CN111209711A - Water flooding reservoir optimal flow field identification method based on flow field diagnosis and clustering - Google Patents
Water flooding reservoir optimal flow field identification method based on flow field diagnosis and clustering Download PDFInfo
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Abstract
The invention discloses a water flooding reservoir optimal flow field identification method based on flow field diagnosis and clustering, belonging to the field of oilfield flooding development and adjustment, and the method comprises the following steps: the model building module is used for building an oil reservoir geological model and simulating a water injection development process; the flow field diagnosis module is used for simulating and dividing different control areas through a numerical tracer and determining a potential flow field target area by combining a flow heterogeneity diagnosis graph; the characteristic extraction module is used for extracting the characteristics of each grid in the target area to obtain the flow field characteristics of each grid; the flow field clustering module is used for clustering each grid by adopting a density peak value clustering method according to the position information of each grid and the flow field characteristics of each grid; and the flow field identification module is used for carrying out region division on the flow field in the target region and determining the distribution position of the dominant flow field. The method integrates visualization and intellectualization, solves the problem of identifying the optimal flow field of the water flooding reservoir, and improves the flow field reconstruction efficiency of scheme designers.
Description
Technical Field
The invention belongs to the field of oilfield flooding development and adjustment, and relates to a water flooding reservoir optimal flow field identification method based on flow field diagnosis and clustering.
Background
Water flooding development is used as a main means for improving the recovery ratio of oil reservoirs, and has extremely wide application. However, due to strong heterogeneity of reservoirs, after many high water-containing reservoirs are subjected to long-term water flooding development, the connectivity among partial injection and production wells becomes good along with the flushing effect of long-term water injection of the gap filler and other supporting substances, so that a large amount of injected water flows in a region with good connectivity, and a dominant channel is formed finally; meanwhile, in the place of the dominant flow place, the water absorption capacity is large, the flow rate of the fluid is high, the invalid circulation of injected water is formed, and the water drive efficiency is reduced. The existence of the dominant channel can also cause difficulty in reservoir production increase, and the formation of the dominant flow field can change the pore structure and seepage characteristics of the reservoir, thereby influencing the understanding of oil field developers on the reservoir.
For flow field identification, because the prior art cannot directly observe the fluid flow condition in a reservoir, a researcher simulates the fluid flow in the reservoir by solving a material conservation equation based on Darcy or non-Darcy law, and then characterizes the flow field. The scholars in China evaluate the flow field by determining the flow field influence parameters to provide support for flow field adjustment decision making, however, the method needs to evaluate by depending on expert experience, and is strong in subjectivity, so that the accuracy of an evaluation result is low, and the flow field reconstruction efficiency is low. Foreign scholars usually adopt methods such as streamline simulation to identify the flow field, but the method has the defect of poor convergence under complex geological conditions.
Therefore, aiming at the problems, the invention provides a water flooding reservoir optimal flow field identification method based on flow field diagnosis and clustering.
Disclosure of Invention
The invention aims to: the method integrates visualization and intellectualization, solves the problem of the identification of the water flooding reservoir optimum flow field, and improves the flow field reconstruction efficiency of scheme designers.
The technical scheme adopted by the invention is as follows:
a water flooding reservoir optimal flow field identification method based on flow field diagnosis and clustering comprises the following steps:
the model building module is used for building a corresponding oil reservoir geological model according to the oil reservoir physical data and simulating the water injection development process;
the flow field diagnosis module is used for simulating and dividing different injection well or extraction well control areas through a numerical tracer, and determining a potential flow field target area by combining a flow heterogeneity diagnosis diagram;
the characteristic extraction module is used for extracting the characteristics of each grid in the flow field distribution diagram of the target area to obtain the flow field characteristics of each grid;
the flow field clustering module is used for clustering each grid by adopting a density peak value clustering method according to the position information of each grid and the flow field characteristics of each grid;
and the flow field identification module is used for carrying out region division on the flow field in the target region according to the flow field clustering result and determining the dominant flow field distribution position by combining each type of flow field characteristics.
Further, the establishing of the corresponding reservoir geological model according to the reservoir physical data and the simulation of the water injection development process comprise the following steps:
acquiring physical data of a target oil reservoir, and establishing a corresponding oil reservoir geological model according to the physical data of the oil reservoir;
and simulating a water flooding development process based on the oil reservoir geological model to obtain oil reservoir water saturation, oil saturation and flow exchange quantity results among grids after water flooding development.
Further, the simulation of the division of control areas of different injection wells or production wells by means of numerical tracers comprises the following steps:
different injection well or production well control areas are divided through tracer distribution in a flow field steady state, the steady state is generally difficult to obtain in a field test, but the calculation is easy by adopting a numerical simulation method;
the concentration of the numerical tracer at the position of an injection well (inflow boundary) or a production well (outflow boundary) is set to be 1, and the tracer concentration distribution of any injection well or production well in a steady state is obtained by solving a numerical tracer transfer equation, wherein the two types of numerical tracer transport equations are as follows:
in the formula
Denotes the fluid flow velocity, m3/s，ciRepresenting hypothetical injected tracer concentration, dimensionless, cpRepresents hypothetical efflux tracer concentration, dimensionless; the calculation result of the two equations gives the concentration distribution when the unit quantity of tracers reach the steady state, and the well with the highest concentration of the tracers is selected as the well to which the grid belongs, so that the control areas of different injection wells or extraction wells can be determined.
Further, the determining a dominant flow field target region in combination with the flow heterogeneity diagnostic map comprises the steps of:
obtaining flow field propagation time by solving a linear stable flow equation according to the flow exchange quantity between grids;
sorting grid nodes in different injection wells or production well control areas according to the propagation time;
then, a flow heterogeneity diagnostic map is made according to the pore volume and the grid flow;
and determining the target area of the dominant flow field by using the Lorentz index and the flow heterogeneity index.
Furthermore, in the flow field propagation time obtained by solving a linear steady-state transport equation according to the flow exchange amount between grids, the linear steady-state transport equation is as follows:
wherein phi represents porosity, decimal; tau isfAnd τbRespectively representing the forward and backward propagation times in units of s.
Furthermore, the method for making the flow heterogeneity diagnosis map according to the pore volume and the grid flow rate comprises the following steps:
obtaining the flow information in the grid according to a flow capacity calculation formula, wherein the flow capacity calculation formula is as follows:
in the formula FiExpressing the normalized cumulative flow capacity of the ith grid after sorting, dimensionless, qiRepresents the ith grid flow after sorting, m3/s；
Obtaining the storage information in the grid according to a storage capacity calculation formula, wherein the storage capacity calculation formula is as follows:
in the formula phiiExpressing the normalized cumulative reservoir Capacity of the ith grid after sorting, dimensionless, ViRepresents the i-th cell pore volume after sorting, m3；
And (4) making an F-phi curve according to the calculation formula, wherein the curve contains most information of the oil reservoir flow, and the heterogeneity degree of the flow field flow can be evaluated according to the curve.
Further, the determination of the target area of the dominant flow field is performed by using a lorentz index and a flow heterogeneity index, wherein the lorentz index is:
the index can be understood as the area, L, contained by the F-phi curveCThe larger the value of (b), the steeper the F-phi curve, indicating that a larger flow rate is contained in the same pore volume, i.e. the stronger the flow heterogeneity; on the contrary, when the F-phi curve is a straight line with the slope of 1, the flow field is in completely homogeneous flowAnd (4) dynamic condition.
The flow heterogeneity index is:
in the formula tauiAnd t*Respectively representing the propagation time of the ith grid and the average propagation time of all grids, and the unit is s; for the homogeneous flow case, the FHI has a value of 1.
Further, the feature extraction is performed on each grid in the target area flow field distribution diagram to obtain the flow field features of each grid, where the flow field features include position information, a water-oil volume ratio and a grid flow rate of each grid, and the water-oil volume ratio calculation formula is as follows:
Vwo＝Swi/Soi
in the formula SwiAnd SoiRespectively representing the water saturation and the oil saturation of the ith grid, and decimal.
Further, the clustering each grid by adopting a density peak value clustering method according to the position information of each grid and the flow field characteristics of each grid comprises the following steps:
after extracting the flow field characteristics, carrying out normalization pretreatment on the characteristic parameters of each grid;
setting a truncation distance, calculating the distance between samples, and calculating the local density according to the distance between samples, wherein the calculation formula is as follows:
in the formula xinAnd xjnRepresenting the ith grid and the nth flow field characteristics of the jth grid, N representing the number of the extracted flow field characteristics, dijDenotes the inter-sample distance, p, between the ith and jth gridsiDenotes the local density of the ith grid, dcRepresents a truncation distance;
sorting in descending order according to the local density, and calculating the minimum distance between the sample and other higher density samples, namely the separation distance, wherein the calculation formula is as follows:
in the formula qiIndicates the ith sequence number sorted in descending order according to local density, dimensionless,
representing the separation distance of the ith sample sorted according to the descending order of the local density, and being dimensionless;
sorting the samples in a descending order according to the local density and the separation distance, and selecting the sample with the front serial number as a clustering center;
and after the clustering centers are determined, classifying the non-clustering center samples and the clustering centers closest to the non-clustering center samples into one class.
Further, the area division of the flow field in the target area according to the flow field clustering result and the determination of the dominant flow field distribution position by combining with each type of flow field characteristics comprises the following steps:
according to the flow field clustering result and the position information of each type of flow field, adopting different types of identifiers to perform region division on the flow field in the target region;
and comparing and analyzing the characteristics of each type of flow field, and determining a flow field area with larger average water-oil volume ratio and larger average grid flow between injection wells and extraction wells as a dominant flow field distribution position by combining the characteristics of the injection well pattern.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
the method for identifying the optimal flow field of the water flooding reservoir based on flow field diagnosis and clustering mainly adopts the flow field diagnosis method to visually represent and evaluate the flow field, adopts the flow field clustering method to intelligently identify and analyze the flow field, integrates visualization and intellectualization, solves the problem of identifying the optimal flow field of the water flooding reservoir, and improves the flow field reconstruction efficiency of scheme designers. Compared with the existing characterization and evaluation method for the water-drive reservoir flow field, the method has less subjective participation, can be evaluated without depending on expert experience, improves the precision and efficiency of dominant flow field identification, provides scientific decision and technical support for schemes such as water injection optimization, well network layer system adjustment, deep profile control and the like, and provides scientific basis for old oil field excavation and further improvement of recovery ratio.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other relevant drawings can be obtained according to the drawings without inventive effort, wherein:
FIG. 1 is a flow chart of a water flooding reservoir dominant flow field identification method based on flow field diagnosis and clustering;
FIG. 2 is a geological model of a reservoir containing a high-permeability channel according to a first embodiment of the invention;
FIG. 3 is a diagram of an injection and production well control area according to a first embodiment of the present invention;
FIG. 4 is a graph of the flow field propagation time distribution according to a first embodiment of the present invention;
FIG. 5 is a diagnostic plot of flow heterogeneity in all injector well control regions in accordance with a first embodiment of the present invention;
FIG. 6 is a single layer flow heterogeneous diagnostic map within the control region of injection well I1 according to a first embodiment of the present invention;
FIG. 7 is a diagnostic plot of flow heterogeneity within the control zone of all production wells in accordance with a first embodiment of the present invention;
FIG. 8 is a single-layer flow heterogeneous diagnostic plot within the control area of a production well P1 in accordance with a first embodiment of the present invention;
FIG. 9 is a graph of the clustering results of layer 2 flow fields in the control area of injection well I1 according to the first embodiment of the present invention;
FIG. 10 is a graph of the clustering results of layer 2 flow fields in the control area of the production well P1 according to the first embodiment of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. The components of embodiments of the present invention generally described herein and illustrated in the figures may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The water flooding reservoir optimal flow field identification method based on flow field diagnosis and clustering integrates visualization and intellectualization, solves the problem of water flooding reservoir optimal flow field identification, and improves the flow field reconstruction efficiency of scheme designers.
The water flooding reservoir optimal flow field identification method based on flow field diagnosis and clustering comprises the following steps:
step 1: establishing a corresponding oil reservoir geological model according to the oil reservoir physical data, and simulating a water injection development process;
step 2: simulating and dividing different injection well or extraction well control areas by using a numerical tracer, and determining a potential flow field target area by combining a flowing heterogeneity diagnostic map;
and step 3: extracting the characteristics of each grid in the flow field distribution diagram of the target area to obtain the flow field characteristics of each grid;
and 4, step 4: clustering each grid by adopting a density peak value clustering method according to the position information of each grid and the flow field characteristics of each grid;
and 5: and performing area division on the flow field in the target area according to the flow field clustering result, and determining the optimal flow field distribution position by combining the characteristics of each type of flow field.
Compared with the existing characterization and evaluation method for the water-drive reservoir flow field, the method has less subjective participation, can be used for evaluation without depending on expert experience, improves the precision and efficiency of dominant flow field identification, provides scientific decision and technical support for schemes such as water injection optimization, well network layer system adjustment, deep profile control and the like, and provides scientific basis for the excavation and potential further improvement of the recovery ratio of old oil fields.
The features and properties of the present invention are described in further detail below with reference to examples.
Example one
In the preferred embodiment of the present invention, a geological model of an oil reservoir containing a high permeability channel is taken as an example, and a method for identifying a water flooding reservoir optimal flow field based on flow field diagnosis and clustering is provided, as shown in fig. 1, the method comprises the following steps:
step 1: establishing a corresponding oil reservoir geological model according to the oil reservoir physical data, and simulating a water injection development process;
step 1.1: acquiring physical data of a target oil reservoir, and establishing a corresponding oil reservoir geological model according to the physical data of the oil reservoir, wherein the model is divided into three layers as shown in figure 2a, a second layer has a high-permeability channel, as shown in figure 2b, the permeability of a high-permeability channel grid in the x direction, the y direction and the z direction is respectively 2000mD, 2000mD and 20mD, and the permeability of other grids in the x direction, the y direction and the z direction is respectively 200mD, 200mD and 20 mD;
step 1.2: and simulating the water injection development process by adopting Eclipse software based on the oil reservoir geological model to obtain the oil reservoir water saturation, oil saturation and flow exchange quantity results among grids after water injection development.
Step 2: simulating and dividing different injection well or extraction well control areas by using a numerical tracer, and determining a potential flow field target area by combining a flowing heterogeneity diagnostic map;
step 2.1: the concentration of the numerical tracer at the position of an injection well (inflow boundary) or a production well (outflow boundary) is set to be 1, and the tracer concentration distribution of any injection well or production well in a steady state is obtained by solving a numerical tracer transfer equation, wherein the two types of numerical tracer transport equations are as follows:
in the formula
Denotes the fluid flow velocity, m3/s，ciRepresenting hypothetical injected tracer concentration, dimensionless, cpRepresents hypothetical efflux tracer concentration, dimensionless; the calculation result of the two equations gives the concentration distribution when the unit quantity of tracers reach the steady state, and the well with the highest concentration of the tracers is selected as the well to which the grid belongs, so that the control areas of different injection wells or extraction wells can be determined. In this embodiment, the injection well control region is shown in fig. 3a, and the production well control region is shown in fig. 3b, in which different shades of color indicate that the spatial regions belong to different injection wells or production wells.
Step 2.2: obtaining flow field propagation time by solving a linear steady state transport equation according to the flow exchange quantity between grids, wherein the linear steady state transport equation is as follows:
wherein phi represents porosity, decimal; tau isfAnd τbRespectively representing the forward and backward propagation times in units of s. In this embodiment, the result of the flow field propagation time distribution is shown in fig. 4, where fig. 4a shows the forward propagation time of the injection well, and fig. 4b shows the backward propagation time of the production well, and the colors in the above-mentioned figures are from dark to light, which indicates that the propagation time is from large to small.
Step 2.3: sorting grid nodes in different injection wells or extraction well control areas according to the propagation time, and making a flowing heterogeneity diagnostic graph according to the pore volume and the grid flow;
step 2.3.1: obtaining the flow information in the grid according to a flow capacity calculation formula, wherein the flow capacity calculation formula is as follows:
in the formula FiExpressing the normalized cumulative flow capacity of the ith grid after sorting, dimensionless, qiRepresents the ith grid flow after sorting, m3/s；
Step 2.3.2: obtaining the storage information in the grid according to a storage capacity calculation formula, wherein the storage capacity calculation formula is as follows:
in the formula phiiExpressing the normalized cumulative reservoir Capacity of the ith grid after sorting, dimensionless, ViRepresenting the ith grid after sortingPore volume, m3；
Step 2.3.3: and (4) making an F-phi curve according to the calculation formula, wherein the curve contains most information of the oil reservoir flow, and the heterogeneity degree of the flow field flow can be evaluated according to the curve.
Step 2.4: determining a dominant flow field target area through the Lorentz index and the flow heterogeneity index;
the lorentz index is:
the index can be understood as the area, L, contained by the F-phi curveCThe larger the value of (b), the steeper the F-phi curve, indicating that a larger flow rate is contained in the same pore volume, i.e. the stronger the flow heterogeneity; on the contrary, when the F- Φ curve is a straight line with a slope of 1, it indicates that the flow field is in a completely homogeneous flow condition.
The flow heterogeneity index was:
in the formula tauiAnd t*Respectively representing the propagation time of the ith grid and the average propagation time of all grids, and the unit is s; for the homogeneous flow case, the FHI has a value of 1.
In this example, the flow heterogeneity diagnosis map in the control region of the 9 injection wells is shown in fig. 5, and as can be seen from fig. 5, the F- Φ curve of the injection well I1 is significantly steeper than that of the other injection wells, and the lorentz index L is also significantly higherCAnd the flow heterogeneity index FHI are both larger, indicating that the flow heterogeneity in the control area of the injection well I1 is stronger. Further, a flow heterogeneity diagnostic was performed for each individual layer in the control region of injection well I1, as shown in FIG. 6, from which FIG. 6 it can be seen that the F- Φ curve at layer 2 of injection well I1 is significantly steeper than the other layers, while the Lorentz index L is significantly steeperCThe flow heterogeneity index FHI is also larger, which indicates that the flow heterogeneity of the 2 nd layer in the control area of the injection well I1 is stronger, and finally the control of the injection well I1 can be determinedLayer 2 in the manufactured area is the target area for further study.
In addition, the flow heterogeneity diagnostic plot in the control area of the 4 production wells is shown in FIG. 7. from FIG. 7, it can be seen that the F-phi curve of the production well P1 is significantly steeper than other production wells, and the Lorentz index L is also significantly steeperCAnd the flow heterogeneity index FHI are both larger, which indicates that the flow heterogeneity in the control area of the production well P1 is stronger. Further, a flow heterogeneity diagnostic plot was made for each individual zone in the control area of the production well P1, as shown in FIG. 8, it can be seen from FIG. 8 that the F-phi curve of the 2 nd zone of the production well P1 is significantly steeper than the other zones, and the Lorentz index L is substantially higher than the Lorentz index LCAnd the flow heterogeneity index FHI is also larger, which indicates that the flow heterogeneity of the layer 2 in the control area of the production well P1 is stronger, and finally the layer 2 in the control area of the production well P1 can be determined as a target area for further research.
And step 3: extracting the characteristics of each grid in the flow field distribution diagram of the target area to obtain the flow field characteristics of each grid; the flow field characteristics comprise position information, a water-oil volume ratio and grid flow of each grid, and the water-oil volume ratio calculation formula is as follows:
Vwo＝Swi/Soi
in the formula SwiAnd SoiRespectively representing the water saturation and the oil saturation of the ith grid, and decimal.
And 4, step 4: clustering each grid by adopting a density peak value clustering method according to the position information of each grid and the flow field characteristics of each grid;
step 4.1: after extracting the flow field characteristics, carrying out normalization pretreatment on the characteristic parameters of each grid;
step 4.2: setting a truncation distance, calculating the distance between samples, and calculating the local density according to the distance between samples, wherein the calculation formula is as follows:
in the formula xinAnd xjnRepresenting the ith grid and the nth flow field characteristics of the jth grid, N representing the number of the extracted flow field characteristics, dijDenotes the inter-sample distance, p, between the ith and jth gridsiDenotes the local density of the ith grid, dcRepresents a truncation distance;
step 4.3: sorting in descending order according to the local density, and calculating the minimum distance between the sample and other higher density samples, namely the separation distance, wherein the calculation formula is as follows:
in the formula qiRepresenting the ith order number, dimensionless, delta, ordered in descending order according to local densityqiRepresenting the separation distance of the ith sample sorted according to the descending order of the local density, and being dimensionless;
step 4.4: sorting the samples in a descending order according to the local density and the separation distance, and selecting the sample with the front serial number as a clustering center;
step 4.5: and after the clustering centers are determined, classifying the non-clustering center samples and the clustering centers closest to the non-clustering center samples into one class.
In this embodiment, the clustering results of the layer 2 in the control area of the injection well I1 are shown in table 1, and it can be seen from table 1 that the flow fields of the layer 2 in the control area of the injection well I1 are divided into 3 types, wherein the flow fields of the first type have larger average water-oil volume ratio and average grid flow rate; in addition, the clustering results of the layer 2 in the control area of the production well P1 are shown in table 2, and it can be seen from table 2 that the flow fields of the layer 2 in the control area of the production well P1 are divided into 8 types, wherein the flow fields of the type 1 have larger water-oil volume ratio and grid flow rate.
TABLE 1
| Cluster classification | Average water to oil volume ratio | |
| 1 | 3.095732 | 0.000013435466 |
| 2 | 3.061739 | 0.000001445936 |
| 3 | 3.059875 | 0.000001445936 |
TABLE 2
| Cluster classification | Average water to oil volume ratio | |
| 1 | 3.347735 | 0.000019313082 |
| 2 | 3.313273 | 0.000002110235 |
| 3 | 3.309949 | 0.000002110236 |
| 4 | 3.290389 | 0.000007632745 |
| 5 | 3.158937 | 0.000001657827 |
| 6 | 3.136659 | 0.000001634498 |
| 7 | 1.061877 | 0.000005151813 |
| 8 | 1.052014 | 0.000003752910 |
And 5: performing region division on the flow field in the target region according to the flow field clustering result, and determining the optimal flow field distribution position by combining the characteristics of each type of flow field;
step 5.1: according to the flow field clustering result and the position information of each type of flow field, adopting different types of identifiers to perform region division on the flow field in the target region;
step 5.2: and comparing and analyzing the characteristics of each type of flow field, and determining a flow field area with larger average water-oil volume ratio and larger average grid flow between injection wells and extraction wells as a dominant flow field distribution position by combining the characteristics of the injection well pattern.
In this example, the results of the division of the flow field region of the 2 nd layer in the control region of the injection well I1 are shown in fig. 9, and it can be seen from fig. 9 that the flow field of type 1 in table 1 is mainly located between the injection well I1 and the production well P1, indicating that there is a dominant flow field between the injection well I1 and the production well P1. As shown in fig. 10, the results of the division of the flow field region of the 2 nd layer in the control region of the production well P1 are shown in fig. 10, and it can be seen from fig. 10 that the flow field of the 1 st type in table 2 is mainly located between the injection well I5 and the production well P1, indicating that there is a dominant flow field between the injection well I5 and the production well P1. The identification result of the dominant flow field is consistent with the established oil reservoir model containing the high-permeability channel, and the effectiveness and the accuracy of the method are demonstrated.
It should be noted that, since the drawings in the specification should not be colored or modified, it is difficult to display a portion where a part of the distinction is obvious in the present invention, and if necessary, a color picture can be provided.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents and improvements made by those skilled in the art within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. The method for identifying the optimal flow field of the water flooding reservoir based on flow field diagnosis and clustering is characterized by comprising the following steps of:
the model building module is used for building a corresponding oil reservoir geological model according to the oil reservoir physical data and simulating the water injection development process;
the flow field diagnosis module is used for simulating and dividing different injection well or extraction well control areas through a numerical tracer, and determining a potential flow field target area by combining a flow heterogeneity diagnosis diagram;
the characteristic extraction module is used for extracting the characteristics of each grid in the flow field distribution diagram of the target area to obtain the flow field characteristics of each grid;
the flow field clustering module is used for clustering each grid by adopting a density peak value clustering method according to the position information of each grid and the flow field characteristics of each grid;
and the flow field identification module is used for carrying out region division on the flow field in the target region according to the flow field clustering result and determining the dominant flow field distribution position by combining each type of flow field characteristics.
2. The method for identifying the optimal flow field of the water flooding reservoir based on flow field diagnosis and clustering according to claim 1, wherein the step of establishing a corresponding reservoir geological model according to reservoir physical data and simulating a water flooding development process comprises the following steps:
acquiring physical data of a target oil reservoir, and establishing a corresponding oil reservoir geological model according to the physical data of the oil reservoir;
and simulating a water flooding development process based on the oil reservoir geological model to obtain oil reservoir water saturation, oil saturation and flow exchange quantity results among grids after water flooding development.
3. The method for identifying a water flooding reservoir preferential flow field based on flow field diagnosis and clustering of claim 1, wherein the step of dividing different injection well or production well control areas through numerical tracer simulation comprises:
different injection well or production well control areas are divided through tracer distribution in a flow field steady state, the steady state is generally difficult to obtain in a field test, but the calculation is easy by adopting a numerical simulation method;
the concentration of the numerical tracer at the position of an injection well (inflow boundary) or a production well (outflow boundary) is set to be 1, and the tracer concentration distribution of any injection well or production well in a steady state is obtained by solving a numerical tracer transfer equation, wherein the two types of numerical tracer transport equations are as follows:
in the formula
Denotes the fluid flow velocity, m3/s，ciRepresenting hypothetical injected tracer concentration, dimensionless, cpRepresents hypothetical efflux tracer concentration, dimensionless; the calculation result of the two equations gives the concentration distribution when the unit quantity of tracers reach the steady state, and the well with the highest concentration of the tracers is selected as the well to which the grid belongs, so that the control areas of different injection wells or extraction wells can be determined.
4. The method for identifying the optimal flow field of the water flooding reservoir based on flow field diagnosis and clustering according to claim 1, wherein the step of determining the target area of the optimal flow field by combining a flow heterogeneity diagnostic map comprises the following steps:
obtaining flow field propagation time by solving a linear stable flow equation according to the flow exchange quantity between grids;
sorting grid nodes in different injection wells or production well control areas according to the propagation time;
then, a flow heterogeneity diagnostic map is made according to the pore volume and the grid flow;
and determining the target area of the dominant flow field by using the Lorentz index and the flow heterogeneity index.
5. The method for identifying the optimal flow field of the water flooding reservoir based on the flow field diagnosis and clustering according to claim 4, wherein in the flow field propagation time obtained by solving a linear steady-state transport equation according to the flow exchange quantity among grids, the linear steady-state transport equation is as follows:
wherein phi represents porosity, decimal; tau isfAnd τbIndividual watchForward and backward propagation times are shown in units of s.
6. The method for identifying the water flooding reservoir preferential flow field based on the flow field diagnosis and the clustering as claimed in claim 4, wherein the steps of making the flow heterogeneity diagnosis map according to the pore volume and the grid flow are as follows:
obtaining the flow information in the grid according to a flow capacity calculation formula, wherein the flow capacity calculation formula is as follows:
in the formula FiExpressing the normalized cumulative flow capacity of the ith grid after sorting, dimensionless, qiRepresents the ith grid flow after sorting, m3/s；
Obtaining the storage information in the grid according to a storage capacity calculation formula, wherein the storage capacity calculation formula is as follows:
in the formula phiiExpressing the normalized cumulative reservoir Capacity of the ith grid after sorting, dimensionless, ViRepresents the i-th cell pore volume after sorting, m3；
And (4) making an F-phi curve according to the calculation formula, wherein the curve contains most information of the oil reservoir flow, and the heterogeneity degree of the flow field flow can be evaluated according to the curve.
7. The method for identifying the water flooding reservoir preferential flow field based on flow field diagnosis and clustering according to claim 4, wherein the determination of the target area of the preferential flow field is performed by using Lorentz index and flow heterogeneity index, wherein the Lorentz index is as follows:
the index can be understood as the area, L, contained by the F-phi curveCThe larger the value of (b), the steeper the F-phi curve, indicating that a larger flow rate is contained in the same pore volume, i.e. the stronger the flow heterogeneity; on the contrary, when the F-phi curve is a straight line with the slope of 1, the flow field is in a completely homogeneous flow condition;
the flow heterogeneity index is:
in the formula tauiAnd t*Respectively representing the propagation time of the ith grid and the average propagation time of all grids, and the unit is s; for the homogeneous flow case, the FHI has a value of 1.
8. The method for identifying the water drive reservoir optimal flow field based on flow field diagnosis and clustering according to claim 1, wherein the feature extraction is performed on each grid in the flow field distribution diagram of the target area to obtain the flow field features of each grid, the flow field features include position information, a water-oil volume ratio and grid flow of each grid, and the water-oil volume ratio calculation formula is as follows:
Vwo＝Swi/Soi
in the formula SwiAnd SoiRespectively representing the water saturation and the oil saturation of the ith grid, and decimal.
9. The method for identifying the optimal flow field of the flooding reservoir based on flow field diagnosis and clustering according to claim 1, wherein the step of clustering each grid by adopting a density peak value clustering method according to the position information of each grid and the flow field characteristics of each grid is as follows:
after extracting the flow field characteristics, carrying out normalization pretreatment on the characteristic parameters of each grid;
setting a truncation distance, calculating the distance between samples, and calculating the local density according to the distance between samples, wherein the calculation formula is as follows:
in the formula xinAnd xjnRepresenting the ith grid and the nth flow field characteristics of the jth grid, N representing the number of the extracted flow field characteristics, dijDenotes the inter-sample distance, p, between the ith and jth gridsiDenotes the local density of the ith grid, dcRepresents a truncation distance;
sorting in descending order according to the local density, and calculating the minimum distance between the sample and other higher density samples, namely the separation distance, wherein the calculation formula is as follows:
in the formula qiIndicates the ith sequence number sorted in descending order according to local density, dimensionless,
representing the separation distance of the ith sample sorted according to the descending order of the local density, and being dimensionless;
sorting the samples in a descending order according to the local density and the separation distance, and selecting the sample with the front serial number as a clustering center;
and after the clustering centers are determined, classifying the non-clustering center samples and the clustering centers closest to the non-clustering center samples into one class.
10. The method for identifying the water drive reservoir optimal flow field based on flow field diagnosis and clustering according to claim 1, wherein the steps of performing region division on the flow field in the target region according to the flow field clustering result and determining the optimal flow field distribution position by combining each type of flow field characteristics are as follows:
according to the flow field clustering result and the position information of each type of flow field, adopting different types of identifiers to perform region division on the flow field in the target region;
and comparing and analyzing the characteristics of each type of flow field, and determining a flow field area with larger average water-oil volume ratio and larger average grid flow between injection wells and extraction wells as a dominant flow field distribution position by combining the characteristics of the injection well pattern.
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