Method and device for determining reservoir permeability

Abstract

The invention provides a method and device for determining reservoir permeability, a processor and a storage medium, and belongs to the technical field of oil and gas development. The method comprises the following steps: acquiring a core sample of a reservoir to be evaluated, and constructing a permeability model reflecting the relationship between permeability and porosity of the core sample under different permeability influence parameters; the permeability influence parameters comprise lithology and a flow unit index; according to the permeability influence parameters of the reservoir to be evaluated, determining a permeability model suitable for the reservoir; and substituting the porosity of the reservoir to be evaluated into a permeability model suitable for the reservoir to obtain the permeability of the reservoir. According to the embodiment of the invention, firstly, the permeability model reflecting the relationship between the permeability and the porosity of the core sample under different permeability influence parameters is constructed, then the permeability model suitable for the reservoir is determined according to the permeability influence parameters of the reservoir to be evaluated, and the permeability is calculated by using the permeability model suitable for the reservoir. Therefore, the permeability prediction precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas development, and in particular to a method, a device, a processor and a storage medium for determining reservoir permeability. Background Art

[0002] Permeability is an important parameter for reservoir evaluation and capacity prediction. The diverse types of reservoir pore structures make logging response characteristics complex, and the errors in calculating permeability using traditional core porosity and permeability statistical regression methods and simple classification model methods are large.

[0003] There are three main existing technical solutions for determining reservoir permeability:

[0004] (1) Based on core experiments, a permeability model is constructed through the permeability-porosity crossplot. This method works better for medium- and high-permeability reservoirs with the same pore structure and good homogeneity.

[0005] (2) Permeability model based on flow unit index (FZI) classification: This method uses the porosity and permeability obtained from core experiments to calculate the flow unit index of the core, and then constructs a permeability model based on the flow unit index classification. It works better for reservoirs with small differences in pore throat structure and permeability.

[0006] (3) The reservoir permeability is predicted using intelligent algorithms. When the difference between the test data and the training data is small, the predicted permeability error is small.

[0007] For (1) when the reservoir pore structure is very complex and the vertical heterogeneity is strong, the porosity-permeability relationship is poor, and the error rate of the model calculated based on the intersection of porosity and permeability is greater than one order of magnitude, which cannot meet the requirements of reservoir evaluation;

[0008] For (2) offshore low-porosity and low-permeability reservoirs, the permeability varies greatly, which is mainly controlled by the lithology and pore throat structure. The flow unit index FZI of different lithology and pore throat structure varies greatly. When the pore structure types are diverse and the logging response characteristics are complex, according to the original classification number, the relationship between the classification parameters and the logging data is not established, so the reservoir permeability cannot be continuously evaluated;

[0009] For (3), when there are many types of reservoir pore structures, uncertainty in model parameters will occur when the intelligent algorithm predicts permeability, and the predicted permeability error will be large. Summary of the invention

[0010] The purpose of the embodiments of the present invention is to provide a method, device, processor and storage medium for determining reservoir permeability. The embodiments of the present invention can solve or partially solve the problems existing in the prior art.

[0011] In order to achieve the above object, an embodiment of the present invention provides a method for determining reservoir permeability, the method comprising:

[0012] Obtaining a core sample of the reservoir to be evaluated, and constructing a permeability model reflecting the relationship between permeability and porosity of the core sample under different permeability influencing parameters; the permeability influencing parameters include lithology and flow unit index;

[0013] Determining a permeability model suitable for the reservoir according to the permeability influencing parameters of the reservoir to be evaluated;

[0014] The porosity of the reservoir to be evaluated is brought into a permeability model suitable for the reservoir to obtain the reservoir permeability.

[0015] Optionally, the permeability influencing parameter also includes an average pore throat radius,

[0016] The step of obtaining a core sample of the reservoir to be evaluated and constructing a permeability model reflecting the relationship between the permeability and porosity of the core sample under different permeability influencing parameters includes:

[0017] According to the rock grain size, the reservoir to be evaluated is classified into lithology;

[0018] Conducting core experiments on core samples of the reservoir to be evaluated under different lithologies respectively to obtain multiple permeabilities and porosities of core samples under different lithologies, and calculating the flow unit index of the core samples based on the permeabilities and porosities;

[0019] Obtaining an average pore throat radius of each core sample, dividing each lithology of the reservoir to be evaluated into at least one range according to the average pore throat radius, and further dividing each range into at least one sub-range according to the flow unit index of the core sample;

[0020] The permeability and porosity of the core samples in each sub-range are fitted to obtain a permeability model reflecting the relationship between the permeability and porosity of the core samples under different permeability influencing parameters.

[0021] Optionally, each lithology of the reservoir to be evaluated is divided into at least one range according to the average pore throat radius, including:

[0022] A relationship curve between the average pore throat radius and permeability of core samples under each lithology is constructed, and the average pore throat radius at the turning point of the relationship curve is taken as the endpoint value, so as to divide each lithology of the reservoir to be evaluated into at least one range.

[0023] Optionally, each range is further divided into at least one sub-range according to the flow unit index of the core sample, including:

[0024] The flow unit index in each range is used as the horizontal axis, and the cumulative frequency of the flow unit index in the range is used as the vertical axis, so as to construct a relationship curve between the flow unit index and the cumulative frequency;

[0025] The flow unit index at the turning point in the relationship curve is taken as the endpoint value, so as to further divide each range into at least one sub-range.

[0026] Optionally, determining a permeability model applicable to the reservoir according to the permeability influencing parameter of the reservoir to be evaluated includes:

[0027] Determine the scope of the reservoir to be evaluated according to the lithology and average pore throat radius of the reservoir to be evaluated;

[0028] The sub-range to which the reservoir to be evaluated belongs is determined according to the flow unit index of the reservoir to be evaluated, and the permeability model obtained by fitting the permeability and porosity of the core samples in the sub-range is determined as the permeability model suitable for the reservoir.

[0029] Optionally, the flow unit index of the reservoir to be evaluated is determined by:

[0030] Inputting the well logging data of the reservoir to be evaluated into the flow unit index prediction model to obtain the flow unit index of the reservoir to be evaluated;

[0031] The flow unit index prediction model is obtained through the following process:

[0032] A sample data set including well logging data and flow unit index is obtained, and a neural network is trained using the sample data set to obtain a flow unit index prediction model.

[0033] Optionally, the logging data includes at least one of the ratio of natural gamma to density, the ratio of deep and shallow lateral resistivity, and neutron and sonic time difference.

[0034] Optionally, the obtaining of a core sample of the reservoir to be evaluated and the construction of a permeability model reflecting the relationship between permeability and porosity of the core sample under different permeability influencing parameters include:

[0035] According to the rock grain size, the reservoir to be evaluated is classified into lithology;

[0036] Conducting core experiments on core samples of the reservoir to be evaluated under different lithologies respectively to obtain multiple permeabilities and porosities of core samples under different lithologies, and calculating the flow unit index of the core samples based on the permeabilities and porosities;

[0037] Dividing each lithology of the reservoir to be evaluated into at least one range according to the flow unit index of the core sample;

[0038] The permeability and porosity of the core samples in each range are fitted to obtain a permeability model reflecting the relationship between the permeability and porosity of the core samples under different permeability influencing parameters.

[0039] Accordingly, an embodiment of the present invention further provides a device for determining reservoir permeability, the device comprising: a model building unit and a calculation unit;

[0040] The model building unit is used to obtain core samples of the reservoir to be evaluated and build a permeability model that reflects the relationship between the permeability and porosity of the core sample under different permeability influencing parameters; the permeability influencing parameters include lithology and flow unit index;

[0041] The calculation unit is used to determine a permeability model suitable for the reservoir according to the permeability influencing parameters of the reservoir to be evaluated; and to bring the porosity of the reservoir to be evaluated into the permeability model suitable for the reservoir to obtain the reservoir permeability.

[0042] Optionally, the permeability influencing parameter also includes an average pore throat radius,

[0043] The step of obtaining a core sample of the reservoir to be evaluated and constructing a permeability model reflecting the relationship between the permeability and porosity of the core sample under different permeability influencing parameters includes:

[0044] According to the rock grain size, the reservoir to be evaluated is classified into lithology;

[0045] Conducting core experiments on core samples of the reservoir to be evaluated under different lithologies respectively to obtain multiple permeabilities and porosities of core samples under different lithologies, and calculating the flow unit index of the core samples based on the permeabilities and porosities;

[0046] Obtaining an average pore throat radius of each core sample, dividing each lithology of the reservoir to be evaluated into at least one range according to the average pore throat radius, and further dividing each range into at least one sub-range according to the flow unit index of the core sample;

[0047] The permeability and porosity of the core samples in each sub-range are fitted to obtain a permeability model reflecting the relationship between the permeability and porosity of the core samples under different permeability influencing parameters.

[0048] Correspondingly, an embodiment of the present invention further provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the method for determining reservoir permeability.

[0049] Accordingly, a processor is provided for running a program, wherein the program is used to execute the method for determining reservoir permeability when being run.

[0050] In an embodiment of the present invention, a permeability model reflecting the relationship between the permeability and porosity of a core sample under different permeability influencing parameters is first constructed, and then a permeability model suitable for the reservoir to be evaluated is determined according to the permeability influencing parameters of the reservoir to be evaluated. The porosity of the reservoir to be evaluated is brought into the permeability model suitable for the reservoir to obtain the reservoir permeability, thereby improving the accuracy of permeability prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0052] Figure 1 is a flow chart of a method for determining reservoir permeability provided by an embodiment of the present invention;

[0053] Figure 2 It is a schematic diagram of the principle of a deep neural network provided by an embodiment of the present invention;

[0054] Figure 3 is a flow chart of a method for determining reservoir permeability provided by a specific embodiment of the present invention;

[0055] Figure 4a This is the porosity and permeability cross-plot of the Paleogene Huagang Formation reservoir (siltstone) in the HG block after classification based on the flow unit index;

[0056] Figure 4b This is the porosity and permeability cross-plot of the Paleogene Huagang Formation reservoir (fine sandstone) in the HG block after classification based on the flow unit index;

[0057] Figure 4c This is the porosity and permeability cross-plot of the Paleogene Huagang Formation reservoir (medium sandstone) in the HG block after classification based on the flow unit index;

[0058] Figure 4d This is the porosity and permeability cross-plot of the Paleogene Huagang Formation reservoir (sand and conglomerate) in the HG block after classification based on the flow unit index;

[0059] Figure 5 is a comparison chart of the calculated permeability and the core test permeability results provided by the embodiment of the present invention;

[0060] Figure 6 This is a well logging interpretation result diagram of the HG2 well provided in an embodiment of the present invention;

[0061] Figure 7It is a structural block diagram of a device for determining reservoir permeability provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0063] Figure 1 is a flow chart of a method for determining reservoir permeability provided by an embodiment of the present invention;

[0064] like Figure 1 As shown, the method for determining reservoir permeability includes:

[0065] S110: obtaining a core sample of the reservoir to be evaluated, and constructing a permeability model reflecting the relationship between the permeability and porosity of the core sample under different permeability influencing parameters;

[0066] In step S110, the permeability influencing parameters may include lithology, average pore throat radius and flow unit index;

[0067] When constructing a permeability model that reflects the relationship between the permeability and porosity of the core sample under different permeability influencing parameters (i.e., when constructing a permeability model that reflects the relationship between the permeability and porosity of the core sample under different lithologies, average pore throat radii and flow unit indices), first, the lithology of the reservoir to be evaluated can be classified according to the rock grain size; then, core experiments are performed on the core samples of the reservoir to be evaluated under each lithology to obtain multiple permeabilities and porosities of the core samples under different lithologies, and the flow unit index of the core sample is calculated based on the permeability and porosity; then, the average pore throat radius of each core sample is obtained, and each lithology of the reservoir to be evaluated is divided into at least one range according to the average pore throat radius, and each range is further divided into at least one sub-range according to the flow unit index of the core sample; finally, the permeability and porosity of the core samples in each sub-range are fitted to obtain a permeability model that reflects the relationship between the permeability and porosity of the core sample under different permeability influencing parameters.

[0068] Among them, when each lithology of the reservoir to be evaluated is divided into at least one range according to the average pore throat radius, a relationship curve between the average pore throat radius and permeability of the core samples under each lithology can be constructed, and the average pore throat radius at the turning point of the relationship curve is taken as the endpoint value, so as to divide each lithology of the reservoir to be evaluated into at least one range.

[0069] In addition, when each range is further divided into at least one sub-range according to the flow unit index of the core sample, the flow unit index in each range can be used as the horizontal coordinate, and the cumulative frequency of the flow unit index in the range can be used as the vertical coordinate to construct a relationship curve between the flow unit index and the cumulative frequency; the flow unit index at the turning point in the relationship curve is taken as the endpoint value, so as to further divide each range into at least one sub-range.

[0070] In the embodiment of the present invention, the permeability of the reservoir determines its seepage capacity, and its size mainly depends on the pore volume, sorting coefficient, and average pore throat radius. The flow unit index FZI is a qualitative and quantitative characterization of the pore structure and seepage capacity of the reservoir from a macroscopic perspective. Core samples with the same FZI have the same pore throat structure and belong to the same flow unit. The larger the FZI, the larger the reservoir pore throat ratio and the higher the seepage capacity. Therefore, in the absence of nuclear magnetic resonance logging data, FZI classification can be used to calculate the reservoir permeability, thereby improving the accuracy of permeability prediction.

[0071] In some optional embodiments, the permeability influencing parameters may include lithology and flow unit index;

[0072] When constructing a permeability model that reflects the relationship between the permeability and porosity of the core sample under different permeability influencing parameters (i.e., when constructing a permeability model that reflects the relationship between the permeability and porosity of the core sample under different lithologies and flow unit indices), first, the lithology of the reservoir to be evaluated is classified according to the rock grain size; then, core experiments are carried out on the core samples of the reservoir to be evaluated under each lithology to obtain multiple permeabilities and porosities of the core samples under different lithologies, and the flow unit index of the core sample is calculated based on the permeability and porosity; then, according to the flow unit index of the core sample, each lithology of the reservoir to be evaluated is divided into at least one range; finally, the permeability and porosity of the core samples in each range are fitted to obtain a permeability model that reflects the relationship between the permeability and porosity of the core sample under different permeability influencing parameters.

[0073] S120: Determine a permeability model suitable for the reservoir according to the permeability influencing parameters of the reservoir to be evaluated;

[0074] In step S120, the range to which the reservoir to be evaluated belongs can be determined based on the lithology and average pore throat radius of the reservoir to be evaluated; then, the sub-range to which the reservoir to be evaluated belongs can be determined based on the flow unit index of the reservoir to be evaluated, and the permeability model obtained by fitting the permeability and porosity of the core samples within the sub-range is determined as the permeability model suitable for the reservoir.

[0075] The permeability influencing parameters of the reservoir to be evaluated may include lithology, average pore throat radius and flow unit index. The lithology can be obtained by dividing according to rock particle size, and the average pore throat radius can be obtained according to the method provided in the prior art.

[0076] The following is an introduction to the method of obtaining the flow unit index of the reservoir to be evaluated.

[0077] The flow unit index is a parameter that determines the pore structure by combining the characteristics of rock mineral composition, rock particle characteristics, and pore throat structure characteristics. It is closely related to the parameters that characterize reservoir permeability. According to the pore geometry phase theory, Kozeny and Kalman defined the ratio of the cross-sectional area to the wetted perimeter as the average pore radius, and used the concept of average hydraulic radius and the Hagen-Poiseuille and Darcy laws to derive the relationship between the porosity and permeability of rock reservoirs:

[0078]

[0079] Where: K is permeability, mD; φ e is the effective porosity, a decimal; F s is the shape coefficient; τ is the tortuosity of the porous medium; S gv is the surface area per unit particle volume; F s τ 2 It is usually called the Kozeny constant, which varies between flow units but is a constant within a flow unit, ranging from 5 to 100 in actual reservoirs. Flow unit index:

[0080]

[0081] It can be seen from the above formula that natural gamma, dual lateral resistivity, acoustic time difference, density, and neutron are all sensitive curves of FZI and can be used to obtain FZI. However, considering that gamma rays are absorbed by the formation when passing through the formation, its absorption capacity depends on the formation density. The ratio of deep and shallow lateral resistivity can reflect the permeability of the reservoir. Therefore, the embodiment of the present invention selects the ratio of natural gamma to density GR / DEN, the ratio of deep and shallow lateral resistivity RD / RS, neutron CNL and acoustic time difference DT to calculate the flow unit index FZI of the reservoir.

[0082] Specifically, the method of calculating the flow unit index FZI of the reservoir using the ratio of natural gamma to density GR / DEN, the ratio of deep and shallow lateral resistivity RD / RS, the neutron CNL and the acoustic time difference DT comprises: inputting the logging data of the reservoir to be evaluated (the ratio of natural gamma to density, the ratio of deep and shallow lateral resistivity, the neutron and acoustic time difference) into the flow unit index prediction model to obtain the flow unit index of the reservoir to be evaluated;

[0083] Furthermore, the flow unit index prediction model is obtained by the following process: acquiring a sample data set including well logging data and flow unit index, and using the sample data set to train a neural network to obtain the flow unit index prediction model.

[0084] Among them, the flow unit index prediction model is a multi-layer unsupervised neural network, and the output features of the previous layer are used as the input of the next layer for feature learning. After layer-by-layer feature mapping, the features of the existing spatial samples are mapped to another feature space, so as to learn better feature expressions for the existing input. The deep neural network has multiple nonlinear mapping feature transformations and can fit highly complex functions. If the deep structure is regarded as a neural network, the core ideas of the deep neural network include: the pre-training of each layer of the network adopts unsupervised learning; unsupervised learning trains each layer layer by layer, that is, the output of the previous layer is used as the input of the next layer; supervised learning is used to fine-tune all layers (plus a classifier for classification). The main difference between deep neural networks and traditional neural networks lies in the training mechanism. The deep neural network is established on the basis of the perceptron model. The perceptron model is a multi-input single-output model. First, a linear relationship between the output and the input is learned to obtain the intermediate output result:

[0085]

[0086] Where w is the linear relationship coefficient and b is the offset.

[0087] Then, through a neuron activation function, the output result is finally obtained.

[0088]

[0089] In order to overcome the shortcomings of traditional neural networks, such as easy overfitting and slow training speed, deep neural networks generally adopt a layer-by-layer pre-training training mechanism instead of the back-propagation training mechanism of traditional neural networks. In addition, deep neural network optimization algorithms are more advanced, including SGD gradient descent, ReLU activation function, early stopping, random inactivation, etc., which can effectively improve the convergence ability of neural network models. Figure 2 It is a schematic diagram of the principle of deep neural network. According to the data of 1200 cores in HG block, GR / DEN, RD / RS, CNCF and DT are selected as input layers to predict the reservoir flow unit index FZI in the study area.

[0090] Taking the Paleogene Huagang Formation reservoir in the HG block of the Xihu Sag in the East China Sea Shelf Basin as an example, the porosity and permeability of 1200 cores were obtained according to the core data of the block, and the flow unit index FZI of the cores was calculated. 1000 core data were selected as training data, 200 of which were used as prediction comparison data. The GR / DEN, RD / RS, CNCF and DT logging data at the corresponding depths of the core data were selected as the input layer to predict the flow unit index FZI of the reservoir in the study area. The prediction error was 0.26, indicating a high accuracy.

[0091] Compared with the prior art that uses natural gamma, dual lateral resistivity, acoustic time difference, density, and neutron as input data, the inventors of the present application consider that gamma rays will be absorbed by the formation when passing through it, and its absorption capacity depends on the formation density. The ratio of deep and shallow lateral resistivity can reflect the permeability of the reservoir. Therefore, the ratio of natural gamma to density GR / DEN, the ratio of deep and shallow lateral resistivity RD / RS, neutron CNL and acoustic time difference DT are selected as input data to calculate the flow unit index FZI of the reservoir, thereby improving the accuracy of obtaining the flow unit index.

[0092] S130: Substitute the porosity of the reservoir to be evaluated into a permeability model suitable for the reservoir to obtain reservoir permeability.

[0093] The porosity of the reservoir to be evaluated can be calculated according to an existing method, and then the porosity is substituted into the permeability model determined in step S120 to obtain the permeability of the reservoir to be evaluated.

[0094] The following is an explanation with reference to specific examples.

[0095] The method for determining reservoir permeability provided by the embodiment of the present invention (the method flow chart is as follows Figure 3 The method was applied to the Paleogene Huagang Formation in the HG block of the Xihu Sag in the East China Sea Shelf Basin.

[0096] The Xihu Sag is part of the eastern depression of the East China Sea Shelf Basin. The Paleogene Huagang Formation located in the HG block of the Xihu Sag is mainly a gas-bearing layer with a burial depth of 3000-5000m. It is an underwater distributary channel sand body of the lake-delta system. The sand body thickness is relatively large, generally tens of meters to more than 100 meters. The reservoir lithology is mainly fine sandstone, with local development of grainy sandstone and conglomerate. The filling material is mainly quartz, and the content of plagioclase and potassium feldspar clay is average. According to the core data analysis of the HG block, the reservoir porosity of the Huagang Formation is basically less than 10%. Due to the variable pore throat structure of the reservoir, the permeability varies widely, ranging from 0.01-100mD; the porosity-permeability relationship is complex, and the vertical heterogeneity is extremely strong. Many places show the phenomenon of small porosity and large permeability or large porosity and extremely small permeability. The core permeability differences of similar porosity or sorting coefficients reach several orders of magnitude, which is a typical low-porosity and low-permeability reservoir with very complex pore structure and strong vertical heterogeneity.

[0097] Figure 4 is a porosity-permeability cross-plot of the Paleogene Huagang Formation reservoirs in the HG block after classification based on the flow unit index. The reservoirs are divided into four categories according to lithology (grain size), and then the four categories of reservoirs are divided into 11 categories according to the average throat radius. Based on the cumulative frequency method, the 11 categories of reservoirs are divided into 21 categories according to the size of the flow unit index FZI. The porosity-permeability relationship after classification is good. Table 1 is the permeability model constructed based on the classification, which divides the HG block into 21 permeability models, with an average correlation coefficient greater than 0.95, and a good correlation.

[0098] Table 1

[0099]

[0100] HG2 is a typical well in the study area. Its permeability was calculated according to the above method. Figure 5 This is a comparison chart of the calculated permeability and the core test permeability. The calculated results are consistent with the core test, indicating that the constructed permeability model is accurate.

[0101] Well HG2 is an important exploration well in this block. The above method is used to classify and construct a permeability calculation model. The reservoir flow unit index FZI is predicted based on the deep neural network algorithm, and the reservoir permeability is calculated based on the prediction results. The calculation results are as follows: Figure 6 As shown, the calculated permeability is consistent with the core permeability, indicating the accuracy of the method;

[0102] In addition, the above method was applied to three other wells in the study area, and the average logarithmic error of the calculated permeability was 0.25, which shows that the above method is accurate and feasible, and the effect is very significant.

[0103] Accordingly, an embodiment of the present invention also provides a device for determining reservoir permeability, such as Figure 7As shown, the device comprises: a model building unit and a calculation unit;

[0104] The model building unit is used to obtain core samples of the reservoir to be evaluated and build a permeability model that reflects the relationship between the permeability and porosity of the core sample under different permeability influencing parameters; the permeability influencing parameters include lithology and flow unit index;

[0105] The calculation unit is used to determine a permeability model suitable for the reservoir according to the permeability influencing parameters of the reservoir to be evaluated; and to bring the porosity of the reservoir to be evaluated into the permeability model suitable for the reservoir to obtain the reservoir permeability.

[0106] Optionally, the permeability influencing parameter also includes an average pore throat radius,

[0107] The step of obtaining a core sample of the reservoir to be evaluated and constructing a permeability model reflecting the relationship between the permeability and porosity of the core sample under different permeability influencing parameters includes:

[0108] According to the rock grain size, the reservoir to be evaluated is classified into lithology;

[0109] Conducting core experiments on core samples of the reservoir to be evaluated under different lithologies respectively to obtain multiple permeabilities and porosities of core samples under different lithologies, and calculating the flow unit index of the core samples based on the permeabilities and porosities;

[0110] Obtaining an average pore throat radius of each core sample, dividing each lithology of the reservoir to be evaluated into at least one range according to the average pore throat radius, and further dividing each range into at least one sub-range according to the flow unit index of the core sample;

[0111] The permeability and porosity of the core samples in each sub-range are fitted to obtain a permeability model reflecting the relationship between the permeability and porosity of the core samples under different permeability influencing parameters.

[0112] Correspondingly, an embodiment of the present invention further provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the method for determining reservoir permeability.

[0113] Accordingly, a processor is provided for running a program, wherein the program is used to execute the method for determining reservoir permeability when being run.

[0114] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0115] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0119] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0120] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0121] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0122] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining reservoir permeability, characterized in that: The method includes: Obtaining a core sample of the reservoir to be evaluated, and constructing a permeability model reflecting the relationship between permeability and porosity of the core sample under different permeability influencing parameters; the permeability influencing parameters include lithology and flow unit index; Determining a permeability model suitable for the reservoir according to the permeability influencing parameters of the reservoir to be evaluated; The porosity of the reservoir to be evaluated is brought into a permeability model suitable for the reservoir to obtain the reservoir permeability.

2. The method for determining reservoir permeability according to claim 1, characterized in that: The permeability influencing parameters also include the average pore throat radius, The step of obtaining a core sample of the reservoir to be evaluated and constructing a permeability model reflecting the relationship between the permeability and porosity of the core sample under different permeability influencing parameters includes: According to the rock grain size, the reservoir to be evaluated is classified into lithology; Conducting core experiments on core samples of the reservoir to be evaluated under different lithologies respectively to obtain multiple permeabilities and porosities of core samples under different lithologies, and calculating the flow unit index of the core samples based on the permeabilities and porosities; Obtaining an average pore throat radius of each core sample, dividing each lithology of the reservoir to be evaluated into at least one range according to the average pore throat radius, and further dividing each range into at least one sub-range according to the flow unit index of the core sample; The permeability and porosity of the core samples in each sub-range are fitted to obtain a permeability model reflecting the relationship between the permeability and porosity of the core samples under different permeability influencing parameters.

3. The method for determining reservoir permeability according to claim 2, characterized in that: According to the average pore throat radius, each lithology of the reservoir to be evaluated is divided into at least one range, including: A relationship curve between the average pore throat radius and permeability of core samples under each lithology is constructed, and the average pore throat radius at the turning point of the relationship curve is taken as the endpoint value, so as to divide each lithology of the reservoir to be evaluated into at least one range.

4. The method for determining reservoir permeability according to claim 3, characterized in that: According to the flow unit index of the core sample, each range is further divided into at least one sub-range, including: The flow unit index in each range is used as the horizontal axis, and the cumulative frequency of the flow unit index in the range is used as the vertical axis, so as to construct a relationship curve between the flow unit index and the cumulative frequency; The flow unit index at the turning point in the relationship curve is taken as the endpoint value, so as to further divide each range into at least one sub-range.

5. The method for determining reservoir permeability according to claim 2, characterized in that: The step of determining a permeability model suitable for the reservoir according to the permeability influencing parameter of the reservoir to be evaluated comprises: Determine the scope of the reservoir to be evaluated according to the lithology and average pore throat radius of the reservoir to be evaluated; The sub-range to which the reservoir to be evaluated belongs is determined according to the flow unit index of the reservoir to be evaluated, and the permeability model obtained by fitting the permeability and porosity of the core samples in the sub-range is determined as the permeability model suitable for the reservoir.

6. The method for determining reservoir permeability according to claim 5, characterized in that: The flow unit index of the reservoir to be evaluated is determined by the following method: Inputting the well logging data of the reservoir to be evaluated into the flow unit index prediction model to obtain the flow unit index of the reservoir to be evaluated; The flow unit index prediction model is obtained through the following process: A sample data set including well logging data and flow unit index is obtained, and a neural network is trained using the sample data set to obtain a flow unit index prediction model.

7. The method for determining reservoir permeability according to claim 6, characterized in that: The logging data includes at least one of a ratio of natural gamma to density, a ratio of deep and shallow lateral resistivity, and neutron and sonic time difference.

8. The method for determining reservoir permeability according to claim 1, characterized in that: The step of obtaining a core sample of the reservoir to be evaluated and constructing a permeability model reflecting the relationship between the permeability and porosity of the core sample under different permeability influencing parameters includes: According to the rock grain size, the reservoir to be evaluated is classified into lithology; Conducting core experiments on core samples of the reservoir to be evaluated under different lithologies respectively to obtain multiple permeabilities and porosities of core samples under different lithologies, and calculating the flow unit index of the core samples based on the permeabilities and porosities; Dividing each lithology of the reservoir to be evaluated into at least one range according to the flow unit index of the core sample; The permeability and porosity of the core samples in each range are fitted to obtain a permeability model reflecting the relationship between the permeability and porosity of the core samples under different permeability influencing parameters.

9. A device for determining reservoir permeability, characterized in that: The device comprises: a model building unit and a calculation unit; The model building unit is used to obtain core samples of the reservoir to be evaluated and build a permeability model that reflects the relationship between the permeability and porosity of the core sample under different permeability influencing parameters; the permeability influencing parameters include lithology and flow unit index; The calculation unit is used to determine a permeability model suitable for the reservoir according to the permeability influencing parameters of the reservoir to be evaluated; and to bring the porosity of the reservoir to be evaluated into the permeability model suitable for the reservoir to obtain the reservoir permeability.

10. The device for determining reservoir permeability according to claim 9, characterized in that: The permeability influencing parameters also include the average pore throat radius, The step of obtaining a core sample of the reservoir to be evaluated and constructing a permeability model reflecting the relationship between the permeability and porosity of the core sample under different permeability influencing parameters includes: According to the rock grain size, the reservoir to be evaluated is classified into lithology; Conducting core experiments on core samples of the reservoir to be evaluated under different lithologies respectively to obtain multiple permeabilities and porosities of core samples under different lithologies, and calculating the flow unit index of the core samples based on the permeabilities and porosities; Obtaining an average pore throat radius of each core sample, dividing each lithology of the reservoir to be evaluated into at least one range according to the average pore throat radius, and further dividing each range into at least one sub-range according to the flow unit index of the core sample; The permeability and porosity of the core samples in each sub-range are fitted to obtain a permeability model reflecting the relationship between the permeability and porosity of the core samples under different permeability influencing parameters.

11. A machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the method for determining reservoir permeability according to any one of claims 1 to 8.

12. A processor, characterized in that: Used to run a program, wherein the program is used to execute the method for determining reservoir permeability according to any one of claims 1 to 8 when being run.

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