CN114154435A - Method, device and equipment for determining porosity of formation fluid - Google Patents

Method, device and equipment for determining porosity of formation fluid Download PDF

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CN114154435A
CN114154435A CN202111420571.7A CN202111420571A CN114154435A CN 114154435 A CN114154435 A CN 114154435A CN 202111420571 A CN202111420571 A CN 202111420571A CN 114154435 A CN114154435 A CN 114154435A
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CN114154435B (en
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谢然红
谷明宣
金国文
郭江峰
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China University of Petroleum Beijing
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E21B49/08—Obtaining fluid samples or testing fluids, in boreholes or wells
    • E21B49/087—Well testing, e.g. testing for reservoir productivity or formation parameters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]

Abstract

The embodiment of the specification provides a method, a device and equipment for determining the porosity of formation fluid, wherein the method comprises the following steps: acquiring an original data set consisting of nuclear magnetic resonance spectrums of different depth points; calculating an AIC value representing the number of the fluid types according to the original data set, and determining the number of the fluid types in the target stratum based on the AIC value; preprocessing an original data set by using a morphological method to obtain a new data set; wherein, the pretreatment comprises expansion treatment and corrosion treatment; decomposing the new data set by using a non-negative matrix algorithm based on the number of the types of the fluids in the target stratum to obtain the characteristics of different fluids on the nuclear magnetic resonance spectrum; determining the saturation of different fluids by using a preset mode based on the characteristics of the different fluids on the nuclear magnetic resonance spectrum and the original data set; the porosities of the different fluids in the different depth points are determined from the saturations of the different fluids and the raw data sets. By using the embodiment of the specification, the porosity of different fluids in the formation can be determined more accurately and quickly.

Description

Method, device and equipment for determining porosity of formation fluid
Technical Field
The application relates to the technical field of oil and gas exploration, in particular to a method, a device and equipment for determining the porosity of formation fluid.
Background
Shale reservoirs are increasingly taking on the leading position in world oil exploration and production. Most oil companies are eagerly required to develop shale oil resources to maintain stable oil production. The porosity of different fluid components is taken as one of important physical parameters for reservoir evaluation, and the accurate evaluation of the porosity can provide reliable reference for the calculation of reserves and the selection of the perforation position of the horizontal well. Therefore, during the exploration and development of shale reservoirs, it is becoming more and more important to accurately find the porosity of different fluids.
The prior art mainly finds the porosity of different fluids in two ways. The first method is based on NMR (Nuclear magnetic resonance) experimental method, which analyzes T of several cores under different conditions (such as original state, centrifugation, saturation, pyrolysis at different temperatures, etc.)1-T2Spectrum, drawing different T1-T2The spectra interpret the template, resulting in porosity for the different fluids. In the mode, not only is coring required to be carried out to carry out a series of complicated experiments, but also T is drawn1-T2Spectrum interpretation templates are susceptible to subjective factors and, in addition, the cored rock sample may not be representative of the fluid properties of all formations throughout the region, making the different fluids sought in this way inaccurate as to porosity. The second approach is based on NMR logging methods by measuring T for successive formations1-T2Spectrum, the characteristics of different fluids are obtained by using a blind source separation method, and therefore, the porosities of the different fluids are obtained. The T of different fluids obtained in this way is influenced by the signal-to-noise ratio of the echo data and by the redundant information present in the formation1-T2The features overlap each other, resulting in large errors in the porosity of the different fluids sought.
Therefore, there is a need for a solution to the above technical problems.
Disclosure of Invention
The embodiment of the specification provides a method, a device and equipment for determining the porosity of formation fluid, which can more accurately and quickly determine the porosity of different fluids in the formation and provide a basis for calculating reserves and determining the perforation position of a horizontal well.
The method, the device and the equipment for determining the porosity of the formation fluid are realized in the following ways.
A method of determining the porosity of a formation fluid, comprising: acquiring an original data set consisting of nuclear magnetic resonance spectrums of different depth points; the nuclear magnetic resonance spectrums of the different depth points are obtained based on the inversion of echo data of the different depth points of the target stratum; calculating an AIC value representing the number of fluid types according to the original data set, and determining the number of the fluid types in the target stratum based on the AIC value; preprocessing the original data set by using a morphological method to obtain a new data set; wherein the pretreatment comprises an expansion treatment and an erosion treatment; decomposing the new data set by using a non-negative matrix algorithm based on the number of the types of the fluids in the target stratum to obtain the characteristics of different fluids on the nuclear magnetic resonance spectrum; determining the saturation of different fluids by using a preset mode based on the characteristics of the different fluids on the nuclear magnetic resonance spectrum and the original data set; determining porosities of different fluids in different depth points from the saturations of the different fluids and the raw data set.
An apparatus for determining the porosity of a formation fluid, comprising: the acquisition module is used for acquiring an original data set consisting of nuclear magnetic resonance spectrums of different depth points; the nuclear magnetic resonance spectrums of the different depth points are obtained based on the inversion of echo data of the different depth points of the target stratum; a first determining module, configured to obtain an AIC value representing the number of fluid types from the raw data set, and determine the number of fluid types in the target formation based on the AIC value; the first obtaining module is used for preprocessing the original data set by using a morphological method to obtain a new data set; wherein the pretreatment comprises an expansion treatment and an erosion treatment; the second obtaining module is used for decomposing the new data set by utilizing a non-negative matrix algorithm based on the number of the types of the fluids in the target stratum to obtain the characteristics of different fluids on the nuclear magnetic resonance spectrum; the second determination module is used for determining the saturation of different fluids by using a preset mode based on the characteristics of the different fluids on the nuclear magnetic resonance spectrum and the original data set; and the third determining module is used for determining the porosity of different fluids in different depth points according to the saturation of the different fluids and the original data set.
An apparatus for determining the porosity of a formation fluid, comprising a processor and a memory for storing executable instructions, the processor when executing the instructions implementing the steps of any one of the method embodiments of the present specification.
The specification provides a method, a device and equipment for determining the porosity of formation fluid. In some embodiments, NMR spectrums obtained by inverting multi-dimensional nuclear magnetic resonance echo data of different depth points are combined into an original data set, and the original data set is subjected to AIC analysis, morphological analysis, nonnegative matrix decomposition, full-constraint least square method and other processing, so that the porosity of different fluids of different depth points in a target stratum can be determined more accurately and more quickly, and a basis can be provided for calculating reserves and determining the perforation position of a horizontal well. With the embodiments provided herein, the porosity of different fluids can be determined more accurately.
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The accompanying drawings, which are included to provide a further understanding of the specification, are incorporated in and constitute a part of this specification, and are not intended to limit the specification. In the drawings:
FIG. 1 is a schematic flow chart diagram illustrating a method for determining formation fluid porosity according to an embodiment of the present disclosure;
FIG. 2 is T provided in the examples of this specification1-T 24 fluid types in the well are NMR T1-T2A feature on a spectrum;
FIG. 3 is a schematic diagram of a morphological method for processing a raw data set according to an embodiment of the present disclosure;
FIG. 4 is a formation having different fluid porosities and saturations provided by embodiments of the present disclosure;
FIG. 5 is a distribution characteristic of a new data set processed by a morphological method according to an embodiment of the present disclosure;
FIG. 6 is a schematic illustration of determining an amount of a type of fluid in a formation based on an AIC value as provided by an embodiment of the present description;
FIG. 7 is a T of different fluids extracted based on a new data set provided by embodiments of the present description1-T2A characteristic schematic diagram;
FIG. 8 is a T of different fluids extracted based on a raw data set provided by embodiments of the present description1-T2A characteristic schematic diagram;
FIG. 9 is a graph of porosity of different fluids derived based on a new data set and an original data set provided by embodiments of the present description;
FIG. 10 shows the effect of different structures on the solution of the present application provided in the examples of the present application;
FIG. 11 is a block diagram illustrating an apparatus for determining formation fluid porosity according to an embodiment of the present disclosure;
fig. 12 is a block diagram of a hardware structure of a server for determining a porosity of a formation fluid according to an embodiment of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments in the present specification, and not all of the embodiments. All other embodiments that can be obtained by a person skilled in the art on the basis of one or more embodiments of the present description without inventive step shall fall within the scope of protection of the embodiments of the present description.
The following describes an embodiment of the present disclosure with a specific application scenario as an example. Specifically, fig. 1 is a schematic flow chart of a method for determining the porosity of a formation fluid according to an embodiment of the present disclosure. Although the present specification provides the method steps or apparatus structures as shown in the following examples or figures, more or less steps or modules may be included in the method or apparatus structures based on conventional or non-inventive efforts.
One embodiment provided by the present specification can be applied to a client, a server, and the like. The client may include a terminal device, such as a smart phone, a tablet computer, and the like. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed system, and the like.
It should be noted that the following description of the embodiments does not limit the technical solutions in other extensible application scenarios based on the present specification. Detailed description of the preferred embodimentsa specific embodiment of a method for determining the porosity of a formation fluid as provided herein may include the following steps, as shown in fig. 1.
S0: acquiring an original data set consisting of nuclear magnetic resonance spectrums of different depth points; and the nuclear magnetic resonance spectrums of the different depth points are obtained by inverting based on the echo data of the different depth points of the target stratum.
Wherein the data in the original data set is converted data.
In some embodiments, T may be performed per depth point1-T2And logging, wherein the multi-dimensional nuclear magnetic resonance echo data of different depth points are obtained, and then the multi-dimensional nuclear magnetic resonance echo data of the different depth points are inverted to obtain nuclear magnetic resonance spectrums (hereinafter, referred to as NMR spectrums) of the different depth points. The echo data may include, among other things, data collected from NMR logging and data measured by NMR instruments in the laboratory.
In some implementations, the NMR spectrum size of each depth point may be represented in a matrix form of n1 × n2, where n1, n2 are the number of points in the NMR spectrum at two coordinates. E.g. T1-T2In well logging, n1 is T1N2 is T2And arranging the number of the dots.
In some embodiments, after the NMR spectra for different depth points are obtained, the NMR spectra for different depth points may be combined to obtain the original data set.
Since the object of processing in the non-negative matrix factorization is linear data and the NMR spectrum is two-dimensional data, the NMR spectrum of each depth point can be converted for the convenience of subsequent processing. In some implementations, before the original data set is obtained based on the NMR spectra of different depth points, the NMR spectra of each depth point may be linearly transformed, and then the original data set is assembled from the transformed data. For example, an n1 × n2 matrix corresponding to the NMR spectrum of each depth point may be converted to a 1 × n matrix, and then the original data set may be composed based on the 1 × n matrix corresponding to each depth point. Where the original data set may be understood as an m × n matrix, n is the product of n1 and n2, m is the number of formation depth points, and m × n represents the size of the matrix.
In the embodiment of the specification, the nuclear magnetic resonance spectrums at different depth points are combined into an original data set, so that a basis can be provided for subsequently determining the number of types of the fluid in the stratum and determining the saturation of different fluids.
S2: and calculating an AIC value representing the number of the fluid types according to the original data set, and determining the number of the fluid types in the target stratum based on the AIC value.
Among them, AIC is a standard for measuring the complexity of statistical models and the superiority of fitting data. The AIC may include a precision penalty term and a complexity penalty term. The fluid types may include bitumen, CBW, oil in OP (OOP), water in IP (WIP), and the like. Wherein oil in OP represents organic pore oil, water in IP represents inorganic pore water, and CBW represents clay bound water.
In some embodiments, the deriving an AIC value representing a number of fluid types from the raw data set, and determining the number of fluid types in the target formation based on the AIC value may include:
the AIC value representing the number of fluid types is found according to the following formula:
AIC＝-2 ln(L)+2c＝F+C (1)
Figure BDA0003376501180000051
C＝r(m+n) (3)
wherein, F ═ 2ln (L) is a precision penalty term, C ═ 2C is a complexity penalty term, L is a likelihood function, C is the number of parameters, m is the number of depth points in the stratum, and n is T1-T2Well logging T1Number of distribution points and T2The product of distribution points, X is an original data set, U and V are two non-negative matrixes obtained by decomposing the original data set by using a non-negative matrix algorithm, the size of the non-negative matrix U is m multiplied by r, the size of the non-negative matrix V is r multiplied by n, r represents the number of fluid types, Xk,iElement of the kth row and ith column of X, (UV)k,iThe element of the ith column of the kth row which is the product of U and V;
standardizing the precision penalty item and the complexity penalty item included in the AIC value to obtain a standardized AIC value;
and taking the number of the fluid types corresponding to the minimum standardized AIC value as the number of the fluid types in the target stratum.
In some implementation scenarios, before obtaining the AIC value representing the number of fluid types, the original data set may be decomposed by using a non-negative matrix algorithm to obtain non-negative matrices U and V, where U is m × r and V is r × n, and then the accuracy penalty term F and the complexity penalty term C are obtained based on the above equations (2) - (3).
Further, the precision penalty term F and the complexity penalty term C obtained as described above may be normalized, thereby obtaining a normalized AIC'. In some implementation scenarios, the accuracy penalty term F and the complexity penalty term C may be normalized in the following manner to obtain a normalized AIC':
Figure BDA0003376501180000061
Figure BDA0003376501180000062
AIC'＝F'+C' (6)
wherein, FmaxIs the maximum value of F, CmaxIs the maximum value in C.
In some implementations, after obtaining the normalized AIC ', the number of fluid types corresponding to the smallest AIC' may be determined as the number of fluid types in the target formation.
Of course, the above description is only exemplary, the manner of determining the amount of the fluid in the formation is not limited to the above examples, and other modifications are possible for those skilled in the art in light of the technical spirit of the present application, and are intended to be included within the scope of the present application as long as they achieve the same or similar functions and effects as the present application.
S4: preprocessing the original data set by using a morphological method to obtain a new data set; wherein the pretreatment comprises an expansion treatment and an erosion treatment.
In some embodiments, after determining the number of fluid types in the target formation, the original data set may be preprocessed by morphological methods to obtain a new data set. Among them, the pretreatment may include an expansion treatment and an etching treatment.
In some embodiments, the preprocessing the original data set by using a morphological method to obtain a new data set may include: calculating the distance sum of the nuclear magnetic resonance spectrum of each depth point in the original data set and the nuclear magnetic resonance spectra of other depth points in the structure range to obtain the distance sum corresponding to each depth point; wherein, the distance of the nuclear magnetic resonance spectra of the two depth points is represented by a preset distance; obtaining the result of the expansion processing of the nuclear magnetic resonance spectrum of each depth point based on the sum of the distances corresponding to the expansion operator and each depth point; obtaining the result of the nuclear magnetic resonance spectrum corrosion treatment of each depth point based on the sum of the distances corresponding to the corrosion operator and each depth point; after the expansion processing of the nuclear magnetic resonance spectrum according to all depth pointsObtaining an expansion data set and a corrosion data set according to the result of the nuclear magnetic resonance spectrum corrosion treatment and the result of the nuclear magnetic resonance spectrum corrosion treatment of all depth points; based on the dilated data set and the eroded data set, a new data set is obtained. Wherein the formation is subjected to T point by point1-T2Logging, the sampling interval is generally 0.125m, and the length of the structure x the sampling interval can be understood as the thickness of the stratum corresponding to the structure. The preset distance may include a cosine distance, a euclidean distance, and the like.
In some implementations, the post-expansion-processing result and the post-erosion-processing result for each depth point may be obtained by:
(fcΘg)＝arg_Max{D(fc,g)} (7)
Figure BDA0003376501180000063
Figure BDA0003376501180000071
Figure BDA0003376501180000072
wherein (f)cΘ g) represents the nuclear magnetic resonance spectrum f of the depth point c in the original datasetcAs a result of the post-expansion treatment,
Figure BDA0003376501180000073
nuclear magnetic resonance spectrum f representing depth point c in the original data setcAnd the expansion operator theta is the expansion operator as a result of the expansion processing,
Figure BDA0003376501180000074
for the erosion operator, D (f)cG) nuclear magnetic resonance spectrum f representing depth point c in the original data setcThe sum of distances from the NMR spectrum at other depth points in the structure g, arg _ Max, is the sum of distances D (f)c,g) Maximum NMR spectrum is achieved, arg _ Min is the distance sum D (f)cG) minimum NMR spectrum, dist (f)c,fs) Nuclear magnetic resonance spectrum f as depth point ccNuclear magnetic resonance spectrum f with depth point ssG is a structure, fcNuclear magnetic resonance spectrum of depth point c, fsIs the nuclear magnetic resonance spectrum of the depth point s, | fcI is fcModulo, | fsI is fsThe die of (1).
In some implementation scenarios, after obtaining the result after the nmr spectrum expansion processing of each depth point and the result after the nmr spectrum corrosion processing of each depth point, when the structure g moves with the depth, the expansion data set and the corrosion data set may be obtained according to the result after the nmr spectrum expansion processing of all depth points and the result after the nmr spectrum corrosion processing of all depth points. Further, the new data set may be obtained by:
Figure BDA0003376501180000075
wherein, new _ data is a new data set, x is data in the new data set, DD is an expansion data set, and EE is a corrosion data set.
S6: and decomposing the new data set by using a non-negative matrix algorithm based on the number of the types of the fluids in the target stratum to obtain the characteristics of different fluids on the nuclear magnetic resonance spectrum.
In some embodiments, after obtaining the new data set, the new data set is decomposed using a non-negative matrix algorithm based on the number of fluid types in the target formation to obtain the characteristics of the different fluids on the nuclear magnetic resonance spectrum. Among them, non-Negative Matrix Factorization (NMF) can be used to solve the blind source separation problem. NMF is widely used because of its non-negative constraint and its better interpretability of the results of its separation. In general, for any given one non-negative matrix Xm×nThe NMF algorithm can find a non-negative matrix U and a non-negative matrix V so as to satisfy Xm×n＝Um×r×Vr×nThus, one non-negative matrix is decomposed into the product of two left and right non-negative matrices. Where r represents the number of fluid types.
In some implementations, the features of different fluids on the nuclear magnetic resonance spectrum can be obtained using a maximum likelihood based NMF algorithm. The specific algorithm is as follows:
inputting: the new data set X is then set to,
and (3) outputting: a non-negative matrix U and a non-negative matrix V. Wherein U comprises the volume fraction of the different fluids and V comprises the characteristic of the different fluids on the nmr spectrum.
Specifically, in a first step, the matrix U is randomly initialized0And V0To make the elements in the matrix positive, for the matrix U0The column vector of (1) is normalized, and the initialization iteration time t is 0;
step two, iterating the U according to the following formula;
Figure BDA0003376501180000081
wherein, UiaThe element in row i and column a of U, j represents the number of columns X, (UV) and V, XijElement of row i and column j of X, VajElement of row a and column j of V, (UV)ijThe element of the ith row and the jth column after the product of U and V. It should be noted that when t is equal to 0, the above iterative formula U, V refers to the initialization matrix U0And V0And t is greater than 0, the above-mentioned iterative formula U, V refers to U and V updated in the last iteration.
Thirdly, normalizing the column vector of the U according to the following formula;
Figure BDA0003376501180000082
step four, iterating the step V according to the following formula;
Figure BDA0003376501180000083
wherein, ViaElement of row i, column a of V, j representing the number of columns of X and (UV), XijElement of row i and column j of X, (UV)ijThe element of the ith row and the jth column after the product of U and V.
And fifthly, updating the iteration time t to t +1, judging U, V whether to converge, if not, continuing to execute the step two, and if so, acquiring a matrix U and a matrix V. Where each row of matrix V, when converged, represents a characteristic of a different fluid on the NMR spectrum.
In the embodiment of the present description, the original data set is preprocessed by using a morphological method, so that the data volume of the obtained new data set is much smaller than that of the original data set, and thus, the efficiency and accuracy of obtaining the features of different fluids on the nuclear magnetic resonance spectrum can be effectively improved.
S8: and determining the saturation of the different fluids by using a preset mode based on the characteristics of the different fluids on the nuclear magnetic resonance spectrum and the original data set.
In some embodiments, after obtaining the features of the different fluids on the nmr spectrum, the saturations of the different fluids may be determined in a predetermined manner based on the features of the different fluids on the nmr spectrum and the raw data set. Wherein the preset mode meets the specified constraint condition.
In some implementation scenarios, the predetermined manner may be a least squares method or an inversion method. It is to be understood that the above description is only exemplary, the preset mode is not limited to the above examples, and other modifications may be made by those skilled in the art within the spirit of the present application, and the scope of the present application should be covered as long as the functions and effects achieved by the present application are the same or similar.
In some implementation scenarios, the specified constraints may include a saturation non-negative constraint and a saturation sum of 1 constraint.
In some implementations, the data set may be based on a raw data set
Figure BDA0003376501180000091
And a matrix V representing the characteristics of different fluids on the nuclear magnetic resonance spectrum, and solving the saturation of different fluids at different depth points by using a fully-constrained least square method. Specifically, the saturation of different fluids at different depth points can be obtained according to the following formula:
Figure BDA0003376501180000092
wherein, S is a saturation matrix to be solved.
Transposing both ends of the equal sign of the formula (15) to obtain a formula (16):
Figure BDA0003376501180000093
since the result obtained by solving the above formula (12) by using the unconstrained least square method is inaccurate, in the embodiment of the present specification, the saturation is added and the constraint of 1 and the constraint of non-negative saturation are solved by using the least square method according to the characteristics of the saturation (i.e., the saturation and the constraint of 1 and the constraint of non-negative saturation), so as to obtain the saturations of different fluids at different depth points.
It should be noted that, the above is only exemplified by using a fully constrained least square method to obtain the saturations of different fluids at different depth points, and in this embodiment of the present disclosure, the saturations of different fluids at different depth points may also be obtained by using other inversion methods. When the saturation of different fluids at different depth points is obtained by other inversion methods, only the saturation non-negative constraint and the saturation sum constraint of 1 are added.
S10: determining porosities of different fluids in different depth points from the saturations of the different fluids and the raw data set.
Where porosity refers to the ratio of the sum of all the pore space volumes in a rock sample to the volume of the rock sample, usually expressed as a percentage. For example, the porosity of a bitumen fluid is the ratio of the volume of bitumen in a rock sample to the volume of the rock sample.
In some embodiments, the determining the porosities of the different fluids in the different depth points from the saturations of the different fluids and the raw data set may include: obtaining the total porosity of each depth point according to the nuclear magnetic resonance spectrum of each depth point in the original data set; and determining the porosity of the different fluids in the different depth points according to the total porosity of each depth point and the saturation of the different fluids.
In some implementations, the porosity of different fluids in different depth points may be determined according to the following equation:
pork,i＝Pork×snk,i (17)
Pork＝sum(fk) (18)
wherein, pork,iPorosity of i-th fluid, Por, at depth point kkTotal porosity, sn, of depth point kk,iSaturation of the ith fluid at depth point k, fkThe nuclear magnetic resonance spectrum of the depth point k.
It is to be understood that the foregoing is only exemplary, and the embodiments of the present disclosure are not limited to the above examples, and other modifications may be made by those skilled in the art within the spirit of the present disclosure, and the scope of the present disclosure is intended to be covered by the claims as long as the functions and effects achieved by the embodiments are the same as or similar to the present disclosure.
The above process is described below with reference to specific examples, however, it is noted that for better illustration of the present application, the following specific examples are given by T1-T2Well logging is illustrated as an example and does not constitute an undue limitation of the present application, e.g. it is still applicable to all multi-dimensional NMR well logging, such as T2-D log and T1-T2D-logging, etc.
Specifically, fig. 2 is T provided in the embodiments of the present disclosure1-T2The characteristics of 4 fluid types in the well logging on the NMR spectrum are shown, wherein the characteristics of the asphalt fluid type on the NMR spectrum and the characteristics of the Clay Bound Water (CBW) fluid type on the NMR spectrum are sequentially shown from top to bottom and from left to rightIs characterized by an oil in OP fluid type (OOP) in the NMR spectrum, a water in IP fluid type (WIP) in the NMR spectrum, and an abscissa T2Representing transverse relaxation time, ordinate T1The longitudinal relaxation time is indicated.
FIG. 3 is a schematic diagram of morphological processing of raw data set provided in the examples of the present specification, wherein K1 and K2 represent two structural bodies, Endmember (Endmember a, Endmember b, Endmember c) being end members, corresponding to T of pure fluid1-T2And (5) characterizing. Bands i and j are used to represent T for different depth points1-T2Spectra. Note that T is1-T2The dimensionality of the spectral data is nt1 × nt2, where nt1 and nt2 are T1And T2The number of the preset distribution points is expressed by two coordinates, i.e., Band i and Band j, for convenience of understanding. The number of fluid types in the formation is unknown, and in this embodiment, the effects of the expansion operator and the erosion operator are shown by the number of three fluid types. Specifically, as shown in fig. 3, the left graph shows the effect of the dilation operator and the right graph shows the effect of the erosion operator. It can be seen that in the K1 neighborhood, the dilation operator can get closer to the end member (corresponding to the T of pure fluid)1-T2Features), the erosion operator then gets data far from the end-members. Since the situation in the actual formation is complex, there are also situations similar to those in the K2 neighborhood, i.e., in the K2 neighborhood, the inflation operator gets data that is not close to the end-members. Therefore, in the embodiment of the present specification, a new data set is obtained by formula (11).
FIG. 4 illustrates a formation with different fluid porosities and saturations according to an embodiment of the present disclosure, wherein the first column DEPTH METERS is a depth channel, the second column P _ B is a porosity of Bitumen (Bitomen), the third column P _ C is a porosity of Clay Bound Water (CBW), the fourth column P _ O is a porosity of oil in organic pores (OOP), the fifth column P _ W is a porosity of water in inorganic pores (WIP), the sixth column P _ T is a total porosity of the formation, the seventh column S _ B is a saturation of Bitumen (Bitomen), the eighth column S _ C is a saturation of Clay Bound Water (CBW), the ninth column S _ O is a saturation of oil in organic pores (OOP), and the tenth column S _ W is a saturation of inorganic poresSaturation of Medium Water (WIP), the eleventh column T1-T2 SPECTRUM being the true T of the formation1-T2The SPECTRUM, the twelfth column ECHO being noisy ECHO data, the thirteenth column INVERTED T1-T2 SPECTRUM being T inverted based on noisy ECHO data1-T2Spectra, top Well in the figure represents the Well.
Fig. 5 is a distribution characteristic of a New data set after being processed by a morphological method according to an embodiment of the present disclosure, where an abscissa represents depth, an ordinate represents saturation, Raw Dataset represents an original data set, and New Dataset represents a New data set, and the New data set corresponds to an asphalt fluid type, a clay-bound water fluid type, an oil in OP fluid type, and a water in IP fluid type in sequence from top to bottom and from left to right.
Fig. 6 is a schematic diagram of determining an amount of a type of fluid in a formation based on an AIC value according to an embodiment of the present disclosure, where an abscissa represents the amount of the type of fluid and an ordinate represents the AIC value.
FIG. 7 is a T of different fluids extracted based on a new data set provided by embodiments of the present description1-T2The characteristic diagram sequentially corresponds to an asphalt fluid type, a clay bound water fluid type, an oil in OP fluid type and a water in IP fluid type from top to bottom and from left to right.
FIG. 8 is a T of different fluids extracted based on a raw data set provided by embodiments of the present description1-T2The characteristic diagram sequentially corresponds to an asphalt fluid type, a clay bound water fluid type, an oil in OP fluid type and a water in IP fluid type from top to bottom and from left to right.
Fig. 9 is a porosity of different fluids obtained based on a New data set and a Raw data set according to an embodiment of the present disclosure, where a horizontal coordinate Real Por is a Real porosity of the fluid, a vertical coordinate Calculated Por is a Calculated porosity of the fluid, Raw Dataset represents the Raw data set, and New Dataset represents the New data set.
Fig. 10 shows the effect of different structures on the solution of the present application provided in the examples of the present application, wherein the abscissa represents the size of the structure,time1 denotes morphological processing Time, Time 2 denotes feature extraction and porosity estimation Time based on the new data set, Time 3 denotes feature extraction and porosity estimation Time based on the original data set, Size of new dataset denotes new data set Size, and E _ por denotes porosity error. Wherein, the porosity error can be determined by the formula E _ por _ cal-por _ real | |2And calculating, wherein por _ cal is porosity calculated by different methods, and por _ real is formation real porosity.
As shown in fig. 10 (a), (b), and (c), it can be seen from (a) that as the structure increases, the data in the neighborhood increases, Time1 increases gradually, the sum of the morphological processing Time and the feature and porosity estimation Time based on the new data set (Time 1+ Time 2) decreases first and then increases, and the estimation Time (Time 3) for the feature and porosity of different fluids based on the original data set is much longer than (Time 1+ Time 2). It can be seen from (b) that as the structure increases, the data size of the new data set becomes smaller, and the same non-negative matrix factorization is less to characterize different fluids and calculate the porosity of different fluids by the fully constrained least squares method (Time 2). As can be seen from (c), the corresponding porosity error of the present embodiment first decreases with increasing structure, then remains relatively stable, and then increases. When the structure is small, certain redundant data still exists in the obtained new data set, and the obtained porosity error is still large although lower than that of the original data set. When the structure body is between 25 and 55, the porosity error calculated by the scheme of the application keeps relatively stable and is positioned in a low valley region of the porosity error, and the scheme of the application can have good fault tolerance when the size of the structure body is selected. As the structure continues to increase, the size of the structure is too large, the processed data is too coarse, so that the useful information in the original data set is filtered out, the useful information is lost, the obtained porosity error gradually increases, and when the size of the structure is 115, the porosity error obtained through the new data set is larger than the porosity error obtained through the original data set.
Therefore, the method and the device can determine the porosity of different fluids more accurately and more quickly.
From the above description, it can be seen that the embodiments of the present application can achieve the following technical effects: the NMR spectrums obtained by inverting the multi-dimensional nuclear magnetic resonance echo data of different depth points are combined into an original data set, and the original data set is subjected to AIC analysis, morphological analysis, nonnegative matrix decomposition, full-constraint least square method and other processing, so that the porosity of different fluids of different depth points in a target stratum can be determined more accurately and more quickly, and a basis can be provided for calculating the reserves and determining the perforation position of a horizontal well.
In the present specification, each embodiment of the method is described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. Reference is made to the description of the method embodiments.
Based on the method for determining the porosity of the formation fluid, one or more embodiments of the specification further provide a device for determining the porosity of the formation fluid. The apparatus may include systems (including distributed systems), software (applications), modules, components, servers, clients, etc. that use the methods described in the embodiments of the present specification in conjunction with any necessary apparatus to implement the hardware. Based on the same innovative conception, embodiments of the present specification provide an apparatus as described in the following embodiments. Since the implementation scheme of the apparatus for solving the problem is similar to that of the method, the specific implementation of the apparatus in the embodiment of the present specification may refer to the implementation of the foregoing method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Specifically, fig. 11 is a schematic block diagram of an apparatus for determining a porosity of a formation fluid according to an embodiment of the present disclosure, and as shown in fig. 11, the apparatus for determining a porosity of a formation fluid according to the present disclosure may include: an obtaining module 120, a first determining module 122, a first obtaining module 124, a second obtaining module 126, a second determining module 128, and a third determining module 130.
An obtaining module 120, configured to obtain an original data set composed of nmr spectra at different depth points; the nuclear magnetic resonance spectrums of the different depth points are obtained based on the inversion of echo data of the different depth points of the target stratum;
a first determining module 122, configured to obtain an AIC value representing the number of fluid types from the raw data set, and determine the number of fluid types in the target formation based on the AIC value;
a first obtaining module 124, configured to perform preprocessing on the original data set by using a morphological method to obtain a new data set; wherein the pretreatment comprises an expansion treatment and an erosion treatment;
a second obtaining module 126, configured to decompose the new data set by using a non-negative matrix algorithm based on the number of fluid types in the target formation, so as to obtain features of different fluids on the nmr spectrum;
a second determining module 128, configured to determine saturation of the different fluids in a preset manner based on the features of the different fluids on the nmr spectrum and the original data set;
a third determining module 130, configured to determine porosities of different fluids in different depth points according to the saturations of the different fluids and the raw data set.
It should be noted that the above-mentioned description of the apparatus according to the method embodiment may also include other embodiments, and specific implementation manners may refer to the description of the related method embodiment, which is not described herein again.
The present description also provides an embodiment of an apparatus for determining formation fluid porosity, comprising a processor and a memory storing processor-executable instructions that, when executed by the processor, implement any of the method embodiments described above. For example, the instructions when executed by the processor implement steps comprising: acquiring an original data set consisting of nuclear magnetic resonance spectrums of different depth points; the nuclear magnetic resonance spectrums of the different depth points are obtained based on the inversion of echo data of the different depth points of the target stratum; calculating an AIC value representing the number of fluid types according to the original data set, and determining the number of the fluid types in the target stratum based on the AIC value; preprocessing the original data set by using a morphological method to obtain a new data set; wherein the pretreatment comprises an expansion treatment and an erosion treatment; decomposing the new data set by using a non-negative matrix algorithm based on the number of the types of the fluids in the target stratum to obtain the characteristics of different fluids on the nuclear magnetic resonance spectrum; determining the saturation of different fluids by using a preset mode based on the characteristics of the different fluids on the nuclear magnetic resonance spectrum and the original data set; determining porosities of different fluids in different depth points from the saturations of the different fluids and the raw data set.
It should be noted that the above-mentioned apparatuses may also include other embodiments according to the description of the method or apparatus embodiments. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.
The method embodiments provided in the present specification may be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Taking an example of the server running on a server, fig. 12 is a block diagram of a hardware structure of a server for determining the porosity of a formation fluid provided in an embodiment of the present disclosure, where the server may be the apparatus for determining the porosity of a formation fluid or the system for determining the porosity of a formation fluid in the above embodiments. As shown in fig. 12, the server 10 may include one or more (only one shown) processors 100 (the processors 100 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 200 for storing data, and a transmission module 300 for communication functions. It will be understood by those skilled in the art that the structure shown in fig. 12 is only an illustration and is not intended to limit the structure of the electronic device. For example, the server 10 may also include more or fewer components than shown in FIG. 12, and may also include other processing hardware, such as a database or multi-level cache, a GPU, or have a different configuration than shown in FIG. 12, for example.
The memory 200 may be used to store software programs and modules for application software, such as program instructions/modules corresponding to the method for determining formation fluid porosity in the embodiments of the present description, and the processor 100 may execute various functional applications and data processing by executing the software programs and modules stored in the memory 200. Memory 200 may include high speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 200 may further include memory located remotely from processor 100, which may be connected to a computer terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission module 300 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission module 300 includes a Network adapter (NIC) that can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission module 300 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The method or apparatus provided by the present specification and described in the foregoing embodiments may implement service logic through a computer program and record the service logic on a storage medium, where the storage medium may be read and executed by a computer, so as to implement the effect of the solution described in the embodiments of the present specification. The storage medium may include a physical device for storing information, and typically, the information is digitized and then stored using an electrical, magnetic, or optical media. The storage medium may include: devices that store information using electrical energy, such as various types of memory, e.g., RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, tapes, core memories, bubble memories, and usb disks; devices that store information optically, such as CDs or DVDs. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth.
The above-described method or apparatus embodiments for determining formation fluid porosity provided in this specification may be implemented in a computer by executing corresponding program instructions by a processor, such as implemented in a PC using windows operating system c + + language, a linux system, or other intelligent terminals using android, iOS system programming languages, and quantum computer-based processing logic.
It should be noted that descriptions of the apparatus, the device, and the system described above according to the related method embodiments may also include other embodiments, and specific implementations may refer to descriptions of corresponding method embodiments, which are not described in detail herein.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.

Claims (10)

1. A method of determining the porosity of a formation fluid, comprising:
acquiring an original data set consisting of nuclear magnetic resonance spectrums of different depth points; the nuclear magnetic resonance spectrums of the different depth points are obtained based on the inversion of echo data of the different depth points of the target stratum;
calculating an AIC value representing the number of fluid types according to the original data set, and determining the number of the fluid types in the target stratum based on the AIC value;
preprocessing the original data set by using a morphological method to obtain a new data set; wherein the pretreatment comprises an expansion treatment and an erosion treatment;
decomposing the new data set by using a non-negative matrix algorithm based on the number of the types of the fluids in the target stratum to obtain the characteristics of different fluids on the nuclear magnetic resonance spectrum;
determining the saturation of different fluids by using a preset mode based on the characteristics of the different fluids on the nuclear magnetic resonance spectrum and the original data set;
determining porosities of different fluids in different depth points from the saturations of the different fluids and the raw data set.
2. The method of claim 1, wherein the AIC includes a precision penalty term and a complexity penalty term; the obtaining an AIC value representing a number of fluid types from the raw data set, and determining the number of fluid types in the target formation based on the AIC value, includes:
the AIC value representing the number of fluid types is found according to the following formula:
AIC＝-2ln(L)+2c＝F+C
Figure FDA0003376501170000011
C＝r(m+n)
wherein, F ═ 2ln (L) is a precision penalty term, C ═ 2C is a complexity penalty term, L is a likelihood function, C is the number of parameters, m is the number of depth points in the stratum, and n is T1-T2Well logging T1Number of distribution points and T2The product of distribution points, X is the original data set, U and V are two nonnegative matrixes obtained by decomposing the original data set by utilizing nonnegative matrix algorithm, the size of the nonnegative matrix U is mxr, and the nonnegative matrix U is non-negativeThe size of the negative matrix V is r × n, r represents the number of fluid types, Xk,iElement of the kth row and ith column of X, (UV)k,iThe element of the ith column of the kth row which is the product of U and V;
standardizing the precision penalty item and the complexity penalty item included in the AIC value to obtain a standardized AIC value;
and taking the number of the fluid types corresponding to the minimum standardized AIC value as the number of the fluid types in the target stratum.
3. The method of claim 1, wherein the preprocessing the original data set using a morphological approach to obtain a new data set comprises:
calculating the distance sum of the nuclear magnetic resonance spectrum of each depth point in the original data set and the nuclear magnetic resonance spectra of other depth points in the structure range to obtain the distance sum corresponding to each depth point; wherein, the distance of the nuclear magnetic resonance spectra of the two depth points is represented by a preset distance;
obtaining the result of the expansion processing of the nuclear magnetic resonance spectrum of each depth point based on the sum of the distances corresponding to the expansion operator and each depth point;
obtaining the result of the nuclear magnetic resonance spectrum corrosion treatment of each depth point based on the sum of the distances corresponding to the corrosion operator and each depth point;
obtaining an expansion data set and a corrosion data set according to the result of the expansion processing of the nuclear magnetic resonance spectrums of all the depth points and the result of the corrosion processing of the nuclear magnetic resonance spectrums of all the depth points;
based on the dilated data set and the eroded data set, a new data set is obtained.
4. The method of claim 3, wherein obtaining the processed result of the expansion of the nuclear magnetic resonance spectrum for each depth point based on the sum of the distance between the expansion operator and each depth point comprises:
the result after the expansion processing of the nuclear magnetic resonance spectrum of each depth point is obtained by the following method:
(fcΘg)＝arg_Max{D(fc,g)}
Figure FDA0003376501170000021
Figure FDA0003376501170000022
wherein (f)cΘ g) represents the nuclear magnetic resonance spectrum f of the depth point c in the original datasetcThe result after the dilation process, Θ, is the dilation operator, D (f)cG) nuclear magnetic resonance spectrum f representing depth point c in the original data setcThe sum of distances from the NMR spectrum at other depth points in the structure g, arg _ Max, is the sum of distances D (f)cG) nuclear magnetic resonance spectrum to maximum, dist (f)c,fs) Nuclear magnetic resonance spectrum f as depth point ccNuclear magnetic resonance spectrum f with depth point ssG is a structure, fcNuclear magnetic resonance spectrum of depth point c, fsIs the nuclear magnetic resonance spectrum of the depth point s, | fcI is fcModulo, | fsI is fsThe die of (1).
5. The method of claim 3, wherein obtaining the NMR-processed result for each depth point based on a sum of distances between the erosion operator and each depth point comprises:
the result after the nuclear magnetic resonance spectrum corrosion treatment of each depth point is obtained by the following method:
Figure FDA0003376501170000031
Figure FDA0003376501170000032
Figure FDA0003376501170000033
wherein the content of the first and second substances,
Figure FDA0003376501170000034
nuclear magnetic resonance spectrum f representing depth point c in the original data setcAs a result of the post-expansion treatment,
Figure FDA0003376501170000035
for the erosion operator, D (f)cG) nuclear magnetic resonance spectrum f representing depth point c in the original data setcThe sum of distances from the NMR spectrum of other depth points in the structure g, arg _ Min is the sum of distances D (f)cG) minimum NMR spectrum, dist (f)c,fs) Nuclear magnetic resonance spectrum f as depth point ccNuclear magnetic resonance spectrum f with depth point ssG is a structure, fcNuclear magnetic resonance spectrum of depth point c, fsIs the nuclear magnetic resonance spectrum of the depth point s, | fcI is fcModulo, | fsI is fsThe die of (1).
6. The method of claim 3, wherein the new data set is obtained by:
Figure FDA0003376501170000036
wherein, new _ data is a new data set, x is data in the new data set, DD is an expansion data set, and EE is a corrosion data set.
7. The method of claim 1, wherein determining the porosity of the different fluids in the different depth points from the saturations of the different fluids and the raw data set comprises:
obtaining the total porosity of each depth point according to the nuclear magnetic resonance spectrum of each depth point in the original data set;
and determining the porosity of the different fluids in the different depth points according to the total porosity of each depth point and the saturation of the different fluids.
8. The method of claim 7, wherein the porosity of different fluids in different depth points is determined according to the following formula:
pork,i＝Pork×snk,i
Pork＝sum(fk)
wherein, pork,iPorosity of i-th fluid, Por, at depth point kkTotal porosity, sn, of depth point kk,iSaturation of the ith fluid at depth point k, fkThe nuclear magnetic resonance spectrum of the depth point k.
9. An apparatus for determining the porosity of a formation fluid, comprising:
the acquisition module is used for acquiring an original data set consisting of nuclear magnetic resonance spectrums of different depth points; the nuclear magnetic resonance spectrums of the different depth points are obtained based on the inversion of echo data of the different depth points of the target stratum;
a first determining module, configured to obtain an AIC value representing the number of fluid types from the raw data set, and determine the number of fluid types in the target formation based on the AIC value;
the first obtaining module is used for preprocessing the original data set by using a morphological method to obtain a new data set; wherein the pretreatment comprises an expansion treatment and an erosion treatment;
the second obtaining module is used for decomposing the new data set by utilizing a non-negative matrix algorithm based on the number of the types of the fluids in the target stratum to obtain the characteristics of different fluids on the nuclear magnetic resonance spectrum;
the second determination module is used for determining the saturation of different fluids by using a preset mode based on the characteristics of the different fluids on the nuclear magnetic resonance spectrum and the original data set;
and the third determining module is used for determining the porosity of different fluids in different depth points according to the saturation of the different fluids and the original data set.
10. An apparatus for determining the porosity of a formation fluid, comprising at least one processor and a memory storing computer executable instructions which when executed by the processor implement the steps of the method of any one of claims 1 to 8.
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