CN108446797B - Method for predicting capacity of tight oil reservoir horizontal well at initial volume fracturing stage - Google Patents
Method for predicting capacity of tight oil reservoir horizontal well at initial volume fracturing stage Download PDFInfo
- Publication number
- CN108446797B CN108446797B CN201810182962.1A CN201810182962A CN108446797B CN 108446797 B CN108446797 B CN 108446797B CN 201810182962 A CN201810182962 A CN 201810182962A CN 108446797 B CN108446797 B CN 108446797B
- Authority
- CN
- China
- Prior art keywords
- fracturing
- productivity
- sub
- influencing
- factor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000011156 evaluation Methods 0.000 claims abstract description 66
- 230000000694 effects Effects 0.000 claims abstract description 47
- 238000004519 manufacturing process Methods 0.000 claims abstract description 30
- 238000009826 distribution Methods 0.000 claims abstract description 8
- 238000010219 correlation analysis Methods 0.000 claims abstract description 7
- 239000011159 matrix material Substances 0.000 claims description 33
- 230000000704 physical effect Effects 0.000 claims description 25
- 230000014509 gene expression Effects 0.000 claims description 18
- 238000010276 construction Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 8
- 239000012530 fluid Substances 0.000 claims description 7
- 230000035699 permeability Effects 0.000 claims description 7
- 239000004576 sand Substances 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 2
- 230000017105 transposition Effects 0.000 claims 1
- 238000012163 sequencing technique Methods 0.000 abstract description 5
- 238000013461 design Methods 0.000 abstract description 4
- 239000003921 oil Substances 0.000 description 35
- 238000011161 development Methods 0.000 description 10
- 239000007789 gas Substances 0.000 description 8
- 206010017076 Fracture Diseases 0.000 description 4
- 230000002596 correlated effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000001965 increasing effect Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 208000010392 Bone Fractures Diseases 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 239000010779 crude oil Substances 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000004836 empirical method Methods 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 208000006670 Multiple fractures Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000000875 corresponding effect Effects 0.000 description 1
- 238000000556 factor analysis Methods 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 239000003209 petroleum derivative Substances 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000011425 standardization method Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Mathematical Physics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Pure & Applied Mathematics (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Computational Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Quality & Reliability (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for predicting capacity of a compact oil reservoir horizontal well at the initial stage of volume fracturing, which comprises the following steps: 1) establishing a database which comprises a sample set, a productivity influencing sub-factor set and an evaluation index main factor set; 2) establishing a multi-level influence productivity factor evaluation system according to a database; 3) calculating weight coefficients influencing the production capacity sub-factors in a multi-level evaluation system by using a grey correlation analysis method, and sequencing the weight coefficients to clearly influence the production capacity main control factors; 4) and sequentially calculating the membership degree of each fracturing well influencing the productivity sub-factors by utilizing a normal distribution membership function, carrying out fuzzy operation on the weight coefficient and the membership degree of each fracturing well influencing the productivity sub-factors, quantitatively and comprehensively scoring the fracturing effect of each well, dividing the comprehensive scores into intervals, and predicting the initial capacity of the volume fracturing of the horizontal well according to the interval where the comprehensive scores of the pre-measured wells are located. The method improves the accuracy of the prediction of the initial production capacity of the horizontal well volume fracturing, and plays an important guiding role in optimizing the design of the fracturing scheme.
Description
Technical Field
The invention relates to the field of petroleum and natural gas development, in particular to a method for predicting the capacity of a compact oil reservoir horizontal well at the initial stage of volume fracturing in a hydraulic fracturing process.
Background
The horizontal well volume fracturing technology is a key technology for realizing shale gas revolution in North America, is widely applied in the world, most compact oil reservoirs in China have the characteristics of poor reservoir physical property, strong heterogeneity, low natural productivity and the like, part of the oil reservoirs cannot be developed by conventional water injection to form an effective injection-production well pattern, and yield increase transformation is required to be carried out before production is put into operation to obtain productivity. In recent years, exploration and field tests on compact oil and gas reservoirs are carried out in various domestic oil and gas fields by using the experience of successful volume transformation abroad for reference, and a good yield increasing effect is achieved. The method has the advantages that the prediction and evaluation of the initial productivity of the production well of the tight oil reservoir have important significance for the exploration and development of the oil and gas field, and are one of key links for improving the exploration effect and important bases for the development planning and deployment of the oil and gas field. However, the factors influencing the volume fracturing productivity of the horizontal well are many, including the factors of reservoir physical properties, fracturing construction, production dynamics and the like of each fracturing well, the relationship among the factors is complex, the fracturing effect is different on different levels, the prediction difficulty is high, and meanwhile, the yield is rapidly decreased due to unclear understanding of various influencing factors such as geology, engineering and the like, the stable production period is short, and the development effect is seriously influenced, so that the influencing factors of the volume fracturing productivity of the horizontal well are comprehensively considered, the main control factors are determined, and the capacity after the pressure is predicted is particularly important for efficiently developing compact oil reservoirs.
Researchers have made a lot of research on fracturing well productivity prediction, mainly including empirical methods and numerical methods, and gradually develop from qualitative evaluation to quantitative evaluation. The empirical method is a research method based on mine field statistical data, and most reservoir engineers analyze the quality of the reservoir quality of the oil and gas well from the perspective of reservoir physical properties and production dynamics, and qualitatively predict the yield increasing effect of a single well after fracturing modification. However, most of the fracturing well development effects are evaluated only from the quality of the reservoir, and the analysis parameters are few. The method comprises the following steps that a scholars establishes a productivity prediction model by using a numerical method, but the numerical method is subject to the problems of simplification of many factors, limited data and the like, and meanwhile, the numerical simulation method is large in workload and is easily influenced by grid division and a calculation method. The existing methods for predicting and evaluating the volume fracturing capacity of the horizontal well mainly comprise the following steps:
(1) liuwan billow and the like (Liuwan billow, Gawu, Zhengguang, Erdos basin length 7 compact oil volume fracturing horizontal well productivity prediction research [ J ] scientific and technical engineering, 2016, 16 (11): 162 and 166) utilize the similarity of a volume fracturing horizontal well and a straight line infinite well row vertical well, neglect the influence of fluid resistance in a horizontal shaft, convert the interference problem among various fracturing cracks into the interference problem among the straight line infinite well row vertical wells, and derive a volume fracturing horizontal well steady-state productivity formula according to the potential superposition principle. According to the method, reservoir physical property parameters and fracturing construction parameters influencing the horizontal well productivity are considered, but partial factors are not considered comprehensively, meanwhile, the horizontal well productivity model is established on the premise that the fluids in the oil reservoir and the fractures flow in a single phase and the parameters of each fracturing section are the same, the actual fluid flow is two-phase seepage, and the parameter difference of each fracturing section is large.
(2) The method for predicting the horizontal well staged multi-cluster fracturing productivity of the medium-grade Yangmu (in Yangmu, beautiful plum and small-rigid. compact reservoir) [ J ] special oil and gas reservoir, 2017, 24 (4): 73-77) is characterized in that a staged multi-cluster fracture network formed after fracturing is equivalent to a high permeability zone according to an equivalent seepage theory, and a relational expression of equivalent permeability and high permeability zone width is deduced. On the basis, a unsteady state capacity prediction model of the compact reservoir segmented multi-cluster fractured horizontal well considering the interference among fractures is established by applying a reset potential theory and a potential superposition principle and applying an analytic method. The method is characterized in that multiple fractures in sections are equivalent to a high permeability zone, actual reservoir physical parameters and construction parameters of each fracturing section of the compact reservoir horizontal well and fracture shapes after fracturing are greatly different, and meanwhile, the simulation result is not accurate enough due to a plurality of assumed conditions.
(3) The influence of Linwang and the like (Linwang, normal flood, Liu Li Feng. engineering parameters on the yield of a horizontal well fractured by a tight oil reservoir [ J ]. oil and gas geology and recovery ratio, 2017, 24 (6): 120-plus 126) utilizes a numerical simulation method to establish a single-well yield model after large-scale volume fracturing and analyze the influence rule of different engineering parameters on the fracturing effect. Meanwhile, the influence degrees of the engineering parameters on the productivity are sequenced by using an orthogonal test method, so that the purpose of evaluating the influence factors of the fracturing effect is achieved. The method only considers the influence of engineering parameters on the fracturing effect, but does not consider reservoir physical parameters and production dynamic parameters.
The three methods do not simultaneously and comprehensively consider the physical properties of the reservoir where each fractured horizontal well is located and the difference of primary fracturing construction, each type of factor has fewer considered parameters and is not comprehensive enough, and meanwhile, the assumed conditions are more and the difference from the actual fractured well condition is large, so that the prediction result is not accurate enough.
Disclosure of Invention
The invention aims to provide a method for predicting capacity at the initial stage of volume fracturing of a horizontal well of a tight oil reservoir. And secondly, calculating the membership degree of each influence factor by using a normal distribution membership function, carrying out fuzzy operation on the weight coefficient and the membership degree of each influence factor, carrying out quantitative comprehensive scoring on the fracturing effect of each fracturing well, and predicting the initial capacity of the horizontal well volume fracturing according to the comprehensive scoring.
In order to achieve the above technical objects, the present invention provides the following technical solutions.
A method for predicting capacity at the initial stage of volume fracturing of a tight oil reservoir horizontal well sequentially comprises the following steps:
1) establishing a database A which comprises a sample set U, a productivity influencing sub-factor set C and an evaluation index main factor set W;
2) establishing a multi-level influence productivity factor evaluation system according to a database;
3) calculating weight coefficients influencing the production capacity sub-factors in a multi-level evaluation system by using a grey correlation analysis method, and sequencing the weight coefficients to clearly influence the production capacity main control factors;
4) and sequentially calculating the membership degree of each fracturing well influencing the productivity sub-factors by utilizing a normal distribution membership function, carrying out fuzzy operation on the weight coefficient and the membership degree of each fracturing well influencing the productivity sub-factors, quantitatively and comprehensively scoring the fracturing effect of each well, dividing the comprehensive scores into intervals, and predicting the initial capacity of the volume fracturing of the horizontal well according to the interval where the comprehensive scores of the pre-logging well are located.
In the invention, the database A established in the step 1) comprises a sample set U, a productivity influencing sub-factor set C and an evaluation index main factor set W, and comprises the following contents:
(1) the sample set U is a volume fracturing horizontal well sample;
(2) the set of capacity affecting sub-factors C includes 12 parameters: length c of oil-containing sandstone1C effective reservoir thickness c2C porosity c3C, permeability c4C oil saturation5Natural gamma c6C of fracturing stage7C, fracturing cluster number8C distance between cracks9Single cluster fracturing fluid amount c10C single cluster of sand amount11C, c12；
(3) And the evaluation index main factor set W is the oil production accumulated in the early stage of each horizontal well after volume fracturing.
In the invention, the step 2) of establishing a multi-level evaluation system for the productivity-affecting factors according to the database means that a three-level evaluation system comprising a target layer, a decision layer and an index layer is established to comprehensively reflect the influence on the productivity, and the specific contents are as follows:
(1) the target layer is a volume fracturing horizontal well sample;
(2) the decision layer is a reservoir physical property parameter set B1And fracturing construction parameter set B2Two major categories;
(3) the index layer is a sub-factor set influencing the productivity, wherein the length c of the oil-containing sandstone1Effective reservoir thickness c2Porosity c3Permeability c4Oil saturation c5Natural gamma c6Belongs to reservoir physical property parameter set B1(ii) a Number of fracturing stages c7Number of fracturing clusters c8Crack spacing c9Amount of single cluster fracturing fluid c10Amount of sand in a single cluster c11And the flow-back rate c12Belongs to fracturing construction parameter set B2。
In the present invention, the step 3) utilizes a gray correlation analysis method (liu si peak. gray system theory and application [ M ]. beijing: scientific publishing agency, 2008), calculating weight coefficients of the factors influencing the productivity in the multi-level evaluation system, and sequencing the weight coefficients to clearly influence the main control factors of the productivity, wherein the weight coefficients comprise the following contents:
(1) establishing an evaluation matrix and an evaluation index main factor set of the sub-factors influencing the production performance: and establishing an evaluation matrix X according to the database, wherein the dimension of the matrix is mxn, and taking the oil yield accumulated in the first year in the initial stage of the known fractured well as a main factor set of the evaluation index.
In the formula: x is an evaluation matrix of the sub-factors influencing the productivity; x0Is a main factor set of evaluation indexes; xi(j) To influence the productivity factor; xi(0) Is a main factor of evaluation indexes; i is 1,2, …, m; j is 1,2, …, n; m is the number of samples of the horizontal well with the volume fractured; n is the number of factors influencing the productivity; in the invention, n is 12.
(2) Data normalization: because the dimensions of the evaluation indexes of different levels are not necessarily the same and the absolute values are difficult to compare, each parameter needs to be standardized and converted into a comparable dimensionless sequence.
① for the index of which the evaluation data is in positive correlation with the fracturing effect, dividing the index data by the maximum value in the index data, and calculating the expression as follows:
② for the index whose evaluation data is negatively correlated with the fracturing effect, the maximum value in the index data is subtracted from the index data, and the difference is divided by the maximum value of the index.
In the formula:
the normalized data; (X)i(j))maxThe maximum value in the jth evaluation index data in all the samples m.
(3) And (3) gray correlation calculation: after the data of each evaluation index is subjected to standardization processing, calculating a gray correlation coefficient between each sub factor influencing the productivity and the main factor of the evaluation index, and determining the gray correlation degree between each sub factor influencing the productivity and the main factor of the evaluation index, wherein the calculation expression is as follows:
in the formula ξi(j) Is a gray correlation coefficient; r isjIs grey correlation degree;
wherein
The data is the data after the main factors of the evaluation indexes are standardized;
the data after the factor standardization of the production capacity is influenced; ρ is a resolution coefficient which has the effect of attenuating distortion due to too large maximum absolute error value, and is usually 0.5.
(4) And (3) calculating a weight coefficient: and measuring the influence degree of each influencing sub-factor on the productivity, and representing by using a weight coefficient. According to the multi-level evaluation system, the weight coefficient set of the factor of the index layer influencing the productivity is C ═ C1,c2,…,cn]And dividing the set of weight coefficients into two parts: c1＝[c1,c2,c3,c4,c5,c6]Is a reservoir physical property parameter sub-factor weight coefficient set; c2＝[c7,c8,c9,c10,c11,c12]For the fracturing construction parameter sub-factor weight coefficient set, the calculation expression is as follows:
in the formula: c. CjTo influence the weight coefficient of the productivity sub-factor.
In the present invention, in the step 4), using a normal distribution membership function (Zeng Fanhui, Cheng Xiaozhao, Guo Jianchun et al. hybrid human judgment, AHP, grey the, and fuzzy bipert systems for candidate well selection in actual resources [ J ]. energier,2017,10,447), sequentially calculating the membership of each fractured well affecting the productivity sub-factors, performing fuzzy operation on the weight coefficients and the membership of the fracturing sub-factors affecting the productivity sub-factors, quantitatively and comprehensively grading the fracturing effect of each well, and predicting the initial capacity of the horizontal well in the volume fracturing according to the section where the comprehensive score of the pre-measured well is located, including the following contents:
(1) sequentially establishing a standard set G of factors of each fracturing well influencing the productivity: and establishing a standard set G according to the distribution range of the sub-factors influencing the energy production of each well, wherein the standard set G is shown as an expression (8). Taking one of the wells as an example, the process is as follows: equally dividing the maximum value interval and the minimum value interval in each child factor data influencing productivity in the expression (1) into four equally divided intervals, and if the child factors influencing productivity are positively correlated with productivity, G1jIs the maximum interval value, G2j、G3j、G4jSequentially decreasing; if the influencer factor is negatively correlated with capacity, G1jIs a minimum interval value, G2j、G3j、G4jSequentially increasing to finally form four grades with different interval values, and simultaneously endowing the grades with different comprehensive fraction values G1Class I-100-75 (min), G2Class II 75-50 (min), G3Class III ═ 50-25 (min), G4Class IV is 25-0 (min), thus forming a standard set G of factors that influence energy production.
In the formula: g is a standard set of factors influencing the productivity; gljAnd the jth influencing sub-factor corresponds to a value range in the ith grade, wherein l is 1,2,3 and 4, which represent different grades.
(2) Calculating the membership degree of each sub-factor influencing the productivity in four levels of the standard set G by using a normal distribution membership function to form a membership degree matrix K ═ (K)lj)TAnd dividing the membership matrix into two different membership matrices: k1A reservoir physical property parameter sub-factor membership matrix; k2Calculating an expression for a sub-factor membership matrix of the fracturing construction parameters as follows:
in the formula: k is a radical ofljIs a membership matrix element, i is 1,2,3, 4; j is 1,2, …, n; xi(j) To influence the productivity factor; a isl＝(Gl1+Gl2)/2；
Wherein G isl1An upper limit value indicating a class i interval; gl2A lower limit value indicating a class i interval; t denotes a transpose operation of the matrix.
(3) Carrying out fuzzy operation on the weight coefficient and the membership degree of each sub-factor influencing the productivity, quantitatively and comprehensively scoring the fracturing effect of each well, and predicting the productivity of the volume fracturing horizontal well, wherein the method specifically comprises the following steps:
①, carrying out fuzzy operation on the weight coefficient and the membership degree of the reservoir physical property parameter influence sub-factors to obtain a reservoir physical property parameter membership degree matrix F, carrying out quantitative comprehensive scoring on the reservoir where the volume fracturing horizontal well is located, classifying the reservoir according to the reservoir comprehensive scoring, wherein the higher the score is, the better the reservoir physical property is, the higher the capacity after volume fracturing is, the more the expression is calculated as follows:
F＝[f1f2f3f4]＝C1·K1(13)
in the formula: f is a reservoir physical property parameter membership matrix; and S is reservoir comprehensive score.
②, carrying out fuzzy operation on the weight coefficients and the membership degrees of all the factors influencing the productivity to obtain a fracturing effect comprehensive membership degree matrix and quantitatively and comprehensively scoring the fracturing effect, wherein the calculation expression is as follows:
E＝[e1e2e3e4]＝C·K (15)
in the formula: e is a fracturing effect comprehensive membership matrix; j is the composite score for fracturing effect.
③ predicting the productivity according to the fracturing effect comprehensive scores of all the fracturing well samples, the process is that firstly, the number of the fracturing well is used as the abscissa, the fracturing effect comprehensive score and the initial stage accumulated oil yield are used as the ordinate, a double-coordinate curve graph is made, secondly, the interval is divided, and the initial stage productivity of the predicted well can be obtained according to the interval where the fracturing effect comprehensive score of the predicted well is located and the known well productivity of the corresponding interval.
Compared with the prior art, the invention has the following beneficial effects:
(1) the method can comprehensively consider the physical property parameters of the reservoir and the fracturing construction parameters which affect the volume fracturing development effect of the horizontal well, establish a multi-level evaluation system, improve the accuracy of evaluation, avoid the unscientific nature that the decision is made only through a single evaluation index, and is also applicable to the evaluation of the influencing factors of the fracturing effect of the vertical well.
(2) According to the method, the weight coefficients of the multilevel influence factors are quantitatively calculated through a grey correlation analysis method, the priority levels of the influence factors are sorted according to the weight coefficient, and the influence on the development effect is larger when the weight value is larger, so that the key influence factors of the productivity are determined, and the decision degree of fracturing scheme design is favorably improved.
(3) According to the invention, through fuzzy comprehensive evaluation, the quality and the fracturing effect of the reservoir where the volume fracturing horizontal well is located are quantitatively scored, the quality of the reservoir is evaluated, the productivity is predicted, and an important guiding function is provided for optimizing the fracturing scheme design.
Drawings
FIG. 1 is a schematic diagram of a multi-level evaluation system for factors affecting productivity due to horizontal well volume fracturing.
FIG. 2 is a classification diagram of a reservoir where the volume fracturing of the horizontal well of the present invention is located.
FIG. 3 is a diagram of comprehensive grading interval division and early productivity prediction of the horizontal well volume fracturing effect.
Detailed Description
The invention is further illustrated below with reference to the figures and examples.
Example 1
The porosity of a reservoir in a certain compact reservoir block is 6.9-13.8%, the average porosity is 10.5%, the permeability is 0.1-10.2 mD, the average porosity is 1.23mD, the saturation of crude oil is 45.2%, the viscosity of crude oil is 9.05mPa.s, the buried depth of the reservoir is 1980m, the initial formation pressure is 19.6MPa, the formation temperature is 87.5 ℃, the physical property and the oil content of the reservoir are poor, and the heterogeneity is strong, so that the sandstone reservoir belongs to a compact sandstone reservoir. The block is developed by adopting a horizontal well large-scale multi-section multi-cluster volume fracturing technology, the initial development effect is greatly different, and the daily oil yield change range is 10.2 t/d-35.7 t/d. The influence factors of the development effect are numerous, the influence factor analysis of the horizontal well volume fracturing productivity needs to be carried out, the main control factor is determined, the initial capacity of the horizontal well volume fracturing is predicted, and theoretical support is provided for the subsequent fracturing well optimization design.
And carrying out quantitative comprehensive scoring on 18 volume fractured horizontal wells in the target block by using a compact reservoir horizontal well volume fracturing initial-stage capacity prediction method, and predicting the capacity of 3 unknown fractured wells according to the known capacity of 15 fractured wells. The specific process comprises the following steps: establishing an evaluation index database, establishing a multi-level evaluation system, calculating the weight coefficient of the sub-factors influencing the productivity by using a gray correlation analysis method, sequencing the sub-factors, determining the main control factors influencing the productivity, calculating the comprehensive score of the fracturing effect of each well, dividing intervals, and predicting the initial capacity of the horizontal well volume fracturing.
1. Establishing an evaluation index database: and (2) counting 15 known volume fractured horizontal wells of the target block as a sample set according to the step 1), wherein the yield sub-factor parameters and the evaluation index main factor parameters (table 1) and (table 2) are influenced, and the accumulated oil yield in the first year in the initial stage is used as the main factor parameter.
Table 1 reservoir property parameter set
TABLE 2 fracturing construction parameters and principal factor parameter sets
2. Establishing a multi-level evaluation system: and (3) establishing a target layer, decision layer and index layer three-level evaluation system according to the evaluation index database in the step 2), as shown in figure 1.
3. Calculating the weight coefficient of the child factors influencing the productivity by using a grey correlation analysis method, sequencing the weight coefficient, and definitely influencing the main control factors influencing the productivity, wherein the specific calculation process according to the step 2) is as follows:
(1) establishing an evaluation matrix and an evaluation index main factor set of the sub-factors influencing the production performance: and establishing a 15-horizontal-well influence productivity sub-factor evaluation matrix according to the evaluation index database, wherein the evaluation matrix is shown as an expression (17). And (3) the oil production accumulated in the first year at the initial stage of each fracturing well is used as a main factor set of the evaluation index, as shown in an expression (18), and is standardized according to the standardization method in the step 3).
X0＝(6282，2800，2705，4347，4745，1506，2937，2596，6115，4558，4688，5931，3872，4142，5925)T(18)
(2) And (3) gray correlation calculation: the gray-level correlation of each evaluation index was calculated from expressions (5) and (6) in step 3), and the result was calculated (table 3).
(3) And (3) calculating a weight coefficient: calculating the weight coefficient of the factor of the index layer influencing the productivity in the multi-layer evaluation system according to the expression (7) in the step 3), wherein the calculation result is as follows:
C＝[0.0966,0.0878,0.0888,0.0824,0.0833,0.0624,0.0913,0.0924,0.0728,0.0843,0.0852,0.0727](19)
C1＝[0.0966,0.0878,0.0888,0.0824,0.0833,0.0624](20)
C2＝[0.0913,0.0924,0.0728,0.0843,0.0852,0.0727](21)
according to the weight coefficient, the energy production sub-factors influenced by the index layer are sequenced (table 3), as can be seen from table 3, the oil-containing sandstone has the largest length weight coefficient, the ranking is 1, the influence on the productivity is the largest, and then the fracturing cluster number and the fracturing segment number are ranked respectively in the ranks 2 nd and 3 rd, so that for a compact reservoir, the fracturing modification volume has the largest influence on the productivity under the condition that the reservoir material basis is certain, the seam distribution density is improved as far as possible on the premise that the interference among seams is not generated, and the volume development effect of the compact reservoir horizontal well can be improved.
TABLE 3 multilevel influence factor Grey correlation degree and weight coefficient ranking table
4. Calculating the comprehensive fracturing effect score of each well, dividing intervals, and predicting the initial capacity of the horizontal well in volume fracturing: the specific calculation process according to step 4) is as follows:
(1) establishing a standard set G of the sub-factors influencing the productivity: and establishing standard sets (tables 4) and (tables 5) of the sub-factors influencing the energy production according to the expression (8) in the step 4).
TABLE 4 reservoir Property parameter criteria set
TABLE 5 Standard set of fracturing parameters
(2) Taking one of the fractured horizontal volume wells P9 as an example, calculating a membership matrix K ═ K (K) of each factor affecting the capacity in four levels of the standard set G according to expressions (9) to (12) in the step 4)lj)TAnd reservoir physical property parameter sub-factor membership matrix K1And fracturing construction parameter sub-factor membership degree matrix K2The calculation results are as follows:
(3) respectively calculating the comprehensive score of the reservoir of the volume fracturing well P9 and the comprehensive score of the fracturing effect according to the expressions (13) to (16) in the step 4), wherein the comprehensive score of the reservoir of the P9 well is 81.1 points, the reservoir belongs to a reservoir I between 75 and 100 points, and the comprehensive score of the fracturing effect is 67.3 points. And calculating the comprehensive scores of the reservoirs and the comprehensive scores of the fracturing effects of other wells in the same way, and determining the types of the reservoirs (table 6) and (figure 2) where the fracturing is located according to the comprehensive scores of the reservoirs, wherein the higher the grade is, the better the physical properties of the reservoirs are.
F＝[f1f2f3f4]＝[0.146 0.333 0.023 0](25)
E＝[e1e2e3e4]＝[0.188 0.383 0.060 0.190](27)
The comprehensive score of the fracturing effect in the table 6 is divided into four intervals (figure 3), wherein the interval is 1 (the score is 45-54 points, and the first year cumulative oil production is 0.15-0.25 multiplied by 104t); interval 2 (score 54-58 points, first year oil production (0.25-0.30) × 104t); interval 3 (score 58-67 points, first year oil production (0.40-0.50) × 104t); interval 4 (score 67-78 points, first year cumulative oil production (0.60-0.65) × 104t). The comprehensive fracturing effect scores of P16, P17 and P18 are respectively 52.3, 73.9 and 54.9, so that the oil yield of the P16, P17 and P18 wells in the first year at the initial stage after volume fracturing is predicted to be (0.15-0.25) multiplied by 104t、(0.60～0.65)×104t、(0.25～0.30)×104And (4) a t interval.
TABLE 6 comprehensive scoring and Productivity prediction Table
While the present invention has been described in detail by way of the embodiments, it should be understood that the present invention is not limited to the embodiments disclosed herein, but is intended to cover other embodiments as well. But all the modifications and simple changes made by those skilled in the art without departing from the technical idea and scope of the present invention belong to the protection scope of the technical solution of the present invention.
Claims (3)
1. A method for predicting capacity at the initial stage of volume fracturing of a tight oil reservoir horizontal well sequentially comprises the following steps:
1) establishing a database A which comprises a sample set U, a productivity influencing sub-factor set C and an evaluation index main factor set W and comprises the following contents:
(1) the sample set U is a volume fracturing horizontal well sample;
(2) the set of capacity affecting sub-factors C includes 12 parameters: length c of oil-containing sandstone1C effective reservoir thickness c2C porosity c3C, permeability c4C oil saturation5Natural gamma c6C of fracturing stage7C, fracturing cluster number8C distance between cracks9Single cluster fracturing fluid amount c10C single cluster of sand amount11C, c12；
(3) The evaluation index main factor set W is the accumulated oil production of each horizontal well in the initial period after volume fracturing;
2) establishing a multi-level influence capacity factor evaluation system according to a database, wherein the specific contents are as follows:
(1) the target layer is a volume fracturing horizontal well sample;
(2) the decision layer is a reservoir physical property parameter set B1And fracturing construction parameter set B2Two major categories;
(3) the index layer is a sub-factor set influencing the productivity, wherein the length c of the oil-containing sandstone1Effective reservoir thickness c2Porosity c3Permeability c4Oil saturation c5Natural gamma c6Belongs to reservoir physical property parameter set B1(ii) a Number of fracturing stages c7Number of fracturing clusters c8Crack spacing c9Amount of single cluster fracturing fluid c10Amount of sand in a single cluster c11And the flow-back rate c12Belongs to fracturing construction parameter set B2；
3) Utilizing a grey correlation analysis method to calculate and sort weight coefficients influencing the productivity sub-factors in a multi-level evaluation system, and definitely influencing main production control factors, wherein the weight coefficients influencing the productivity sub-factors comprise the following contents:
(1) establishing an evaluation matrix X, wherein the matrix dimension is mxn, and taking the oil production accumulated in the first year at the initial stage of the known fractured well as a main factor set of evaluation indexes:
in the formula: x is an evaluation matrix of the sub-factors influencing the productivity; x0Is a main factor set of evaluation indexes; xi(j) To influence the productivity factor; xi(0) Is a main factor of evaluation indexes; i is 1,2, …, m; j is 1,2, …, n; m is the number of samples of the horizontal well with the volume fractured; n is the number of factors influencing the productivity; n is 12;
(2) carrying out standardization processing on each parameter, and converting the parameter into a comparable dimensionless sequence;
(3) calculating gray correlation coefficients between the sub factors influencing the productivity and the main factors of the evaluation index, and determining the gray correlation degrees of the sub factors influencing the productivity and the main factors of the evaluation index:
in the formula ξi(j) Is a gray correlation coefficient; r isjIs grey correlation degree;
wherein
The data is the data after the main factors of the evaluation indexes are standardized;
the data after the factor standardization of the production capacity is influenced; rho is a resolution coefficient;
(4) according to the multi-level evaluation system, the weight coefficient set of the factor of the index layer influencing the productivity is C ═ C1,c2,…,cn]The set of weight coefficients is divided into two parts, C1＝[c1,c2,c3,c4,c5,c6]Is a reservoir physical property parameter sub-factor weight coefficient set, C2＝[c7,c8,c9,c10,c11,c12]Calculating the weight coefficient c of the sub-factor influencing the productivity for the weight coefficient set of the sub-factor of the fracturing construction parameters through the following formulaj：
4) The method comprises the following steps of sequentially calculating the membership degree of each fracturing well influencing the productivity sub-factors by utilizing a normal distribution membership function, carrying out fuzzy operation on the weight coefficient and the membership degree of each fracturing well influencing the productivity sub-factors, quantitatively and comprehensively scoring the fracturing effect of each well, dividing the comprehensive score into sections, and predicting the initial capacity of the horizontal well volume fracturing according to the section where the comprehensive score of the pre-logging well is located, wherein the method comprises the following steps:
(1) the maximum value interval and the minimum value interval in each sub-factor data influencing the productivity are equally divided into four equally divided intervals, and comprehensive point values G of different grades are given1Class I-100-75 (min), G2Class II 75-50 (min), G3Class III ═ 50-25 (min), G4Class IV is 25-0 (min), forming a set of criteria G that influence energy production subfactor:
in the formula: g is a standard set of factors influencing the productivity; gljThe jth influencing sub-factor corresponds to a value range in the ith grade, wherein l is 1,2,3 and 4Different grades;
(2) calculating the membership degree of each factor influencing the productivity in four levels of the standard set G to form a membership degree matrix K ═ K (K)lj)TAnd dividing the membership matrix into two different membership matrices, K1Is a reservoir physical property parameter sub-factor membership matrix, K2A fracture construction parameter sub-factor membership matrix:
in the formula: k is a radical ofljIs a membership matrix element, i is 1,2,3, 4; j is 1,2, …, n; xi(j) To influence the productivity factor; a isl＝(Gl1+Gl2)/2；
T represents the transposition operation of the matrix;
(3) carrying out fuzzy operation on the weight coefficient and the membership degree of each sub-factor influencing the productivity, quantitatively and comprehensively scoring the fracturing effect of each well, and predicting the productivity of the volume fracturing horizontal well, wherein the specific process comprises the following steps:
①, carrying out fuzzy operation on the weight coefficient and the membership degree of the reservoir physical property parameter influence sub-factors to obtain a reservoir physical property parameter membership degree matrix F, carrying out quantitative comprehensive scoring on the reservoir where the volume fracturing horizontal well is located, classifying the reservoir according to the reservoir comprehensive scoring, wherein the higher the score is, the better the reservoir physical property is, the higher the capacity after volume fracturing is, the more the expression is calculated as follows:
F＝[f1f2f3f4]＝C1·K1
in the formula: f is a reservoir physical property parameter membership matrix; s is comprehensive scoring of the reservoir;
②, carrying out fuzzy operation on the weight coefficients and the membership degrees of all the factors influencing the productivity to obtain a fracturing effect comprehensive membership degree matrix and quantitatively and comprehensively scoring the fracturing effect, wherein the calculation expression is as follows:
E＝[e1e2e3e4]＝C·K
in the formula: e is a fracturing effect comprehensive membership matrix; j is the comprehensive score of the fracturing effect;
③ taking the number of the fracturing well as the abscissa and the comprehensive fracturing effect score and the initial accumulated oil yield as the ordinate, making a two-coordinate curve chart, dividing the curve chart into intervals, and obtaining the initial productivity of the predicted well according to the interval where the comprehensive fracturing effect score of the predicted well is located and the known well productivity of the corresponding interval.
2. The method for predicting the initial capacity of the tight reservoir horizontal well in the volumetric fracturing process according to claim 1, wherein rho is a resolution coefficient and is 0.5.
3. The method for predicting the capacity at the initial stage of the volume fracturing of the tight reservoir horizontal well according to claim 1, wherein the step of standardizing the parameters comprises the following steps:
for the indexes that the evaluation data and the fracturing effect are in positive correlation:
for the indexes that the evaluation data is in negative correlation with the fracturing effect:
in the formula:
the normalized data; (X)i(j))maxThe maximum value in the jth evaluation index data in all the samples m.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810182962.1A CN108446797B (en) | 2018-03-06 | 2018-03-06 | Method for predicting capacity of tight oil reservoir horizontal well at initial volume fracturing stage |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810182962.1A CN108446797B (en) | 2018-03-06 | 2018-03-06 | Method for predicting capacity of tight oil reservoir horizontal well at initial volume fracturing stage |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108446797A CN108446797A (en) | 2018-08-24 |
| CN108446797B true CN108446797B (en) | 2020-02-07 |
Family
ID=63193773
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810182962.1A Active CN108446797B (en) | 2018-03-06 | 2018-03-06 | Method for predicting capacity of tight oil reservoir horizontal well at initial volume fracturing stage |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108446797B (en) |
Families Citing this family (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109209321B (en) * | 2018-09-03 | 2020-01-21 | 中国石油大学(华东) | Fracturing potential-based horizontal well fracturing design method and device to be fractured |
| CN109711595A (en) * | 2018-09-20 | 2019-05-03 | 西安石油大学 | A kind of hydraulic fracturing operation effect evaluation method based on machine learning |
| CN110070282B (en) * | 2019-04-17 | 2023-06-20 | 国网安徽省电力公司 | Low-voltage transformer area line loss influence factor analysis method based on comprehensive relevance |
| CN112308269A (en) * | 2019-07-30 | 2021-02-02 | 中国石油化工股份有限公司 | Yield prediction method and device for low-permeability oil and gas reservoir |
| CN110533237A (en) * | 2019-08-21 | 2019-12-03 | 中国石油化工股份有限公司 | A kind of sandstone reservoir oily PRODUCTION FORECASTING METHODS |
| CN111441767B (en) * | 2020-05-11 | 2022-05-20 | 中国石油大学(华东) | Oil reservoir production dynamic prediction method and device |
| CN111401669A (en) * | 2020-06-05 | 2020-07-10 | 西南石油大学 | Shale oil pressure post-flowback rate prediction method based on wavelet neural network |
| CN113803043B (en) * | 2020-06-17 | 2023-07-07 | 中国石油化工股份有限公司 | Fracturing transformation volume prediction method and device for shale gas reservoir |
| CN111894566A (en) * | 2020-07-30 | 2020-11-06 | 中国石油天然气股份有限公司 | Single-production gas well unobstructed flow prediction method based on fracturing construction parameters |
| CN112257892B (en) * | 2020-08-27 | 2024-04-16 | 中国石油化工股份有限公司 | Optimization method for drainage gas production process system of complex gas reservoir |
| CN112305634B (en) * | 2020-10-31 | 2023-11-17 | 中国海洋石油集团有限公司 | Method for predicting sand content of bottom fan of lake based on cause and morphological analysis |
| CN112561144B (en) * | 2020-12-07 | 2021-09-21 | 西安石油大学 | Method for evaluating main control factor of productivity of tight oil fracturing horizontal well and predicting productivity |
| CN115110936B (en) * | 2021-03-23 | 2024-04-30 | 中国石油天然气股份有限公司 | Method and device for determining clustering perforation position of tight oil horizontal well |
| CN112966978B (en) * | 2021-04-02 | 2024-01-23 | 中国石油天然气股份有限公司 | Oil reservoir gas drive recovery ratio calibration method, data processing device, computer equipment and readable storage medium |
| CN113807021B (en) * | 2021-09-29 | 2022-08-26 | 西南石油大学 | Gas well productivity grade prediction method based on index analysis and multi-model fusion |
| CN114320266B (en) * | 2021-11-17 | 2023-11-28 | 陕西延长石油（集团）有限责任公司 | Dense oil reservoir conventional well yield prediction method based on support vector machine |
| CN116066072A (en) * | 2022-12-20 | 2023-05-05 | 中国石油大学(华东) | Method and processing device for predicting productivity by logging |
| CN115660294B (en) * | 2022-12-22 | 2023-03-10 | 西南石油大学 | Horizontal well full life cycle EUR tracking evaluation method, equipment and readable storage medium |
| CN117077573B (en) * | 2023-10-16 | 2024-01-26 | 西安石油大学 | Quantitative characterization method and system for shale oil reservoir laminated fracture network morphology |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104018831A (en) * | 2014-06-24 | 2014-09-03 | 西南石油大学 | Method for evaluating reservoir of fractured well |
| CN106703776A (en) * | 2016-12-23 | 2017-05-24 | 西南石油大学 | Method for optimizing fracturing parameters |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105095986B (en) * | 2015-06-23 | 2018-12-25 | 中国石油天然气股份有限公司 | The method of stratified reservoir overall yield prediction |
-
2018
- 2018-03-06 CN CN201810182962.1A patent/CN108446797B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104018831A (en) * | 2014-06-24 | 2014-09-03 | 西南石油大学 | Method for evaluating reservoir of fractured well |
| CN106703776A (en) * | 2016-12-23 | 2017-05-24 | 西南石油大学 | Method for optimizing fracturing parameters |
Non-Patent Citations (1)
| Title |
|---|
| 致密油藏水平井压裂后产能预测方法;纪天亮 等;《大庆石油地质与开发》;20160430;第35卷(第2期);第166-168页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108446797A (en) | 2018-08-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108446797B (en) | Method for predicting capacity of tight oil reservoir horizontal well at initial volume fracturing stage | |
| CN108009716B (en) | Multi-level evaluation method for influence factors of horizontal well volume fracturing effect | |
| CN108960651B (en) | Comprehensive evaluation method for completion efficiency of compact oil and gas reservoir multi-stage fractured horizontal well | |
| CN112561144B (en) | Method for evaluating main control factor of productivity of tight oil fracturing horizontal well and predicting productivity | |
| CN107387051B (en) | Repeated fracturing well selection method for multi-stage fractured horizontal well with low-permeability heterogeneous oil reservoir | |
| CN112016175B (en) | Water supply pipe network pressure measuring point optimal arrangement method based on tree hierarchical clustering | |
| CN106056252B (en) | Shale gas field well group division three-level optimization method | |
| CN109214026A (en) | A kind of shale gas horizontal well initial productivity prediction technique | |
| CN110992200A (en) | Shale gas well staged fracturing effect evaluation and yield prediction method based on random forest | |
| CN109034647A (en) | A kind of method that densification oil-gas reservoir volume fracturing horizontal well refracturing selects well | |
| CN107965301B (en) | Quantitative evaluation method for water injection process of digital oil field | |
| CN106355540A (en) | Small- and medium-sized reservoir dam safety evaluating method based on GRA-BP (grey relational analysis and back propagation) neural network | |
| CN112541571A (en) | Injection-production connectivity determination method based on machine learning of double parallel neural networks | |
| CN115422740A (en) | Method for predicting height of water flowing fractured zone of layered fully-mechanized caving mining of huge thick coal seam | |
| CN112049629B (en) | Fracture-cavity type oil reservoir recovery ratio prediction method based on A-type water drive characteristic curve | |
| CN111325441B (en) | Quantitative evaluation method for shale gas target preservation conditions | |
| CN111622724A (en) | Method for calculating and optimizing injection allocation amount of layered water injection layer section | |
| CN111783205A (en) | Intelligent prediction method for jacking force in vertical jacking construction process | |
| CN116384618A (en) | Method and system for profile control and flooding well selection of large well spacing oil reservoir | |
| CN112228052B (en) | Shale and tight rock oil gas productivity prediction method, evaluation method and system | |
| CN113705917A (en) | Method for predicting initial productivity of fractured horizontal well of tight reservoir | |
| CN113887049A (en) | Drilling speed prediction method and system for petroleum drilling based on machine learning | |
| CN111274736A (en) | Water flowing fractured zone prediction method based on supervised learning neural network algorithm | |
| CN107818189B (en) | Method for calculating recoverable resource amount of shale gas | |
| CN114676631B (en) | Shale gas reservoir fracturing horizontal well fracturing transformation parameter recommendation method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |