CN112412443B - Quantitative evaluation method for adaptability of ultra-low permeability reservoir polymer microspheres - Google Patents
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Abstract
The invention provides a method for quantitatively evaluating the adaptability of polymer microspheres of an ultra-low permeability oil reservoir, which comprises the following steps: s001: determining quantitative evaluation parameters: determining eight parameters including comprehensive water absorption coefficient, injection pressure of a water injection well, water injection intensity, pressure maintenance level, water storage rate, taper rate, water content rising amplitude and stable production time as quantitative evaluation parameters; s002: determining evaluation parameter weights and determining classification coefficients: determining the weight of the evaluation parameter by collecting evaluation parameter block data; determining a classification coefficient according to the multiple between the expected beneficial change amount and the actual beneficial change amount of each production parameter of the ultra-low permeability reservoir development; s003: calculating a comprehensive evaluation index: the classification coefficient and the evaluation parameter weight obtained by calculation are calculated, and comprehensive evaluation index calculation is carried out; s004: adjusting the injection parameters of the oil reservoir microspheres: and adjusting microsphere injection parameters of the oil reservoir with the comprehensive evaluation index less than 50.
Description
Technical Field
The invention belongs to the technical field of geological research of oil and gas field exploitation, and particularly relates to a quantitative evaluation method for adaptability of polymer microspheres of an ultra-low permeability oil reservoir.
Background
Ultra-low permeability reservoirs are mainly developed by water injection, but due to the influences of reservoir heterogeneity, natural micro-cracks and artificial fracturing, injected water can preferentially enter a high-permeability layer with smaller seepage resistance. As reservoirs enter medium-high water-cut periods, waterflooding can result in inefficient and ineffective cycles. Some measures for improving the water flooding effect are provided according to the development characteristics, seepage rules, development contradiction and the like of the oil reservoir: such as encryption adjustment, injection and production policy optimization, conventional profile control and the like; although the above measures achieve a certain effect, the defects of low production of the encrypted well, short effective period of the measures, gradual deterioration of the conventional profile control multiple-time adjustment effect and the like are gradually revealed. In recent years, the polymer microsphere is widely applied to oil field development as a deep profile control agent, and a large number of indoor experiments and practices prove that the polymer microsphere can achieve good dewatering and oil increasing effects. Polymer microspheres in long-term celebration oil fields have been developed for four years on a large scale, and the basis for comprehensively evaluating the effect of the microspheres is provided. The method for comprehensively evaluating the microsphere effect is not reported yet, and the main evaluation methods at present are as follows: a single index mathematical model evaluation method, a typical index qualitative description and the like; the single index mathematical model evaluation method refers to the comparison of a theoretical curve and an actual curve, is simple and clear, is widely applied to oil fields, but cannot reflect systematic characteristics in the water injection development process; typical qualitative description refers to analysis of the description by indexes such as a decreasing rate, a water content rising rate and the like to judge the implementation effect, but the qualitative description does not have a clear evaluation index limit.
Disclosure of Invention
The invention provides a quantitative evaluation method for the adaptability of polymer microspheres of an ultra-low permeability oil reservoir, which is used for overcoming the problems or at least partially solving or relieving the problems.
Therefore, the invention provides a method for quantitatively evaluating the adaptability of polymer microspheres of an ultra-low permeability oil reservoir, which comprises the following steps:
s001: determining quantitative evaluation parameters: determining eight parameters including comprehensive water absorption coefficient, injection pressure of a water injection well, water injection intensity, pressure maintenance level, water storage rate, taper rate, water content rising amplitude and stable production time as quantitative evaluation parameters;
s002: determining evaluation parameter weights and determining classification coefficients: determining the weight of the evaluation parameter by collecting evaluation parameter block data; determining a classification coefficient according to the multiple between the expected beneficial change amount and the actual beneficial change amount of each production parameter of the ultra-low permeability reservoir development;
s003: calculating a comprehensive evaluation index: the classification coefficient and the evaluation parameter weight obtained by calculation are calculated, and comprehensive evaluation index calculation is carried out;
s004: adjusting the injection parameters of the oil reservoir microspheres: and adjusting microsphere injection parameters of the oil reservoir with the comprehensive evaluation index less than 50.
In step S001, the comprehensive water absorption coefficient refers to the uniform water absorption area divided by the total water absorption area, the uniform water absorption area is the product of the minimum value of the gamma values before and after the test in each test point in the test section and the effective water absorption thickness, and the comprehensive water absorption coefficient parameter is based on the water absorption profile test data, and is used for uniformly evaluating the quantization standard of the water absorption effect, and more accurately and rapidly quantitatively evaluating the water absorption effect of the water injection well;
the formula is as follows:
S 0 ＝min(GR j -YGR j )×L0；
the total water absorption area S is the sum of the water absorption areas of all the test points, and the formula is as follows:
S total (S) ＝∑L j ×(GR j -YGR j )/B0；
Comprehensive water absorption coefficient
Wherein S0 is a uniform water absorption area; s is the total water absorption area; lj is the water absorption thickness of the test point; l0 is the effective water absorption thickness; GRj is the gamma value after test; YGRj is the gamma value before testing; b0 is gamma value of standard length;
the remaining seven parameters can be obtained by mine statistics.
In step S001, the water absorption effect of the water injection well is classified by using the comprehensive water absorption coefficient θ, and the water absorption effect of the water injection well is classified into five categories according to quantitative evaluation of the water absorption effect of the water injection well, and when l0=0, the water absorption effect is classified as non-water absorption; when L0 is more than or equal to 0, classifying 0.6< theta less than or equal to 1 as level I, uniformly absorbing water, classifying 0.4< theta less than or equal to 0.6 as level II, non-uniformly absorbing water, classifying theta less than or equal to 0.4 as level III, and absorbing water abnormally.
In step S002, the step two is to determine the weight of the evaluation parameter, and the gray correlation method is used to process the data, where the processing procedure is as follows:
the first step: data dimensionless treatment:
taking average single well annual oil increment as a reference sequence, and taking evaluation parameters as a comparison sequence; let x be 0 ＝{x 0 (k) I k=1, 2, …, m } is a reference series of numbers, x i ＝{x i (k) I k=1, 2, …, m } (i=1, 2, …, n) is a comparison array; because the dimensions of the parameters are different, in order to make the parameters have comparability, the following formula is adopted to carry out dimensionless treatment on the parameters;
x i (k)′＝[x i (k)-minx i (k)]/[maxx i (k)-minx i (k)]
wherein: i is an evaluation block row; k is an evaluation parameter column; x is x i (k) A value of the ith row and the kth column;
and a second step of: solving a correlation coefficient:
after the data is subjected to the first step of dimensionless treatment, x is calculated by the following formula i (k) And x 0 (k) Is a correlation coefficient of (2);
wherein: i is the number of evaluation block lines; k is an evaluation parameter column; x is x 0 (k) Is a reference sequence; x is x i (k) A value of the ith row and the kth column; and (V) i (k) Is X 0 Array and X i Absolute differences of the number columns at the kth point; zeta type i (k) For the association coefficient ρ to be a resolution coefficient, the value range (0, 1) is usually 0.5;
and a third step of: determining the association degree and calculating the weight coefficient of the evaluation parameter:
and calculating the association coefficient calculated in the second step, and calculating the association degree by using an average value method:
wherein: r is (r) k Is the degree of association;
after the association degree is obtained, the weight coefficient is obtained through the following normalization processing:
wherein: w (w) k Is a weight coefficient.
In step S002, a classification coefficient is determined according to a multiple between the expected beneficial amount of change and the actual beneficial amount of change of each production parameter of the ultra-low permeability reservoir development; the specific values of the evaluation parameters are divided into three stages i, ii and iii.
In step S002, the class coefficients of the class i, ii and iii values are 1, 0.5 and 0, respectively.
In step S003, the calculated comprehensive evaluation index is obtained by using the following formula:
wherein: k is the number of the evaluation parameter; n is a classification coefficient; w is the weight of the evaluation parameter; s is a comprehensive evaluation index;
the comprehensive evaluation index S is more than or equal to 70, which indicates that the microsphere has good effect; s is more than or equal to 50 and less than 70, which means that the microsphere has better effect; s is less than 50, which means that the microsphere effect is poor.
In step S004, when the oil well comprehensive evaluation index is less than 50 after the polymer microspheres are injected into the oil well, the particle size and concentration of the injected microspheres are further adjusted.
According to the ultra-low permeability oil reservoir polymer microsphere adaptability quantitative evaluation method, the four steps of determining quantitative evaluation parameters, determining evaluation parameter weights, calculating comprehensive evaluation indexes and adjusting the parameters of the injected microspheres for the oil reservoir with poor effect are carried out, the unknown information is predicted and quantized through processing of known information by using a gray correlation method, the relevance of each evaluation parameter is found out, quantitative description of the evaluation parameters is realized, the influence of human factors on weight distribution is eliminated, the polymer microsphere effect is accurately and rapidly evaluated through reaction, and a basis is provided for the next adjustment of the microsphere parameters for the oil reservoir.
Detailed Description
The invention is further illustrated by a specific example.
10 main force blocks of the ultra-low permeability oil reservoir are selected, and the adaptability of the polymer microsphere is evaluated according to the method.
Example 1
The invention provides a method for quantitatively evaluating the adaptability of polymer microspheres of an ultra-low permeability oil reservoir, which comprises the following steps:
s001: determining quantitative evaluation parameters: determining eight parameters including comprehensive water absorption coefficient, injection pressure of a water injection well, water injection intensity, pressure maintenance level, water storage rate, taper rate, water content rising amplitude and stable production time as quantitative evaluation parameters; s002: determining evaluation parameter weights and determining classification coefficients: determining the weight of the evaluation parameter by collecting evaluation parameter block data; determining a classification coefficient according to the multiple between the expected beneficial change amount and the actual beneficial change amount of each production parameter of the ultra-low permeability reservoir development; s003: calculating a comprehensive evaluation index: the classification coefficient and the evaluation parameter weight obtained by calculation are calculated, and comprehensive evaluation index calculation is carried out; s004: adjusting the injection parameters of the oil reservoir microspheres: and adjusting microsphere injection parameters of the oil reservoir with the comprehensive evaluation index less than 50.
Example 2
S001: determining quantitative evaluation parameters: determining eight parameters including comprehensive water absorption coefficient, injection pressure of a water injection well, water injection intensity, pressure maintenance level, water storage rate, taper rate, water content rising amplitude and stable production time as quantitative evaluation parameters;
in step S001, the comprehensive water absorption coefficient is the uniform water absorption area divided by the total water absorption area, where the uniform water absorption area is the product of the minimum value of the difference between the gamma values before and after the test in each test point in the test section and the effective water absorption thickness, and the formula is as follows:
S 0 ＝min(GR j -YGR j )×L0；
the total water absorption area S is the sum of the water absorption areas of all the test points, and the formula is as follows:
S total (S) ＝∑L j ×(GR j -YGR j )/B0；
Comprehensive water absorption coefficient
Wherein S0 is a uniform water absorption area; s is the total water absorption area; lj is the water absorption thickness of the test point; l0 is the effective water absorption thickness; GRj is the gamma value after test; YGRj is the gamma value before testing; b0 is gamma value of standard length;
classifying the water absorption effect of the water injection well by using the comprehensive water absorption coefficient theta, classifying the water absorption effect of the water injection well into five types according to quantitative evaluation of the water absorption effect of the water injection well, and classifying the water absorption effect of the water injection well as non-water absorption when L0=0; when L0 is more than or equal to 0, classifying 0.6< theta less than or equal to 1 as level I, uniformly absorbing water, classifying 0.4< theta less than or equal to 0.6 as level II, non-uniformly absorbing water, classifying theta less than or equal to 0.4 as level III, and absorbing water abnormally.
The remaining seven parameters can be obtained by mine statistics.
Example 3
S002: determining evaluation parameter weights and determining classification coefficients: determining the weight of the evaluation parameter by collecting evaluation parameter block data; determining a classification coefficient according to the multiple between the expected beneficial change amount and the actual beneficial change amount of each production parameter of the ultra-low permeability reservoir development;
in step S002, the step two is to determine the weight of the evaluation parameter, and the gray correlation method is used to process the data, where the processing procedure is as follows:
the first step: the data was dimensionless processed as shown in table 1.
Taking average single well annual oil increment as a reference sequence, and taking evaluation parameters as a comparison sequence; let x be 0 ＝{x 0 (k) I k=1, 2, …, m } is a reference series of numbers, x i ＝{x i (k) I k=1, 2, …, m } (i=1, 2, …, n) is a comparison array; due to the ginseng of each kindThe number of the non-dimensionality processing is carried out on each parameter by adopting the following formula in order to make the number of the non-dimensionality processing have comparability;
x i (k)′＝[x i (k)-minx i (k)]/[maxx i (k)-minx i (k)]
wherein: i is an evaluation block row; k is an evaluation parameter column; x is x i (k) A value of the ith row and the kth column;
table 1 ultra-low permeability reservoir evaluation index parameters were not processed as follows:
and a second step of: solving a correlation coefficient:
after the data is subjected to the first step of dimensionless treatment, x is calculated by the following formula i (k) And x 0 (k) Is a correlation coefficient of (2);
wherein: i is the number of evaluation block lines; k is an evaluation parameter column; x is x 0 (k) Is a reference sequence; x is x i (k) A value of the ith row and the kth column; and (V) i (k) Is X 0 Array and X i Absolute differences of the number columns at the kth point; zeta type i (k) For the association coefficient ρ to be a resolution coefficient, the value range (0, 1) is usually 0.5;
and a third step of: the degree of association was determined and the evaluation parameter weight coefficient was calculated as shown in table 2.
And calculating the association coefficient calculated in the second step, and calculating the association degree by using an average value method:
wherein: r is (r) k Is the degree of association;
after the association degree is obtained, the weight coefficient is obtained through the following normalization processing:
wherein: w (w) k Is a weight coefficient.
TABLE 2 microsphere Effect influencing parameter correlation coefficient and weight coefficient
Example 4
In the step S002, the specific numerical value of the evaluation parameter is divided into three stages I, II and III, wherein the classification coefficients of the stages I, II and III are respectively 1, 0.5 and 0; i.e. the actual beneficial change of each production parameter of the oil well after the oil well is injected with the microspheres is greater than 1 time of the expected beneficial change, the II is the actual beneficial change of the oil well after the oil well is injected with the microspheres is 0.5-1 time of the expected beneficial change, and the III is the actual beneficial change of the oil well after the oil well is injected with the microspheres is less than 0.5 time of the expected beneficial change, wherein the evaluation parameters comprise: water injection pressure, pressure maintenance, water storage rate, descending, water content descending, comprehensive water absorption coefficient, water injection strength and stable production time.
Example 5
S003: calculating a comprehensive evaluation index: the classification coefficient and the evaluation parameter weight obtained by calculation are calculated, and comprehensive evaluation index calculation is carried out;
in step S003, the calculated comprehensive evaluation index is obtained by using the following formula:
wherein: k is the number of the evaluation parameter; n is a classification coefficient; w is the weight of the evaluation parameter; s is a comprehensive evaluation index;
the comprehensive evaluation index S is more than or equal to 70, which indicates that the microsphere has good effect; s is more than or equal to 50 and less than 70, which means that the microsphere has better effect; s is less than 50, which means that the microsphere effect is poor. The water absorption coefficient, the injection pressure of the water injection well, the water injection intensity, the pressure maintaining level, the water storage rate, the progressive rate, the water content rising amplitude and the stable production time are integrated
The results are shown in tables 4 and 5
TABLE 4 ultra-low permeability reservoir Polymer microsphere suitability evaluation index
TABLE 5 ultra-low permeability reservoir Polymer microsphere suitability evaluation results
Example 6
S004: adjusting the injection parameters of the oil reservoir microspheres: and adjusting microsphere injection parameters of the oil reservoir with the comprehensive evaluation index less than 50.
In step S004, when the oil well comprehensive evaluation index is less than 50 after the polymer microspheres are injected into the oil well, the particle size and concentration of the injected microspheres are further adjusted.
If a certain oil reservoir is developed by adopting a diamond reverse nine-point well pattern water injection in 2010, the permeability is 1.3mD, the average porosity is 12.0%, the median radius of pore throats is 0.13 mu m, the heterogeneity is strong, natural microcracks develop, uneven planar water flooding is caused, spike-shaped water absorption or single-section water absorption on the section is caused, and the water flooding condition is poor. Microsphere tests were continuously carried out in 2017-2018, and injection parameters: particle size 300nm, concentration 0.15%, effective period 9 months, stable period 5 months, increased water content in later period, continuous decrease in yield, and poor effect. And after the particle size of the microsphere is reduced from 300nm to 50nm by adjusting injection parameters, the yield reduction trend is relieved, and the water content is reduced.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (7)
1. The method for quantitatively evaluating the adaptability of the ultra-low permeability reservoir polymer microsphere is characterized by comprising the following steps of:
s001: determining quantitative evaluation parameters: determining eight parameters including comprehensive water absorption coefficient, injection pressure of a water injection well, water injection intensity, pressure maintenance level, water storage rate, taper rate, water content rising amplitude and stable production time as quantitative evaluation parameters;
s002: determining evaluation parameter weights and determining classification coefficients: determining the weight of the evaluation parameter by collecting evaluation parameter block data; determining a classification coefficient according to the multiple between the expected beneficial change amount and the actual beneficial change amount of each production parameter of the ultra-low permeability reservoir development;
s003: calculating a comprehensive evaluation index: the classification coefficient and the evaluation parameter weight obtained by calculation are calculated, and comprehensive evaluation index calculation is carried out;
s004: adjusting the injection parameters of the oil reservoir microspheres: adjusting microsphere injection parameters of the oil reservoir with the comprehensive evaluation index less than 50;
in step S001, the comprehensive water absorption coefficient is the uniform water absorption area divided by the total water absorption area, where the uniform water absorption area is the product of the minimum value of the difference between the gamma values before and after the test in each test point in the test section and the effective water absorption thickness, and the formula is as follows:
S 0 ＝min(GR j -YGR j )×L 0 ；
the total water absorption area S is the sum of the water absorption areas of all the test points, and the formula is as follows:
comprehensive water absorption coefficient
Wherein S0 is a uniform water absorption area; s is the total water absorption area; lj is the water absorption thickness of the test point; l0 is the effective water absorption thickness; GRj is the gamma value after test; YGRj is the gamma value before testing; b0 is gamma value of standard length; the remaining seven parameters were obtained by mine statistics.
2. The quantitative evaluation method for the adaptability of the polymer microspheres of the ultra-low permeability reservoir according to claim 1, wherein the water absorption effect of the water injection well is classified by using the comprehensive water absorption coefficient theta, and the water absorption effect of the water injection well is classified into five types according to the quantitative evaluation of the water absorption effect of the water injection well, and when L0=0, the water absorption effect is classified as non-water absorption; when L0 is more than or equal to 0, classifying 0.6< theta less than or equal to 1 as level I, uniformly absorbing water, classifying 0.4< theta less than or equal to 0.6 as level II, non-uniformly absorbing water, classifying theta less than or equal to 0.4 as level III, and absorbing water abnormally.
3. The quantitative evaluation method of the adaptability of the ultra-low permeability reservoir polymer microsphere according to claim 1, wherein in the step S002, the evaluation parameter weight is determined in the step S002, and the data is processed by adopting a gray correlation method, and the processing process is as follows:
the first step: data dimensionless treatment:
taking average single well annual oil increment as a reference sequence, and taking evaluation parameters as a comparison sequence; let x be 0 ＝{x 0 (k) I k=1, 2, …, m } is a reference series of numbers, x i ＝{x i (k) I k=1, 2, …, m } (i=1, 2, …, n) is a comparison array; due toThe dimensionality of each parameter is different, and in order to make the parameters have comparability, the dimensionless treatment is carried out on each parameter by adopting the following formula;
x i (k)′＝[x i (k)-minx i (k)]/[maxx i (k)-minx i (k)]
wherein: i is an evaluation block row; k is an evaluation parameter column; x is x i (k) A value of the ith row and the kth column;
and a second step of: solving a correlation coefficient:
after the data is subjected to the first step of dimensionless treatment, x is calculated by the following formula i (k) And x 0 (k) Is a correlation coefficient of (2);
wherein: i is the number of evaluation block lines; k is an evaluation parameter column; x is x 0 (k) Is a reference sequence; x is x i (k) A value of the ith row and the kth column; and (V) i (k) Is X 0 Array and X i Absolute differences of the number columns at the kth point; zeta type i (k) As the association coefficient, ρ is the resolution coefficient, and the value range is (0, 1);
and a third step of: determining the association degree and calculating the weight coefficient of the evaluation parameter:
and calculating the association coefficient calculated in the second step, and calculating the association degree by using an average value method:
wherein: r is (r) k Is the degree of association;
after the association degree is obtained, the weight coefficient is obtained through the following normalization processing:
wherein: w (w) k Is a weight coefficient.
4. The method for quantitatively evaluating the suitability of the polymer microspheres for the ultra-low permeability reservoir according to claim 1, wherein in the step S002, specific values of the evaluation parameters are divided into three stages of i, ii and iii.
5. The quantitative evaluation method for the adaptability of the polymer microspheres of the ultra-low permeability reservoir according to claim 4, wherein the classification coefficients of the I, II and III grades are respectively 1, 0.5 and 0.
6. The quantitative evaluation method for the adaptability of the polymer microspheres of the ultra-low permeability reservoir according to claim 1, wherein in the step S003, the calculated comprehensive evaluation index is obtained by adopting the following formula:
wherein: k is the number of the evaluation parameter; n is a classification coefficient; w is the weight of the evaluation parameter; s is a comprehensive evaluation index;
the comprehensive evaluation index S is more than or equal to 70, which indicates that the microsphere has good effect; s is more than or equal to 50 and less than 70, which means that the microsphere has better effect; s is less than 50, which means that the microsphere effect is poor.
7. The quantitative evaluation method for the adaptability of the polymer microspheres of the ultra-low permeability reservoir according to claim 1, wherein in the step S004, when the comprehensive evaluation index of the oil well is less than 50 after the polymer microspheres are injected into the oil well, the particle size and the concentration of the injected microspheres are further adjusted.
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