Lake facies carbonate reservoir identification method, device, equipment and medium

Abstract

The invention belongs to the technical field of seismic comprehensive interpretation and research, and discloses a lacustrine carbonate reservoir identification method, device, equipment and medium, the method comprises the following steps: classifying the lithology of lacustrine carbonate, and summarizing the characteristics of the lacustrine carbonate reservoir according to the core scale; according to fine well seismic calibration, determining seismic reflection characteristics of different lithologies, and summarizing elastic parameter characteristics of different lithologies; performing rock physical modeling according to the pore structure; establishing a rock physical quantity plate; carrying out pre-stack inversion to obtain a geophysical elastic parameter body; and distinguishing different lithological characters according to the rock physical quantity version, and predicting the spatial distribution of the lacustrine carbonate rock according to the pre-stack inversion data volume. According to the method, the lacustrine carbonate rocks with different lithologies are accurately distinguished, the lacustrine carbonate reservoir is accurately predicted, and the method is suitable for identifying the lacustrine carbonate reservoir.

Description

Technical Field

[0001] This invention relates to the technical field of comprehensive seismic interpretation research, specifically to a method, apparatus, equipment, and medium for identifying lacustrine carbonate reservoirs. Background Technology

[0002] In recent years, lacustrine carbonate rocks have received increasing attention as a new type of reservoir in the exploration field. Reservoirs in saline lacustrine basins are mainly lacustrine carbonate rocks, primarily including algal limestone and dolomite. Due to the influence of special geological environments, the lithology of lacustrine carbonate rocks is impure, mainly composed of a mixture of four lithologies: calcite, dolomite, mudstone, and sandstone. Core sampling results show that the calcite content of algal limestone is higher than 50%, and the dolomite content is between 15% and 30%; the calcite content of dolomite is between 15% and 30%, and the dolomite content is higher than 50%; the total calcite and dolomite content of lithologies other than algal limestone and dolomite is less than 50%. The porosity of these lithologies is generally low, typically between 5% and 8%, which is unfavorable for predicting the spatial distribution of lacustrine carbonate reservoirs.

[0003] Furthermore, due to the unique diagenetic environment, the seismic reflection characteristics of lacustrine carbonate rocks are similar to those of reservoirs with other lithologies. Conventional geophysical methods, such as impedance inversion, make it difficult to predict and effectively distinguish the lithology of lacustrine carbonate rocks. Current commercial software lacks a specific petrophysical modeling process for lacustrine carbonate rocks, resulting in significant deviations from measured data when predicting shear waves and elastic parameters, thus failing to effectively predict lacustrine carbonate reservoirs. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention aims to provide a method, apparatus, equipment, and medium for identifying lacustrine carbonate reservoirs, enabling precise and accurate prediction of lacustrine carbonate reservoir distribution.

[0005] To achieve the above objectives, the technical solution adopted by this invention is as follows: A method for identifying lacustrine carbonate reservoirs includes the following steps performed sequentially: S1, classifying the lithology of lacustrine carbonate rocks and summarizing the characteristics of lacustrine carbonate reservoirs based on core calibration; S2, clarifying the seismic reflection characteristics of different lithologies based on fine well seismic calibration and summarizing the elastic parameter characteristics of different lithologies; S3, performing rock physics modeling based on pore structure; S4, establishing a rock physics model; S5, conducting pre-stack inversion to obtain a geophysical elastic parameter volume; S6, distinguishing different lithologies based on the rock physics model and predicting the spatial distribution of lacustrine carbonate rocks based on the pre-stack inversion data volume.

[0006] As a limitation of the present invention: step S1 specifically includes S11, analyzing and statistically analyzing the proportion of different lithologies based on conventional logging data, lithological scanning data, and imaging logging data of the drilled wells in the study area, and clarifying the main lithologies of lacustrine carbonate rocks; S12, summarizing the mineral and physical property characteristics of the main lithologies of lacustrine carbonate rocks based on data from multiple core wells, conventional logging data, lithological scanning data, and imaging logging data.

[0007] As a limitation of the present invention: Step S3 specifically includes S31, conducting core thin section observations, summarizing the pore distribution and pore structure characteristics of different lithologies, and determining the pore aspect ratio of different lithologies; S32, considering the characteristics of lacustrine carbonate rocks, using calcite, dolomite, sandstone, and mudstone as the framework, and using lithological scanning data, inputting different proportions of calcite, dolomite, sandstone, and mudstone into the software for framework mixing; S33, based on DEM theory, inputting the total porosity and effective porosity aspect ratio, calculating the dry rock modulus using the Kuster-Toksoz equation, calculating the fluid elastic parameters using the Brie equation, and finally achieving framework and fluid mixing through the Biot-Gassmann equation to simulate reservoir elastic parameters; S34, verifying the rationality of the rock physics modeling by comparing with measured curves; the expression of the Kuster-Toksoz equation is...

[0008] Where k d and u d k is the equivalent elastic modulus of rock. m and u m k is the equivalent elastic modulus of the rock matrix. p and u p φ is the equivalent elastic modulus of the porous material. i Porosity is the volume fraction of pore inclusions, α is the pore aspect ratio, and the coefficients T(α) and F(α) are related to the pore aspect ratio, representing the effect of adding pore inclusions on the background matrix; the expression of the Brie equation is: Where K dry The bulk modulus of dry rock, μ dry μ is the shear modulus of dry rock. m For the shear modulus of the matrix, (v p / v s ) dry The ratio of P-wave to S-wave velocity in dry rock is given by φ, porosity is given by φ, and c is an empirical value that varies with the degree of compaction, lithology, etc. The expression for the Biot-Gassmann equation is: Where K sat K represents the bulk modulus of saturated rock. dry K represents the bulk modulus of dry rock. m K is the bulk modulus of the matrix.f φ represents the bulk modulus of the porous fluid, and φ represents the porosity.

[0009] As a limitation of the present invention: step S4 specifically includes S41, establishing a rock physical quantity plate based on the input carbonate content, porosity, longitudinal wave impedance, and longitudinal wave velocity ratio of different lithologies.

[0010] As a limitation of the present invention: step S5 specifically includes S51, performing pre-stack seismic inversion to obtain three data volumes: P-wave impedance, S-wave impedance, and density; S52, stacking the data volumes at different angles and extracting the well-side seismic wavelet through repeated iterations; S53, performing inversion on each data volume stacked at different angles using the corresponding seismic wavelet; S54, obtaining the geophysical elastic parameter volumes of Lamé coefficient, shear modulus, and Poisson's ratio based on mathematical calculations.

[0011] As a limitation of the present invention: step S6 specifically includes S61, according to the different regions of different lithologies in the rock physical scale, finely distinguishing the main lithologies of lacustrine carbonate rocks in the cross plot of P-wave impedance and P-wave velocity ratio; S62, predicting the spatial distribution of algal limestone and dolomite lacustrine carbonate rocks based on the pre-stack inversion data volume.

[0012] As a limitation of the present invention: the main lithologies of lacustrine carbonate rocks are algal limestone, dolomite, sandstone, and mudstone.

[0013] As a limitation of the present invention: a lacustrine carbonate reservoir identification device includes a lacustrine carbonate reservoir characteristic summary module, used to classify lacustrine carbonate lithology and summarize lacustrine carbonate reservoir characteristics based on core calibration; a lithological elastic parameter characteristic summary module, used to clarify the seismic reflection characteristics of different lithologies based on fine well seismic calibration and summarize the elastic parameter characteristics of different lithologies; a rock physics modeling module, used to perform rock physics modeling based on pore structure; a rock physics scale module, used to establish a rock physics scale; a geophysical elastic parameter volume acquisition module, used to perform pre-stack inversion and obtain a geophysical elastic parameter volume; and a lacustrine carbonate spatial distribution prediction module, used to distinguish different lithologies based on the rock physics scale and predict the spatial distribution of lacustrine carbonate rocks based on the pre-stack inversion data volume.

[0014] As a limitation of the present invention: a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the lacustrine carbonate reservoir identification method.

[0015] As a limitation of the present invention: a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program that executes the lacustrine carbonate reservoir identification method.

[0016] Due to the adoption of the above technical solution, the beneficial effects achieved by this invention compared with the prior art are as follows: The above method was applied in a certain area of ​​a basin. By summarizing the composition and pore characteristics of lacustrine carbonate reservoirs, a rock physical model was constructed in combination with the pore characteristics. Then, a rock physical scale was established, and fine lithology and reservoir prediction were carried out. Lacustrine carbonate rocks of different lithologies were accurately distinguished. By combining pre-stack elastic inversion, the spatial distribution of algal limestone and dolomite was obtained, and the lacustrine carbonate reservoirs were accurately predicted.

[0017] Both wells drilled in the aforementioned area achieved high production, and the experimental results obtained from the wells were in good agreement with the predicted spatial distribution of lacustrine carbonate reservoirs, proving that the method for predicting lacustrine carbonate reservoir distribution is accurate and feasible. This method can be used to characterize lacustrine carbonate reservoirs and is applicable to all stages of oilfield development, improving the accuracy and reliability of lacustrine carbonate reservoir prediction. Attached Figure Description

[0018] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0019] Figure 1 This is a flowchart of a method for identifying lacustrine carbonate reservoirs according to Embodiment 1 of the present invention;

[0020] Figure 2 This is a comprehensive lithological interpretation diagram of well A1 in a certain area of ​​a basin, according to Embodiment 1 of the present invention.

[0021] Figure 3 This is a diagram showing the aspect ratio types of pores in different lithologies in Example 1 of the present invention;

[0022] Figure 4 This is the petrological modeling process for lacustrine carbonate rocks in Embodiment 1 of the present invention;

[0023] Figure 5 This is a comparison diagram of shear wave fitting in Embodiment 1 of the present invention;

[0024] Figure 6 This is the physical quantity version of lacustrine carbonate rocks in Example 1 of the present invention;

[0025] Figure 7 This is the pre-stack inversion prediction profile of Embodiment 1 of the present invention;

[0026] Figure 8 This is a location distribution diagram of the wells drilled in Embodiment 1 of the present invention;

[0027] Figure 9 This is a structural block diagram of a lacustrine carbonate reservoir identification device according to Embodiment 2 of the present invention;

[0028] Figure 10 This is a schematic diagram of the structure of a computer device according to Embodiment 3 of the present invention. Detailed Implementation

[0029] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the methods, apparatus, equipment, and media for identifying lacustrine carbonate reservoirs described herein are all preferred embodiments and are only used for illustration and explanation of the present invention, and do not constitute a limitation thereof.

[0030] Example 1: A method for identifying lacustrine carbonate reservoirs

[0031] like Figure 1 As shown, a method for identifying lacustrine carbonate reservoirs includes the following steps performed sequentially:

[0032] S1. Classify the lithology of lacustrine carbonate rocks and summarize the characteristics of lacustrine carbonate reservoirs based on core scale.

[0033] S1 specifically includes the following steps:

[0034] S11. Based on conventional logging data, lithological scanning data, and imaging logging data from existing wells in the study area, analyze and statistically analyze the proportion of different lithologies to clarify the main lithologies of lacustrine carbonate rocks.

[0035] S12. Based on data from multiple core wells, conventional logging data, lithological scanning data, and imaging logging data, summarize the mineral and physical properties of the main lithologies of lacustrine carbonate rocks.

[0036] Specifically, lacustrine carbonate rocks can be mainly classified into algal limestone, dolomite, sandstone, and mudstone. For example... Figure 2 As shown, through core measurement calibration and characteristic analysis of minerals and rocks, it was determined that the total content of calcite and dolomite in the algal limestone and dolomite reservoirs exceeds 50%. Specifically, the sedimentary microfacies of the algal limestone reservoir are mainly algal mats and grain shoals, with calcite content greater than 40% and clay content less than 25% in the algal limestone; the sedimentary microfacies of the dolomite reservoir are mainly dolomite flats, with dolomite content greater than 40% and clay content less than 30% in the dolomite. By statistically analyzing the elastic parameters of different lithologies, the P-wave / S-wave velocity ratio was selected as the most sensitive parameter for distinguishing carbonate rocks.

[0037] S2. Based on the fine well seismic calibration, clarify the seismic reflection characteristics of different lithologies and summarize the elastic parameter characteristics of different lithologies.

[0038] S3. Based on the pore structure, perform rock physical modeling;

[0039] S3 specifically includes the following steps:

[0040] S31. Conduct core thin section observations, summarize the pore distribution and pore structure characteristics of different lithologies, and determine the pore length-to-width ratio of different lithologies;

[0041] Specifically, such as Figure 3 As shown, the porosity aspect ratio of algal limestone was summarized through observation of core thin sections. The porosity aspect ratio was determined using the intersection diagram of P-wave velocity and porosity and the Voigt-Reuss limit model. The rock elastic parameters were simulated using the Xu-White rock physics modeling process.

[0042] S32. Based on the characteristics of lacustrine carbonate rocks, calcite, dolomite, sandstone, and mudstone are used as the framework. Using lithological scanning data, different proportions of calcite, dolomite, sandstone, and mudstone are input into the software to mix the framework.

[0043] S33. Based on DEM theory, input the total porosity and the aspect ratio of the effective porosity, calculate the dry rock modulus using the Kuster-Toksoz equation, calculate the fluid elastic parameters using the Brie equation, and finally achieve skeleton-fluid mixing using the Biot-Gassmann equation. Figure 4 As shown, simulated reservoir elastic parameters;

[0044] S34. By comparing with the measured curves, the rationality of the rock physics model is verified;

[0045] Specifically, such as Figure 5 As shown, by comparing with the measured longitudinal wave curves and transverse wave curves, adjusting the reservoir elastic parameters can more realistically reflect the distribution of underground reservoirs.

[0046] The expression for the Kuster-Toksoz equation is as follows:

[0047]

[0048] Where k d and u d k is the equivalent elastic modulus of rock. m and u m k is the equivalent elastic modulus of the rock matrix. p and u p φ is the equivalent elastic modulus of the porous material. i Porosity is the volume fraction of the pore inclusions, and α is the pore aspect ratio. The coefficients T(α) and F(α) are related to the pore aspect ratio and represent the effect of adding pore inclusions on the background matrix.

[0049] The expression for the Brie equation is as follows:

[0050] Where K dry The bulk modulus of dry rock, μdry μ is the shear modulus of dry rock. m For the shear modulus of the matrix, (v p / v s ) dry The ratio of longitudinal and transverse wave velocities in dry rock is given by φ, porosity is given by c, and this index value varies with the degree of compaction, lithology, etc.

[0051] The expression for the Biot-Gassmann equation is as follows: Where K sat K represents the bulk modulus of saturated rock. dry K represents the bulk modulus of dry rock. m K is the bulk modulus of the matrix. f φ represents the bulk modulus of the porous fluid, and φ represents the porosity.

[0052] S4. Establish a rock physical quantity panel;

[0053] S4 specifically includes the following steps:

[0054] S41. Based on the input of carbonate content, porosity, P-wave impedance, and P-wave / S-wave velocity ratio of different lithologies, establish a rock physical quantity model.

[0055] Specifically, using the established rock physical scale, and considering that algal limestone has a calcite content higher than 50% and a dolomite content between 15% and 30%; dolomite has a calcite content between 15% and 30% and a dolomite content higher than 50%; and the total calcite and dolomite content of lithologies other than algal limestone and dolomite is less than 50%, the main lithology of lacustrine carbonate rocks can be predicted based on the different regions where different lithologies are located in the rock physical scale, and the main lithology of lacustrine carbonate rocks can be effectively distinguished, including algal limestone, dolomite, sandstone, and mudstone. Figure 6 As shown, the porosity of algal limestone ranges from 4% to 12%, the porosity of limestone is greater than 4%, and the porosity of sandstone and mudstone is less than 6%.

[0056] S5. Conduct pre-stack inversion to obtain geophysical elastic parameter volume;

[0057] S5 specifically includes the following steps:

[0058] S51. Conduct pre-stack seismic inversion to obtain three data volumes: P-wave impedance, S-wave impedance, and density.

[0059] S52. The angle-separated superimposed data volume is used to extract the seismic wavelet of the well-side channel through repeated iterations.

[0060] S53. For each data volume superimposed at different angles, inversion is performed using the corresponding seismic wavelet;

[0061] S54. Based on mathematical calculations, obtain the Lamé coefficient, shear modulus, and Poisson's ratio geophysical elastic parameters.

[0062] Specifically, such as Figure 7 As shown, the process of extracting the wavelet is an iterative process. For each data volume superimposed at different angles, the corresponding seismic wavelet is used for inversion. This can avoid errors caused by the wavelet's variation with the offset. At the same time, the quality of the wavelet also affects the final inversion result.

[0063] S6. Differentiate different lithologies based on rock physical quantities, and predict the spatial distribution of lacustrine carbonate rocks based on pre-stack inversion data.

[0064] S6 specifically includes the following steps:

[0065] S61. Based on the different regions of different lithologies in the rock physical scale, the main lithologies of lacustrine carbonate rocks are finely distinguished in the cross-plot of P-wave impedance and P-wave / S-wave velocity ratio.

[0066] S62. Based on the pre-stack inversion data, predict the spatial distribution of lacustrine carbonate rocks.

[0067] Specifically, such as Figure 6 As shown, in the rock physics scale, the P-wave velocity ratio and P-wave impedance of different lithologies are different, which can finely distinguish the main lithologies such as lacustrine carbonate rocks, algal limestone, and limestone. Among them, the P-wave velocity ratio of algal limestone is between 1.68 and 1.99, and the P-wave impedance is between 22,500 and 37,500.

[0068] like Figure 8 As shown, core data were obtained through drilling wells A1, A2, A3, and A5. After research, the characteristics of the algal limestone were found to be: lacustrine carbonate rock content greater than 40%, and the porosity distribution of the algal limestone is between 5% and 11%. Combined with the geophysical elastic parameters obtained from pre-stack inversion, the spatial distribution of algal limestone and lacustrine carbonate rocks was predicted.

[0069] Table 1:

[0070] hashtag Actual drilled thickness of algal limestone (m) Predicted thickness of algal limestone (m) Error thickness (m) A1 12.8 14.5 -1.7 A2 11.5 10.3 1.2 A3 12.9 12.4 0.5 A5 8.7 11.2 -2.5

[0071] The predicted results are close to the thickness of algal limestone obtained from the actual core data of wells A1, A2, A3, and A5, as shown in Table 1. The error in the thickness of algal limestone is within 10%, and the correspondence is good. The consistency between the actual drilling and the pre-stack inversion results is also good, indicating that the above method is suitable for predicting effective reservoirs of lacustrine carbonate rocks and has achieved good results.

[0072] Example 2: A device for identifying lacustrine carbonate reservoirs

[0073] A device for identifying lacustrine carbonate reservoirs, such as Figure 9 As shown, it includes:

[0074] The module summarizing the characteristics of lacustrine carbonate reservoirs is used to classify the lithology of lacustrine carbonate rocks and summarize the characteristics of lacustrine carbonate reservoirs based on core scales. Specifically:

[0075] Based on conventional logging data, lithological scanning data, and imaging logging data from existing wells in the study area, the proportion of different lithologies was analyzed and statistically analyzed to clarify the main lithologies of lacustrine carbonate rocks. Based on data from multiple core wells, conventional logging data, lithological scanning data, and imaging logging data, the mineral and physical property characteristics of the main lithologies of lacustrine carbonate rocks were summarized. The main lithologies of lacustrine carbonate rocks can be divided into algal limestone, dolomite, sandstone, and mudstone. Through core measurement calibration and characteristic analysis of minerals and rocks, it was determined that the total content of calcite and dolomite in algal limestone and dolomite reservoirs exceeds 50%. Specifically, the sedimentary microfacies of algal limestone reservoirs are mainly algal mats and grain shoals, with calcite content greater than 40% and clay content less than 25% in algal limestone; the sedimentary microfacies of dolomite reservoirs are mainly dolomite flats, with dolomite content greater than 40% and clay content less than 30% in dolomite. By statistically analyzing the elastic parameter characteristics of different lithologies, the P-wave / S-wave velocity ratio was selected as the most sensitive parameter for distinguishing carbonate rocks.

[0076] The lithological elastic parameter characteristic summary module is used to clarify the seismic reflection characteristics of different lithologies and summarize the elastic parameter characteristics of different lithologies based on fine well seismic calibration.

[0077] The rock physics modeling module is used to perform rock physics modeling based on pore structure, specifically:

[0078] Core thin section observations were conducted to summarize the pore distribution and pore structure characteristics of different lithologies, and to determine the pore aspect ratio of different lithologies. Through core thin section observations, the porosity aspect ratio of algal limestone was summarized. The porosity aspect ratio was determined using the intersection diagram of P-wave velocity and porosity, as well as the Voigt-Reuss limit model. The rock elastic parameters were simulated using the Xu-White rock physics modeling workflow. Considering the characteristics of lacustrine carbonate rocks, calcite, dolomite, sandstone, and mudstone were used as the framework. Lithological scanning data was utilized, and different proportions of calcite, dolomite, sandstone, and mudstone were input into the software for framework mixing. Based on DEM theory, total porosity and effective porosity aspect ratios were input, and the dry rock modulus was calculated using the Kuster-Toksoz equation. The fluid elastic parameters were calculated using the Brie equation, and finally, the framework and fluid were mixed using the Biot-Gassmann equation to simulate reservoir elastic parameters. The rationality of the rock physics model was verified by comparing with measured curves. By comparing with measured P-wave and S-wave curves, the reservoir elastic parameters were adjusted to more realistically reflect the distribution of underground reservoirs.

[0079] The expression for the Kuster-Toksoz equation is as follows:

[0080]

[0081] Where k d and u d k is the equivalent elastic modulus of rock. m and u m k is the equivalent elastic modulus of the rock matrix. p and u p φ is the equivalent elastic modulus of the porous material. i Porosity is the volume fraction of the pore inclusions, and α is the pore aspect ratio. The coefficients T(α) and F(α) are related to the pore aspect ratio and represent the effect of adding pore inclusions on the background matrix.

[0082] The expression for the Brie equation is as follows:

[0083] Where K dry The bulk modulus of dry rock, μ dry μ is the shear modulus of dry rock. m For the shear modulus of the matrix, (v p / v s ) dry The ratio of longitudinal and transverse wave velocities in dry rock is given by φ, porosity is given by c, and this index value varies with the degree of compaction, lithology, etc.

[0084] The expression for the Biot-Gassmann equation is as follows: Where K sat K represents the bulk modulus of saturated rock. dry K represents the bulk modulus of dry rock. m K is the bulk modulus of the matrix. f φ represents the bulk modulus of the porous fluid, and φ represents the porosity.

[0085] The Rock Physical Quantity Module is used to create rock physical quantity modules, specifically:

[0086] A rock physical scale is established based on the input of carbonate content, porosity, P-wave impedance, and P-wave / S-wave velocity ratio for different lithologies. Using this scale, and considering that algal limestone has a calcite content higher than 50% and a dolomite content between 15% and 30%; dolomite has a calcite content between 15% and 30% and a dolomite content higher than 50%; and that the total calcite and dolomite content of lithologies other than algal limestone and dolomite is less than 50%, the main lithology of lacustrine carbonate rocks can be predicted based on the different regions of each lithology within the rock physical scale, and the main lithology can be effectively distinguished between algal limestone, dolomite, sandstone, and mudstone. The porosity of algal limestone is between 4% and 12%, that of dolomite is greater than 4%, and that of sandstone and mudstone is less than 6%.

[0087] The geophysical elastic parameter volume acquisition module is used to perform pre-stack inversion and obtain the geophysical elastic parameter volume, specifically:

[0088] Pre-stack seismic inversion was performed to obtain three data volumes: P-wave impedance, S-wave impedance, and density. Seismic wavelets were extracted from the stacked data volumes at different angles through iterative processing. For each data volume stacked at different angles, the corresponding seismic wavelet was used for inversion. Based on mathematical calculations, the Lamé coefficient, shear modulus, and Poisson's ratio geophysical elastic parameters were obtained. The wavelet extraction process was an iterative process. Using the corresponding seismic wavelet for inversion of each data volume stacked at different angles avoids errors caused by variations in wavelet offset. Furthermore, the quality of the wavelet also affects the final inversion result.

[0089] The lacustrine carbonate spatial distribution prediction module is used to distinguish different lithologies based on rock physical quantities and to predict the spatial distribution of lacustrine carbonate rocks based on pre-stack inversion data. Specifically:

[0090] Based on the different regions of different lithologies in the rock physics scale, the main lithologies of lacustrine carbonate rocks are finely distinguished in the cross plot of P-wave impedance and P-wave / S-wave velocity ratio; and the spatial distribution of lacustrine carbonate rocks is predicted based on the pre-stack inversion data.

[0091] In rock physics quantification, different lithologies exhibit varying P-wave and S-wave velocity ratios and P-wave impedances, allowing for precise differentiation of major lithologies such as lacustrine carbonate rocks, algal limestone, and dolomite. The P-wave and S-wave velocity ratio of algal limestone ranges from 1.68 to 1.99, while its P-wave impedance ranges from 22,500 to 37,500. Core data obtained from wells A1, A2, A3, and A5 were analyzed, revealing the following characteristics of algal limestone: lacustrine carbonate content greater than 40%, and porosity distribution ranging from 5% to 11%. Combined with geophysical elastic parameters obtained from pre-stack inversion, the spatial distribution of algal limestone and dolomite lacustrine carbonate rocks was predicted. The predicted results are close to the thickness of algal limestone obtained from the actual core data of wells A1, A2, A3, and A5. The error in the thickness of algal limestone is within 10%, and the correspondence is good. The consistency between the actual drilling and the pre-stack inversion results is also good, indicating that the above method is suitable for predicting effective reservoirs in lacustrine carbonate rocks and has achieved good results.

[0092] Example 3: An electronic device

[0093] The electronic device of this embodiment includes a memory and a processor. The memory stores a computer program, and the processor calls the computer program in the memory to execute a lacustrine carbonate reservoir identification method of Embodiment 1. Figure 10 This is a schematic diagram of the structure of the electronic device provided in this embodiment. The electronic device can be a terminal device or a server. The terminal device can include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, personal digital assistants (PDAs), portable Android devices (PADs), portable media players (PMPs), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 10 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of this embodiment.

[0094] like Figure 10As shown, electronic devices may include processing units, such as central processing units (CPUs) and graphics processors (GPUs), which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) or programs loaded from storage devices into random access memory (RAM). RAM also stores various programs and data required for the operation of the electronic device. The processing unit, ROM, and RAM are interconnected via a bus. Input devices, output devices, communication devices, and storage devices are also connected to the bus via I / O interfaces.

[0095] Typically, the following devices can be connected to an I / O interface: input devices such as touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices such as liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices such as magnetic tapes, hard drives, etc.; and communication devices. Communication devices allow electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 10 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0096] Example 4: A computer-readable medium

[0097] The computer-readable storage medium of this embodiment stores a computer program, which, when executed by a processor, is used to implement a lacustrine carbonate reservoir identification method of Embodiment 1. The computer-readable storage medium of this embodiment may be included in an electronic device; alternatively, it may exist independently and not assembled into an electronic device.

[0098] The computer-readable storage medium of this embodiment may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit them. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A method for identifying lacustrine carbonate reservoirs, characterized in that, The method includes the following steps performed sequentially: S1. Classify the lithology of lacustrine carbonate rocks and summarize the characteristics of lacustrine carbonate reservoirs based on core scale. S2. Based on the fine-scale well seismic calibration, clarify the seismic reflection characteristics of different lithologies and summarize the elastic parameter characteristics of different lithologies; S3. Based on the pore structure, perform rock physical modeling; S4. Establish a rock physical quantity panel; S5. Conduct pre-stack inversion to obtain geophysical elastic parameter volume; S6. Differentiate different lithologies based on rock physical quantities, and predict the spatial distribution of lacustrine carbonate rocks based on pre-stack inversion data.

2. The method for identifying lacustrine carbonate reservoirs according to claim 1, characterized in that, Step S1 specifically includes: S11. Based on conventional logging data, lithological scanning data, and imaging logging data from existing wells in the study area, analyze and statistically analyze the proportion of different lithologies to clarify the main lithologies of lacustrine carbonate rocks. S12. Based on data from multiple core wells, conventional logging data, lithological scanning data, and imaging logging data, summarize the mineral and physical properties of the main lithologies of lacustrine carbonate rocks.

3. The method for identifying lacustrine carbonate reservoirs according to claim 1, characterized in that, Step S3 specifically includes: S31. Conduct core thin section observations, summarize the pore distribution and pore structure characteristics of different lithologies, and determine the pore length-to-width ratio of different lithologies; S32. Based on the characteristics of lacustrine carbonate rocks, calcite, dolomite, sandstone, and mudstone are used as the framework. Using lithological scanning data, different proportions of calcite, dolomite, sandstone, and mudstone are input into the software to mix the framework. S33. Based on DEM theory, input the total porosity and the aspect ratio of the effective porosity, calculate the dry rock modulus using the Kuster-Toksoz equation, calculate the fluid elastic parameters using the Brie equation, and finally realize the mixing of the skeleton and fluid through the Biot-Gassmann equation to simulate the reservoir elastic parameters. S34. By comparing with the measured curves, the rationality of the rock physics model is verified; The expression for the Kuster-Toksoz equation is as follows: Where k d and u d k is the equivalent elastic modulus of rock. m and u m k is the equivalent elastic modulus of the rock matrix. p and u p φ is the equivalent elastic modulus of the porous material. i Porosity is the volume fraction of the pore inclusions, and α is the pore aspect ratio. The coefficients T(α) and F(α) are related to the pore aspect ratio and represent the effect of adding pore inclusions on the background matrix. The expression for the Brie equation is as follows: Where K dry The bulk modulus of dry rock, μ dry μ is the shear modulus of dry rock. m For the shear modulus of the matrix, (v p / v s ) dry The ratio of longitudinal and transverse wave velocities in dry rock is given by φ, porosity is given by c, and this index value varies with the degree of compaction, lithology, etc. The expression for the Biot-Gassmann equation is as follows: Where K sat K represents the bulk modulus of saturated rock. dry K represents the bulk modulus of dry rock. m K is the bulk modulus of the matrix. f φ represents the bulk modulus of the porous fluid, and φ represents the porosity.

4. The method for identifying lacustrine carbonate reservoirs according to claim 1, characterized in that, Step S4 specifically includes: S41. Based on the input of carbonate content, porosity, P-wave impedance, and P-wave / S-wave velocity ratio of different lithologies, establish a rock physical quantity model.

5. The method for identifying lacustrine carbonate reservoirs according to claim 1, characterized in that, Step S5 specifically includes: S51. Conduct pre-stack seismic inversion to obtain three data volumes: P-wave impedance, S-wave impedance, and density. S52. The angle-separated superimposed data volume is used to extract the seismic wavelet of the well-side channel through repeated iterations. S53. For each data volume superimposed at different angles, inversion is performed using the corresponding seismic wavelet; S54. Based on mathematical calculations, obtain the Lamé coefficient, shear modulus, and Poisson's ratio geophysical elastic parameters.

6. The method for identifying lacustrine carbonate reservoirs according to claim 1, characterized in that, Step S6 specifically includes: S61. Based on the different regions of different lithologies in the rock physical scale, the main lithologies of lacustrine carbonate rocks are finely distinguished in the cross-plot of P-wave impedance and P-wave / S-wave velocity ratio. S62. Based on the pre-stack inversion data, predict the spatial distribution of algal limestone and lacustrine carbonate rocks.

7. The method for identifying lacustrine carbonate reservoirs according to claim 2 or 6, characterized in that, The main lithologies of lacustrine carbonate rocks are algal limestone, dolomite, sandstone, and mudstone.

8. A device for identifying lacustrine carbonate reservoirs, characterized in that, include: The Lacustrine Carbonate Reservoir Characteristics Summary Module is used to classify the lithology of lacustrine carbonate rocks and summarize the characteristics of lacustrine carbonate reservoirs based on core scale. The lithological elastic parameter characteristic summary module is used to clarify the seismic reflection characteristics of different lithologies and summarize the elastic parameter characteristics of different lithologies based on fine well seismic calibration. The rock physics modeling module is used to perform rock physics modeling based on pore structure. The Rock Physical Quantity Module is used to create rock physical quantity modules; The geophysical elastic parameter volume acquisition module is used to perform pre-stack inversion and obtain the geophysical elastic parameter volume. The lacustrine carbonate spatial distribution prediction module is used to distinguish different lithologies based on rock physical quantities and to predict the spatial distribution of lacustrine carbonate rocks based on pre-stack inversion data.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the lacustrine carbonate reservoir identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that executes the lacustrine carbonate reservoir identification method according to any one of claims 1 to 7.

Images