Three-dimensional geological modeling method

Abstract

The present invention provides a three-dimensional geological modeling method, including obtaining layered data, logging data, reservoir data, drilling data and experimental data for simulating pore throat changes; determining original attribute model parameters and pore throat parameter change rules according to the above data; training the original attribute model parameters according to a predetermined algorithm to form model parameters to be verified with predicted data, and when the similarity between the change rules of the predicted data in the model parameters to be verified and the change rules of the pore throat parameters is greater than a preset value, outputting the model parameters to be verified as predicted attribute model parameters; and determining a predicted reservoir attribute model according to the layered data, logging data and predicted attribute model parameters. The three-dimensional geological modeling method provided by the present invention introduces the simulated pore throat change experimental data as a reference, so that the generated predicted reservoir attribute model is more in line with the operation characteristics of the gas storage.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas exploitation, and particularly relates to a three-dimensional geological modeling method. Background Art

[0002] Gas injection and production in a gas storage reservoir means that in order to cope with the higher natural gas demand in winter than in summer, a part of natural gas is injected into the underground gas storage reservoir during the period of lower natural gas demand, and then produced during the period of higher natural gas demand. An underground gas storage reservoir refers to an artificial gas field or gas reservoir formed by re-injecting the obtained natural gas into the underground space. Compared with the gas storage methods such as surface spherical tanks, the underground gas storage reservoir has a large storage capacity and high safety. A depleted oil and gas reservoir gas storage reservoir is a gas storage reservoir built by using a gas reservoir that has been exploited and depleted or a retired gas reservoir exploited to a certain extent. When performing three-dimensional geological modeling for such a gas storage reservoir, during the operation of the depleted gas reservoir gas storage reservoir, the physical properties of the reservoir in the gas storage reservoir will change with the change of the injection and production cycle, resulting in the difficulty of the three-dimensional geological model established by the existing three-dimensional geological modeling method to conform to the operation characteristics of the depleted gas reservoir type gas storage reservoir. Summary of the Invention

[0003] In view of the above defects or deficiencies, the present invention provides a three-dimensional geological modeling method, aiming to solve the technical problem that the three-dimensional geological model established by the existing three-dimensional geological modeling method is difficult to conform to the operation characteristic that the physical properties of the reservoir in the gas storage reservoir change with the change of the injection and production cycle.

[0004] To achieve the above object, the present invention provides a three-dimensional geological modeling method, wherein the three-dimensional geological modeling method includes:

[0005] Obtain layered data, logging data, reservoir data, drilling data, and experimental data on simulated pore throat changes;

[0006] Determine the original attribute model parameters according to the logging data, reservoir data, and drilling data, and determine the variation law of the pore throat parameters according to the experimental data on simulated pore throat changes;

[0007] Train the original attribute model parameters according to a predetermined algorithm to form the model parameters to be verified with prediction data. When the similarity between the variation law of the prediction data in the model parameters to be verified and the variation law of the pore throat parameters is greater than a preset value, output the model parameters to be verified as the predicted attribute model parameters;

[0008] Determine the predicted reservoir attribute model according to the layered data, logging data, and predicted attribute model parameters.

[0009] In an embodiment of the present invention, the experimental data on simulated pore throat changes includes the change data of the porosity of the rock sample, the change data of the permeability of the rock sample, and the change data of the compressibility coefficient of the rock sample.

[0010] In an embodiment of the present invention, the step of obtaining experimental data of simulated pore throat changes includes:

[0011] Select rock samples;

[0012] Carry out injection and production simulation for the rock sample for a corresponding number of times according to the predetermined number of injection and production cycles;

[0013] Measure and record the change data of the rock sample porosity, the change data of the rock sample permeability and the change data of the rock sample compression coefficient during each injection and production simulation process.

[0014] In an embodiment of the present invention, determining the variation law of pore throat parameters according to the experimental data of simulated pore throat variation specifically includes:

[0015] Determine the porosity variation law of the rock samples in each injection-production simulation cycle according to the porosity variation data of the rock samples;

[0016] Determine the changing law of the permeability of the rock sample in each injection-production simulation cycle according to the changing data of the permeability of the rock sample;

[0017] The variation law of the compression coefficient of the rock samples in each injection-production simulation cycle is determined based on the variation data of the compression coefficient of the rock samples.

[0018] In an embodiment of the present invention, the preset algorithm is a BP neural network algorithm in Python.

[0019] In an embodiment of the present invention, the step of adjusting the original attribute model parameters according to a preset algorithm to generate the model parameters to be verified with prediction data includes:

[0020] Establish the Transformer neural network attribute model based on the BP neural network algorithm;

[0021] By using the Transformer DataProcessor algorithm in the BP neural network algorithm, the logging data is input into the input layer of the Transformer neural network attribute model to form input layer data;

[0022] The training function is defined by using the Transformer-train algorithm in the BP neural network algorithm, and the original attribute model parameters are trained according to the input layer data and the training function to form a trained model;

[0023] The Transformer prediction algorithm in the BP neural network algorithm is used to generate model parameters to be verified with prediction data based on the trained model.

[0024] In an embodiment of the present invention, the well logging data includes a plurality of single well logging data, and the step of inputting the well logging data into the input layer in the Transformer neural network attribute model to form input layer data includes:

[0025] Through well logging interpretation, multiple groups of single well logging data are converted into multiple groups of corresponding single well original attribute model parameters;

[0026] The simulated pore throat change experimental data are compared with multiple groups of single well original attribute model parameters for error, and the single well original attribute model parameters with error values ​​within the preset threshold are selected;

[0027] The corresponding preferred single well logging data is determined according to the parameters of the preferred single well original attribute model, and the preferred single well logging data is input into the input layer of the Transformer neural network attribute model.

[0028] In an embodiment of the present invention, the step of adjusting the original attribute model parameters according to the preset algorithm to generate the model parameters to be verified with the prediction data also includes:

[0029] Using the Transformer TimeSeries algorithm in the BP neural network algorithm to construct a neural network as the basis of the Transformer neural network attribute model;

[0030] Determine the forecast data for the next time node based on the existing forecast data and the forecast rules of the time series.

[0031] In an embodiment of the present invention, the step of determining a predicted reservoir attribute model according to layering data, logging data, drilling data and predicted attribute model parameters comprises:

[0032] Load the layering data, logging data, drilling data and prediction attribute model parameters in the Petrel software;

[0033] According to the Petrel software operation process, a reservoir attribute prediction model is established.

[0034] In an embodiment of the present invention, well logging data, reservoir data and drilling data are obtained from physical well logging data through well logging interpretation.

[0035] Through the above technical solution, the crane counterweight device provided by the embodiment of the present invention has the following beneficial effects:

[0036] The three-dimensional geological modeling method provided by the present invention introduces experimental data of simulated pore throat changes, and determines the variation law of pore throat parameters based on the experimental data of simulated pore throat changes, so that the variation law of injection and production pore throats of gas storage reservoirs in different injection and production cycles can be simulated, and then the original attribute model parameters are trained according to a predetermined algorithm to generate model parameters to be verified with predicted data, and the variation law of these predicted data is compared with the variation law of pore throat parameters. When the variation law of the predicted data in the model parameters to be verified is similar to the variation law of pore throat parameters, it means that the variation trend of the predicted data in the model parameters to be verified obtained through training is similar to the variation law of injection and production pore throats of gas storage reservoirs in different injection and production cycles, so that the generated predicted reservoir attribute model is more in line with the operating characteristics of the gas storage reservoir when the corresponding injection and production cycle is updated.

[0037] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide an understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention, but do not constitute a limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 is a flowchart of the steps of a three-dimensional geological modeling method according to an embodiment of the present invention;

[0040] Figure 2 is a specific step diagram of step S10 in an embodiment of the present invention;

[0041] Figure 3 is a specific step diagram of step S30 in an embodiment of the present invention;

[0042] Figure 4 is a specific step diagram of step S320 in an embodiment of the present invention;

[0043] Figure 5 is a specific step diagram of step S40 in an embodiment of the present invention;

[0044] Figure 6 It is an example of the porosity variation rule of the rock sample according to the embodiment of the present invention;

[0045] Figure 7 This is an example of the variation law of rock sample permeability in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0047] The three-dimensional geological modeling method of the present invention is described below with reference to the accompanying drawings.

[0048] The present invention provides a three-dimensional geological modeling method, such as Figure 1 As shown, the three-dimensional geological modeling method includes:

[0049] S10: Acquire stratification data, logging data, reservoir data, drilling data and experimental data for simulating pore throat changes;

[0050] S20: determining the original attribute model parameters according to the well logging data, reservoir data and drilling data, and determining the variation law of pore throat parameters according to the experimental data of simulating pore throat variation;

[0051] S30: training the original attribute model parameters according to a predetermined algorithm to form the model parameters to be verified with the predicted data, and outputting the model parameters to be verified as the predicted attribute model parameters when the similarity between the change law of the predicted data in the model parameters to be verified and the change law of the pore throat parameters is greater than a preset value;

[0052] S40: Determine a predicted reservoir attribute model according to the layering data, the logging data and the predicted attribute model parameters.

[0053] The three-dimensional geological modeling method provided by the present invention introduces experimental data for simulating pore throat changes, and determines the variation law of pore throat parameters based on the experimental data for simulating pore throat changes, so that the variation law of injection and production pore throats in different injection and production cycles of a gas storage reservoir can be simulated, and then the original attribute model parameters are trained according to a predetermined algorithm to generate model parameters to be verified with predicted data, and the variation law of these predicted data is compared with the variation law of pore throat parameters. When the variation law of the predicted data in the model parameters to be verified is similar to the variation law of the pore throat parameters, it means that the variation trend of the predicted data in the model parameters to be verified obtained through training is similar to the variation law of the injection and production pore throats in different injection and production cycles of a gas storage reservoir, thereby making the generated predicted reservoir attribute model more in line with the operating characteristics of the gas storage reservoir.

[0054] It should be specifically noted that multiple sets of prediction data will be generated among the parameters of the model to be verified. The multiple sets of prediction data include not only the prediction data for the attribute model parameters of the current injection-production cycle, but also the prediction data for the attribute model parameters of subsequent injection-production cycles. According to the multiple sets of prediction data, the variation law of the attribute model parameters with the alternation of injection-production cycles can be determined. When the variation law of the attribute model parameters is similar to the variation law of the experimental data of simulated pore-throat changes, the predicted reservoir attribute model generated based on these attribute model parameters will be more consistent with the actual changes of the gas storage reservoir.

[0055] As Figure 2 shown, in the embodiments of the present invention, the experimental data of simulated pore-throat changes include the change data of the porosity of the rock sample, the change data of the permeability of the rock sample, and the change data of the compressibility coefficient of the rock sample.

[0056] In the embodiments of the present invention, the steps for obtaining the experimental data of simulated pore-throat changes include:

[0057] S110: Select rock samples;

[0058] S120: Conduct corresponding numbers of injection-production simulations on the rock samples according to the predetermined number of injection-production cycles;

[0059] S130: Measure and record the change data of the porosity of the rock sample, the change data of the permeability of the rock sample, and the change data of the compressibility coefficient of the rock sample during each injection-production simulation.

[0060] It should be specifically noted that the above-mentioned recording of the change data of the porosity of the rock sample, the change data of the permeability of the rock sample, and the change data of the compressibility coefficient of the rock sample during each injection-production simulation refers to recording the porosity data of the rock sample at each pressure recording point, the permeability data of the rock sample at each pressure recording point, and the compressibility coefficient data of the rock sample at each pressure recording point during each injection-production simulation.

[0061] Specifically, select representative rocks, drill rock samples with a length of 5 cm and a diameter of 2.5 cm. Two rock samples form a group, and mark the forward displacement direction;

[0062] Clean and dry the rock samples of the plunger-like core according to the SY / T 5336 standard;

[0063] Put the cleaned and dried rock samples into a vacuum pump for vacuum treatment for 24 h;

[0064] Conduct porosity tests on the vacuumed cores and record the measured porosity φ;

[0065] Calculate the confining pressure required to be loaded according to the formation pressure, and load the confining pressure on the rock samples to simulate the underground environment;

[0066] Use the method of forward gas drive to water to simulate the gas injection process for the rock sample until no more water is produced and record the displacement time;

[0067] After the simulation of the gas injection process is completed, keep the confining pressure unchanged and then perform reverse constant-flow rate water drive. The fluid used is the water in the gas injection simulation process, and the flow rate of the water is the same as that in the gas injection simulation process. The displacement duration is the displacement time recorded in the above gas injection simulation process. This is the simulation of the gas production process.

[0068] Perform the simulation of gas injection and gas production cycles multiple times until the preset number of cycles is reached. At the same time, measure and record the change data of the porosity of the rock sample, the change data of the permeability of the rock sample, and the change data of the compressibility coefficient of the rock sample during the injection and production process. Of course, the present invention can also introduce other parameters on the basis of the above three parameters, so as to further increase the accuracy of the final predicted reservoir property model.

[0069] By performing multiple simulations of gas injection and gas production on the rock sample, the variation law of pore throats in multiple injection and production cycles of the gas storage reservoir can be simulated. Therefore, when generating prediction data, the prediction data will also reflect the data variation law in multiple injection and production cycles, so that the final predicted reservoir property model can change with the law of the injection and production cycles of the gas storage reservoir.

[0070] In the embodiment of the present invention, determining the variation law of pore throat parameters according to the experimental data of simulated pore throat changes specifically includes:

[0071] Determine the variation law of the porosity of the rock sample in each injection and production simulation cycle according to the change data of the porosity of the rock sample;

[0072] Determine the variation law of the permeability of the rock sample in each injection and production simulation cycle according to the change data of the permeability of the rock sample;

[0073] Determine the variation law of the compressibility coefficient of the rock sample in each injection and production simulation cycle according to the change data of the compressibility coefficient of the rock sample.

[0074] Take Figure 6 and Figure 7 as an example. Since the experimental data of simulated pore throat changes include the change data of the porosity of the rock sample, the change data of the permeability of the rock sample, and the change data of the compressibility coefficient of the rock sample, the variation law of pore throat parameters determined according to the experimental data of simulated pore throat changes is to respectively determine the variation law of the porosity of the rock sample, the variation law of the permeability of the rock sample, and the variation law of the compressibility coefficient of the rock sample.

[0075] In the embodiment of the present invention, the preset algorithm can be the BP neural network algorithm in Python.

[0076] The BP neural network algorithm is a common data analysis method at present, with good accuracy. And when the similarity between the variation law of the predicted data in the model parameters to be verified and the variation law of the pore-throat parameters is less than the preset value, it indicates a low fitting degree. The computer recalculates and continuously calculates the fitting until the similarity error value between the two is reduced to within the preset value. At this time, it indicates a high fitting degree. Of course, the BP neural network algorithm of the present invention can also be implemented by other software.

[0077] As Figure 3 shown, in the embodiment of the present invention, the steps of adjusting the original attribute model parameters according to the BP neural network algorithm to generate the model parameters to be verified with predicted data include:

[0078] S310: Establish a Transformer neural network attribute model according to the BP neural network algorithm;

[0079] S320: Use the Transformer DataProcessor algorithm in the BP neural network algorithm to input the logging data into the input layer of the Transformer neural network attribute model to form input layer data;

[0080] S330: Use the Transformer-train algorithm in the BP neural network algorithm to define a training function, and train the original attribute model parameters according to the input layer data and the training function to form a trained model;

[0081] S340: Use the Transformer prediction algorithm in the BP neural network algorithm to generate the model parameters to be verified with the predicted data according to the trained model.

[0082] In the Transformer neural network attribute model, generally including an input layer, a hidden layer and an output layer, by taking the logging data as the input layer of the Transformer neural network attribute model, and taking the training function defined by the Transformer-train algorithm as the hidden layer to calculate the data of the input layer, so as to obtain the predicted data of the output layer. It should be particularly noted that the predicted data of the output layer is in multiple groups, and each group corresponds to each injection-production cycle. The variation law of the predicted data is determined through multiple groups of predicted data, and then compared with the variation law of the pore-throat parameters determined by the simulated pore-throat change experimental data, so as to judge whether the variation law of the predicted data conforms to the operation characteristics of the gas storage reservoir. Among them, the data input format of the logging data is (N, Nb of timepoints), where N is the number of ts rows, and nb of timepoints is the number of ts time points in one row. The porosity data of a single formation and a single injection-production cycle is one row of ts.

[0083] As Figure 4 shown, in the embodiments of the present invention, the logging data includes multiple single-well logging data. The steps of inputting the logging data into the input layer of the Transformer neural network attribute model to form the input layer data include:

[0084] S321: Convert multiple groups of single-well logging data into multiple groups of corresponding single-well original attribute model parameters respectively;

[0085] S322: Compare the error between the simulated pore throat change experimental data and multiple groups of single-well original attribute model parameters respectively, and select the preferred single-well original attribute model parameters with the error value within the preset threshold;

[0086] S323: Determine the corresponding preferred single-well logging data according to the preferred single-well original attribute model parameters, and input the preferred single-well logging data into the input layer of the Transformer neural network attribute model.

[0087] That is, when inputting the logging data into the input layer of the Transformer neural network attribute model, a part of the logging data with strong correlation in the logging data is selected as the input data. Through the optimization of the input data, the accuracy of the prediction result can be guaranteed.

[0088] In the embodiments of the present invention, the steps of adjusting the original attribute model parameters according to the preset algorithm to generate the to-be-verified model parameters with the prediction data further include;

[0089] Use the Transformer TimeSeries algorithm in the BP neural network algorithm to construct a neural network as the basis of the Transformer neural network attribute model;

[0090] Determine the prediction data of the next time node according to the existing prediction data and the prediction law of the time series.

[0091] The prediction law of the time series refers to using the continuous regularity of the development of objective things, applying the past historical data, and further inferring the future development trend through statistical analysis. That is, the prediction data of the next time node is determined based on the existing prediction data. Since the pore throat change of the gas storage reservoir is a continuous process, by introducing the time series, the dependence between multiple prediction data can be guaranteed, so that the final prediction result is more in line with the operation law of the gas storage reservoir. And the Log Sparse mechanism can be introduced into the prediction law of the time series, so as to reduce the data processing volume.

[0092] As Figure 5As shown, in the embodiments of the present invention, the steps of determining the predicted reservoir property model based on the layered data, logging data, drilling data, and predicted property model parameters include:

[0093] S410: Load the layered data, logging data, drilling data, and predicted property model parameters in Petrel software respectively;

[0094] S420: Establish a predicted reservoir property model according to the Petrel software operation process.

[0095] Petrel software is a common 3D geological modeling software. It is convenient to build a model through Petrel software. Of course, 3D geological modeling can also be achieved through other geological modeling software. The order of loading data in Petrel software is preferably wellhead, well deviation, logging data, and then other data.

[0096] In the embodiments of the present invention, the logging data, reservoir data, and drilling data are obtained by logging interpretation of physical logging data. Among them, for the physical logging data including resistivity data RD, natural gamma data GR, acoustic data AC, density data DEN, spontaneous potential data SP, etc., logging interpretation of these data can obtain the logging data, reservoir data, and drilling data.

[0097] In the embodiments of the present invention, the steps of determining the original property model parameters from the above-mentioned logging data, reservoir data, and drilling data only need to import the above-mentioned data into the modeling software to construct the original model, and the original property model parameters can be obtained according to the original model.

[0098] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0099] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection, an electrical connection, or communicable with each other; it can be directly connected, or indirectly connected through an intermediate medium, and can be the communication inside two components or the interaction relationship between two components, unless otherwise clearly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0100] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0101] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A three-dimensional geological modeling method, characterized in that, The three-dimensional geological modeling method includes: Obtaining layered data, logging data, reservoir data, drilling data, and experimental data on simulated pore throat changes; Determining the original attribute model parameters according to the logging data, the reservoir data, and the drilling data, and determining the variation law of pore throat parameters according to the experimental data on simulated pore throat changes; Training the original attribute model parameters according to a preset algorithm to generate unverified model parameters with prediction data. When the similarity between the variation law of the prediction data in the unverified model parameters and the variation law of the pore throat parameters is greater than a preset value, output the unverified model parameters as predicted attribute model parameters; Determining a predicted reservoir attribute model according to the layered data, the logging data, and the predicted attribute model parameters; The experimental data on simulated pore throat changes includes the change data of rock sample porosity, the change data of rock sample permeability, and the change data of rock sample compressibility. The preset algorithm is the BP neural network algorithm in Python. The step of adjusting the original attribute model parameters according to the preset algorithm to generate the unverified model parameters with the prediction data includes: Establishing a Transformer neural network attribute model according to the BP neural network algorithm; Using the Transformer DataProcessor algorithm in the BP neural network algorithm, inputting the logging data into the input layer of the Transformer neural network attribute model to form input layer data; Using the Transformer train algorithm in the BP neural network algorithm to define a training function, and training the original attribute model parameters according to the input layer data and the training function to form a trained model; Using the Transformer prediction algorithm in the BP neural network algorithm to generate the unverified model parameters with the prediction data according to the trained model; The logging data includes multiple single-well logging data. The step of inputting the logging data into the input layer of the Transformer neural network attribute model to form the input layer data includes: Respectively converting multiple groups of the single-well logging data into multiple groups of corresponding single-well original attribute model parameters; Comparing the experimental data on simulated pore throat changes with multiple groups of the single-well original attribute model parameters respectively for error, and selecting the preferred single-well original attribute model parameters with error values within a preset threshold; Determining the corresponding preferred single-well logging data according to the preferred single-well original attribute model parameters, and inputting the preferred single-well logging data into the input layer of the Transformer neural network attribute model; The step of adjusting the original attribute model parameters according to the preset algorithm to generate the unverified model parameters with the prediction data further includes; Construct a neural network using the Transformer TimeSeries algorithm in the BP neural network algorithm as the basis for the Transformer neural network attribute model; Determine the predicted data at the next time node according to the existing predicted data and the prediction law of the time series.

2. The 3D geological modeling method according to claim 1, wherein The steps of obtaining the experimental data of the simulated pore-throat change include: Select rock samples; Carry out corresponding times of injection-production simulation on the rock samples according to the predetermined number of injection-production cycles; Measure and record the change data of the porosity of the rock samples, the change data of the permeability of the rock samples, and the change data of the compressibility coefficient of the rock samples during each injection-production simulation process.

3. The 3D geological modeling method according to claim 2, characterized in that Determining the change law of the pore-throat parameters according to the experimental data of the simulated pore-throat change specifically includes: Determine the change law of the porosity of the rock samples in each injection-production simulation cycle according to the change data of the porosity of the rock samples; Determine the change law of the permeability of the rock samples in each injection-production simulation cycle according to the change data of the permeability of the rock samples; Determine the change law of the compressibility coefficient of the rock samples in each injection-production simulation cycle according to the change data of the compressibility coefficient of the rock samples.

4. The 3D geological modeling method according to any one of claims 1 to 3, characterized in that The steps of determining the predicted reservoir attribute model according to the layered data, the logging data, the drilling data, and the predicted attribute model parameters include: Load the layered data, the logging data, the drilling data, and the predicted attribute model parameters in Petrel software respectively; Establish the predicted reservoir attribute model according to the Petrel software operation process.

5. The 3D geological modeling method according to any one of claims 1 to 3, characterized in that, The logging data, the reservoir data, and the drilling data are obtained by logging interpretation of physical logging data.

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