CN108763648B - Method and device for acquiring capillary pressure curve based on nuclear magnetic resonance T2 distribution - Google Patents

Abstract

The invention provides a nuclear magnetic resonance T₂Method and device for acquiring capillary pressure curve in distribution mode, and nuclear magnetic resonance T based on capillary pressure points₂First model of distribution transformation capillary pressure curve and nuclear magnetic resonance T based on non-wetting phase saturation point₂The second model of the distribution conversion capillary pressure curve respectively predicts the advantages of capillary pressure curves of small pore parts (high pressure and high saturation of non-wetting phase) and large pore parts (low saturation of non-wetting phase) to form the nuclear magnetic resonance T₂The mixed model of the capillary pressure curve is distributed and converted, the complete capillary pressure curve with higher precision can be obtained, and the nuclear magnetic resonance T of any sample to be detected can be obtained₂After distribution, the mixing model is applied to predict the capillary pressure curve of the sample to be measured, so that favorable technical support is provided for accurately calculating the pore structure parameters of the complex reservoir.

Description

Method and device for acquiring capillary pressure curve based on nuclear magnetic resonance T2 distribution

Technical Field

The invention relates to the technical field of oil and gas exploration and development, in particular to a nuclear magnetic resonance-based T₂A method and a device for acquiring capillary pressure curves in a distributed manner are provided.

Background

The capillary pressure curve and the form thereof can represent the pore throat size and distribution of reservoir rock, and are one of important means for evaluating the pore structure of the reservoir rock. At present, capillary pressure curves used for researching a rock pore structure are mainly obtained through a rock core mercury intrusion experiment, however, in actual production, due to the fact that coring is less, coring cost is high, application range of the capillary pressure curves is limited to a certain extent, and how to continuously obtain the capillary pressure curves of reservoir rocks is always a subject of research of oil and gas exploration and development workers. Transverse relaxation time T of nuclear magnetic resonance₂The distribution can reflect the size and distribution information of rock pores to a certain extent, and the nuclear magnetic resonance is utilized to log well T₂The capillary pressure curve of the reservoir rock is obtained through distribution, on one hand, the defect that the capillary pressure curve of the reservoir rock continuous with the logging depth cannot be obtained through rock experiment measurement can be overcome, and meanwhile, a foundation is laid for evaluating the pore structure of the rock and classifying the reservoir by utilizing nuclear magnetic resonance logging information.

Marschall et al (1995) first proposed the use of a linear transformation method to establish T₂The relationship between profile and capillary pressure curve. Subsequently, a series of methods suitable for different research areas are developed based on a linear transformation method, wherein the methods comprise an average saturation error minimum value method, a Volokitn empirical formula method, a direct transformation method, a scale variation method, a similar comparison method, a matrix method and the like. He Yudan et al (2005) hypothesis T₂The relationship between the distribution and the pore size distribution is in the form of a power function, and a piecewise power function method is proposed. The scale factors of the models for the linear transformation method and the power function method vary from core sample to core sample, and these scale factors are dependent on the other parameters of the sample (porosity, permeability and T₂Geometric mean, etc.) do not have a significant correlation. Therefore, the linear conversion method and the power function method have certain limitations when applied to a compact reservoir. Xiao et al (2012) proposes a method based on the nonwetting phase saturation and T at different capillary pressure points₂Geometric mean (T)_2lm) And a statistical relationship model between the total porosity for predicting a capillary pressure curve. Subsequently, Xiao et al, (2016a) improved the method by first classifying the samples into three categories using flow cell indices and then predicting the capillary pressure curve using the method, with improved prediction. Xiaoet al (2016b) nuclear magnetic resonance T₂The distribution is divided into 8 parts, the percentage content of the porosity component of the 8 parts is calculated, and a conversion model between the mercury inlet saturation and the percentage content of the nuclear magnetic resonance porosity component under different mercury inlet pressures is established. However, this method has two problems: only use nuclear magnetic resonance T₂Distributed T_2lmAnd total porosity information or nuclear magnetic resonance interval porosity information; the capillary pressure curve predicted by the method is greatly different from the capillary pressure curve obtained by the experiment in the large pore part.

Disclosure of Invention

The invention provides a nuclear magnetic resonance T₂The method and the device for acquiring the capillary pressure curve in a distributed manner are used for providing a capillary pressure curve prediction method with higher precision, so that favorable technical support is provided for accurately calculating the pore structure parameters of the complex reservoir.

One aspect of the invention is to provide a nuclear magnetic resonance T-based method₂The method for acquiring the capillary pressure curve in a distributed manner comprises the following steps:

establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point₂The relation of a plurality of characteristic parameters is distributed to obtain the nuclear magnetic resonance T based on the capillary pressure points₂A first model of a distribution conversion capillary pressure curve;

establishing corresponding capillary pressure and nuclear magnetic resonance T for each non-wetting phase saturation point₂Obtaining the nuclear magnetic resonance T based on the non-wetting phase saturation point through the relationship of a plurality of distributed characteristic parameters₂A second model of the distribution conversion capillary pressure curve;

solving model parameters in the first model and the second model and a splicing point of the first model and the second model according to pre-acquired sample data to obtain the nuclear magnetic resonance T₂Distributing and converting a mixed model of capillary pressure curves;

according to nuclear magnetic resonance T of the sample to be measured₂And the capillary pressure curve of the sample to be measured is obtained through the distribution and the mixed model.

Further, establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point₂The relation of a plurality of characteristic parameters is distributed to obtain the nuclear magnetic resonance T based on the capillary pressure points₂A first model of a profile-transformed capillary pressure curve, comprising:

establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point according to J function representing capillary pressure curve characteristics and SDR model for evaluating permeability through nuclear magnetic resonance logging₂Distributing the relationship of a plurality of characteristic parameters;

establishing non-wetting phase saturation and nuclear magnetic resonance T according to each capillary pressure point₂Distributing a plurality of characteristic parametersObtaining the following nuclear magnetic resonance T based on capillary pressure points by using the relationship and the pre-acquired sample data₂First model of profile transition capillary pressure curve:

S_n×e＝T_n×qC_q×e+D_n×e

wherein n is the number of samples participating in modeling, and e is the number of effective capillary pressure points; q is nuclear magnetic resonance T₂The number of characteristic parameters which participate in modeling and are extracted from the distribution; s is the non-wetting phase saturation S of the sample corresponding to different capillary pressure points_nwLog S of (1)_nwA matrix of formations; t is the nuclear magnetic resonance T of the sample participating in the modeling₂Characteristic parameter T in distribution and involved in modeling_pLog value of (1)_pFormed matrix, T_pIncluding geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xQ of (1); c is a model coefficient matrix; d is a constant coefficient matrix.

Further, establishing a capillary pressure and a nuclear magnetic resonance T corresponding to each non-wetting phase saturation point₂Obtaining the nuclear magnetic resonance T based on the non-wetting phase saturation point through the relationship of a plurality of distributed characteristic parameters₂A second model of a profile-transformed capillary pressure curve, comprising:

according to permeability and NMR T₂Establishing corresponding capillary pressure and nuclear magnetic resonance T for each non-wetting phase saturation point according to the relationship of a plurality of distributed characteristic parameters, the relationship of permeability and throat radius and the relationship of capillary pressure and throat radius₂A relationship of the distributed plurality of characteristic parameters;

establishing corresponding capillary pressure and nuclear magnetic resonance T according to each non-wetting phase saturation point₂The relation of a plurality of distributed characteristic parameters and pre-acquired sample data are used for obtaining the following nuclear magnetic resonance T based on the non-wetting phase saturation point₂Second model of distribution transformation capillary pressure curve:

P_n×m＝T_n×qA_q×m+B_n×m

wherein n is the number of samples participating in modeling, and m is the number of effective non-wetting phase saturation points; q is nuclear magnetic resonance T₂The number of characteristic parameters which participate in modeling and are extracted from the distribution; capillary pressure P of samples with different non-wetting phase saturation points_cLog P of (1)_cA matrix of formations; t is the nuclear magnetic resonance T of the sample participating in the modeling₂Characteristic parameter T in distribution and involved in modeling_pLog value of (1)_pFormed matrix, T_pIncluding geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xQ of (1); a is a model coefficient matrix; b is a constant coefficient matrix.

Further, the model parameters in the first model and the second model and the splicing point of the first model and the second model are solved according to the pre-obtained sample data, so that the nuclear magnetic resonance T is obtained₂A hybrid model of a profile-transformed capillary pressure curve, comprising:

according to the sample data acquired in advance, solving model parameters in the first model and the second model by adopting a partial least squares regression method;

obtaining the optimal splicing point of the first model and the second model according to the sample data, the first model and the second model so as to obtain the nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves.

Further, the model parameters in the first model and the second model and the splicing point of the first model and the second model are solved according to the pre-obtained sample data, so that the nuclear magnetic resonance T is obtained₂A hybrid model of a profile-transformed capillary pressure curve, comprising:

determining the number h of latent variables in different first models_{Pc_opt}Number h of latent variables in different second models_{Snw_opt}And different splicing points S_nwCut；

For each group h_{Pc_opt}、h_{Snw_opt}And S_nwCutObtaining a nuclear magnetic resonance T according to the sample data by adopting leave-one-out cross validation and partial least square regression method₂Distributing and converting alternative mixed models and model errors of the capillary pressure curve;

comparing the model errors of the alternative mixed models, and taking the alternative mixed model with the minimum model error as the optimal nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves.

Another aspect of the invention is to provide a method for detecting nuclear magnetic resonance T₂The device for acquiring capillary pressure curve in a distributed manner comprises:

a first model obtaining module for establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point₂The relation of a plurality of characteristic parameters is distributed to obtain the nuclear magnetic resonance T based on the capillary pressure points₂A first model of a distribution conversion capillary pressure curve;

a second model obtaining module for establishing capillary pressure and nuclear magnetic resonance T corresponding to each non-wetting phase saturation point₂Obtaining the nuclear magnetic resonance T based on the non-wetting phase saturation point through the relationship of a plurality of distributed characteristic parameters₂A second model of the distribution conversion capillary pressure curve;

a hybrid model obtaining module, configured to solve model parameters in the first model and the second model and a splicing point of the first model and the second model according to pre-obtained sample data, so as to obtain a nuclear magnetic resonance T₂Distributing and converting a mixed model of capillary pressure curves;

a capillary pressure curve prediction module for nuclear magnetic resonance T according to the sample to be measured₂And the capillary pressure curve of the sample to be measured is obtained through the distribution and the mixed model.

Further, the first model obtaining module is specifically configured to:

establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point according to J function representing capillary pressure curve characteristics and SDR model for evaluating permeability through nuclear magnetic resonance logging₂Distributing the relationship of a plurality of characteristic parameters;

establishing non-wetting phase saturation and nuclear magnetic resonance T according to each capillary pressure point₂The relation of a plurality of characteristic parameters and the pre-acquired sample data are distributed to obtain the following nuclear magnetic resonance T based on capillary pressure points₂First model of profile transition capillary pressure curve:

S_n×e＝T_n×qC_q×e+D_n×e

wherein n is the number of samples participating in modeling, and e is the number of effective capillary pressure points; q is nuclear magnetic resonance T₂The number of characteristic parameters which participate in modeling and are extracted from the distribution; s is the non-wetting phase saturation S of the sample corresponding to different capillary pressure points_nwLog S of (1)_nwA matrix of formations; t is the nuclear magnetic resonance T of the sample participating in the modeling₂Characteristic parameter T in distribution and involved in modeling_pLog value of (1)_pFormed matrix, T_pIncluding geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xQ of (1); c is a model coefficient matrix; d is a constant coefficient matrix.

Further, the second model obtaining module is specifically configured to:

according to permeability and NMR T₂Establishing corresponding capillary pressure and nuclear magnetic resonance T for each non-wetting phase saturation point according to the relationship of a plurality of distributed characteristic parameters, the relationship of permeability and throat radius and the relationship of capillary pressure and throat radius₂A relationship of the distributed plurality of characteristic parameters;

establishing corresponding capillary pressure and nuclear magnetic resonance T according to each non-wetting phase saturation point₂The relation of a plurality of distributed characteristic parameters and pre-acquired sample data are used for obtaining the following nuclear magnetic resonance T based on the non-wetting phase saturation point₂Second model of distribution transformation capillary pressure curve:

P_n×m＝T_n×qA_q×m+B_n×m

wherein n is the number of samples participating in modeling, and m isThe number of effective non-wetting phase saturation points; q is nuclear magnetic resonance T₂The number of characteristic parameters which participate in modeling and are extracted from the distribution; capillary pressure P of samples with different non-wetting phase saturation points_cLog P of (1)_cA matrix of formations; t is the nuclear magnetic resonance T of the sample participating in the modeling₂Characteristic parameter T in distribution and involved in modeling_pLog value of (1)_pFormed matrix, T_pIncluding geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xQ of (1); a is a model coefficient matrix; b is a constant coefficient matrix.

Further, the hybrid model obtaining module is specifically configured to:

according to the sample data acquired in advance, solving model parameters in the first model and the second model by adopting a partial least squares regression method;

obtaining the optimal splicing point of the first model and the second model according to the sample data, the first model and the second model so as to obtain the nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves.

Further, the hybrid model obtaining module is specifically configured to:

determining the number h of latent variables in different first models_{Pc_opt}Number h of latent variables in different second models_{Snw_opt}And different splicing points S_nwCut；

For each group h_{Pc_opt}、h_{Snw_opt}And S_nwCutObtaining a nuclear magnetic resonance T according to the sample data by adopting leave-one-out cross validation and partial least square regression method₂Distributing and converting alternative mixed models and model errors of the capillary pressure curve;

comparing the model errors of the alternative mixed models, and taking the alternative mixed model with the minimum model error as the optimal nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves.

The invention provides a nuclear magnetic resonance-based methodT₂Method and device for distributed acquisition of capillary pressure curve by means of nuclear magnetic resonance T based on capillary pressure points₂First model of distribution transformation capillary pressure curve and nuclear magnetic resonance T based on non-wetting phase saturation point₂A second model of the distribution conversion capillary pressure curve; solving model parameters in the first model and the second model and a splicing point of the first model and the second model according to pre-acquired sample data to obtain the nuclear magnetic resonance T₂The mixed model of the distribution conversion capillary pressure curve integrates the advantages of the first model and the second model in predicting the capillary pressure curves of a small pore part (high pressure, high saturation of a non-wetting phase) and a large pore part (low saturation of a non-wetting phase) respectively to form the nuclear magnetic resonance T₂The mixed model of the capillary pressure curve is distributed and converted, the complete capillary pressure curve with higher precision can be obtained, and the nuclear magnetic resonance T of any sample to be detected can be obtained₂After distribution, the mixing model is applied to predict the capillary pressure curve of the sample to be measured, so that favorable technical support is provided for accurately calculating the pore structure parameters of the complex reservoir.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a diagram of a nuclear magnetic resonance-based method for detecting T₂A flow chart of a method for acquiring a capillary pressure curve in a distributed manner;

FIG. 2 is a graph showing model errors of a predicted capillary pressure curve and an experimental capillary pressure curve when different model parameters are combined in an embodiment of the present invention;

FIG. 3 is a flowchart of a cyclic process employing an exhaustion method in an embodiment of the present invention;

FIG. 4 is an experimental capillary pressure curve and a predicted capillary pressure curve of three samples #5, #11 and #19 in an example of the present invention;

FIG. 5 is a plot of a log of an actual well and a processing result provided by an embodiment of the present invention;

FIG. 6 shows an example of a nuclear magnetic resonance-based magnetic resonance method T₂And distributing and acquiring a device structure diagram of the capillary pressure curve.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

FIG. 1 is a diagram of a nuclear magnetic resonance-based method for detecting T₂And distributing and acquiring a method flow chart of the capillary pressure curve. As shown in FIG. 1, the present embodiment provides a magnetic resonance based T₂The method for acquiring the capillary pressure curve in a distributed manner comprises the following specific steps:

s101, establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point₂The relation of a plurality of characteristic parameters is distributed to obtain the nuclear magnetic resonance T based on the capillary pressure points₂A first model of a profile transformation capillary pressure curve.

In the embodiment, the nuclear magnetic resonance T based on the pressure point of the capillary₂The first model of the distribution conversion capillary pressure curve has better prediction effect on the capillary pressure curve of a small pore part (high pressure, non-wetting phase and high saturation). Therefore, the embodiment can establish non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point₂The relation of a plurality of characteristic parameters is distributed, and the nonwetting phase saturation and the nuclear magnetic resonance T are established for each capillary pressure point through a J function representing capillary pressure curve characteristics and an SDR model for evaluating permeability through nuclear magnetic resonance logging₂The relation of a plurality of characteristic parameters is distributed, so that the nuclear magnetic resonance T based on the capillary pressure point is obtained₂A first model of a profile transformation capillary pressure curve.Wherein nuclear magnetic resonance T₂Distributing the plurality of feature parameters may include geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xOf the above. Of course, the nuclear magnetic resonance T based on the capillary pressure point can be obtained in other modes₂A first model of a profile transformation capillary pressure curve.

S102, establishing corresponding capillary pressure and nuclear magnetic resonance T for each non-wetting phase saturation point₂Obtaining the nuclear magnetic resonance T based on the non-wetting phase saturation point through the relationship of a plurality of distributed characteristic parameters₂And distributing a second model of the transformed capillary pressure curve.

In this example, NMR T based on non-wetting phase saturation Point₂The second model of the distribution conversion capillary pressure curve has better prediction effect on the capillary pressure curve of a large pore part (non-wetting phase low saturation), so that the corresponding capillary pressure and nuclear magnetic resonance T can be established by establishing the saturation point of each non-wetting phase₂The relationship between the distribution of multiple characteristic parameters can be determined by permeability and NMR T₂Establishing corresponding capillary pressure and nuclear magnetic resonance T for each non-wetting phase saturation point according to the relationship of a plurality of distributed characteristic parameters, the relationship of permeability and throat radius and the relationship of capillary pressure and throat radius₂A plurality of distributed characteristic parameters, thereby obtaining the nuclear magnetic resonance T based on the non-wetting phase saturation point₂And distributing a second model of the transformed capillary pressure curve. Wherein nuclear magnetic resonance T₂Distributing the plurality of feature parameters may include geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xThe characteristic parameters in the second model may be different from those in the first model. Of course, the NMR T based on the non-wetting phase saturation point can be obtained in other ways₂And distributing a second model of the transformed capillary pressure curve.

S103, solving the first model and the second model according to pre-acquired sample dataModel parameters in the second model and a splicing point of the first model and the second model to obtain the nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves.

In the embodiment, the nuclear magnetic resonance T based on the pressure point of the capillary₂The first model of the distribution conversion capillary pressure curve has better prediction effect on the capillary pressure curve of a small pore part (high pressure and high saturation of a non-wetting phase) and has poorer prediction effect on a large pore part, and the nuclear magnetic resonance T based on a non-wetting phase saturation point₂The second model of the distribution conversion capillary pressure curve has a good prediction effect on the capillary pressure curve of a large pore part (non-wetting phase low saturation), but cannot predict the capillary pressure curve of a small pore part, and based on the situation, the first model and the second model are spliced together through a splicing point to form a nuclear magnetic resonance T₂The mixed model for distributing and converting the capillary pressure curve is a complete capillary pressure curve formed by splicing and fusing the capillary pressure curve predicted by the first model and the capillary pressure curve predicted by the second model at the splicing point, so that the defects of the two models are effectively avoided, and the complete capillary pressure curve with higher precision can be obtained. In this embodiment, model parameters in the first model and the second model and a splicing point of the first model and the second model are solved through pre-obtained sample data, where the sample data at least includes actually measured capillary pressure curves and nuclear magnetic resonance T of a plurality of samples₂For the data such as distribution, any optimization algorithm for obtaining an optimal solution in the prior art, such as a genetic algorithm, an ant colony algorithm, a hill climbing algorithm, etc., may be used in the solving process, and will not be described herein again.

S104, according to the nuclear magnetic resonance T of the sample to be measured₂And the capillary pressure curve of the sample to be measured is obtained through the distribution and the mixed model.

In this example, nuclear magnetic resonance T is obtained₂After the mixed model of the capillary pressure curve is distributed and converted, the nuclear magnetic resonance T of any sample to be detected can be obtained₂After distribution, the mixed model is applied to predict the capillary pressure curve of the sample to be measured, so as to accurately calculate the complex reservoirThe pore structure parameters provide advantageous technical support.

The embodiment provides a nuclear magnetic resonance-based T₂Method for obtaining capillary pressure curve by distribution, through nuclear magnetic resonance T based on capillary pressure points₂First model of distribution transformation capillary pressure curve and nuclear magnetic resonance T based on non-wetting phase saturation point₂A second model of the distribution conversion capillary pressure curve; solving model parameters in the first model and the second model and a splicing point of the first model and the second model according to pre-acquired sample data to obtain the nuclear magnetic resonance T₂The mixed model of the distribution conversion capillary pressure curve integrates the advantages of the first model and the second model in predicting the capillary pressure curves of a small pore part (high pressure, high saturation of a non-wetting phase) and a large pore part (low saturation of a non-wetting phase) respectively to form the nuclear magnetic resonance T₂The mixed model of the capillary pressure curve is distributed and converted, the complete capillary pressure curve with higher precision can be obtained, and the nuclear magnetic resonance T of any sample to be detected can be obtained₂After distribution, the mixing model is applied to predict the capillary pressure curve of the sample to be measured, so that favorable technical support is provided for accurately calculating the pore structure parameters of the complex reservoir.

On the basis of the above embodiments, the present embodiment is based on nuclear magnetic resonance T₂The method for acquiring the capillary pressure curve by distribution is explained in detail.

Specifically, the establishing of the non-wetting phase saturation and the NMR T for each capillary pressure point in S101₂The relation of a plurality of characteristic parameters is distributed to obtain the nuclear magnetic resonance T based on the capillary pressure points₂A first model of a profile-transformed capillary pressure curve, comprising:

s1011, establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point according to J function representing capillary pressure curve characteristics and SDR model for evaluating permeability through nuclear magnetic resonance logging₂Distributing the relationship of the plurality of characteristic parameters.

The J function characterizing the capillary pressure curve first is:

wherein, P_cIs capillary pressure; σ is interfacial tension; θ is the contact angle of the inner surface of the fluid and the pore wall; j (S)_w) Is a dimensionless function; s_wThe wetting phase water saturation; k is the core permeability; phi is the core porosity.

The SDR model for evaluating permeability by nuclear magnetic resonance logging is as follows:

k＝cφ^mT_2lm ⁿ

wherein: t is_2lmIs nuclear magnetic resonance T₂A geometric mean of the distribution; c. and m and n are model coefficients.

The two formulas are combined to obtain:

from the above formula, at a given capillary pressure P_cIn the case of (2), the wetting phase saturation S_wAnd J (S)_w) Often there is a power functional relationship between them. Due to non-wetting phase saturation S_nwSaturation with wetting phase S_wThere is a relationship: s_nw＝1-S_wThe non-wetting phase saturation S is known for each capillary pressure point_nwPhi, T₂Distribution logarithmic mean (T)_2lm) There is a functional relationship between them, denoted as log (S)_nw)＝G(log(T_p)). The SDR model for evaluating the formation permeability by nuclear magnetic resonance logging only considers two parameters (phi and T)_2lm) And in fact permeability and NMR T₂Distributed multiple characteristic parameters (geometric mean T)_2lmTotal porosity phi, T₂Distribution from T₂Relaxation time T corresponding to x% saturation by maximum value accumulation (hereinafter referred to as "inverse accumulation") toward minimum value_x) It is related. Thus, the pressure point P is pressed for each capillary_c,jEstablishing the non-wetting phase saturation S of all samples_nwAnd nuclear magnetic resonance T₂Distributed multiple parameters (T)_p) The relationship of (1):

wherein, P_c,jIs the pressure point of capillary, j is belonged to [1, e]E is the number of effective capillary pressure points, S_nw,jThe pressure point of the capillary is P_c,jNon-wetting phase saturation of time, T_pIs nuclear magnetic resonance T₂Characteristic parameters of the distribution, including the geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xA plurality of characteristic parameters of G_jIndicates the pressure point of the capillary is P_c,jTimes log S_nw,jAnd logT_pFunctional relationship therebetween.

When G is a linear function, the above equation can be written in matrix form, i.e.:

s1012, establishing non-wetting phase saturation and nuclear magnetic resonance T according to each capillary pressure point₂The relation of a plurality of characteristic parameters and the pre-acquired sample data are distributed to obtain the following nuclear magnetic resonance T based on capillary pressure points₂First model of profile transition capillary pressure curve:

S_n×e＝T_n×qC_q×e+D_n×e

wherein n is the number of samples participating in modeling, and e is the number of effective capillary pressure points; q is nuclear magnetic resonance T₂The number of characteristic parameters which participate in modeling and are extracted from the distribution; s is the non-wetting phase saturation S of the sample corresponding to different capillary pressure points_nwLog S of (1)_nwA matrix of formations; t is the nuclear magnetic resonance T of the sample participating in the modeling₂Characteristic parameter T in distribution and involved in modeling_pLog value of (1)_pFormed matrix, T_pIncluding geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xQ of (1); c is a model coefficient matrix; d is a constant coefficient matrix.

Further, the establishment of the non-wetting phase saturation point for each non-wetting phase is described in S102Corresponding capillary pressure and NMR T₂Obtaining the nuclear magnetic resonance T based on the non-wetting phase saturation point through the relationship of a plurality of distributed characteristic parameters₂A second model of a profile-transformed capillary pressure curve, comprising:

s1021, according to permeability and nuclear magnetic resonance T₂Establishing corresponding capillary pressure and nuclear magnetic resonance T for each non-wetting phase saturation point according to the relationship of a plurality of distributed characteristic parameters, the relationship of permeability and throat radius and the relationship of capillary pressure and throat radius₂A relationship of the distributed plurality of characteristic parameters.

First according to permeability and NMR T₂Establishing a multi-parameter permeability calculation model according to the relationship of the distributed characteristic parameters:

wherein k is the permeability; q is nuclear magnetic resonance T₂The number of distributed characteristic parameters; t is_p,iIs the ith characteristic parameter; a is₀、a_iAre model coefficients.

There is a close relationship between permeability k and throat radius r:

logk＝b₁+b₂logφ+b₃logr_x

wherein, b₁、b₂、b₃Is the model coefficient; r is_xThe radius of the throat corresponding to x% saturation of the non-wetting phase.

Capillary pressure P_cThe relationship to throat radius r is:

the three formulas are combined to obtain:

wherein: c. C₀、c_iIs the model coefficient;P_c,xthe capillary pressure corresponding to x% saturation of the non-wetting phase.

Thus, a capillary pressure and NMR T corresponding to each non-wetting phase saturation point is established₂Relationship of distributed multiple characteristic parameters:

wherein S is_nw,xFor the non-wetting phase saturation point, x is equal to [1, m ]]M is the number of effective non-wetting phase saturation points, P_c,xAs the non-wetting phase saturation point is S_nw,xCapillary pressure of time, T_pIs nuclear magnetic resonance T₂Characteristic parameters of the distribution, including the geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xA plurality of characteristic parameters of (1), F_xIndicates a non-wetting phase saturation point of S_nw,xTemporal logP_c,xAnd logT_pFunctional relationship therebetween.

When F is a linear function, the above equation can be written in matrix form, i.e.:

s1022, establishing corresponding capillary pressure and nuclear magnetic resonance T according to each non-wetting phase saturation point₂The relation of a plurality of distributed characteristic parameters and pre-acquired sample data are used for obtaining the following nuclear magnetic resonance T based on the non-wetting phase saturation point₂Second model of distribution transformation capillary pressure curve:

P_n×m＝T_n×qA_q×m+B_n×m

wherein n is the number of samples participating in modeling, and m is the number of effective non-wetting phase saturation points; q is nuclear magnetic resonance T₂The number of characteristic parameters which participate in modeling and are extracted from the distribution; capillary pressure P of samples with different non-wetting phase saturation points_cLog P of (1)_cA matrix of formations; t is the nuclear magnetic resonance T of the sample participating in the modeling₂Characteristic parameter T in distribution and involved in modeling_pLog value of (1)_pFormed matrix，T_pIncluding geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xQ of (1); a is a model coefficient matrix; b is a constant coefficient matrix.

Further, in S103, according to the pre-obtained sample data, model parameters in the first model and the second model and a splicing point of the first model and the second model are solved, so as to obtain the nuclear magnetic resonance T₂A hybrid model of a profile-transformed capillary pressure curve, comprising:

according to the sample data acquired in advance, solving model parameters in the first model and the second model by adopting a partial least squares regression method; obtaining the optimal splicing point of the first model and the second model according to the sample data, the first model and the second model so as to obtain the nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves.

Taking into account T₂The distribution characteristic parameters have a multiple collinearity relation, the number of variables is more, and the number of samples is relatively less, so that the model parameters of the two models are solved by adopting a partial least squares regression method. The Partial Least Squares Regression (PLSR) is a regression modeling method of multiple dependent variables to multiple independent variables, and can realize comprehensive application of various data analysis methods by finding the optimal function matching of a group of data by minimizing the sum of squares of errors. The currently common partial least squares regression method is nonlinear iterative partial least squares. The general process is as follows:

let the argument X be (X)₁,x₂,…,x_p) The dependent variable Y is (Y)₁,y₂,…,y_q) In X, Y, the components L ═ (L) are extracted respectively₁,l₂,…,l_h) And U ═ U (U)₁,u₂,…,u_h) So that L and U carry as much information as possible in the original matrix and the correlation is maximized. Thus, for each fraction l extracted_i、u_iAnd satisfies the following conditions:

the corresponding mathematical expression is to solve the following optimization problem:

max＜X_iw_i,Y_ic_i>

wherein: w is a_iAnd c_iIs a weight vector, X_iAnd Y_iResidual matrixes after (i-1) latent variables (LatentVariable) are extracted for X and Y respectively.

Solving a first latent variable l by a Lagrange algorithm₁And u₁And X, Y are respectively aligned to l₁And u₁Obtaining a residual matrix X by regression₁,Y₁(ii) a By X₁And Y₁Respectively replacing X and Y, and extracting a second latent variable by the same method; iterating the process until the number of latent variables reaches the optimal number of latent variables h or the residual error Y_hLess than a given error. After h latent variables are extracted:

wherein: z and Q are load matrixes; x_hAnd Y_hAnd extracting a residual matrix after h latent variables are extracted.

After extracting h latent variables, the regression equation can be expressed as:

Y＝X(ZBQ^T)+Y_h

wherein: and B is a coefficient matrix of the regression equation.

For the input variable X, its predicted value is:

specifically, for the first model S in the present embodiment_n×e＝T_n×qC_q×e+D_n×eWith T as independent variable X and S as dependent variable Y, nuclear magnetic resonance T₂Characteristic parameter T in distribution and involved in modeling_pThe number of (a) is the number h of latent variables, and is counted as h_{Pc_opt}(ii) a For P_n×m＝T_n×qA_q×m+B_n×mWith T as independent variable X and P as dependent variable Y, nuclear magnetic resonance T₂Characteristic parameter T in distribution and involved in modeling_pThe number of (a) is the number h of latent variables, and is counted as h_{Snw_opt}. Since the number h of latent variables may determine the iteration number of partial least squares regression and thus the accuracy of the model, it is also necessary to determine the optimal h_{Pc_opt}And h_{Snw_opt}So that the optimal model parameters can be obtained. And then, by acquiring the optimal splicing point of the first model and the second model, the defects of the first model and the second model are avoided, namely the first model is paired with the P_cData points with values less than the maximum expulsion pressure of all samples (i.e., capillary pressure curves for the large porosity fraction) are less predictive, while the second model can predict capillary pressure curves with non-wetting phase saturations of 1% -m% (m is the number of effective non-wetting phase saturation points), but cannot predict S_nwData points with values greater than m%, i.e. the capillary pressure curve of the smaller pore section cannot be predicted, leading to an optimum NMR T₂And distributing and converting a mixed model of capillary pressure curves.

Specifically, the following steps are adopted in this embodiment:

s1031, determining the number h of latent variables in different first models_{Pc_opt}Number h of latent variables in different second models_{Snw_opt}And different splicing points S_nwCut；

S1031, h for each group_{Pc_opt}、h_{Snw_opt}And S_nwCutObtaining a nuclear magnetic resonance T according to the sample data by adopting leave-one-out cross validation and partial least square regression method₂Distributing and converting alternative mixed models and model errors of the capillary pressure curve;

s1033, comparing model errors of the alternative mixed models to obtain the alternative mixture with the minimum model errorNuclear magnetic resonance T with synthetic model as optimum₂And distributing and converting a mixed model of capillary pressure curves.

In this embodiment, because the number of samples is relatively small, the sample data utilization rate can be improved by one-out-of-one cross validation, which is to leave only one sample as a test set and other samples as training sets at a time, and if there are a samples, training is required a times and testing is required a times.

In this embodiment, h may be determined first_{Pc_opt}Has a value range of (0, np1) and h_{Snw_opt}The value range of (2) is (0, np2), and specifically, for example, any integer of 1 to 10 can be taken; splicing point S_nwCutHas a value range of (minS)_nw,maxS_nw) For example, 5% to 65% can be selected, and the value can be obtained by 5% step length; after the value range is determined, a plurality of groups h can be obtained_{Pc_opt}、h_{Snw_opt}And S_nwCut。

For each group h_{Pc_opt}、h_{Snw_opt}And S_nwCutSelecting training samples and test samples from the samples according to the sample data, for example, the number of the samples is 19, selecting sample 1 as the test sample, samples 2-19 as the training samples, solving model parameters of a first model and a second model by adopting a partial least squares regression method according to the data of the samples 2-19, and reaching h by the number of latent variables h_{Pc_opt}、h_{Snw_opt}Or residual Y_hLess than the given error is used as the iteration ending condition of the partial least squares regression method, so as to obtain a first model and a second model, and the first model and the second model are spliced at a splicing point S_nwCutSplicing the first model and the second model to form an alternative mixed model, and obtaining a model error S of the alternative mixed model by sample data of the sample 1_error1(ii) a Selecting a sample 2 as a test sample, and the other samples as training samples, and repeating the steps until a model error S is obtained_error2(ii) a Iterating the steps until the leave-one-out cross validation is completed to obtain S_error1～S_error19Obtaining the set h by obtaining the root mean square error_{Pc_opt}、h_{Snw_opt}And S_nwCutModel error S of corresponding candidate mixture model_error(h_{Pc_opt},h_{Snw_opt},S_nwCut)。

Iterating the above process until each group h is completed_{Pc_opt}、h_{Snw_opt}And S_nwCutComparing the model errors of the candidate mixture models, and as shown in FIG. 2, selecting the candidate mixture model with the smallest model error as the optimal nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves. When the parameters are the best model parameter combination (e.g. h)_{Pc_opt}＝4,h_{Snw_opt}＝4,S_{nwCut_opt}＝65％)。

This embodiment may employ a cyclic process of the exhaustion method as shown in fig. 3 for each group h_{Pc_opt}、h_{Snw_opt}And S_nwCutAnd the leave-one-out method carries out value taking through cross validation, the exhaustion method is simple and has high accuracy, and the optimal mixed model can be conveniently obtained.

Obtaining the optimal nuclear magnetic resonance T₂After the mixed model of the capillary pressure curve is subjected to distribution conversion, samples with large difference in extractability can be selected to verify the mixed model, so as to verify the adaptability of the mixed model, for example, three samples with large difference in physical properties, namely 5#, 11# and 19# in 19 samples are selected to be calculated in the embodiment, so that the capillary pressure curve prediction result shown in fig. 4 can be obtained (in the figure, a solid line is the prediction result, and a hollow point is a data point obtained through a core mercury intrusion experiment).

Further, in this embodiment, according to the step described in S104, the nmr T of the sample to be measured₂And the capillary pressure curve of the sample to be measured is obtained through the distribution and the mixed model. For example, this example predicts the capillary pressure curve of a tight sandstone reservoir segment of an oil field H well, and the results are shown in fig. 5. The first trace in the graph is the natural Gamma (GR) and caliper Curves (CAL). The second pass is the CNL, DEN and AC three porosity curves. Third pass is NMR T₂Distribution and T₂Geometric mean. The fourth path is T₂Normalized inverse cumulative distribution of the distribution, different grays representing different saturation values; t is_xThe transverse relaxation time corresponding to the saturation of x%. Fifth lane is the utilization of the method proposed herein, according to T₂Distribution pre-treatmentAnd (4) measuring a capillary pressure curve, wherein different colors represent different capillary pressure values corresponding to the change of the non-wetting phase saturation. The sixth step is the pore structure type divided according to the calculated capillary pressure, wherein black represents the first pore structure, the porosity and the permeability are high, and the displacement pressure and the median pressure are low; dark gray represents a second type of pore structure, with relatively low porosity and permeability, high expulsion pressure and median pressure; the light gray represents a third type of pore structure with lower porosity and permeability, higher displacement pressure and higher median pressure. The seventh path is the displacement pressure P calculated according to the predicted capillary pressure curve_dAnd P obtained by rock core mercury intrusion experiment_d. The eighth step is that the saturation median pressure P is calculated according to the predicted capillary pressure curve_c50And P of core analysis_c50The ninth is nuclear magnetic resonance porosity and core analysis porosity, and the tenth is nuclear magnetic resonance T₂Distributing the calculated permeability and the core analysis permeability. Displacement pressure P calculated from predicted capillary pressure curve_dAnd a median pressure P_c50The method is better corresponding to the analysis result of the rock core mercury intrusion experiment, and the effectiveness of the method is verified. By adopting the method provided by the invention to predict the capillary pressure curve, the pore structure of the reservoir is finely divided, and the method has important significance for finding favorable reservoirs and formulating reasonable development schemes.

FIG. 6 shows an example of a nuclear magnetic resonance-based magnetic resonance method T₂And distributing and acquiring a device structure diagram of the capillary pressure curve. The embodiment provides a nuclear magnetic resonance-based T₂The device for acquiring capillary pressure curve in distribution can execute the method based on nuclear magnetic resonance T₂As shown in fig. 6, the processing flow provided by the embodiment of the method for distributively acquiring the capillary pressure curve includes a first model acquiring module 21, a second model acquiring module 22, a hybrid model acquiring module 23, and a capillary pressure curve predicting module 24.

Wherein, the first model obtaining module 21 is used for establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point₂The relation of a plurality of characteristic parameters is distributed to obtain the nuclear magnetic resonance T based on the capillary pressure points₂A first model of a distribution conversion capillary pressure curve;

a second model obtaining module 22 for establishing a capillary pressure and a nuclear magnetic resonance T corresponding to each non-wetting phase saturation point₂Obtaining the nuclear magnetic resonance T based on the non-wetting phase saturation point through the relationship of a plurality of distributed characteristic parameters₂A second model of the distribution conversion capillary pressure curve;

a hybrid model obtaining module 23, configured to solve model parameters in the first model and the second model and a splicing point of the first model and the second model according to sample data obtained in advance, so as to obtain a nuclear magnetic resonance T₂Distributing and converting a mixed model of capillary pressure curves;

a capillary pressure curve prediction module 24 for predicting the nuclear magnetic resonance T of the sample to be measured₂And the capillary pressure curve of the sample to be measured is obtained through the distribution and the mixed model.

Further, the first model obtaining module 21 is specifically configured to:

establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point according to J function representing capillary pressure curve characteristics and SDR model for evaluating permeability through nuclear magnetic resonance logging₂Distributing the relationship of a plurality of characteristic parameters;

establishing non-wetting phase saturation and nuclear magnetic resonance T according to each capillary pressure point₂The relation of a plurality of characteristic parameters and the pre-acquired sample data are distributed to obtain the following nuclear magnetic resonance T based on capillary pressure points₂First model of profile transition capillary pressure curve:

S_n×e＝T_n×qC_q×e+D_n×e

wherein n is the number of samples participating in modeling, and e is the number of effective capillary pressure points; q is nuclear magnetic resonance T₂The number of characteristic parameters which participate in modeling and are extracted from the distribution; s is the non-wetting phase saturation S of the sample corresponding to different capillary pressure points_nwLog S of (1)_nwA matrix of formations; t is the nuclear magnetic resonance T of the sample participating in the modeling₂Characteristic parameter T in distribution and involved in modeling_pLogarithm of (a)Value logT_pFormed matrix, T_pIncluding geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xQ of (1); c is a model coefficient matrix; d is a constant coefficient matrix.

Further, the second model obtaining module 22 is specifically configured to:

according to permeability and NMR T₂Establishing corresponding capillary pressure and nuclear magnetic resonance T for each non-wetting phase saturation point according to the relationship of a plurality of distributed characteristic parameters, the relationship of permeability and throat radius and the relationship of capillary pressure and throat radius₂A relationship of the distributed plurality of characteristic parameters;

establishing corresponding capillary pressure and nuclear magnetic resonance T according to each non-wetting phase saturation point₂The relation of a plurality of distributed characteristic parameters and pre-acquired sample data are used for obtaining the following nuclear magnetic resonance T based on the non-wetting phase saturation point₂Second model of distribution transformation capillary pressure curve:

P_n×m＝T_n×qA_q×m+B_n×m

wherein n is the number of samples participating in modeling, and m is the number of effective non-wetting phase saturation points; q is nuclear magnetic resonance T₂The number of characteristic parameters which participate in modeling and are extracted from the distribution; capillary pressure P of samples with different non-wetting phase saturation points_cLog P of (1)_cA matrix of formations; t is the nuclear magnetic resonance T of the sample participating in the modeling₂Characteristic parameter T in distribution and involved in modeling_pLog value of (1)_pFormed matrix, T_pIncluding geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xQ of (1); a is a model coefficient matrix; b is a constant coefficient matrix.

Further, the hybrid model obtaining module 23 is specifically configured to:

according to the sample data acquired in advance, solving model parameters in the first model and the second model by adopting a partial least squares regression method;

obtaining the optimal splicing point of the first model and the second model according to the sample data, the first model and the second model so as to obtain the nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves.

Further, the hybrid model obtaining module 23 is specifically configured to:

determining the number h of latent variables in different first models_{Pc_opt}Number h of latent variables in different second models_{Snw_opt}And different splicing points S_nwCut；

For each group h_{Pc_opt}、h_{Snw_opt}And S_nwCutObtaining a nuclear magnetic resonance T according to the sample data by adopting leave-one-out cross validation and partial least square regression method₂Distributing and converting alternative mixed models and model errors of the capillary pressure curve;

comparing the model errors of the alternative mixed models, and taking the alternative mixed model with the minimum model error as the optimal nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves.

The embodiment of the invention provides a nuclear magnetic resonance-based T₂The device for distributing and acquiring the capillary pressure curve may be specifically used for executing the method embodiment provided in fig. 1, and specific functions are not described herein again.

The embodiment provides a nuclear magnetic resonance-based T₂Means for distributing the pressure curve of the capillary, by nuclear magnetic resonance T based on the pressure points of the capillary₂First model of distribution transformation capillary pressure curve and nuclear magnetic resonance T based on non-wetting phase saturation point₂A second model of the distribution conversion capillary pressure curve; solving model parameters in the first model and the second model and a splicing point of the first model and the second model according to pre-acquired sample data to obtain the nuclear magnetic resonance T₂A mixed model of distributed conversion capillary pressure curve integrates a first model and a second model to predict small pore parts (high pressure, non-wetting phase and high saturation degree) respectively) And the advantage of capillary pressure curve of large pore part (non-wetting phase and low saturation), forming nuclear magnetic resonance T₂The mixed model of the capillary pressure curve is distributed and converted, the complete capillary pressure curve with higher precision can be obtained, and the nuclear magnetic resonance T of any sample to be detected can be obtained₂After distribution, the mixing model is applied to predict the capillary pressure curve of the sample to be measured, so that favorable technical support is provided for accurately calculating the pore structure parameters of the complex reservoir.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions. For the specific working process of the device described above, reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. Based on nuclear magnetic resonance T₂The method for acquiring the capillary pressure curve in a distributed manner is characterized by comprising the following steps:

establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point₂The relation of a plurality of characteristic parameters is distributed to obtain the nuclear magnetic resonance T based on the capillary pressure points₂A first model of a distribution conversion capillary pressure curve;

establishing corresponding capillary pressure and nuclear magnetic resonance T for each non-wetting phase saturation point₂The relationship of the distributed plurality of characteristic parameters,obtaining a nuclear magnetic resonance T based on a non-wetting phase saturation point₂A second model of the distribution conversion capillary pressure curve;

solving model parameters in the first model and the second model and a splicing point of the first model and the second model according to pre-acquired sample data to obtain the nuclear magnetic resonance T₂Distributing and converting a mixed model of capillary pressure curves;

according to nuclear magnetic resonance T of the sample to be measured₂And the capillary pressure curve of the sample to be measured is obtained through the distribution and the mixed model.

2. The method of claim 1, wherein the establishing of non-wetting phase saturation and NMR T for each capillary pressure point₂The relation of a plurality of characteristic parameters is distributed to obtain the nuclear magnetic resonance T based on the capillary pressure points₂A first model of a profile-transformed capillary pressure curve, comprising:

establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point according to J function representing capillary pressure curve characteristics and SDR model for evaluating permeability through nuclear magnetic resonance logging₂Distributing the relationship of a plurality of characteristic parameters;

establishing non-wetting phase saturation and nuclear magnetic resonance T according to each capillary pressure point₂The relation of a plurality of characteristic parameters and the pre-acquired sample data are distributed to obtain the following nuclear magnetic resonance T based on capillary pressure points₂First model of profile transition capillary pressure curve:

S_n×e＝T_n×qC_q×e+D_n×e

wherein n is the number of samples participating in modeling, and e is the number of effective capillary pressure points; q is nuclear magnetic resonance T₂The number of characteristic parameters which participate in modeling and are extracted from the distribution; s is the non-wetting phase saturation S of the sample corresponding to different capillary pressure points_nwLog S of (1)_nwA matrix of formations; t is the nuclear magnetic resonance T of the sample participating in the modeling₂Characteristic parameter T in distribution and involved in modeling_pLog value of (1)_pFormed matrix, T_pIncluding geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xQ of (1); c is a model coefficient matrix; d is a constant coefficient matrix.

3. The method of claim 1, wherein the establishing a capillary pressure and a nuclear magnetic resonance T for each non-wetting phase saturation point is performed in a corresponding manner₂Obtaining the nuclear magnetic resonance T based on the non-wetting phase saturation point through the relationship of a plurality of distributed characteristic parameters₂A second model of a profile-transformed capillary pressure curve, comprising:

according to permeability and NMR T₂Establishing corresponding capillary pressure and nuclear magnetic resonance T for each non-wetting phase saturation point according to the relationship of a plurality of distributed characteristic parameters, the relationship of permeability and throat radius and the relationship of capillary pressure and throat radius₂A relationship of the distributed plurality of characteristic parameters;

establishing corresponding capillary pressure and nuclear magnetic resonance T according to each non-wetting phase saturation point₂The relation of a plurality of distributed characteristic parameters and pre-acquired sample data are used for obtaining the following nuclear magnetic resonance T based on the non-wetting phase saturation point₂Second model of distribution transformation capillary pressure curve:

P_n×m＝T_n×qA_q×m+B_n×m

wherein n is the number of samples participating in modeling, and m is the number of effective non-wetting phase saturation points; q is nuclear magnetic resonance T₂The number of characteristic parameters which participate in modeling and are extracted from the distribution; capillary pressure P of samples with different non-wetting phase saturation points_cLog P of (1)_cA matrix of formations; t is the nuclear magnetic resonance T of the sample participating in the modeling₂Characteristic parameter T in distribution and involved in modeling_pLog value of (1)_pFormed matrix, T_pIncluding geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xIn (1)q are provided; a is a model coefficient matrix; b is a constant coefficient matrix.

4. The method according to any one of claims 1-3, wherein said solving model parameters in said first model and said second model and a splicing point of said first model and said second model from pre-acquired sample data results in a nuclear magnetic resonance T₂A hybrid model of a profile-transformed capillary pressure curve, comprising:

according to the sample data acquired in advance, solving model parameters in the first model and the second model by adopting a partial least squares regression method;

obtaining the optimal splicing point of the first model and the second model according to the sample data, the first model and the second model so as to obtain the nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves.

5. The method of claim 4, wherein the solving of the model parameters in the first model and the second model and the splicing point of the first model and the second model from the pre-obtained sample data results in the nuclear magnetic resonance T₂A hybrid model of a profile-transformed capillary pressure curve, comprising:

determining the number h of latent variables in different first models_{Pc_opt}Number h of latent variables in different second models_{Snw_opt}And different splicing points S_nwCut；

For each group h_{Pc_opt}、h_{Snw_opt}And S_nwCutObtaining a nuclear magnetic resonance T according to the sample data by adopting leave-one-out cross validation and partial least square regression method₂Distributing and converting alternative mixed models and model errors of the capillary pressure curve;

comparing the model errors of the alternative mixed models, and taking the alternative mixed model with the minimum model error as the optimal nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves.

6. Based on nuclear magnetic resonance T₂Device that capillary pressure curve was obtained in distribution, its characterized in that includes:

a first model obtaining module for establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point₂The relation of a plurality of characteristic parameters is distributed to obtain the nuclear magnetic resonance T based on the capillary pressure points₂A first model of a distribution conversion capillary pressure curve;

a second model obtaining module for establishing capillary pressure and nuclear magnetic resonance T corresponding to each non-wetting phase saturation point₂Obtaining the nuclear magnetic resonance T based on the non-wetting phase saturation point through the relationship of a plurality of distributed characteristic parameters₂A second model of the distribution conversion capillary pressure curve;

a hybrid model obtaining module, configured to solve model parameters in the first model and the second model and a splicing point of the first model and the second model according to pre-obtained sample data, so as to obtain a nuclear magnetic resonance T₂Distributing and converting a mixed model of capillary pressure curves;

a capillary pressure curve prediction module for nuclear magnetic resonance T according to the sample to be measured₂And the capillary pressure curve of the sample to be measured is obtained through the distribution and the mixed model.

7. The apparatus of claim 6, wherein the first model obtaining module is specifically configured to:

establishing non-wetting phase saturation and nuclear magnetic resonance T for each capillary pressure point according to J function representing capillary pressure curve characteristics and SDR model for evaluating permeability through nuclear magnetic resonance logging₂Distributing the relationship of a plurality of characteristic parameters;

establishing non-wetting phase saturation and nuclear magnetic resonance T according to each capillary pressure point₂The relation of a plurality of characteristic parameters and the pre-acquired sample data are distributed to obtain the following nuclear magnetic resonance T based on capillary pressure points₂First model of profile transition capillary pressure curve:

S_n×e＝T_n×qC_q×e+D_n×e

wherein n is the number of samples participating in modeling, and e is the number of effective capillary pressure points; q is nuclear magnetic resonance T₂The number of characteristic parameters which participate in modeling and are extracted from the distribution; s is the non-wetting phase saturation S of the sample corresponding to different capillary pressure points_nwLog S of (1)_nwA matrix of formations; t is the nuclear magnetic resonance T of the sample participating in the modeling₂Characteristic parameter T in distribution and involved in modeling_pLog value of (1)_pFormed matrix, T_pIncluding geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xQ of (1); c is a model coefficient matrix; d is a constant coefficient matrix.

8. The apparatus of claim 6, wherein the second model obtaining module is specifically configured to:

according to permeability and NMR T₂Establishing corresponding capillary pressure and nuclear magnetic resonance T for each non-wetting phase saturation point according to the relationship of a plurality of distributed characteristic parameters, the relationship of permeability and throat radius and the relationship of capillary pressure and throat radius₂A relationship of the distributed plurality of characteristic parameters;

establishing corresponding capillary pressure and nuclear magnetic resonance T according to each non-wetting phase saturation point₂The relation of a plurality of distributed characteristic parameters and pre-acquired sample data are used for obtaining the following nuclear magnetic resonance T based on the non-wetting phase saturation point₂Second model of distribution transformation capillary pressure curve:

P_n×m＝T_n×qA_q×m+B_n×m

wherein n is the number of samples participating in modeling, and m is the number of effective non-wetting phase saturation points; q is nuclear magnetic resonance T₂The number of characteristic parameters which participate in modeling and are extracted from the distribution; capillary pressure P of samples with different non-wetting phase saturation points_cLog P of (1)_cA matrix of formations; t is nuclear magnetic resonance of a sample participating in modelingT₂Characteristic parameter T in distribution and involved in modeling_pLog value of (1)_pFormed matrix, T_pIncluding geometric mean T_2lmTotal porosity φ, and T₂Distribution from T₂The relaxation time T corresponding to the maximum value accumulated to the minimum value with the saturation of x%_xQ of (1); a is a model coefficient matrix; b is a constant coefficient matrix.

9. The apparatus according to any one of claims 6 to 8, wherein the hybrid model acquisition module is specifically configured to:

according to the sample data acquired in advance, solving model parameters in the first model and the second model by adopting a partial least squares regression method;

obtaining the optimal splicing point of the first model and the second model according to the sample data, the first model and the second model so as to obtain the nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves.

10. The apparatus of claim 9, wherein the hybrid model acquisition module is specifically configured to:

determining the number h of latent variables in different first models_{Pc_opt}Number h of latent variables in different second models_{Snw_opt}And different splicing points S_nwCut；

For each group h_{Pc_opt}、h_{Snw_opt}And S_nwCutObtaining a nuclear magnetic resonance T according to the sample data by adopting leave-one-out cross validation and partial least square regression method₂Distributing and converting alternative mixed models and model errors of the capillary pressure curve;

comparing the model errors of the alternative mixed models, and taking the alternative mixed model with the minimum model error as the optimal nuclear magnetic resonance T₂And distributing and converting a mixed model of capillary pressure curves.

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