A method for nuclear magnetic logging inversion of particle size parameters based on lithofacies classification

Abstract

The application discloses a kind of based on diagenetic facies classification nuclear magnetic logging inversion granularity parameter method, comprising the following steps: S1: carry out nuclear magnetic resonance and granularity analysis test experiment, obtain saturated water T2 spectrum cumulative curve and granularity cumulative curve;S2: draw transverse relaxation time T2-particle size relationship curve, scale range division is carried out to particle size, obtain the relationship formula one of transverse relaxation time T2 and particle size under each scale range;S3: the diagenetic lithofacies type of target area is divided;S4: with diagenetic lithofacies type as constraint, relationship formula one is optimized, and the relationship formula two of transverse relaxation time T2 and particle size under different diagenetic lithofacies types and different scale ranges is obtained;Establish the standard of logging curve identification diagenetic lithofacies;S5: obtain the logging curve of target wellbore profile, identify the diagenetic lithofacies of target wellbore profile, and obtain the continuous granularity parameter of target wellbore profile by inversion in combination with relationship formula two.The present application can quantitatively invert the particle size distribution on wellbore profile by nuclear magnetic logging.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of well logging, in particular to a method for nuclear magnetic logging inversion of grain size parameters based on diagenetic facies classification. BACKGROUND

[0002] The grain size parameter is an important basis for reflecting the microstructure characteristics of clastic rocks, identifying sedimentary environments, and distinguishing sedimentary microfacies and lithology. The research on the distribution of grain size parameters in the vertical direction and the fine characterization of lithology is an important prerequisite for realizing the breakthrough of deep basin oil and gas exploration and development. However, the drilling cost is high in deep water area of the basin, and it is difficult to obtain continuous drilling core grain size information.

[0003] At present, in the inversion of grain size parameters, Oyeneyin (1999), Zhang (2022), and Wang Lihua et al. (2016) use various neural network technologies to establish conventional logging Md model for predicting grain size. Yang Ning et al. (2012) used binary wavelet transform method to calculate the grain size median Md and other grain size parameters through natural gamma curve. Gao Yang et al. (2021) established a grain size median calculation model based on acoustic travel time, neutron and density logging data, which eliminated the influence of pore fluid on logging response and improved the grain size median calculation model. After determining the grain size conversion distribution coefficient by combining nuclear magnetic experiment, He Shenglin et al. (2017) realized the continuous calculation of rock grain size. Sun Jianmeng et al. (2013) and Qu Le (2014) used nuclear magnetic resonance T2 spectrum combined with digital core technology to calculate the continuous rock grain size of the formation, and realized the dynamic extraction of grain size distribution by nuclear magnetic resonance T2 spectrum.

[0004] The above methods all use logging data to obtain grain size parameters or grain size distribution, but the above methods have certain degree of deficiency. Machine learning directly calculates grain size parameters, ignoring the geological significance behind logging curves and grain size parameters. Mathematical models such as wavelet transform are relatively simple, ignoring the complexity of actual strata, and the calculation of complex strata often has poor accuracy. The grain size parameters that can be inverted by conventional logging curves are limited, and usually only have certain effect on rock average particle size and grain size median. The comprehensive understanding of grain size parameters (particle size distribution) in the vertical direction has certain limitations. The existing nuclear magnetic calculation grain size model only considers the influence of sedimentation on calculation and ignores the influence of diagenesis on calculation, which has poor application effect on actual strata. SUMMARY

[0005] In view of the above problems, the present application aims to provide a method for nuclear magnetic logging inversion of grain size parameters based on diagenetic facies classification.

[0006] The technical scheme of the present application is as follows:

[0007] A method for nuclear magnetic logging inversion of grain size parameters based on diagenetic facies classification, comprising the following steps:

[0008] S1: Obtain a plurality of rock samples of a target zone, and carry out nuclear magnetic resonance and particle size analysis test experiments to obtain a saturated water T2 spectrum cumulative curve and a particle size cumulative curve of each sample;

[0009] S2: Draw a transverse relaxation time T2-particle size relationship curve according to the saturated water T2 spectrum cumulative curve and the particle size cumulative curve, and divide the particle size into a scale range according to the transverse relaxation time T2-particle size relationship curve to obtain a relationship between the transverse relaxation time T2 and the particle size in each scale range;

[0010] S3: Obtain rock thin sections and / or scanning electron microscope data of the target zone, and divide the diagenetic lithofacies types of the target zone according to the rock thin sections and / or scanning electron microscope data;

[0011] S4: With the diagenetic lithofacies types as constraints, optimize the relationship between the transverse relaxation time T2 and the particle size in each scale range to obtain a relationship between the transverse relaxation time T2 and the particle size in different diagenetic lithofacies types and different scale ranges;

[0012] Obtain well logging curves of the target zone, establish a diagenetic lithofacies cross-plot according to the well logging curves combined with the diagenetic lithofacies types, and establish a standard for identifying diagenetic lithofacies from well logging curves according to the diagenetic lithofacies cross-plot;

[0013] S5: Obtain well logging curves of a target wellbore profile of the target zone, identify the diagenetic lithofacies of the target wellbore profile according to the well logging curves of the target wellbore profile combined with the standard, and obtain continuous particle size parameters of the target wellbore profile by inversion according to the diagenetic lithofacies of the target wellbore profile combined with the relationship between the transverse relaxation time T2 and the particle size.

[0014] Preferably, in step S2, when the scale range is divided, the particle size is divided into three scale ranges.

[0015] Preferably, the three scale ranges include a scale range one in which the transverse relaxation time T2 and the particle size are in a linear relationship, a scale range two in which both the transverse relaxation time T2 and the particle size are in a power function relationship, and a scale range three.

[0016] Preferably, in step S3, the diagenetic lithofacies types include a medium compaction-weak cementation-dissolution facies, a medium-strong compaction-medium cementation-dissolution facies, and a medium-strong compaction-strong cementation-weak dissolution facies.

[0017] Preferably, in step S4, the well logging curves include a natural gamma curve, a density curve, and an acoustic time difference curve.

[0018] Preferably, the diagenetic lithofacies cross-plot includes a natural gamma-density cross-plot and an acoustic time difference-density cross-plot.

[0019] The beneficial effects of the present application are:

[0020] The present application can quantitatively invert the particle size distribution on the wellbore profile by using nuclear magnetic logging, and provide accurate lithology particle size information for fine interpretation of lithology analysis, sedimentary sequence and reservoir configuration, and also expand the application of nuclear magnetic resonance logging in the interpretation of clastic rock lithology. BRIEF DESCRIPTION OF DRAWINGS

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

[0022] Figure 1 The flowchart of the method for nuclear magnetic logging inversion of particle size parameters based on diagenetic facies classification of the present application;

[0023] Figure 2 The schematic diagram of the actual core equivalent small ball packing model;

[0024] Figure 3 The schematic diagram of the saturated water T2 spectrum cumulative curve and the particle size cumulative curve of X-1 well in one specific embodiment;

[0025] Figure 4 The schematic diagram of the saturated water T2 spectrum cumulative curve and the particle size cumulative curve of X-2d well in one specific embodiment;

[0026] Figure 5 The schematic diagram of the corresponding relationship between the saturated cumulative T2 spectrum curve and the particle size cumulative curve in one specific embodiment;

[0027] Figure 6 The schematic diagram of the characteristics of different diagenetic facies types of the sandstone of the third member of the Lingshui Formation in the X area on the thin section and the scanning electron microscope in one specific embodiment;

[0028] Figure 7 The gamma-ray-density crossplot in one specific embodiment;

[0029] Figure 8 The acoustic travel time-density crossplot in one specific embodiment;

[0030] Figure 9 The schematic diagram of the color image combination mode of the diagenetic facies logging sensitive curve of the third member of the Lingshui Formation in X-1 well in one specific embodiment;

[0031] Figure 10An illustrative diagram of the grain size distribution calculated by the T2 spectrum of the nuclear magnetic resonance logging in the deep clastic rock of the third member of the Shuijing Formation in the X-2d well is shown. DETAILED DESCRIPTION

[0032] The application will be further described below in conjunction with the drawings and examples. It should be noted that the examples and technical features in the examples can be combined with each other without conflict. It should be noted that all technical and scientific terms used in the present application have the same meaning as generally understood by those skilled in the art to which the present application belongs, unless otherwise specified. The present application discloses that the "including" or "containing" and similar words mean that the elements or objects before the words cover the elements or objects listed after the words and their equivalents, and do not exclude other elements or objects.

[0033] As Figure 1 shown, the present application provides a method for inverting grain size parameters based on rock facies classification by nuclear magnetic logging, comprising the following steps:

[0034] S1: Obtain a plurality of rock samples of a target area, and carry out nuclear magnetic resonance and grain size analysis test experiments to obtain the saturated water T2 spectrum cumulative curve and the grain size cumulative curve of each sample.

[0035] In one specific embodiment, when the scale range is divided, the particle size is divided into three scale ranges. Alternatively, the three scale ranges include scale range one in which the transverse relaxation time T2 is linearly related to the particle size, scale range two in which the transverse relaxation time T2 and the particle size are both in a power function relationship, and scale range three.

[0036] S2: Draw a transverse relaxation time T2-particle size relationship curve according to the saturated water T2 spectrum cumulative curve and the grain size cumulative curve, and divide the particle size into scale ranges according to the transverse relaxation time T2-particle size relationship curve to obtain a relationship one between the transverse relaxation time T2 and the particle size in each scale range.

[0037] The grain size distribution of rock is the statistical volume or weight percentage of various different particle sizes of clastic rock. The particle size is usually characterized by the volume value method, which equates the sedimentary particles to spheres of the same volume, and the standard diameter of the sphere represents the particle size of the particles. When the particle size parameter "particle size" is characterized, the actual rock core particles can be simplified into a small ball packing model as Figure 2 shown, and the diameter of the sphere is then characterized by classification.

[0038] According to the NMR relaxation mechanism, the transverse relaxation time T2 is:

[0039]

[0040]

[0041] where T 2B is the free relaxation time of fluid, ms; p2 is the transverse surface relaxation strength of rock, pmms -1 ; S is the pore surface area, cm 2 ; V is the pore volume, cm 3 ; D is the diffusion coefficient, pmms 2 ; y is the gyromagnetic ratio (ratio of nuclear magnetic moment to angular momentum, which is a constant for protons (hydrogen nuclei)); G is the magnetic field gradient, Gcm -1 ; T -1 is the echo interval, ms; E

[0042] In practical applications, T 2B is much larger than T2, and when the magnetic field is uniform, the corresponding G value, T E is small enough, therefore, the T2 relaxation time of fluid in rock pores is directly related to the specific surface S / V of the pores, i.e.:

[0043]

[0044] The following relationship exists in the skeleton model of rock:

[0045] S = S 骨架 (3)

[0046]

[0047] where S 骨架 is the surface area of skeleton particles, cm 2 ; V 骨架 is the total volume of skeleton particles, cm 3 ; φ is the porosity of rock, %;

[0048] The ratio of the specific surface of skeleton particles to the total volume of skeleton particles can be derived from the equivalent sphere packing model as follows:

[0049]

[0050] where r is the particle size of rock, pm;

[0051] Combining the above equations, we can obtain:

[0052]

[0053] ​Therefore, the transverse relaxation time T2 is indirectly proportional to the radius r of the rock particles. The application draws a transverse relaxation time T2-particle size relationship curve by using the saturated water T2 spectrum cumulative curve and the particle size cumulative curve, and then fits to obtain a relationship formula one between the transverse relaxation time T2 and the particle size in different scale ranges. According to the relationship formula, the transverse relaxation time T2 can be used to calculate the particle size in the corresponding scale range.

[0054] S3: Obtain rock thin sections and / or scanning electron microscope data of the target area, and divide the diagenetic lithofacies types of the target area according to the rock thin sections and / or scanning electron microscope data.

[0055] In a specific embodiment, the diagenetic lithofacies types include a medium compaction-weak cementation-dissolution facies, a medium-strong compaction-medium cementation-dissolution facies, and a medium-strong compaction-strong cementation-weak dissolution facies.

[0056] S4: Optimizing the relationship formula one between the transverse relaxation time T2 and the particle size in each scale range with the diagenetic lithofacies types as constraints, to obtain a relationship formula two between the transverse relaxation time T2 and the particle size in different diagenetic lithofacies types and different scale ranges.

[0057] Obtaining well logging curves of the target area, establishing a diagenetic lithofacies cross-plot according to the well logging curves combined with the diagenetic lithofacies types, and establishing a standard for identifying diagenetic lithofacies from well logging curves according to the diagenetic lithofacies cross-plot.

[0058] After the end of clastic sedimentation, compaction in the actual formation will cause deformation of the clastic particles, cementation will fill the pores between the particles with calcite, clay and other authigenic minerals, and authigenic overgrowth may occur on the surface of quartz particles. Dissolution will destroy the integrity of the particles. Therefore, the relationship between rock particles and NMR T2 spectrum under different diagenesis is different. In the application, the relationship formula one is optimized with the diagenetic lithofacies types as constraints, which can eliminate the influence of diagenesis on the calculation of particle size distribution, and thus further improve the accuracy of the rock particle size r obtained from NMR T2 spectrum.

[0059] In a specific embodiment, the well logging curves include natural gamma ray curves, density curves, and acoustic time difference curves. Optionally, the diagenetic lithofacies cross-plot includes a natural gamma ray-density cross-plot and an acoustic time difference-density cross-plot.

[0060] S5: Obtaining well logging curves of the target wellbore profile of the target area, identifying the diagenetic lithofacies of the target wellbore profile according to the well logging curves of the target wellbore profile combined with the standard, and inverting to obtain the continuous particle size parameters of the target wellbore profile according to the diagenetic lithofacies of the target wellbore profile combined with the relationship formula two between the transverse relaxation time T2 and the particle size.

[0061] In a specific embodiment, taking the third member of the Lingshui Formation in the X area of the western South China Sea Q Basin as an example, the method for obtaining the continuous grain size parameter of the target wellbore profile based on the rock facies classification of the nuclear magnetic logging inversion grain size parameter is used, and specifically includes the following steps:

[0062] (1) Obtain a plurality of rock samples of the target area, and carry out nuclear magnetic resonance and grain size analysis test experiments to obtain the saturated water T2 spectrum cumulative curve and the grain size cumulative curve of each sample;

[0063] In this embodiment, nuclear magnetic resonance and grain size analysis test experiments are carried out on rock samples of different lithologies at 25 different depths of 2 wells in the third member of the Lingshui Formation in the X area, wherein the saturated water T2 spectrum cumulative curve and the grain size cumulative curve of two different samples (X-1 well, 4001.8 m; X-2d well, 4198 m) are shown in Figure 3 and Figure 4 .

[0064] (2) Draw a transverse relaxation time T2-grain size relationship curve, and divide the grain size into a scale range according to the transverse relaxation time T2-grain size relationship curve, to obtain a relationship between the transverse relaxation time T2 and the grain size in each scale range;

[0065] In this embodiment, the transverse relaxation time T2-grain size relationship curve of one of the samples is shown in Figure 5 . By comparing and observing the test results of 25 groups of nuclear magnetic and grain size experiments, it is found that the rock grain size can be divided into three scale ranges: 0.002 mm-0.12 mm, 0.12 mm-0.5 mm and 0.5 mm-2.0 mm.

[0066] Figure 5 In the transverse relaxation time T2-grain size relationship curve of the sample shown in

[0067] r=0.006*T2-0.006 (7)

[0068] The relationship between the transverse relaxation time T2 and the grain size in the 0.12 mm-0.5 mm scale range is:

[0069] r=0.022*T2 0.52 (8)

[0070] The relationship between the transverse relaxation time T2 and the grain size in the 0.5 mm-2.0 mm scale range is:

[0071] r=0.016*T2 0.55 (9)。

[0072] (3) obtaining rock thin section and / or scanning electron microscope data of the target area, and dividing the diagenetic lithofacies types of the target area according to the rock thin section and / or scanning electron microscope data;

[0073] In the embodiment, the core data such as rock thin section image observation, scanning electron microscope image and physical property data are used to analyze the diagenesis of the area, and the skeleton, mineral and pore of each thin section image are quantitatively calculated, so as to calculate the diagenetic intensity coefficients of each sample, i.e., apparent compaction rate (a), apparent cementation rate (β) and apparent dissolution rate (γ). The diagenetic intensity coefficients and physical property values are used as the basis for diagenetic lithofacies judgment, and the diagenetic lithofacies types of the clastic rock of the third member of the Lingshui Formation in the X area are divided. The characteristics of different diagenetic lithofacies types of the sandstone of the third member of the Lingshui Formation in the X area on the thin section and scanning electron microscope are shown in FIG. 1, wherein (a) is a medium compaction-calcitic weak cementation-dissolution facies; (b) is a medium compaction-siderite weak cementation-dissolution facies; (c) is a medium compaction-siliceous weak cementation-dissolution facies; (d) is a medium compaction-clay mineral weak cementation-dissolution facies; (e) is a medium compaction-glaucophane weak cementation-dissolution facies; (f) is a medium-strong compaction-calcitic medium cementation-dissolution facies; (g) is a medium-strong compaction-siderite medium cementation-dissolution facies; (h) is a medium-strong compaction-calcitic strong cementation-weak dissolution facies; and (i) is a medium-strong compaction-siderite strong cementation-weak dissolution facies. Figure 6

[0074] In view of the resolution of the logging curve and the operability of the judgment, the 9 small categories are summarized into 3 large categories:

[0075] 1) The medium compaction-calcitic weak cementation-dissolution facies, the medium compaction-siderite weak cementation-dissolution facies, the medium compaction-siliceous weak cementation-dissolution facies, the medium compaction-clay mineral weak cementation-dissolution facies and the medium compaction-glaucophane weak cementation-dissolution facies are summarized as a medium compaction-weak cementation-dissolution facies (I). The medium compaction-weak cementation-dissolution facies (I) is characterized by strong degree of water erosion, low content of argillaceous matrix, good particle sorting, and high content of quartz in rock components. The particles are in linear contact and point-linear contact, the apparent compaction rate is 38.09% to 65.68%, and the compaction degree is medium. The thin section shows that the calcite, siderite, clay mineral, siliceous and autogenous glaucophane cement fill part of the secondary dissolution pores (a)-(e), the overall content of the cement is low, the apparent cementation rate is 0.11% to 29.62%, and the cementation is weak. A large number of original pores are lost due to compaction, but a small amount of residual intergranular pores (c) are still retained, which are in triangular shape. The secondary pores are developed, mainly including dissolution intergranular pores, feldspar dissolution pores and mold pores, the apparent dissolution rate is 39.02% to 90.90%. The reservoir property is better than that of other diagenetic lithofacies types, the porosity is distributed in the range of 10.16% to 17.68%, and the permeability is in the range of 0.16 mD to 17.88 mD. Figure 6 Figure 6 (e)) Figure 6 (c))​​

[0076] 2) The two kinds of medium-strong compaction-calcitic medium cementation-dissolution facies and medium-strong compaction-siderite medium cementation-dissolution facies are summarized as medium-strong compaction-medium cementation-dissolution facies (II). The medium-strong compaction-medium cementation-dissolution facies (II) is characterized by being located in a layer with better particle sorting and less muddy miscellaneous base deposited. The particles are in linear contact, the apparent compaction rate is 41.35%-75.19%, and the compaction degree is medium-strong. The carbonate cementation is mainly composed of calcite, dolomite and siderite, the cementation degree is medium, and the apparent cementation rate is 30.43%-67.53%. Part of the secondary dissolution pores is filled with carbonate cementation, and part of the pores are preserved Figure 6 (f)- Figure 6 (g)) The apparent dissolution rate is 11.76%-81.59%. The physical properties of this kind of diagenetic facies are slightly worse than those of the first kind, the porosity is distributed in 5.82%-14.10%, and the permeability is between 0.07 mD and 8.61 mD.

[0077] 3) The two kinds of medium-strong compaction-calcitic strong cementation-weak dissolution facies and medium-strong compaction-siderite strong cementation-weak dissolution facies are summarized as medium-strong compaction-strong cementation-weak dissolution facies (III). The medium-strong compaction-strong cementation-weak dissolution facies (III) is characterized by relatively high content of muddy miscellaneous base and poor particle sorting, the apparent compaction rate is 50.37%-86.54%, showing medium-strong compaction, and the primary pores are poorly preserved, and the particles are mainly in point-linear contact and filled with cementation. The apparent cementation rate is 71.51%-99.70%, and the strong cementation makes the secondary pores almost disappear after being filled with cementation, showing weak dissolution, and the apparent dissolution rate is 0.00-66.67%. Thin sections show that calcite and siderite completely fill the intergranular space Figure 6 (h)- Figure 6 (i)). The diagenetic facies of this kind has poor porosity and permeability, the porosity is distributed in 3.17%-9.75%, and the permeability is usually <3.5 mD.

[0078] (4) Obtain the logging curve of the target area, establish a diagenetic facies intersection identification chart according to the logging curve combined with the diagenetic facies type, and establish a standard for identifying diagenetic facies from the logging curve according to the diagenetic facies intersection identification chart;

[0079] In this embodiment, the established diagenetic facies intersection identification chart is shown in Figure 7 and Figure 8 . From Figure 7 and Figure 8It can be seen that the GR value of mudstone is large (greater than 100 API), the GR value of the middle strong compaction-medium cementation-dissolution phase (II) is smaller than that of the middle compaction-weak cementation-dissolution phase (I) and the middle strong compaction-strong cementation-weak dissolution phase (III), the density value of the middle compaction-weak cementation-dissolution phase (I), the middle strong compaction-medium cementation-dissolution phase (II) and the middle strong compaction-strong cementation-weak dissolution phase (III) shows an increasing trend, and the acoustic time difference shows a decreasing trend.

[0080] The logging identification of various types of diagenetic lithofacies can be intuitively performed according to the limit value through the diagenetic lithofacies intersection identification chart. However, it is not convenient for simultaneous observation of multiple curves, and the judgment in the overlapping area near the limit has multiple solutions, so the embodiment further introduces a color image combination mode of multiple sensitive logging curves to more intuitively identify the types of diagenetic lithofacies, wherein the color image combination mode of the logging sensitive curves of the diagenetic lithofacies of the third member of the Lingshui Formation in well X-1 is as shown in Figure 9 .

[0081] From Figure 9 it can be seen that the GR value of the middle compaction-weak cementation-dissolution phase (I) (see ④, ⑤ and ⑥ in Figure 9 ) is low, the imaging is beige or light brown; the acoustic time difference value is large, and the imaging is yellow green; the density value is small, and the imaging is green. The GR, AC and DEN image combination presents two modes of “beige+orange yellow+green” and “light brown+orange yellow+green”. Compared with the type I, the acoustic time difference value of the middle strong compaction-medium cementation-dissolution phase (II) (see ③ and ⑦ in Figure 9 ) is low, the curve color imaging is yellow green, and the GR, AC and DEN image presents two modes of “beige+yellow green+green” and “light brown+yellow green+green”. The GR curve imaging of the middle strong compaction-strong cementation-weak dissolution phase (III) (see ① and ② in Figure 9 ) is brown; the acoustic time difference value is slightly smaller than that of the type II, and is also basically yellow green; the density is large, and the imaging is yellow. The GR, AC and DEN image presents a mode of “brown+yellow green+yellow / yellow green”.

[0082] The type identification standard of the diagenetic lithofacies obtained by the above crossplot method and the logging curve color image combination mode (GR+AC+DEN) is shown in Table 1:

[0083] Table 1 Type identification standard of diagenetic lithofacies

[0084]

[0085]

[0086] (5) optimizing the relationship between the transverse relaxation time T2 and the particle size under each scale range to obtain the relationship between the transverse relaxation time T2 and the particle size under different diagenetic lithofacies types and different scale ranges;

[0087] In the embodiment, the relationship between the transverse relaxation time T2 and the particle size in different diagenetic lithofacies types and different scale ranges is shown in Table 2:

[0088] Table 2 Relationship between the transverse relaxation time T2 and the particle size

[0089]

[0090] (6) obtaining a well logging curve of a target wellbore profile of a target area, identifying diagenetic lithofacies of the target wellbore profile according to the well logging curve of the target wellbore profile and the standard, and inversely obtaining a continuous particle size parameter of the target wellbore profile according to the diagenetic lithofacies of the target wellbore profile and the relationship between the transverse relaxation time T2 and the particle size;

[0091] In the embodiment, the continuous particle size parameter of the X-2d well in the X area of the Q basin is obtained by inversely obtaining the continuous particle size parameter according to the nuclear magnetic resonance logging T2 spectrum of the X-2d well in the X area of the Q basin and the results of steps (4)-(5), and the results are shown in Table 3. Figure 10 Figure 10 The 8th, 9th and 10th traces constitute a sensitive well logging curve color image combination mode for identifying diagenetic lithofacies, the 11th trace is a result of judging diagenetic lithofacies types, the 12th trace is a nuclear magnetic resonance logging T2 spectrum, the 13th and 14th traces are respectively a particle size cumulative curve and a particle size distribution curve which are continuous in the vertical direction and are calculated from the nuclear magnetic logging T2 spectrum, the 15th trace is a comparison between a particle size median Md curve extracted from the 14th trace and a particle size median of a core test of a partial cored layer, the 16th trace is a result of identifying a sedimentary microfacies by combining core description with well logging curves, and the 17th trace is a comparison result between a particle size cumulative curve obtained by inversely calculating the nuclear magnetic logging T2 spectrum and actual core particle size test data.

[0092] As can be seen from Table 3, Figure 10 it can be seen that the Md value of the core test is highly consistent with the Md value extracted from the calculated particle size distribution curve. The result of identifying the sedimentary microfacies by combining the core description with the well logging curves is consistent with the distribution characteristics of the calculated particle size distribution and the particle size median curve in the vertical direction. At 4338m to 4364.1m, the GR well logging curve is approximately box-shaped, which is underwater distributary channel deposition, and the particle size is relatively coarse as a whole, and gradually becomes fine upwards. At 4323.5m to 4330.2m, the GR well logging curve is funnel-shaped, which is a river mouth bar deposition, and the particle size shows an inverse rhythm of becoming fine from bottom to top. The comparison between the calculated particle size cumulative curves at 4277.9m, 4303.29m, 4319m and 4341.9m and the particle size cumulative curves of the core particle size test shows high similarity.

[0093] ​Therefore, the reliability of the continuous calculation of the particle size distribution by the nuclear magnetic resonance logging T2 spectrum under the constraint of the lithofacies classification of diagenesis can be proved, the particle size information is more intuitive and accurate in the longitudinal direction, the lithological sequence of the deep clastic rock formation is quantitatively obtained, and the further development and application of the nuclear magnetic resonance logging in clastic rock reservoir geology and sedimentology are deepened. Compared with the prior art, the present application has significant progress.

[0094] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above with a preferred embodiment, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, as long as the changes or modifications do not deviate from the technical solution of the present application. Any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application still belong to the scope of the technical solution of the present application.

Claims

1. A method of nuclear magnetic logging inversion of grain size parameters based on petrographic facies classification, characterized in that, The method comprises the following steps: S1: Obtain a plurality of rock samples of a target area, and perform nuclear magnetic resonance and particle size analysis tests to obtain a saturated water T2 spectrum cumulative curve and a particle size cumulative curve of each sample; S2: Draw a transverse relaxation time T2-particle size relationship curve according to the saturated water T2 spectrum cumulative curve and the particle size cumulative curve, and divide the particle size into a scale range according to the transverse relaxation time T2-particle size relationship curve to obtain a relationship between the transverse relaxation time T2 and the particle size in each scale range; S3: Obtain rock thin sections and / or scanning electron microscope data of the target area, and divide the diagenetic lithofacies types of the target area according to the rock thin sections and / or scanning electron microscope data; S4: With the diagenetic lithofacies types as constraints, optimize the relationship between the transverse relaxation time T2 and the particle size in each scale range to obtain a relationship between the transverse relaxation time T2 and the particle size in different diagenetic lithofacies types and different scale ranges; Obtain well logging curves of the target area, establish a diagenetic lithofacies cross-plot identification plate according to the well logging curves combined with the diagenetic lithofacies types, and establish a standard for identifying diagenetic lithofacies from well logging curves according to the diagenetic lithofacies cross-plot identification plate; S5: Obtain well logging curves of a target wellbore profile of the target area, identify the diagenetic lithofacies of the target wellbore profile according to the well logging curves of the target wellbore profile combined with the standard, and inversely obtain continuous particle size parameters of the target wellbore profile according to the diagenetic lithofacies of the target wellbore profile combined with the relationship between the transverse relaxation time T2 and the particle size.

2. The method for petrophysical facies classification based on inversion of grain size parameters from NMR logs according to claim 1, wherein, In step S2, when the scale range is divided, the particle size is divided into three scale ranges.

3. The method for petrophysical facies classification based on inversion of grain size parameters from NMR logs according to claim 2, wherein, The three scale ranges include a scale range one in which the transverse relaxation time T2 and the particle size are in a linear relationship, a scale range two in which the transverse relaxation time T2 and the particle size are in a power function relationship, and a scale range three.

4. The method for petrophysical facies classification based on NMR inversion of grain size parameters according to claim 1, wherein, In step S3, the diagenetic lithofacies types include a medium compaction-weak cementation-dissolution facies, a medium-strong compaction-medium cementation-dissolution facies, and a medium-strong compaction-strong cementation-weak dissolution facies.

5. The method for petrophysical facies classification based on NMR inversion of grain size parameters according to claim 1, wherein, In step S4, the well logging curves include a natural gamma curve, a density curve, and an acoustic time difference curve.

6. The method for petrophysical facies classification based on inversion of grain size parameters from NMR logs according to claim 5, wherein, The diagenetic lithofacies cross-plot identification plate includes a natural gamma-density cross-plot plate and an acoustic time difference-density cross-plot plate.

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