherein the cave-type reservoir is not filled with sandstone and mudstone.

[0075] From the above description, it can be seen that an embodiment of the present invention provides a method for predicting cave-type reservoirs in carbonate formations, including: first, generating a straight low-frequency model of the target work area based on the well logging data of the target work area; then, performing constrained sparse pulse inversion on the straight low-frequency model based on the seismic data of the target work area to generate a cave low-frequency model; finally, in the cave low-frequency model, the wave impedance value of the carbonate rock is assigned the wave impedance value of the mudstone to generate a cave-type reservoir prediction model to identify the cave-type reservoirs in the target work area; wherein the cave-type reservoirs are not filled with sandstone and mudstone.

[0076] The method for predicting cave-type reservoirs in carbonate formations provided by the embodiment of the present invention overcomes the limitations of existing technologies, amplifies the wave impedance difference between sandstone and mudstone and oil, gas and water, and realizes the prediction of effective cave-type reservoirs.

[0077] Specifically, the present invention uses iterative constrained sparse pulse inversion to assign the wave impedance value of mudstone to carbonate rocks, highlighting the wave impedance difference between sandstone and mudstone and oil, gas and water, and accurately predicting effective reservoirs not filled with sandstone and mudstone. First, a straight low-frequency model of carbonate rocks is established, and constrained sparse pulse inversion is performed to determine the cave threshold value. A low-frequency model reflecting caves is established, and then iterative constrained sparse pulse inversion is performed. Then, the wave impedance value of the mudstone filling the cave is determined using logging and well data. The wave impedance value of mudstone is assigned to carbonate rocks, highlighting the wave impedance difference between sandstone and mudstone and oil, gas and water, which is transformed into predicting oil, gas and water in clastic rock formations. Finally, a threshold value for the effective reservoir is given, and cave-type reservoirs not filled with sandstone and mudstone are depicted in carbonate formations.

[0078] Example 2:

[0079] Regarding step 100, in geophysical logging, a straight plate model generally refers to a simplified formation model that assumes that the formation is uniform and horizontal in the logging area, that is, the formation is straight and has consistent physical properties. This model is used to interpret and process logging data.

[0080] When implementing step 100, a formation model (e.g., a straight plate model) is first used as a background, combined with the results of rock physics analysis. The low-frequency components of the model (typically those that cannot be directly resolved in seismic data interpretation, such as velocity and density variations in the background formation) are defined using well logging data and their interpretation. For carbonate rocks, the focus is on the effect of pore structure on seismic wave velocity and attenuation. This model is used to simulate seismic attributes, such as reflection coefficient and velocity. The simulation results are calibrated to ensure they are consistent with the actual seismic data.

[0081] Preferably, the established low-frequency model can be used to combine high-resolution well logging data with lower-resolution seismic data to perform seismic inversion to extract more detailed formation attribute information from the seismic data.

[0082] Use independent data sets, such as well log data from newly drilled wells, to validate the accuracy of the model.

[0083] Regarding step 200, constrained sparse spike inversion (CSSI) is a seismic data processing technique that combines the concept of sparse inversion with certain constraints to improve the reliability and resolution of the inversion results. In step 200, the goal of sparse inversion is to reconstruct the reflection coefficient sequence of the formation from the seismic data, which results in a sparse solution in which only points near the formation interface have significant reflection coefficients. In this method, sparsity is promoted by using L1 norm regularization (compared to L2 norm) during the inversion process. The L1 norm tends to produce many zero or near-zero coefficients while retaining a few larger coefficients.

[0084] In this application, CSSI adds additional constraints on the basis of sparse pulse inversion. These constraints can be based on a variety of prior knowledge, including but not limited to:

[0085] Geological constraints: Use geological models to guide the inversion process and ensure that the inversion results are consistent with known stratigraphic structure and lithologic variations.

[0086] Physical constraints: These include minimizing non-physical solutions, such as negative acoustic impedance, or ensuring that the acoustic impedance is within a reasonable physical range.

[0087] Smoothness constraint: Smooths the solution to remove high-frequency noise that is unlikely to be a true reflection of the geology.

[0088] Amplitude constraint: By limiting the dynamic range of the inversion results, it is consistent with the actual formation reflection intensity.

[0089] Phase constraint: Ensures that the phase of the inversion results matches the seismic data to obtain the correct bed interface location.

[0090] Sparsity constraint: Improving the sparsity of the solution through L1 norm regularization or other methods can help reflect the sharpness of the geological interface.

[0091] The constrained sparse pulse inversion of the straight plate low-frequency model based on the seismic data of the target area involves the following steps:

[0092] Apply the above constraints to form an optimization problem, which is then solved using an algorithm (such as linear programming, Bayesian methods, or iterative reweighted least squares). Validate the inversion results, which may include comparison with other geological or geophysical data.

[0093] Constrained sparse pulse inversion helps to extract more precise formation parameters that are more consistent with actual geological conditions, and can enhance the resolution and credibility of seismic inversion, especially in the identification of complex geological bodies or reservoir characteristics.

[0094] It can be understood that step 300 amplifies the wave impedance difference between sandstone and oil, gas and water in the cave-type reservoir, and changes the purpose to predict oil, gas and water in sandstone.

[0095] In addition, the cave-type reservoir in step 300, also known as a karst reservoir, is a reservoir formed by groundwater dissolution in soluble rocks such as carbonate rocks or evaporites. This type of reservoir has unique geological characteristics, including high heterogeneity, complex pore networks, and possible large-scale cavities. The characteristics of cave-type reservoirs are as follows:

[0096] High porosity and high permeability: The development of caves and fractures results in this type of reservoir usually having high porosity and permeability, which can become high-quality oil and gas storage space.

[0097] Heterogeneity: Because dissolution is highly dependent on the dynamics of water flow and the chemical properties of the rocks, the size, shape and distribution of caves vary greatly, resulting in strong heterogeneity of the reservoir.

[0098] Large differences in reservoir connectivity: The connectivity between caves varies greatly due to secondary sediment filling or insufficient dissolution.

[0099] Multi-scale pore structure: This type of reservoir usually contains pores of various scales, ranging from micrometers to several meters or even tens of meters.

[0100] Complex fluid flow characteristics: Due to the complexity of the pore structure, the flow characteristics of the fluid in it are also quite complex, which poses a challenge to oil and gas extraction.

[0101] The key points of exploration and development of cave-type reservoirs include: Detailed geological surveys: Through surface geological surveys, drilling and logging, seismic exploration and other methods, the structure and characteristics of the reservoir are understood as detailed as possible. High-resolution seismic data: High-quality, high-resolution seismic data are required to identify and map reservoirs, and seismic attribute analysis and seismic inversion play a key role here. Advanced seismic interpretation methods: For example, rock physics models based on seismic attribute analysis can reveal the characteristics of cave-type reservoirs to a certain extent. High-precision numerical simulation: Use geological modeling and reservoir simulation technology to predict the distribution and flow characteristics of oil and gas to guide development strategies. Special completion and production technologies: Due to the complexity of reservoir characteristics, special completion and production technologies are required to effectively develop such reservoirs.

[0102] In some embodiments of the present invention, see Figure 2 , a method for predicting cave-type reservoirs in carbonate formations, further comprising:

[0103] Step 400: Determine the wave impedance value of the mudstone based on the core data of the target work area and the logging data.

[0104] The wave impedance AI here is the product of the formation material density (ρ) and the propagation velocity (V), expressed as:

[0105] AI=ρ×V

[0106] Where: ρ is the density of the medium, usually expressed in grams per cubic centimeter (g / cm 3 ) or kilograms / cubic meter (kg / m 3 ) in units of meters per second (m / s). V is the speed of the seismic wave as it travels through the medium. For longitudinal waves (P waves), this is the P-wave velocity, usually measured in meters per second (m / s).

[0107] Wave impedance is one of the key parameters reflecting formation characteristics because it combines two basic physical properties of the formation: density and velocity. Changes in wave impedance generally indicate changes in formation properties, such as rock type, porosity, and saturation. During seismic wave propagation, when a wave propagates from one stratum with a different wave impedance value to another, a portion of the energy is reflected back to the ground. This is the basis for capturing and analyzing reflected waves in seismic exploration. Preferably, the wave impedance of step 400 can also be used to perform the following steps:

[0108] Calculation of reflection coefficient: The reflection coefficient of seismic waves at the interface between two media with different wave impedances can be calculated using wave impedance.

[0109] Seismic inversion Seismic inversion technology can be used to estimate the wave impedance distribution from reflection seismic data and then infer the rock physical properties of the formation.

[0110] Rock physics analysis: Wave impedance is related to rock parameters such as porosity, fluid saturation and rock type. These parameters can be indirectly estimated through wave impedance analysis.

[0111] Reservoir feature identification: For oil and gas exploration and production, changes in wave impedance can help identify reservoir boundaries, reservoir fluid properties, and fluid contacts.

[0112] An important role of wave impedance in seismic exploration is its application in seismic inversion, which converts seismic data into models that more directly reflect the physical properties of the formation, such as the wave impedance model. This provides more detailed formation information than traditional seismic attribute analysis, helping to inform exploration and production decisions.

[0113] In some embodiments of the present invention, see Figure 3 , step 400 includes:

[0114] Step 401: Determine the depth of the mudstone-filled cave based on the core data;

[0115] Step 402: Determine the wave impedance value of the mudstone according to the acoustic transit time logging curve and the density logging curve corresponding to the depth.

[0116] The purpose of step 400 is to calculate the wave impedance value of the mudstone filling the cave. For steps 401 and 402, since the wave impedance value of mudstone is smaller than that of sandstone, the depth of the mudstone-filled cave can be determined based on the core data of the actual drilling, and the wave impedance of the mudstone filling the cave can be calculated based on the logging curve.

[0117] In some embodiments of the present invention, see Figure 4 , a method for predicting cave-type reservoirs in carbonate formations, further comprising:

[0118] Step 500: Determine the wave impedance value of the carbonate rock according to the cave low-frequency model.

[0119] In some embodiments of the present invention, step 500 includes:

[0120] An iterative constrained sparse pulse inversion is performed on the cave low-frequency model to determine the wave impedance value of the carbonate rock.

[0121] Iterative constrained sparse pulse inversion (ICI) is designed to extract more detailed formation parameter information, particularly reflection coefficients, from seismic data. This technique simplifies the complex relationship between seismic records and the reflection coefficients of a formation model into a sparse pulse sequence, thereby finding a formation parameter model that best matches the observed data.

[0122] It is understood that the reflection coefficient of a formation can be represented by discrete pulses (spikes), whose positions correspond to the reflection interfaces of the formation, and whose amplitudes correspond to the reflection intensity. This representation assumes that the reflection characteristics of the formation are locally concentrated, that is, most of the formation is flat and non-reflective, and reflection occurs only at specific interfaces.

[0123] The iterative constrained sparse pulse inversion process includes the following steps:

[0124] First, based on the current stratigraphic model, the propagation of seismic waves and their reflection at various interfaces are calculated to generate synthetic seismic records. Next, the generated synthetic seismic records are compared with the actual observed seismic data to assess the error between the two. Based on the results of the error assessment, optimization algorithms (such as least squares and genetic algorithms) are used to adjust the stratigraphic model, optimize the distribution of reflection coefficients, and increase the sparsity of reflection coefficients in the model. During the optimization process, various geological constraints can be applied, such as known stratigraphic interfaces, lithologic information, and sparsity constraints, to ensure the geological plausibility of the inversion results.

[0125] Repeat the above steps until the degree of matching between the synthetic seismic record and the actual seismic data reaches a satisfactory level, or the preset number of iterations is reached.

[0126] Iterative constrained sparse pulse inversion usually has the following characteristics:

[0127] High resolution: Due to the sparsity constraint, higher resolution inversion results can be obtained than traditional methods.

[0128] Stability: The stability and reliability of the inversion results can be improved through reasonable geological constraints.

[0129] Complexity: The algorithm is complex to implement and requires high computing resources.

[0130] In some embodiments of the present invention, see Figure 5 , a method for predicting cave-type reservoirs in carbonate formations, further comprising:

[0131] Step 600: Determine the approximate range of the wave impedance of the cave-type reservoir based on the cave low-frequency model.

[0132] In some embodiments of the present invention, see Figure 6 , step 300 includes:

[0133] Step 301: within the approximate range of the wave impedance of the cave-type reservoir, determine the accurate range of the wave impedance of the cave-type reservoir according to the cave-type reservoir prediction model to predict the cave-type reservoir in the target work area.

[0134] From the above description, it can be seen that an embodiment of the present invention provides a method for predicting cave-type reservoirs in carbonate formations, including: first, generating a straight low-frequency model of the target work area based on the well logging data of the target work area; then, performing constrained sparse pulse inversion on the straight low-frequency model based on the seismic data of the target work area to generate a cave low-frequency model; finally, in the cave low-frequency model, the wave impedance value of the carbonate rock is assigned the wave impedance value of the mudstone to generate a cave-type reservoir prediction model to identify the cave-type reservoirs in the target work area; wherein the cave-type reservoirs are not filled with sandstone and mudstone.

[0135] The method for predicting cave-type reservoirs in carbonate formations provided by the embodiments of the present invention uses iterative constrained sparse pulse inversion to assign the carbonate rocks the wave impedance value of mudstone, highlighting the wave impedance difference between sandstone and mudstone and oil, gas and water, and accurately predicting effective reservoirs not filled with sandstone and mudstone. First, a straight low-frequency model of carbonate rock is established, and constrained sparse pulse inversion is performed to determine the cave threshold value. A low-frequency model reflecting caves is established, and then iterative constrained sparse pulse inversion is performed. Then, the wave impedance value of the mudstone filling the cave is determined using logging and well data. The carbonate rocks are assigned the wave impedance value of mudstone, highlighting the wave impedance difference between sandstone and mudstone and oil, gas and water, which is a disguised prediction of oil, gas and water in clastic formations. Finally, a threshold value for the effective reservoir is given, and cave-type reservoirs not filled with sandstone and mudstone are depicted in the carbonate formation.

[0136] Example 3:

[0137] In a specific embodiment, the present invention also takes the carbonate strata of the Middle Ordovician Yingshan Formation in a certain three-dimensional block in the northern Tarim region as an example to provide a specific embodiment of a method for predicting cave-type reservoirs in carbonate strata. Figure 7 as well as Figure 8 , specifically including the following steps.

[0138] Step S1: Perform iterative constrained sparse pulse inversion.

[0139] See also Figure 9 Based on the logging data of actual drilling, a low-frequency model of carbonate rock straight plate is established, and the constrained sparse pulse inversion is performed on the seismic data volume to determine the approximate threshold value of the cave and establish a low-frequency model reflecting the cave ( Figure 10 ), and then perform constrained sparse pulse inversion to obtain the longitudinal impedance Imp of the formation 地层 Specifically, iterative constrained sparse pulse inversion is performed to more clearly depict the boundaries of the cave and more accurately invert the longitudinal wave impedance values ​​of the carbonate rock and the cave interior ( Figure 11 ).

[0140] Step S2: Calculate the wave impedance value of the mudstone filling the cave.

[0141] Since the wave impedance of mudstone is smaller than that of sandstone, the depth of mudstone-filled caves is determined based on the core data of actual drilling, and the wave impedance Imp of mudstone filling the caves is calculated based on the well logging curve. 泥岩 The depth of the cave filled with mudstone was determined by core observation, and the wave impedance of the mudstone at this depth was calculated to be approximately 1.293*10 7 km / m 3 *m / s.

[0142] Step S3: Assign the carbonate rock the wave impedance value of the mudstone.

[0143] The wave impedance of carbonate rock is obviously greater than that of mudstone. 地层 >Imp 泥岩 , Imp 泥岩 , Imp 地层 ), assign the wave impedance value of mudstone to carbonate rock, and obtain the wave impedance body Imp after assignment 赋值 ;

[0144] Specifically, by the formula IF(Imp 地层 >Imp 泥岩 , Imp 泥岩 , Imp 地层 ) Assign the wave impedance value of carbonate rock to 1.293*10 7 km / m 3 *m / s, highlighting the difference in wave impedance between sandstone and oil, gas and water filling the cave, which is a good way to predict oil, gas and water in clastic rocks ( Figure 12 ).

[0145] Step S4: Carving of effective reservoir bodies.

[0146] By using the assigned wave impedance body, given the wave impedance of the cave-type effective reservoir and the threshold value of the size, the unfilled caves are carved out and the effective porosity is calculated.

[0147] Specifically, given the threshold values ​​of wave impedance and size of cave-type effective reservoirs, the unfilled caves were carved out. From the results, the beads drilled in Well A appear as low anomalies on the wave impedance profile. The profile that assigns the surrounding rock to the mudstone wave impedance value clearly depicts the cave-type reservoir that is not completely filled with sandstone and mudstone ( Figure 11 、 Figure 12 ), the actual drilling situation of Well A was that the venting loss occurred at 5817m, with a loss volume of 36.4m 3 Well A is the most productive exploration well in the block, with a cumulative production of 5.67 wt. Well B also drilled strong-amplitude beads on the profile. Although the wave impedance value of the bead inversion is relatively low, the profile that assigns the surrounding rock to the mudstone wave impedance value through Well B does not clearly identify unfilled caves ( Figure 13、 Figure 14 ). Core data from Well B show that the caves are completely filled with sandstone and mudstone, producing only a small amount of water and no production has been established. This shows that the present invention accurately predicts cave-type reservoirs that are not filled with sandstone and mudstone, and the inversion carving results match well with the actual drilling results.

[0148] The method provided in the specific embodiment of the present invention is applied to the prediction of carbonate rock reservoirs in the Yingshan Formation of the Middle Ordovician in a three-dimensional block in the northern Tarim region, and the caves filled with sandstone and mudstone are finely identified, achieving good application results.

[0149] As can be seen from the above description, a specific embodiment of the present invention provides a method for predicting cave-type reservoirs in carbonate formations, comprising: first, generating a straight low-frequency model of the target work area based on the well logging data of the target work area; then, performing constrained sparse pulse inversion on the straight low-frequency model based on the seismic data of the target work area to generate a cave low-frequency model; finally, in the cave low-frequency model, assigning the wave impedance value of the carbonate rock to the wave impedance value of the mudstone to generate a cave-type reservoir prediction model to identify the cave-type reservoirs in the target work area; wherein the cave-type reservoirs are not filled with sandstone and mudstone.

[0150] Specifically, the present invention uses iterative constrained sparse pulse inversion to assign the wave impedance value of mudstone to carbonate rocks, highlighting the wave impedance difference between sandstone and mudstone and oil, gas and water, and accurately predicting effective reservoirs not filled with sandstone and mudstone. First, a straight low-frequency model of carbonate rocks is established, and constrained sparse pulse inversion is performed to determine the cave threshold value. A low-frequency model reflecting caves is established, and then iterative constrained sparse pulse inversion is performed. Then, the wave impedance value of the mudstone filling the cave is determined using logging and well data. The wave impedance value of mudstone is assigned to carbonate rocks, highlighting the wave impedance difference between sandstone and mudstone and oil, gas and water, which is transformed into predicting oil, gas and water in clastic rock formations. Finally, a threshold value for the effective reservoir is given, and cave-type reservoirs not filled with sandstone and mudstone are depicted in carbonate formations.

[0151] Cave-type reservoirs are mainly developed in carbonate formations, but they are generally filled by the collapse of overlying sandstone and mudstone formations, which restricts the exploration of cave-type oil and gas reservoirs. The present invention uses iterative constrained sparse pulse inversion to establish the longitudinal wave impedance of the formation. By assigning values ​​to remove the high impedance values ​​of carbonate rocks, the wave impedance difference between sandstone and mudstone and oil, gas and water in the reservoir is amplified, which is transformed into the prediction of oil, gas and water in clastic rocks. This realizes the detailed prediction and carving of cave-type reservoirs in carbonate formations that are not filled by sandstone and mudstone, provides a basis for studying the filling laws of cave-type reservoirs, and provides a basis and direction for the next step of well site deployment.

[0152] Example 4:

[0153] Based on the same inventive concept, the embodiments of the present application also provide a device for predicting cave-type reservoirs in carbonate formations, which can be used to implement the methods described in the above embodiments, such as the following embodiments. Since the principle of solving the problem by the device for predicting cave-type reservoirs in carbonate formations is similar to that of the method for predicting cave-type reservoirs in carbonate formations, the implementation of the device for predicting cave-type reservoirs in carbonate formations can refer to the implementation of the method for predicting cave-type reservoirs in carbonate formations, and the repeated parts will not be repeated. As used below, the terms "unit" or "module" can be a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceived.

[0154] The embodiment of the present invention provides a specific embodiment of a device for predicting cave-type reservoirs in carbonate formations, which can realize a method for predicting cave-type reservoirs in carbonate formations. Figure 15 A device for predicting cave-type reservoirs in carbonate formations comprises:

[0155] A straight plate low-frequency model generation module 10 is used to generate a straight plate low-frequency model of the target work area according to the well logging data of the target work area;

[0156] a cave low-frequency model generating module 20, configured to perform constrained sparse pulse inversion on the straight plate low-frequency model according to the seismic data of the target work area to generate a cave low-frequency model;

[0157] The cave-type reservoir prediction module 30 is used to assign the wave impedance value of carbonate rock to the wave impedance value of mudstone in the cave low-frequency model to generate a cave-type reservoir prediction model and predict the cave-type reservoir in the target work area; wherein the cave-type reservoir is not filled with sandstone and mudstone.

[0158] In some embodiments of the present invention, a device for predicting cave-type reservoirs in carbonate formations further includes:

[0159] The mudstone wave impedance value determination module is used to determine the wave impedance value of the mudstone according to the core data of the target work area and the well logging data.

[0160] In some embodiments of the present invention, the mudstone wave impedance value determination module includes:

[0161] a mudstone cave depth determination unit, configured to determine the depth of the mudstone-filled cave based on the core data;

[0162] The mudstone wave impedance value determining unit is used to determine the wave impedance value of the mudstone according to the acoustic time difference logging curve and the density logging curve corresponding to the depth.

[0163] In some embodiments of the present invention, a device for predicting cave-type reservoirs in carbonate formations further includes:

[0164] The carbonate rock wave impedance value determination module is used to determine the wave impedance value of the carbonate rock according to the cave low-frequency model.

[0165] In some embodiments of the present invention, the carbonate rock wave impedance value determination module includes:

[0166] The carbonate rock wave impedance value determination unit is used to perform iterative constrained sparse pulse inversion on the cave low-frequency model to determine the wave impedance value of the carbonate rock.

[0167] In some embodiments of the present invention, a device for predicting cave-type reservoirs in carbonate formations further includes:

[0168] The approximate range determination module is used to determine the approximate range of the wave impedance of the cave-type reservoir based on the cave low-frequency model.

[0169] In some embodiments of the present invention, the cavernous reservoir prediction module includes:

[0170] The cave type reservoir prediction unit is used to determine the accurate range of the wave impedance of the cave type reservoir according to the cave type reservoir prediction model within the approximate range of the wave impedance of the cave type reservoir, so as to predict the cave type reservoir in the target work area.

[0171] As can be seen from the above description, an embodiment of the present invention provides a device for predicting cave-type reservoirs in carbonate formations, comprising: a straight low-frequency model generation module, for generating a straight low-frequency model of a target work area based on well logging data of the target work area; a cave low-frequency model generation module, for performing constrained sparse pulse inversion on the straight low-frequency model based on seismic data of the target work area to generate a cave low-frequency model; and a cave-type reservoir prediction module, for assigning the wave impedance value of carbonate rock to the wave impedance value of mudstone in the cave low-frequency model to generate a cave-type reservoir prediction model to predict the cave-type reservoir in the target work area; wherein the cave-type reservoir is not filled with sandstone or mudstone.

[0172] The device for predicting cave-type reservoirs in carbonate formations provided in an embodiment of the present invention obtains a wave impedance body reflecting caves through iterative constrained sparse pulse inversion, and assigns a value to remove the high impedance value of carbonate rock, thereby amplifying the wave impedance difference between sandstone and mudstone and oil, gas and water in the cave-type reservoirs, and predicting oil, gas and water in sandstone in disguise, thereby realizing the fine prediction and carving of cave-type reservoirs in carbonate formations that are not filled with sandstone and mudstone, providing a basis for studying the filling law of cave-type reservoirs, and providing a basis and direction for the next step of well site deployment.

[0173] Embodiment 5:

[0174] The present application also provides a specific implementation of an electronic device capable of implementing all steps of the method for predicting cave-type reservoirs in carbonate formations in the above embodiment, see Figure 16 , electronic equipment specifically includes the following:

[0175] Processor 1201, memory 1202, communications interface 1203, and bus 1204;

[0176] The processor 1201, the memory 1202, and the communication interface 1203 communicate with each other via the bus 1204; the communication interface 1203 is used to implement information transmission between the server device and the client device and other related devices;

[0177] The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, all steps of the method for predicting cave-type reservoirs in carbonate formations in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0178] generating a straight plate low-frequency model of the target work area according to the well logging data of the target work area;

[0179] Performing constrained sparse pulse inversion on the straight plate low-frequency model according to the seismic data of the target work area to generate a cave low-frequency model;

[0180] In the cave low-frequency model, the wave impedance value of carbonate rock is assigned as the wave impedance value of mudstone to generate a cave-type reservoir prediction model to predict the cave-type reservoir in the target work area; wherein the cave-type reservoir is not filled with sandstone and mudstone.

[0181] In one embodiment, a method for predicting cave-type reservoirs in carbonate formations further includes:

[0182] The wave impedance value of the mudstone is determined based on the core data of the target work area and the well logging data.

[0183] In one embodiment, determining the wave impedance value of the mudstone based on the core data of the target work area and the well logging data includes:

[0184] determining the depth of the mudstone-filled cave based on the core data;

[0185] The wave impedance value of the mudstone is determined according to the acoustic transit time logging curve and the density logging curve corresponding to the depth.

[0186] In one embodiment, a method for predicting cave-type reservoirs in carbonate formations further includes:

[0187] The wave impedance value of the carbonate rock is determined according to the cave low-frequency model.

[0188] In one embodiment, determining the wave impedance value of the carbonate rock according to the cave low-frequency model includes:

[0189] An iterative constrained sparse pulse inversion is performed on the cave low-frequency model to determine the wave impedance value of the carbonate rock.

[0190] In one embodiment, a method for predicting cave-type reservoirs in carbonate formations further includes:

[0191] The approximate range of the wave impedance of the cave-type reservoir is determined based on the cave low-frequency model.

[0192] In one embodiment, in the cave low-frequency model, the wave impedance value of carbonate rock is assigned as the wave impedance value of mudstone to generate a cave-type reservoir prediction model, and the cave-type reservoir in the target work area is predicted, including:

[0193] Within the approximate range of the wave impedance of the cave-type reservoir, the accurate range of the wave impedance of the cave-type reservoir is determined according to the cave-type reservoir prediction model to predict the cave-type reservoir in the target work area.

[0194] Example 6:

[0195] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps of the method for predicting cave-type reservoirs in carbonate formations in the above-mentioned embodiments. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all steps of the method for predicting cave-type reservoirs in carbonate formations in the above-mentioned embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0196] generating a straight plate low-frequency model of the target work area according to the well logging data of the target work area;

[0197] Performing constrained sparse pulse inversion on the straight plate low-frequency model according to the seismic data of the target work area to generate a cave low-frequency model;

[0198] In the cave low-frequency model, the wave impedance value of carbonate rock is assigned as the wave impedance value of mudstone to generate a cave-type reservoir prediction model to predict the cave-type reservoir in the target work area; wherein the cave-type reservoir is not filled with sandstone and mudstone.

[0199] In one embodiment, a method for predicting cave-type reservoirs in carbonate formations further includes:

[0200] The wave impedance value of the mudstone is determined based on the core data of the target work area and the well logging data.

[0201] In one embodiment, determining the wave impedance value of the mudstone based on the core data of the target work area and the well logging data includes:

[0202] determining the depth of the mudstone-filled cave based on the core data;

[0203] The wave impedance value of the mudstone is determined according to the acoustic transit time logging curve and the density logging curve corresponding to the depth.

[0204] In one embodiment, a method for predicting cave-type reservoirs in carbonate formations further includes:

[0205] The wave impedance value of the carbonate rock is determined according to the cave low-frequency model.

[0206] In one embodiment, determining the wave impedance value of the carbonate rock according to the cave low-frequency model includes:

[0207] An iterative constrained sparse pulse inversion is performed on the cave low-frequency model to determine the wave impedance value of the carbonate rock.

[0208] In one embodiment, a method for predicting cave-type reservoirs in carbonate formations further includes:

[0209] The approximate range of the wave impedance of the cave-type reservoir is determined based on the cave low-frequency model.

[0210] In one embodiment, in the cave low-frequency model, the wave impedance value of carbonate rock is assigned as the wave impedance value of mudstone to generate a cave-type reservoir prediction model, and the cave-type reservoir in the target work area is predicted, including:

[0211] Within the approximate range of the wave impedance of the cave-type reservoir, the accurate range of the wave impedance of the cave-type reservoir is determined according to the cave-type reservoir prediction model to predict the cave-type reservoir in the target work area.

[0212] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the hardware + program embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0213] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0214] Although the present application provides method operation steps such as embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative work. The order of steps listed in the embodiments is only one way of executing the steps among many steps and does not represent the only execution order. When an actual device or client product is executed, it can be executed in the order shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0215] For the convenience of description, the above devices are described in terms of functions divided into various modules. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or multiple software and / or hardware, or the modules that implement the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0216] Those skilled in the art will also appreciate that, in addition to implementing the controller in pure computer-readable program code, it is entirely possible to implement the same functionality by logically programming the method steps in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, and the like. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered structures within the hardware component. Alternatively, the devices for implementing various functions can be considered both software modules implementing the method and structures within the hardware component.

[0217] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0218] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0219] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between the various embodiments can be referenced across them. Each embodiment focuses on the differences from the other embodiments. In particular, since the system embodiments are generally similar to the method embodiments, their description is relatively simple. For relevant parts, reference can be made to the description of the method embodiments. Throughout this specification, reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the embodiments in this specification. In this specification, the schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and integrate the different embodiments or examples, and features of different embodiments or examples, described in this specification, without conflict.

[0220] The above description is merely an example of the embodiments of this specification and is not intended to limit the embodiments of this specification. For those skilled in the art, various modifications and variations of the embodiments of this specification are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of this specification shall be included within the scope of the claims of the embodiments of this specification.

Claims

1. A method for predicting cave-type reservoirs in carbonate formations, characterized in that: include: generating a straight plate low-frequency model of the target work area according to the well logging data of the target work area; Performing constrained sparse pulse inversion on the straight plate low-frequency model according to the seismic data of the target work area to generate a cave low-frequency model; In the cave low-frequency model, the wave impedance value of carbonate rock is assigned as the wave impedance value of mudstone to generate a cave-type reservoir prediction model to predict the cave-type reservoir in the target work area; wherein the cave-type reservoir is not filled with sandstone and mudstone.

2. The method for predicting cave-type reservoirs in carbonate formations according to claim 1, characterized in that: Also includes: The wave impedance value of the mudstone is determined based on the core data of the target work area and the well logging data.

3. The method for predicting cave-type reservoirs in carbonate formations according to claim 2, characterized in that: Determining the wave impedance value of the mudstone according to the core data of the target work area and the well logging data includes: determining the depth of the mudstone-filled cave based on the core data; The wave impedance value of the mudstone is determined according to the acoustic transit time logging curve and the density logging curve corresponding to the depth.

4. The method for predicting cave-type reservoirs in carbonate formations according to claim 1, wherein: Also includes: The wave impedance value of the carbonate rock is determined according to the cave low-frequency model.

5. The method for predicting cave-type reservoirs in carbonate formations according to claim 4, characterized in that: Determining the wave impedance value of the carbonate rock according to the cave low-frequency model includes: An iterative constrained sparse pulse inversion is performed on the cave low-frequency model to determine the wave impedance value of the carbonate rock.

6. The method for predicting cave-type reservoirs in carbonate formations according to claim 1, characterized in that: Also includes: The approximate range of the wave impedance of the cave-type reservoir is determined based on the cave low-frequency model.

7. The method for predicting cave-type reservoirs in carbonate formations according to claim 6, characterized in that: In the cave low-frequency model, the wave impedance value of carbonate rock is assigned to the wave impedance value of mudstone to generate a cave-type reservoir prediction model, and the cave-type reservoir in the target work area is predicted, including: Within the approximate range of the wave impedance of the cave-type reservoir, the accurate range of the wave impedance of the cave-type reservoir is determined according to the cave-type reservoir prediction model to predict the cave-type reservoir in the target work area.

8. A device for predicting cave-type reservoirs in carbonate formations, characterized in that: include: A straight plate low-frequency model generation module is used to generate a straight plate low-frequency model of the target work area according to the well logging data of the target work area; a cave low-frequency model generation module, configured to perform constrained sparse pulse inversion on the straight plate low-frequency model according to the seismic data of the target work area to generate a cave low-frequency model; The cave-type reservoir prediction module is used to assign the wave impedance value of carbonate rock to the wave impedance value of mudstone in the cave low-frequency model to generate a cave-type reservoir prediction model to predict the cave-type reservoir in the target work area; wherein the cave-type reservoir is not filled with sandstone and mudstone.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for predicting cave-type reservoirs in carbonate formations according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting cave-type reservoirs in carbonate formations according to any one of claims 1 to 7 are implemented.

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