Granularity parameter inversion method for nuclear magnetic logging based on lithogenous phase classification

Abstract

The invention discloses a lithogenous phase classification-based nuclear magnetic well logging particle size parameter inversion method, which comprises the following steps of S1, carrying out nuclear magnetic resonance and particle size analysis test experiments to obtain a saturated water T2 spectrum accumulation curve and a particle size accumulation curve; s2, drawing a transverse relaxation time T2-particle size relation curve, performing scale range division on the particle size, and obtaining a relational expression I of the transverse relaxation time T2 and the particle size in each scale range; s3, diagenetic lithofacies types of the target area are divided; s4, by taking the diagenetic lithofacies type as a constraint, optimizing the relational expression 1 to obtain a relational expression 2 between the transverse relaxation time T2 and the particle size under different diagenetic lithofacies types and different scale ranges; establishing a standard for identifying diagenetic lithofacies of the logging curve; and S5, obtaining a logging curve of the target wellbore profile, identifying diagenetic lithofacies of the target wellbore profile, and obtaining continuous granularity parameters of the target wellbore profile in combination with second inversion of the relational expression. According to the method, the particle size distribution on the wellbore section can be accurately and quantitatively inverted by using nuclear magnetic logging.

Description

Technical Field

[0001] The present invention relates to the technical field of logging, and particularly relates to a method for inverting grain size parameters by nuclear magnetic logging based on diagenetic facies classification. Background Art

[0002] Grain size parameters are important bases for reflecting the microscopic rock structure characteristics of clastic rocks, identifying sedimentary environments, and sedimentary microfacies and lithology. Conducting research on the vertical distribution law of grain size parameters and fine lithology characterization is an important prerequisite task for achieving breakthroughs in deep basin oil and gas exploration and development. However, the drilling cost in the deep water area of the basin is high, and the coring difficulty is great, so that continuous drilling core grain size information cannot be obtained.

[0003] Currently, in grain size parameter inversion, Oyeneyin (1999), Zhang (2022), Wang Lihua et al. (2016) established a conventional logging prediction model for median grain size Md by means of various neural network technologies. Yang Ning et al. (2012) used the binary wavelet transform method to calculate grain size parameters such as median grain size Md through the natural gamma curve. Gao Yang et al. (2021) improved the median grain size calculation model based on logging data such as acoustic travel time, neutron, and density, after eliminating the influence of pore fluid on logging response by using the skeleton index parameter. He Shenglin et al. (2017) realized continuous calculation of rock grain size after determining the grain size conversion distribution coefficient in combination with nuclear magnetic experiments. 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 dynamic extraction of grain size distribution from 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 all have certain deficiencies to some extent. 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 the actual formation, and the calculation accuracy for complex formations is often poor; the grain size parameters that can be inverted by conventional logging curves are limited, usually only having a certain effect on the average grain size and median grain size of rocks, and having certain limitations for a comprehensive understanding of vertical grain size parameters (grain size distribution); the existing nuclear magnetic calculation grain size models only consider reservoir sedimentation and ignore the influence of diagenesis on the calculation, resulting in poor application effects for actual formations. Summary of the Invention

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

[0006] The technical solution of the present invention is as follows:

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

[0008] S1: Obtain multiple rock samples from the target area, and conduct nuclear magnetic resonance and particle size analysis test experiments to obtain the saturated water T2 spectrum cumulative curve and particle size cumulative curve of each sample;

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

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

[0011] S4: Optimize the first relationship formula between the transverse relaxation time T2 and the particle size in each scale range with the diagenetic lithofacies type as a constraint to obtain the second relationship formula between the transverse relaxation time T2 and the particle size in different diagenetic lithofacies types and different scale ranges;

[0012] Obtain the logging curves of the target area, establish a diagenetic lithofacies cross - identification chart based on the logging curves in combination with the diagenetic lithofacies type, and establish the standard for identifying diagenetic lithofacies from logging curves according to the diagenetic lithofacies cross - identification chart;

[0013] S5: Obtain the logging curves of the target wellbore profile in the target area, identify the diagenetic lithofacies of the target wellbore profile according to the logging curves of the target wellbore profile in combination with the standard, and inversely obtain the continuous particle size parameters of the target wellbore profile according to the diagenetic lithofacies of the target wellbore profile in combination with the second relationship formula between the transverse relaxation time T2 and the particle size.

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

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

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

[0017] Preferably, in step S4, the logging curves include natural gamma curve, density curve, and acoustic travel - time curve.

[0018] Preferably, the diagenetic lithofacies cross - identification chart includes natural gamma - density cross - plot and acoustic travel - time - density cross - plot.

[0019] The beneficial effects of the present invention are as follows:

[0020] The present invention can accurately and quantitatively invert the particle size distribution on the wellbore profile by using nuclear magnetic logging, providing accurate lithologic grain size information for lithologic analysis, sedimentary sequence, and fine interpretation of reservoir configuration. At the same time, it also expands the application of nuclear magnetic resonance logging in the lithologic interpretation of clastic rocks. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It is a schematic flow chart of the method for inverting particle size parameters by nuclear magnetic logging based on diagenetic facies classification of the present invention;

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

[0024] Figure 3 It is a schematic diagram of the cumulative curve of saturated water T2 spectrum and the cumulative curve of particle size of Well X-1 in a specific embodiment;

[0025] Figure 4 It is a schematic diagram of the cumulative curve of saturated water T2 spectrum and the cumulative curve of particle size of Well X-2d in a specific embodiment;

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

[0027] Figure 6 It is a schematic diagram of the characteristics of different diagenetic lithofacies types of sandstones in the third member of the Lingshui Formation in Area X on thin sections and scanning electron microscopes in a specific embodiment;

[0028] Figure 7 It is a natural gamma-density cross plot in a specific embodiment;

[0029] Figure 8 It is an acoustic time difference-density cross plot in a specific embodiment;

[0030] Figure 9 It is a schematic diagram of the color image combination mode of well logging sensitive curves of diagenetic lithofacies in the third member of the Lingshui Formation of Well X-1 in a specific embodiment;

[0031] Figure 10Schematic diagram of the calculation results of the particle size distribution of the deep clastic rock nuclear magnetic resonance logging T2 spectrum in the third member of the Linghuashui Formation in Well X-2d in a specific embodiment. Detailed implementation manners

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, without conflict, the embodiments in the present application and the technical features in the embodiments may be combined with each other. It should be pointed out that, unless otherwise specified, all technical and scientific terms used in the present application have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs. The terms "including" or "comprising" and the like used in the present invention disclosure mean that the elements or objects appearing before the term cover the elements or objects listed after the term and their equivalents, without excluding other elements or objects.

[0033] As Figure 1 shown, the present invention provides a method for inverting particle size parameters of nuclear magnetic resonance logging based on diagenetic facies classification, including the following steps:

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

[0035] In a specific embodiment, when performing the scale range division, the particle size is divided into three scale ranges. Optionally, the three scale ranges include scale range one where the transverse relaxation time T2 has a linear relationship with the particle size, scale range two where the transverse relaxation time T2 has a power function relationship with the particle size, 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 particle size cumulative curve, and perform scale range division on the particle size according to the transverse relaxation time T2 - particle size relationship curve to obtain relationship formula one between the transverse relaxation time T2 and the particle size in each scale range.

[0037] The particle size distribution of rocks is the statistical result of the volume or weight percentage of various different particle sizes that make up clastic rocks. The particle size is usually characterized by the volume value method, and the sediment particles are equivalent to spheres of the same volume, and the standard diameter of the sphere represents the particle size. When characterizing the particle size parameter "particle size", the actual rock core particles can be simplified into a Figure 2 small ball packing model as shown, and then the sphere diameters are classified and characterized.

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

[0039]

[0041] In the formula: T2B is the free relaxation time of the fluid, in ms; ρ2 is the transverse surface relaxation strength of the rock, in μm·ms -1 ; S is the pore surface area, in cm 2 ; V is the pore volume, in cm 3 ; D is the diffusion coefficient, in μm 2 ·ms -1 ; γ is the gyromagnetic ratio (the ratio of the nuclear magnetic moment to the angular momentum, which is a constant for protons (hydrogen nuclei)); G is the magnetic field gradient, in G·cm -1 ; T E is the echo spacing, in ms;

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

[0043]

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

[0045] S = S 骨架 (3)

[0046]

[0047] In the formula: S 骨架 is the surface area of the skeleton particles, in cm 2 ; V 骨架 is the volume of the skeleton particles, in cm 3 ; φ is the porosity of the rock, in %;

[0048] Through the equivalent sphere packing model, it can be deduced that the ratio of the specific surface area of the skeleton particles to the total volume of the skeleton particles is:

[0049]

[0050] In the formula: r is the particle size of the rock particles, in μm;

[0051] Combining the above formulas, it can be obtained that:

[0052]

[0053] It can be seen from this that there is an indirect proportional relationship between the transverse relaxation time T2 and the rock particle radius r. In the present invention, the transverse relaxation time T2-particle size relationship curve is drawn through the saturated water T2 spectrum cumulative curve and the particle size cumulative curve, and then the relational expression one between the transverse relaxation time T2 and the particle size in different scale ranges is obtained by fitting. According to this relational expression, the particle size in the corresponding scale range can be calculated using the transverse relaxation time T2.

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

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

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

[0057] Obtain the logging curves of the target area, establish a diagenetic lithofacies cross - plot identification chart according to the logging curves in combination with the diagenetic lithofacies types, and establish the standard for identifying diagenetic lithofacies from logging curves according to the diagenetic lithofacies cross - plot identification chart.

[0058] After the clastic sedimentation ends, compaction in the actual formation will cause deformation of clastic particles, cementation will fill the pores between particles with authigenic minerals such as calcite and clay, and autogenous enlargement may occur on the surface of quartz particles, and dissolution will damage the integrity of particles. Therefore, the corresponding relationship between rock particles and the nuclear magnetic T2 spectrum under different diagenetic processes is different. In the present invention, optimizing the relationship formula one with the diagenetic lithofacies types as constraints can eliminate the influence of diagenesis on the calculation of particle size distribution, so as to further improve the accuracy of obtaining the rock particle size r from the nuclear magnetic resonance T2 spectrum.

[0059] In a specific embodiment, the logging curves include natural gamma ray curve, density curve, and acoustic travel - time curve. Optionally, the diagenetic lithofacies cross - plot identification chart includes natural gamma ray - density cross - plot and acoustic travel - time - density cross - plot.

[0060] S5: Obtain the logging curves of the target wellbore profile in the target area, identify the diagenetic lithofacies of the target wellbore profile according to the logging curves of the target wellbore profile in combination with the standard, and inversely calculate the continuous grain size parameters of the target wellbore profile according to the diagenetic lithofacies of the target wellbore profile in combination with the relationship formula two between the transverse relaxation time T2 and particle size.

[0061] In a specific embodiment, taking the third member of the Lingshui Formation in Area X of the Q Basin in the western South China Sea as an example, the method for inversely calculating grain size parameters from nuclear magnetic logging based on diagenetic facies classification of the present invention is used to obtain the continuous grain size parameters of the target wellbore profile, which specifically includes the following steps:

[0062] (1) Obtain multiple rock samples from the target area, and conduct nuclear magnetic resonance and particle size analysis test experiments to obtain the cumulative curves of saturated water T2 spectra and particle size cumulative curves of each sample;

[0063] In this embodiment, nuclear magnetic resonance and particle size analysis test experiments are carried out on 25 rock samples with different depths in two wells in the third member of the Lingshui Formation in Area X, which can cover different lithologies. Among them, the cumulative curves of saturated water T2 spectra and particle size cumulative curves of two different samples (Well X-1, 4001.8 m; Well X-2d, 4198 m) are as Figure 3 and Figure 4 shown.

[0064] (2) Plot the relationship curve of transverse relaxation time T2 - particle size, and divide the particle size into scale ranges according to the relationship curve of transverse relaxation time T2 - particle size, and obtain the first relationship formula between transverse relaxation time T2 and particle size in each scale range;

[0065] In this embodiment, the relationship curve of transverse relaxation time T2 - particle size of one of the samples is as Figure 5 shown. By comparing and observing the results of 25 groups of nuclear magnetic resonance and particle size test experiments, it is found that the rock particle sizes 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 relationship curve of transverse relaxation time T2 - particle size of the sample shown as

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

[0068] In the scale range of 0.12 mm - 0.5 mm, the relationship formula between transverse relaxation time T2 and particle size is:

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

[0070] In the scale range of 0.5 mm - 2.0 mm, the relationship formula between transverse relaxation time T2 and particle size is:

[0071] r = 0.016 * T2 0.55 (9).

[0072] (3) 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;

[0073] In this embodiment, core data such as thin-section images of rocks, scanning electron microscope images, and physical property data are used to analyze the diagenesis in the area. The framework, minerals, and pores of each thin-section image are quantitatively calculated, and the diagenetic intensity coefficients of each sample are calculated: the apparent compaction rate (α), the apparent cementation rate (β), and the apparent dissolution rate (γ). Taking the diagenetic intensity coefficients and physical property values as the basis for diagenetic facies judgment, the diagenetic facies types of the clastic rocks in the third member of the Lingshui Formation in Area X are divided. The characteristics of different diagenetic facies types of sandstones in the third member of the Lingshui Formation in Area X on thin sections and scanning electron microscopes are as Figure 6 shown, where (a) is the medium compaction-calcite weak cementation-dissolution facies; (b) is the medium compaction-siderite weak cementation-dissolution facies; (c) is the medium compaction-silica weak cementation-dissolution facies; (d) is the medium compaction-clay mineral weak cementation-dissolution facies; (e) is the medium compaction-glauconite weak cementation-dissolution facies; (f) is the medium-strong compaction-calcite medium cementation-dissolution facies; (g) is the medium-strong compaction-siderite medium cementation-dissolution facies; (h) is the medium-strong compaction-calcite strong cementation-weak dissolution facies; (i) is the medium-strong compaction-siderite strong cementation-weak dissolution facies.

[0074] In view of the resolution of logging curves and the operability of identification, the 9 sub-categories are grouped into 3 major categories:

[0075] 1) The five types of medium compaction-calcite weak cementation-dissolution facies, medium compaction-siderite weak cementation-dissolution facies, medium compaction-silica weak cementation-dissolution facies, medium compaction-clay mineral weak cementation-dissolution facies, and medium compaction-glauconite weak cementation-dissolution facies are grouped into the medium compaction-weak cementation-dissolution facies (Ⅰ). The characteristics of the medium compaction-weak cementation-dissolution facies (Ⅰ) are as follows: the degree of fluvial erosion is strong, the content of muddy matrix is low, the grain sorting is good, and the quartz content in the rock components is high. The grains are in line contact and point-line contact, the apparent compaction rate is 38.09% - 65.68%, and the compaction degree is medium. Calcite, siderite, clay minerals, silica, and authigenic glauconite can be seen in thin sections filling some secondary dissolution pores ([[]] Figure 6 (a)- Figure 6 (e)), the overall content of cement is small, the apparent cementation rate is 0.11% - 29.62%, belonging to weak cementation. Compaction causes a considerable part of the original pores to disappear, but a small amount of residual intergranular pores still remain ([[]] Figure 6 (c)), showing a triangular shape. Secondary pores develop, mainly including dissolution intergranular pores, feldspar dissolution pores, and mold pores, and the apparent dissolution rate is 39.02% - 90.90%. The physical properties of the reservoir are better than those of other diagenetic facies types, the porosity is distributed between 10.16% and 17.68%, and the permeability ranges from 0.16 mD to 17.88 mD.

[0076] 2) The two types, i.e., medium-strong compaction-calcite medium cementation-dissolution facies and medium-strong compaction-siderite medium cementation-dissolution facies, are classified into medium-strong compaction-medium cementation-dissolution facies (Ⅱ). The characteristics of the medium-strong compaction-medium cementation-dissolution facies (Ⅱ) are as follows: it is located in the horizons where the detrital grains are well sorted and the deposited argillaceous matrix is less. The grains are in line contact, the apparent compaction rate is 41.35% - 75.19%, and the compaction degree is medium-strong. The carbonate cements are mainly calcite, dolomite and siderite, the cementation degree is medium, and the apparent cementation rate is 30.43% - 67.53%. Some of the secondary dissolution pores are filled with carbonate cements, and some pores are retained ( Figure 6 (f)- Figure 6 (g)), and the apparent dissolution rate is 11.76% - 81.59%. The physical properties of this type of diagenetic lithofacies are slightly worse than those of type Ⅰ, the porosity is distributed in 5.82% - 14.10%, and the permeability ranges between 0.07 mD and 8.61 mD.

[0077] 3) The two types, i.e., medium-strong compaction-calcite strong cementation-weak dissolution facies and medium-strong compaction-siderite strong cementation-weak dissolution facies, are classified into medium-strong compaction-strong cementation-weak dissolution facies (Ⅲ). The characteristics of the medium-strong compaction-strong cementation-weak dissolution facies (Ⅲ) are as follows: the content of argillaceous matrix is relatively high, the grain sorting is poor, the apparent compaction rate is 50.37% - 86.54%, showing medium-strong compaction, few primary pores are preserved, the intergranular spaces are filled with cements, and the contact is mainly point-line contact. The apparent cementation rate is 71.51% - 99.70%, and the strong cementation makes the secondary pores almost disappear after the cements are filled, showing weak dissolution, and the apparent dissolution rate is 0.00 - 66.67%. It can be seen from the thin section that calcite and siderite completely fill the intergranular spaces ( Figure 6 (h)- Figure 6 (i)). The pore and permeability of this type of diagenetic lithofacies are poor, the porosity is distributed in 3.17% - 9.75%, and the permeability is usually < 3.5 mD.

[0078] (4) Obtain the logging curves of the target area, establish a diagenetic lithofacies cross-plot identification chart according to the logging curves in combination with the types of diagenetic lithofacies, and establish the standard for identifying diagenetic lithofacies from logging curves according to the diagenetic lithofacies cross-plot identification chart;

[0079] In this embodiment, the established diagenetic lithofacies cross-plot identification chart is as shown in Figure 7 and Figure 8 . As shown in Figure 7 and Figure 8It can be seen that the GR value of mudstone is relatively large (greater than 100 API). The GR value of the medium-strong compaction-medium cementation-dissolution facies (Ⅱ) is smaller than that of the medium compaction-weak cementation-dissolution facies (Ⅰ) and the medium-strong compaction-strong cementation-weak dissolution facies (Ⅲ). The density values of the three types of diagenetic lithofacies, namely the medium compaction-weak cementation-dissolution facies (Ⅰ), the medium-strong compaction-medium cementation-dissolution facies (Ⅱ), and the medium-strong compaction-strong cementation-weak dissolution facies (Ⅲ), show an increasing trend in sequence, while the acoustic travel time decreases in sequence.

[0080] Through the diagenetic lithofacies intersection identification plate, the logging identification of various diagenetic lithofacies can be visually carried out based on the boundary values. However, it is not convenient to observe multiple curves simultaneously, and the judgment in the overlapping area near the boundary has multiple solutions. Therefore, in this embodiment, a combination mode of color images of multiple sensitive logging curves is introduced to more visually identify the types of diagenetic lithofacies. The combination mode of color images of logging sensitive curves of the third member of the Lingshui Formation in Well X-1 is as Figure 9 shown.

[0081] From Figure 9 it can be seen that the GR value of the medium compaction-weak cementation-dissolution facies (Ⅰ) (see Figure 9 ④, ⑤, ⑥ in it) is relatively low, and the imaging is beige or light brown; the acoustic travel time value is large, showing yellowish green; the density value is relatively small, being green. The GR, AC, and DEN image combinations present two modes of "beige + orange yellow + green" and "light brown + orange yellow + green". The medium-strong compaction-medium cementation-dissolution facies (Ⅱ) (see Figure 9 ③, ⑦ in it) has a lower acoustic travel time value compared to the first type, and the curve color imaging shows yellowish green. The GR, AC, and DEN images present two modes of "beige + yellowish green + green" and "light brown + yellowish green + green". The medium-strong compaction-strong cementation-weak dissolution facies (Ⅲ) (see Figure 9 ①, ② in it) has a brown GR curve imaging; the acoustic travel time value is slightly smaller than that of the second type, and it is also basically yellowish green; the density is relatively large, and the image shows yellow. The GR, AC, and DEN images present the mode of "brown + yellowish green + yellow / yellowish green".

[0082] The identification criteria for the diagenetic lithofacies types in this embodiment obtained through the above crossplot method and the combination mode of logging curve color images (GR + AC + DEN) are shown in Table 1:

[0083] Table 1 Identification criteria for diagenetic lithofacies types

[0084]

[0085]

[0086] (5) Taking the diagenetic lithofacies type as a constraint, optimize the relationship formula one between the transverse relaxation time T2 and the particle size at each scale range to obtain the relationship formula two between the transverse relaxation time T2 and the particle size for different diagenetic lithofacies types and different scale ranges;

[0087] In this embodiment, the relationship formula II between the transverse relaxation time T2 and the particle size under different diagenetic facies types and different scale ranges is shown in Table 2 as follows:

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

[0089]

[0090] (6) Obtain the logging curves of the target wellbore profile in the target area, identify the diagenetic facies of the target wellbore profile according to the logging curves of the target wellbore profile in combination with the above standards, and inversely obtain the continuous grain size parameters of the target wellbore profile according to the diagenetic facies of the target wellbore profile in combination with the relationship formula II between the transverse relaxation time T2 and the particle size;

[0091] In this embodiment, obtain the nuclear magnetic resonance logging T2 spectrum of Well X-2d in Area X of Basin Q, and inversely obtain its continuous grain size parameters in combination with the results of steps (4)-(5). The results are as follows Figure 10 shown. Figure 10 In the figure, the color image combination mode of the sensitive logging curves for identifying diagenetic facies is composed of the 8th, 9th, and 10th tracks. The 11th track is the result of judging the diagenetic facies type. The 12th track is the nuclear magnetic resonance logging T2 spectrum. The 13th and 14th tracks are respectively the longitudinal continuous grain size cumulative curve and grain size distribution curve calculated from the nuclear magnetic logging T2 spectrum. The 15th track is the comparison between the grain size median Md curve extracted from the data of the 14th track and the grain size median of the core test in part of the cored interval. The 16th track is the result of identifying sedimentary microfacies by combining core description and logging curves. The 17th track is the comparison result between the grain size cumulative curve inversed from the nuclear magnetic logging T2 spectrum and the actual core grain size test data.

[0092] From Figure 10 it can be seen that the Md value obtained from the core test is highly consistent with the Md value extracted from the calculated particle size distribution curve. The result of identifying sedimentary microfacies by combining core description and logging curves is consistent with the longitudinal distribution characteristics of the calculated grain size distribution and grain size median curve. At 4338m to 4364.1m, the GR logging curve is approximately box-shaped, which is underwater distributary channel sedimentation, and the grain size is generally coarser, gradually becoming finer upward; at 4323.5m to 4330.2m, the GR logging curve is funnel-shaped, which is estuary bar sedimentation, and the grain size shows an inverse rhythm from fine to coarse from bottom to top. The comparison between the calculated grain size cumulative curve at four locations of 4277.9m, 4303.29m, 4319m, and 4341.9m and the grain size cumulative curve of the core grain size test shows a high similarity between the two.

[0093] Thus, it can be proved the reliability of the continuous calculation of grain size distribution by nuclear magnetic resonance logging T2 spectrum under the constraint of diagenetic lithofacies classification in the present invention, making the grain size information more intuitive and accurate vertically, quantitatively obtaining the lithologic sequence of deep clastic rock formations, and also deepening the further development and application of nuclear magnetic resonance logging in clastic rock reservoir geology and sedimentology. Compared with the prior art, the present invention has made remarkable progress.

[0094] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the disclosed technical content within the scope of the technical solution of the present invention. However, as long as the content does not depart from the technical solution of the present invention, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for inverting grain size parameters from nuclear magnetic logging based on diagenetic facies classification, characterized in that, It includes the following steps: S1: Obtain multiple rock samples from the target area, and conduct nuclear magnetic resonance and grain size analysis test experiments to obtain the cumulative curves of saturated water T2 spectra and grain size cumulative curves of each sample; S2: Draw a transverse relaxation time T2 - particle size relationship curve based on the cumulative curve of the saturated water T2 spectrum 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 the first relationship formula between the transverse relaxation time T2 and the particle size in each scale range; S3: Obtain thin sections of rocks and / or scanning electron microscope data of the target area, and divide the diagenetic lithofacies types of the target area according to the thin sections of rocks and / or scanning electron microscope data; S4: Optimize the first relationship formula between the transverse relaxation time T2 and the particle size in each scale range with the diagenetic lithofacies type as a constraint to obtain the second relationship formula between the transverse relaxation time T2 and the particle size in different diagenetic lithofacies types and different scale ranges; Obtain the logging curves of the target area, establish a diagenetic lithofacies cross - plot identification chart based on the logging curves in combination with the diagenetic lithofacies type, and establish the standard for identifying diagenetic lithofacies from logging curves according to the diagenetic lithofacies cross - plot identification chart; S5: Obtain the logging curves of the target wellbore profile in the target area, identify the diagenetic lithofacies of the target wellbore profile according to the logging curves of the target wellbore profile in combination with the standard, and inversely obtain the continuous grain size parameters of the target wellbore profile according to the diagenetic lithofacies of the target wellbore profile in combination with the second relationship formula between the transverse relaxation time T2 and the particle size; 2. The method for inverting grain size parameters by nuclear magnetic logging based on diagenetic facies classification according to claim 1, wherein In step S2, when dividing the scale ranges, the particle size is divided into three scale ranges.

3. The method for inverting grain size parameters by nuclear magnetic logging based on diagenetic facies classification according to claim 2, wherein The three scale ranges include scale range one where the transverse relaxation time T2 and the particle size show a linear relationship, scale range two where both the transverse relaxation time T2 and the particle size show a power function relationship, and scale range three.

4. The method for inverting grain size parameters by nuclear magnetic logging based on diagenetic facies classification according to claim 1, wherein In step S3, the diagenetic lithofacies types include medium compaction - weak cementation - dissolution facies, medium - strong compaction - medium cementation - dissolution facies, and medium - strong compaction - strong cementation - weak dissolution facies.

5. The method for inverting grain size parameters by nuclear magnetic logging based on diagenetic facies classification according to claim 1, wherein In step S4, the logging curves include natural gamma curve, density curve, and acoustic time - difference curve.

6. The method for inverting grain size parameters by nuclear magnetic logging based on diagenetic facies classification according to claim 5, wherein The diagenetic lithofacies cross - plot identification chart includes natural gamma - density cross - plot chart and acoustic time - difference - density cross - plot chart.

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