CN117808986A - Water-drive reservoir dominant channel identification and quantification method - Google Patents
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Abstract
The invention relates to the technical field of petroleum exploitation, and discloses a water-drive reservoir dominant channel identification quantification method, which comprises the following steps: s1, obtaining the number N of production wells according to an actual geological model of the oil field P Number N of water injection wells I The effective grid quantity M and the horizon quantity F of small layers in the actual geological model of the oil field; s2, setting an oil reservoir numerical simulation method and stable conditions for tracing the tracer; s3, according to N P ·N I The tracer simulation results of the well pairs are respectively and correspondingly manufactured into N well pair grid slices, and the spatial attribute of each grid slice is subjected to average treatment according to the layers to finally form a feature matrix X; s4, defining a parameter vector C to be solved, and calculating and generating a potential index theta through elements in the parameter vector C to be solved; s5, drawing three-dimensional stereo and two-dimensional plane distribution of space attribute thetaThe high value region characterizes the hypertonic channel. The invention introduces the time-of-flight space attribute quantity obtained by numerical simulation and has certain flow characteristic characterization capability.
Description
Technical Field
The invention relates to the technical field of petroleum exploitation, in particular to a water-drive reservoir dominant channel identification quantification method.
Background
Once the dominant channel is developed in the oil reservoir, the inefficient or ineffective circulation flow of the injected water in the dominant channel causes the difficulty in the effectiveness of other parts in the reservoir, the sweep efficiency of the oil reservoir is severely restricted, and the water flooding development effect is affected. The method enhances the identification and quantitative description research of the reservoir dominant channels, and has important significance for guiding the water injection development of oil reservoirs, profile control of injection and production wells and optimization design of tertiary oil recovery.
The identification of the hypertonic channels in the reservoir by the traditional method mainly comprises an indoor physical simulation experiment, an inter-well tracer test, inversion of reservoir attribute parameters by production dynamic data, an inter-well dynamic connectivity evaluation method and the like. The traditional method adopts few data samples, the quantitative description method is based on some assumptions, the assumptions are quite different from the actual situation, and the obtained result has low reliability.
At present, with the development of an oil reservoir fine modeling method and a numerical simulation technology, how to build a self-adaptive specific oil field condition based on a model has good oil reservoir geology and engineering theoretical background, has good interpretability and robustness, and becomes a leading-edge subject of dominant channel research by using dominant channel qualitative identification and quantitative description technology for assisting efficient development of a water-flooding reservoir.
Disclosure of Invention
The invention provides a water-flooding reservoir dominant channel identification quantification method, which combines an oil reservoir numerical simulation and a data-driven optimization algorithm, wherein three geological space attributes of a specific oil field work area, namely porosity, permeability and net-to-gross ratio, and forward, backward and average flight time obtained by the oil reservoir numerical simulation are mapped into a hypertonic channel development index through a model, and an optimization target is set to maximize a Spearman coefficient of a liquid production profile obtained by the oil reservoir numerical simulation and the hypertonic channel development index, so that the complex nonlinear mapping problem is avoided, and the stability of the model is improved; based on a linear equation instead of a complex nonlinear machine learning model, the complexity is effectively reduced, and the model has better interpretability. The method can intuitively represent the hypertonic channel in the quantized geological three-dimensional model, thereby providing reference for optimizing the water injection scheme. The method is based on oil reservoir numerical simulation, has good theoretical basis and practical operability, and is suitable for design and later adjustment of water-flooding field development schemes.
The invention is realized by the following technical scheme:
a water-flooding reservoir dominant channel identification quantification method comprises the following steps:
s1, obtaining the number N of production wells according to an actual geological model of the oil field P Number of water injection wellsQuantity N I The effective grid quantity M and the horizon quantity F of small layers in the actual geological model of the oil field;
s2, setting a simulation method and a stable condition for tracing a tracer by oil reservoir numerical simulation, and simulating a well pair consisting of a production well and a water injection well in the actual geological model of the oil field;
s3, according to N P ·N I The tracer simulation result of the group well pairs is that N communicating well pairs are extracted from the tracer simulation result and grid sections are correspondingly manufactured respectively, and the spatial attribute of each grid section is processed according to the horizon to finally form a feature matrix X;
s4, according to tracking test of oil field tracer or according to N P ·N I The tracer simulation result of the group well pairs is used for obtaining the flow distribution of the injection and production sections of the N groups of injection and production well pairs on F small layers
S5, defining a parameter vector C to be solved, wherein each element of the parameter vector C to be solved is a weight coefficient of an element of the feature matrix X; then based on the flow distributionSolving elements in the parameter vector C to be solved, and calculating to generate a potential index theta through the elements in the parameter vector C to be solved;
s6, drawing a three-dimensional stereo and two-dimensional plane distribution diagram of the spatial attribute theta, and representing a hypertonic channel in a high-value area.
As optimization, the simulation method and the stabilization conditions for carrying out the numerical simulation tracking of the oil reservoir by the tracer are specifically as follows:
the simulation method comprises the following steps: single phase seepage and in N P Production well, N I The mouth water injection well is respectively provided with constant pressure production and quantitative injection;
the stabilizing conditions are as follows: simulation was performed until the tracer concentration field reached steady state.
As optimization, in S3, for N P ·N I Tool for assembling well pair to manufacture well pair grid slicesThe body method comprises the following steps:
s3.1, pair N P Any nth production well p A production well, a cable leading out and the nth p The well bore track of the production well is overlapped or adjacent to the space grid, and the total grid number of the space grid isThe set of spatial grids is expressed as:wherein f is the number of the small layer where the grid is located; f is the total number of small layers; />Represents the nth p A 1 st grid of a shaft of the production well and the nearby thereof, wherein the grid is provided with a small layer number f;
s3.2 at arbitrary nth p Grid for production wellOn the basis of (1), examine the nth p Production well and nth i Relationship of water injection well: according to the tracer tracking result, nth i Tracer of water injection well is in space grid +.>A grid with medium concentration greater than 0, denoted +.>
S3.3, p n p ＝{1,2,…,N P }、n i ＝{1,2,…,N I Repeating the steps S3.1-S3.2, and recording the well pairs consisting of the production wells and the water injection wells with the element number more than 0Is N.
As optimization, in S3, the specific process of performing the horizon-based average processing on the spatial attribute of each grid slice to finally form the feature matrix X is as follows:
s3.4, carrying out min-max normalization treatment on the porosity field, the permeability field and the net Mao Bichang of all grids in the oilfield actual geological model to obtain a normalized porosity field phi, a normalized permeability field K and a normalized net Mao Bichang psi;
s3.5, calculating the forward flight time field T under each group of well positions according to the tracer simulation result F Rearward time of flight field T B Average time of flight field T A ；
S3.6, for each group of production well-water injection well pairs (n p ,n i ) To collect corresponding space gridsInner porosity field->Permeability field->Net hair ratio field->Forward time of flight field->Rearward time of flight fieldMean time of flight field>Average over F small layers to obtain each attribute the well pair (n p ,n i ) Row vector +.>The specific expression of (2) is:
s3.7, acquiring the flow distribution of the tracer of the well pair on F small layers according to the tracer tracking test or the tracer simulation result of the well pair in the oilfield site
As optimization, in S4, flow distribution of N groups of injection and production wells to injection and production sections on F small layers respectivelyExpressed as:wherein: n is n p ＝{1,2,…,N P }，n i ＝{1,2,…,N I }，f＝{1,2,…,F}，/>Is the nth i Tracer of water injection well at nth p Flow percentage of f-zone of the production well.
As optimization, in S3.4, the specific expression of the normalized porosity field Φ is:
wherein,represents n p Production well shaft and adjacent production well shaft and n i The porosity vector of the grid with the tracer concentration of the water injection well being greater than 0, m being the grid number,/->A porosity vector of the spatial grid denoted m;
the specific expression of the normalized permeability field K is:
wherein,represents n p Production well shaft and adjacent production well shaft and n i Permeability vector of grid with tracer concentration of water injection well greater than 0, m is grid number, and ∈10>A permeability vector representing a spatial grid of m;
the specific expression of the normalized net-to-gross field ψ is as follows:
wherein,represents n p Production well shaft and adjacent production well shaft and n i Net wool ratio vector of grid with tracer concentration of water injection well greater than 0, m is grid number,/->A net-to-gross vector representing the spatial grid of m; .
As an optimization, in S3.5, the forward time-of-flight field T is calculated F Rearward time of flight field T B Average time of flight field T A The specific process of (2) is as follows:
s3.5.1 on the basis of the actual geological model and well position of the oil field given in S1, taking single-phase fluid into consideration, carrying out oil reservoir numerical simulation until reaching a steady state, obtaining a speed field, namely M speed vectors, and recording the i-th group speed field as follows:
s3.5.2 calculating the forward and backward time-of-flight fields of any mth grid, i.e.Andare scalar quantities:
representing the porosity of a mesh numbered m;
s3.5.3 calculating the average time-of-flight field at the grid, i.e
S3.5.4, obtaining vectors of the marker composition in the M spatial grids, respectively:
s3.5.5 well pair consisting of production well and water injection well, and guiding forward flight time field T F Rearward time of flight field T B Average time of flight field T A The method is divided into:
wherein,represents n p Production well-n i Forward time-of-flight field of a well pair consisting of water injection wells,>represents n p Production well-n i A backward time-of-flight field of a well pair consisting of water injection wells,>represents n p Production well-n i An average time-of-flight field for a well pair of water injection wells.
As an optimization, in S3.6, the row vector of the individual properties at the f-th minor layer with respect to the average value of the well pair on each minor layer is expressed as:
wherein,represents n p Production well-n i Column vectors of each attribute of the well pair consisting of the water injection wells in the f-th small layer +.>Represents n p Production well-n i The porosity vector of the well pair formed by the water injection well at the f-th small layer>Represents n p Production well-n i Permeability vector of well pair formed by water injection well at f-th small layer, +.>Represents n p Production well-n i The net ratio vector of the well pair formed by the water injection well at the f-th small layer is +.>Represents n p Production well-n i The well pair consisting of water injection wells is in the forward time-of-flight field of the f-th small layer +.>Represents n p Production well-n i Well pairs consisting of water injection wells are in the backward time-of-flight field of the f-th small layer +.>Represents n p Production well-n i The average time-of-flight field at the f-th floor is the well pair consisting of the water injection wells.
As optimization, the specific steps of S5 are:
s5.1, defining a parameter vector C to be solved, and recording the parameter vector C as a column vector form matrix with the size of (6 multiplied by 1), wherein each element of the parameter vector C to be solved is respectively a porosity field phi, a permeability field K, a net Mao Bichang psi and a forward flight time field T F And then (b) backTo a time of flight field T B Average time of flight field T A Weight coefficient of (2);
s5.2, calculating the index Y column vector of the hypertonic channel of the production well-water injection well-small layer arrangement:
Y＝XC；
s5.3, calculating the flow distribution of the hypertonic channel index Y and the actual corresponding flow distributionSpearman coefficient ρ (C);
s5.4, setting C as a variable to be solved, wherein any C i The search range is [ -1,1]Repeating the steps S5.2-S5.3 by using a simulated annealing algorithm, and solving C by using a random disturbance mode * Such that:
C * ＝argmax(ρ(C))；
s5.5, pair C * Normalization processing is carried out to obtainThe specific mode is as follows:
wherein,the element in the parameter vector C to be solved, namely the weight corresponding to each attribute.
As an optimization, the potential index θ is:
compared with the prior art, the invention has the following advantages and beneficial effects:
the invention establishes a set of process method with high practicability based on the oil reservoir numerical simulation result and the data-driven optimization algorithm, and maps three geological space attributes of porosity, permeability and net-to-gross ratio of a specific oil field work area and forward, backward and average flight time obtained by oil reservoir numerical simulation into a hypertonic channel development index through a model. Processing the grid data into average values according to the horizon and the production well-water injection well through the tracer concentration and the section of the well periphery area; the flight time space attribute quantity obtained by numerical simulation is introduced, and the flight time space attribute quantity has certain flow characteristic characterization capability; the method fully considers the highly complex nonlinear relation between the average spatial attribute of the well pair and the industrial profile, so that an optimization object is determined to be a Spearman coefficient between a potential index and the accumulated yield instead of the traditional regression error directly using the accumulated yield value, the rapid convergence of a mathematical optimization model and the most important generalization capability for field application are ensured, and the established potential index and the accumulated yield are ensured to have obvious positive correlation in statistical sense, so that a stable and reliable hypertonic channel identification index is provided for a reservoir engineer.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention. In the drawings:
FIG. 1 is a schematic view of a real geologic model attribute field of a work area;
FIG. 2 is a schematic illustration of the communication area between pairs of production wells from tracer tracking;
FIG. 3 is a schematic representation of an oilfield time-of-flight field;
FIG. 4 is a plot of production well fluid profile and source contribution obtained by tracer tracking;
FIG. 5 is a schematic diagram of the distribution of the index planes of the dominant channels of the oil reservoirs;
FIG. 6 is a schematic diagram of the distribution and classification of the reservoir dominant channel index;
FIG. 7 is a schematic diagram of a dominant channel planar distribution;
FIG. 8 is a schematic diagram of a cross-sectional profile of a dominant channel well connection.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
The embodiment 1 provides a water-flooding reservoir dominant channel identification quantification method, as shown in fig. 1-8, comprising:
s1, obtaining the number N of production wells according to an actual geological model of the oil field P Number N of water injection wells I The effective grid quantity M and the horizon quantity F of small layers in the actual geological model of the oil field;
s2, setting a simulation method and a stable condition for tracing a tracer by oil reservoir numerical simulation, and simulating a well pair consisting of a production well and a water injection well in the actual geological model of the oil field;
the simulation method comprises the following steps: single phase seepage and in N P Production well, N I The mouth water injection well is respectively provided with constant pressure production and quantitative injection; the tracer is pressed in from the water injection well and then flows into the production well;
the stabilizing conditions are as follows: simulation was performed until the tracer concentration field reached steady state.
S3, according to N P ·N I The tracer simulation result of the group well pairs is that N communicating well pairs are extracted from the tracer simulation result and grid sections are correspondingly manufactured respectively, and the spatial attribute of each grid section is processed according to the horizon to finally form a feature matrix X;
the specific method comprises the following steps:
s3.1, pair N P Any nth production well p A production well, a cable leading out and the nth p The well bore track of the production well coincides with or is adjacent to the remaining grids which are in contact with the grid under investigation, the total number of grids of the space grid beingThe set of spatial grids is expressed as：/>Wherein f is the number of the small layer where the grid is located; />Represents the nth p A 1 st grid of a shaft of the production well and the nearby thereof, wherein the grid is provided with a small layer number f;
s3.2 at arbitrary nth p Grid for production wellOn the basis of (1), examine the nth p Production well and nth i Relationship of water injection well: according to the tracer tracking result, nth i Tracer of water injection well is in space grid +.>A grid with medium concentration greater than 0, denoted +.>
S3.3, p n p ＝{1,2,…,N P }、n i ＝{1,2,…,N I Repeating the steps S3.1-S3.2, and recording the well pairs consisting of the production wells and the water injection wells with the element number more than 0(that is, the number of the space grids with the concentration greater than 0 exists, namely, on the basis of S3.1 and S3.2, all the space grids with the concentration greater than 0) is N;
s3.4, carrying out min-max normalization treatment on the porosity field, the permeability field and the net Mao Bichang of all grids in the oilfield actual geological model to obtain a normalized porosity field phi, a normalized permeability field K and a normalized net Mao Bichang psi;
according to the production well-water injection well pair, the normalized porosity field, permeability field and net wool ratio field data after the actual geological model S3.4 of the oil field are derived, and the normalized porosity field, the permeability field and the net wool ratio field data are respectively:
the specific expression of the porosity field phi after normalization is as follows:
wherein,represents n p Production well shaft and adjacent production well shaft and n i The porosity vector of the grid with the tracer concentration of the water injection well being greater than 0, m being the grid number,/->A porosity vector of the spatial grid denoted m;
the specific expression of the normalized permeability field K is:
wherein,represents n p Production well shaft and adjacent production well shaft and n i Permeability vector of grid with tracer concentration of water injection well greater than 0, m is grid number, and ∈10>A permeability vector representing a spatial grid of m;
the specific expression of the normalized net-to-gross field ψ is as follows:
wherein,represents n p Production well shaft and adjacent production well shaft and n i Net wool ratio vector of grid with tracer concentration of water injection well greater than 0, m is grid number,/->A net-to-gross vector representing the spatial grid of m.
S3.5, calculating the forward flight time field T under each group of well positions (which are uniformly calculated at one time and are given according to the oil field or oil reservoir level without distinguishing the production well and the water injection well) according to the tracer simulation result F Rearward time of flight field T B Average time of flight field T A ；
Calculating forward, backward, average time-of-flight fields at each group of well sites, TOFF (Time of Flight Forward, denoted T F ) TOFB (Time ofFlight Backward, denoted T B ) TOFA (Time of Flight Averaged, denoted T) A ). TOFF refers to the time that the fluid starts from the injection point to a certain spatial grid of the reservoir, TOFB refers to the time that the fluid starts from a certain spatial grid of the reservoir to the production point, and TOFA is the average of TOFB and TOFF.
The specific process is as follows:
s3.5.1 on the basis of the actual geological model and well position of the oil field given in S1, taking single-phase fluid into consideration, carrying out oil reservoir numerical simulation until reaching a steady state, obtaining a speed field, namely M speed vectors, and recording the i-th group speed field as follows:
s3.5.2 calculating the forward and backward time-of-flight fields of any mth grid, i.e.Andare scalar quantities:
representing the porosity of a mesh numbered m;
s3.5.3 calculating the average time-of-flight field at the grid, i.e
S3.5.4, obtaining vectors of the marker composition in the M spatial grids, respectively:
s3.5.5 well pair consisting of production well and water injection well, and guiding forward flight time field T F Rearward time of flight field T B Average time of flight field T A The method is divided into:
wherein,represents n p Production well-n i Forward time-of-flight field of a well pair consisting of water injection wells,>represents n p Production well-n i A backward time-of-flight field of a well pair consisting of water injection wells,>represents n p Production well-n i An average time-of-flight field for a well pair of water injection wells;
s3.6, for each group of production well-water injection well pairs (n p ,n i ) To collect corresponding space gridsInner porosity field->Permeability field->Net hair ratio field->Forward time of flight field->Rearward time of flight fieldMean time of flight field>Average over F small layers to obtain each attribute the well pair (n p ,n i ) Row vector of the average value of the f-th layer of +.>
For any n p Production well-n i The water injection well pair has more than 6 attribute vectors Average over F small layers to obtain each attribute the well pair (n p Nx) row vector composed of the average value of the f-th small layer:
wherein,represents n p Production well-n i Column vectors of each attribute of the well pair consisting of the water injection wells in the f-th small layer +.>Represents n p Production well-n i The porosity vector of the well pair formed by the water injection well at the f-th small layer>Represents n p Production well-n i Permeability vector of well pair formed by water injection well at f-th small layer, +.>Represents n p Production well-n i The net ratio vector of the well pair formed by the water injection well at the f-th small layer is +.>Represents n p Production well-n i The well pair consisting of water injection wells is in the forward time-of-flight field of the f-th small layer +.>Represents n p Production well-n i Well pairs consisting of water injection wells are in the backward time-of-flight field of the f-th small layer +.>Represents n p Production well-n i An average time-of-flight field at the f-th small layer for a well pair consisting of water injection wells;
s3.7, sequentially arranging the obtained N.F ordered row vectors according to rows to form a matrix with the size of ((N.F). Times.6), and taking the matrix as a feature matrix X.
S4, flow distribution of N groups of injection and production wells to injection and production sections on F small layersExpressed as:wherein: n is n p ＝{1,2,…,N P }，n i ＝{1,2,…,N I }，f＝{1,2,…,F}，/>Is the nth i Tracer of water injection well at nth p Flow percentage of f-zone of the production well.
S5, defining a parameter vector C to be solved, wherein each element of the parameter vector C to be solved is a weight coefficient of an element of the feature matrix X; then flow distributionSolving elements in the parameter vector C to be solved, and calculating to generate a potential index theta through the elements in the parameter vector C to be solved;
the method comprises the following specific steps:
s5.1, defining a parameter vector C to be solved, and recording the parameter vector C as a column vector form matrix with the size of (6 multiplied by 1), wherein each element of the parameter vector C to be solved is respectively a porosity field phi, a permeability field K, a net Mao Bichang psi and a forward flight time field T F Rearward time of flight field T B Average time of flight field T A Weight coefficient of (2);
s5.2, calculating the index Y column vector of the hypertonic channel of the production well-water injection well-small layer arrangement:
Y＝XC；
s5.3, calculating the flow distribution of the hypertonic channel index Y and the actual corresponding flow distributionSpearman coefficient ρ (C);
the Spearman coefficient is used to characterize the nonlinear correlation between variables, with a range of [ -1, +1], negative values representing the negative correlation, and positive values representing the positive correlation. The method comprises the following specific steps:
1) For each sample data point P i And Y i The data are ranked according to their values. P (P) i For the flow distribution of the ith grid, Y i For the hypertonic channel index Y of the ith grid, rank means that data is arranged from small to large and marked with 1,2 and 3 … N, and if the data with the same value is found, the average rank order is taken.
2) Calculate P i And Y i The absolute value of the difference between the ranks of (also called rank difference), noted D i For example, if a row Pi has a ranking value of 9 (9 th large), yi has a ranking of 5, di is 4.
3) Calculating Spearman coefficients as follows:
in this embodiment, N is 8721.
S5.4, setting C as a variable to be solved, wherein any C i The search range is [ -1,1]Repeating the steps S5.2-S5.3 by using a simulated annealing algorithm, and solving C by using a random disturbance mode * Such that:
C * ＝argmax(ρ(C))；
s5.5, pair C * Normalization processing is carried out to obtainThe specific mode is as follows:
wherein,the element in the parameter vector C to be solved, namely the weight corresponding to each attribute.
The potential index θ is:
s5, drawing a three-dimensional stereo and two-dimensional plane distribution diagram of the spatial attribute theta, and representing a hypertonic channel in a high-value area.
(taking as an example the dominant seepage path identification of a certain field development scheme):
1. tracer tracking using reservoir numerical modeling:
according to the actual geological three-dimensional model of the oilfield work area, the total number of small layers is F=20, and 3 ports (I 1 ,I 2 ,I 3 ) Production well 10 port (P) 1 ,P 2 ,…,P 10 ) As shown in fig. 1, the set conditions are: (1) single-phase seepage;(2) simulation was performed until the tracer concentration field reached steady state.
The communication area between the production well and the water injection well is examined according to the tracer concentration, as shown in fig. 2.
The production well-water injection well pairs are combined into 10.3=30 groups, and grid slices of the production well-water injection well are manufactured according to the tracer simulation result, and the specific steps are as follows:
any nth of the above 10 production wells p The well is opened, the space grids which are coincident with or adjacent to the well shaft track are indexed, and the total number of grids isThese grids form a set->Wherein f is the number of the small layer where the grid is located; />Represents the nth p The 1 st grid of the well shaft and the nearby grid is provided with a small layer number f.
At any nth p Grid for production wellOn the basis of (1), examine the n p Well and nth i Relationship of water injection well: according to the tracer tracking result, nth i The tracer of the water injection well is->The part of the grid with the medium concentration greater than 0 is marked as +.>
For n p ＝{1,2,…,N P }、n i ＝{1,2,…,N I Repeating the steps (1) and (2), wherein the number of the record elements is more than 0Is N.
N=23 valid well pair grid slices were obtained in total.
The flow rate of each production well-water injection well pair on each layer, namely the liquid production section,wherein: n is n p ＝{1,2,…,N P }，n i ＝{1,2,…,N I }，f＝{1,2,…,F}，/>Is the nth i Tracer of water injection well at nth p Flow percentage of f-zone of the production well.
2. Obtaining an average spatial attribute representation of the production well-water injection well pair:
for any n p Production well-n i A water injection well pair for carrying out the above 6 attribute vectors Average over 20 small layers to obtain each attribute the well pair (n p ,n i ) Row vector composed of the average value of the f-th layer:
the obtained n·f=23×20=460 ordered vectors are sequentially arranged in rows to form a matrix with a size of (460×6), and a feature matrix X is formed.
Defining a parameter vector to be solved, and recording the parameter vector as a column vector form matrix with the size of (6 multiplied by 1), namely:
3. solving the coefficient of the hypertonic channel index model:
the index row vector matrix Y of the hypertonic channel arranged according to the pairs of the production well and the water injection well is calculated in trial mode, wherein Y=XC;
setting C as a variable to be solved, wherein any C i The search range is [ -1,1]. Solving for C maximizing Spearman coefficients using a simulated annealing algorithm using random perturbation and iterative approach * The method can be used for obtaining:
C * ＝[0.23,0.35,0.12,0.21,0.22,0.34]
the weight normalized by the step J is as follows:
utilization of any ith effective grid in oil layer of whole three-dimensional geological modelCalculating production potential index θ i 。
Calculating a spatial attribute value, namely a production potential index theta, of any ith effective grid in an oil layer of the whole three-dimensional geological model i ：
4. And (3) researching the development condition of the oil reservoir hypertonic channel:
the solution process is to divide the high value interval into 3 stages according to the distribution of the maximized hypertonic channel index (fig. 6) by maximizing the positive correlation of the hypertonic channel index and the fluid production profile: class 3 is between 0.534-0.6; class 2 is between 0.6 and 0.666; class 1 is 0.666 or more.
As can be seen from the plan view of FIG. 7, the level 1 dominant percolation channel is mainly distributed in I1-P2, I1-P6, I2-P3, I3-P4.
The oil reservoir hypertonic channel index is a three-dimensional space attribute, and the development condition of the dominant seepage channel among multiple layers among wells can be more intuitively inspected on a well-by-well section, as shown in fig. 8.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The method for identifying and quantifying the dominant channels of the water-flooding reservoir is characterized by comprising the following steps of:
s1, obtaining the number N of production wells according to an actual geological model of the oil field P Number N of water injection wells I The effective grid quantity M and the horizon quantity F of small layers in the actual geological model of the oil field;
s2, setting a simulation method and a stable condition for tracing a tracer by oil reservoir numerical simulation, and simulating a well pair consisting of a production well and a water injection well in the actual geological model of the oil field;
s3, according to N P ·N I The tracer simulation result of the group well pairs is that N communicating well pairs are extracted from the tracer simulation result and grid sections are correspondingly manufactured respectively, and the spatial attribute of each grid section is processed according to the horizon to finally form a feature matrix X;
s4, according to tracking test of oil field tracer or according to N P ·N I The tracer simulation result of the group well pairs is used for obtaining the flow distribution of the injection and production sections of the N groups of injection and production well pairs on F small layers
S5, defining a parameter vector C to be solved, wherein each element of the parameter vector C to be solved is a weight coefficient of an element of the feature matrix X; then based on the flow distributionSolving the elements in the parameter vector C to be solved, and passing through the parameter vector C to be solvedCalculating elements in the parameter vector C to generate a potential index theta;
s6, drawing a three-dimensional stereo and two-dimensional plane distribution diagram of the spatial attribute theta, and representing a hypertonic channel in a high-value area.
2. The method for identifying and quantifying the dominant channels of the water-flooding reservoir according to claim 1 is characterized in that the simulation method for carrying out reservoir numerical simulation tracking through the tracer and the stability conditions are as follows:
the simulation method comprises the following steps: single phase seepage and in N P Production well, N I The mouth water injection well is respectively provided with constant pressure production and quantitative injection;
the stabilizing conditions are as follows: simulation was performed until the tracer concentration field reached steady state.
3. The method for identifying and quantifying the dominant channel of a water-flooding reservoir according to claim 1, wherein in S3, for N P ·N I The specific method for manufacturing the well pair grid slice by the well pair comprises the following steps:
s3.1, pair N P Any nth production well p A production well, a cable leading out and the nth p Wellbore trajectory coincidence and adjacent space grids of production wellsThe total of these grids>The set of elements is expressed as: /> Wherein F is the number of the small layer where the grid is located, and f=1, 2, …, F; f is the total number of small layers; />Represents the nth p Wellbore of production well and adjacent ith grid thereof, i=1, 2, …, +.>And the number of the small layer where the grid is positioned is f;
s3.2 at arbitrary nth p Grid for production wellOn the basis of (1), examine the nth p Production well and nth i Relationship of water injection well: according to the tracer tracking result, nth i Tracer of water injection well is in space grid +.>A grid with medium concentration greater than 0, denoted +.>
S3.3, p n p ＝{1,2,…,N P }、n i ＝{1,2,…,N I Repeating the steps S3.1-S3.2, and recording the well pairs consisting of the production wells and the water injection wells with the element number more than 0Is N.
4. The water-flooding reservoir dominant channel identification and quantization method according to claim 3, wherein in S3, the specific process of performing the horizon-based average processing on the spatial attribute of each grid slice to finally form the feature matrix X is as follows:
s3.4, carrying out min-max normalization treatment on the porosity field, the permeability field and the net Mao Bichang of all grids in the oilfield actual geological model to obtain a normalized porosity field phi, a normalized permeability field K and a normalized net Mao Bichang psi;
s3.5, calculating each group of well positions according to the tracer simulation resultForward time of flight field T F Rearward time of flight field T B Average time of flight field T A ；
S3.6, for each group of production well-water injection well pairs (n p ,n i ) To collect corresponding space gridsPorosity field inPermeability field->Net hair ratio field->Forward time of flight field->Backward time of flight field->Mean time of flight field>Average over F small layers to obtain each attribute the well pair (n p ,n i ) Row vector +.>The specific expression of (2) is:
s3.7, sequentially arranging the obtained N.F ordered row vectors according to rows to form a matrix with the size of ((N.F). Times.6), and taking the matrix as a feature matrix X.
5. The method for identifying and quantifying the dominant channel of a water-flooding reservoir according to claim 4, wherein in S4, the flow distribution of the injection and production sections of the N groups of injection and production wells on the F small layers respectivelyExpressed as: />Wherein: n is n p ＝{1,2,…,N P }，n i ＝{1,2,…,N I }，f＝{1,2,…,F}，/>Is the nth i Tracer of water injection well at nth p Flow percentage of f-zone of the production well.
6. The water-flooding reservoir dominant channel identification quantification method according to claim 5, wherein in S3.4, the specific expression of the normalized porosity field phi is:
wherein,represents n p Production well shaft and adjacent production well shaft and n i The porosity vector of the grid with the tracer concentration of the water injection well being greater than 0, m being the grid number,/->A porosity vector of the spatial grid denoted m;
the specific expression of the normalized permeability field K is:
wherein,represents n p Production well shaft and adjacent production well shaft and n i Permeability vector of grid with tracer concentration of water injection well greater than 0, m is grid number, and ∈10>A permeability vector representing a spatial grid of m;
the specific expression of the normalized net-to-gross field ψ is as follows:
wherein,represents n p Production well shaft and adjacent production well shaft and n i Net wool ratio vector of grid with tracer concentration of water injection well greater than 0, m is grid number; />A net-to-gross vector representing the spatial grid of m.
7. The method for identifying and quantifying the dominant channel of a water-drive reservoir according to claim 6, wherein in S3.5, a forward time-of-flight field T is calculated F Rearward time of flight field T B Average time of flight field T A The specific process of (2) is as follows:
s3.5.1 on the basis of the actual geological model and well position of the oil field given in S1, taking single-phase fluid into consideration, carrying out oil reservoir numerical simulation until reaching a steady state, obtaining a speed field, namely M speed vectors, and recording the i-th group speed field as follows:
s3.5.2 calculating the forward and backward time-of-flight fields of any mth grid, i.e.And->Are scalar quantities:
representing the porosity of a mesh numbered m;
s3.5.3 calculating the average time-of-flight field at the grid, i.e
S3.5.4, obtaining vectors of the marker composition in the M spatial grids, respectively:
s3.5.5 well pair consisting of production well and water injection well, and guiding forward flight time field T F Rearward time of flight field T B Average time of flight field T A The method is divided into:
wherein,represents n p Production well-n i Forward time-of-flight field of a well pair consisting of water injection wells,>represents n p Production well-n i A backward time-of-flight field of a well pair consisting of water injection wells,>represents n p Production well-n i Comprising water injection wellsAverage time-of-flight field for a well pair.
8. The method for identifying and quantifying the dominant channels of a water reservoir according to claim 7, wherein in S3.6, the row vector of each attribute of the f-th small layer with respect to the average value of the well pair on each small layer is expressed as:
wherein,represents n p Production well-n i Each attribute of the well pair consisting of the water injection wells is in a row vector consisting of the f-th small layer,represents n p Production well-n i The porosity vector of the well pair formed by the water injection well at the f-th small layer>Represents n p Production well-n i Permeability vector of well pair formed by water injection well at f-th small layer, +.>Represents n p Production well-n i The net ratio vector of the well pair formed by the water injection well at the f-th small layer is +.>Represents n p Production well-n i The well pair consisting of water injection wells is in the forward time-of-flight field of the f-th small layer +.>Represents n p Production well-n i Well composed of water injection wellFor the backward time-of-flight field at the f-th floor,represents n p Production well-n i The average time-of-flight field at the f-th floor is the well pair consisting of the water injection wells.
9. The water-flooding reservoir dominant channel identification quantification method according to claim 8, wherein the specific steps of S5 are as follows:
s5.1, defining a parameter vector C to be solved, and recording the parameter vector C as a column vector form matrix with the size of (6 multiplied by 1), wherein each element of the parameter vector C to be solved is respectively a porosity field phi, a permeability field K, a net Mao Bichang psi and a forward flight time field T F Rearward time of flight field T B Average time of flight field T A Weight coefficient of (2);
s5.2, calculating the index Y column vector of the hypertonic channel of the production well-water injection well-small layer arrangement:
Y＝XC；
s5.3, calculating the flow distribution of the hypertonic channel index Y and the actual corresponding flow distributionSpearman coefficient ρ (C);
s5.4, setting C as a variable to be solved, wherein any C i The search range is [ -1,1]Repeating the steps S5.2-S5.3 by using a simulated annealing algorithm, and solving C by using a random disturbance mode * Such that:
C * ＝arfmax(ρ(C))；
s5.5, pair C * Normalization processing is carried out to obtainThe specific mode is as follows:
wherein,the element in the parameter vector C to be solved, namely the weight corresponding to each attribute.
10. The method for identifying and quantifying the dominant channel of the water-flooding reservoir according to claim 9, wherein the potential index θ is:
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