Reservoir fluid identification method and device, storage medium and processor

Abstract

The invention relates to the field of oil and gas exploration and development, and discloses a reservoir fluid identification method and device, a storage medium and a processor, and the identification method comprises the steps: obtaining a deep resistivity value and a density value of a water layer in a target reservoir, and obtaining a deep resistivity value and a density value of a gas layer in the target reservoir; determining a target curve from a plurality of natural gamma-ray spectrum logging curves corresponding to the target reservoir; the deep resistivity value and the density value of the water layer are corrected based on the target curve, and the deep resistivity value and the density value of the gas layer are corrected based on the target curve; and establishing a target chart based on the corrected deep resistivity value and density value of the water layer and the corrected deep resistivity value and density value of the gas layer, and identifying the water layer and the gas layer in the target reservoir based on the target chart. Based on the scheme provided by the embodiment of the invention, the difference between the water layer and the gas layer in the chart can be increased, so that the gas layer and the water layer can be distinguished easily.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, specifically to a reservoir fluid identification method, a reservoir fluid identification device, a machine-readable storage medium, and a processor. Background Technology

[0002] In ultra-deep oil and gas exploration, due to the deep burial of reservoirs and high formation pressure, the clay minerals in mudstone are subjected to long-term compaction and diagenesis, and the water in pores and interlayers is discharged, resulting in mudstone exhibiting logging response characteristics such as high resistivity and high density.

[0003] In existing technologies, the distinction between gas and water layers in a reservoir is typically based on deep resistivity-density cross plots, utilizing the significant difference in deep resistivity values ​​between the two. However, for argillaceous sandstone reservoirs containing the aforementioned high-resistivity mudstone (referred to as high-resistivity argillaceous reservoirs), these reservoirs usually have a high mud content, and clay minerals fill the pores, narrowing or even blocking the throats, leading to increased water resistivity. Consequently, the difference in deep resistivity values ​​between gas and water layers is not significant, making it difficult to distinguish between gas and water layers in this type of reservoir using existing deep resistivity-density cross plots. Summary of the Invention

[0004] The purpose of this invention is to overcome the problem in the prior art that it is difficult to distinguish between gas layers and water layers in reservoirs containing high-resistivity mud, and to provide a reservoir fluid identification method, device, storage medium and processor.

[0005] To achieve the above objectives, the present invention provides a reservoir fluid identification method, comprising:

[0006] Obtain the deep resistivity and density values ​​of the water layer in the target reservoir, and obtain the deep resistivity and density values ​​of the gas layer in the target reservoir;

[0007] The target curve is determined from multiple natural gamma spectral logging curves corresponding to the target reservoir;

[0008] The deep resistivity and density values ​​of the water layer are corrected based on the target curve, and the deep resistivity and density values ​​of the gas layer are corrected based on the target curve.

[0009] Based on the corrected deep resistivity and density values ​​of the water layer and the gas layer, a target map is established, and the water layer and gas layer in the target reservoir are identified based on the target map.

[0010] In this embodiment of the application, obtaining the deep resistivity and density values ​​of the water layer in the target reservoir includes:

[0011] Obtain multiple deep resistivity values ​​and multiple density values ​​of the water layer in the target reservoir, wherein the multiple deep resistivity values ​​and multiple density values ​​of the water layer correspond one-to-one;

[0012] The acquisition of the deep resistivity and density values ​​of the gas layer in the target reservoir includes:

[0013] Multiple deep resistivity values ​​and multiple density values ​​of the gas layer in the target reservoir are obtained, and the multiple deep resistivity values ​​and multiple density values ​​of the gas layer correspond one-to-one.

[0014] In this embodiment of the application, the multiple natural gamma ray spectral logging curves include the total natural gamma ray spectral logging curve, the uranium-free gamma ray spectral logging curve, the thorium gamma ray spectral logging curve, the uranium gamma ray spectral logging curve, and the potassium gamma ray spectral logging curve.

[0015] In this embodiment of the application, determining the target curve from multiple natural gamma ray spectral logging curves corresponding to the target reservoir includes:

[0016] Obtain X-ray diffraction whole-rock mineral experimental data corresponding to the target reservoir, wherein the X-ray diffraction whole-rock mineral experimental data includes the reference clay content of the target reservoir;

[0017] Determine the clay content calculation formula corresponding to each natural gamma ray spectral logging curve, and calculate the first clay content corresponding to each natural gamma ray spectral logging curve based on the clay content calculation formula.

[0018] The content of each first clay is compared with the reference clay content, and the target curve is determined from the multiple natural gamma ray spectral logging curves based on the comparison results.

[0019] In this embodiment of the application, before calculating the first clay content corresponding to each natural gamma ray spectral logging curve based on the formula for calculating each clay content, the identification method further includes:

[0020] The X-ray diffraction whole-rock mineral experimental data and the multiple natural gamma ray spectral logging curves were used to determine the reservoir depth.

[0021] In this embodiment of the application, determining the target curve from the plurality of natural gamma ray spectral logging curves based on the comparison results includes:

[0022] The natural gamma ray spectral logging curve corresponding to the first clay content with the smallest error compared to the reference clay content is taken as the target curve.

[0023] In this embodiment of the application, the correction of the deep resistivity and density values ​​of the water layer based on the target curve, and the correction of the deep resistivity and density values ​​of the gas layer based on the target curve, include:

[0024] Determine a first functional relationship between the deep resistivity values ​​of the water layer and the deep resistivity values ​​of the gas layer and the element content values ​​in the target curve; and determine a second functional relationship between the density values ​​of the water layer and the density values ​​of the gas layer and the element content values ​​in the target curve.

[0025] The deep resistivity values ​​of the water layer and the gas layer are corrected based on the first functional relationship, and the density values ​​of the water layer and the gas layer are corrected based on the second functional relationship.

[0026] A second aspect of the present invention provides a reservoir fluid identification device, comprising:

[0027] The acquisition module is used to acquire the deep resistivity and density values ​​of the water layer in the target reservoir, and the deep resistivity and density values ​​of the gas layer in the target reservoir.

[0028] The screening module is used to determine the target curve from multiple natural gamma spectral logging curves corresponding to the target reservoir;

[0029] The correction module is used to correct the deep resistivity and density values ​​of the water layer based on the target curve, and to correct the deep resistivity and density values ​​of the gas layer based on the target curve.

[0030] The identification module is used to establish a target map based on the corrected deep resistivity and density values ​​of the water layer and the corrected deep resistivity and density values ​​of the gas layer, and to identify the water layer and gas layer in the target reservoir based on the target map.

[0031] A third aspect of this application provides a processor configured to perform the above-described reservoir fluid identification method.

[0032] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned reservoir fluid identification method.

[0033] The above technical solution includes: obtaining the deep resistivity and density values ​​of the water layer and the gas layer in the target reservoir; determining a target curve from multiple natural gamma-ray spectral logging curves corresponding to the target reservoir; correcting the deep resistivity and density values ​​of the water layer and the gas layer based on the target curve; establishing a target map based on the corrected deep resistivity and density values ​​of the water layer and the gas layer; and identifying the water layer and gas layer in the target reservoir based on the target map. The solution provided by this application increases the difference between the water layer and the gas layer on the map, thereby making it easier to distinguish between them.

[0034] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0035] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0036] Figure 1 The schematic diagram illustrates a flow chart of a reservoir fluid identification method according to an embodiment of this application;

[0037] Figure 2 The illustration shows the first clay content curve calculated from each natural gamma ray spectroscopy logging curve according to the embodiments of this application, and a schematic diagram of the reference clay content curve in the X-ray diffraction whole-rock mineral experimental data.

[0038] Figure 3 This schematic diagram illustrates the error analysis results between the first clay content curves and X-ray diffraction whole-rock mineral experiment clay content data according to embodiments of this application.

[0039] Figure 4 This illustration schematically shows a first functional relationship between the deep resistivity value and the thorium content value in the target curve according to an embodiment of this application;

[0040] Figure 5 This illustration schematically shows a second functional relationship between the density value and the thorium content value in the target curve according to an embodiment of this application;

[0041] Figure 6 A schematic diagram of a target plate according to an embodiment of this application is shown;

[0042] Figure 7 This schematic diagram illustrates a structural block diagram of a reservoir fluid identification device according to an embodiment of this application;

[0043] Figure 8 The diagram illustrates the internal structure of a computer device according to an embodiment of this application.

[0044] Explanation of reference numerals in the attached figures

[0045] 210 - Acquisition module; 220 - Filtering module; 230 - Correction module; 240 - Identification module; A01 - Processor; A02 - Network interface; A03 - Internal memory; A04 - Display screen; A05 - Input device; A06 - Non-volatile storage medium; B01 - Operating system; B02 - Computer program. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0047] It should be noted that if any directional indications (such as up, down, left, right, front, back, etc.) are involved in the embodiments of this application, these directional indications are only used to explain the relative positional relationships and movement of the components in a specific posture (as shown in the attached figures). If the specific posture changes,

[0048] The directional indication will then change accordingly.

[0049] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0050] As described in the background section, in ultra-deep oil and gas exploration, due to the deep burial of reservoirs and high formation pressure, clay minerals (including kaolinite, illite, montmorillonite, chlorite, etc.) in mudstone undergo long-term compaction and diagenesis, causing water to be expelled from pores and interlayers, resulting in mudstone exhibiting high resistivity and high density logging response characteristics. In existing technologies, the distinction between gas and water layers in a reservoir is generally based on a deep resistivity-density cross-plot, utilizing the significant difference in deep resistivity values ​​between the gas and water layers. However, for argillaceous sandstone reservoirs containing the aforementioned high-resistivity mudstone (referred to as high-resistivity argillaceous reservoirs), these reservoirs typically have a high mud content, and clay minerals fill the pores, narrowing or even blocking the throats, thus leading to increased water layer resistivity. Therefore, the difference in deep resistivity values ​​between gas and water layers is not significant, making it difficult to distinguish between gas and water layers in this type of reservoir using existing deep resistivity-density cross plots.

[0051] To address this, one embodiment of this application provides a reservoir fluid identification method that can be used to distinguish between gas and water layers in reservoirs containing highly resistive clay. For example... Figure 1 As shown, the reservoir fluid identification method may include the following steps:

[0052] Step 101: Obtain the deep resistivity and density values ​​of the water layer in the target reservoir, and obtain the deep resistivity and density values ​​of the gas layer in the target reservoir.

[0053] The target reservoir can be the aforementioned high-resistivity mud-bearing reservoir. In practical applications, the high-resistivity mud-bearing reservoir can be identified based on the oil testing data of the study area. Then, the deep resistivity and density values ​​of the water layer and the gas layer in the high-resistivity mud-bearing reservoir can be obtained.

[0054] Regarding the acquisition of deep resistivity and density values ​​of water layers:

[0055] In this embodiment, step 101, obtaining the deep resistivity and density values ​​of the water layer in the target reservoir, may include: obtaining multiple deep resistivity and density values ​​of the water layer in the target reservoir, wherein the multiple deep resistivity and density values ​​correspond one-to-one, and one set of deep resistivity and density values ​​corresponds to one depth layer. That is, when obtaining the deep resistivity and density values ​​of the water layer, multiple sets of data can be obtained, each set of data including one deep resistivity value and one density value, and each set of data corresponds to one depth layer, with each set of data corresponding to a different depth layer.

[0056] Furthermore, to improve the accuracy of subsequent target maps, in one embodiment, the plurality of deep resistivity values ​​include target deep resistivity values, the plurality of density values ​​include target density values, and the target deep resistivity values ​​and the target density values ​​correspond to a target depth layer; wherein, the target deep resistivity values ​​and the target density values ​​are the deep resistivity value and density value in any one of the plurality of data sets; the reservoir fluid identification method provided in this application embodiment may further include: selecting a characteristic deep resistivity value from a plurality of original deep resistivity values ​​corresponding to the target depth layer as the target deep resistivity value, and selecting a characteristic density value from a plurality of original density values ​​corresponding to the target depth layer as the target density value.

[0057] In existing technologies, reservoirs are typically divided into multiple depth layers. For a water layer at a specific target depth (e.g., a depth of A meters to B meters), the oil testing data generally includes multiple raw deep resistivity values ​​and multiple raw density values. In practice, a characteristic deep resistivity value can be selected from these multiple raw deep resistivity values ​​as the target deep resistivity value for that target depth layer, and a characteristic density value can be selected from these multiple raw density values ​​as the target density value for that target depth layer.

[0058] In the above embodiments regarding obtaining the deep resistivity and density values ​​of the water layer, the principle for selecting the characteristic deep resistivity value can be: the characteristic deep resistivity value is neither the maximum nor the minimum among the plurality of original deep resistivity values. Similarly, the principle for selecting the characteristic density value can be: the characteristic density value is neither the maximum nor the minimum among the plurality of original density values.

[0059] Regarding the acquisition of deep resistivity and density values ​​of gas layers:

[0060] Similar to the water layer, step 101, obtaining the deep resistivity and density values ​​of the gas layer in the target reservoir, may include: obtaining multiple deep resistivity and density values ​​of the gas layer in the target reservoir, wherein the multiple deep resistivity and density values ​​correspond one-to-one, and one set of deep resistivity and density values ​​corresponds to one depth layer. That is, when obtaining the deep resistivity and density values ​​of the gas layer, multiple sets of data can be obtained, each set of data including one deep resistivity value and one density value, and each set of data corresponds to one depth layer, with each set of data corresponding to a different depth layer.

[0061] Furthermore, to improve the accuracy of subsequent target maps, in one embodiment, the plurality of deep resistivity values ​​include target deep resistivity values, the plurality of density values ​​include target density values, and the target deep resistivity values ​​and the target density values ​​correspond to a target depth layer; wherein, the target deep resistivity values ​​and the target density values ​​are the deep resistivity value and density value in any one of the plurality of data sets; the reservoir fluid identification method provided in this application embodiment may further include: selecting a characteristic deep resistivity value from a plurality of original deep resistivity values ​​corresponding to the target depth layer as the target deep resistivity value, and selecting a characteristic density value from a plurality of original density values ​​corresponding to the target depth layer as the target density value.

[0062] In existing technologies, reservoirs are typically divided into multiple depth layers. For a gas layer at a specific target depth layer (e.g., a depth of A meters to B meters), the oil testing data generally includes multiple raw deep resistivity values ​​and multiple raw density values. In practice, a characteristic deep resistivity value can be selected from these multiple raw deep resistivity values ​​as the target deep resistivity value for that target depth layer, and a characteristic density value can be selected from these multiple raw density values ​​as the target density value for that target depth layer.

[0063] In the above embodiments regarding obtaining the deep resistivity and density values ​​of the gas layer, the principle for selecting the characteristic deep resistivity value can be: the characteristic deep resistivity value is neither the maximum nor the minimum among the plurality of original deep resistivity values. Similarly, the principle for selecting the characteristic density value can be: the characteristic density value is neither the maximum nor the minimum among the plurality of original density values.

[0064] In practical applications, for water layers in a target reservoir, it is possible to obtain as many sets of deep resistivity and density values ​​as possible; similarly, for gas layers in a target reservoir, it is possible to obtain as many sets of deep resistivity and density values ​​as possible. This will make the boundary between water and gas layers in the subsequent target map clearer, making it easier to distinguish and identify the two layers.

[0065] Furthermore, after obtaining the deep resistivity and density values ​​of the water layer and the gas layer in the target reservoir in step 101, points corresponding to each set of deep resistivity and density values ​​of the water layer and the gas layer can be plotted on a coordinate system with density as the abscissa and deep resistivity as the ordinate to obtain an initial "deep resistivity-density" cross-plot for comparison with the subsequent target plot. Because the water layer exhibits high resistivity, the boundary between the water layer and the gas layer is not obvious and difficult to distinguish in this initial "deep resistivity-density" cross-plot.

[0066] Step 102: Determine the target curve from multiple natural gamma spectral logging curves corresponding to the target reservoir.

[0067] The multiple natural gamma ray spectral logging curves may include a total natural gamma ray spectral logging curve, a uranium-free gamma ray spectral logging curve, a thorium gamma ray spectral logging curve, a uranium gamma ray spectral logging curve, and a potassium gamma ray spectral logging curve.

[0068] In this embodiment of the application, step 102, which determines the target curve from multiple natural gamma ray spectral logging curves corresponding to the target reservoir, may include steps one and two, as follows:

[0069] Step 1: Obtain X-ray diffraction whole-rock mineral experimental data corresponding to the target reservoir, including the reference clay content of the target reservoir.

[0070] In practical applications, the above X-ray diffraction whole-rock mineral experimental data can be compared with multiple natural gamma ray spectroscopy logging curves to determine the reservoir depth, thereby improving the accuracy of the comparison results in the subsequent step three.

[0071] Step 2: Determine the clay content calculation formula corresponding to each natural gamma ray spectral logging curve, and calculate the first clay content corresponding to each natural gamma ray spectral logging curve based on the clay content calculation formula.

[0072] The formulas for calculating the clay content corresponding to the total natural gamma ray spectral logging curve, the uranium-free gamma ray spectral logging curve, the thorium gamma ray spectral logging curve, the uranium gamma ray spectral logging curve, and the potassium gamma ray spectral logging curve can be shown in the following formulas (1), (2), (3), (4), and (5):

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] In the above formula, SH1, SH2, SH3, SH4, and SH5 represent the first clay content corresponding to the total natural gamma (GR) energy spectrum logging curve, the uranium-free gamma (KTH) energy spectrum logging curve, the thorium gamma (TH) energy spectrum logging curve, the uranium gamma (U) energy spectrum logging curve, and the potassium gamma (K) energy spectrum logging curve, respectively; ΔGR, ΔKTH, ΔTH, ΔU, and ΔK represent the relative values ​​of natural gamma, uranium-free gamma, thorium gamma, uranium gamma, and potassium gamma for a certain calculation interval, respectively. The relative value is calculated as: (current logging value - minimum logging value) / (maximum logging value - minimum logging value).

[0079] Step 3: Compare the contents of each first clay with the reference clay contents, and determine the target curve from the multiple natural gamma ray spectral logging curves based on the comparison results.

[0080] In this embodiment of the application, the target curve is determined from the plurality of natural gamma ray spectral logging curves based on the comparison results. Specifically, it may include: taking the natural gamma ray spectral logging curve corresponding to the first clay content with the smallest error with the reference clay content as the target curve.

[0081] In practical applications, step three above can be performed in different well sections. Taking a specific calculation section as an example, the first clay content calculated based on the clay content calculation formula in that calculation section can be compared with the reference clay content corresponding to that calculation section in the X-ray diffraction whole-rock mineral experimental data. Based on the comparison results, the target curve corresponding to that calculation section can be determined from the multiple natural gamma ray spectral logging curves. That is, step three can be performed in each calculation section.

[0082] It is understandable that by using the natural gamma ray spectral logging curve corresponding to the first clay content with the smallest error to the reference clay content as the target curve, the curve that can most accurately calculate the true clay content of the reservoir can be determined from the multiple natural gamma ray spectral logging curves.

[0083] Step 103: Correct the deep resistivity and density values ​​of the water layer based on the target curve, and correct the deep resistivity and density values ​​of the gas layer based on the target curve.

[0084] In this embodiment of the application, correcting the deep resistivity and density values ​​of the water layer based on the target curve, and correcting the deep resistivity and density values ​​of the gas layer based on the target curve, may include steps ① and ②, as follows:

[0085] Step ①: Determine the first functional relationship between the deep resistivity values ​​of the water layer and the deep resistivity values ​​of the gas layer and the element content values ​​in the target curve, and determine the second functional relationship between the density values ​​of the water layer and the density values ​​of the gas layer and the element content values ​​in the target curve.

[0086] It is understandable that there is a depth layer correspondence between the deep resistivity values ​​of water layers, the deep resistivity values ​​of gas layers, and the elemental content values ​​in the target curve. That is, there are multiple deep resistivity values ​​for water layers, multiple deep resistivity values ​​for gas layers, and multiple elemental content values. These multiple deep resistivity values ​​for water layers, multiple deep resistivity values ​​for gas layers, and multiple elemental content values ​​correspond one-to-one. A set of deep resistivity values ​​for water layers, deep resistivity values ​​for gas layers, and elemental content values ​​corresponds to one depth layer, and each set of data can correspond to a different depth layer.

[0087] In practical implementation, element content can be used as the horizontal axis and deep resistivity as the vertical axis. Each point can be plotted on a coordinate system based on its coordinates (element content value, deep resistivity value). Then, it can be...

[0088] By fitting data to each point, the first functional relationship is obtained.

[0089] Similarly, there is a depth layer correspondence between the density values ​​of the water layer, the density values ​​of the gas layer, and the element content values ​​corresponding to the target curve. That is, there are multiple density values ​​of water layers, multiple density values ​​of gas layers, and multiple element content values. The density values ​​of multiple water layers, multiple density values ​​of multiple gas layers, and multiple element content values ​​correspond one-to-one. A set of density values ​​of water layers, density values ​​of gas layers, and element content values ​​corresponds to a depth layer. Each set of data can correspond to a different depth layer.

[0090] In practical implementation, element content can be used as the x-axis and density as the y-axis. Points can be plotted on a coordinate system based on their coordinates (element content value, density value). Then, a fitting function can be performed based on these points to obtain the second functional relationship.

[0091] Generally speaking, as the element content increases, the deep resistivity and density also increase. However, depending on the different trends of increase, different functional relationships may be presented, such as linear relationship, logarithmic relationship, exponential relationship, etc.

[0092] Step ②: Correct the deep resistivity values ​​of the water layer and the gas layer based on the first functional relationship, and correct the density values ​​of the water layer and the gas layer based on the second functional relationship.

[0093] In this embodiment of the application, when correcting the deep resistivity values ​​of the water layer and the gas layer based on the first functional relationship, the correction method can be determined according to the type of the first functional relationship.

[0094] For example, when the first functional relationship is a linear function, the deep resistivity values ​​of the water layer and the gas layer can be directly divided by the corresponding element content values ​​to correct their values. Taking a point in a coordinate system (with element content as the x-axis and deep resistivity as the y-axis) as an example, the deep resistivity value at that point can be divided by the element content value to correct its value.

[0095] When the first functional relationship is a logarithmic function, such as a function containing lnx (where x is the element content value), the deep resistivity values ​​of the water layer and the gas layer can be divided by the corresponding logarithmic part, i.e., divided by lnx, to correct the deep resistivity values ​​of the water layer and the gas layer. Taking a point in a coordinate system (where element content is the x-axis and deep resistivity is the y-axis) as an example, the deep resistivity value at that point can be divided by the logarithmic part of the element content at that point, thereby correcting the deep resistivity value at that point.

[0096] When the first function relation is an exponential function, for example, containing x aThe function (where x is the element content value and a is the exponent) can be used to divide the deep resistivity values ​​of water layers and gas layers by the corresponding exponent, i.e., divide by x. a This is to correct the deep resistivity values ​​of both water and gas layers. Taking a point in a coordinate system (with element content as the horizontal axis and deep resistivity as the vertical axis) as an example, the deep resistivity value at that point can be divided by the exponential element content at that point, thereby correcting the deep resistivity value at that point.

[0097] Similarly, when correcting the density values ​​of the water layer and the air layer based on the second functional relationship, the correction method can be determined according to the type of the second functional relationship.

[0098] For example, when the second functional relationship is a linear function, the density values ​​of the water layer and the gas layer can be directly divided by the corresponding element content values ​​to correct their density values. Taking a point in a coordinate system (with element content as the horizontal axis and density as the vertical axis) as an example, the density value at that point can be divided by the element content value at that point to correct its density value.

[0099] When the second functional relationship is a logarithmic function, such as a function containing lnx (where x is the elemental content value), the density values ​​of the water layer and the gas layer can be divided by the corresponding logarithmic part, i.e., divided by lnx, to correct the density values ​​of the water layer and the gas layer. Taking a point in a coordinate system (where elemental content is the x-axis and density is the y-axis) as an example, the density value at that point can be divided by the logarithmic part of the elemental content at that point, thereby correcting the density value at that point.

[0100] When the second function relation is an exponential function, for example, containing x a The function (where x is the elemental content value and a is the exponent) can be used to divide the density values ​​of the water layer and the air layer by the corresponding exponent, i.e., divide by x. a This is done to correct the density values ​​of the water layer and the air layer. Taking a point in a coordinate system (with element content as the horizontal axis and density as the vertical axis) as an example, the density value at that point can be divided by the exponential element content at that point, thereby correcting the density value at that point.

[0101] Step 104: Based on the corrected deep resistivity and density values ​​of the water layer and the corrected deep resistivity and density values ​​of the gas layer, establish a target map, and identify the water layer and gas layer in the target reservoir based on the target map.

[0102] In the embodiments of this application, the horizontal axis of the target pattern is related to density, and the vertical axis of the target pattern is related to deep resistivity.

[0103] The horizontal axis of the target map corresponds to the correction method for the density values ​​of the water and air layers. For example, when the correction method is to directly divide the density values ​​of the water and air layers by the corresponding elemental content values, the horizontal axis of the target map can be calculated as density divided by elemental content (density / elemental content). When the correction method is to divide the density values ​​of the water and air layers by the logarithm of the elemental content, the horizontal axis of the target map can be calculated as density divided by the logarithm of the elemental content. When the correction method is to divide the density values ​​of the water and air layers by the exponential part of the elemental content, the horizontal axis of the target map can be calculated as density divided by the exponential part of the elemental content.

[0104] Similarly, the vertical axis of the target map corresponds to the correction method for the deep resistivity values ​​of the water and gas layers. For example, when the correction method for the deep resistivity values ​​of the water and gas layers is to directly divide the deep resistivity values ​​of the water and gas layers by the corresponding element content values, the vertical axis of the target map can be calculated as deep resistivity divided by element content, i.e., deep resistivity / element content. When the correction method for the deep resistivity values ​​of the water and gas layers is to divide the deep resistivity values ​​of the water and gas layers by the logarithmic part of the element content, the vertical axis of the target map can be calculated as deep resistivity divided by the logarithmic part of the element content. When the correction method for the deep resistivity values ​​of the water and gas layers is to divide the deep resistivity values ​​of the water and gas layers by the exponential part of the element content, the vertical axis of the target map can be calculated as deep resistivity divided by the exponential part of the element content.

[0105] Once the x-coordinate and y-coordinate of the target map are determined, the points corresponding to the water layer and gas layer can be plotted on the coordinate system to obtain the target map, which can also be called the fluid identification map of the high-resistivity silt reservoir. Then, based on the distribution of points of the water layer and gas layer in the target map, the water layer and gas layer are distinguished and identified.

[0106] It is understood that the reservoir fluid identification method provided in this application includes: obtaining the deep resistivity and density values ​​of the water layer and the gas layer in the target reservoir; determining a target curve from multiple natural gamma-ray spectral logging curves corresponding to the target reservoir; correcting the deep resistivity and density values ​​of the water layer and the gas layer based on the target curve; establishing a target map based on the corrected deep resistivity and density values ​​of the water layer and the gas layer; and identifying the water layer and gas layer in the target reservoir based on the target map. The solution provided in this application increases the difference between the water layer and the gas layer on the map, thereby making it easier to distinguish between them.

[0107] The reservoir fluid identification method provided in this application will be described below with specific examples. It should be understood that the following examples are only some specific implementation methods and do not imply an improper limitation of the solution in this application.

[0108] Example 1

[0109] Taking a deep gas reservoir in western China as an example, the mudstone in this reservoir exhibits high resistivity and high density logging response characteristics. The process of establishing the target chart is as follows:

[0110] Step 1): Based on the oil testing data of the gas reservoir, obtain the deep resistivity and density values ​​of the water layer and the gas layer. Each data set includes one deep resistivity value and one density value, for a total of 64 data sets. All obtained deep resistivity and density values ​​are characteristic deep resistivity and density values, respectively. The initial deep resistivity-density cross-plot shows that the water layer exhibits high resistivity characteristics, and the deep resistivity values ​​are not significantly different from those of the gas layer. The boundary between the water and gas layers is indistinct and difficult to distinguish.

[0111] Step 2) Compare the X-ray diffraction whole-rock mineral experimental data from the core well of the gas reservoir with the natural gamma-ray spectroscopy.

[0112] The well logging curves are used to locate the reservoir depth, and the first clay content corresponding to each natural gamma ray spectrum well logging curve is calculated based on the clay content calculation formulas corresponding to each natural gamma ray spectrum well logging curve (as shown in the previous formulas (1)-(5)).

[0113] The total natural gamma ray spectral logging curves, uranium-free gamma ray spectral logging curves, thorium gamma ray spectral logging curves, uranium gamma ray spectral logging curves, and potassium gamma ray spectral logging curves, along with the first clay content curves SH1, SH2, SH3, SH4, and SH5 calculated from each natural gamma ray spectral logging curve, and the reference clay content curve from the X-ray diffraction whole-rock mineral experimental data, are shown below. Figure 2 As shown.

[0114] Error analysis was performed on the five clay content curves (SH1, SH2, SH3, SH4, SH5) and the clay content data from the X-ray diffraction whole-rock mineral experiments. The results are as follows: Figure 3 And as shown in Table 1 below. Based on the analysis results, the thorium gamma ray spectral logging curve was selected as the target curve.

[0115] Table 1 Error Analysis Results

[0116] curve <![CDATA[SH1]]> <![CDATA[SH2]]> <![CDATA[SH3]]> <![CDATA[SH4]]> <![CDATA[SH5]]> average error 23.03％ 18.71％ 12.15％ 24.43％ 20.01％

[0117] Step 3), the first functional relationship between the deep resistivity values ​​of the water layer and the gas layer and the thorium content value in the target curve can be as follows: Figure 4 As shown, the deep resistivity value is directly proportional to the thorium content value; the second functional relationship between the density values ​​of the water layer and the gas layer and the thorium content value in the target curve can be expressed as follows: Figure 5 As shown, the density value is proportional to ln(thorium).

[0118] Step 4) Divide the deep resistivity values ​​of the water layer and the gas layer by the thorium content to correct the deep resistivity values ​​of the water layer and the gas layer; divide the density values ​​of the water layer and the gas layer by ln(thorium) to correct the density values ​​of the water layer and the gas layer.

[0119] Step 5): Using deep resistivity divided by thorium content (i.e., deep resistivity / thorium) as the ordinate and density divided by ln(thorium) (i.e., density / ln(thorium)) as the abscissa, construct a cross-plot, i.e., the target plot, as shown below. Figure 6 As shown. By Figure 6 As can be seen, the water layer and the gas layer have a clear boundary, making them easy to identify and distinguish. Furthermore, compared with the initial "deep resistivity-density" cross-plot, the plot accuracy improved from 79.68% to 96.87%. The accuracy rate refers to the proportion of oil test results indicating a gas layer falling within the gas layer region.

[0120] Based on the same inventive concept, such as Figure 7 As shown, Figure 7 This schematically illustrates a structural block diagram of a reservoir fluid identification device according to an embodiment of the present application. In one embodiment, a reservoir fluid identification device 200 is provided, including an acquisition module 210, a filtering module 220, a correction module 230, and an identification module 240, wherein:

[0121] The acquisition module 210 is used to acquire the deep resistivity and density values ​​of the water layer in the target reservoir, and to acquire the deep resistivity and density values ​​of the gas layer in the target reservoir.

[0122] The screening module 220 is used to determine the target curve from multiple natural gamma ray spectral logging curves corresponding to the target reservoir;

[0123] The correction module 230 is used to correct the deep resistivity and density values ​​of the water layer based on the target curve, and to correct the deep resistivity and density values ​​of the gas layer based on the target curve.

[0124] The identification module 240 is used to establish a target map based on the corrected deep resistivity and density values ​​of the water layer and the corrected deep resistivity and density values ​​of the gas layer, and to identify the water layer and gas layer in the target reservoir based on the target map.

[0125] In one embodiment, the acquisition module 210 is used to acquire multiple deep resistivity values ​​and multiple density values ​​of a water layer in a target reservoir, wherein the multiple deep resistivity values ​​and multiple density values ​​of the water layer correspond one-to-one; and to acquire multiple deep resistivity values ​​and multiple density values ​​of a gas layer in a target reservoir, wherein the multiple deep resistivity values ​​and multiple density values ​​of the gas layer correspond one-to-one.

[0126] In one embodiment, the multiple natural gamma ray spectral logging curves include a total natural gamma ray spectral logging curve, a uranium-free gamma ray spectral logging curve, a thorium gamma ray spectral logging curve, a uranium gamma ray spectral logging curve, and a potassium gamma ray spectral logging curve.

[0127] In one embodiment, the screening module 220 is used to obtain X-ray diffraction whole-rock mineral experimental data corresponding to the target reservoir, wherein the X-ray diffraction whole-rock mineral experimental data includes the reference viscosity of the target reservoir.

[0128] Soil content;

[0129] Determine the clay content calculation formula corresponding to each natural gamma ray spectral logging curve, and calculate the first clay content corresponding to each natural gamma ray spectral logging curve based on the clay content calculation formula.

[0130] The content of each first clay is compared with the reference clay content, and the target curve is determined from the multiple natural gamma ray spectral logging curves based on the comparison results.

[0131] In one embodiment, before calculating the first clay content corresponding to each natural gamma ray spectral logging curve based on each clay content calculation formula, the screening module 220 performs reservoir depth relocation between the X-ray diffraction whole-rock mineral experimental data and the multiple natural gamma ray spectral logging curves.

[0132] In one embodiment, the screening module 220 is used to select the natural gamma spectral logging curve corresponding to the first clay content with the smallest error to the reference clay content as the target curve.

[0133] In one embodiment, the correction module 230 is used to determine a first functional relationship between the deep resistivity values ​​of the water layer and the deep resistivity values ​​of the gas layer and the element content values ​​in the target curve, and to determine a second functional relationship between the density values ​​of the water layer and the density values ​​of the gas layer and the element content values ​​in the target curve.

[0134] The deep resistivity values ​​of the water layer and the gas layer are corrected based on the first functional relationship, and the density values ​​of the water layer and the gas layer are corrected based on the second functional relationship.

[0135] The reservoir fluid identification device includes a processor and a memory. The acquisition module 210, screening module 220, correction module 230 and identification module 240 are all stored in the memory as program units. The processor executes the program modules stored in the memory to implement the corresponding functions.

[0136] A processor contains a core, which retrieves the corresponding program unit from memory. One or more cores can be configured, and by adjusting the core parameters, fast and efficient computation can be achieved at the entire chip scale.

[0137] Memory may include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM.

[0138] The memory includes at least one memory chip.

[0139] This application provides a machine-readable storage medium storing a program that, when executed by a processor, implements the above-described reservoir fluid identification method.

[0140] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown in the figure, the computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the computer program is executed by the processor A01, it implements a reservoir fluid identification method. The display screen A04 can be a liquid crystal display (LCD) or an e-ink display. The input device A05 can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0141] Those skilled in the art will understand that Figure 8The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0142] In one embodiment, the reservoir fluid identification device provided in this application can be implemented as a computer program, which can be implemented in the form of, for example... Figure 8 The computer device shown runs on this system. The computer device's memory can store the various program modules that make up the intelligent scheduling device for this construction task, for example... Figure 7 The diagram shows an acquisition module 210, a filtering module 220, a correction module 230, and an identification module 240. The computer program comprised of these modules causes a processor to execute the steps in the reservoir fluid identification methods of the various embodiments of this application described in this specification.

[0143] Figure 8 The computer device shown can be used as follows Figure 7 The acquisition module 210, screening module 220, correction module 230 and identification module 240 in the reservoir fluid identification device shown execute the method.

[0144] This application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps:

[0145] Obtain the deep resistivity and density values ​​of the water layer in the target reservoir, and obtain the deep resistivity and density values ​​of the gas layer in the target reservoir;

[0146] The target curve is determined from multiple natural gamma spectral logging curves corresponding to the target reservoir;

[0147] The deep resistivity and density values ​​of the water layer are corrected based on the target curve, and the deep resistivity and density values ​​of the gas layer are corrected based on the target curve.

[0148] Based on the corrected deep resistivity and density values ​​of the water layer and the gas layer, a target map is established, and the water layer and gas layer in the target reservoir are identified based on the target map.

[0149] In one embodiment, obtaining the deep resistivity and density values ​​of the water layer in the target reservoir includes:

[0150] Obtain multiple deep resistivity values ​​and multiple density values ​​of the water layer in the target reservoir, wherein the multiple deep resistivity values ​​and multiple density values ​​of the water layer correspond one-to-one;

[0151] The acquisition of the deep resistivity and density values ​​of the gas layer in the target reservoir includes:

[0152] Multiple deep resistivity values ​​and multiple density values ​​of the gas layer in the target reservoir are obtained, and the multiple deep resistivity values ​​and multiple density values ​​of the gas layer correspond one-to-one.

[0153] In one embodiment, the multiple natural gamma ray spectral logging curves include a total natural gamma ray spectral logging curve, a uranium-free gamma ray spectral logging curve, a thorium gamma ray spectral logging curve, a uranium gamma ray spectral logging curve, and a potassium gamma ray spectral logging curve.

[0154] In one embodiment, determining the target curve from multiple natural gamma-ray spectral logging curves corresponding to the target reservoir includes:

[0155] Obtain X-ray diffraction whole-rock mineral experimental data corresponding to the target reservoir, wherein the X-ray diffraction whole-rock data...

[0156] Mineral experimental data include the reference clay content of the target reservoir;

[0157] Determine the clay content calculation formula corresponding to each natural gamma ray spectral logging curve, and calculate the first clay content corresponding to each natural gamma ray spectral logging curve based on the clay content calculation formula.

[0158] The content of each first clay is compared with the reference clay content, and the target curve is determined from the multiple natural gamma ray spectral logging curves based on the comparison results.

[0159] In one embodiment, before calculating the first clay content corresponding to each natural gamma ray spectral logging curve based on the clay content calculation formula, the identification method further includes:

[0160] The X-ray diffraction whole-rock mineral experimental data and the multiple natural gamma ray spectral logging curves were used to determine the reservoir depth.

[0161] In one embodiment, determining the target curve from the plurality of natural gamma-ray spectral logging curves based on the comparison results includes:

[0162] The natural gamma ray spectral logging curve corresponding to the first clay content with the smallest error compared to the reference clay content is taken as the target curve.

[0163] In one embodiment, the correction of the deep resistivity and density values ​​of the water layer based on the target curve, and the correction of the deep resistivity and density values ​​of the gas layer based on the target curve, include:

[0164] Determine a first functional relationship between the deep resistivity values ​​of the water layer and the deep resistivity values ​​of the gas layer and the element content values ​​in the target curve; and determine a second functional relationship between the density values ​​of the water layer and the density values ​​of the gas layer and the element content values ​​in the target curve.

[0165] The deep resistivity values ​​of the water layer and the gas layer are corrected based on the first functional relationship, and the density values ​​of the water layer and the gas layer are corrected based on the second functional relationship.

[0166] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0167] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0168] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0169] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0170] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0171] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0172] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0173] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0174] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for identifying reservoir fluids, characterized in that, The identification method includes: Obtain the deep resistivity and density values ​​of the water layer in the target reservoir, and obtain the deep resistivity and density values ​​of the gas layer in the target reservoir; The target curve is determined from multiple natural gamma spectral logging curves corresponding to the target reservoir; The deep resistivity and density values ​​of the water layer are corrected based on the target curve, and the deep resistivity and density values ​​of the gas layer are corrected based on the target curve. Based on the corrected deep resistivity and density values ​​of the water layer and the gas layer, a target map is established, and the water layer and gas layer in the target reservoir are identified based on the target map.

2. The reservoir fluid identification method according to claim 1, characterized in that, The acquisition of the deep resistivity and density values ​​of the water layer in the target reservoir includes: Obtain multiple deep resistivity values ​​and multiple density values ​​of the water layer in the target reservoir, wherein the multiple deep resistivity values ​​and multiple density values ​​of the water layer correspond one-to-one; The acquisition of the deep resistivity and density values ​​of the gas layer in the target reservoir includes: Multiple deep resistivity values ​​and multiple density values ​​of the gas layer in the target reservoir are obtained, and the multiple deep resistivity values ​​and multiple density values ​​of the gas layer correspond one-to-one.

3. The reservoir fluid identification method according to claim 1, characterized in that, The multiple natural gamma ray spectrum logging curves include the total natural gamma ray spectrum logging curve, the uranium-free gamma ray spectrum logging curve, the thorium gamma ray spectrum logging curve, the uranium gamma ray spectrum logging curve, and the potassium gamma ray spectrum logging curve.

4. The reservoir fluid identification method according to claim 1, characterized in that, Determining the target curve from multiple natural gamma ray spectral logging curves corresponding to the target reservoir includes: Obtain X-ray diffraction whole-rock mineral experimental data corresponding to the target reservoir, wherein the X-ray diffraction whole-rock mineral experimental data includes the reference clay content of the target reservoir; Determine the clay content calculation formula corresponding to each natural gamma ray spectral logging curve, and calculate the first clay content corresponding to each natural gamma ray spectral logging curve based on the clay content calculation formula. The content of each first clay is compared with the reference clay content, and the target curve is determined from the multiple natural gamma ray spectral logging curves based on the comparison results.

5. The reservoir fluid identification method according to claim 4, characterized in that, Before calculating the first clay content corresponding to each natural gamma ray spectral logging curve based on the formula for calculating each clay content, the identification method further includes: The X-ray diffraction whole-rock mineral experimental data and the multiple natural gamma ray spectral logging curves were used to determine the reservoir depth.

6. The reservoir fluid identification method according to claim 4, characterized in that, The determination of the target curve from the multiple natural gamma ray spectral logging curves based on the comparison results includes: The natural gamma ray spectral logging curve corresponding to the first clay content with the smallest error compared to the reference clay content is taken as the target curve.

7. The reservoir fluid identification method according to claim 1, characterized in that, The correction of the deep resistivity and density values ​​of the water layer based on the target curve, and the correction of the deep resistivity and density values ​​of the gas layer based on the target curve, include: Determine a first functional relationship between the deep resistivity values ​​of the water layer and the deep resistivity values ​​of the gas layer and the element content values ​​in the target curve; and determine a second functional relationship between the density values ​​of the water layer and the density values ​​of the gas layer and the element content values ​​in the target curve. The deep resistivity values ​​of the water layer and the gas layer are corrected based on the first functional relationship, and the density values ​​of the water layer and the gas layer are corrected based on the second functional relationship.

8. A reservoir fluid identification device, characterized in that, include: The acquisition module is used to acquire the deep resistivity and density values ​​of the water layer in the target reservoir, and the deep resistivity and density values ​​of the gas layer in the target reservoir. The screening module is used to determine the target curve from multiple natural gamma spectral logging curves corresponding to the target reservoir; The correction module is used to correct the deep resistivity and density values ​​of the water layer based on the target curve, and to correct the deep resistivity and density values ​​of the gas layer based on the target curve. The identification module is used to establish a target map based on the corrected deep resistivity and density values ​​of the water layer and the corrected deep resistivity and density values ​​of the gas layer, and to identify the water layer and gas layer in the target reservoir based on the target map.

9. A processor, characterized in that, It is configured to perform the reservoir fluid identification method according to any one of claims 1 to 7.

10. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the reservoir fluid identification method according to any one of claims 1 to 7.

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