CN111577267A - Reservoir sensitivity prediction method based on whole rock and clay mineral composition - Google Patents
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- 230000035945 sensitivity Effects 0.000 title claims abstract description 101
- 239000011435 rock Substances 0.000 title claims abstract description 86
- 239000000203 mixture Substances 0.000 title claims abstract description 48
- 239000002734 clay mineral Substances 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims abstract description 12
- 229910052500 inorganic mineral Inorganic materials 0.000 claims abstract description 34
- 239000011707 mineral Substances 0.000 claims abstract description 34
- 238000012360 testing method Methods 0.000 claims abstract description 23
- 238000002474 experimental method Methods 0.000 claims abstract description 12
- 238000010586 diagram Methods 0.000 claims description 10
- 239000002253 acid Substances 0.000 claims description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 9
- 239000003513 alkali Substances 0.000 claims description 7
- 238000013178 mathematical model Methods 0.000 claims description 6
- 230000035699 permeability Effects 0.000 claims description 6
- 238000002441 X-ray diffraction Methods 0.000 claims description 4
- 238000005553 drilling Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 2
- 238000007620 mathematical function Methods 0.000 abstract description 2
- 229910052900 illite Inorganic materials 0.000 description 7
- VGIBGUSAECPPNB-UHFFFAOYSA-L nonaaluminum;magnesium;tripotassium;1,3-dioxido-2,4,5-trioxa-1,3-disilabicyclo[1.1.1]pentane;iron(2+);oxygen(2-);fluoride;hydroxide Chemical compound [OH-].[O-2].[O-2].[O-2].[O-2].[O-2].[F-].[Mg+2].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[K+].[K+].[K+].[Fe+2].O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2 VGIBGUSAECPPNB-UHFFFAOYSA-L 0.000 description 7
- 229910021532 Calcite Inorganic materials 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 6
- NLYAJNPCOHFWQQ-UHFFFAOYSA-N kaolin Chemical compound O.O.O=[Al]O[Si](=O)O[Si](=O)O[Al]=O NLYAJNPCOHFWQQ-UHFFFAOYSA-N 0.000 description 6
- 229910052622 kaolinite Inorganic materials 0.000 description 6
- 230000009286 beneficial effect Effects 0.000 description 3
- 239000002585 base Substances 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 206010043087 Tachyphylaxis Diseases 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 229910001919 chlorite Inorganic materials 0.000 description 1
- 229910052619 chlorite group Inorganic materials 0.000 description 1
- QBWCMBCROVPCKQ-UHFFFAOYSA-N chlorous acid Chemical compound OCl=O QBWCMBCROVPCKQ-UHFFFAOYSA-N 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000010453 quartz Substances 0.000 description 1
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 1
- -1 speed sensitivity Substances 0.000 description 1
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Abstract
A reservoir sensitivity prediction method based on whole rock and clay mineral composition. The method is based on a small amount of reservoir sensitivity experiment test results, combines the whole rock and clay mineral composition data of the tested rock sample, finds out the specific types of the whole rock and the clay mineral influencing reservoir sensitivity through data correlation identification and analysis, analyzes the influence rule of various rock minerals on the sensitivity, establishes a single-factor influence regression model, and sets the influence weights of various rock minerals according to the correlation coefficient after regression. And a multivariate mathematical function model which takes the sensitivity as an objective function, the contents of various rock minerals as variables and the correlation coefficient as weight is constructed on the basis of a single-factor regression model, a small amount of reservoir sensitivity flow experimental data and whole rock and clay mineral composition data are utilized to carry out multivariate regression, the regression coefficient is solved, and the regression coefficient is substituted into the multivariate function, so that the reservoir sensitivity of rock mineral compositions in a certain range can be predicted by the function, and a large amount of sensitivity test work is reduced.
Description
Technical Field
The invention relates to the technical field of oil and gas geology and development, in particular to a reservoir sensitivity prediction method based on whole rock and clay mineral composition.
Background
Reservoir sensitivity evaluation is an important work for evaluating reservoirs by geological and reservoir engineering technicians, the existing reservoir sensitivity evaluation method mainly depends on indoor experiments, the sensitivities of reservoir rocks such as speed sensitivity, water sensitivity, acid sensitivity, alkali sensitivity and the like are evaluated according to the existing reservoir sensitivity flow experiment evaluation method (SY/T5358-2010) in the China oil and gas industry standard, a large amount of rock cores are required to be taken for indoor rock core flow experiments, and downhole reservoir rock samples required by the experiments are more, the test time is long, and the cost is high. The method is not beneficial to large-scale test work, and limits the early quick understanding of the reservoir.
Disclosure of Invention
In order to solve the problems of more underground reservoir rock samples, long testing time and high cost required by a reservoir sensitivity evaluation experiment, the invention provides a reservoir sensitivity prediction method based on whole rock and clay mineral composition, which has low testing time and low cost, and comprises the following steps:
the first step is as follows: taking an underground core sample, drilling and cutting the underground core sample into a cylindrical core with the diameter of 25mm and the length of between 5 and 10mm, carrying out a permeability sensitivity test, and calculating the sensitivity index of the experimental core sample according to the test result;
the second step is that: taking a small amount of crushed rock debris samples cut by the cylindrical rock core to perform an X-ray diffraction experiment, and measuring the composition of whole rock and clay minerals of the experimental rock core sample;
the third step: the permeability sensitivity index of the columnar rock core and corresponding all-rock and clay mineral composition data are arranged, and a data distribution scatter diagram between the all-rock and clay mineral content and the sensitivity index is drawn;
the fourth step: observing a correlation distribution diagram in a data distribution scatter diagram of the whole rock and clay mineral content and the sensitivity index, and finding out an influence sensitivity function Y1One-factor variable X of unitary function relational expression1、X2、……、Xn,X1、X2、……、XnRepresenting the rock mineral composition, n represents the mineral type, performing one-element regression to determine a unary function relation formula of the sensitive function Y1, and determining the regression correlation coefficient R of each rock mineral composition1 2、R2 2……、Rn 2；
The fifth step: constructing a multi-factor influence multi-element function model according to the single-factor influence number of the sensitivity index and a unitary function relation obtained by regression, wherein the multi-element function model uses a sensitivity index multi-element function Y2Aiming at the target, the rock mineral composition is a function variable, and a correlation coefficient obtained by single-factor regression in the fourth step is taken as the weight of each variable;
and a sixth step: for the constructed multivariate function Y2Performing regression to determine a regression coefficient;
the seventh step: and (4) bringing the regression coefficient back to a multivariate function to obtain a sensitivity index prediction mathematical model based on the whole rock and clay mineral composition, and predicting the reservoir sensitivity of a specific composition interval according to the mineral composition time of the rock and the sensitivity index prediction mathematical model.
Further, the sensitivity index includes, speed sensitive or water sensitive or acid sensitive or alkali sensitive.
Further, function Y1The relation of unary function includes linear unary function or exponential unary function or logarithmic unary function or polynomial unary function
The invention has the beneficial effects that: by adopting the technical scheme of the invention, the reservoir sensitivity of rock mineral composition in a certain range can be predicted, so that a large amount of sensitivity test work is reduced, the test time is low, and the cost is low.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a graph of the velocity sensitivity index versus various rock mineral compositions.
FIG. 3 is a graph of water sensitivity index versus various rock mineral compositions.
FIG. 4 is a graph of acid sensitivity index versus various rock mineral compositions.
FIG. 5 is a graph of alkali sensitivity index versus various rock mineral compositions.
FIG. 6 is a comparison graph of the prediction result and the measured sensitivity index.
Detailed Description
The invention has the following inventive concept: based on a small amount of reservoir sensitivity test results, the specific types of the whole rocks and the clay minerals influencing reservoir sensitivity are found out by combining the whole rock and clay mineral composition data of the tested rock samples and identifying and analyzing data correlation, the influence rules of various rock minerals on the sensitivity are analyzed, a single-factor influence regression model is established, and the influence weights of various rock minerals are set according to the correlation coefficients after regression. And a multivariate mathematical function model which takes the sensitivity as an objective function, the contents of various rock minerals as variables and the correlation coefficient as weight is constructed on the basis of a single-factor regression model, a small amount of reservoir sensitivity flow experimental data and whole rock and clay mineral composition data are utilized to carry out multivariate regression, the regression coefficient is solved, and the regression coefficient is substituted into the multivariate function, so that the reservoir sensitivity of rock mineral compositions in a certain range can be predicted by the function, and a large amount of sensitivity test work is reduced.
As shown in fig. 1, the present invention provides a reservoir sensitivity prediction method based on whole rock and clay mineral composition, which comprises the following seven operation steps.
The first step is as follows: taking a plurality of different underground core samples, drilling and cutting the samples into cylindrical cores with the diameter of 25mm and the length of 5-10 mm, carrying out permeability sensitivity tests such as speed sensitivity, water sensitivity, acid sensitivity, alkali sensitivity and the like, and calculating various sensitivity indexes of different experimental core samples according to test results.
The second step is that: and (3) when the cylindrical rock core is drilled and cut, taking a small amount of rock debris samples cut from the cylindrical rock core to perform an X-ray diffraction experiment, and measuring the composition of the whole rock and the clay minerals of the sample.
The third step: and (3) sorting the various permeability sensitivity indexes of the columnar rock cores and the corresponding data of the whole rock and clay mineral composition, and drawing a data distribution scatter diagram between the whole rock and clay mineral content and the sensitivity indexes.
The fourth step: in a data distribution scatter diagram of the contents of various whole rocks and clay minerals and the sensitivity index, a distribution diagram with better correlation relations (linear, exponential, logarithmic, polynomial and the like) is observed, and an influence target function Y is found out1Single factor variable X of (sensitivity index)1、X2、……、Xn(various rock mineral compositions) are subjected to one-by-one unitary regression, and respective regression correlation coefficients R are determined1 2、R2 2……、Rn 2。
The one-factor unitary function is:
Y1(Xi)quick-acting＝fi(Xi) Weight is Ri 2And i ranges from 1 to n.
In the practice of the present invention, the sensitivity index may also be water sensitive or acid sensitive or base sensitive.
The fifth step: and constructing a multi-factor influence multi-function model according to the single-factor influence number n of various sensitivity indexes and the regression obtained unitary function relational expression. The function takes various sensitivity indexes Y as targets and various rock mineral compositions (X)1、X2、……、Xn) As a function, a correlation coefficient R obtained by single-factor regression1 2、R2 2……、Rn 2Is the weight of each variable.
The multifactor multivariate function is:
Y2(X1、X2、……、Xn)quick-acting＝a1*f1(X1)+a2*f2(X2)+……an*fn(Xn)
And a sixth step: regression is carried out on the constructed multivariate function Y to determine a regression coefficient ai。
The seventh step: the regression coefficient aiAnd (4) carrying back to the multivariate function to obtain a sensitivity index prediction mathematical model based on the whole rock and clay mineral composition, and predicting the reservoir sensitivity of a specific composition interval according to the mathematical model when the mineral composition of the rock is known.
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. The components of embodiments of the present invention generally described and illustrated in the figures herein 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.
The embodiment comprises the following steps:
1. according to the X-ray diffraction experiment and the test results of the core sensitivity experiment, scatter diagrams of 4 clay minerals (chlorite, kaolinite, illite and illite) and 2 rock minerals (calcite and quartz) with quick-sensitive, water-sensitive, acid-sensitive and alkali-sensitive indexes are drawn (see fig. 2-5).
2. Firstly, analyzing the quick sensitivity, observing and finding in figure 2 that the influence of the mineral content of 3 kinds of rocks (kaolinite, illite and calcite) on the quick sensitivity index has a better logarithmic rule, and respectively carrying out logarithmic curve regression to obtain a kaolinite regression equation y126.746ln (x2) -64.208, illite regression equation y120.468ln (x3) -33.492 and calcite regression equation y113.174ln (x5) +0.8972, the correlation coefficients of regression are kaolinite 0.7950, illite 0.8609 and calcite 0.5384, respectively.
In the step, a regression equation does not need to be constructed for 3 rocks with poor correlation observed and found, so that the condition that a subsequently constructed multiple regression function is too complex due to the fact that the regression equation is constructed for each rock is avoidedAnd (3) mixing. In the step, through observation of single-factor regression and determination of single-factor regression equation, the rock type with high sensitive index correlation can be selected, the type of regression equation is determined, so that the subsequent determination of multiple regression function can select the same regression equation type and the rock type with high sensitive index correlation, and the multiple regression function Y is solved2The technical problem of selecting rock types and selecting regression equation types.
3. Constructing a multiple regression function Y by taking the contents of kaolinite, illite and calcite as variables and taking a speed sensitivity index as a target function2The experimental data were subjected to multiple regression to a1 × ln (x2) + a2 × ln (x3) + a3 × ln (x5) + a4, and the correlation coefficients of the single-factor regression were used as weights to find coefficients a1 — 0.33894, a2 — 21.1501, a3 — 6.54471, a4 — 59.4649, and a correlation coefficient 0.9147.
4. The prediction model is Y2Given the percentages x2, x3 and x5 of the contents of kaolinite, illite and calcite in any rock sample, the sensitivity index Y can be predicted for 0.33894 x ln (x2) +21.1501 x ln (x3) +6.54471 x ln (x5) -59.46492。
In other embodiments, the predictive models for the water sensitivity index, acid sensitivity index, and base sensitivity index are built and predicted in a manner similar to that for tachyphylaxis. The difference is that the variables selected are not the same as the multivariate function constructed.
The following matters need to be noted in the implementation of the present invention.
1. The samples tested by the experiment are not suitable to be too few, the rules are difficult to find if the samples are too few, and the relation function between the rock mineral composition and the sensitivity index is difficult to establish; meanwhile, the reliability of the prediction result in a wider rock mineral composition distribution interval is difficult to guarantee. Not less than 10 groups are recommended for each sensitivity and rock mineral composition test sample.
2. The number of samples for experimental testing is not too large, the samples are too large, more experimental investment is needed, the advantages of the method cannot be shown, and the improvement range of the reliability of the prediction result by too many samples is limited. No more than 20 groups are recommended per sensitivity and rock mineral composition test sample.
3. When the experimental data sample is selected, the composition distribution and the sensitivity index distribution range of the rock mineral are wider as possible.
4. No matter the single-factor unitary function or the multi-factor multivariate function is established and regressed, the function with a simple structure is adopted as much as possible, and meanwhile, the number of variables is reduced, and the number of fitting coefficients is reduced, so that undetermined coefficients in the function can be fitted more quickly.
5. Regression of unary and multivariate functions can employ programming or other data processing software such as EXCEL, Matlab, Origin, etc.
The invention has the beneficial effects that: through the prediction results and error analysis, in the comparison of the 48 calculation prediction data and the actual test results, 80% of the sample prediction errors are less than 20%, only the water sensitivity index evaluation effect is relatively poor, and 90% of the sample prediction errors of the quick sensitivity, the acid sensitivity and the alkali sensitivity are less than 20%, so that the engineering error range of the reservoir sensitivity evaluation is met, as shown in fig. 6.
Although the present invention has been described in detail hereinabove with reference to a general description and specific examples, it will be appreciated that the present invention may be implemented to predict reservoir sensitivity for a range of rock mineral compositions, thereby reducing the number of sensitivity testing operations, the testing time and the cost, and that modifications and improvements may be made based on the present invention, as will be apparent to those skilled in the art. Therefore, it is intended that the present invention covers such modifications and variations as fall within the true spirit of the invention.
Claims (3)
1. A reservoir sensitivity prediction method based on whole rock and clay mineral composition is characterized by comprising the following steps:
the first step is as follows: taking an underground core sample, drilling and cutting the underground core sample into a cylindrical core with the diameter of 25mm and the length of between 5 and 10mm, carrying out a permeability sensitivity test, and calculating the sensitivity index of the experimental core sample according to the test result;
the second step is that: taking a small amount of crushed rock debris samples cut by the cylindrical rock core to perform an X-ray diffraction experiment, and measuring the composition of whole rock and clay minerals of the experimental rock core sample;
the third step: the permeability sensitivity index of the columnar rock core and corresponding all-rock and clay mineral composition data are arranged, and a data distribution scatter diagram between the all-rock and clay mineral content and the sensitivity index is drawn;
the fourth step: observing a correlation distribution diagram in a data distribution scatter diagram of the whole rock and clay mineral content and the sensitivity index, and finding out an influence sensitivity function Y1One-factor variable X of unitary function relational expression1、X2、……、Xn,X1、X2、……、XnRepresenting the mineral composition of the rock, n representing the type of the mineral, and performing one-by-one regression to determine the sensitivity function Y1And determining regression correlation coefficient R of each rock mineral composition1 2、R2 2……、Rn 2；
The fifth step: constructing a multi-factor influence multi-element function model according to the single-factor influence number of the sensitivity index and a unitary function relation obtained by regression, wherein the multi-element function model uses a sensitivity index multi-element function Y2Aiming at the target, the rock mineral composition is a function variable, and a correlation coefficient obtained by single-factor regression in the fourth step is taken as the weight of each variable;
and a sixth step: for the constructed multivariate function Y2Performing regression to determine a regression coefficient;
the seventh step: and (4) bringing the regression coefficient back to a multivariate function to obtain a sensitivity index prediction mathematical model based on the whole rock and clay mineral composition, and predicting the reservoir sensitivity of a specific composition interval according to the mineral composition time of the rock and the sensitivity index prediction mathematical model.
2. The method of claim 1, wherein the sensitivity index comprises a rate sensitivity, a water sensitivity, an acid sensitivity, or an alkali sensitivity.
3. The method of claim 1, wherein the function Y is a function of the reservoir sensitivity prediction based on the composition of whole rocks and clay minerals1Unitary function gateThe system includes a linear or exponential or logarithmic or polynomial basis function.
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