Deep coal reservoir in-situ apparent permeability calculation method applied to nuclear magnetic logging

Abstract

The invention belongs to the technical field of geophysical logging and oil-gas field development, and discloses a deep coal reservoir in-situ apparent permeability calculation method applied to nuclear magnetic logging. The method comprises the following steps: acquiring a deep coal reservoir in-situ nuclear magnetic resonance spectrum; calculating the accumulated signal amplitude of the spectrum; fitting the relationship under a double logarithmic coordinate system, and solving fractal dimensions representing the heterogeneity of the pore structure; the fractal absolute permeability is calculated according to the Lisdarcy law; and finally, in combination with a Klinkenberg equation, establishing a complete model for predicting the apparent permeability under any pore pressure. The permeability result calculated through the method is highly consistent with the in-situ real flow capacity, if the relative error in the embodiment is only 1.34%, the technical problem that an existing model loses the actual guiding significance for productivity prediction due to result distortion is solved.

Description

Technical Field

[0001] This invention belongs to the field of geophysical logging and oil and gas field development technology, and particularly relates to a method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging. Background Technology

[0002] my country possesses abundant deep coalbed methane resources, representing a crucial next step in increasing natural gas reserves and production. Accurately obtaining the apparent permeability of deep coalbed methane reservoirs under in-situ conditions is key to production capacity prediction and efficient development. Compared to shallow coal seams, deep coalbed methane reservoirs are characterized by high ground stress, high fluid pressure, and high formation temperature, resulting in relatively poor reservoir properties and lower absolute permeability. When gas flows within the reservoir's pore-throat channels, the wall velocity is not zero, exhibiting a significant slippage effect, making the apparent permeability much higher than the absolute permeability. This characteristic makes traditional permeability evaluation methods difficult to apply directly. Therefore, accurately characterizing and predicting the apparent permeability of deep coalbed methane reservoirs under in-situ conditions is a current technical challenge in the efficient exploitation of deep coalbed methane.

[0003] Nuclear magnetic resonance logging (NMR) technology, with its unique advantages of being able to penetrate deep into the formation and conduct continuous, non-destructive exploration of reservoirs in real "high-temperature, high-volume, and high-pressure" environments, has become the most important technical means for evaluating the in-situ apparent permeability of reservoirs. Among them, the SDR model and the Coates model are commonly used NMR logging permeability calculation models in the field. However, the above models all have certain limitations: (1) For the Coates model, its calculation results are extremely dependent on the selection of the T2 cutoff value. Due to the extremely complex pore structure of deep coal seams, there is no unique T2 cutoff value. In the current field logging interpretation work, due to the lack of core experimental data calibrated point by point, engineers are forced to use a fixed empirical T2 cutoff value. This approach cannot be adapted to the highly heterogeneous pore system of deep coal seams. The permeability evaluation results will inevitably deviate significantly from the actual in-situ flow capacity of the reservoir, thus losing the practical guiding significance for production capacity prediction. (2) The SDR model oversimplifies the pore structure and completely ignores the influence of reservoir heterogeneity on pore size, resulting in a serious overestimation of permeability. (3) Both the SDR model and the Coates model calculate the absolute permeability of the rock. The gas slippage effect was not considered during the model construction process, resulting in a large error in the apparent permeability calculation results.

[0004] Current permeability calculation methods suffer from limitations such as insufficient consideration of influencing factors (heterogeneity, slippage effect) and overly idealized models, leading to significant errors and even distortions in permeability test results. Therefore, there is an urgent need to develop a method for calculating reservoir apparent permeability in situ based on nuclear magnetic resonance logging data that can comprehensively consider the influence of multiple factors.

[0005] Limitations of the SDR / Coates model: Traditional models (such as the SDR model) assume that the pores are ideal, smooth circular tubes, oversimplifying the pore structure and ignoring the extremely strong heterogeneity and roughness of deep coal reservoirs. This idealized assumption leads to calculated permeability that is often much higher than the actual value.

[0006] Ignoring the slippage effect: Existing models typically calculate absolute permeability (liquid permeability), completely ignoring the slippage effect of gas in micro- and nano-pores. Under deep high-pressure conditions, the slippage effect is significant, and ignoring it will lead to calculated results that are lower than the actual flow capacity of the gas.

[0007] Based on the above analysis, the problems and shortcomings of the existing technology are as follows: (1) The calculation results of the Coates model are extremely dependent on the selection of the T2 cutoff value. Due to the extremely complex pore structure of deep coal seams, there is no unique T2 cutoff value. The method of using fixed empirical values ​​cannot be adapted to the highly heterogeneous pore system, resulting in the distortion of the permeability evaluation results. The SDR model oversimplifies the pore structure and completely ignores the influence of reservoir heterogeneity on pore size. The pore structure of deep coal seams is complex, and the radius of different pore throats varies greatly, resulting in a serious overestimation of permeability.

[0008] (2) Both the SDR model and the Coates model calculate the absolute permeability of the rock, ignoring the influence of gas slippage under deep high pressure. The gas slippage effect was not considered at all during the model construction process, resulting in the apparent permeability being much higher than the absolute permeability. Ignoring this factor will lead to a large error in the apparent permeability calculation results.

[0009] (3) Existing permeability calculation methods mainly focus on parameters such as porosity and pore throat radius, and do not adequately consider factors such as heterogeneity and slippage effect. Deep coal seam reservoirs have complex physical properties, are extremely heterogeneous, and have a significant gas slippage effect. Ignoring these factors will lead to large errors or even distortions in the permeability test results.

[0010] To overcome the problems existing in related technologies, the present invention discloses an embodiment of a method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging, and particularly relates to an in-situ apparent permeability calculation method considering reservoir heterogeneity and slippage effect. The technical solution is as follows: This invention is implemented as follows: a method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging. This method integrates the characterization of reservoir pore structure heterogeneity with the coupling mechanism of gas slippage effect, and achieves accurate prediction of in-situ apparent permeability through the following steps: S1, Under in-situ high temperature and high pressure conditions in deep coal reservoirs, nuclear magnetic resonance logging was carried out on the target well section to obtain in-situ nuclear magnetic resonance data reflecting the spatial distribution characteristics of reservoir pores. Spectrum; S2, for the obtained in-situ nuclear magnetic resonance Spectral data are processed and calculated. Time from minimum value to any value cumulative signal amplitude ; S3, based on accumulated signal amplitude data Compared with lateral relaxation time data Plot the cumulative signal amplitude logarithm in a double logarithmic coordinate system. The cross plot of the logarithms over time was used, and the slope was obtained by linear fitting of the data points relating the two logarithms. Determine the fractal dimension that characterizes the heterogeneity of reservoir pore structure. ; S4, Define the effective radius With fractal dimension The mathematical relationship is used to correct the pore flow boundary, thereby quantitatively characterizing the influence of true roughness and heterogeneity on the flow. S5, effective radius As a new boundary condition, it is substituted into the classical fluid equilibrium equations for solution, establishing a single-pore fluid total flow rate model that incorporates real roughness and heterogeneous characteristics. ; S6, Combined Total Flow Model Using Darcy's law to calculate fractal absolute permeability ; S7 will increase the absolute permeability of fractals. Combined with the Klinkenberg equation describing the gas slip effect, a method for predicting arbitrary pore pressure is established. Apparent penetration rate The complete model.

[0011] In step S2, the signal amplitude data is accumulated. The calculation formula is: ; In the formula, For the minimum relaxation time, Let the relaxation time be arbitrary. Let the signal amplitude correspond to any relaxation time. This is the sum of all relaxation times.

[0012] In step S3, the cumulative signal amplitude logarithm and logarithm of time The linear fitting expression is: ; In the formula, The slope of the expression. This is the intercept of the expression.

[0013] Furthermore, fractal dimension The calculation formula is: .

[0014] In step S4, the effective radius The calculation formula is: ; In the formula, Where is the throat radius, For fractal dimension, For calibration coefficients, The equivalent height of the roughness element refers to a physical parameter characterizing the roughness of the inner wall of the pores in a coal reservoir. It represents the average height characteristic of irregular protrusions on the pore wall. This parameter reflects the geometric deviation between the real pore wall and the ideal smooth wall and is a key factor affecting the flow resistance of fluid in the pores.

[0015] Calibration coefficient Obtained by laboratory testing and analysis of representative core samples from the well section; when When the pore throat model is considered to be smooth, without roughness, and ideal, the ideal apparent permeability can be calculated using the SDR model. The expression is: ; In the formula, The permeability calculated using the SDR model; These are the fitting coefficients for the SDR model; Porosity; for The geometric mean, which is directly calculated by the accompanying software for nuclear magnetic resonance (NMR); Permeability approximately satisfies Poiseuille's law with respect to radius. The ratio of actual apparent permeability to ideal apparent permeability is: ; Combined effective radius Ideal apparent penetration rate and actual apparent penetration rate The formula for calculating the ratio of the apparent permeability to the ideal permeability yields the calibration coefficient. The expression is: ; Among them, actual apparent penetration rate These values ​​were obtained directly from laboratory core displacement experiments.

[0016] In step S5, substitute the effective radius. Post-total flow model The expression is: ; In the formula, The pressure difference across the pore model. For fluid dynamic viscosity, For sample length, The effective radius.

[0017] In step S6, the flow rate expression for Darcy permeability is: ; Fractal absolute penetration The expression is: ; In the formula, the orifice throat radius Depend on The value is obtained through conversion.

[0018] Furthermore, pore throat radius data With nuclear magnetic resonance The data transformation relationship is as follows: ; In the formula, The surface relaxation rate, This is the shape factor.

[0019] In step S7, in-situ environmental data and high-pressure physical property data of reservoir fluids are collected from the deep reservoir, including formation temperature. and pore pressure By combining the absolute permeability expression with the Klinkenberg equation describing the gas slippage effect, a method for predicting arbitrary pore pressure is established. Apparent penetration rate The complete model; Apparent penetration rate The complete model expression is: ; ; In the formula, The slip coefficient; Mass of gas molecules; It is a constant, with a value of 0.9; is the dynamic viscosity coefficient of the gas; For temperature; The ideal gas constant is 8.314 J·mol⁻¹. -1 ·K -1 .

[0020] Combining all the above technical solutions, the beneficial effects of this invention are as follows: First, this invention achieves accurate calculation of apparent permeability by processing in-situ nuclear magnetic resonance logging data, reservoir temperature and pressure environment data, and fluid high-pressure physical property data of deep coal reservoirs.

[0021] Second, this invention quantitatively characterizes the effect of reservoir heterogeneity on permeability: this invention utilizes nuclear magnetic resonance. Spectral calculation of fractal dimension And innovatively introduce the effective radius This method corrects the pore flow boundary, thereby deriving the fractal absolute permeability. It overcomes the oversimplification of the pore structure in the SDR model and the extreme dependence of the Coates model on a single pore structure. The cutoff value is insufficient to adapt to the strong heterogeneity of deep coal seams.

[0022] This invention couples the gas slip effect with the heterogeneity of pore structure: It combines the derived fractal absolute permeability (which already includes heterogeneity information) with the Klinkenberg equation describing the gas slip effect to establish a comprehensive model. This solves the fundamental problem of traditional models that only calculate absolute permeability and completely ignore the slip effect, leading to results that severely deviate from the actual gas flow capacity.

[0023] This invention provides high-precision key parameters for production capacity prediction, improving the reliability of the prediction: The invention aims to obtain accurate in-situ apparent permeability, a crucial parameter for production capacity prediction and efficient development. By comprehensively considering heterogeneity and slippage effects, the permeability results calculated by this method are highly consistent with the actual in-situ flow capacity (e.g., the relative error in the example is only 1.34%), solving the technical problem that existing models lose their practical guiding significance for production capacity prediction due to distorted results. Therefore, this invention can provide high-precision core input parameters for numerical simulation and production capacity prediction of deep coalbed methane reservoirs, greatly improving the accuracy of development scheme formulation and mining effect prediction.

[0024] Third, this invention provides a high-precision in-situ apparent permeability calculation method for deep coal reservoirs, which can be widely applied to well logging interpretation and reservoir evaluation of unconventional oil and gas reservoirs such as coalbed methane and shale gas. By optimizing fracturing schemes and production parameters, it can increase single-well production, reduce development costs, and improve permeability prediction accuracy. This provides a reliable basis for optimizing oil and gas field development schemes, fracturing design, production capacity prediction, and economic evaluation, significantly reducing development risks and costs. It has significant engineering application value and broad market prospects.

[0025] Currently, domestic and international methods for evaluating the permeability of deep coal reservoirs mainly focus on traditional well logging interpretation methods and core analysis methods. A complete methodological system has not yet been formed that can simultaneously couple nuclear magnetic resonance (NMR) fractal heterogeneity characterization with gas slippage effects and directly extrapolate from in-situ well logging data. These methods suffer from dependence on idealized models and difficulty adapting to complex geological conditions, leading to significant errors in permeability evaluation results. This invention is the first to achieve permeability evaluation from NMR... The integrated, high-precision prediction of apparent permeability from spectrum to in-situ fills a technological gap in this field.

[0026] Deep coal reservoirs are characterized by "three highs" (high ground stress, high pore pressure, and high geothermal temperature) and strong heterogeneity. Traditional permeability models, due to excessive simplification assumptions, cannot accurately reflect the true flow characteristics, leading to predictions that deviate significantly from reality. This invention, by introducing fractal dimension to correct the flow boundary and coupling it with the Klinkenberg equation, for the first time unifies the influence of heterogeneity and slippage effect on permeability at the mechanistic level, successfully solving the long-standing technical problem of accurately obtaining in-situ apparent permeability in deep coal seams.

[0027] Traditional nuclear magnetic resonance logging permeability models (such as SDR and Coates models) are generally based on the assumption of ideal smooth pores and are only applicable to liquid or absolute permeability calculations, neglecting the slippage effect of gas in micro- and nano-pores. This invention overcomes the dependence of traditional permeability evaluation methods on idealized models by introducing the concepts of fractal dimension and effective radius, which better reflects the actual situation of deep coal seam reservoirs. It overcomes the long-standing simplification and underestimation of the gas permeation mechanism in deep coal seams, providing a new theoretical framework and technical approach for permeability evaluation of complex reservoirs. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure; Figure 1 This is a flowchart of the in-situ apparent permeability calculation method for deep coal reservoirs applied to nuclear magnetic resonance logging, provided by an embodiment of the present invention. Figure 2 This is a nuclear magnetic resonance logging curve corresponding to the target depth of well B in Block A of the Ordos Basin, provided in an embodiment of the present invention. Figure 3 This is the nuclear magnetic resonance T2 spectrum corresponding to the target depth of well B in block A of the Ordos Basin provided in this embodiment of the invention; Figure 4 This is a nuclear magnetic resonance fractal dimension fitting diagram of the target depth of well B in Block A of the Ordos Basin provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a flat, rough pore model provided in an embodiment of the present invention. Detailed Implementation

[0029] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0030] The innovation of this invention lies in: (1) By nuclear magnetic resonance Spectral calculation of fractal dimension This method quantitatively characterizes the heterogeneity of reservoir pore structure, overcomes the dependence of traditional models on idealized models, and achieves in-situ quantitative characterization of reservoir pore structure heterogeneity.

[0031] (2) Define the effective radius With fractal dimension The mathematical relationship is used to correct the pore flow boundary, thereby more accurately reflecting the influence of true roughness and heterogeneity on the flow.

[0032] (3) Fractal absolute permeability Combined with the Klinkenberg equation describing the gas slip effect, a method is established to predict arbitrary pore pressure while simultaneously considering pore structure heterogeneity and the gas slip mechanism. Subsoil apparent permeability The complete model overcomes the problem of traditional models ignoring the slippage effect, which leads to results that deviate from reality.

[0033] (4) This method uses in-situ nuclear magnetic resonance Using the spectrum as the sole input, the fractal dimension calculation, effective radius correction, fractal absolute permeability derivation, and slippage effect correction are completed sequentially, forming a complete, closed-loop, and field-applicable permeability calculation process, achieving a high degree of unity between theory and engineering practice.

[0034] Example 1, such as Figure 1 As shown in the embodiments of the present invention, the method for calculating the in-situ apparent permeability of deep coal reservoirs applied to nuclear magnetic resonance logging includes the following steps: S1, Under in-situ high temperature and high pressure conditions in deep coal reservoirs, nuclear magnetic resonance logging was carried out on the target well section to obtain in-situ nuclear magnetic resonance data reflecting the spatial distribution characteristics of reservoir pores. Spectrum; S2, for the obtained in-situ nuclear magnetic resonance Spectral data are processed and calculated. Time from minimum value to any value cumulative signal amplitude ; S3, based on accumulated signal amplitude data Compared with lateral relaxation time data Plot the cumulative signal amplitude logarithm in a double logarithmic coordinate system. The cross plot of the logarithms over time was used, and the slope was obtained by linear fitting of the data points relating the two logarithms. Determine the fractal dimension that characterizes the heterogeneity of reservoir pore structure. ; S4, Define the effective radius With fractal dimension The mathematical relationship is used to correct the pore flow boundary, thereby quantitatively characterizing the influence of true roughness and heterogeneity on the flow. S5, effective radius As a new boundary condition, it is substituted into the classical fluid equilibrium equations for solution, establishing a single-pore fluid total flow rate model that incorporates real roughness and heterogeneous characteristics. ; S6, Combined Total Flow Model Using Darcy's law to calculate fractal absolute permeability ; S7 will increase the absolute permeability of fractals. Combined with the Klinkenberg equation describing the gas slip effect, a method for predicting arbitrary pore pressure is established. Apparent penetration rate The complete model.

[0035] Taking Block A of the Ordos Basin as an example, nuclear magnetic resonance logging was conducted on a certain section of Well B in Block A. The nuclear magnetic resonance logging curve is as follows: Figure 2 As shown, nuclear magnetic resonance imaging of the target depth under in-situ conditions was obtained. Spectrum, such as Figure 3 As shown.

[0036] Furthermore, based on the obtained nuclear magnetic resonance images... Spectrum, Calculation Time from minimum value to any value cumulative signal amplitude .

[0037] The formula for calculating the cumulative signal amplitude is: ; In the formula, The minimum relaxation time is expressed in milliseconds (ms). The relaxation time is arbitrary and is expressed in milliseconds (ms). The signal amplitude corresponding to any relaxation time, in au; This is the sum of all relaxation times.

[0038] In a double logarithmic coordinate system, plot the logarithm of the cumulative signal amplitude ( )and logarithm of time The intersection diagram, In the formula, The slope of the expression. This is the intercept of the expression. and The slope is obtained by linear fitting of the relational data points. ,like Figure 4 As shown. Fractal dimension .

[0039] like Figure 5 As shown, the effective radius in a real reservoir environment Significantly smaller than the geometric radius Based on geometric radius Calculate the effective radius under the influence of pore roughness and heterogeneity. The fractal dimension can be used to simultaneously characterize reservoir roughness and heterogeneity.

[0040] Furthermore, define the effective radius. With fractal dimension The mathematical relationship is as follows: ; In the formula, Where is the throat radius, For fractal dimension, Calibration coefficients, dimensionless, calibration coefficients It can be obtained by testing representative core samples from the well section in the laboratory; The equivalent height of the roughness element is expressed in μm; it refers to the physical parameter characterizing the roughness of the inner wall of the pores in a coal reservoir. It represents the average height of irregular protrusions on the pore wall. This parameter reflects the geometric deviation between the real pore wall and the ideal smooth wall and is a key factor affecting the flow resistance of fluid in the pores.

[0041] when When the pore throat model is considered to be smooth, without roughness, and ideal, the ideal apparent permeability can be calculated using the SDR model. The expression is: ; In the formula, The permeability calculated using the SDR model, in mD / ms 2 ; These are the fitting coefficients for the SDR model; Porosity, expressed as % for The geometric mean, in milliseconds, is calculated directly by the NMR software. Permeability approximately satisfies Poiseuille's law with respect to radius. The ratio of actual apparent permeability to ideal apparent permeability is: ; Solving equations (2), (3), and (4) simultaneously, we obtain the effective radius. Ideal apparent penetration rate and actual apparent penetration rate The formula for calculating the ratio of the apparent permeability to the ideal permeability yields the calibration coefficient. The expression is: ; Among them, actual apparent penetration rate These values ​​were obtained directly from laboratory core displacement experiments.

[0042] Table 1 Calculation results of parameters required for calibration coefficient C

[0043] Furthermore, the test and calculation results of the above parameters are summarized in Table 1, and substituted into formula (5) to obtain the calibration coefficients. .

[0044] Furthermore, the effective radius The new boundary condition is introduced into the classical fluid dynamics equilibrium equation for solution to obtain the total flow rate under the influence of roughness and heterogeneity. The expression.

[0045] Total flow rate under the influence of roughness and heterogeneity The expression is: ; In the formula, The pressure difference across the pore model is expressed in MPa. The viscosity is the fluid dynamic viscosity, expressed in Pa·s. The sample length is in meters. The effective radius.

[0046] Furthermore, since the flow rate in Darcy's permeability is the same as the total flow rate mentioned above, combining the total flow rate expression with Darcy's law, the resulting expression is: ; Fractal absolute penetration The expression is: ; In the formula, the orifice throat radius Depend on The value is converted to obtain the pore throat radius. The unit is nm.

[0047] Throat radius data With nuclear magnetic resonance The data transformation relationship is as follows: ; In the formula, Surface relaxation rate, in nm / ms; The shape factor is 1 for flat pores, 2 for columnar pores, and 3 for spherical pores.

[0048] The pores in Block A of the Ordos Basin are mostly flat plate-shaped pores, therefore Set to 1; Surface relaxation rate The surface relaxation rate is between 0.26 and 0.68 nm / ms, with an average of 0.38 nm / ms. Therefore, this invention selects 0.38 nm / ms as the surface relaxation rate for the relaxation time T2 and aperture conversion. The calculation results from the accompanying NMR software show... It takes 3.203ms.

[0049] By combining the derived absolute permeability expression with the Klinkenberg equation describing the gas slippage effect, a final formula capable of predicting arbitrary pore pressure can be established. Apparent penetration rate The complete model.

[0050] The apparent permeability The complete mathematical expression of the model is: ; ; In the formula, The slip coefficient; This refers to the molecular mass of a gas, expressed in kg / mol. It is a constant, with a value of 0.9; ρ is the dynamic viscosity coefficient of the gas, with units of Pa·s; Temperature, in Kelvin (K). The ideal gas constant is given by a value of 8.314 J·mol⁻¹. -1 ·K -1 .

[0051] Furthermore, the model parameter values ​​were determined: the main component of deep coalbed methane is methane, with a molecular weight of 0.01604 kg / mol; the target depth of the B well section in Block A of the Ordos Basin is 1856.62 m, the average geothermal gradient is 2.21 ℃ / hm, and the surface temperature is 10.09 ℃. According to the formation temperature calculation formula, the formation temperature at 1856.62 m can be calculated to be 324.27 K; the pore pressure can be estimated from the hydrostatic pressure, and its magnitude is 18.57 MPa.

[0052] Substituting the above parameter values ​​into formulas (10) and (11), the in-situ apparent permeability of well B at the target depth in block A of the Ordos Basin can be obtained. The calculated apparent permeability is 0.1105 mD.

[0053] In the laboratory, permeability tests were conducted on standard plunger samples at the target depth using a high-temperature, high-pressure permeability testing device. The apparent permeability test result was 0.112 mD. The relative error between the model prediction and the experimental true value was only 1.34%, fully demonstrating the high accuracy and reliability of the model.

[0054] Example 2: The in-situ apparent permeability calculation system for deep coal reservoirs applied to nuclear magnetic resonance logging provided in this embodiment of the invention includes: The data acquisition module performs nuclear magnetic resonance logging on the target well section under in-situ high temperature and high pressure conditions in deep coal reservoirs to obtain in-situ nuclear magnetic resonance data reflecting the spatial distribution characteristics of reservoir pores. Spectrum; The signal processing module communicates with the data acquisition module to process the acquired in-situ nuclear magnetic resonance images. Spectral data are processed and calculated. Time from minimum value to any value cumulative signal amplitude ; The fractal dimension calculation module is communicatively connected to the signal processing module, and is based on accumulated signal amplitude data. Compared with lateral relaxation time data Plot the cumulative signal amplitude logarithm in a double logarithmic coordinate system. The cross plot of the logarithms over time was used, and the slope was obtained by linear fitting of the data points relating the two logarithms. Determine the fractal dimension that characterizes the heterogeneity of reservoir pore structure. ; The effective radius calculation module communicates with the fractal dimension calculation module to define the effective radius. With fractal dimension The mathematical relationship is used to correct the pore flow boundary, thereby quantitatively characterizing the influence of true roughness and heterogeneity on the flow. The flow model construction module communicates with the effective radius calculation module to calculate the effective radius. As a new boundary condition, it is substituted into the classical fluid equilibrium equations for solution, establishing a single-pore fluid total flow rate model that incorporates real roughness and heterogeneous characteristics. ; The absolute penetration rate calculation module communicates and connects with the flow model construction module to establish a total flow model. Using Darcy's law to calculate fractal absolute permeability ; The apparent permeability prediction module communicates with the absolute permeability calculation module to calculate the fractal absolute permeability. Combined with the Klinkenberg equation describing the gas slip effect, a method for predicting arbitrary pore pressure is established. Apparent penetration rate The complete model.

[0055] To further demonstrate the positive effects of the above embodiments, the present invention conducts the following experiments based on the above technical solutions.

[0056] 1. Application Scenario Description: Well B, a deep coalbed methane exploration well in the Ordos Basin, is selected as a typical application scenario. The target coal seam depth of this well is 1856.62m, and the gas molecular mass M... g =0.01604 kg / mol. It exhibits typical "three highs" characteristics: high ground stress (equivalent confining pressure > 25 MPa), high reservoir pressure (measured pore pressure P = 18.57 MPa), and high formation temperature (measured T = 324.27℃). The coal and rock pore structure in this well section is complex and highly heterogeneous. Under these conditions, accurate in-situ apparent permeability is urgently needed to guide subsequent fracturing scheme design. However, traditional nuclear magnetic resonance logging interpretation methods (such as SDR models) and conventional core analysis methods are limited by idealized assumptions and are difficult to adapt to these complex geological conditions.

[0057] Core intermediate parameter calculation results: Fractal dimension fitting: Based on fitting lg(S) and lg(T2) in a double logarithmic coordinate system, the slope k=0.476 was obtained, and the fractal dimension D=3-k=2.524 was calculated. Combined with laboratory data, the calibration coefficient C=0.735 was calculated.

[0058] Final results comparison and verification: The predicted value of this invention, when substituted into the complete model calculation, is ka = 0.1105 mD in situ. The laboratory true value, measured using a high-temperature, high-pressure device, is 0.112 mD in the core sample. Error analysis: The relative error is only 1.34%, demonstrating the high accuracy of the model.

[0059] 2. Comparative Scheme Design: To verify the advancement of the in-situ apparent permeability calculation method considering heterogeneity and slippage effect proposed in this invention, three sets of comparative experiments were designed at the same depth (1856.62m): (1) Comparison with Scheme 1 (existing technology - SDR model): The permeability is calculated using the classic nuclear magnetic resonance SDR model. This model assumes that the pores are smooth circular tubes and ignores the roughness and heterogeneity of the reservoir pores.

[0060] (2) Comparison Scheme 2 (Prior Art - Absolute Permeability Model Without Considering Slippage Effect): Only the absolute permeability of the rock is calculated (similar to the one obtained in the intermediate step of this invention). However, the Klinkenberg correction in step 7 was not performed, which ignored the significant gas slippage effect under deep high pressure.

[0061] (3) The present invention solution: Completely execute steps 1-7 of the present invention, and obtain the results by nuclear magnetic resonance imaging. The fractal dimension D = 2.524 was calculated using spectral analysis to characterize heterogeneity, and an effective radius was introduced. The flow boundary was modified, and the slippage effect was corrected by coupling in-situ temperature and pressure parameters (T=324.27K, p=18.57MPa).

[0062] 3. Experimental results and data comparison: The true apparent permeability of the core obtained by high temperature and high pressure off-site test in the laboratory ($0.112mD$7) was used as the benchmark true value. The comparison results are shown in Table 2.

[0063] Table 2 Comparative Test Results of Nuclear Magnetic Logging Permeability Calculation Methods in Deep Coal Seam Sections of Well B in the Ordos Basin

[0064] 4. Analysis of experimental results and beneficial effects; (1) Overcoming “serious overestimation”: Compared with Scheme 1 (SDR model), it does not take into account the effect of the fractal dimension D proposed in this invention on the effective radius r. e The correction effect (i.e., the ideal smooth model assuming C=0) causes the calculated result to be more than twice as high as the true value. This invention introduces D=2.524 to reduce noise in the pore structure, accurately restoring the true low-permeability characteristics.

[0065] (2) Overcame the "significant underestimation": Although Scheme 2 considered heterogeneity, it ignored the gas slip effect. Under the high reservoir pressure of 18.57 MPa, gas slip still contributed approximately 19.6% of the additional apparent permeability ( This invention accurately captures this increment through step 7.

[0066] (3) Achieved “accurate prediction”: The present invention ultimately reduced the prediction error from >17% in the prior art to 1.34%, meeting the stringent requirements of the engineering site for the accuracy of production capacity prediction.

[0067] Application scenario: Deep coalbed methane exploration and evaluation. After drilling is completed, nuclear magnetic resonance logging tools are used to collect data.

[0068] Specific workflow: ① Data acquisition: Obtain in-situ T2 NMR spectra in high-temperature, high-pressure wells. ② Heterogeneity characterization: On-site engineers use supporting software modules to calculate the cumulative signal amplitude of the T2 spectrum and automatically fit the fractal dimension D. ③ Parameter correction: Input formation temperature (e.g., 324.27 K) and pressure (e.g., 18.57 MPa) data. ④ Result output: The system directly outputs the corrected in-situ apparent permeability curve for geologists to determine reservoir sweet spots.

[0069] (4) Application in specific products / systems; This invention can be embedded in nuclear magnetic resonance logging interpretation workstation software as a core interpretation module. Module composition: includes a data acquisition module (reading logging files), a fractal dimension calculation module (algorithm core), an effective radius calculation module (correcting boundaries), and an apparent permeability prediction module (coupling slip effect).

[0070] (5) Extension of Technological Value: This invention provides guidance for fracturing schemes: accurate permeability is a key input for fracturing design. If the high estimate (0.245mD) of the SDR model is used, it will lead to insufficient fracturing scale design and failure to meet production targets. Using the accurate value (0.1105mD) provided by this invention, a more aggressive fracturing volume can be designed to ensure single-well production. This invention realizes production capacity simulation: it provides a real permeability input for numerical simulation software, greatly improving the accuracy of gas reservoir recovery rate prediction.

[0071] This invention features fractal dimension-corrected flow boundary. It calculates the fractal dimension D using NMR T2 spectroscopy and innovatively introduces an effective radius to correct the pore flow boundary. The pore size correction formula quantitatively characterizes the hindering effect of true roughness on flow, restoring the true low-permeability characteristics. This invention also features coupled gas slippage effect. Going beyond absolute permeability, this invention combines fractal absolute permeability with the Clinkerberg equation, incorporating formation temperature and pressure parameters to establish a complete model for predicting in-situ apparent permeability. Comparative experiments at the same depth (1856.62m) revealed that the relative error of the SDR model was as high as +118.7%; the relative error of the model considering only heterogeneity but not slippage effect was -17.5%; while the relative error of the method in this invention was only 1.34%. This fundamentally solves the technical problem of "inaccuracy" in existing models.

[0072] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging, characterized in that, This method integrates the characterization of reservoir pore structure heterogeneity with the coupling mechanism of gas slippage effect, and achieves accurate in-situ prediction of apparent permeability through the following steps: S1, Under in-situ high temperature and high pressure conditions in deep coal reservoirs, nuclear magnetic resonance logging was carried out on the target well section to obtain in-situ nuclear magnetic resonance data reflecting the spatial distribution characteristics of reservoir pores. Spectrum; S2, for the obtained in-situ nuclear magnetic resonance Spectral data are processed and calculated. Time from minimum value to any value cumulative signal amplitude ; S3, based on accumulated signal amplitude data Compared with lateral relaxation time data Plot the cumulative signal amplitude logarithm in a double logarithmic coordinate system. The cross plot of the logarithms over time was used, and the slope was obtained by linear fitting of the data points relating the two logarithms. Determine the fractal dimension that characterizes the heterogeneity of reservoir pore structure. ; S4, Define the effective radius With fractal dimension The mathematical relationship is used to correct the pore flow boundary, thereby quantitatively characterizing the influence of true roughness and heterogeneity on the flow. S5, effective radius As a new boundary condition, it is introduced into the classical fluid equilibrium equations for solution, establishing a single-pore fluid total flow rate model that incorporates real roughness and heterogeneous characteristics. ; S6, Combined Total Flow Model Using Darcy's law to calculate fractal absolute permeability ; S7 will increase the absolute permeability of fractals. Combined with the Klinkenberg equation describing the gas slip effect, a method for predicting arbitrary pore pressure is established. Apparent penetration rate The complete model.

2. The method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging as described in claim 1, characterized in that, In step S2, the signal amplitude data is accumulated. The calculation formula is: ； In the formula, For the minimum relaxation time, Let the relaxation time be arbitrary. Let the signal amplitude correspond to any relaxation time. This is the sum of all relaxation times.

3. The method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging as described in claim 1, characterized in that, In step S3, the cumulative signal amplitude logarithm and logarithm of time The linear fitting expression is: ； In the formula, The slope of the expression. This is the intercept of the expression.

4. The method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging as described in claim 3, characterized in that, fractal dimension The calculation formula is: 。 5. The method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging as described in claim 1, characterized in that, In step S4, the effective radius The calculation formula is: ； In the formula, Where is the throat radius, For fractal dimension, For calibration coefficients, The equivalent height of the roughness element refers to a physical parameter characterizing the roughness of the inner wall of the pores in a coal reservoir. It represents the average height characteristic of irregular protrusions on the pore wall. This parameter reflects the geometric deviation between the real pore wall and the ideal smooth wall and is a key factor affecting the flow resistance of fluid in the pores.

6. The method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging as described in claim 5, characterized in that, Calibration coefficient Obtained by laboratory testing and analysis of representative core samples from the well section; when When the pore throat model is considered to be smooth, without roughness, and ideal, the ideal apparent permeability can be calculated using the SDR model. The expression is: ； In the formula, The permeability calculated using the SDR model; These are the fitting coefficients for the SDR model; Porosity; for The geometric mean, which is directly calculated by the accompanying software for nuclear magnetic resonance (NMR); Permeability approximately satisfies Poiseuille's law with respect to radius. The ratio of actual apparent permeability to ideal apparent permeability is: ； Combined effective radius Ideal apparent penetration rate and actual apparent penetration rate The formula for calculating the ratio of the apparent permeability to the ideal permeability yields the calibration coefficient. The expression is: ； Among them, actual apparent penetration rate These values ​​were obtained directly from laboratory core displacement experiments.

7. The method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging as described in claim 1, characterized in that, In step S5, substitute the effective radius. Post-total flow model The expression is: ； In the formula, The pressure difference across the pore model. For fluid dynamic viscosity, For sample length, The effective radius.

8. The method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging as described in claim 7, characterized in that, In step S6, the flow rate expression for Darcy permeability is: ； Fractal absolute penetration The expression is: ； In the formula, the orifice throat radius Depend on The value is obtained through conversion.

9. The method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging as described in claim 8, characterized in that, Throat radius data With nuclear magnetic resonance The data transformation relationship is as follows: ； In the formula, The surface relaxation rate, This is the shape factor.

10. The method for calculating the in-situ apparent permeability of deep coal reservoirs using nuclear magnetic resonance logging as described in claim 9, characterized in that, In step S7, in-situ environmental data and high-pressure physical property data of reservoir fluids are collected from the deep reservoir, including formation temperature. and pore pressure By combining the absolute permeability expression with the Klinkenberg equation describing the gas slippage effect, a method for predicting arbitrary pore pressure is established. Apparent penetration rate The complete model; Apparent penetration rate The complete model expression is: ； ； In the formula, The slip coefficient; Mass of gas molecules; It is a constant, with a value of 0.9; is the dynamic viscosity coefficient of the gas; For temperature; The ideal gas constant is 8.314 J·mol⁻¹. -1 ·K -1 .

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