A method, apparatus, equipment and medium for determining reservoir rock types

Abstract

This application discloses a method, apparatus, equipment, and medium for determining reservoir rock types. The method includes: determining reservoir density data and reservoir cross-plot data for each reservoir segment based on seismic data; wherein the reservoir cross-plot data is cross-plot data of reservoir density data and reservoir elastic parameters; determining conventional and unconventional sandstones for each reservoir segment based on a comparison of the reservoir density data with a density threshold; and determining low-permeability sandstones within the unconventional sandstones based on a comparison of the reservoir cross-plot data with a cross-plot data threshold. The technical solution of this application effectively identifies low-permeability sandstone reservoirs, providing strong comprehensive geological support for oilfield development.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of reservoir seismic prediction, and in particular to a reservoir rock type determination method, device, equipment and medium. BACKGROUND

[0002] Low-permeability sandstone mainly includes calcareous siltstone, argillaceous siltstone, and grey siltstone. The identification of low-permeability sandstone and the prediction of favorable zones in low-permeability reservoirs are the basis and key for providing comprehensive geological support for oilfield development.

[0003] Lithology identification and favorable zone prediction are usually achieved by a series of processing and attribute extraction, seismic inversion, etc. on a three-dimensional seismic data volume. Common methods include frequency division attribute technology, well-constrained seismic inversion technology, pattern recognition, and reservoir lateral prediction technology.

[0004] However, low-permeability sandstone is similar to mudstone, siltstone, and coarse sandstone in terms of conventional well logging curves and various elastic parameters, and there is no obvious difference, so that conventional seismic methods cannot effectively identify low-permeability sandstone. SUMMARY

[0005] The present application provides a reservoir rock type determination method, device, equipment and medium, which can effectively identify low-permeability sandstone reservoirs and provide strong comprehensive geological support for oilfield development.

[0006] According to an aspect of the present application, a reservoir rock type determination method is provided, which comprises:

[0007] According to seismic data of the reservoir, reservoir density data of each layer of the reservoir and reservoir crossplot data are determined. The reservoir crossplot data are crossplot data of the reservoir density data and reservoir elastic parameters.

[0008] According to the comparison result of the reservoir density data and a density threshold value, conventional sandstone and unconventional sandstone of each layer of the reservoir are determined.

[0009] According to the comparison result of the reservoir crossplot data of the unconventional sandstone and a crossplot threshold value, low-permeability sandstone in the unconventional sandstone is determined.

[0010] According to another aspect of the present application, a reservoir rock type determination device is provided, which comprises:

[0011] A threshold value determination module is configured to determine reservoir density data of each layer of the reservoir and reservoir crossplot data according to seismic data of the reservoir. The reservoir crossplot data are crossplot data of the reservoir density data and reservoir elastic parameters.

[0012] a conventional sandstone determining module configured to determine conventional sandstone and unconventional sandstone of each interval of the reservoir according to a comparison result of the reservoir density data and a density threshold value;

[0013] a low-permeability sandstone determining module configured to determine low-permeability sandstone in the unconventional sandstone according to a comparison result of the reservoir crossplot data of the unconventional sandstone and a crossplot data threshold value.

[0014] According to another aspect of the present application, an electronic device is provided, which comprises:

[0015] at least one processor; and

[0016] a memory connected to the at least one processor in communication; wherein,

[0017] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the reservoir rock type determining method according to any one of the embodiments of the present application.

[0018] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to perform the reservoir rock type determining method according to any one of the embodiments of the present application when executed by the processor.

[0019] The technical solution of the embodiments of the present application comprises: determining reservoir density data and reservoir crossplot data of each interval of the reservoir according to seismic data of the reservoir; wherein, the reservoir crossplot data is crossplot data of the reservoir density data and reservoir elastic parameters; determining conventional sandstone and unconventional sandstone of each interval of the reservoir according to a comparison result of the reservoir density data and a density threshold value; and determining low-permeability sandstone in the unconventional sandstone according to a comparison result of the reservoir crossplot data of the unconventional sandstone and a crossplot data threshold value. The technical solution of the embodiments of the present application effectively identifies low-permeability sandstone reservoirs, and provides strong comprehensive geological support for oilfield development.

[0020] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0022] Figure 1 is a flow chart of a reservoir rock type determination method according to the first embodiment of the present application;

[0023] Figure 2 is a flow chart of a reservoir rock type determination method according to the second embodiment of the present application;

[0024] Figure 3 is a target well point density histogram of a reservoir rock type determination method according to the second embodiment of the present application;

[0025] Figure 4 is a target well point P-S wave velocity ratio histogram of a reservoir rock type determination method according to the second embodiment of the present application;

[0026] Figure 5 is a target well point crossplot data schematic diagram of a reservoir rock type determination method according to the second embodiment of the present application;

[0027] Figure 6 is a boundary line rotation schematic diagram of a reservoir rock type determination method according to the second embodiment of the present application;

[0028] Figure 7a is a conventional sandstone identification result schematic diagram of a reservoir rock type determination method according to the second embodiment of the present application;

[0029] Figure 7b is a low-permeability sandstone identification result schematic diagram of a reservoir rock type determination method according to the second embodiment of the present application;

[0030] Figure 8 is a structural schematic diagram of a reservoir rock type determination device according to the third embodiment of the present application;

[0031] Figure 9 is a structural schematic diagram of an electronic device implementing a reservoir rock type determination method according to the embodiments of the present application. DETAILED DESCRIPTION

[0032] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0033] It is to be understood that the terms "first", "second", "target", etc. in the description and claims of the application and the above drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0034] Embodiment one

[0035] Figure 1 A flowchart of a reservoir rock type determination method is provided for the first embodiment of the application. The first embodiment of the application can be applicable to identifying low-permeability sandstone. The method can be executed by a reservoir rock type determination device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device with data processing capability. As shown in FIG. 1, the method comprises: Figure 1

[0036] S110, determining reservoir density data of each layer of the reservoir and reservoir crossplot data according to seismic data of the reservoir; wherein the reservoir crossplot data is crossplot data of the reservoir density data and reservoir elastic parameters.

[0037] The reservoir refers to a rock layer with connected pores, allowing oil and gas to be stored and percolated therein. The oil and gas storage capacity of the reservoir is determined by the petrophysical properties of the rock, usually including its porosity and permeability. The porosity determines the size of the storage capacity of the reservoir, and the permeability determines the percolation capacity of the reservoir. The seismic data refers to seismic wave data obtained through seismic exploration. The seismic data can be the propagation data of artificially excited seismic waves in the stratum, and can reflect the properties and structure of the reservoir. Different layers can be divided according to the depth and rock distribution characteristics of the reservoir. The density, wave impedance, elastic parameters, P-S wave velocity ratio and gamma value of each layer of the reservoir can be different. The reservoir density data can reflect the density of the reservoir. The reservoir crossplot data can comprehensively reflect the density of the reservoir and the elastic parameters of the reservoir. The reservoir crossplot data can be represented by the horizontal coordinate representing the density of the reservoir and the vertical coordinate representing the elastic parameters of the reservoir in the coordinate system.

[0038] ​In the embodiments of the present application, the seismic data of the reservoirs reflect the geological information of each layer of the reservoirs. According to the seismic data of the reservoirs, density inversion and elastic parameter inversion of each layer of the reservoirs are performed to determine reservoir density data and reservoir elastic parameters of each layer of the reservoirs. The crossplot data of the reservoirs are obtained by combining the density data and the elastic parameters of each layer of the reservoirs.

[0039] In S120, the conventional sandstone and the unconventional sandstone of each layer of the reservoirs are determined according to the comparison result of the reservoir density data and the density threshold.

[0040] The density threshold can be determined according to actual conditions, and the embodiments of the present application do not limit this. The conventional sandstone mainly includes siltstone, medium-fine sandstone, coarse sandstone, etc. The unconventional sandstone mainly includes low-permeability sandstone, mudstone, etc.

[0041] In the embodiments of the present application, it is difficult to distinguish the conventional sandstone and the unconventional sandstone by using wave impedance, P-S wave velocity ratio, gamma value and elastic parameters, etc. The conventional sandstone and the unconventional sandstone have certain distinguishability in density. Therefore, the density threshold is determined according to the difference in density between the conventional sandstone and the unconventional sandstone, so as to determine the conventional sandstone and the unconventional sandstone of each layer of the reservoirs according to the comparison result of the reservoir density data and the density threshold.

[0042] In S130, the low-permeability sandstone in the unconventional sandstone is determined according to the comparison result of the crossplot data of the unconventional sandstone and the crossplot data threshold.

[0043] The crossplot data threshold can be determined according to actual conditions, and the embodiments of the present application do not limit this. The low-permeability sandstone is also called low-permeability sandstone, which has the characteristics of low permeability and mainly includes calcareous siltstone, argillaceous siltstone, calcareous siltstone, etc.

[0044] In the embodiments of the present application, the conventional sandstone and the unconventional sandstone can be distinguished by S120, but it is difficult to distinguish each component in the unconventional sandstone by using a series of single characteristics such as elastic parameters, wave impedance and gamma value, and it is not easy to distinguish the low-permeability sandstone in the unconventional sandstone. The embodiments of the present application distinguish the low-permeability sandstone by combining the density and the elastic parameter characteristics of the reservoirs through the crossplot data of the reservoirs. Since the crossplot data of the reservoirs of the low-permeability sandstone and the mudstone have certain distinguishability, the crossplot data threshold can be adaptively determined to distinguish the low-permeability sandstone in the unconventional sandstone.

[0045] The technical scheme of the embodiment of the present application comprises: determining reservoir density data of each layer of the reservoir and reservoir crossplot data according to seismic data of the reservoir; wherein the reservoir crossplot data are crossplot data of the reservoir density data and reservoir elastic parameters; determining conventional sandstone and unconventional sandstone of each layer of the reservoir according to a comparison result of the reservoir density data and a density threshold; and determining low-permeability sandstone in the unconventional sandstone according to a comparison result of reservoir crossplot data of the unconventional sandstone and a crossplot data threshold. The technical scheme of the embodiment of the present application effectively identifies the low-permeability sandstone reservoir, and provides strong comprehensive geological support for oilfield development.

[0046] Embodiment two

[0047] Figure 2 A flowchart of a reservoir rock type determination method provided for the embodiment two of the present application is based on the above-described embodiment and is optimized.

[0048] As Figure 2 shown, the method of the embodiment of the present application specifically comprises the following steps:

[0049] S210, determining reservoir density data of each layer of the reservoir and reservoir crossplot data according to seismic data of the reservoir; wherein the reservoir crossplot data are crossplot data of the reservoir density data and reservoir elastic parameters.

[0050] S220, determining well point density data of a rock layer of a target well point and well point crossplot data according to a well logging curve of the target well point; wherein the well point crossplot data are crossplot data of the well point density data and well point elastic parameters.

[0051] The target well point refers to a well point to be analyzed, and the target well point can be several core wells. The well logging data of the target well point can be plotted as a well logging curve. The well point density data can reflect the rock density of the target well point, and the well point density obtained from the well logging curve has high accuracy and is valuable for analysis. The well point crossplot data can reflect the rock density and elastic parameters of the target well point, and the well point crossplot data combine the two angles of density and elastic parameters, and more details of the rock properties of the target well point are reflected, which provides a basis for distinguishing low-permeability sandstone in the unconventional sandstone in the subsequent steps.

[0052] Specifically, the density threshold for distinguishing the conventional sandstone and the unconventional sandstone and the crossplot data threshold for identifying the low-permeability sandstone in the unconventional sandstone can be obtained according to the well logging data, so as to ensure the accuracy. Therefore, several core wells are selected as the target well points, the well point density data of the rock layer of the target well point and the well point crossplot data are determined according to the well logging curve of the target well point. Further, the elastic parameter μ can be calculated through the well logging curve, and the calculation formula is as follows:

[0053]

[0054] Where ρ represents density, with units of g / cm³. 3 DTs represents transverse wave time difference, with units of us / ft or s / m.

[0055] S230, Based on the wellpoint density data, determine the density threshold used to distinguish between conventional and unconventional sandstone.

[0056] For example, Figure 3 This is a density histogram for the target well point, where ρ represents density. Unconventional sandstone includes low-permeability sandstone and mudstone. Since conventional and unconventional sandstones are distinguishable in density, there are differences between the conventional and unconventional sandstones in the well point density data. Based on this, a density threshold for distinguishing between conventional and unconventional sandstones can be determined. Figure 4 For the histogram of the P-wave and S-wave velocity ratios at the target well point, from Figure 4 It can be seen that it is not easy to distinguish between conventional sandstone and unconventional sandstone based on the ratio of longitudinal and transverse wave velocities.

[0057] In this embodiment of the application, optionally, determining a density threshold for distinguishing between conventional and unconventional sandstone based on the wellpoint density data includes: determining the wellpoint density data range corresponding to conventional sandstone and the wellpoint density data range corresponding to unconventional sandstone based on the distribution characteristics of conventional and unconventional sandstone corresponding to different wellpoint density data; and determining the density threshold for distinguishing between conventional and unconventional sandstone based on the wellpoint density data range corresponding to conventional sandstone and the wellpoint density data range corresponding to unconventional sandstone.

[0058] Distribution characteristics refer to the distribution range and quantity of conventional and unconventional sandstones within each range. The wellpoint density data range corresponding to conventional sandstone reflects its density range, and the wellpoint density data range corresponding to unconventional sandstone reflects its density range. For example, if the wellpoint density data ranges for conventional and unconventional sandstones do not overlap, a specific value within the range between these two ranges can be used as the density threshold to distinguish between conventional and unconventional sandstones, such as the midpoint of that range. If the wellpoint density data ranges for conventional and unconventional sandstones overlap, the density threshold can be determined based on the proportion of conventional and unconventional sandstones in the overlapping portion. For example,... Figure 3 As shown, the density threshold for distinguishing between conventional and unconventional sandstone can be 2.284 g / cm³. 3 .

[0059] S240, Based on the well point intersection data, determine the intersection data threshold for identifying low-permeability sandstone in unconventional sandstone.

[0060] Specifically, well point cross-plot data includes two factors: density and elastic parameters. Low-permeability sandstones in unconventional sandstones differ from other lithologies in well point cross-plot data. The cross-plot data threshold can be determined to identify low-permeability sandstones in unconventional sandstones.

[0061] In this embodiment of the application, optionally, determining a cross-connection data threshold for identifying low-permeability sandstone in unconventional sandstone based on the well point cross-connection data includes: determining the distribution density of low-permeability sandstone and other lithologies in unconventional sandstone based on the well point cross-connection data; determining a boundary line for distinguishing low-permeability sandstone from other lithologies based on the distribution density; and determining the cross-connection data threshold based on the boundary line.

[0062] Among them, the distribution density can reflect the distribution of low-permeability sandstone and other lithologies. If there is more low-permeability sandstone or other lithologies corresponding to the data range of a certain well point, then the distribution density of the sandstone at the corresponding location is greater.

[0063] Specifically, in well point intersection data, the quantity of low-permeability sandstone corresponding to a unit density is determined as the distribution density, or the quantity of low-permeability sandstone corresponding to a unit elastic parameter is determined as the distribution density, or the quantity of low-permeability sandstone in a unit region composed of a unit density and a unit elastic parameter is determined as the distribution density. Unit regions with distribution densities greater than a threshold are merged to obtain the main distribution area of ​​low-permeability sandstone, and the boundary between the main distribution area of ​​low-permeability sandstone and the main distribution areas of other rocks is taken as the dividing line.

[0064] In this embodiment, mudstone is used as an example of other lithologies. Obviously, any rock dissimilar to low-permeability sandstone can be considered as other lithologies, and this embodiment does not limit the definition of other lithologies. For example, well point intersection data is placed in a Cartesian coordinate system, such as... Figure 5 As shown, the distribution density of low-permeability sandstone is distinguishable from that of mudstone. Based on the distribution density, a boundary line is determined to distinguish low-permeability sandstone from other lithologies. This boundary line can be the threshold of the intersection data.

[0065] In one feasible embodiment, wellpoint intersection data such as Figure 5 As shown, the median value of the elastic parameters of low-permeability sandstone and mudstone at the same density is used as the coordinate point. The coordinate points at each density value are traversed, and the coordinate points where the elastic parameters of low-permeability sandstone are less than those of mudstone are removed. The remaining coordinate points are fitted into a straight line, which is the dividing line. The threshold of the intersection data is determined based on the dividing line.

[0066] In one feasible embodiment, the boundary line input by the operator can be directly obtained, and the intersection data threshold can be determined based on the boundary line.

[0067] S250, Based on the comparison results between the reservoir density data and the density threshold, determine the conventional and unconventional sandstone in each layer of the reservoir.

[0068] S260, Based on the comparison results of reservoir cross-connection data and cross-connection data thresholds of unconventional sandstone, the low-permeability sandstone in the unconventional sandstone is determined.

[0069] In a feasible embodiment, the low-permeability sandstone in the unconventional sandstone is determined based on the positional relationship between reservoir cross-hatching data and cross-hatching data thresholds. Specifically, the boundary line can be represented as: μ = kρ + b, where μ is the elastic parameter, ρ is the density, k is the slope of the boundary line, and b is the intercept of the boundary line. Substituting the reservoir density data from the reservoir cross-hatching data into the boundary line expression, if the result is less than or equal to the elastic parameter corresponding to the reservoir density data, the low-permeability sandstone in the unconventional sandstone corresponding to the reservoir cross-hatching data is determined.

[0070] In this embodiment of the application, optionally, the intersection data is plotted in a two-dimensional coordinate system; determining the intersection data threshold based on the boundary line includes: rotating the wellpoint intersection data until the slope of the boundary line is zero, and using the value of the vertical coordinate of the boundary line as the intersection data threshold; or, rotating the wellpoint intersection data until the slope of the boundary line does not exist, and using the value of the horizontal coordinate of the boundary line as the intersection data threshold.

[0071] In this plan, such as Figure 5 As shown, if the boundary line is not parallel to the coordinate axes, the well point intersection data can be rotated to make the boundary line parallel to the coordinate axes, such as... Figure 6 As shown, the boundary line with a slope of zero or no slope is obtained, and then the cross-plot data threshold is determined. This scheme determines the boundary line with a slope of zero or no slope by rotating the well point cross-plot data, so that the obtained cross-plot data threshold is the ordinate or abscissa of the boundary line. In the subsequent identification of low-permeability sandstone in unconventional sandstone, the relationship between the rotated reservoir cross-plot data and the cross-plot data threshold can be used to quickly determine whether it is a low-permeability sandstone.

[0072] In this embodiment of the application, optionally, a preset rotation angle is determined based on the slope of the rotated boundary line.

[0073] Specifically, the preset rotation angle is the angle between the boundary line before rotation and the boundary line after rotation. The preset rotation angle is adaptively determined based on whether the slope of the boundary line after rotation is zero or does not exist.

[0074] In this embodiment of the application, optionally, determining low-permeability sandstone in the unconventional sandstone based on the comparison result of reservoir cross-connection data and cross-connection data threshold includes: rotating the reservoir cross-connection data corresponding to the unconventional sandstone by a preset rotation angle; if the slope of the boundary line is zero, then determining the low-permeability sandstone in the unconventional sandstone based on the comparison result of the vertical coordinate value of the rotated reservoir cross-connection data and the cross-connection data threshold; or, rotating the reservoir cross-connection data corresponding to the unconventional sandstone by a preset rotation angle; if the slope of the boundary line does not exist, then determining the low-permeability sandstone in the unconventional sandstone based on the comparison result of the horizontal coordinate value of the rotated reservoir cross-connection data and the cross-connection data threshold.

[0075] In this embodiment, the reservoir cross-section data corresponding to unconventional sandstone is rotated according to a preset rotation angle. The rotation formula is as follows:

[0076]

[0077] Where: ρ represents density, unit: g / cm³ 3 μ represents the elastic parameter in GPa, θ represents the preset rotation angle in radians, and x and y represent the coordinates of the ρ-μ coordinates after rotation, which are dimensionless.

[0078] Furthermore, if the slope of the rotated boundary line is zero, and the threshold of the intersection data is denoted as y0, then the low-permeability sandstone in the unconventional sandstone can be determined by comparing the value of the vertical axis of the rotated reservoir intersection data with y0; if the slope of the rotated boundary line does not exist, and the threshold of the intersection data is denoted as x0, then the low-permeability sandstone in the unconventional sandstone can be determined by comparing the value of the horizontal axis of the rotated reservoir intersection data with x0.

[0079] This scheme improves the efficiency of identifying and determining low-permeability sandstone by rotating the coordinates so that the comparison process between the reservoir cross-section data and the cross-section data threshold only requires comparing the cross-section data threshold with the horizontal or vertical coordinates of the rotated reservoir cross-section data, without having to compare density and elastic parameters separately.

[0080] In this embodiment of the application, the conventional sandstone identification results are as follows: Figure 7a As shown, Well A is a target well point, and HJ530 and other identifiers are geological strata names. The low-permeability sandstone identification results are as follows: Figure 7bAs shown, Well A is a target well point, and HJ530 and other identifiers are geological stratification names. It is evident that in the process of plotting the low-permeability sandstone identification results, because the reservoir cross-section data corresponding to unconventional sandstones is rotated according to a preset rotation angle, the comparison process between the reservoir cross-section data and the cross-section data threshold only requires comparing one data point. In the process of characterizing the low-permeability sandstone identification results, based on the comparison results between the rotated reservoir cross-section data and the cross-section data threshold at each actual location, it is possible to quickly determine whether the corresponding location in the low-permeability sandstone identification result map is low-permeability sandstone.

[0081] The technical solution of this application embodiment determines the well point density data and well point cross-plot data of the target well point strata through the logging curve of the target well point; based on the well point density data and well point cross-plot data, it determines a density threshold for distinguishing between conventional and unconventional sandstone and a cross-plot data threshold for identifying low-permeability sandstone in unconventional sandstone; based on the comparison result of the reservoir density data and the density threshold, it determines the conventional and unconventional sandstone in each layer of the reservoir; based on the comparison result of the reservoir cross-plot data of unconventional sandstone and the cross-plot data threshold, it determines the low-permeability sandstone in the unconventional sandstone. This technical solution addresses the problem that conventional seismic inversion cannot effectively identify low-permeability sandstone in the prediction of favorable zones of low-permeability reservoirs. It fully leverages the advantages of cross-analysis of wellpoint elastic parameters and density to achieve a fine characterization of conventional and low-permeability sandstone in the target layer. The identification effect of low-permeability sandstone is good. Conventional sandstone with a thickness greater than 2 meters can be basically identified from the density inversion results, with a relative error in the predicted thickness of less than 20%. Most of the low-permeability sandstone with a thickness greater than 2 meters can be identified, with a relative error in the predicted thickness of less than 25%. It has broad application prospects.

[0082] In this embodiment, the density ρ and elastic parameter μ of each reservoir segment need to be obtained through seismic inversion. Regarding the seismic inversion problem, given seismic data at a point in space, the elements of the model space that generated the data are calculated, expressed by the formula:

[0083] F -1 [e] = m;

[0084] Where: m represents the model, F represents the mapping relationship, and e represents the observed seismic data. In traditional wave impedance inversion, m represents the wave impedance in three-dimensional space, and e represents the observed seismic data.

[0085] In this embodiment, sensitive logging curves (density ρ and elastic parameter μ) of conventional sandstone, low-permeability sandstone and mudstone are identified by each layer. Well point density ρ and elastic parameter μ are used as constraints. Through well-constrained seismic inversion method, three-dimensional data volumes of ρ and μ can be obtained from the three-dimensional seismic data volume, thereby realizing the identification of low-permeability sandstone in each layer in three-dimensional space.

[0086] In this embodiment of the application, based on the seismic data of the reservoir, the reservoir density data of each segment of the reservoir is determined, and the density curves of the target well points of each segment of the reservoir and the elastic parameter μ calculated from the logging curves are used as well point constraints for the reservoir cross-hatching data. This utilizes the well-constrained seismic inversion method.

[0087] The basic steps of well-constrained seismic inversion include A1-A5:

[0088] Step A1: Establish the initial model.

[0089] Based on the similarity of seismic waveforms and spatial distribution distance, all wells were sorted according to their correlation, and effective sample wells were selected. The common structural information of their logging curves was extracted as the initial model.

[0090] Step A2: Determine the distribution of the prior probability.

[0091] By analyzing well logging data, the prior probability distribution Pm(m) based on the initial formation model parameters is statistically determined, where m represents the model parameters.

[0092] Step A3: Establish the likelihood function.

[0093] The initial model is matched with the seismic wave impedance and the maximum likelihood function Lm(m) is calculated.

[0094] Step A4: Determine the posterior probability distribution.

[0095] Within the Bayesian framework, the objective function is determined by combining the likelihood function and prior probability:

[0096] F(m) = kPm(m)Lm(m);

[0097] Where F(m) is the posterior probability distribution and k is the probability normalization factor.

[0098] Step A5 involves continuously perturbing the model parameters to maximize the posterior probability density function in order to obtain a feasible stochastic implementation.

[0099] Example 3

[0100] Figure 8 This is a schematic diagram of a reservoir rock type determination device provided in Embodiment 3 of this application. This device can execute the reservoir rock type determination method provided in any embodiment of the present invention, and possesses the corresponding functional modules and beneficial effects of the method. Figure 8 As shown, the device includes:

[0101] The threshold determination module 310 is used to determine the reservoir density data and reservoir cross-hatching data of each segment of the reservoir based on the seismic data of the reservoir; wherein, the reservoir cross-hatching data is the cross-hatching data of the reservoir density data and the reservoir elastic parameters.

[0102] The conventional sandstone determination module 320 is used to determine the conventional and unconventional sandstone in each layer of the reservoir based on the comparison results between the reservoir density data and the density threshold.

[0103] The low-permeability sandstone determination module 330 is used to determine the low-permeability sandstone in the unconventional sandstone based on the comparison results of reservoir cross-connection data and cross-connection data thresholds.

[0104] Optionally, the device further includes:

[0105] The target well point threshold determination module is used to determine the well point density data of the rock formation at the target well point, as well as the well point intersection data, based on the logging curve of the target well point; wherein, the well point intersection data is the intersection data of the well point density data and the well point elastic parameters;

[0106] The density threshold determination module is used to determine a density threshold for distinguishing between conventional and unconventional sandstone based on the well point density data.

[0107] The intersection data threshold determination module is used to determine the intersection data threshold for identifying low-permeability sandstone in unconventional sandstone based on the well point intersection data.

[0108] Optionally, the density threshold determination module includes:

[0109] The density data interval determination unit is used to determine the well point density data interval corresponding to conventional sandstone and the well point density data interval corresponding to unconventional sandstone based on the distribution characteristics of conventional and unconventional sandstone corresponding to different well point density data.

[0110] The density threshold determination unit is used to determine the density threshold used to distinguish between conventional and unconventional sandstones based on the wellpoint density data range corresponding to conventional sandstones and the wellpoint density data range corresponding to unconventional sandstones.

[0111] Optionally, the intersection data threshold determination module includes:

[0112] The distribution density determination unit is used to determine the distribution density of low-permeability sandstone and other lithologies in unconventional sandstone based on the well point intersection data.

[0113] Boundary line determination unit, used to determine the boundary line that distinguishes low-permeability sandstone from other lithologies based on distribution density;

[0114] The intersection data threshold determination unit is used to determine the intersection data threshold based on the boundary line.

[0115] Furthermore, the intersection data is plotted in a two-dimensional coordinate system;

[0116] Optionally, the rendezvous data threshold determination unit includes:

[0117] The intersection data rotation subunit is used to rotate the well point intersection data until the slope of the boundary line is zero, and use the value of the vertical coordinate of the boundary line as the intersection data threshold; or, rotate the well point intersection data until the slope of the boundary line does not exist, and use the value of the horizontal coordinate of the boundary line as the intersection data threshold.

[0118] Optionally, the device further includes:

[0119] The preset rotation angle determination module is used to determine the preset rotation angle based on the slope of the dividing line after rotation.

[0120] Optionally, the low-permeability sandstone identification module 330 includes:

[0121] The reservoir intersection data rotation unit is used to rotate the reservoir intersection data corresponding to unconventional sandstones according to a preset rotation angle.

[0122] A low-permeability sandstone identification unit is used to determine the low-permeability sandstone in the unconventional sandstone by comparing the value of the ordinate of the rotated reservoir cross-hatching data with the cross-hatching data threshold if the slope of the boundary line is zero; or...

[0123] Rotate the reservoir cross-section data corresponding to unconventional sandstone according to a preset rotation angle;

[0124] If the slope of the boundary line does not exist, the low-permeability sandstone in the unconventional sandstone is determined by comparing the value of the abscissa of the rotated reservoir intersection data with the threshold of the intersection data.

[0125] The reservoir rock type determination device provided in this application embodiment can execute the reservoir rock type determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0126] Example 4

[0127] Figure 9A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device 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 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0128] like Figure 9 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0129] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0130] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as reservoir rock type determination methods.

[0131] In some embodiments, the reservoir rock type determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the reservoir rock type determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the reservoir rock type determination method by any other suitable means (e.g., by means of firmware).

[0132] Various embodiments 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-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0133] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0134] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0135] To provide interaction with a user, the systems and techniques described herein can 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 pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0136] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0137] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0138] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0139] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method of reservoir rock type determination, characterized by, The method comprises: According to the seismic data of the reservoir, the reservoir density data of each layer of the reservoir is determined, and the reservoir crossplot data is determined; wherein, the reservoir crossplot data is the crossplot data of the reservoir density data and the reservoir elastic parameters; According to the well logging curve of the target well point, the well point crossplot data of the rock layer of the target well point is determined; wherein, the well point crossplot data is the crossplot data of the well point density data and the well point elastic parameters; According to the well point crossplot data, the crossplot data threshold value for identifying the low permeability sandstone in the unconventional sandstone is determined; According to the comparison result of the reservoir density data and the density threshold value, the conventional sandstone and the unconventional sandstone of each layer of the reservoir are determined; According to the comparison result of the reservoir crossplot data of the unconventional sandstone and the crossplot data threshold value, the low permeability sandstone in the unconventional sandstone is determined; According to the well point crossplot data, the distribution density of the low permeability sandstone and other rock types in the unconventional sandstone is determined; the distribution density can reflect the distribution of the low permeability sandstone and other rock types, and the more the low permeability sandstone corresponding to the well point crossplot data range, the greater the distribution density of the sandstone at the corresponding position; According to the distribution density, the demarcation line for distinguishing the low permeability sandstone and other rock types is determined; The crossplot data threshold value is determined according to the demarcation line. Before the step of determining the conventional sandstone and the unconventional sandstone of each layer of the reservoir according to the comparison result of the reservoir density data and the density threshold value, the method further comprises:

2. The method of claim 1, wherein, According to the well logging curve of the target well point, the well point density data of the rock layer of the target well point is determined; According to the well point density data, the density threshold value for distinguishing the conventional sandstone and the unconventional sandstone is determined. According to the well point density data, the density threshold value for distinguishing the conventional sandstone and the unconventional sandstone is determined, which comprises:

3. The method of claim 2, wherein, According to the distribution characteristics of the conventional sandstone and the unconventional sandstone corresponding to different well point density data, the well point density data interval corresponding to the conventional sandstone and the well point density data interval corresponding to the unconventional sandstone are determined; According to the well point density data interval corresponding to the conventional sandstone and the well point density data interval corresponding to the unconventional sandstone, the density threshold value for distinguishing the conventional sandstone and the unconventional sandstone is determined. The crossplot data is plotted in a two-dimensional coordinate system; 4. The method of claim 1, wherein, According to the demarcation line to determine the crossplot data threshold value, which comprises: The well point crossplot data is rotated until the slope of the demarcation line is zero, and the value of the ordinate of the demarcation line is taken as the crossplot data threshold value; Or, The well point crossplot data is rotated until the slope of the demarcation line does not exist, and the value of the abscissa of the demarcation line is taken as the crossplot data threshold value. The method further comprises:

5. The method of claim 4, wherein, According to the slope of the rotated demarcation line, a preset rotation angle is determined. According to the comparison result of the reservoir crossplot data of the unconventional sandstone and the crossplot data threshold value, the low permeability sandstone in the unconventional sandstone is determined, which comprises:

6. The method of claim 5, wherein, The reservoir crossplot data corresponding to the unconventional sandstone is rotated by a preset rotation angle; ​ If the slope of the demarcation line is zero, then according to the value of the longitudinal coordinate of the reservoir crossplot data after rotation, and the comparison result of the crossplot data threshold, the low-permeability sandstone in the unconventional sandstone is determined; or, The reservoir crossplot data corresponding to the unconventional sandstone is rotated according to a preset rotation angle; If the slope of the demarcation line does not exist, then the value of the longitudinal coordinate of the reservoir crossplot data after rotation, and the comparison result of the crossplot data threshold, are used to determine the low-permeability sandstone in the unconventional sandstone.

7. A reservoir rock category determining apparatus characterized by comprising: The device comprises: A threshold determination module configured to determine reservoir density data of each layer of a reservoir and reservoir crossplot data according to seismic data of the reservoir; wherein the reservoir crossplot data is crossplot data of the reservoir density data and reservoir elastic parameters; A conventional sandstone determination module configured to determine conventional sandstone and unconventional sandstone of each layer of the reservoir according to a comparison result of the reservoir density data and a density threshold; A low-permeability sandstone determination module configured to determine low-permeability sandstone in the unconventional sandstone according to a comparison result of the reservoir crossplot data of the unconventional sandstone and a crossplot data threshold; The device further comprises: A target well point threshold determination module configured to determine well point crossplot data of a target well point formation according to a well logging curve of the target well point; wherein the well point crossplot data is crossplot data of well point density data and well point elastic parameters; A crossplot data threshold determination module configured to determine a crossplot data threshold for identifying low-permeability sandstone in the unconventional sandstone according to the well point crossplot data; The crossplot data threshold determination module comprises: A distribution density determination unit configured to determine distribution density of low-permeability sandstone and other lithologies in the unconventional sandstone according to the well point crossplot data; the distribution density can reflect the distribution of the low-permeability sandstone and other lithologies, and the more the low-permeability sandstone in the well point crossplot data range, the greater the distribution density of the sandstone at the corresponding position; A demarcation line determination unit configured to determine a demarcation line for distinguishing the low-permeability sandstone and other lithologies according to the distribution density; A crossplot data threshold determination unit configured to determine the crossplot data threshold according to the demarcation line.

8. An electronic device, comprising: The electronic device comprises: At least one processor; and A memory connected in communication with the at least one processor; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the reservoir rock type determination method in any one of claims 1-6.

9. A computer readable storage medium characterized by, The computer readable storage medium stores computer instructions for enabling the processor to execute the reservoir rock type determination method in any one of claims 1-6 when executed.

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