CN113705917A - Method for predicting initial productivity of fractured horizontal well of tight reservoir - Google Patents
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Abstract
The invention provides a method for predicting initial productivity of a fractured horizontal well of a tight oil reservoir, and belongs to the field of tight oil development. The method mainly comprises the following steps: acquiring productivity data of a fractured horizontal well in a region to be researched and geological and engineering parameters influencing productivity factors; determining main control factors influencing the initial productivity of the compact oil horizontal well from the influence factors by using a grey correlation method, a factor analysis method and an analytic hierarchy process-based subjective and objective combination weight method; and establishing a model for predicting the initial productivity of the compact oil horizontal well by using a multiple linear regression equation, and predicting the productivity of the newly drilled compact oil horizontal well. Compared with the actual result, the relative error of the prediction result is less than 15%, and the method has higher productivity prediction precision.
Description
Technical Field
The invention relates to the technical field of compact oil development, in particular to a compact oil horizontal well initial-stage capacity prediction method.
Background
China oil gas resources are in a shortage state for a long time, and low-permeability oil gas is hidden in oil gas development in China, so that the method has important significance. The low-permeability oil-gas resource distribution in China has the characteristics of more oil-gas, more oil-gas reservoir types, wide distribution area and the combination of 'upper-gas lower-oil and sea-phase gas-bearing and continental-phase oil-gas', and in the ascertained reserves, the proportion of the low-permeability oil reservoir reserves is very high, which accounts for more than 2/3 of the national reserves, so that the development potential is huge. However, the oil and gas reservoir is compact in reservoir, small in pore throat, strong in heterogeneity, complex in underground condition, influenced by multiple engineering factors such as fracturing series, construction displacement and the like in the construction process, unclear in understanding of main control factors of productivity, and limited in oil well productivity.
Scholars at home and abroad make a lot of research on the problem that the master control factors of productivity are not clear. Yellow Wen Feng et al (2000) determined that the primary capacity controlling factors of the orphan east oilfield in the seven-zone West exploitation stage are crude oil viscosity and permeability using a grey correlation method. Tunde Adebovale Osholake et al (2013) use an oil deposit numerical simulation technology to obtain that the main factor influencing the capacity of the Marcellus shale reservoir is the change of the permeability of the reservoir. The major of torx (2014) found that the matrix permeability of shale reservoir has the most significant influence on the productivity through the BP test method. yongjun Yao et al (2019) determined that the main controlling factor for natural fracture wells and fracture wells was gas production using principal component analysis and cluster analysis. In addition, the predecessors also use a factor analysis method, a fuzzy comprehensive evaluation method, a theoretical analysis method and the like to research the productivity main control factors. The methods are applied to the analysis of the main control factors of the horizontal well productivity, but have the following problems: the numerical simulation method has high requirements on the integrity and accuracy of historical data, and has a plurality of assumed conditions, so that the result is easily influenced; the gray correlation method needs to carry out current determination on the optimal values of all indexes, the subjectivity is too strong, and meanwhile, the optimal values of part of indexes are difficult to determine; the BP test method has many parameters, and the convergence speed is too low because a large number of thresholds and weights need to be updated every time; the factor analysis method only faces comprehensive evaluation, and has requirements on data quantity and components of data, so that KOM (KOM-KOM) is required to detect whether the data can be used or not. The fuzzy comprehensive evaluation method is complex in calculation and strong in subjectivity for determining the index weight vector. In addition, the predecessors mostly adopt a single method or objective analysis to draw conclusions when discussing main control factors of horizontal well productivity, so that certain deviation exists between the results and the actual results.
Based on the method, the influence factors influencing the horizontal well productivity in the research well zone are analyzed by combining a gray correlation method, a factor analysis method and an analytic hierarchy process, and the weight is obtained by using a subjective and objective evaluation method, so that the main control factors influencing the horizontal well productivity in the research zone are disclosed more reasonably. On the basis, the yield of the research area is predicted by using a multiple linear regression model, and the research result provides guiding opinions for the deployment and development of the subsequent well area.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for predicting the initial capacity of a compact oil horizontal well aiming at the defects of the predecessors and the prior art.
The technical scheme of the invention is implemented by the following steps:
s1: establishing a compact oil horizontal well logging interpretation and construction fracturing database;
s2: carrying out compact oil productivity influence factor analysis by utilizing database data, wherein the main influence factors of the compact oil productivity comprise: the number of fracturing stages, the sand amount of each cluster, the number of fracturing clusters, the gap distance, the liquid amount of each cluster, the sand amount of each section, the porosity, the permeability, the oil saturation, the Poisson ratio, the Young modulus, the brittleness index, the length of a horizontal section, the oil layer drilling rate, the oil layer drilling layer thickness and the oil layer drilling rate of one type;
s3: analyzing influence factors influencing the horizontal well productivity in the research well zone by utilizing a gray correlation method, a factor analysis method and an analytic hierarchy process, obtaining weight coefficients obtained by calculation of each method, performing combined sorting on the weight coefficients obtained by the three methods by utilizing an objective and subjective combined weight method, and simultaneously taking the top six ranking main control factors influencing productivity;
s4: regression analysis is carried out on the obtained main control factors and productivity by using a multiple regression analysis method, a prediction equation of the initial productivity of the compact oil horizontal well is established,
Q＝β0+β1X1+β2X2+β3X3+…+βmXm+ζ；
s5: and determining the coefficient of the multiple regression model according to the fitting result on the basis of the multiple linear fitting method to obtain the prediction equation of the initial capacity of the compact oil horizontal well.
Further, the single-factor single-well parameter in step S2 is calculated by weighted average.
Further, the three methods used in step S3 need to have a subjective method and an objective method.
Further, the first six influencing factors influencing the productivity determined in the step S3 are: permeability, porosity, horizontal segment length, number of fracturing stages, brittleness index, and number of fracturing clusters.
Further, the value of each single factor in step S4 has different unit and magnitude, and the data is normalized before the multiple regression is performed.
Further, the coefficients of the initial capacity prediction equation in the step S5 satisfy the site requirement.
The invention provides a method for determining capacity influence factors of a compact oil horizontal well, and performs initial capacity prediction on the basis, the main control factors obtained based on the subjective and objective combination weight can be more accurately applied to capacity prediction, the average relative error between the predicted yield and the tested yield is smaller, and the result has greater application value in the optimized design of a new well of a well area to be researched. Compared with the existing method, the method has higher practicability and higher precision, and simultaneously provides a good method for exploration well testing, stratum selection and the like.
Has the advantages that:
compared with the prior art, the invention has the following beneficial effects:
the weight ratio of each influence factor obtained by combining the gray correlation method, the factor analysis method and the analytic hierarchy process is found, and if the three methods are analyzed independently, the result has larger error. The subjective and objective combination weights can effectively make up for the deficiency. The master control factors obtained based on the subjective and objective combination weights can be more accurately applied to capacity prediction, the average relative error between the predicted yield and the tested yield is small, and the result has a great application value in the optimized design of a well area new well to be researched. Compared with the existing method, the method has higher practicability and higher precision, provides a good method for exploration well testing, stratum selection and the like, and has wide application value.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 shows the gray correlation calculation steps.
FIG. 3 shows the calculation steps of the factor analysis method.
FIG. 4 is a step of the calculation of the analytic hierarchy process.
FIG. 5 is a graph comparing predicted and tested yields for each method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example (b):
as shown in fig. 1, a method for predicting and determining factors affecting initial productivity of a tight oil horizontal well includes the following steps:
1: establishing a compact oil horizontal well logging interpretation and construction fracturing database;
2: carrying out compact oil productivity influence factor analysis by utilizing database data, wherein the main influence factors of the compact oil productivity comprise: the number of fracturing stages, the sand amount of each cluster, the number of fracturing clusters, the gap distance, the liquid amount of each cluster, the sand amount of each section, the porosity, the permeability, the oil saturation, the Poisson ratio, the Young modulus, the brittleness index, the length of a horizontal section, the oil layer drilling rate, the oil layer drilling layer thickness and the oil layer drilling rate of one type; specific numerical values are shown in table 1;
table 1 study of well zone horizontal well parameters
3: influence factors influencing the horizontal well productivity in the well area are analyzed by using a gray correlation method, a factor analysis method and an analytic hierarchy process, and respective weight coefficients are calculated by three methods.
S1: gray correlation method: performing weight calculation on the selected main influence factors influencing the capacity of the compact oil by using a gray correlation method and combining the steps shown in FIG. 2;
s2: factor analysis method: carrying out weight calculation on the selected main influence factors influencing the capacity of the compact oil by using a factor analysis method and combining the steps shown in the figure 3;
s3: analytic hierarchy process: carrying out weight calculation on the selected main influence factors influencing the capacity of the compact oil by applying an analytic hierarchy process and combining the steps shown in FIG. 4;
4: under the calculation of the foregoing three methods, 3 sets of weight data are obtained, as shown in table 2:
TABLE 2 methods weights and their ordering
5: after obtaining the data with different weights, calculating the combination weight by using an objective and subjective combination weight method, wherein the formula is as follows:
wherein W (i) is the combining weight, theta is the comprehensive weight coefficient, AiObjective weight of i-th attribute of the first method, BiObjective weight of i-th attribute of second method, CiIs the subjective weight of the ith attribute, n is the index number, GAHPDifference coefficient, g, for each index weight determined in the analytic hierarchy process1,g2,...,gnThe weights of all indexes determined in the analytic hierarchy process are sorted from small to large.
6: the combination weights are calculated by applying the subjective and objective combination weight method, and the obtained combination weight coefficients are shown in table 3:
TABLE 3 weight table for each influence factor combination
7: the weight proportion of each influence factor can be reasonably reflected by using the subjective and objective combination weight method. Meanwhile, the weight of the top six in the results in table 3 is the main control factor affecting productivity, and is the permeability, the porosity, the horizontal segment length, the fracturing stage number, the brittleness index and the fracturing cluster number in sequence.
8: establishing an initial capacity prediction equation of the compact oil horizontal well of the research well region by using a multiple linear regression equation:
Q＝43.894-1.401R+0.931L-1.475S+4.407K+0.097P-0.009I
wherein R is the fracturing stage number (grade); l is the number of fracturing clusters (clusters), S is the porosity (%), K is the permeability (mD), P is the brittleness index (m), and I is the horizontal segment length (m).
9: through a prediction model, 10 horizontal wells of the block are selected to carry out prediction sample data as shown in table 4, and when the main control factors obtained by the combined weight are subjected to capacity prediction, the main control factors obtained by the three methods are subjected to capacity prediction respectively, and results are compared. The predicted results are shown in fig. 5 and table 5.
TABLE 4 prediction sample data
TABLE 5 predicted yield results and relative errors for each model
The average relative error of the predicted yield and the tested yield of the combined weights was 13.87%. The results obtained using the combination weight method have a smaller average relative error relative to the predicted yield of other methods. The multiple linear regression model can quickly and accurately predict the yield.
Compared with the existing method, the method has higher practicability and higher precision, and simultaneously provides a good method for exploration well testing, stratum selection and the like.
Although the present invention has been described with reference to the above embodiments, it should be understood that the present invention is not limited to the above embodiments, and those skilled in the art can make various changes and modifications without departing from the scope of the present invention.
Claims (6)
1. The method for predicting the initial productivity of the compact oil horizontal well is characterized by comprising the following steps of:
s1: establishing a compact oil horizontal well logging interpretation and construction fracturing database;
s2: carrying out compact oil productivity influence factor analysis by utilizing database data, wherein the main influence factors of the compact oil productivity comprise: the number of fracturing stages, the sand amount of each cluster, the number of fracturing clusters, the gap distance, the liquid amount of each cluster, the sand amount of each section, the porosity, the permeability, the oil saturation, the Poisson ratio, the Young modulus, the brittleness index, the length of a horizontal section, the oil layer drilling rate, the oil layer drilling layer thickness and the oil layer drilling rate of one type;
s3: analyzing influence factors influencing the horizontal well productivity in the research well zone by utilizing a gray correlation method, a factor analysis method and an analytic hierarchy process, obtaining weight coefficients obtained by calculation of each method, performing combined sorting on the weight coefficients obtained by the three methods by utilizing an objective and subjective combined weight method, and simultaneously taking the top six ranking main control factors influencing productivity;
s4: performing regression analysis on the obtained main control factors and productivity by using a multiple regression analysis method, and establishing a prediction equation of the initial productivity of the compact oil horizontal well;
Q＝β0+β1X1+β2X2+β3X3+…+βmXm+ζ
s5: and determining the coefficient of the multiple regression model according to the fitting result on the basis of the multiple linear fitting method to obtain the prediction equation of the initial capacity of the compact oil horizontal well.
2. The method of claim 1, wherein the single-factor individual well parameter of step S2 is calculated by a weighted average.
3. The method of claim 1, wherein the three methods used in step S3 include subjective methods and objective methods.
4. The method of claim 1, wherein the first six influencing factors influencing the productivity determined in step S3 are: permeability, porosity, horizontal segment length, number of fracturing stages, brittleness index, and number of fracturing clusters.
5. The method of claim 1, wherein each single factor value in step S4 has different unit and magnitude levels, and the data is normalized before performing the multiple regression.
6. The method of claim 1, wherein the initial capacity forecast equation coefficients satisfy the site demand in step S5.
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| CN115330060A (en) * | 2022-08-17 | 2022-11-11 | 中国科学院大学 | Shale oil horizontal well productivity calculation method based on reservoir and engineering factor analysis |
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| CN112561144A (en) * | 2020-12-07 | 2021-03-26 | 西安石油大学 | Method for evaluating main control factor of productivity of tight oil fracturing horizontal well and predicting productivity |
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| 张富: ""水平井分段压裂效果评价方法及应用"", 《中国石油和化工标准与质量》, no. 14, pages 167 - 168 * |
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