A method for determining the water-flooded level of a reservoir by using nuclear magnetic resonance double T E Method for logging water-flooded level

Abstract

This invention discloses a method utilizing nuclear magnetic resonance double-T E The method for classifying water flooding levels in well logging is as follows: First, the transverse relaxation rate of nuclear magnetic resonance (NMR), the geometric mean of the mobile fluid portion of the T2 spectrum, and the double T2 spectrum are calculated sequentially using NMR logging data. E The geometric mean difference of the mobile fluid portion of the T2 spectrum was measured. Then, the porosity of different components was obtained by inversion of echo data under long waiting times and short echo intervals. Finally, the percentage content of macroporous components in the total porosity was plotted against the T2 spectrum. E Cross plot of geometric mean difference of the mobile fluid portion of T2 spectrum and plotting of array induced resistivity AT90 and double T E The cross plot of the geometric mean difference of the mobile fluid portion of the T2 spectrum is measured to identify the flooding level of the flooded layer: unflooded layer, low flooded layer, medium flooded layer, and high flooded layer.

Description

Technical Field

[0001] This invention belongs to the technical field of flooding layer identification and flooding level classification, and relates to a method using nuclear magnetic resonance double-T... E Methods for classifying water flooding levels using well logging. Background Technology

[0002] As water injection development in oilfields progresses, more and more development blocks are forming water-flooded reservoirs. Infilling and adjusting old wells in water-flooded areas is necessary for tapping potential within the reservoir. The infilling area is gradually extending to ultra-low permeability and edge expansion areas, where reservoir properties are worse, heterogeneity is stronger, and the oil-water relationship and remaining oil distribution patterns are more complex. Infilling and adjusting pose new challenges to the identification of water-flooded layers.

[0003] Injected water alters the pore structure and electrical properties of the original formation, making it difficult to interpret water-flooded reservoirs using conventional logging methods. Nuclear magnetic resonance (NMR) logging utilizes the interaction between hydrogen protons and an applied magnetic field to observe reservoir information, remaining largely unaffected by formation water salinity, thus making it more advantageous for identifying the fluid properties of water-flooded reservoirs. NMR logging observation methods primarily include long and short waiting times (double T). W Well logging and long / short echo intervals (dual-T) E Well logging. Double-T nuclear magnetic resonance logging. E In the observation mode, oil, gas, and water have different diffusion coefficients, and their influence on the T2 time and its distribution varies in the gradient magnetic field. Increasing the echo interval T... E This will lead to a decrease in T2, and the T2 distribution will shift in the direction of decrease (spectral shift). In oil-water two-phase fluids, the diffusion coefficient of water is larger than that of oil, and the spectral shift phenomenon is more obvious. Therefore, the nuclear magnetic resonance double T... E Well logging shift spectrum analysis is one of the most effective methods for identifying water-flooded layers and classifying water-flood levels.

[0004] The main drawbacks of existing methods for identifying and classifying flooded layers are:

[0005] (1) Formation resistivity is related to both the salinity of formation water and injected water, as well as the water-flooding stage of the reservoir. Different injection types and injection volumes result in different resistivity variation patterns, making it unreliable to interpret water-flooded reservoirs using resistivity. (2) When interpreting water-flooded reservoirs using conventional logging, even if the salinity of formation water and injected water is known, the water-flooding stage is affected by injection time, injection scale, and complex geological factors. Calculating oil saturation based on the salinity of formation water and injected water and their corresponding formation resistivity curves still presents some ambiguity. (3) Most methods utilize nuclear magnetic resonance logging with dual-T... W Double T E The model was used to identify the original reservoir fluid type, but it was not used to explain the flooded layer or classify the flooding level of the flooded layer. Summary of the Invention

[0006] The purpose of this invention is to provide a method utilizing nuclear magnetic resonance dual-T E The method for classifying water flooding levels through well logging aims to solve the problem of difficulty in identifying water flooding layers in infiltrated wells in old areas.

[0007] The technical solution adopted in this invention is a method utilizing nuclear magnetic resonance dual-T... E The method for classifying water flooding levels using well logging is implemented according to the following steps:

[0008] Step 1: Calculate the nuclear magnetic resonance transverse relaxation rate using nuclear magnetic resonance logging data;

[0009] Step 2: Calculate the geometric mean of the mobile fluid portion of the T2 spectrum using nuclear magnetic resonance logging data;

[0010] Step 3, using nuclear magnetic resonance double-T E The geometric mean difference of the movable fluid portion of the T2 spectrum at long and short echo intervals is calculated from well logging data.

[0011] Step 4: Invert echo data with long waiting time and short echo interval to obtain the porosity of multiple components;

[0012] Step 5: Plot the percentage of macroporous components in total porosity against the double T value. E The geometric mean difference cross plot of the mobile fluid portion of the T2 spectrum was used to distinguish between the intermediate and high water-flooded layers.

[0013] Step 6, plot the array induced resistivity AT90 and double T E The geometric mean difference of the mobile fluid portion of the T2 spectrum was measured and cross plotted to distinguish between unflooded and low-flooded layers.

[0014] The invention is further characterized in that,

[0015] In step 1, the expression for the transverse relaxation rate of nuclear magnetic resonance is:

[0016]

[0017] In equation (1), T2 is the transverse relaxation time; T 2B ρ2 represents the transverse relaxation time of the volumetric fluid; D represents the fluid diffusion coefficient; ρ2 represents the surface relaxation rate. γ is the ratio of pore area to volume; T is the gyromagnetic ratio; E G is the echo interval, and G is the gradient magnetic field.

[0018] In step 2, the expression for the geometric mean of the mobile fluid component of the T2 spectrum is:

[0019]

[0020] In equation (2), T 2L,MC T represents the geometric mean of the T2 spectrum of a mobile fluid, in milliseconds (ms); T 2c The cutoff value is in milliseconds (ms); A j For the corresponding T 2j Porosity; A n For the corresponding T 2n Porosity; T 2j Let T2 be the relaxation time constant of the j-th relaxation component, and T be the relaxation time constant of the j-th relaxation component. 2n T2 is the relaxation time constant of the nth relaxation component.

[0021] In step 3, the expression for the geometric mean difference of the mobile fluid portion of the T2 spectrum at different echo intervals is:

[0022] ΔT 2LM =T 2LM，S -T 2LM,L (3)

[0023] In equation (3), ΔT 2LM For double T E Measure the geometric mean difference of the mobile fluid portion of the T2 spectrum, in milliseconds; T 2LM，S The geometric mean of the moving fluid portion of the T2 spectrum with long waiting time and short echo interval, in milliseconds; T 2LM，L The geometric mean of the moving fluid portion of the T2 spectrum with long waiting time and long echo interval is given in milliseconds (ms).

[0024] Step 4 specifically involves: obtaining the porosity of various components, i.e., the porosity at different relaxation times, through echo data inversion under long waiting times and short echo intervals. The porosity of different components is then classified according to the relaxation time. Specifically, the percentage content of porosity components in the 1–10 ms interval is represented as the percentage content of small-sized porosity components in the total porosity; the percentage content of porosity components in the 10–100 ms interval is represented as the percentage content of medium-sized porosity components in the total porosity; and the percentage content of porosity components in the 100–10000 ms interval is represented as the percentage content of large-sized porosity components in the total porosity.

[0025] In step 5, the intermediate water-flooded layer: double T E Measure the difference in geometric mean ΔT between T2 2LM For samples with a systolic velocity greater than 150 ms, the percentage of macroporous components in the total porosity is between 11% and 18%.

[0026] High flooding layer: Double T E Measure the difference in geometric mean ΔT between T2 2LM The time limit is greater than 150ms, and the percentage of macroporous components in the total porosity is greater than 18%.

[0027] In step 6, the unflooded layer: double T E Measure the difference in geometric mean ΔT between T22LM The induced resistivity of the array (AT90) is less than 18 Ω·m, and the ms time is less than 40.

[0028] Low flooding layer: Double T E Measure the difference in geometric mean ΔT between T2 2LM Between 40ms and 150ms, the array induced resistivity AT90 is greater than 18Ω·m.

[0029] The beneficial effect of this invention is that it utilizes nuclear magnetic resonance double-T... E The method for classifying water flooding levels in well logging considers not only the percentage of macroporous components in total porosity, but also array induction logging with different radial detection depths, utilizing dual-T... E The geometric mean difference of the mobile fluid portion of the measured T2 spectrum was combined with the percentage content of macroporous components and resistivity information to construct cross plots, determine the boundary values ​​for water flooding layer identification, and identify the type of water flooding layer point by point or layer by layer, thereby realizing the identification of water flooding layers in complex oil-water layers and achieving accurate classification of water flooding levels. Attached Figure Description

[0030] Figure 1 The water-flooded layer nuclear magnetic resonance double-T method described in this embodiment of the invention. E Schematic diagram of numerical simulation of relaxation mechanism;

[0031] Figure 2 This is a comparison diagram of the nuclear magnetic resonance T2 distribution of reservoirs with different degrees of water flooding as described in the embodiments of the present invention;

[0032] Figure 3 The percentage content of macroporous components in total porosity and the double T content described in the embodiments of the present invention are... E Cross plot of geometric mean difference of the mobile fluid portion of the T2 spectrum;

[0033] Figure 4 The array induced resistivity AT90 and dual T described in this embodiment of the invention E Cross plot of geometric mean difference of the mobile fluid portion of the T2 spectrum;

[0034] Figure 5 This is an embodiment of the invention that utilizes nuclear magnetic resonance double-T... E A diagram showing the results of the well logging method for classifying water flooding levels. Detailed Implementation

[0035] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0036] The fluids in the reservoir mainly include mobile oil, mobile water, residual oil, and bound water. With the development of water injection in the oil field, the content of mobile oil in the reservoir decreases, while the content of mobile water increases, forming a water-flooded layer. As the degree of water flooding increases, the water cut of the reservoir increases, but residual oil still exists.

[0037] In nuclear magnetic resonance logging, the transverse relaxation time T2 and the longitudinal relaxation time T1 are both physical quantities caused by the interaction of hydrogen protons in a magnetic field, and the relaxation rate is represented by 1 / T1 or 1 / T2.

[0038] Example 1

[0039] This invention provides a method for utilizing nuclear magnetic resonance double-T E The method for classifying water flooding levels using well logging is implemented according to the following steps:

[0040] Step 1: Calculate the nuclear magnetic resonance transverse relaxation rate using nuclear magnetic resonance logging data;

[0041] There are three relaxation modes of hydrogen nuclei in nuclear magnetic resonance logging: particle surface relaxation, volume relaxation induced by fluid flow, and diffusion relaxation induced by molecular diffusion in a gradient field. Correspondingly, the relaxation time is also composed of these three parts, and its transverse relaxation rate is:

[0042]

[0043] In equation (1), T2 is the transverse relaxation time; T 2B ρ2 represents the transverse relaxation time of the volumetric fluid; D represents the fluid diffusion coefficient; ρ2 represents the surface relaxation rate. γ is the ratio of pore area to volume; T is the gyromagnetic ratio; E is the echo interval; G is the gradient magnetic field.

[0044] Step 2: Calculate the geometric mean of the mobile fluid component of the T2 spectrum using nuclear magnetic resonance logging data. The expression is:

[0045]

[0046] In equation (2), T 2L,MC T represents the geometric mean of the T2 spectrum of a mobile fluid, in milliseconds (ms); T 2c The cutoff value is in milliseconds (ms); A j For the corresponding T 2j Porosity; A n For the corresponding T 2n Porosity; T 2j Let T2 be the relaxation time constant of the j-th relaxation component, and T be the relaxation time constant of the j-th relaxation component. 2n T2 is the relaxation time constant of the nth relaxation component.

[0047] Step 3, using nuclear magnetic resonance double-T EThe geometric mean difference of the mobile fluid portion of the T2 spectrum under long and short echo intervals is calculated from well logging data. This is equivalent to the difference between the geometric mean of the T2 spectrum under long waiting times and short echo intervals and the geometric mean of the T2 spectrum under long waiting times and long echo intervals. E The difference in geometric mean of the mobile fluid portion of the T2 spectrum at (3.6 ms) is:

[0048] ΔT 2LM =T 2LM，S -T 2LM,L (3)

[0049] In equation (3), ΔT 2LM For double T E Measure the geometric mean difference of the mobile fluid portion of the T2 spectrum, in milliseconds; T 2LM，S The geometric mean of the moving fluid portion of the T2 spectrum with long waiting time and short echo interval, in milliseconds; T 2LM，L The geometric mean of the moving fluid portion of the T2 spectrum with long waiting time and long echo interval is given in milliseconds (ms).

[0050] The key to fluid identification in nuclear magnetic resonance logging is eliminating the influence of pore structure factors on fluid identification. The pore structure information S / V is determined by the reservoir itself, while the fluid diffusion coefficient D is related to the type of pore fluid. The diffusion coefficients of the three fluids are as follows: D g >D w >D o In the formula D g D is the diffusion coefficient of the gas. w D is the diffusion coefficient of water. o Let T be the oil diffusion coefficient. Therefore, after reservoir water injection, the volume of movable water in the large pores increases, resulting in a longer echo interval T. E (3.6ms) The T2 spectrum shifts in the direction of decreasing, and the spectrum shift phenomenon is obvious.

[0051] Step 4: Nuclear magnetic resonance logging can obtain the total porosity, effective porosity, movable fluid porosity, and bound fluid porosity of the formation. By inverting echo data under long waiting times and short echo intervals, the porosity of different components is obtained, i.e., the porosity under different relaxation times. The porosity of different components is classified according to relaxation time. The specific classification rules are as follows: relaxation time ranges of 1-10 ms, 10-100 ms, and 100-10000 ms represent the percentage content of porosity components. Among them, the percentage content of porosity components in the 1-10 ms range represents the percentage content of small-sized porosity components in total porosity, the percentage content of porosity components in the 10-100 ms range represents the percentage content of medium-sized porosity components in total porosity, and the percentage content of porosity components in the 100-10000 ms range represents the percentage content of large-sized porosity components in total porosity.

[0052] Step 5: Plot the percentage of macroporous components in total porosity against the double T value.E Cross plot of geometric mean difference of the mobile fluid portion of the T2 spectrum, with the horizontal axis representing the biT spectrum. E The geometric mean difference of the mobile fluid portion of the T2 spectrum was measured, with the vertical axis representing the percentage content of macroporous components in the total porosity.

[0053] Medium-water-flooded layer: Double T E Measure the difference in geometric mean ΔT between T2 2LM For samples with a systolic velocity greater than 150 ms, the percentage of macroporous components in the total porosity is between 11% and 18%.

[0054] High flooding layer: Double T E Measure the difference in geometric mean ΔT between T2 2LM The time limit is greater than 150ms, and the percentage of macroporous components in the total porosity is greater than 18%.

[0055] However, using the percentage of macroporous components in the total porosity in the cross plot is not very effective in distinguishing between unflooded and low-flooded layers.

[0056] The research area involved in this invention is the infill zone of water-flooded old wells, which gradually extends towards ultra-low permeability and widened perimeter zones, where reservoir properties are worse, fractures are more developed, and oil-water relationships are more complex. For the identification of water-flooded layers, dual-T nuclear magnetic resonance imaging is used. E Well logging has significant advantages; the more severe the water flooding, the more pronounced the nuclear magnetic resonance (NMR) response characteristics become. This study aims to investigate the NMR double-T characteristics of water-flooded layers. E Numerical simulations were performed to analyze the well logging response characteristics. Figure 1 This is a schematic diagram of the numerical simulation of the double-TE NMR relaxation mechanism in a water-flooded layer. Due to the influence of diffusion relaxation, the magnitude of the T2 distribution shift varies with different oil and water contents. The oil peak follows the water peak, and the greater the degree of water flooding, the more the long echo interval T2 spectrum shifts towards a decreasing direction. This shows that the double-TE NMR distribution shifts differently with varying oil and water contents. E The observed T2 distributions have different geometric means.

[0057] Furthermore, after long-term scouring by injected water, the clay adhering to the pore walls of the reservoir is stripped away. High-porosity, high-permeability formations are typically flooded first, and the scouring force of the injected water washes away the clay in the large pores, increasing the throat radius. In the T2 distribution of nuclear magnetic resonance (NMR), the amplitude of the short relaxation region decreases, while the amplitude of the long relaxation region increases. Figure 2 The nuclear magnetic resonance response characteristics of flooded layers with different degrees of flooding are shown. The T2 distribution of flooded layers with different degrees of flooding is different. The higher the degree of flooding, the greater the content of macropores.

[0058] Step 6, plot the array induced resistivity AT90 and double T E Cross plot of the geometric mean difference of the movable fluid portion of the T2 spectrum, with the horizontal axis representing the difference between the two T values. EThe geometric mean difference of the mobile fluid portion of the T2 spectrum was measured, with the ordinate representing the array induced resistivity AT90.

[0059] Unflooded layer: Double T E Measure the difference in geometric mean ΔT between T2 2LM The induced resistivity of the array (AT90) is less than 18 Ω·m, and the ms time is less than 40.

[0060] Low flooding layer: Double T E Measure the difference in geometric mean ΔT between T2 2LM Between 40ms and 150ms, the array induced resistivity AT90 is greater than 18Ω·m;

[0061] This cross-plot can effectively distinguish between unflooded and low-flooded layers. However, it is less effective for medium-flooded and high-flooded layers because it is affected by the type of water injection (saltwater injection or freshwater injection).

[0062] Array-based resistivity logging can reflect the oil and water information of intact formations, but the resistivity logging response of water-flooded formations is complex. For brine-injected water-flooded formations, the formation resistivity decreases with increasing injected water volume; for freshwater injection, the resistivity initially decreases and then increases with increasing injected fluid volume, exhibiting a more complex pattern, making it difficult to identify water-flooded formations using resistivity alone. However, nuclear magnetic resonance (NMR) measurements, which assess the relaxation characteristics of fluids, are less affected by the salinity of intact formation water and injected water, and can identify the type of fluid.

[0063] Example 2

[0064] The example is Well A in the flooded old well reinforcement zone, at a depth of 1894-1900m. Following steps 1-4, a double T is obtained. E The geometric mean difference of the mobile fluid portion of the T2 spectrum and the percentage content of macroporous components in the total porosity were measured, such as... Figure 3 As shown, the percentage content of macroporous components in total porosity and the relationship between T and T are plotted. E Cross-plot of the geometric mean difference of the mobile fluid portion of the T2 spectrum, as shown. Figure 4 As shown, the array induced resistivity AT90 and dual T are plotted. E The geometric mean difference cross-plot of the mobile fluid portion of the T2 spectrum is obtained by measuring the cross-plot of ... E The results of well logging data processing, such as Figure 5 As shown, the first channel represents component porosity, the third channel represents resistivity, the fourth channel represents the standard T² distribution, and the fifth channel represents the double T² distribution. E Measurements were taken; the sixth track represents macropore content, the seventh track represents porosity, and the eighth track represents interpretation conclusions; double-T measurements were performed at depths of 1894-1900 m. EThe geometric mean difference of the mobile fluid portion of the T2 spectrum was approximately 150 ms, with a macropore content of approximately 17%, which can be interpreted as a medium-water-flooded layer. The average production volume over the first three months of operation was 3.62 m³. 3 / d, with a water content of 37.21%, and the test data also confirmed that this layer is a medium-water-flooded layer, which confirms that the conclusions explained using the above methods and techniques are correct.

[0065] Example 3

[0066] This invention provides a method utilizing nuclear magnetic resonance double-T E The method for classifying water flooding levels using well logging is implemented according to the following steps:

[0067] Step 1: Calculate the nuclear magnetic resonance transverse relaxation rate using nuclear magnetic resonance logging data;

[0068] Step 2: Calculate the geometric mean of the mobile fluid portion of the T2 spectrum using nuclear magnetic resonance logging data;

[0069] Step 3, using nuclear magnetic resonance double-T E The geometric mean difference of the movable fluid portion of the T2 spectrum at long and short echo intervals is calculated from well logging data.

[0070] Step 4: Invert echo data with long waiting time and short echo interval to obtain the porosity of multiple components;

[0071] Step 5: Plot the percentage of macroporous components in total porosity against the double T value. E The geometric mean difference cross plot of the mobile fluid portion of the T2 spectrum was used to distinguish between the intermediate and high water-flooded layers.

[0072] Step 6, plot the array induced resistivity AT90 and double T E The geometric mean difference of the mobile fluid portion of the T2 spectrum was measured and cross plotted to distinguish between unflooded and low-flooded layers.

[0073] Example 4

[0074] The difference from Example 3 is as follows:

[0075] In step 1, the expression for the transverse relaxation rate of nuclear magnetic resonance is:

[0076]

[0077] In equation (1), T2 is the transverse relaxation time; T 2B ρ2 represents the transverse relaxation time of the volumetric fluid; D represents the fluid diffusion coefficient; ρ2 represents the surface relaxation rate. γ is the ratio of pore area to volume; T is the gyromagnetic ratio; E G is the echo interval, and G is the gradient magnetic field.

[0078] In step 2, the expression for the geometric mean of the mobile fluid component of the T2 spectrum is:

[0079]

[0080] In equation (2), T 2L,MC T represents the geometric mean of the T2 spectrum of a mobile fluid, in milliseconds (ms); T 2c The cutoff value is in milliseconds (ms); A j For the corresponding T 2j Porosity; A n For the corresponding T 2n Porosity; T 2j Let T2 be the relaxation time constant of the j-th relaxation component, and T be the relaxation time constant of the j-th relaxation component. 2n T2 is the relaxation time constant of the nth relaxation component.

[0081] In step 3, the expression for the geometric mean difference of the mobile fluid portion of the T2 spectrum at different echo intervals is:

[0082] ΔT 2LM =T 2LM，S -T 2LM,L (3)

[0083] In equation (3), ΔT 2LM For double T E Measure the geometric mean difference of the mobile fluid portion of the T2 spectrum, in milliseconds; T 2LM，S The geometric mean of the moving fluid portion of the T2 spectrum with long waiting time and short echo interval, in milliseconds; T 2LM，L The geometric mean of the moving fluid portion of the T2 spectrum with long waiting time and long echo interval is given in milliseconds (ms).

[0084] Step 4 specifically involves: obtaining the porosity of various components, i.e., the porosity at different relaxation times, through echo data inversion under long waiting times and short echo intervals. The porosity of different components is then classified according to the relaxation time. Specifically, the percentage content of porosity components in the 1–10 ms interval is represented as the percentage content of small-sized porosity components in the total porosity; the percentage content of porosity components in the 10–100 ms interval is represented as the percentage content of medium-sized porosity components in the total porosity; and the percentage content of porosity components in the 100–10000 ms interval is represented as the percentage content of large-sized porosity components in the total porosity.

Claims

1. Using nuclear magnetic resonance double-T E The method for classifying water flooding levels through well logging is characterized by, The specific steps are as follows: Step 1: Calculate the nuclear magnetic resonance transverse relaxation rate using nuclear magnetic resonance logging data; Step 2: Calculate the geometric mean of the mobile fluid portion of the T2 spectrum using nuclear magnetic resonance logging data; Step 3, using nuclear magnetic resonance double-T E The geometric mean difference between the movable fluid portions of the well logging data with long and short echo intervals is calculated. Step 4: Obtain the porosity of different components using echo data with long waiting time and short echo interval; Step 5: Plot the percentage of macroporous components in total porosity against the double T value. E The geometric mean difference cross plot of the mobile fluid portion of the T2 spectrum was used to distinguish between the intermediate and high water-flooded layers. Step 6, plot the array induced resistivity AT90 and double T E The geometric mean difference cross plot of the mobile fluid portion of the T2 spectrum is used to distinguish between unflooded and low-flooded layers.

2. The method of utilizing nuclear magnetic resonance double-T as described in claim 1 E The method for classifying water flooding levels through well logging is characterized by, In step 1, the expression for the transverse relaxation rate of nuclear magnetic resonance is: In equation (1), T2 is the transverse relaxation time; T 2B ρ2 represents the transverse relaxation time of the volumetric fluid; D represents the fluid diffusion coefficient; ρ2 represents the surface relaxation rate. γ is the ratio of pore area to volume; T is the gyromagnetic ratio; E is the echo interval; G is the gradient magnetic field.

3. The method of utilizing nuclear magnetic resonance double-T as described in claim 1 E The method for classifying water flooding levels through well logging is characterized by, In step 2, the expression for the geometric mean of the mobile fluid component of the T2 spectrum is: In equation (2), T 2L,MC T represents the geometric mean of the T2 spectrum of a mobile fluid, in milliseconds (ms); T 2c The cutoff value is in milliseconds (ms); A j For the corresponding T 2j Porosity; A n For the corresponding T 2n Porosity; T 2j Let T2 be the relaxation time constant of the j-th relaxation component, and T be the relaxation time constant. 2n T2 is the relaxation time constant of the nth relaxation component.

4. The method of utilizing nuclear magnetic resonance double-T as described in claim 1 E The method for classifying water flooding levels through well logging is characterized by, In step 3, the expression for the geometric mean difference of the movable fluid portion with long and short echo intervals is: ΔT 2LM =T 2LM，S -T 2LM,L (3) In equation (3), ΔT 2LM The geometric mean difference of the mobile fluid portion of the spectrum at different echo intervals T2, in milliseconds; T 2LM，S The geometric mean of the moving fluid portion of the T2 spectrum with long waiting time and short echo interval, in milliseconds; T 2LM，L The geometric mean of the moving fluid portion of the T2 spectrum with long waiting time and long echo interval is given in milliseconds.

5. The method of utilizing nuclear magnetic resonance double-T as described in claim 1 E The method for classifying water flooding levels through well logging is characterized by, Step 4 specifically involves obtaining the porosity of different components, i.e., the porosity at different relaxation times, through echo data with long waiting times and short echo intervals. The porosity of different components is then classified according to relaxation time. Specifically, the percentage content of porosity components in the 1–10 ms interval is represented as the percentage content of small-sized porosity components in the total porosity; the percentage content of porosity components in the 10–100 ms interval is represented as the percentage content of medium-sized porosity components in the total porosity; and the percentage content of porosity components in the 100–10000 ms interval is represented as the percentage content of large-sized porosity components in the total porosity.

6. The method of utilizing nuclear magnetic resonance double-T as described in claim 1 E The method for classifying water flooding levels through well logging is characterized by, In step 5, the intermediate water-flooded layer: double T E Measure the difference in geometric mean ΔT between T2 2LM For samples with a systolic velocity greater than 150 ms, the percentage of macroporous components in the total porosity is between 11% and 18%. High flooding layer: Double T E Measure the difference in geometric mean ΔT between T2 2LM The time limit is greater than 150ms, and the percentage of macroporous components in the total porosity is greater than 18%.

7. The method of utilizing nuclear magnetic resonance double-T as described in claim 1 E The method for classifying water flooding levels through well logging is characterized by, In step 6, the unflooded layer: double T E Measure the difference in geometric mean ΔT between T2 2LM The induced resistivity of the array (AT90) is less than 18 Ω·m, and the ms time is less than 40. Low flooding layer: Double T E Measure the difference in geometric mean ΔT between T2 2LM Between 40ms and 150ms, the array induced resistivity AT90 is greater than 18Ω·m.

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