A well logging method for identifying a medium-high porosity dry layer

Abstract

The present application relates to oil and gas field exploration and development technical field, especially relate to a kind of well logging discrimination method of medium-high porosity dry layer, comprising the following steps: calculating acoustic porosity;Establish acoustic porosity-resistivity crossplot, wherein, X axis is acoustic porosity, Y axis is resistivity, and both are logarithmic coordinate system;Different acoustic porosity interval is divided, and the distribution area boundary 1 of medium-high porosity dry layer is divided according to the interval on acoustic porosity-resistivity crossplot;The distribution area boundary 2 of medium-high porosity dry layer is divided on acoustic porosity-resistivity crossplot;According to boundary 1 and boundary 2, the distribution area of medium-high porosity dry layer is determined on acoustic porosity-resistivity crossplot.By this method, the problems of time-consuming, labor-intensive and high cost can be effectively solved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field exploration and development technology, and in particular to a logging method for identifying medium- and high-porosity dry formations. Background Technology

[0002] Medium- to high-porosity carbonate reservoirs, characterized by poor specular pore size, intragranular pore size, and poor pore connectivity, are a typical type of complex oil and gas reservoir. They often exhibit medium- to high porosity and resistivity, making them difficult to distinguish from oil-bearing layers using conventional logging data. This results in significant waste of funds and time in perforation, acid fracturing, and oil testing for these layers. Medium- to high-porosity reservoirs include medium-porosity and high-porosity reservoirs. The standard for calculating oil and gas reserves, DZ / T 0217-2005, defines medium-porosity reservoirs as those with a matrix porosity greater than or equal to 5% and less than 10% in non-clastic rocks, and high-porosity reservoirs as those with a porosity greater than 10%.

[0003] For the identification of the aforementioned medium- and high-porosity carbonate reservoirs, current technical methods include: first, conducting experiments, including thin section analysis, mercury intrusion porosimetry, and nuclear magnetic resonance analysis; second, acquiring new technical data, such as nuclear magnetic resonance and MDT sampling; and third, direct testing.

[0004] In the prior art, a Chinese invention patent document with publication number CN109374757A and publication date of February 22, 2019, was proposed. The technical solution disclosed in this patent document is as follows: A method for evaluating the effectiveness of igneous reservoirs using quantitative processing of acoustic wave amplitude: performing array acoustic logging within a depth range; calculating the amplitude of the array acoustic logging data using the root mean square method; obtaining the ratio of the calculated amplitude values ​​of the effective reservoir section to the ineffective reservoir section under the same lithological conditions in the same well; calculating the amplitude productivity index using the effective thickness indicated by the Stoneley wave and shear wave amplitude ratios; establishing a reservoir effectiveness evaluation chart based on the amplitude ratio using the Stoneley wave amplitude ratio and the shear wave amplitude ratio; dividing the reservoir effectiveness into three intervals according to the amplitude productivity index, the Stoneley wave amplitude ratio, and the shear wave amplitude ratio, namely, Class I reservoir area, Class II reservoir area, and Class III reservoir area; processing the single-well acoustic logging data, and making a rapid prediction of reservoir effectiveness and productivity based on the area where the processing results are located in the chart.

[0005] In the prior art, a Chinese invention patent document with publication number CN106126936A and publication date of November 16, 2016 has been proposed. The technical solution disclosed in this patent document is as follows: a comprehensive evaluation method for the effectiveness of fractures in tight low-permeability reservoirs, the method comprising: calculating the single-factor index corresponding to the fracture effectiveness factor, and calculating the fracture effectiveness index based on the index.

[0006] In the prior art, a Chinese invention patent document with publication number CN102200008A and publication date of September 28, 2011, was proposed. The technical solution disclosed in this patent document is as follows: a reservoir effectiveness identification method based on electro-imaging logging; calculating the porosity distribution spectrum through electro-imaging logging; calculating the mean of the porosity spectrum distribution, the variance of the porosity spectrum morphology change, and the porosity distribution ratio; evaluating the reservoir effectiveness on a two-dimensional plane composed of the mean and variance of the porosity spectrum, and dividing the area into four regions according to the range of the mean and variance distribution of the porosity spectrum; adding the porosity distribution ratio as a third-dimensional information for reservoir effectiveness identification on the basis of the two-dimensional plane composed of the mean and variance of the porosity spectrum, and performing a three-parameter effectiveness evaluation in a three-dimensional space.

[0007] In the prior art, a Chinese invention patent document with publication number CN104314558A and publication date of January 28, 2015 has been proposed. The technical solution disclosed in this patent document is as follows: a method for determining reservoir effectiveness using Stoneley wave energy loss degree, comprising: regarding the Stoneley wave energy extracted from array acoustic logging data as a comprehensive response of the Stoneley wave energy of the formation rock and the degree of Stoneley wave energy loss in the pores, establishing the relationship between Stoneley wave energy and porosity, calculating the Stoneley wave energy loss degree in the pores, and determining the reservoir effectiveness based on the Stoneley wave energy loss degree.

[0008] The above technical solution may encounter the following problems during actual use: None of the above technical solutions propose a method for determining the effectiveness of medium- and high-porosity carbonate reservoirs using conventional logging data. Instead, they often rely on methods such as recording new logging technologies or conducting experiments to evaluate reservoir effectiveness. However, in practice, the vast majority of wells do not record such data, let alone cor the samples. Recording such data would increase operating time and costs. Summary of the Invention

[0009] To address the aforementioned technical problems, this invention proposes a logging identification method for medium- and high-porosity dry reservoirs. This method eliminates the need for additional logging data from other new technologies or coring experiments to identify such medium- and high-porosity carbonate reservoirs, effectively solving the problems of time-consuming, labor-intensive, and costly processes.

[0010] This invention is achieved by adopting the following technical solution: A logging method for identifying medium-to-high porosity dry formations, characterized by the following steps: S1. Calculate acoustic porosity; S2. Establish an acoustic porosity-resistivity cross plot, where the X-axis represents acoustic porosity and the Y-axis represents resistivity, and both are logarithmic coordinate systems; S3. Divide the intervals of different acoustic porosity, and divide the distribution area boundary 1 of the medium and high porosity dry layer on the acoustic porosity-resistivity intersection chart according to the interval; S4. Delineate the distribution area boundary 2 of the medium-high porosity dry layer on the acoustic porosity-resistivity cross plot; S5. Based on boundary 1 and boundary 2, determine the distribution area of ​​the medium-high porosity dry layer on the acoustic porosity-resistivity cross plot.

[0011] The method for calculating acoustic porosity in step S1 is as follows:

[0012] in, For acoustic porosity, For the acoustic transit time of the rock skeleton, For the acoustic transit time of formation fluids, The content of clay in the formation, This is the acoustic compaction correction factor. The sonic transit time logging value is for the target layer.

[0013] In step S3, boundary 1 is the acoustic porosity, which is a constant.

[0014] The step S3 of dividing the intervals of different acoustic porosity specifically refers to dividing the intervals according to the porosity corresponding to the permeability distribution range.

[0015] By establishing a permeability-porosity relationship chart, intervals are divided according to the porosity corresponding to the permeability distribution range.

[0016] When the permeability distribution range is 0.01-0.1 mD, the corresponding porosity distribution range is 7-10%; when the permeability distribution range is 0.1-1 mD, the corresponding porosity distribution range is 10-16%; and when the permeability distribution range is 1-10 mD, the corresponding porosity distribution range is 16-25%.

[0017] The boundary 1 is 7%, 10%, 16%, and 25%.

[0018] The boundary 2 is:

[0019] Among them, S w R represents the water saturation level. w denoted as ρ, where m is the formation water resistivity, n is the porosity index, and a and b are lithology-related coefficients.

[0020] The specific method for determining boundary 2 is as follows: According to Archie's formula: Take the logarithm of both sides:

[0021] make , ,have to:

[0022] Among them, S w R represents the water saturation level. t R is the formation resistivity. w For formation water resistivity, denoted as acoustic porosity, m as porosity index, n as saturation index, and a and b as coefficients related to lithology.

[0023] In step S5, the distribution area of ​​the medium-high porosity dry layer is the upper trapezoidal area of ​​the region enclosed by boundary 1 and boundary 2 within the range of different acoustic porosities.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Compared with the prior art, the objects evaluated in this invention are different. The prior art is mostly aimed at reservoirs with fracture-vuggy structures and complex rock structures, while this application is aimed at medium- and high-porosity carbonate reservoirs.

[0025] 2. This invention comprehensively uses three parameters that are most sensitive to the reservoir's modulus pores, intragranular pores, and pore connectivity to identify medium- to high-porosity dry reservoirs. Instead of using a resistivity-total porosity chart, the three parameters that are most sensitive to non-connected pore reservoirs are simplified and comprehensively reflected on the acoustic porosity-resistivity cross plot.

[0026] Existing technologies often rely on new technology data or experimental data. The direct cost of conducting related experiments and acquiring new technology data for a single well can range from tens of thousands to hundreds of thousands of yuan, and in some cases even reach millions. Compared to existing technologies, this invention makes full use of existing data, saving significant financial, human, and time resources.

[0027] Using this discrimination method, the effectiveness rate can reach over 85% in some blocks. Attached Figure Description

[0028] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments, wherein: Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a schematic diagram of the acoustic porosity-resistivity intersection diagram, boundary 1, and boundary 2 in this invention; Figure 3 This is a schematic diagram of the permeability-porosity relationship in this invention; Figure 4 This is a schematic diagram of the high-porosity dry layer discrimination region in this invention; Figure 5 This is a schematic diagram showing the relationship between the porosity index m and the relative porosity of non-connected structures under different total porosity conditions in this invention. Figure 6 This is a schematic diagram of the simplified rock electrical model and the series-parallel model for resistivity calculation in this invention; Figure 7 This is a schematic diagram comparing the acoustic porosity of the medium-high porosity dry layer with the core porosity and density porosity in this invention. Figure 8 This is a schematic diagram illustrating the variation characteristics of lines with the same water saturation as the value of m increases in this invention. Detailed Implementation

[0029] Example 1 As a basic embodiment of the present invention, the present invention includes a well logging identification method for medium-high porosity dry formations, comprising the following steps: S1. Calculate acoustic porosity.

[0030] S2. Establish an acoustic porosity-resistivity cross plot, where the X-axis represents acoustic porosity and the Y-axis represents resistivity, and both are logarithmic coordinate systems.

[0031] S3. Divide the intervals of different acoustic porosity, and divide the distribution area boundary 1 of the medium and high porosity dry layer on the acoustic porosity-resistivity intersection chart according to the interval.

[0032] S4. Delineate the distribution area boundary 2 of the medium-high porosity dry layer on the acoustic porosity-resistivity cross plot.

[0033] S5. Based on boundary 1 and boundary 2, determine the distribution area of ​​the medium-high porosity dry layer on the acoustic porosity-resistivity cross plot.

[0034] Example 2 As a preferred embodiment of the present invention, the present invention includes a logging identification method for medium-high porosity dry formations, comprising the following steps: S1. Calculate acoustic porosity.

[0035] S2. Establish an acoustic porosity-resistivity cross plot, where the X-axis represents acoustic porosity and the Y-axis represents resistivity, and both are logarithmic coordinate systems.

[0036] S3. Divide the region into intervals with different acoustic porosities, and delineate the distribution boundary 1 of the medium-high porosity dry layer on the acoustic porosity-resistivity cross plot based on these intervals. The functional relationship of boundary 1 is acoustic porosity = constant. Boundary 1 can be obtained by dividing the intervals according to the porosity corresponding to the permeability distribution range.

[0037] S4. Delineate the distribution region boundary 2 of the medium-high porosity dry layer on the acoustic porosity-resistivity cross-plot. Boundary 2 is:

[0038] Among them, S w R represents the water saturation level. w denoted as ρ, where m is the formation water resistivity, n is the porosity index, and a and b are lithology-related coefficients.

[0039] S5. Based on boundary 1 and boundary 2, determine the distribution area of ​​the medium-high porosity dry layer on the acoustic porosity-resistivity cross plot. More specifically, the distribution area of ​​the medium-high porosity dry layer is the upper trapezoidal region within the area enclosed by boundary 1 and boundary 2, within the range of different acoustic porosities.

[0040] Example 3 As the preferred embodiment of the present invention, the present invention includes a well logging discrimination method for medium-high porosity dry reservoirs. This discrimination method is based on the comprehensive application of three parameters: porosity index *m*, rock resistivity, and sonic porosity. Through numerical simulation of porosity index, analysis and research of experimental results, theoretical derivation of resistivity response, theoretical analysis and verification of sonic logging response characteristics, it has been shown that porosity index *m*, resistivity, and sonic porosity are the three most sensitive parameters to carbonate reservoirs characterized by poor granular pore size, intragranular pore size, and poor pore connectivity.

[0041] (1) Refer to the appendix of the instruction manual Figure 5 Analysis of the response of the pore structure index m to reservoirs with poor pore connectivity, including those with modulus pores, intragranular pores, and pore structure shows that: the larger the pore-throat ratio, the larger the value of m; the larger the non-connected porosity, the larger the value of m; and when the non-connected porosity is constant, the larger the porosity, the smaller the value of m.

[0042] (2) Refer to the appendix of the instruction manual. Figure 6 Based on the following derivation of the calculation model for rock resistivity, the calculation results show that the smaller the throat, the higher the formation resistivity. Differences in pore structure cause significant differences in the distribution of current linear density in the pore space. The smaller the diameter of the pore throat, the greater its current density, which is one of the biggest factors affecting rock resistivity. Theoretical derivation results also show that the throat has a great influence on rock resistivity and will significantly increase the rock resistivity.

[0043]

[0044]

[0045]

[0046] In the formula: , , , , respectively indicating the instruction manual attached Figure 6 Mesopore type a, pore type b, resistivity of the framework and rock; , These represent the rock resistivity and the formation water resistivity, respectively. , , , These represent the length and cross-sectional area of ​​pore a and pore b, respectively. The length of the rock is indicated. This indicates the volume of the rock.

[0047] (3) There are many methods for calculating porosity, such as using neutron, density, neutron-density intersection, neutron-acoustic intersection, and density-acoustic intersection methods. This method chooses acoustic porosity instead of other porosity methods because acoustic measurement measures the fastest first wave reaching the acoustic probe. Waves that propagate fastest along the wave velocity are recorded, while waves passing through particle mold pores, intraparticle pores, etc., cannot be recorded. Refer to the appendix of the instruction manual. Figure 7 Well logging data also shows that acoustic porosity is significantly lower than density porosity.

[0048] (4) Refer to the appendix of the instruction manual. Figure 8 On the acoustic porosity-resistivity cross plot, lines with the same water saturation gradually rotate to the right as the m value increases. For reservoirs with the same porosity and water saturation, pore connectivity gradually deteriorates, the m value gradually increases, and the intersection point on the acoustic porosity-resistivity plot increases perpendicularly to the direction of increasing resistivity, gradually transitioning from an effective reservoir to an ineffective reservoir. Therefore, its dry layer distribution area is located above a certain acoustic porosity range.

[0049] Based on the above analysis, it is known that the three parameters most sensitive to carbonate reservoirs with poor pore size, intragranular pore size, and poor pore connectivity are porosity index m, rock resistivity, and acoustic porosity.

[0050] Based on this, please refer to the appendix to the instruction manual. Figure 1 The discrimination method of the present invention specifically includes the following steps: S1. Calculate acoustic porosity. The specific calculation method is as follows:

[0051] in, Acoustic porosity, decimal; , These are the acoustic transit times of the rock skeleton and the acoustic transit times of the formation fluids, respectively. This represents the clay content of the formation, as a decimal. This is the acoustic compaction correction factor; The sonic transit time logging value is for the target layer.

[0052] S2. Refer to the instruction manual appendix. Figure 2 A cross plot of acoustic porosity and resistivity is established, where the X-axis represents acoustic porosity and the Y-axis represents resistivity, and both are logarithmic coordinate systems.

[0053] S3. Divide the intervals of different acoustic porosity, and divide the distribution area boundary 1 of the medium and high porosity dry layer on the acoustic porosity-resistivity intersection chart according to the interval.

[0054] To distinguish between medium and high porosity dry layers, different acoustic porosity ranges are defined. The functional relationship at boundary 1 is acoustic porosity = constant. (Refer to the appendix in the instruction manual.) Figure 3 A permeability-porosity relationship chart can be established to divide the data into intervals based on the porosity corresponding to the permeability distribution range. When the permeability distribution range is 0.01-0.1 mD, the corresponding porosity distribution range is 7-10%; when the permeability distribution range is 0.1-1 mD, the corresponding porosity distribution range is 10-16%; and when the permeability distribution range is 1-10 mD, the corresponding porosity distribution range is 16-25%. Finally, boundary 1 was determined as acoustic porosity = 7%, 10%, 16%, and 25%, based on which three intervals were identified.

[0055] S4. Delineate the distribution region boundary 2 of the medium-high porosity dry layer on the acoustic porosity-resistivity cross-plot. The specific method for determining boundary 2 is as follows: According to Archie's formula: Take the logarithm of both sides:

[0056] make , ,have to:

[0057] Among them, S w R represents the degree of water saturation, a decimal. t R represents the formation resistivity, in Ω·m. w The resistivity of formation water is given in Ω·m. denoted as acoustic porosity (decimal); m is the porosity index (dimensionless); n is the saturation index (dimensionless). a and b are coefficients related to lithology.

[0058] From the above equation, it can be seen that: in the acoustic porosity-resistivity intersection diagram, boundary 2, Rt, and... The relationship between them is a set of slopes with a value of -m and an intercept of . A straight line.

[0059] S5. Based on boundary 1 and boundary 2, determine the distribution area of ​​the medium-high porosity dry layer on the acoustic porosity-resistivity cross plot. Refer to the appendix of the instruction manual. Figure 4 The medium-high porosity dry layer distribution is the upper part of the area enclosed by boundary 1 and boundary 2, as shown in the instruction manual. Figure 4 Regions 1, 2, and 3, i.e., the medium-high porosity dry layer, are trapezoidal regions within different acoustic porosity zones.

[0060] In summary, any other corresponding modifications made by those skilled in the art after reading this invention document, without requiring creative mental effort, based on the technical solutions and concepts of this invention, are all within the scope of protection of this invention.

Claims

1. A logging method for identifying medium-to-high porosity dry formations, characterized in that: Includes the following steps: S1. Calculate acoustic porosity; S2. Establish an acoustic porosity-resistivity cross plot, where the X-axis represents acoustic porosity and the Y-axis represents resistivity, and both are logarithmic coordinate systems; S3. Divide the intervals of different acoustic porosity, and divide the distribution area boundary 1 of the medium and high porosity dry layer on the acoustic porosity-resistivity intersection chart according to the interval. Boundary 1 is the acoustic porosity, which is a constant. S4. Delineate the distribution area boundary 2 of the medium-high porosity dry layer on the acoustic porosity-resistivity cross plot; S5. Based on boundary 1 and boundary 2, determine the distribution area of ​​the medium-high porosity dry layer on the acoustic porosity-resistivity intersection chart; the distribution area of ​​the medium-high porosity dry layer is the upper trapezoidal area of ​​the region enclosed by boundary 1 and boundary 2 within the range of different acoustic porosities. The specific meaning of dividing the intervals of different acoustic porosity in step S3 is: to divide the intervals according to the porosity corresponding to the permeability distribution range by establishing a permeability-porosity relationship chart; The specific method for determining boundary 2 is as follows: According to Archie's formula: Take the logarithm of both sides: make , ,have to: Among them, S w R represents the water saturation level. t R is the formation resistivity. w For formation water resistivity, denoted as acoustic porosity, m as porosity index, n as saturation index, and a and b as coefficients related to lithology.

2. The logging identification method for medium-high porosity dry formations according to claim 1, characterized in that: The method for calculating acoustic porosity in step S1 is as follows: in, For acoustic porosity, For the acoustic transit time of the rock skeleton, For the acoustic transit time of formation fluids, The content of clay in the formation, This is the acoustic compaction correction factor. The sonic transit time logging value is for the target layer.

3. The logging identification method for medium-high porosity dry formations according to claim 1, characterized in that: When the permeability distribution range is 0.01-0.1 mD, the corresponding porosity distribution range is 7-10%; when the permeability distribution range is 0.1-1 mD, the corresponding porosity distribution range is 10-16%; and when the permeability distribution range is 1-10 mD, the corresponding porosity distribution range is 16-25%.

4. The logging identification method for medium-high porosity dry formations according to claim 3, characterized in that: The boundary 1 is 7%, 10%, 16%, and 25%.

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