Prediction method, device and equipment of fracture-cave type reservoir and storage medium

Abstract

The embodiment of the application discloses a kind of prediction method, device and equipment of fracture-cave type reservoir and storage medium, it is related to oil and gas exploration field.The method comprises: based on the core data and well logging data of target well in target area, determine target well curve and cut-off value;Based on target well curve, at least two target seismic attributes are determined from the seismic data of target area;Based on target well curve and the target seismic attribute curve extracted along well track of target seismic attribute, the fitting relationship of at least two target seismic attribute curves and target well curve is determined;Based on fitting relationship, the seismic data of target area and cut-off value, fracture-cave type reservoir prediction is carried out to target area.The scheme provided in the embodiment of the application can improve the prediction accuracy of fracture-cave type reservoir, thereby reducing the exploration risk and exploration cost, improve the efficiency and benefit of oil and gas production.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration, and in particular to a method, apparatus, equipment and storage medium for predicting fractured-vuggy reservoirs. Background Technology

[0002] As oil and gas exploration and development deepen, drilling difficulty has increased significantly, and the accuracy requirements for predicting small-scale fractured-vuggy reservoirs are constantly rising.

[0003] Fractured-vuggy reservoir prediction methods in related technologies can be divided into two main categories: qualitative prediction and quantitative prediction. Qualitative prediction can extract different seismic attributes from seismic data volumes to predict fractured-vuggy reservoirs, while quantitative prediction can use impedance inversion or geostatistical inversion to predict fractured-vuggy reservoirs.

[0004] However, due to limitations in the methods, the prediction results of fractured-vuggy reservoirs in related technologies have poor well-to-well matching rates and cannot effectively guide the fine exploration and development of wells. Therefore, how to improve the accuracy of fractured-vuggy reservoir prediction has become an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for predicting fractured-vuggy reservoirs. The technical solution is as follows:

[0006] On one hand, embodiments of this application provide a method for predicting fractured-vuggy reservoirs, the method comprising:

[0007] Based on the core data and logging data of the target well in the target area, the target logging curve and cutoff value are determined. The target logging curve is used to characterize the relationship between fracture and vulcanization development and drilling depth. The cutoff value is a threshold used to determine the location of fracture and vulcanization reservoirs based on the target logging curve. The correlation between the target logging curve and the fracture and vulcanization reservoir is higher than the correlation between other logging curves in the logging data and the fracture and vulcanization reservoir.

[0008] Based on the target logging curve, at least two target seismic attributes are determined from the seismic data of the target area. The correlation between the target seismic attributes and the fractured-vuggy reservoir is higher than the correlation between other seismic attributes in the seismic data and the fractured-vuggy reservoir.

[0009] Based on the target logging curve and the target seismic attribute curve extracted along the well trajectory, at least two fitting relationships between the target seismic attribute curve and the target logging curve are determined;

[0010] Based on the fitting relationship, the seismic data of the target area, and the cutoff value, fracture-vuggy reservoir prediction is performed in the target area.

[0011] On the other hand, embodiments of this application provide a prediction device for fractured-vuggy reservoirs, the device comprising:

[0012] The first determining module is used to determine the target logging curve and cutoff value based on the core data and logging data of the target well in the target area. The target logging curve is used to characterize the relationship between fracture and vulcanization development and drilling depth. The cutoff value is a threshold used to determine the location of fracture and vulcanization reservoirs based on the target logging curve. The correlation between the target logging curve and the fracture and vulcanization reservoir is higher than the correlation between other logging curves in the logging data and the fracture and vulcanization reservoir.

[0013] The second determining module is used to determine at least two target seismic attributes from the seismic data of the target area based on the target logging curve, wherein the correlation between the target seismic attributes and the fractured-vuggy reservoir is higher than the correlation between other seismic attributes in the seismic data and the fractured-vuggy reservoir;

[0014] The third determining module is used to determine at least two fitting relationships between the target seismic attribute curve and the target logging curve based on the target logging curve and the target seismic attribute curve extracted along the well trajectory.

[0015] The prediction module is used to predict fracture-vuggy reservoirs in the target area based on the fitting relationship, the seismic data of the target area, and the cutoff value.

[0016] On the other hand, embodiments of this application provide a computer device including a processor and a memory, wherein the memory stores at least one program, which is loaded and executed by the processor to implement the prediction method for fractured-vuggy reservoirs as described above.

[0017] On the other hand, embodiments of this application provide a computer-readable storage medium storing at least one program that is loaded and executed by a processor to implement the prediction method for fractured-vuggy reservoirs as described above.

[0018] On the other hand, embodiments of this application provide a computer program product including computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform a method for predicting fractured-vuggy reservoirs as described above.

[0019] In this embodiment, the computer equipment first determines the target logging curve and cutoff value based on the core data and logging data of the target well in the target area. Then, based on the target logging curve, it determines at least two target seismic attributes from the seismic data of the target area. Furthermore, based on the target logging curve and the target seismic attribute curve extracted along the well trajectory, it determines the fitting relationship between the at least two target seismic attribute curves and the target logging curve. Finally, based on the fitting relationship, the seismic data of the target area, and the cutoff value, it predicts fractured-vuggy reservoirs in the target area. The scheme provided in this embodiment effectively improves the prediction accuracy of fractured-vuggy reservoirs, provides the distribution range of underground fractured-vuggy reservoirs for oil and gas exploration and development, thereby reducing exploration risks and costs, and improving the efficiency and benefits of oil and gas production. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a method for predicting fractured-vuggy reservoirs provided in an exemplary embodiment of this application is shown.

[0022] Figure 2 This illustration shows a schematic diagram of determining target seismic properties according to an exemplary embodiment of this application;

[0023] Figure 3 A flowchart illustrating a method for predicting fractured-vuggy reservoirs provided in another exemplary embodiment of this application is shown;

[0024] Figure 4 This illustration shows a schematic diagram of determining a target logging curve according to an exemplary embodiment of this application;

[0025] Figure 5 This illustration shows a schematic diagram of determining the characteristic values ​​of a well logging curve according to another exemplary embodiment of this application;

[0026] Figure 6 A schematic diagram illustrating the determination of a cutoff value provided by another exemplary embodiment of this application is shown;

[0027] Figure 7 A schematic diagram illustrating the determination of a fitting relationship provided in an exemplary embodiment of this application is shown;

[0028] Figure 8 This is an exemplary embodiment of this application illustrating a comparison of the prediction results of an embodiment of this application with that of a fractured-vuggy reservoir with a single seismic attribute;

[0029] Figure 9 A structural block diagram of an oil and gas prediction device according to an embodiment of this application is shown;

[0030] Figure 10 A schematic diagram of the structure of a computer device provided in an exemplary embodiment of this application is shown. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0032] In this article, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0033] With the increasing demand for oil and gas resources in modern society and the continuous deepening of oil exploration and development, the prediction of fractured-vuggy reservoirs has become increasingly important for achieving efficient oilfield development.

[0034] Fracture-vuggy reservoirs refer to reservoirs where fractures and cavities serve as oil and gas storage spaces. These reservoirs are mostly carbonate rocks, and their storage spaces and seepage channels are primarily composed of fractures and connected solution pores and cavities. These reservoirs are typically high-permeability reservoirs, mainly influenced by the combined effects of original lithology, structure, and karst. Having undergone multiple tectonic movements and intense weathering, erosion, and leaching, fracture-vuggy reservoirs exhibit diverse storage space types, with fractures and cavities exhibiting highly irregular and randomly distributed sizes and shapes.

[0035] In related technologies, the prediction methods for fractured-vuggy reservoirs can be divided into two main categories: qualitative prediction and quantitative prediction. Qualitative prediction can use seismic attributes such as coherence, curvature, texture, symmetry, ant-like size, maximum likelihood, and P-wave azimuth anisotropy to predict fractured-vuggy reservoirs, while quantitative prediction can use impedance inversion or geostatistical inversion to predict fractured-vuggy reservoirs.

[0036] However, due to methodological limitations, the fractured-vuggy reservoir prediction methods in related technologies have a low well-to-well matching rate, and cannot effectively guide the fine exploration and development of wells.

[0037] In view of this, in the embodiments of this application, the computer device first determines the target logging curve and cutoff value based on the core data and logging data of the target well in the target area. Then, based on the target logging curve, it determines at least two target seismic attributes from the seismic data of the target area. Furthermore, based on the target logging curve and the target seismic attribute curve extracted along the well trajectory, it determines the fitting relationship between the at least two target seismic attribute curves and the target logging curve. Finally, based on the fitting relationship, the seismic data of the target area, and the cutoff value, it performs fractured-vuggy reservoir prediction in the target area, effectively improving the prediction accuracy of fractured-vuggy reservoirs and providing the distribution range of underground fractured-vuggy reservoirs for oil and gas exploration and development. This can reduce exploration risks and costs, and improve the efficiency and benefits of oil and gas production.

[0038] Please refer to Figure 1 This document illustrates a flowchart of a method for predicting fractured-vuggy reservoirs provided in an exemplary embodiment of this application. This embodiment uses the method applied to a computer device as an example, and the method includes the following steps:

[0039] Step 101: Based on the core data and logging data of the target well in the target area, determine the target logging curve and cutoff value.

[0040] In one possible implementation, the computer device determines the target logging curve and cutoff value based on the core data and logging data of the target well in the target area. The target logging curve is used to characterize the relationship between fracture and vulcanization development and drilling depth. The cutoff value is a threshold used to determine the location of fracture and vulcanization reservoirs based on the target logging curve. The correlation between the target logging curve and fracture and vulcanization reservoirs is higher than the correlation between other logging curves in the logging data and fracture and vulcanization reservoirs.

[0041] Core refers to ore-bearing rock or ore extracted from a solid mineral body or stratum using a core drill bit and other core-taking tools. Core data is important physical geological data for studying and understanding underground geology and mineral resources.

[0042] The logging data for the target well includes conventional logging data and special logging data. Conventional logging data refers to logging data obtained through conventional logging methods, including natural gamma logging, shallow and deep resistivity logging, sonic transit logging, density logging, and neutron logging. Special logging data refers to logging data obtained through special logging methods, including cable formation logging, formation dip logging, imaging logging, nuclear magnetic resonance logging, and full-wavelength logging. This embodiment does not limit the specific conventional logging methods and special logging methods.

[0043] Optionally, the target logging curve characteristic value refers to the average value of the target logging curves within the depth range. The depth range corresponding to the curve segment with a target curve characteristic value greater than the cutoff value is the depth range where the fractured-vuggy reservoir is located, and the depth range corresponding to the curve segment with a target curve characteristic value less than the cutoff value is the depth range where the non-fractured-vuggy reservoir is located.

[0044] Step 102: Based on the target logging curves, determine at least two target seismic attributes from the seismic data of the target area.

[0045] In one possible implementation, the computer device determines at least two target seismic attributes from seismic data of the target area based on the target logging curve. The target seismic attributes have a higher correlation with fractured-vuggy reservoirs than other seismic attributes in the seismic data have a higher correlation with fractured-vuggy reservoirs.

[0046] Seismic attributes are specific measurements of geometric, kinematic, and dynamic characteristics derived from seismic data volumes. They are intricately linked to the physical properties of underground rocks, and the relationships between seismic attributes can be influenced by different regions, lithologies, and reservoir configurations.

[0047] Optionally, earthquake attributes include amplitude-related attributes (such as root mean square amplitude, absolute amplitude sum, amplitude kurtosis, total amplitude energy, instantaneous amplitude, etc.), frequency-related attributes (such as instantaneous frequency, instantaneous phase, average Q, etc.), spectrum-related attributes (such as spectral absorption factor, dominant frequency, frequency attenuation, bandwidth energy, etc.), and structural attributes (such as coherence, similarity, curvature, texture, etc.), etc. This embodiment does not limit these attributes.

[0048] Because the relationship between seismic attributes and the predicted object is complex, different work areas and reservoirs have different sensitivities to seismic attributes for the predicted object. Even within the same work area and reservoir, different predicted objects will have different corresponding sensitive seismic attributes. Therefore, it is necessary to select the seismic attributes sensitive to the problem being solved. Seismic attribute optimization methods aim to select the combination of seismic attributes that are most sensitive, effective, and representative of the problem being solved, thereby improving the effectiveness of processing and interpretation methods related to seismic attributes. In this embodiment, the seismic attributes with the highest correlation to fractured-vuggy reservoirs are selected. Seismic attribute selection methods mainly fall into two categories: seismic attribute selection and seismic attribute dimensionality reduction mapping.

[0049] In this embodiment of the application, the target seismic attributes ultimately determined by the computer device include at least two of the following: root mean square amplitude, total amplitude energy, and instantaneous frequency.

[0050] Regarding the specific method by which computer equipment determines at least two target seismic attributes from seismic data of a target area, in one possible implementation, after extracting at least two candidate seismic attributes from the seismic data of the target area, the computer equipment determines the correlation between the target well logging curve and the candidate seismic attribute curve corresponding to the candidate seismic attribute, and then determines at least two target seismic attributes from at least two candidate seismic attributes based on the correlation.

[0051] In a schematic manner, after the computer equipment identifies candidate seismic attribute A, candidate seismic attribute B, and candidate seismic attribute C from the seismic data of the target area, it obtains the distribution of fractured-vuggy reservoirs corresponding to the target well location, such as... Figure 2 As shown in the figure, the shaded area represents the distribution area of ​​fractured-vuggy reservoirs within the target area determined by the candidate seismic attributes. If the drilling depth range of the fractured-vuggy reservoir in well W is known to be [3865, 3960] and [4040, 4065], then it can be determined that the distribution of fractured-vuggy reservoirs in the target well reflected by candidate seismic attributes A and C is relatively accurate. That is, the correlation between the candidate seismic attribute curves corresponding to candidate seismic attributes A and C and the target logging curve is greater than the correlation between the candidate seismic attribute curve corresponding to candidate seismic attribute B and the target logging curve. Therefore, the computer equipment determines that candidate seismic attributes A and C are two target seismic attributes.

[0052] Step 103: Based on the target logging curve and the target seismic attribute curve extracted along the well trajectory, determine the fitting relationship between at least two types of target seismic attribute curves and the target logging curve.

[0053] In one possible implementation, a computer device determines the fitting relationship between at least two target seismic attribute curves and the target logging curve based on the target logging curve and the target seismic attribute curve extracted along the well trajectory.

[0054] Optionally, after the computer equipment acquires the target well logging curve and the target seismic curve, it uses regression analysis to determine the fitting relationship between the target seismic attributes and the target well logging attributes.

[0055] Regression analysis is a statistical analysis method used to determine the quantitative relationship of interdependence between two or more variables. Based on the number of variables involved, it is divided into univariate regression and multivariate regression analysis; based on the number of dependent variables, it can be divided into simple regression analysis and multiple regression analysis; based on the type of relationship between independent and dependent variables, it can be divided into linear regression analysis and nonlinear regression analysis.

[0056] Optionally, regression analysis methods include multiple linear regression, logistic regression, multiple multinomial regression, stepwise regression, ridge regression, and lasso regression, etc., which are not limited in this embodiment.

[0057] Step 104: Based on the fitting relationship, seismic data of the target area, and cutoff values, predict fracture-vuggy reservoirs in the target area.

[0058] In one possible implementation, the computer device fits the target seismic attribute data volume in the seismic data of the target area based on the fitting relationship to obtain the fitted well logging data volume, and then predicts fractured-vuggy reservoirs in the target area based on the fitted well logging data volume and the cutoff value.

[0059] Optionally, in this embodiment of the application, after the computer device obtains the fitted logging data volume of the target area, it removes the fitted logging data volume that is less than the cutoff value, that is, it removes the fitted logging data volume corresponding to the non-fractured and vulcanized reservoir and retains the fitted logging data volume corresponding to the fractured and vulcanized reservoir, thereby obtaining the distribution range of fractured and vulcanized reservoirs in the target area.

[0060] Furthermore, in one possible implementation, the computer device performs vertical thickness accumulation on the predicted fracture-vuggy reservoirs within the target area to obtain a plane distribution map of the fracture-vuggy reservoir thickness in the target area. The plane distribution map of the fracture-vuggy reservoir thickness is used to characterize the thickness of the fracture-vuggy reservoirs corresponding to different coordinate points within the target area, so that users can obtain the distribution of fracture-vuggy reservoirs in the target area based on the plane distribution map of the fracture-vuggy reservoir thickness in the target area.

[0061] Optionally, if the computer equipment obtains that the drilling depth range of the fractured-vuggy reservoir of the target well is [3865, 3960] and [4040, 4065], that is, the thickness of the fractured-vuggy reservoir is 95 and 25 respectively, then after the longitudinal thickness accumulation, the thickness of the fractured-vuggy reservoir at the target well indicated by the fractured-vuggy reservoir thickness planar distribution map is 120.

[0062] In summary, in this embodiment, the computer equipment first determines the target logging curve and cutoff value based on the core data and logging data of the target well in the target area. Then, based on the target logging curve, it determines at least two target seismic attributes from the seismic data of the target area. Furthermore, based on the target logging curve and the target seismic attribute curve extracted along the well trajectory, it determines the fitting relationship between the at least two target seismic attribute curves and the target logging curve. Finally, based on the fitting relationship, the seismic data of the target area, and the cutoff value, it predicts fractured-vuggy reservoirs in the target area. The scheme provided in this embodiment effectively improves the prediction accuracy of fractured-vuggy reservoirs, provides the distribution range of underground fractured-vuggy reservoirs for oil and gas exploration and development, thereby reducing exploration risks and costs, and improving the efficiency and effectiveness of oil and gas production.

[0063] In one possible implementation, the computer equipment first performs lithological, physical, and hydrocarbon-bearing analysis on the target well based on well logging data, and then determines the target well logging attribute from at least two candidate well logging attributes based on the analysis results, and then determines the target well logging curve and cutoff value.

[0064] Please refer to Figure 3 This document illustrates a flowchart of a method for predicting fractured-vuggy reservoirs provided in an exemplary embodiment of this application. This embodiment uses the method applied to a computer device as an example, and the method includes the following steps:

[0065] Step 301: Based on the core data and logging data of the target well, determine the target logging curve from at least two candidate logging curves.

[0066] In one possible implementation, a computer device determines a target logging curve from at least two candidate logging curves based on core data and logging data from the target well.

[0067] Optionally, the computer equipment performs lithological, physical, and hydrocarbon-bearing analysis on the target well using reservoir evaluation methods, and then determines the logging curve with the highest correlation to the fractured-vuggy reservoir from at least two candidate logging curves based on the analysis results.

[0068] Optionally, reservoir evaluation methods may include weighted analysis, Q-type clustering, analytic hierarchy process (AHP), principal component analysis, fuzzy theory, Kriging, etc. This embodiment does not limit the specific reservoir evaluation method.

[0069] In one possible implementation, a computer device acquires raw curves that characterize geological conditions from the logging data of the target well, such as natural gamma curves, shallow and deep resistivity curves, sonic curves, density curves, and neutron curves. Then, it uses a Q-type clustering analysis algorithm to perform lithological, physical, and hydrocarbon-bearing analysis on the target well based on the raw curves, and obtains clustering analysis results. The clustering analysis results include candidate logging curves that can characterize the lithology, physical properties, and hydrocarbon-bearing properties of the formation at different depths of the target well.

[0070] Optionally, candidate logging curves may include porosity curves, permeability curves, oil saturation curves, and sandstone thickness curves, etc., but this embodiment does not limit them.

[0071] Furthermore, based on the cluster analysis results, the computer equipment determines at least two candidate logging curves, and based on the candidate logging curves of the target well, the core data and the location of the fractured-vuggy reservoir indicated by the logging data of the target well, determines the correlation between the candidate logging curves and the fractured-vuggy reservoir, and then determines the candidate logging curve corresponding to the highest correlation as the target logging curve.

[0072] In an illustrative example, please refer to Figure 4 The computer equipment uses the Q-type clustering analysis algorithm to perform lithological, physical, and hydrocarbon-bearing analysis on the target well, obtaining candidate logging curves A, B, and C. Then, based on the core data and logging data of the target well indicating the location of the fractured-vuggy reservoir, the correlation between candidate logging curves A, B, and C and the fractured-vuggy reservoir is determined. Finally, the candidate logging curve with the highest correlation is determined as the target logging curve.

[0073] In this embodiment of the application, after the computer device determines the candidate logging curves, based on the candidate logging curves, core data, and the location of the fractured-vuggy reservoir of the target well indicated by the logging data, it determines that the porosity curve has a higher correlation with the fractured-vuggy reservoir than the correlation between other candidate logging curves and the fractured-vuggy reservoir. Therefore, the computer device determines the porosity curve as the target logging curve.

[0074] Step 302: Determine the cutoff value based on the target logging curve, core data, and logging data.

[0075] Furthermore, the computer equipment determines the cutoff value based on the target logging curve, core data, and the location of the fractured-vuggy reservoir indicated by the logging data.

[0076] In one possible implementation, the computer equipment determines the first drilling depth of the fractured-vuggy reservoir and the second drilling depth of the non-fractured-vuggy reservoir based on the core data and logging data of the target well. Then, based on the first drilling depth, the second drilling depth, and the target logging curve, it determines the characteristic value of the first target logging curve corresponding to the fractured-vuggy reservoir and the characteristic value of the second target logging curve corresponding to the non-fractured-vuggy reservoir. Finally, based on the characteristic values ​​of the first and second target logging curves, a cutoff value is determined through multi-well statistical analysis.

[0077] Optionally, after the computer equipment determines the first drilling depth of the fractured-vuggy reservoir and the second drilling depth of the non-fractured-vuggy reservoir in the target well, it calculates the average value of the target logging curves corresponding to the first drilling depth and the second drilling depth to obtain the characteristic value of the first target logging curve and the characteristic value of the second target logging curve.

[0078] Optionally, to improve the accuracy of the cutoff value determined by the computer equipment, after acquiring core data and logging data of at least two target wells within the target area, the computer equipment determines the candidate logging curves of at least two target wells, as well as the first drilling depth of fractured-vuggy reservoirs and the second drilling depth of non-fractured-vuggy reservoirs in each target well. Then, it determines the first candidate logging curve characteristic value corresponding to the first drilling depth and the second candidate logging curve characteristic value corresponding to the second drilling depth.

[0079] Indicative, such as Figure 5 As shown, after the computer equipment acquires the core data and logging data of target well 1, target well 2 and target well 3, it determines the candidate logging curves A, B and C for each of target well 1, target well 2 and target well 3, and then determines the first candidate logging curve characteristic value and the second candidate logging curve characteristic value corresponding to the first drilling depth of the fractured-vuggy reservoir and the second drilling depth of the non-fractured-vuggy reservoir in each target well. For example, if target well 1 has a fractured-vuggy reservoir within the drilling depth range of [5102.6, 5110.6], and the computer equipment takes the first drilling depth within this range, then the characteristic values ​​of the first candidate logging curves A, B, and C at the first drilling depth for target well 1 are 4.5, 1.987, and 55.3, respectively. If target well 1 has a non-fractured-vuggy reservoir within the drilling depth range of [5315.1, 5321.3], and the computer equipment takes the second drilling depth within this range, then the characteristic values ​​of the second candidate logging curves A, B, and C at the second drilling depth for target well 1 are 2.4, 0.064, and 43.8, respectively.

[0080] Furthermore, the computer equipment determines the cutoff value by performing multi-well statistical analysis on at least two target wells within the target area.

[0081] Schematic illustration: After the computer equipment acquires the candidate logging curve feature values ​​corresponding to candidate logging curves A, B, and C for target wells 1, 2, and 3 respectively, for the same target well within the same drilling depth range, it randomly selects two candidate logging curve feature values ​​from the three candidate logging curve feature values ​​as the horizontal and vertical coordinates to obtain a two-dimensional coordinate point. For example, taking the candidate logging curve feature value 4.5 of candidate logging curve A and the candidate logging curve feature value 55.3 of candidate logging curve C for target well 1 within the depth range of [5102.6, 5110.6] as the horizontal and vertical coordinates respectively, a two-dimensional coordinate point (4.5, 55.3) is obtained. Furthermore, the computer equipment uses the above method to obtain multiple two-dimensional coordinate points for the same target well within multiple drilling depth ranges.

[0082] Computer equipment uses the aforementioned method to obtain multiple two-dimensional coordinate points for different target wells at different drilling depths, thereby constructing multiple two-dimensional coordinate systems. Cutoff value analysis is then performed on the characteristic values ​​of the first and second candidate logging curves for each coordinate system based on multiple candidate curves from the multiple target wells. (Illustrative example, such as...) Figure 6As shown, after the computer equipment obtains the first candidate logging curve feature values ​​and the second candidate logging curve feature values ​​corresponding to the candidate logging curves A, B, and C of target wells 1, 2, and 3 respectively, it constructs a two-dimensional coordinate system using multiple coordinate points of the candidate logging curve feature values ​​of the corresponding candidate logging curves A and C extracted from target wells 1, 2, and 3. The horizontal and vertical axes of this two-dimensional coordinate system are the candidate logging curve feature values ​​of candidate logging curve C and candidate logging curve feature values ​​of candidate logging curve A, respectively. The computer equipment then marks each coordinate point in the two-dimensional coordinate system based on the first candidate logging curve feature value and the second candidate logging curve feature value corresponding to the candidate logging curves A and C of the target wells 1, 2 and 3, respectively. After obtaining the coordinate points of the corresponding fractured-vuggy reservoirs and the corresponding non-fractured-vuggy reservoirs, the computer equipment performs cutoff value analysis on the two-dimensional coordinate system to obtain the threshold values ​​of the first candidate logging curve feature value of the corresponding fractured-vuggy reservoir and the second candidate logging curve feature value of the corresponding non-fractured-vuggy reservoir among the different candidate logging curves. This threshold value is the cutoff value for determining the location of the fractured-vuggy reservoir by the candidate logging curve. For example, if the computer equipment determines, based on the analysis results, that the candidate logging curve C cannot clearly distinguish the first candidate logging curve characteristic value corresponding to the fractured-vuggy reservoir and the second candidate logging curve characteristic value corresponding to the non-fractured-vuggy reservoir within the corresponding candidate logging curve characteristic value range [40, 60], that is, the computer equipment cannot determine the cutoff value of the candidate logging curve C for determining the location of the fractured-vuggy reservoir within the candidate logging curve characteristic value range [40, 60] corresponding to the selected logging curve C, and the computer equipment determines, based on the analysis results, that the threshold value of the first candidate logging curve characteristic value corresponding to the fractured-vuggy reservoir and the second candidate logging curve characteristic value corresponding to the non-fractured-vuggy reservoir in the candidate logging curve A is 3.0, that is, the cutoff value of the computer equipment for determining the location of the fractured-vuggy reservoir based on the candidate logging curve A is 3.0, and if the candidate logging curve A is a porosity curve, since the computer equipment has already determined the porosity curve as the target logging curve, then the computer equipment can determine the cutoff value of the target logging curve as 3.0.

[0083] In this embodiment of the application, the target logging curve is a porosity curve, and the computer equipment determines the cutoff value to be 3.0. That is, in the target logging curve, the depth range corresponding to the curve segment with a characteristic value greater than 3.0 is the depth range of the fractured-vuggy reservoir, and the depth range corresponding to the curve segment with a characteristic value less than 3.0 is the depth range of the non-fractured-vuggy reservoir.

[0084] Step 303: Based on the target logging curves, determine at least two target seismic attributes from the seismic data of the target area.

[0085] The implementation method of this step can be referred to step 102, and will not be repeated here in this embodiment.

[0086] Step 304: Perform multiple linear regression on the target logging curve and at least two target seismic attribute curves to obtain the fitting relationship between the at least two target seismic attribute curves and the target logging curve.

[0087] In one possible implementation, before the computer device performs multiple regression on the target logging curve and at least two target seismic attribute curves to obtain the fitting relationship between the at least two target seismic attribute curves and the target logging curve, a seismic wavelet is extracted from the seismic data of the target area and the seismic wavelet is convolved to obtain a synthetic seismic record. Then, based on the synthetic seismic record and the propagation velocity of the wavelet, the correspondence between the wavelet propagation time and the drilling depth is determined. Based on the correspondence between the wavelet propagation time and the drilling depth, the target logging curve is converted from the depth domain to the time domain. The converted target logging curve is used to characterize the relationship between fracture and cavity development and the wavelet propagation time.

[0088] Optionally, synthetic seismic records are seismic records artificially synthesized from sonic logging or vertical seismic profile data. They are a widely used type of seismic modeling technology and are the basis for tasks such as stratigraphic calibration and reservoir description. They serve as an intermediate medium for converting geological models into seismic information.

[0089] Optionally, a seismic wavelet is a signal with a definite start time, finite energy, and a certain duration, and is the basic unit in a seismic record.

[0090] Furthermore, the computer equipment performs multiple regression on the converted target logging curve and at least two target seismic attribute curves to obtain the fitting relationship between at least two target seismic attributes and target logging attributes.

[0091] Indicative, such as Figure 7 As shown, if the computer equipment obtains the target seismic attribute curves extracted along the well trajectory for three types of target seismic attributes, the computer equipment performs multiple linear regression on the target seismic attribute curves and the target logging curves converted to the time domain to obtain the fitting relationship between at least two types of target seismic attributes and target logging attributes.

[0092] Optionally, the target logging curve determined by the computer equipment is the porosity curve Y, and the target seismic attribute curves are the root mean square amplitude curve X1, the total amplitude energy curve X2, and the instantaneous frequency curve X3. The computer equipment obtains a fitting equation that can characterize the fitting relationship between the porosity curve Y and the root mean square amplitude curve X1, the total amplitude energy curve X2, and the instantaneous frequency curve X3. This fitting equation is Y = 13.25 - 9.6 × 10⁻⁶. -4The computer then calculates R1 = ×X1 - 0.085×X2 - 0.899×X3, and the fitted relation is obtained by the computer. 2 R 2 Also known as the coefficient of determination of the fitted relationship, it represents the degree to which variable X explains Y in the equation. 2 The value ranges from 0 to 1; the closer it is to 1, the stronger the explanatory power of X for Y in the equation. In this embodiment, the computer device calculates the value of R to be 0.892, i.e., R0. 2 The value is 0.795664, indicating that the root mean square amplitude curve X1, the total amplitude energy curve X2, and the instantaneous frequency curve X3 can well characterize the porosity curve Y.

[0093] Step 305: Based on the fitting relationship, seismic data of the target area, and cutoff values, predict fracture-vuggy reservoirs in the target area.

[0094] The implementation method of this step can be referred to step 104, and will not be repeated here in this embodiment.

[0095] In this embodiment, after the computer device determines the target logging attribute, it determines the target logging curve and cutoff value based on the target logging attribute. Then, by extracting wavelets and creating synthetic seismic records, the logging curve in the depth domain is unified to the time domain of the seismic data volume. This facilitates the subsequent multivariate linear regression of the target curve and seismic attribute in the same time domain to obtain at least two fitting relationships between the target seismic attribute and the target logging attribute. Based on the fitting relationship, the seismic data of the target area, and the cutoff value, the computer device can predict fractured-vuggy reservoirs in the target area.

[0096] Indicative, such as Figure 8As shown, compared to related technologies that extract only a single seismic attribute from the seismic data of the target area and predict fractured-vuggy reservoirs based on that seismic attribute, the fractured-vuggy reservoir prediction method provided in this application can extract target seismic attributes with high correlation to fractured-vuggy reservoirs from multiple seismic attributes. Since the target seismic attribute can be directly extracted from the seismic data of the target area by computer equipment, but the target logging curve must be obtained from accurate logging data, and the target logging curve has higher accuracy in predicting fractured-vuggy reservoirs than the target seismic attribute, the computer equipment obtains the target seismic attribute curve extracted along the well trajectory based on the target seismic attribute, and then determines the fitting relationship between the target seismic attribute curve and the target logging curve. Based on the fitting relationship and the target seismic attribute data volume of the target area, the computer equipment obtains the fitted logging data volume of the entire target area, and then uses the fitted logging data volume to predict the distribution of fractured-vuggy reservoirs. This method has higher accuracy than predicting the distribution of fractured-vuggy reservoirs using only a single seismic attribute, indicating that the fractured-vuggy reservoir prediction method provided in this application can predict the distribution of fractured-vuggy reservoirs more accurately.

[0097] Please refer to Figure 9 This illustration shows a structural block diagram of a prediction device for fractured-vuggy reservoirs provided in an exemplary embodiment of this application. The device may include:

[0098] The first determining module 901 is used to determine the target logging curve and cutoff value based on the core data and logging data of the target well in the target area. The target logging curve is used to characterize the relationship between fracture and vulcanization development and drilling depth. The cutoff value is a threshold used to determine the location of fracture and vulcanization reservoirs based on the target logging curve. The correlation between the target logging curve and the fracture and vulcanization reservoir is higher than the correlation between other logging curves in the logging data and the fracture and vulcanization reservoir.

[0099] The second determining module 902 is used to determine at least two target seismic attributes from the seismic data of the target area based on the target logging curve, wherein the correlation between the target seismic attributes and the fractured-vuggy reservoir is higher than the correlation between other seismic attributes in the seismic data and the fractured-vuggy reservoir;

[0100] The third determining module 903 is used to determine at least two fitting relationships between the target seismic attribute curve and the target logging curve based on the target logging curve and the target seismic attribute curve extracted along the well trajectory.

[0101] The prediction module 904 is used to predict fracture-vuggy reservoirs in the target area based on the fitting relationship, the seismic data of the target area, and the cutoff value.

[0102] Optionally, the first determining module 901 is used for:

[0103] Based on the core data and logging data of the target well, the target logging curve is determined from at least two candidate logging curves;

[0104] The cutoff value is determined based on the target logging curve, the core data, and the logging data.

[0105] Optionally, the first determining module 901 is used for:

[0106] Based on the logging data of the target well, the Q-type clustering analysis algorithm is used to analyze the lithology, physical properties and hydrocarbon content of the target well, and the clustering analysis results are obtained.

[0107] Based on the cluster analysis results, at least two candidate logging curves were determined;

[0108] Based on the candidate logging curves, the core data, and the location of the fractured-vuggy reservoir of the target well indicated by the logging data, the correlation between the candidate logging curves and the fractured-vuggy reservoir is determined.

[0109] The candidate logging curve corresponding to the highest correlation is determined as the target logging curve.

[0110] Optionally, the first determining module 901 is used for:

[0111] Based on the core data and logging data of the target well, the first drilling depth of the fractured-vuggy reservoir and the second drilling depth of the non-fractured-vuggy reservoir in the target well are determined.

[0112] Based on the first drilling depth, the second drilling depth, and the target logging curve, determine the characteristic value of the first target logging curve corresponding to the fractured-vuggy reservoir and the characteristic value of the second target logging curve corresponding to the non-fractured-vuggy reservoir.

[0113] Based on the characteristic values ​​of the first target logging curve and the second target logging curve, the cutoff value is determined through multi-well statistical analysis.

[0114] Optionally, the second determining module 902 is used for:

[0115] Extract at least two candidate seismic attributes from the seismic data of the target area;

[0116] Determine the correlation between the target logging curve and the candidate seismic attribute curves extracted along the well trajectory;

[0117] Based on the correlation, at least two target seismic attributes are determined from at least two candidate seismic attributes.

[0118] Optionally, the third determining module 903 is used for:

[0119] Multiple linear regression is performed on the target well logging curve and at least two target seismic attribute curves to obtain the fitting relationship between the at least two target seismic attribute curves and the target well logging curve.

[0120] Optionally, the device further includes:

[0121] An extraction module is used to extract seismic wavelets from the seismic data of the target area;

[0122] The synthesis module is used to convolve seismic wavelets to obtain synthetic seismic records;

[0123] The fourth determining module is used to determine the correspondence between the wavelet propagation time and the drilling depth based on the synthetic seismic record and the wavelet propagation velocity;

[0124] The conversion module is used to convert the target logging curve from the depth domain to the time domain based on the correspondence between the wavelet propagation time and the drilling depth. The converted target logging curve is used to characterize the relationship between the fracture and cavity development and the wavelet propagation time.

[0125] The third determining module 903 is used to perform multiple regression on the converted target logging curve and at least two target seismic attribute curves to obtain the fitting relationship between at least two target seismic attributes and the target logging attribute.

[0126] Optionally, the prediction module 904 is used for:

[0127] Based on the fitting relationship, the target seismic attribute data volume in the seismic data is fitted to obtain the fitted well logging data volume;

[0128] Based on the fitted logging data and the cutoff value, fractured-vuggy reservoirs are predicted for the target area.

[0129] Optionally, the device further includes:

[0130] The accumulation module is used to accumulate the longitudinal thickness of the predicted fractured-vuggy reservoirs within the target area to obtain a plane distribution map of the fractured-vuggy reservoir thickness in the target area. The plane distribution map of the fractured-vuggy reservoir thickness is used to characterize the thickness of the fractured-vuggy reservoirs corresponding to different coordinate points within the target area.

[0131] In summary, in this embodiment, the computer equipment first determines the target logging curve and cutoff value based on the core data and logging data of the target well in the target area. Then, based on the target logging curve, it determines at least two target seismic attributes from the seismic data of the target area. Furthermore, based on the target logging curve and the target seismic attribute curve extracted along the well trajectory, it determines the fitting relationship between the at least two target seismic attribute curves and the target logging curve. Finally, based on the fitting relationship, the seismic data of the target area, and the cutoff value, it predicts fractured-vuggy reservoirs in the target area. The scheme provided in this embodiment effectively improves the prediction accuracy of fractured-vuggy reservoirs, provides the distribution range of underground fractured-vuggy reservoirs for oil and gas exploration and development, thereby reducing exploration risks and costs, and improving the efficiency and effectiveness of oil and gas production.

[0132] Please refer to Figure 10 This illustration shows a schematic diagram of the structure of a computer device provided in an exemplary embodiment of this application. Specifically, the computer device 1000 includes a Central Processing Unit (CPU) 1001, a system memory 1004 including a random access memory 1002 and a read-only memory 1003, and a system bus 1005 connecting the system memory 1004 and the CPU 1001. The computer device 1000 may also include a basic input / output system (I / O system) 1006 to facilitate the transfer of information between various devices within the computer, and a mass storage device 1007 for storing the operating system 1013, application programs 1014, and other program modules 1015.

[0133] In some embodiments, the basic input / output system 1006 may include a display 1008 for displaying information and an input device 1009 for user input, such as a mouse or keyboard. Both the display 1008 and the input device 1009 are connected to the central processing unit 1001 via an input / output controller 1010 connected to the system bus 1005. The basic input / output system 1006 may also include the input / output controller 1010 for receiving and processing input from multiple other devices such as a keyboard, mouse, or electronic stylus. Similarly, the input / output controller 1010 also provides output to a display screen, printer, or other types of output devices.

[0134] The mass storage device 1007 is connected to the central processing unit 1001 via a mass storage controller (not shown) connected to the system bus 1005. The mass storage device 1007 and its associated computer-readable media provide non-volatile storage for the computer device 1000. That is, the mass storage device 1007 may include computer-readable media (not shown) such as a hard disk or drive.

[0135] Without loss of generality, the computer-readable medium may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include random access memory (RAM), read-only memory (ROM), flash memory or other solid-state storage technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that the computer storage media are not limited to the above-mentioned types. The system memory 1004 and mass storage device 1007 described above can be collectively referred to as memory.

[0136] The memory stores one or more programs, which are configured to be executed by one or more central processing units 1001. The one or more programs contain instructions for implementing the methods described above, and the central processing unit 1001 executes the one or more programs to implement the methods provided in the various method embodiments described above.

[0137] According to various embodiments of this application, the computer device 1000 can also be connected to a remote computer on a network, such as the Internet. That is, the computer device 1000 can be connected to the network 1012 via the network interface unit 1011 connected to the system bus 1005, or the network interface unit 1011 can be used to connect to other types of networks or remote computer systems (not shown).

[0138] The memory further includes one or more programs stored in the memory, and the one or more programs include steps performed by a computer device in the methods provided in the embodiments of this application.

[0139] This application also provides a computer-readable storage medium storing at least one program, which is loaded and executed by a processor to implement the oil and gas prediction method as described in the above embodiments.

[0140] This application provides a computer program product containing computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the oil and gas prediction method as described in the above embodiments.

[0141] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0142] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting fractured-vuggy reservoirs, characterized in that, The method includes: Based on the logging data of the target wells within the target area, the Q-type clustering analysis algorithm is used to analyze the lithology, physical properties and hydrocarbon content of the target wells, and the clustering analysis results are obtained. Based on the cluster analysis results, at least two candidate logging curves were determined; Based on the candidate logging curves, core data, and the location of the fractured-vuggy reservoir in the target well indicated by the logging data, the correlation between the candidate logging curves and the fractured-vuggy reservoir is determined. The candidate logging curve corresponding to the highest correlation is determined as the target logging curve. The target logging curve is used to characterize the relationship between fracture and vulcanization development and drilling depth. The correlation between the target logging curve and fracture and vulcanization reservoirs is higher than the correlation between other logging curves in the logging data and fracture and vulcanization reservoirs. Based on the core data and logging data of the target well, the first drilling depth of the fractured-vuggy reservoir and the second drilling depth of the non-fractured-vuggy reservoir in the target well are determined. Based on the first drilling depth, the second drilling depth, and the target logging curve, determine the characteristic value of the first target logging curve corresponding to the fractured-vuggy reservoir and the characteristic value of the second target logging curve corresponding to the non-fractured-vuggy reservoir. Based on the characteristic values ​​of the first target logging curve and the second target logging curve, a cutoff value is determined through multi-well statistical analysis. The cutoff value is a threshold value used to determine the location of fractured-vuggy reservoirs based on the target logging curve. Based on the target logging curve, at least two target seismic attributes are determined from the seismic data of the target area. The correlation between the target seismic attributes and the fractured-vuggy reservoir is higher than the correlation between other seismic attributes in the seismic data and the fractured-vuggy reservoir. Based on the target logging curve and the target seismic attribute curve extracted along the well trajectory, at least two fitting relationships between the target seismic attribute curve and the target logging curve are determined; Based on the fitting relationship, the seismic data of the target area, and the cutoff value, fracture-vuggy reservoir prediction is performed in the target area.

2. The method according to claim 1, characterized in that, Based on the target logging curve, at least two target seismic attributes are determined from the seismic data of the target area, including: Extract at least two candidate seismic attributes from the seismic data of the target area; Determine the correlation between the target logging curve and the candidate seismic attribute curves extracted along the well trajectory; Based on the correlation, at least two target seismic attributes are determined from at least two candidate seismic attributes.

3. The method according to claim 1, characterized in that, The method involves determining at least two fitting relationships between the target seismic attribute curve and the target well logging curve, based on the target logging curve and the target seismic attribute curve extracted along the well trajectory. These relationships include: Multiple linear regression is performed on the target well logging curve and at least two target seismic attribute curves to obtain the fitting relationship between the at least two target seismic attribute curves and the target well logging curve.

4. The method according to claim 3, characterized in that, Before performing multiple linear regression on the target well logging curve and at least two target seismic attribute curves to obtain the fitting relationship between the at least two target seismic attribute curves and the target well logging curve, the method further includes: Extract seismic wavelets from the seismic data of the target region; Synthetic seismic records are obtained by convolving seismic wavelets; Based on the synthetic seismic record and the propagation velocity of the wavelet, the correspondence between the wavelet propagation time and the drilling depth is determined; Based on the correspondence between the wavelet propagation time and the drilling depth, the target logging curve is converted from the depth domain to the time domain, wherein the converted target logging curve is used to characterize the relationship between the fracture and cavity development and the wavelet propagation time. The step of performing multiple linear regression on the target well logging curve and at least two target seismic attribute curves to obtain the fitting relationship between the at least two target seismic attribute curves and the target well logging curve includes: Multiple linear regression is performed on the transformed target logging curve and at least two target seismic attribute curves to obtain the fitting relationship between the at least two target seismic attribute curves and the target logging curve.

5. The method according to claim 1, characterized in that, The method of predicting fractured-vuggy reservoirs in the target area based on the fitting relationship, the seismic data of the target area, and the cutoff value includes: Based on the fitting relationship, the target seismic attribute data volume in the seismic data is fitted to obtain the fitted well logging data volume. Based on the fitted logging data and the cutoff value, fractured-vuggy reservoirs are predicted for the target area.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: The predicted fractured-vuggy reservoirs within the target area are longitudinally accumulated to obtain a plane distribution map of the fractured-vuggy reservoir thickness in the target area. This plane distribution map is used to characterize the fractured-vuggy reservoir thickness at different coordinate points within the target area.

7. A device for predicting fractured-vuggy reservoirs, characterized in that, The device includes: The first determining module is used to perform lithological, physical, and hydrocarbon-bearing analysis on the target wells based on the logging data of the target wells within the target area using the Q-type clustering analysis algorithm, and obtain the clustering analysis results. Based on the cluster analysis results, at least two candidate logging curves were determined; Based on the candidate logging curves, core data, and the location of the fractured-vuggy reservoir in the target well indicated by the logging data, the correlation between the candidate logging curves and the fractured-vuggy reservoir is determined. The candidate logging curve corresponding to the highest correlation is determined as the target logging curve. The target logging curve is used to characterize the relationship between fracture and vulcanization development and drilling depth. The correlation between the target logging curve and fracture and vulcanization reservoirs is higher than the correlation between other logging curves in the logging data and fracture and vulcanization reservoirs. Based on the core data and logging data of the target well, the first drilling depth of the fractured-vuggy reservoir and the second drilling depth of the non-fractured-vuggy reservoir in the target well are determined. Based on the first drilling depth, the second drilling depth, and the target logging curve, determine the characteristic value of the first target logging curve corresponding to the fractured-vuggy reservoir and the characteristic value of the second target logging curve corresponding to the non-fractured-vuggy reservoir. Based on the characteristic values ​​of the first target logging curve and the second target logging curve, a cutoff value is determined through multi-well statistical analysis. The cutoff value is a threshold value used to determine the location of fractured-vuggy reservoirs based on the target logging curve. The second determining module is used to determine at least two target seismic attributes from the seismic data of the target area based on the target logging curve, wherein the correlation between the target seismic attributes and the fractured-vuggy reservoir is higher than the correlation between other seismic attributes in the seismic data and the fractured-vuggy reservoir; The third determining module is used to determine at least two fitting relationships between the target seismic attribute curve and the target logging curve based on the target logging curve and the target seismic attribute curve extracted along the well trajectory. The prediction module is used to predict fracture-vuggy reservoirs in the target area based on the fitting relationship, the seismic data of the target area, and the cutoff value.

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