CN110500083B - Method for judging dynamic connectivity of oil-water well - Google Patents
Method for judging dynamic connectivity of oil-water well Download PDFInfo
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- CN110500083B CN110500083B CN201910716460.7A CN201910716460A CN110500083B CN 110500083 B CN110500083 B CN 110500083B CN 201910716460 A CN201910716460 A CN 201910716460A CN 110500083 B CN110500083 B CN 110500083B
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- 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
- E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
- E21B43/16—Enhanced recovery methods for obtaining hydrocarbons
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- 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
- E21B47/00—Survey of boreholes or wells
Abstract
The invention belongs to the technical field of oilfield development, and particularly relates to a method for judging dynamic connectivity of an oil-water well. The invention determines the connectivity between oil wells and water wells by judging the degree of association between two subsystems of an oil well and a water well. If the variation trends of the oil well and the water well are consistent, namely the synchronous variation degree is higher, the correlation between the oil well and the water well is larger, and the connectivity is better; otherwise, the correlation degree of the two is small, and the connectivity is poor. The analysis method quantifies the dynamic process of the development of the connectivity between the oil and the water wells by using the related coefficients. By comprehensively analyzing a plurality of correlation coefficients, the invention can classify injection and production wells by qualitatively judging whether the channels have advantages or not, and provides guidance for further improvement measures.
Description
Technical Field
The invention belongs to the technical field of oilfield development, and particularly relates to a method for judging dynamic connectivity of an oil-water well.
Background
At present, most domestic oil fields are developed by water injection, and the water injection is utilized to supplement formation energy so as to drive oil reservoirs to exploit. The water injection exploitation of oil reservoir area usually adopts the technological process of 'water source-water injection station-multi-hole single well'. The water injection station boosts the pressure of the incoming water of the water source and then injects the water into a single well through a pipe network. After a single well is injected with water for a period of time, the phenomenon of insufficient injection and non-injection (called short injection) often occurs. The key to solve the problem is to make sure the dynamic connectivity between injection wells and production wells, and then take corresponding measures to solve the problem of insufficient injection. To determine the connectivity between injection wells and production wells, the prior art generally uses two methods:
1. conventional methods
The method comprises an interwell tracer method, a pressure testing method, a well testing analysis method and the like, and the methods need targeted construction, are long in time consumption, influence normal production and are high in construction cost.
2. Inversion method
The inversion method is to regard an oil reservoir block developed by water flooding as a dynamic balance system, change of water injection parameters of an injection well leads to change of output data of a production well communicated with the injection well, and a plurality of inversion models are established by researchers to invert connectivity among the injection wells, such as linear regression, nonlinear regression, sliding regression, correlation coefficient method and other models. A more direct method is to calculate the positive correlation between the similarity degree of the change rule of the injection and production data and the strength of the well connectivity, and to express the strength of the well connectivity by the gray correlation degree between the injection data curve and the production data curve. In the existing method, a dune grey correlation formula is adopted, but the dune correlation formula reflects the proximity between sequences through displacement difference, namely the absolute distance between the sequences is more concerned. An article published in 1997 by Shownin, theoretical research and review on a quantitative model of gray relevance, states that dimensionless operations may change the ordering of the relative strengths of connectivity between wells. In 2015, a patent application number: 201510733980.0, application publication number: CN 105389467a applied by chen islets and the like, a dune grey correlation formula is still adopted to calculate a grey correlation value between injection-production data sequences, a DWT algorithm is adopted to calculate a dynamic time similarity value between injection-production data sequences (to reflect a time lag effect between injection-production data), then a linear weighted sum of the grey correlation value and the dynamic time similarity value is calculated, and the stronger the inter-well connectivity is considered as the weighted sum is, but the obtained result has larger deviation and cannot provide better guidance and suggestion for actual production.
Disclosure of Invention
The invention provides a method for judging the dynamic connectivity of an oil-water well, aiming at providing a method for judging the dynamic connectivity of an oil-water well, which has short time, does not influence the normal production and has lower construction cost; the second purpose is to provide a method for judging the dynamic connectivity of the oil-water well, which has accurate results and better guidance and suggestion on actual production.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method for judging the dynamic connectivity of an oil-water well comprises the following steps:
the method comprises the following steps: determining data types to be employed for data analysis
Determining the monthly injection volume of the water well as necessary data for data analysis;
step two: determination of analytical sequences
Selecting an injection well or a production well as a concerned central well, taking the concerned central well data as a reference series, and taking the other well data as a comparison series;
y is set as the reference number sequence, i.e. Y is
Y＝{y(k)|k＝1,2,…n}； (1)
X is set as a comparison sequence, i.e. X is
Xi＝{xi(k)|k＝1,2,…n},i＝1,2,…m； (2)
Wherein:
y (k) is a reference sequence of data elements;
x (k) is a comparison sequence of data elements;
k is the serial number of the elements in the array;
i is a comparison sequence type mark number;
step three: dimensionless formulation of variables
Carrying out non-dimensionalization on the data of the reference series and the comparison series determined in the step two by adopting an averaging or interval mode;
step four: calculating the degree of association
Calculating weight by using an entropy weight method, and further determining the degree of association;
step five: rank the degree of association and judge connectivity
And sorting the relevance values calculated in the fourth step in size, wherein the connectivity of the relevance values with the concerned central well is stronger than that of the relevance values with the concerned central well.
The data type in the first step further comprises one or two selected from the two data of the monthly liquid production amount and the monthly water content of the oil well.
In the third step, non-dimensionalization is performed in an averaging or interval mode, and the operations are performed according to the following conditions:
initial value processing: xi/xi(1)＝{x′i(k)|k＝1,2,…n,xi(1)≠0},i＝1,2,…m； (3)
For the accumulated injection-production data, because the data has obvious increasing trend, an averaging non-dimensionalization processing mode is adopted;
for monthly data of injection and collection, an interval dimensionless processing mode is adopted because the data has no obvious increasing trend;
wherein: i is a comparative number series type mark number;
k is the number of the array elements after dimensionless;
n is the number of elements in the array;
xi(k) is the kth element in the original sequence;
the weight is obtained by adopting an entropy weight method, and the relevance is further determined by adopting the following method:
in a system of m indexes and n evaluated objects, the original evaluation matrix is Dn,mStandardizing the matrix to obtain a normalized matrix Rn,mAccording to the entropy weight theory, the entropy value of the j term is calculated by the following formula
The entropy weight (weight) of the j-th item is calculated by the following formula:
in the above formula:
fi,jwhen equal to 0, fi,jlnfi,j＝0；
Wherein: hjEntropy value of j-th item;
i is an evaluated object mark number;
n is the total number of the evaluated objects;
fi,jis an intermediate variable;
ωjis the entropy weight of item j.
The correlation value r in the fourth step0,iIs calculated by the following formula
Wherein i is 1,2, … m.
Has the advantages that:
the invention provides a method for judging the dynamic connectivity of the oil-water well, which has the advantages of short time, no influence on normal production and low construction cost by determining the data type adopted by data analysis, determining an analysis sequence, nondimensionating variables, calculating the relevance and ranking the relevance and judging the connectivity.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to clearly understand the technical solutions of the present invention and to implement the technical solutions according to the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram showing the trend of the reference sequence and the comparative sequence in an example of the present invention;
figure 3 is a schematic of the distribution of injection and production wells of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
the method for judging the dynamic connectivity of the oil-water well shown in the figure 1 comprises the following steps:
the method comprises the following steps: determining data types to be employed for data analysis
Determining the monthly injection volume of the water well as necessary data for data analysis;
step two: determination of analytical sequences
Selecting an injection well or a production well as a concerned central well, taking the concerned central well data as a reference series, and taking the other well data as a comparison series;
y is set as the reference number sequence, i.e. Y is
Y＝{y(k)|k＝1,2,…n}； (1)
X is set as a comparison series, i.e. X is
Xi＝{xi(k)|k＝1,2,…n},i＝1,2,…m； (2)
Wherein:
y (k) is a reference sequence of data elements;
x (k) is a comparison sequence of data elements;
k is the serial number of the elements in the array;
i is a comparison sequence type mark number;
step three: dimensionless formulation of variables
Carrying out non-dimensionalization on the data of the reference series and the comparison series determined in the step two by adopting an averaging or interval mode;
step four: calculating the degree of association
Calculating weight by using an entropy weight method, and further determining the degree of association;
step five: rank the degree of association and judge connectivity
And sorting the relevance values calculated in the step four in size, wherein the connectivity of the relevance values with the concerned central well is stronger than the connectivity of the relevance values with the concerned central well.
The degree of correlation reflects the similarity of the injection-production data sequence and indirectly reflects the inter-well connectivity, so that the inter-well connectivity can be judged according to the degree of correlation. The method can make a judgment by adopting the relevance of single injection data and single output data (called as a group of data) according to the content of daily data, can comprehensively analyze the relevance of multiple groups of data, judges the relative strength of the connectivity between injection and production wells, further qualitatively judges whether an advantageous channel exists, can provide guidance for next injection enhancement improvement measures, and enables the water injection station to achieve the purpose of energy-saving and efficient operation.
In actual use, in order to overcome the influence of time lag between injection and production wells, the injection and production data sequence selected in the method adopts a daily average value of a certain time period (such as one month, half month, one week and the like). This can overcome the effect of the time lag to some extent and is convenient to operate.
The method for judging the dynamic connectivity of the oil-water well has the advantages of short time, no influence on normal production, low construction cost, accurate judgment result and good guiding significance for actual production.
Example two:
the method for judging the dynamic connectivity of the oil-water well shown in the figure 1 is different from the first embodiment in that: the data type in the first step further comprises one or two selected from the two data of the monthly liquid production amount and the monthly water content of the oil well.
In actual use, the monthly injection amount of the water well is the necessary data, and the data of the adopted oil well can be used in one or two types according to the data condition, thereby obtaining satisfactory effect. Because the oil production is of concern, it is more effective to use monthly fluid production data for the well data.
Example three:
the method for judging the dynamic connectivity of the oil-water well shown in the figure 1 is different from the first embodiment in that: in the third step, non-dimensionalization is performed in an averaging or interval mode, and the operations are performed according to the following conditions:
initial value processing: xi/xi(1)＝{x′i(k)|k＝1,2,…n,xi(1)≠0},i＝1,2,…m； (3)
For the accumulated injection-production data, because the data has obvious increasing trend, an averaging non-dimensionalization processing mode is adopted;
for monthly injection and collection data, an interval non-dimensionalization processing mode is adopted because the data has no obvious increasing trend;
wherein: i is a comparison sequence type mark number;
k is the serial number of the array elements after non-dimensionalization;
n is the number of elements in the array;
xi(k) is the kth element in the original sequence;
in practical use, since the data in the factor columns in the system may be different in dimension, it is inconvenient to compare or it is difficult to obtain correct conclusions in comparison. By adopting the technical scheme, the dimension is unified, and the subsequent comparison is more convenient.
Example four:
the method for judging the dynamic connectivity of the oil-water well shown in the figure 1 is different from the first embodiment in that: the weight is obtained by adopting an entropy weight method, and the relevance is further determined by adopting the following method:
in a system of m indexes and n evaluated objects, the original evaluation matrix is Dn,mStandardizing the matrix to obtain a normalized matrix Rn,mAccording to the entropy weight theory, the entropy value of the j term is calculated by the following formula
The entropy weight (weight) of the j-th item is calculated by the following formula:
in the above formula:
fi,jwhen equal to 0, fi,jlnfi,j＝0。
Wherein: hjEntropy value of j item;
i is an evaluated object mark number;
n is the total number of the evaluated objects;
fi,jis an intermediate variable;
ωjis the entropy weight of item j.
In actual use, the weight can be determined in a self-adaptive manner from the perspective of effective information quantity by adopting the technical scheme, and the stability of an analysis result is improved.
Example five:
the method for judging the dynamic connectivity of the oil-water well shown in the figure 1 is different from the first embodiment in that: the correlation value r in the fourth step0,iIs calculated by the following formula
Wherein i is 1,2, … m.
In actual use, the calculated correlation coefficient reflects the correlation degree of the injection and production amount, and indirectly reflects the connectivity between oil wells and water wells. By comprehensively analyzing the plurality of correlation coefficients, whether the oil-water well has an advantageous channel or not can be qualitatively judged, guidance is provided for the next improvement measure, and injection and production well parameters are well adjusted and optimized.
The degree of association is sorted by size if r0,i<r0,jThen the sequences x are comparedjWith reference sequence x0Comparison of sequences xiWith reference sequence x0More similarly, i.e., the connectivity of producer well j with injector well 0 is stronger than the connectivity of producer well i with injector well 0.
Example six:
take the reference sequence as x0(1,2,3,4,5,6,7,8), comparativeThe sequence is as follows: x is the number of1＝(21,23,22,24,26,25,27,26,28)，x2(3,2,3,4,3,4,6,7,6). Three sequences are shown in FIG. 2:
from the similarity perspective of the trend of change, sequence x0Has the same variation trend with the sequence (can be overlapped after translation), and the two show positive correlation, the correlation degree x1High; sequence x0And sequence x2The variation trend difference of (2) is larger, and the correlation degree is lower.
However, the calculation results using the dung correlation model are as follows:
γ0,1＝0.3333，γ0,2＝0.8868，
indicating the sequence x0And sequence x2More similarly, contrary to the conclusions of the previous analysis. This also indicates that the dung correlation model is not suitable for studying the connectivity between wells based on the injection-production data sequence.
Meanwhile, the correlation result calculated by the new model is as follows:
γ0,1＝1，γ0,2＝-0.1886
indicating the sequence x0And sequence more x1Similarly, it is consistent with the conclusions of the foregoing analysis. This also indicates that the new correlation model is suitable for studying interwell connectivity based on the injection and production data sequences.
Example seven:
take the reference sequence as x0(1,2,3,4,5,6,7,8,9), the comparative sequence is: x is the number of1When (9,8,7,6,5,4,3,2,1) it is clear that the reference sequence is in an increasing state and the comparison sequence is in a decreasing state. If the reference sequence is regarded as the injection quantity data and the comparison sequence is regarded as the production quantity data, the non-existence of strong connectivity between the wells corresponding to the reference sequence and the comparison sequence can be visually seen from the variation trend. The following table 1 shows the results calculated based on the dune correlation model and the new correlation model herein:
TABLE 1 comparison of the calculated results
Obviously, the dune correlation value indicates that a strong correlation exists between the two sequences, and misleading is easily formed. The new relevance model indicates that a negative correlation exists between the two sequences, and the output is not increased due to the increase of the injection quantity. This shows that for the problem of inter-well connectivity research based on the injection-production data sequence, the new model is superior to the dune correlation model.
Example eight:
and selecting an injection and production well group of the XX block, wherein the well group consists of a water injection well WJW (an under injection well) and two corresponding production wells OW-1 and OW-2, and the relative positions of the wells are shown in figure 3. The records show that the water injection well WJW belongs to an under-injection well. The basic data of the injection and production during the period of 2015 to 2017 and 6 are selected for grey correlation analysis. The selected base data is the monthly water injection (m) of the injection well3D) monthly fluid production volume (m) of producing well3And/d) as shown in Table 2.
TABLE 22015 6 months-2017 6 months basis data (m)3/d)
Based on the data, a dune correlation model and the new correlation model are respectively adopted to calculate the gray correlation between injection wells and production wells, and the result is shown in the following table 3:
TABLE 3 calculated values of different relevance models
As can be seen from the above table, the value of the Duncus correlation degree exceeds 0.5, and the difference is small, which is not in accordance with the actual under-fill condition. And the relevance values calculated by the relevance model provided by the method are small, so that the fact that the connectivity among injection wells and production wells is poor is prompted, and the actual situation of insufficient injection is met. In addition, the relevance model can distinguish that the difference of the connectivity of two corresponding production wells and a water injection well is large, and the connectivity of the OW-1 well and the WJW well is better than that of the OW-2 well and the WJW well.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
In the case of no conflict, a person skilled in the art may combine the relevant technical features in the foregoing examples with each other according to an actual situation to achieve a corresponding technical effect, and details of various combining situations are not described herein again.
The foregoing is illustrative of the preferred embodiments of the present invention, and the present invention is not to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. Any simple modification, equivalent change and modification of the above embodiments according to the technical spirit of the present invention still fall within the scope of the technical solution of the present invention.
Claims (3)
1. A method for judging the dynamic connectivity of an oil-water well is characterized by comprising the following steps:
the method comprises the following steps: determining data types for data analysis
Determining the monthly injection amount of the water well as necessary data for data analysis;
step two: determination of analytical sequences
Selecting an injection well or a production well as a concerned central well, taking the concerned central well data as a reference series, and taking the other well data as a comparison series;
y is set as the reference number sequence, i.e. Y is
Y＝{y(k)|k＝1,2,…n}； (1)
X is set as a comparison series, i.e. X is
Xi＝{xi(k)|k＝1,2,…n},i＝1,2,…m； (2)
Wherein:
y (k) is a reference sequence of data elements;
x (k) is a comparison sequence of data elements;
k is the serial number of the elements in the array;
i is a comparison sequence type mark number;
step three: dimensionless formulation of variables
Carrying out non-dimensionalization on the data of the reference series and the comparison series determined in the step two by adopting an averaging or interval mode;
step four: calculating a correlation value
Step five: sorting the correlation values and judging connectivity
Sorting the correlation values calculated in the fourth step in size, wherein the connectivity of the correlation values with the concerned central well is stronger than the connectivity of the correlation values with the concerned central well;
the correlation value r in the fourth step0,iIs calculated by the following formula
Wherein i is 1,2, … m;
the correlation values are sorted by size if r0,i<r0,jThen the sequences x are comparedjWith reference sequence x0Comparison of sequences xiWith reference sequence x0More similarly, i.e., the connectivity of producer well j with injector well 0 is stronger than the connectivity of producer well i with injector well 0.
2. The method for determining the dynamic connectivity of an oil-water well as claimed in claim 1, wherein: the data type in the first step further comprises one or two selected from the two data of the monthly liquid production amount and the monthly water content of the oil well.
3. The method for determining the dynamic connectivity of an oil-water well according to claim 1, wherein the third step is conducted in a non-dimensionalized manner by means of averaging or interval, and is conducted as follows:
initial value processing: xi/xi(1)＝{xi′(k)|k＝1,2,…n,xi(1)≠0},i＝1,2,…m； (3)
For the accumulated injection-production data, because the data has obvious increasing trend, an averaging non-dimensionalization processing mode is adopted;
for monthly injection and collection data, an interval non-dimensionalization processing mode is adopted because the data has no obvious increasing trend;
wherein: i is a comparison sequence type mark number;
k is the serial number of the array elements after non-dimensionalization;
n is the number of elements in the array;
xi(k) is the kth element in the original sequence.
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