Reservoir space permeability prediction method and device, electronic equipment and medium

Abstract

The embodiment of the invention discloses a reservoir space permeability prediction method and device, electronic equipment and a medium. Comprising the following steps: classifying pore structures according to a first flow unit index of a mercury injection sample, and determining flow unit index ranges corresponding to different pore structures; according to the first flow unit index and a second flow unit index of the target core sample, constructing permeability calculation models corresponding to the different pore structures; determining a third flow unit index of the non-coring sample according to the relationship between the second flow unit index and the logging curve; reversely deducing a fourth flow unit index of the well-side unsampled reservoir according to the relationship between the third flow unit index and the well-side seismic parameters, and constructing a three-dimensional space flow unit index prediction model; and constructing a three-dimensional space permeability model according to the three-dimensional space flow unit index prediction model, and predicting the reservoir space permeability. By adopting the scheme, the quantitative characterization of the permeability of the offshore low-permeability-ultra-low-permeability reservoir from a single well to a three-dimensional space is realized.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of reservoir exploration technology, and in particular to a reservoir space permeability prediction method, device, electronic equipment and medium. Background Art

[0002] The pore structure of low-permeability and ultra-low-permeability reservoirs is complex, often manifested as a permeability range of two orders of magnitude difference under the same porosity. The permeability of such reservoirs calculated by traditional porosity and permeability statistical regression methods and conventional logging interpretation methods has low accuracy. Previous researchers have explored methods to establish porosity and permeability relationships corresponding to different pore structures, which greatly improved the calculation accuracy of single well permeability.

[0003] However, the accuracy of spatial permeability prediction is more important for improving the drilling rate of "sweet spots" with high relative permeability and releasing the production capacity of low-permeability and ultra-low-permeability gas reservoirs. At present, the prediction of spatial permeability is mostly based on the direct conversion of spatial porosity under the permeability constraint of a single well into a permeability field. In the case of few wells or large well spacing at sea, this approach cannot accurately characterize the changes in permeability between wells, resulting in large errors in spatial permeability prediction.

[0004] Therefore, how to improve the prediction accuracy of spatial permeability of offshore low-permeability and ultra-low-permeability reservoirs is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0005] The embodiments of the present invention provide a reservoir space permeability prediction method, device, electronic equipment and medium to achieve quantitative characterization of offshore low-permeability and ultra-low-permeability reservoir permeability from a single well to three-dimensional space, improve the prediction accuracy of three-dimensional space permeability under offshore few-well conditions, and provide guidance for pre-drilling trajectory optimization and production capacity prediction.

[0006] In a first aspect, an embodiment of the present invention provides a reservoir space permeability prediction method, comprising:

[0007] Determine the first flow unit index and pore structure type of the mercury injection sample according to the core mercury injection test curve, and determine the flow unit index range corresponding to different types of pore structures according to the first flow unit index; wherein reservoirs with different types of pore structures represent different flow units;

[0008] Determine a second flow unit index of the target core sample according to the permeability and porosity of the target core sample, and determine a target pore structure type corresponding to the target core sample according to the second flow unit index; wherein the target core sample has not been subjected to a mercury injection test;

[0009] Determining a first corresponding relationship between permeability and porosity corresponding to different types of pore structures, and constructing a permeability calculation model corresponding to different types of pore structures based on the first corresponding relationship;

[0010] Determine a second corresponding relationship between the second flow unit index and the logging curve in the vertical direction, and determine a third flow unit index of the uncored sample according to the second corresponding relationship;

[0011] Constructing a third corresponding relationship between the third flow unit index and the seismic parameter near the well, inferring the fourth flow unit index of the unsampled reservoir near the well according to the third corresponding relationship, and constructing a three-dimensional space flow unit index prediction model;

[0012] A three-dimensional space permeability model is constructed based on the three-dimensional space flow unit index prediction model and the permeability calculation model, and the three-dimensional space permeability model is used to predict the reservoir space permeability.

[0013] In a second aspect, an embodiment of the present invention further provides a reservoir space permeability prediction device, comprising:

[0014] A mercury injection sample information determination module determines the first flow unit index and pore structure type of the mercury injection sample according to the core mercury injection experimental curve, and determines the flow unit index range corresponding to different types of pore structures according to the first flow unit index; wherein reservoirs with different types of pore structures represent different flow units;

[0015] A target core sample information determination module is used to determine a second flow unit index of the target core sample according to the permeability and porosity of the target core sample, and determine a target pore structure type corresponding to the target core sample according to the second flow unit index; wherein the target core sample has not been subjected to a mercury injection experiment;

[0016] A permeability calculation model determination module is used to determine a first corresponding relationship between permeability and porosity corresponding to different types of pore structures, and to construct a permeability calculation model corresponding to different types of pore structures based on the first corresponding relationship;

[0017] An uncored sample information determination module is used to determine a second corresponding relationship between a second flow unit index and a logging curve in the vertical direction, and determine a third flow unit index of the uncored sample according to the second corresponding relationship;

[0018] A three-dimensional space flow unit index prediction model construction module is used to construct a third corresponding relationship between the third flow unit index and the wellside seismic parameter, and to reversely infer the fourth flow unit index of the unsampled reservoir beside the well based on the third corresponding relationship and to construct a three-dimensional space flow unit index prediction model;

[0019] The three-dimensional space permeability model construction module is used to construct a three-dimensional space permeability model based on the three-dimensional space flow unit index prediction model and the permeability calculation model, and use the three-dimensional space permeability model to predict the reservoir space permeability.

[0020] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0021] one or more processors;

[0022] A storage device for storing one or more programs;

[0023] When the one or more programs are executed by the one or more processors, the one or more processors implement the reservoir space permeability prediction method described in any embodiment of the present invention.

[0024] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the reservoir space permeability prediction method described in any embodiment of the present invention.

[0025] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the reservoir space permeability prediction method as described in any embodiment of the present invention.

[0026] The embodiment of the present invention provides a reservoir space permeability prediction method, device, electronic device and storage medium, which classifies the pore structure by determining the first flow unit index of the mercury injection sample subjected to the mercury injection experiment, and determines the flow unit index range corresponding to different types of pore structures; constructs a permeability calculation model corresponding to different types of pore structures according to the first flow unit index and the second flow unit index of the target core sample; determines a second corresponding relationship between the second flow unit index and the logging curve in the vertical direction to determine the third flow unit index of the uncored sample; reversely infers the fourth flow unit index of the unsampled reservoir near the well according to the third corresponding relationship between the third flow unit index and the seismic parameter near the well and constructs a three-dimensional space flow unit index prediction model; constructs a three-dimensional space permeability model according to the three-dimensional space flow unit index prediction model and the permeability calculation model, and uses the three-dimensional space permeability model to predict the reservoir space permeability. By adopting the technical solution of the embodiment of the present invention, the pore structure, a key factor affecting the permeability, is reflected in the prediction of spatial permeability, and the quantitative characterization of the permeability of offshore low-permeability and ultra-low-permeability reservoirs from a single well to three-dimensional space is achieved, thereby improving the prediction accuracy of three-dimensional spatial permeability under offshore conditions of few wells, and providing guidance for pre-drilling trajectory optimization and production capacity prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts. In the drawings:

[0028] Figure 1 is a flow chart of a reservoir space permeability prediction method provided in Example 1 of the present invention;

[0029] Figure 2 is a flow chart of a reservoir space permeability prediction method provided by the second embodiment of the present invention;

[0030] Figure 3 It is a schematic diagram of a permeability model based on flow unit index classification provided by the second embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram of a probabilistic neural network prediction principle provided by Embodiment 2 of the present invention;

[0032] Figure 5 This is a schematic diagram of the correspondence between a target curve and seismic parameters provided in the second embodiment of the present invention;

[0033] Figure 6 This is a schematic diagram of operator length sensitivity analysis provided by Embodiment 2 of the present invention;

[0034] Figure 7 This is a schematic diagram of average error and number of attributes provided by the second embodiment of the present invention;

[0035] Figure 8 This is a schematic diagram of comparing a probabilistic neural network prediction curve and an actual curve provided in the second embodiment of the present invention;

[0036] Fig. 9 This is a schematic diagram of the intersection of a probabilistic neural network prediction curve and an actual curve provided in the second embodiment of the present invention;

[0037] Fig.10 This is a schematic diagram of a three-dimensional space flow unit index prediction model provided by the second embodiment of the present invention;

[0038] Fig.11 It is a schematic diagram of a three-dimensional permeability model of a target area established by a traditional method provided in the second embodiment of the present invention;

[0039] Fig.12 is a schematic diagram of a three-dimensional space permeability model provided by the second embodiment of the present invention;

[0040] Fig.13is a schematic structural diagram of a reservoir space permeability prediction device provided in an embodiment of the present invention;

[0041] Fig.14 It is a structural schematic diagram of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0043] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0044] Among them, the acquisition, storage, use and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations.

[0045] Embodiment 1

[0046] Figure 1 This is a flow chart of a reservoir space permeability prediction method provided in an embodiment of the present invention. This embodiment is applicable to the case of reservoir space permeability prediction under low permeability and ultra-low permeability background. The method of this embodiment can be executed by a reservoir space permeability prediction device, which can be implemented in hardware and / or software. The device can be configured in a server for reservoir space permeability prediction. The method specifically includes the following steps:

[0047] S110, determining a first flow unit index and a pore structure type of the mercury injection sample according to a core mercury injection test curve, and determining a flow unit index range corresponding to different types of pore structures according to the first flow unit index.

[0048] Among them, the core mercury injection experiment is an important experimental method for studying the pore structure of rocks, especially suitable for the study of low permeability rocks. It can help researchers understand key parameters such as the pore size, distribution and starting pressure of rocks, and then evaluate the permeability and production potential of the reservoir.

[0049] Pore ​​structure refers to the geometric shape, size, distribution, interconnectedness, and configuration relationship between pores and throats of rocks. Pore structure can reflect the combination of various pores and throats connecting pores in the reservoir, and is the overall picture of pore and throat development.

[0050] Flow Zone Indicator (FZI) is a parameter used to describe regions with similar fluid flow characteristics in reservoir rocks and is widely used to quantitatively identify and divide flow units.

[0051] In an embodiment of the present invention, the first flow unit index of different mercury injection samples is determined based on the core mercury injection test curve, and the pore structure is classified according to the first flow unit index; the first flow unit indexes of different pore structures are matched, and the flow unit index ranges corresponding to different types of pore structures are determined; wherein reservoirs of different types of pore structures represent different flow units.

[0052] S120. Determine a second flow unit index of the target core sample according to the permeability and porosity of the target core sample, and determine a target pore structure type corresponding to the target core sample according to the second flow unit index.

[0053] Wherein, the target core sample has not been subjected to mercury injection experiment. The permeability and porosity of the target core sample are determined, and the second flow unit index of the target core sample is determined by the permeability and porosity. The second flow unit index is matched with the first flow unit index to determine the target pore structure type corresponding to the target core sample. In the embodiment of the present invention, the second flow unit index of the target core sample is determined based on the permeability and porosity, and the target core sample may be obtained in a low permeability-ultra-low permeability reservoir; the second flow unit index is matched with the first flow unit index to determine the target pore structure type corresponding to the target core sample.

[0054] It can be understood that the first flow unit index can be a reference to distinguish different flow unit indices that appear before and after in the embodiment to execute corresponding logic. Any flow unit index selected from the flow unit index can be used to explain the execution logic from the selected flow unit index. Therefore, the flow unit index that appears for the first time in this article is called the first flow unit index, and other flow unit indices that appear subsequently and are different from the first flow unit index are called second flow unit indices, which will not be repeated later.

[0055] S130, determining a first corresponding relationship between permeability and porosity corresponding to different types of pore structures, and constructing a permeability calculation model corresponding to different types of pore structures based on the first corresponding relationship.

[0056] Among them, permeability is a parameter that characterizes the ability of rocks to allow fluids to pass through. It is the physical property basis of oil (gas) reservoir rocks and has an important impact on oil and gas migration, seepage mechanics, etc. The numerical value of permeability can indicate the quality of rock permeability.

[0057] The core sample includes a target core sample and a mercury injection sample for a core mercury injection experiment. The embodiment of the present invention constructs a first corresponding relationship between the permeability and porosity of the core sample based on the relationship between the permeability and porosity of the target core sample and the relationship between the permeability and porosity of the mercury injection sample. Because the porosity and permeability of low-permeability and ultra-low-permeability reservoirs have a poor correlation, the porosity and permeability of the core mercury injection experiment analysis of different flow units are intersected to obtain the permeability calculation models of different flow units, that is, the permeability calculation models of different types of pore structures are obtained.

[0058] S140, determining a second corresponding relationship between the second flow unit index and the logging curve in the vertical direction, and determining a third flow unit index of the uncored sample according to the second corresponding relationship.

[0059] Among them, the logging curve is a curve of changes in the physical properties of the formation rock measured and drawn by logging technology in petroleum geological exploration. The logging curve reflects various physical properties of underground rock formations and fluids, and can indirectly reflect the pore structure of low-permeability and ultra-low-permeability reservoirs. The logging curve includes but is not limited to natural gamma logging curves, neutron logging curves, density logging curves, sonic time difference logging curves, and resistivity logging curves.

[0060] In the embodiment of the present invention, after determining the second flow unit index of the target core sample and the target pore structure type, a relationship between the flow unit index FZI of all core analysis samples and the corresponding depth point logging curve is constructed according to a machine learning algorithm (K-Nearest Neighbor, KNN), thereby obtaining the third flow unit index of the non-coring sample.

[0061] S150, constructing a third corresponding relationship between the third flow unit index and the seismic parameters near the well, inferring the fourth flow unit index of the unsampled reservoir near the well based on the third corresponding relationship, and constructing a three-dimensional space flow unit index prediction model.

[0062] Among them, seismic parameters may refer to the quantitative description of earthquake source characteristics based on seismic data analysis, including basic seismic parameters, seismic mechanism solutions and source dynamic parameters. The seismic parameters include but are not limited to density, longitudinal and transverse wave velocities, frequency, longitudinal wave impedance, Poisson's ratio, transverse wave impedance and longitudinal and transverse wave velocity ratio. The wellside seismic parameters may refer to the seismic parameters of the reservoir between the target well and the adjacent wells. The target well in the embodiment of the present invention may refer to the well where the target core sample is sampled.

[0063] In the embodiment of the present invention, the corresponding third flow unit index is determined based on the wellbore seismic parameters, and a third corresponding relationship between the third flow unit index and the wellbore seismic parameters is constructed, and the fourth flow unit index flow unit index on other reservoirs is reversed based on the third corresponding relationship; a three-dimensional space flow unit index prediction model is constructed to predict the flow unit index of other unsampled wells or inter-well reservoirs in three-dimensional space.

[0064] S160, constructing a three-dimensional space permeability model based on the three-dimensional space flow unit index prediction model and the permeability calculation model, and using the three-dimensional space permeability model to predict the reservoir space permeability.

[0065] According to the relationship between the flow unit index and the permeability, a three-dimensional space permeability model is constructed on the basis of the three-dimensional space flow unit index prediction model; and the three-dimensional space permeability model is used to predict the reservoir space permeability.

[0066] The embodiment of the present invention provides a reservoir space permeability prediction method, which comprises the following steps: determining a first flow unit index and a pore structure type of a mercury injection sample according to a core mercury injection test curve, and determining a flow unit index range corresponding to different types of pore structures according to the first flow unit index; determining a second flow unit index of a target core sample according to the permeability and porosity of the target core sample, and determining a target pore structure type corresponding to the target core sample according to the second flow unit index; determining a first corresponding relationship between permeability and porosity corresponding to different types of pore structures, and constructing a permeability calculation model corresponding to different types of pore structures according to the first corresponding relationship; determining a second corresponding relationship between the second flow unit index and a logging curve in the vertical direction, and determining a third flow unit index of an uncored sample according to the second corresponding relationship; constructing a third corresponding relationship between the third flow unit index and a wellside seismic parameter, and inferring a fourth flow unit index of an unsampled reservoir near the well according to the third corresponding relationship and constructing a three-dimensional space flow unit index prediction model; constructing a three-dimensional space permeability model according to the three-dimensional space flow unit index prediction model and a permeability calculation model, and using the three-dimensional space permeability model to predict the reservoir space permeability. By adopting the technical solution of the embodiment of the present invention, the pore structure, a key factor affecting the permeability, is reflected in the prediction of spatial permeability, and the quantitative characterization of the permeability of offshore low-permeability and ultra-low-permeability reservoirs from a single well to three-dimensional space is achieved, thereby improving the prediction accuracy of three-dimensional spatial permeability under offshore conditions of few wells, and providing guidance for pre-drilling trajectory optimization and production capacity prediction.

[0067] Embodiment 2

[0068] Figure 2The flowchart of a reservoir space permeability prediction method provided in an embodiment of the present invention is shown in FIG. The embodiment of the present invention further optimizes the above embodiment on the basis of the above embodiment, and the embodiment of the present invention can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the reservoir space permeability prediction method provided in the embodiment of the present invention may include the following steps:

[0069] S210, determining a first flow unit index and a pore structure type of the mercury injection sample according to the core mercury injection test curve, and determining a flow unit index range corresponding to different types of pore structures according to the first flow unit index.

[0070] Among them, based on the core mercury injection test analysis results of the target area low-permeability and ultra-low-permeability reservoir, the pore structure is classified according to the morphological characteristics of the mercury injection curve, and the flow unit index range corresponding to different types of pore structures is determined. Among them, the porosity and permeability data of the core analysis are used to calculate the first flow unit index corresponding to different types of pore structures according to the Kozeny-Carman equation; Among them, the Kozeny-Carman equation is a formula that describes the relationship between rock permeability and porosity and rock specific surface area, which can be expressed as:

[0071]

[0072] Where K is the permeability, mD; φ is the effective porosity; H c Represents the structural performance constant, dimensionless.

[0073] Due to the influence of sediment heterogeneity, the pore tortuosity and shape coefficient vary greatly. Also due to the influence of cementation, pore roughness and other factors, H c There have been huge changes.

[0074]

[0075] The reservoir quality index (RQI) is defined and can be expressed as:

[0076]

[0077] Define the ratio of pore volume to particle volume (φ z ), which can be expressed as:

[0078]

[0079] The flow unit index is expressed as:

[0080] FZI=RQI / φ z

[0081] Taking the logarithm on both sides, it can be expressed as:

[0082] lgRQI = lgFZI + lgφ x

[0083] It can be seen from this that under logarithmic coordinates, RQI and φ z have a linear correlation. The flow unit index FZI corresponding to different pore structure types corresponds to different ranges between 0 and 1.

[0084] In the embodiments of the present invention, the pore structure is divided into six categories, and the ranges of the flow unit indices corresponding to the different types of pore structures are respectively: for type I, FZI > 0.5; for type II, 0.3 < FZI ≤ 0.5; for type III, 0.15 < FZI ≤ 0.3; for type IV, 0.1 < FZI ≤ 0.15; for type V, 0.05 < FZI ≤ 0.1; for type VI, FZI ≤ 0.05.

[0085] S220. Determine the second flow unit index of the target core sample according to the permeability and porosity of the target core sample, and determine the target pore structure type corresponding to the target core sample according to the second flow unit index.

[0086] Among them, based on the calculation method of the first flow unit index FZI in step A1, calculate the second flow unit index of the target core sample without performing the mercury injection experiment on the core, and classify the target core sample into the corresponding pore structure classification according to the second flow unit index.

[0087] As an optional but non-limiting implementation manner, the determining the second flow unit index of the target core sample according to the permeability and porosity of the target core sample, and determining the target pore structure type corresponding to the target core sample according to the second flow unit index includes, but is not limited to, steps A1 - A2:

[0088] Step A1: Determine the permeability and porosity of the target core sample, and determine the second flow unit index of the target core sample according to the permeability and porosity.

[0089] Step A2: Correlate the second flow unit index with the first flow unit index to determine the target pore structure type corresponding to the target core sample.

[0090] Among them, since the data of the core mercury injection test analysis is relatively small, in order to improve the accuracy of the permeability calculation model, the porosity and permeability of the target core sample that has not been subjected to the core mercury injection test analysis are determined through the well logging curve to determine the second flow unit index of the target core sample. Specifically, based on the calculation method of the first flow unit index FZI in step A1, the permeability and porosity of the target core sample are determined according to the well logging curve, and the second flow unit index of the target core sample that has not been subjected to the core mercury injection test is calculated. The second flow unit index is matched with the first flow unit index to determine the target pore structure type corresponding to the target core sample.

[0091] S230, determining a first corresponding relationship between permeability and porosity corresponding to different types of pore structures, and constructing a permeability calculation model corresponding to different types of pore structures based on the first corresponding relationship.

[0092] Among them, the embodiment of the present invention constructs a first corresponding relationship between the permeability and porosity of the core sample based on the relationship between the permeability and porosity of the target core sample and the relationship between the permeability and porosity of the mercury injection sample; and constructs a permeability calculation model corresponding to different types of pore structures based on the first corresponding relationship.

[0093] As an optional but non-limiting implementation, the determining of the first corresponding relationship between the permeability and porosity corresponding to different types of pore structures, and constructing the permeability calculation model corresponding to different types of pore structures according to the first corresponding relationship, includes but is not limited to steps B1-B2:

[0094] Step B1: Determine a first corresponding relationship between the permeability and porosity of the core sample according to a first flow unit index of the mercury injection sample and a second flow unit index of the target core sample; the core sample includes the target core sample and the mercury injection sample for the core mercury injection experiment.

[0095] Step B2: constructing a permeability calculation model corresponding to different types of pore structures based on the first corresponding relationship.

[0096] Among them, different pore structure types correspond to different flow unit index ranges, and the corresponding relationship between permeability and porosity corresponding to different flow unit index ranges is determined to construct a permeability calculation model corresponding to different types of pore structures. Specifically, the different pore structure types of the core sample are determined by determining the first flow unit index and the second flow unit index, and when determining the first flow unit index and the second flow unit index, they are determined based on the permeability and porosity of the mercury injection sample and the permeability and porosity of the target core sample. Therefore, based on the first flow unit index and the second flow unit index, the first corresponding relationship between the permeability and porosity of the core sample is determined; and based on the first corresponding relationship, the permeability calculation model corresponding to different types of pore structures is constructed.

[0097] Optionally, the porosity and permeability of six types of flow units in low-permeability and ultra-low-permeability reservoirs in different target areas are intersected respectively by the core mercury injection test analysis, and the permeability calculation models of six different flow units are obtained. Figure 3 , the correlation between porosity and permeability increased to more than 90%.

[0098] S240: Determine a second corresponding relationship between the second flow unit index and the logging curve in the vertical direction, and determine a third flow unit index of the uncored sample based on the second corresponding relationship.

[0099] Optionally, the uncored samples may belong to different types of pore structures in the vertical direction, and the corresponding type of pore structure is determined based on the third flow unit index of the uncored samples; the corresponding permeability calculation model is determined based on the pore structure, and the permeability of the uncored samples belonging to different types of pore structures is determined based on the corresponding permeability calculation model.

[0100] In the embodiment of the present invention, the permeability of uncored samples with different types of pore structures in the vertical direction is determined so as to correct the permeability predicted by the constructed three-dimensional spatial permeability model.

[0101] S250, constructing a third corresponding relationship between the third flow unit index and the seismic parameters near the well, and determining a fourth flow unit index of the unsampled reservoir near the well according to the third corresponding relationship and the seismic parameters of the unsampled reservoir near the well.

[0102] In the embodiment of the present invention, a third corresponding relationship is established between the third flow unit index calculated by the logging curve and the seismic parameters near the well, and the fourth flow unit index on the unsampled reservoir near the well is inferred by using this corresponding relationship. The seismic parameters near the well include density, longitudinal and transverse wave velocity, frequency, longitudinal wave impedance, Poisson's ratio, transverse wave impedance and longitudinal and transverse wave velocity ratio. The third corresponding relationship includes a linear relationship or a nonlinear relationship.

[0103] S260. Construct a reservoir flow unit index curve according to the second flow unit index and the fourth flow unit index.

[0104] Wherein, a reservoir flow unit index curve is constructed in the vertical direction according to the second flow unit index and the fourth flow unit index.

[0105] S270, using the flow unit index curve and wellbore seismic parameters as training samples to construct a three-dimensional space flow unit index prediction model.

[0106] Among them, the corresponding relationship between the flow unit index and the wellside seismic parameters includes a linear relationship or a nonlinear relationship. If there is a linear relationship between the flow unit index and the wellside seismic parameters, the correlation coefficient can be derived using the well and wellside seismic parameters within a certain time window. If there is a nonlinear relationship between the flow unit index and the wellside seismic parameters, the well and wellside seismic parameters within a certain time window can be used as training samples to derive the implicit relationship using a neural network.

[0107] Among them, multiple types of seismic data bodies can be used simultaneously in the prediction process, such as AVO attribute bodies and conventional acoustic impedance inversion bodies, and multiple characteristic parameter bodies (such as instantaneous frequency) can be derived from each data body. Among the numerous parameters, the EMERGE module of HRS can automatically find a set of seismic parameters that best matches the target curve in a given mode to participate in the prediction. Among them, the EMERGE module of HRS is a powerful software tool that focuses on analyzing well logging curves and seismic data for multi-attribute analysis and prediction; the target curve refers to the flow unit index curve. This algorithm is actually a process of interpolating known well point information in a multi-dimensional seismic characteristic parameter space. Since it does not require a fixed mathematical model, it can better adapt to local data and can give inversion results with a high consistency rate with known well points. Its disadvantage is that the lateral stability of the inversion results is difficult to control, so repeated parameter experiments are required.

[0108] The most distinctive feature of this method is that it can use a variety of different seismic data bodies at the same time, combined with the AVO processing results, which is equivalent to introducing pre-stack seismic information in the inversion, providing a more sufficient basis for inverting other rock physical parameters besides acoustic impedance.

[0109] In the linear relationship model, it is assumed that the corresponding relationship between the flow unit index and the wellbore seismic parameters can be expressed by the following linear equation:

[0110] M=a11S 11 +a12S 12 +…+a1mS 1m +a21S 21 +a22S 22 +…+a2mS2

[0111] +an1S n1 +an2S n2 +…+anmS nm

[0112] Among them, S ij is the jth sample point of the i-th seismic parameter curve; a is a constant coefficient, which is determined by the criterion of best matching with the target parameter curve M; n is the number of seismic parameter types used, which is determined by the minimum cross-check error. The so-called cross-check error is to remove a known well in a loop, use the remaining wells to determine the prediction parameters and predict the target parameters of the removed well points. The average value of the difference between the two for all known well points is called the cross-check error. The minimum cross-check error criterion is the core of almost all modules of EMERGE. The selection of seismic parameters is determined by a single-step incremental method. First, the seismic parameter that best matches the well point is selected, and the remaining parameters are tested one by one. The second parameter that best matches the well point under the given regression mode is selected, and the remaining seismic parameters are selected in this way.

[0113] After constructing the linear relationship between the third flow unit index and the wellside seismic parameter, the fourth flow unit index of the unsampled reservoir near the well is inferred based on the linear relationship and a three-dimensional space flow unit index prediction model is constructed.

[0114] As an optional but non-limiting implementation, when there is a nonlinear relationship between the third flow unit index and the wellbore seismic parameters, the flow unit index curve and the wellbore seismic parameters are used as training samples to construct a three-dimensional space flow unit index prediction model, including but not limited to steps C1-C3:

[0115] Step C1: A cross-check error algorithm is used to determine target seismic parameters that match the flow unit index curve in a preset regression mode from the wellbore seismic parameters.

[0116] Step C2: using the target earthquake parameters and the flow unit index curve as training samples, and using a probabilistic neural network to train a three-dimensional flow unit index prediction model to obtain a first three-dimensional flow unit index prediction model.

[0117] Step C3: using the seismic parameters of the sampled adjacent wells to verify the first three-dimensional spatial flow unit index prediction model and adjust the parameters to determine the second three-dimensional spatial flow unit index prediction model.

[0118] Among them, the embodiment of the present invention uses the target seismic parameters and the flow unit index curve as training samples, and the matching rate of the prediction results through the probabilistic neural network (PNN) will be greatly improved. The relevant mode is predicted by the (PNN) method, that is, the well and seismic data within a certain time window range are used as training samples, and the implicit relationship is derived by using the neural network. The target seismic parameters are consistent with the way of determining seismic parameters in the linear relationship. Multiple types of seismic data bodies can be used simultaneously in the prediction process, such as AVO attribute body, conventional acoustic impedance inversion body, etc., and each data body can derive multiple characteristic parameter bodies (such as instantaneous frequency) from it. A set of target seismic parameters that best match the target curve under a given mode is found among the numerous parameters to participate in the prediction.

[0119] See also Figure 4 The difference between probabilistic neural network and the commonly used BP neural network is its boundary stability, which means that it can often produce more stable prediction results for predictions far away from the sample space. Another significant difference is that PNN abandons the "black box" characteristics of BP and replaces it with a clear mathematical model, so its prediction performance can be roughly judged in advance, and the parameters of the trained network can be appropriately adjusted to obtain the best prediction effect.

[0120] Optionally, given a set of training samples: {x i1 , x i2 , …, x i2 , L i}, the PNN network output value L at a point x in the earthquake parameter space is given by the following formula:

[0121]

[0122] in: It represents the n-dimensional space distance between the earthquake parameter space point x and the i-th training sample; the weight coefficient r is a pending network parameter, which is used to control the sensitivity of different types of earthquake parameters to the prediction results; n represents the number of earthquake parameter types involved in the training, m represents the number of samples, and L can refer to the objective function value.

[0123] It can be seen that the PNN network prediction process is actually an interpolation process of well point information in the seismic parameter space. The determination of the network parameter r is based on the criterion of minimizing the cross-check error of each well. At the same time, it can also be combined with the geological understanding of the local area and artificially adjusted through repeated trial calculations of several key lines. The smaller the value of r, the closer the prediction result is to the known well point.

[0124] Based on the prediction of single well training simulation of probabilistic neural network (PNN), this nonlinear relationship is applied to the entire seismic data volume, and the seismic prediction of the entire FZI data volume can be realized. Based on the FZI data volume predicted by the probabilistic neural network (PNN) method, according to the results of variogram analysis (primary and secondary ranges and source direction), the sequential Gaussian simulation method is used to establish a three-dimensional spatial flow unit index prediction model for the target area with trend constraint co-simulation.

[0125] In an optional solution of the embodiment of the present invention, see Figure 5 , using the probabilistic neural network (PNN) method to predict, that is, the FZI data and seismic data on the well within a certain time window range are used as training samples, and the neural network is used to derive implicit relationships. Through the analysis of the flow unit index curve (FZI curve) and seismic related multi-attributes, it is found that Figure 6 as well as Figure 7 , when the operator length is 5 and the number of attributes is 7, the average value of the root mean square error between the predicted result and the actual FZI curve is the smallest. Wherein, the operator length is the number of seismic sampling points symmetrically distributed with the seismic sampling point corresponding to the target curve sample point as the center, and the seismic multi-attribute values ​​at multiple points are convoluted to characterize the value at a sample point of the target curve. Preferred seismic parameters include but are not limited to average frequency, inverse Poisson's ratio, instantaneous phase cosine, seismic bandpass filtering, shear wave impedance squared, longitudinal wave impedance squared, and longitudinal and shear wave velocity ratio squared.

[0126] Based on the prediction results of earthquake multi-attribute analysis, the probabilistic neural network (PNN) is used for continuous iterative training and learning, which significantly improves the prediction consistency rate and reduces the prediction error. Figure 8 as well as Fig. 9 ,From the comparison and intersection diagram of the PNN prediction and ,actual curves, it can be seen that the two curves of the four wells have the same ,trend vertically, the curve change positions are unified, and the ,correlation coefficient is increased to 76.7%.

[0127] Based on the probabilistic neural network (PNN) single well training simulation prediction, this nonlinear relationship is applied to the entire seismic data volume to achieve seismic prediction of the entire FZI curve. Fig.10 Based on the FZI data predicted by the probabilistic neural network (PNN) method, according to the variogram analysis results (primary and secondary ranges and source direction), the sequential Gaussian simulation method is used to establish a three-dimensional spatial flow unit index prediction model for the target area with trend constraint co-simulation.

[0128] S280, constructing a three-dimensional space permeability model based on the three-dimensional space flow unit index prediction model and the permeability calculation model, and using the three-dimensional space permeability model to predict the reservoir space permeability.

[0129] Wherein, a three-dimensional space permeability model is constructed according to the three-dimensional space flow unit index prediction model, and the three-dimensional space permeability model is used to predict the reservoir space permeability.

[0130] As an optional but non-limiting implementation, the three-dimensional space permeability model is constructed based on the three-dimensional space flow unit index prediction model and the permeability calculation model, and the three-dimensional space permeability model is used to predict the reservoir space permeability, including but not limited to steps D1-D3:

[0131] Step D1: According to the second flow unit index of the target core sample and the target permeability calculation model, the corresponding relationship between the flow unit index and the permeability calculation model is determined.

[0132] Step D2: According to the corresponding relationship, a three-dimensional space permeability model is constructed based on the three-dimensional space flow unit index prediction model.

[0133] Step D3: using the three-dimensional spatial permeability model to predict the reservoir spatial permeability.

[0134] Among them, according to the relationship between the flow unit index and the permeability, the corresponding relationship between the flow unit index and the permeability calculation model is determined; based on the three-dimensional space flow unit index prediction model, a three-dimensional space permeability model is constructed, and the three-dimensional space permeability model is used to predict the reservoir space permeability.

[0135] Optionally, the prediction of the three-dimensional spatial permeability of low-permeability and ultra-low-permeability reservoirs can refer to firstly establishing the target area porosity model based on the porosity inversion prediction results, according to the variance function analysis parameters (primary and secondary variation ranges and source direction), and using the sequential Gaussian simulation method through lithofacies control to perform trend constraints. According to different FZI values, the corresponding porosity-permeability relationship prediction results are established, and on this basis, the trend constraint co-simulation is performed to establish the target area permeability three-dimensional attribute model. See Fig.11 as well as Fig.12 The results show that the permeability plane characteristics of the target area are quite different from the original permeability model calculated directly using the porosity model according to a single pore-permeability relationship, and the heterogeneity of low permeability and ultra-low permeability is more refined: the permeability around the anticline 4 well and the northwest area is higher, but the heterogeneity around the well is stronger.

[0136] In an optional solution of the embodiment of the present invention, the permeability predicted by the three-dimensional space permeability model can be calibrated according to the permeability of the uncored sample, and the three-dimensional space permeability model can be optimized. For example, the first permeability of the uncored sample A is determined, and the second permeability of the sample A is determined using the three-dimensional space permeability model. The first permeability is compared with the second permeability. If the difference exceeds a preset threshold, the parameters of the three-dimensional space permeability model are adjusted to calibrate and optimize the three-dimensional space permeability model.

[0137] The embodiment of the present invention provides a reservoir space permeability prediction method. According to the core mercury injection experimental data, the low-permeability and ultra-low-permeability reservoirs are classified according to the pore structure type, and the first flow unit index reflecting the quality of each type of pore structure reservoir is constructed to obtain the flow unit index range of different types of reservoirs; then the relationship between the second flow unit index of the target core sample and the logging curve is established, and the third flow unit index value of the uncored layer section is calculated, and the permeability calculation model of different types of reservoirs is established according to the classification. Probabilistic neural network (PNN) prediction is performed in combination with the original seismic data, prestack inversion results, seismic internal operation attributes, etc., and the spatial reservoir flow zone index FZI data body is inverted. The sequential Gaussian simulation method is used to establish a three-dimensional spatial flow unit index prediction model with trend constraint co-simulation, and the permeability is predicted according to the pore-permeability relationship corresponding to different FZI values. On this basis, trend constraint co-simulation is performed to establish a three-dimensional spatial permeability model of the target area. Compared with the traditional direct conversion of spatial porosity field into permeability field under the constraint of single well permeability, the present invention better overcomes the problem of poor correlation between porosity and permeability of low-permeability and extra-low permeability reservoirs, and can reflect the pore structure, a key factor affecting the permeability of such reservoirs, in the prediction of three-dimensional spatial permeability, thereby realizing quantitative characterization of the permeability of offshore low-permeability and extra-low permeability reservoirs from single well to three-dimensional space, making the prediction of spatial permeability under the constraint of few offshore wells more reasonable and more accurate, thereby improving the prediction accuracy of three-dimensional spatial permeability under the condition of few offshore wells, and providing guidance for pre-drilling trajectory optimization and production capacity prediction.

[0138] Embodiment 3

[0139] Fig.13 : is a schematic diagram of the structure of a reservoir space permeability prediction device provided in an embodiment of the present invention. The technical solution of this embodiment can be applied to the case of reservoir space permeability prediction. The device can be implemented by software and / or hardware and is generally integrated on any electronic device with network communication function, including but not limited to: servers, computers, personal digital assistants and other devices. Figure 4As shown, the reservoir space permeability prediction device provided in this embodiment may include: a mercury injection sample information determination module 1310, a target core sample information determination module 1320, a permeability calculation model determination module 1330, an uncored sample information determination module 1340, a three-dimensional space flow unit index prediction model construction module 1350 and a three-dimensional space permeability model construction module 1360; wherein,

[0140] The mercury injection sample information determination module 1310 determines the first flow unit index and pore structure type of the mercury injection sample according to the core mercury injection experimental curve, and determines the flow unit index range corresponding to different types of pore structures according to the first flow unit index; wherein reservoirs with different types of pore structures represent different flow units;

[0141] The target core sample information determination module 1320 is used to determine the second flow unit index of the target core sample according to the permeability and porosity of the target core sample, and determine the target pore structure type corresponding to the target core sample according to the second flow unit index; wherein the target core sample has not been subjected to mercury injection experiment;

[0142] The permeability calculation model determination module 1330 is used to determine a first corresponding relationship between the permeability and porosity of a core sample, and to construct a permeability calculation model corresponding to different types of pore structures according to the first corresponding relationship; the core sample includes a target core sample and a mercury injection sample for a core mercury injection experiment;

[0143] The uncored sample information determination module 1340 is used to determine a second corresponding relationship between the second flow unit index and the logging curve in the vertical direction, and determine a third flow unit index of the uncored sample according to the second corresponding relationship;

[0144] The three-dimensional space flow unit index prediction model construction module 1350 is used to construct a third corresponding relationship between the third flow unit index and the wellside seismic parameter, and to reversely infer the fourth flow unit index of the unsampled reservoir beside the well based on the third corresponding relationship and to construct a three-dimensional space flow unit index prediction model;

[0145] The three-dimensional space permeability model construction module 1360 is used to construct a three-dimensional space permeability model based on the three-dimensional space flow unit index prediction model and the permeability calculation model, and use the three-dimensional space permeability model to predict the reservoir space permeability.

[0146] Based on the above embodiment, optionally, the target core sample information determination module is specifically used to:

[0147] Determining the permeability and porosity of the target core sample, and determining a second flow unit index of the target core sample based on the permeability and porosity;

[0148] The second flow unit index is matched with the first flow unit index to determine the target pore structure type corresponding to the target core sample.

[0149] Based on the above embodiment, optionally, the permeability calculation model determination module is specifically used to:

[0150] Determining a first corresponding relationship between the permeability and the porosity of the core sample according to a first flow unit index of the mercury injection sample and a second flow unit index of the target core sample; the core sample includes the target core sample and the mercury injection sample for the core mercury injection experiment;

[0151] A permeability calculation model corresponding to different types of pore structures is constructed based on the first corresponding relationship.

[0152] Based on the above embodiment, optionally, the three-dimensional space flow unit index prediction model construction module is specifically used to:

[0153] Constructing a third corresponding relationship between the third flow unit index and the wellbore seismic parameters, and determining a fourth flow unit index of the unsampled reservoir beside the well based on the third corresponding relationship and the seismic parameters of the unsampled reservoir beside the well; wherein the wellbore seismic parameters include density, longitudinal and transverse wave velocities, frequency, longitudinal wave impedance, Poisson's ratio, transverse wave impedance, and longitudinal and transverse wave velocity ratio, and the corresponding relationship includes a linear relationship or a nonlinear relationship;

[0154] constructing a reservoir flow unit index curve according to the second flow unit index and the fourth flow unit index;

[0155] The flow unit index curve and wellside seismic parameters are used as training samples to construct a three-dimensional space flow unit index prediction model.

[0156] On the basis of the above embodiment, optionally, when there is a nonlinear relationship between the third flow unit index and the wellbore seismic parameter, the three-dimensional space flow unit index prediction model construction module is further specifically used for:

[0157] A cross-check error algorithm is used to determine target seismic parameters that match the flow unit index curve in a preset regression mode from the wellbore seismic parameters;

[0158] The target earthquake parameter and the flow unit index curve are used as training samples, and a probabilistic neural network is used to train a three-dimensional space flow unit index prediction model to obtain a first three-dimensional space flow unit index prediction model;

[0159] The seismic parameters of sampled adjacent wells are used to verify the first three-dimensional spatial flow unit index prediction model and adjust the parameters to determine the second three-dimensional spatial flow unit index prediction model.

[0160] Based on the above embodiment, optionally, the three-dimensional space permeability model construction module is specifically used to:

[0161] According to the second flow unit index of the target core sample and the target permeability calculation model, the corresponding relationship between the flow unit index and the permeability calculation model is determined;

[0162] According to the corresponding relationship, a three-dimensional space permeability model is constructed on the basis of the three-dimensional space flow unit index prediction model;

[0163] The three-dimensional spatial permeability model is used to predict the reservoir spatial permeability.

[0164] The reservoir space permeability prediction device provided in the embodiment of the present invention can execute the reservoir space permeability prediction method provided in any embodiment of the present invention mentioned above, and has the corresponding functions and beneficial effects of executing the reservoir space permeability prediction method. For detailed process, please refer to the relevant operations of the reservoir space permeability prediction method in the above embodiment.

[0165] Embodiment 4

[0166] Fig.14 1 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0167] like Fig.14As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0168] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0169] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a reservoir space permeability prediction method.

[0170] In some embodiments, the reservoir space permeability prediction method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the reservoir space permeability prediction method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the reservoir space permeability prediction method in any other appropriate manner (for example, by means of firmware).

[0171] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0172] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0173] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0174] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0175] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0176] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0177] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0178] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

[0179] Embodiment 5

[0180] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the reservoir space permeability prediction method provided in any embodiment of the present application.

[0181] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

Claims

1. A reservoir space permeability prediction method, characterized in that: The method comprises: Determine the first flow unit index and pore structure type of the mercury injection sample according to the core mercury injection test curve, and determine the flow unit index range corresponding to different types of pore structures according to the first flow unit index; wherein reservoirs with different types of pore structures represent different flow units; Determine a second flow unit index of the target core sample according to the permeability and porosity of the target core sample, and determine a target pore structure type corresponding to the target core sample according to the second flow unit index; wherein the target core sample has not been subjected to a mercury injection test; Determining a first corresponding relationship between permeability and porosity corresponding to different types of pore structures, and constructing a permeability calculation model corresponding to different types of pore structures based on the first corresponding relationship; Determine a second corresponding relationship between the second flow unit index and the logging curve in the vertical direction, and determine a third flow unit index of the uncored sample according to the second corresponding relationship; Constructing a third corresponding relationship between the third flow unit index and the seismic parameter near the well, inferring the fourth flow unit index of the unsampled reservoir near the well according to the third corresponding relationship, and constructing a three-dimensional space flow unit index prediction model; A three-dimensional space permeability model is constructed based on the three-dimensional space flow unit index prediction model and the permeability calculation model, and the three-dimensional space permeability model is used to predict the reservoir space permeability.

2. The method according to claim 1, characterized in that The method of determining a second flow unit index of the target core sample according to the permeability and porosity of the target core sample, and determining a target pore structure type corresponding to the target core sample according to the second flow unit index, includes: Determining the permeability and porosity of the target core sample, and determining a second flow unit index of the target core sample based on the permeability and porosity; The second flow unit index is matched with the first flow unit index to determine the target pore structure type corresponding to the target core sample.

3. The method according to claim 1, characterized in that: The determining of a first corresponding relationship between permeability and porosity corresponding to different types of pore structures, and constructing a permeability calculation model corresponding to different types of pore structures according to the first corresponding relationship, includes: Determining a first corresponding relationship between the permeability and the porosity of the core sample according to a first flow unit index of the mercury injection sample and a second flow unit index of the target core sample; the core sample includes the target core sample and the mercury injection sample for the core mercury injection experiment; A permeability calculation model corresponding to different types of pore structures is constructed based on the first corresponding relationship.

4. The method according to claim 1, characterized in that: The method of constructing a third corresponding relationship between the third flow unit index and the wellside seismic parameter, inferring the fourth flow unit index of the unsampled reservoir near the well based on the third corresponding relationship, and constructing a three-dimensional space flow unit index prediction model includes: Constructing a third corresponding relationship between the third flow unit index and the wellbore seismic parameters, and determining a fourth flow unit index of the unsampled reservoir beside the well based on the third corresponding relationship and the seismic parameters of the unsampled reservoir beside the well; wherein the wellbore seismic parameters include density, longitudinal and transverse wave velocities, frequency, longitudinal wave impedance, Poisson's ratio, transverse wave impedance, and longitudinal and transverse wave velocity ratio, and the corresponding relationship includes a linear relationship or a nonlinear relationship; constructing a reservoir flow unit index curve according to the second flow unit index and the fourth flow unit index; The flow unit index curve and wellside seismic parameters are used as training samples to construct a three-dimensional space flow unit index prediction model.

5. The method according to claim 4, characterized in that When there is a nonlinear relationship between the third flow unit index and the wellbore seismic parameters, the flow unit index curve and the wellbore seismic parameters are used as training samples to construct a three-dimensional space flow unit index prediction model, including: A cross-check error algorithm is used to determine target seismic parameters that match the flow unit index curve in a preset regression mode from the wellbore seismic parameters; The target earthquake parameter and the flow unit index curve are used as training samples, and a probabilistic neural network is used to train a three-dimensional space flow unit index prediction model to obtain a first three-dimensional space flow unit index prediction model; The seismic parameters of sampled adjacent wells are used to verify the first three-dimensional spatial flow unit index prediction model and adjust the parameters to determine the second three-dimensional spatial flow unit index prediction model.

6. The method according to claim 1, characterized in that The three-dimensional space permeability model is constructed based on the three-dimensional space flow unit index prediction model and the permeability calculation model, and the three-dimensional space permeability model is used to predict the reservoir space permeability, including: According to the second flow unit index of the target core sample and the target permeability calculation model, the corresponding relationship between the flow unit index and the permeability calculation model is determined; According to the corresponding relationship, a three-dimensional space permeability model is constructed on the basis of the three-dimensional space flow unit index prediction model; The three-dimensional spatial permeability model is used to predict the reservoir spatial permeability.

7. A reservoir space permeability prediction device, characterized in that: The device comprises: A mercury injection sample information determination module determines the first flow unit index and pore structure type of the mercury injection sample according to the core mercury injection experimental curve, and determines the flow unit index range corresponding to different types of pore structures according to the first flow unit index; wherein reservoirs with different types of pore structures represent different flow units; A target core sample information determination module is used to determine a second flow unit index of the target core sample according to the permeability and porosity of the target core sample, and determine a target pore structure type corresponding to the target core sample according to the second flow unit index; wherein the target core sample has not been subjected to a mercury injection experiment; A permeability calculation model determination module is used to determine a first corresponding relationship between permeability and porosity corresponding to different types of pore structures, and to construct a permeability calculation model corresponding to different types of pore structures based on the first corresponding relationship; An uncored sample information determination module is used to determine a second corresponding relationship between a second flow unit index and a logging curve in the vertical direction, and determine a third flow unit index of the uncored sample according to the second corresponding relationship; A three-dimensional space flow unit index prediction model construction module is used to construct a third corresponding relationship between the third flow unit index and the wellside seismic parameter, and to reversely infer the fourth flow unit index of the unsampled reservoir beside the well based on the third corresponding relationship and to construct a three-dimensional space flow unit index prediction model; The three-dimensional space permeability model construction module is used to construct a three-dimensional space permeability model based on the three-dimensional space flow unit index prediction model and the permeability calculation model, and use the three-dimensional space permeability model to predict the reservoir space permeability.

8. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the reservoir space permeability prediction method described in any one of claims 1-6.

9. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute the reservoir space permeability prediction method as described in any one of claims 1-6 when executed by a computer processor.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for predicting reservoir spatial permeability according to any one of claims 1 to 6 is implemented.

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