CN110533237A - A kind of sandstone reservoir oily PRODUCTION FORECASTING METHODS - Google Patents
A kind of sandstone reservoir oily PRODUCTION FORECASTING METHODS Download PDFInfo
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
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Abstract
The present invention relates to petroleum exploration and development technical field, especially a kind of sandstone reservoir oily PRODUCTION FORECASTING METHODS.The prediction technique is by selecting reasonable evaluation parameter, according to having logged well, the existing modes such as rock core actual measurement obtain the sample data of evaluation parameter, correlation between rear Calculation Estimation parameter is standardized to these evaluation parameters, and then determine weight of each evaluation parameter in entirety, predictive factor is determined according to each evaluation parameter and its weight, and reservoir productivity prediction model is obtained with practical day oil production level, for realizing the prediction of the daily oil production of reservoir to be evaluated, due to consideration that the Different Effects of each evaluation parameter, the precision of forecasting model built is high, prediction result accuracy is high, convenient for the exploration and development decision in oil field.
Description
Technical field
The present invention relates to petroleum exploration and development technical field, especially a kind of sandstone reservoir oily PRODUCTION FORECASTING METHODS.
Background technique
Oily capability forecasting is the technology that comprehensive evaluation is carried out to reservoir oil productive capacity, and reservoir productivity is by reservoir
The co-determinations such as self-condition, engineering factor and oil gas performance.And the factor for influencing geologic characteristics is complicated and more
Aspect, capability forecasting is carried out to reservoir with these complicated parameters and reasonable evaluation of classification is also very difficult.For example,
Oil-production capacity is controlled primarily by the factors such as reservoir effective permeability, Reservoir and its electrical response characteristic, they are applied to
In practice, oil-water seepage substantially conforms to darcy capability forecasting equation, and capability forecasting result is basic for the mining site production of high porosity permeability reservoir
Reliably.But the conventional saturating sandstone reservoir of low porosity and low permeability causes electrical property feature by object since porosity type multiplicity, heterogeneity are strong
Property and rock matrix be affected, logging response character is complicated, is unable to accurate quantitative analysis evaluation, simple to be easy to lose according to well log interpretation
Leakage even wrong identification effective reservoir.
The scholar of studies in China reservoir has made many researchs in terms of selection evaluation parameter and reservoir productivity prediction, selected
Evaluation index it is different, some are the principal element that production capacity is influenced by determining, qualitative evaluation reservoir and capability forecasting；Have
It is the reservoir productivity sort research using pore throat character analysis as core a bit；It is a certain that some multiple parameters selected only evaluate reservoir
Multiple performances of aspect characteristic；Besides replacement pressure classification is respectively adopted to reservoir identification and classification using pattern-recongnition method
Method, production capacity composite index law and Reservoir levels index method carry out quantitative forecast etc. to the production capacity of reservoir.It is heterogeneous due to reservoir
Property, it is simple to be carried out reservoir classification and evaluation with macroscopic view or microcosmic reservoir parameter and had its one-sidedness, in parameter selection, be not also
Parameter is The more the better, and parameter excessively will increase calculation amount, and suppresses the big parameter of correlation, influences prediction effect.Distinct methods
The evaluation parameter of selection can not represent multiple main aspects of evaluation reservoir, even if using more evaluation parameter,
Not accounting for different evaluation parameter influences different situations on capability forecasting, so that it cannot with fine mathematical method come accurate
Quantitatively characterizing reservoir productivity causes prediction result inaccurate, and Comprehensive Evaluation evaluation is unreliable.Therefore, capability forecasting and evaluation side
The selection of method will meet the geologic feature in research area, select some representative parameters, neither ignore principal element, again
Make model simple possible.
Summary of the invention
The object of the present invention is to provide a kind of sandstone reservoir oily PRODUCTION FORECASTING METHODSs, to solve prior art prediction
As a result inaccuracy, the insecure problem of evaluation.
To achieve the goals above, the present invention provides a kind of sandstone reservoir oily PRODUCTION FORECASTING METHODS, including following step
It is rapid:
1) obtain the porosity of the known any reservoir of oil reservoir, permeability, shale content, median grain diameter, depth in the middle part of reservoir,
Reservoir effective thickness, formation resistivity and practical day oil production level；
2) according to depth and practical day oil production level in the middle part of the reservoir, the km well depth daily output of any reservoir is determined
Oil mass；Determine that the quality of any reservoir refers to according to the porosity, permeability, reservoir effective thickness and formation resistivity
Number, flow coefficient, packing coefficient, seepage coefficient and oil saturation；
3) with depth, reservoir effective thickness, formation resistivity, km well depth day in the middle part of shale content, median grain diameter, reservoir
At least six in oil production, qualitative index, flow coefficient, packing coefficient, seepage coefficient and oil saturation join as evaluation
Number, the raw data matrix that building is made of reservoir and corresponding evaluation parameter numerical value, obtains normalized number by standardization
According to matrix；
4) related coefficient between any two evaluation parameter numerical value is calculated according to standardized data matrix, any one evaluation ginseng
The ratio of the average value of several related coefficients and the sum of the average value of related coefficient of other evaluation parameters is any evaluation
The weight coefficient of parameter；
5) according to the evaluation parameter of the weight coefficient of evaluation parameter and each reservoir, the predictive factor of each reservoir is calculated；
According to the practical day oil production level and predictive factor of each reservoir, fitting obtains reservoir productivity prediction model；
6) evaluation parameter for obtaining reservoir to be evaluated obtains storage to be evaluated as the input quantity of reservoir productivity prediction model
The prediction daily oil production of layer.
Beneficial effect is, according to logged well, the existing mode such as rock core actual measurement obtains the sample data of evaluation parameter, to this
A little evaluation parameters are standardized the correlation between rear Calculation Estimation parameter, and then determine each evaluation parameter in entirety
Weight, predictive factor is determined according to each evaluation parameter and its weight, and obtain with practical day oil production level regression analysis
Reservoir productivity prediction model, for realizing the prediction of the daily oil production of reservoir to be evaluated, due to consideration that each evaluation parameter
Different Effects, the precision of forecasting model built is high, and prediction result accuracy is high, convenient for the exploration and development decision in oil field.
Further, the calculation formula of the predictive factor is as follows:
In formula, z is the predictive factor of reservoir, λjFor the weight coefficient of j-th of evaluation parameter, yjFor j-th evaluation parameter
Numerical value after standardization.
Further, the calculation formula of the related coefficient are as follows:
In formula, rjkFor the related coefficient between evaluation parameter j and evaluation parameter k, rjk∈ [- 1,1],To comment
The average value of valence parameter j,For the average value of evaluation parameter k, n is reservoir quantity, yijFor the jth of i-th of reservoir
Numerical value after the standardization of a evaluation parameter.
Further, the calculation formula of the weight coefficient is as follows:
In formula, λjFor the weight coefficient of j-th of evaluation parameter,For in correlation matrix with jth
The weighted arithmetic average of the related coefficient of the relevant m-1 evaluation parameter of a evaluation parameter, m are total evaluation parameter number.
Further, for simple, accurate realization standardization, the standardization is standardized using z-score
Method.
Further, preferable fitting result, the fitting use statistical regression method in order to obtain.
Detailed description of the invention
Fig. 1 is a kind of flow chart of sandstone reservoir oily PRODUCTION FORECASTING METHODS of the invention；
Fig. 2 is the predictive factor of known reservoir of the invention and the correlation analysis figure of practical daily oil production；
Fig. 3 is the correlation analysis figure of the prediction daily output and the practical daily output of known reservoir of the invention；
Fig. 4 is the prediction daily oil production of known reservoir of the invention and the boundary standard drawing of predictive factor；
Fig. 5 is the prediction daily oil production and evaluation of classification figure of reservoir to be evaluated of the invention.
Specific embodiment
The present invention will be further described in detail with reference to the accompanying drawing.
The present invention provides a kind of sandstone reservoir oily PRODUCTION FORECASTING METHODS, as shown in Figure 1, comprising the following steps:
1) porosity, permeability, shale content, the median grain diameter, reservoir middle part depth, storage of each reservoir of known oil reservoir are obtained
Layer effective thickness, formation resistivity and practical day oil production level.
Porosity, permeability, shale content, median grain diameter, reservoir middle part depth, the reservoir of the known any reservoir of oil reservoir have
Effect thickness, formation resistivity and practical day oil production level can be analyzed to obtain by the prior art, wherein pass through known oil reservoir
The available reservoir of well logging well-log information in the middle part of depth value, reservoir effective thickness, formation resistivity, natural gamma and when sound wave
Difference log value, by the measured data of rock core assay it is available fathom, median grain diameter, porosity, permeability,
Shale content and oil viscosity.
Data in the well logging well-log information of the measured data of rock core assay and known oil reservoir are subjected to multiple linear
It returns and calculates, respectively obtain the log interpretation model of porosity, permeability, median grain diameter and shale content, as follows:
Md=α × kβ
In formula,For porosity, unit %；K is permeability, unit mD；VshFor shale content, unit %；AC
For well logging sonic differential time value, unit is μ s/s；GR is natural gamma ray logging value, unit API；RtFor the ground of any of the above-described reservoir
The log value of layer resistivity, unit are Ω m；RlimFor the log value of the maximum formation resistivity of known oil reservoir, unit Ω
m；RshFor the log value of pure shale formation resistivity, unit is Ω m；MdFor median grain diameter, unit is μm；α, β, γ are constant
Coefficient, numerical value are obtained by multiple linear regression.
The log value of one reservoir of known oil reservoir be it is known, sample the rock core of the reservoir by rock core assay i.e.
Measured data can be obtained, i.e. the median grain diameter of the reservoir, porosity, permeability, shale content and oil viscosity is
Therefore the amount of knowing porosity, permeability, shale content and the grain of any reservoir can be obtained by above-mentioned log interpretation model
Spend intermediate value.
It can count to obtain and depth of reservoirs pair according to data such as oil well formation testing, pilot production, operation creation data, well test datas
The practical day oil production level for the reservoir answered, day oil production level can be referred to as daily oil production or the daily output again.
2) according to depth in the middle part of reservoir and practical day oil production level, the km well depth daily oil production of each reservoir is determined；According to
Porosity, permeability, reservoir effective thickness and formation resistivity determine the qualitative index of each reservoir, flow coefficient, packing coefficient,
Seepage coefficient and oil saturation.
The calculation formula of the km well depth daily oil production of above-mentioned each reservoir is as follows:
In formula, QeFor km well depth daily oil production, Q0For the practical day oil production level of reservoir, H is depth in the middle part of reservoir.
Qualitative index, flow coefficient, packing coefficient, the calculation formula of seepage coefficient and oil saturation are successively as follows:
P=k × h/ μ
In formula, h is reservoir effective thickness, unit m；μ is oil viscosity, is constant；RwFor formation water resistivity,
Unit is Ω m；T is cementation factor, and v is saturation exponent, and a, b are lithology factor, and parameter a, b, t, v are constant, passes through rock
Electricity experiment can be obtained.
3) with depth, reservoir effective thickness, formation resistivity, km well depth day in the middle part of shale content, median grain diameter, reservoir
In oil production, qualitative index, flow coefficient, packing coefficient, seepage coefficient and oil saturation at least median grain diameter, in the middle part of reservoir
Depth, reservoir effective thickness, formation resistivity, qualitative index, flow coefficient and oil saturation are as evaluation parameter, and building is not
With the raw data matrix of reservoir evaluation parameter numerical value composition, standardized data matrix is obtained by standardization；
With depth, reservoir effective thickness, formation resistivity, the daily output of km well depth in the middle part of shale content, median grain diameter, reservoir
Oil mass, qualitative index, flow coefficient, packing coefficient, seepage coefficient and oil saturation as evaluation parameter, building by reservoir and
The raw data matrix X of corresponding evaluation parameter numerical value composition, as follows:
X=(xij)n×m
In formula, i=1,2 ..., n, n indicate reservoir quantity；J=1,2 ..., m, m indicate the quantity of evaluation parameter；xijIt indicates
The numerical value of j-th of evaluation parameter of i-th of reservoir.
Since each evaluation parameter dimension is inconsistent, X is standardized using z-score standardized method, is asked
The average value of each evaluation parameter outAnd standard deviation sigma, specific steps are as follows:
The observed value of n sample data of n reservoir of each evaluation parameter is subtracted to the average value of the evaluation parameterThen divided by the standard deviation sigma of the evaluation parameter, data fit standardized normal distribution after standardization, i.e. mean value are 0, standard deviation
It is 1, converts function are as follows:
In formula: x' is the value of the evaluation parameter after standardization, xiFor i-th of sample data of each evaluation parameter.
After z-score standardization, raw data matrix X is become into the matrix Y after standardization, as follows:
Y=(yij)n×m
In formula, i=1,2 ..., n, n indicate reservoir quantity；J=1,2 ..., m, m indicate the quantity of evaluation parameter；yijIt indicates
Numerical value after the standardization of j-th of evaluation parameter of i-th of reservoir.
The present embodiment is by shale content, median grain diameter, depth, reservoir effective thickness, formation resistivity, km in the middle part of reservoir
This 11 amount all conducts of well depth daily oil production, qualitative index, flow coefficient, packing coefficient, seepage coefficient and oil saturation
Evaluation parameter can also be used as evaluation parameter using six of them or seven as other embodiments.
In the present embodiment, raw data matrix is standardized using z-score standardized method, as other
Embodiment can also be handled using existing other standardized methods.
4) related coefficient between any two evaluation parameter numerical value is calculated according to standardized data matrix, any one evaluation ginseng
The ratio of the average value of several related coefficients and the sum of the average value of related coefficient of other evaluation parameters is any evaluation parameter
Weight coefficient.
In one reservoir, the calculation formula of the related coefficient between j-th of evaluation parameter and k-th of evaluation parameter are as follows:
In formula, rjkFor the related coefficient between evaluation parameter j and evaluation parameter k, rjk∈ [- 1,1],For
The average value of evaluation parameter j,For the average value of evaluation parameter k, n is reservoir quantity, yijFor i-th reservoir
Numerical value after the standardization of j-th of evaluation parameter.
Correlation matrix R is obtained after correlation, as follows:
R=(rjk)m×m
In formula, j=1,2 ..., m；K=1,2 ..., m；rjkIndicate that j-th of evaluation parameter is related to k-th evaluation parameter
Coefficient, for the absolute value of r closer to 1, degree of correlation is higher.
Weight coefficient is calculated, that is, the average value for calculating the related coefficient of each evaluation parameter accounts for the phase of other evaluation parameters
The calculation formula of the percentage of the sum of the average value of relationship number, weight coefficient is as follows:
In formula, λjFor the weight coefficient of j-th of evaluation parameter,For in correlation matrix with
The weighted arithmetic average of the related coefficient of the relevant m-1 evaluation parameter of j evaluation parameter, m are total evaluation parameter number.
5) according to the evaluation parameter of the weight coefficient of evaluation parameter and each reservoir, the predictive factor of each reservoir is calculated；
According to the practical day oil production level and predictive factor of each reservoir, fitting obtains reservoir productivity prediction model.
The predictive factor of each reservoir, i.e. comprehensive evaluation value is calculated, formula is as follows:
In formula, z is the predictive factor of reservoir, λjFor the weight coefficient of j-th of evaluation parameter, yjFor j-th evaluation parameter
Numerical value after standardization.
By the practical day oil production level of each reservoir of statistics, predictive factor with the reservoir, as two-dimensional coordinate number
Strong point obtains a kind of higher functional relation of the degree of correlation, as reservoir productivity prediction model using statistical regression methods.
The daily output is predicted according to the functional value that the reservoir productivity prediction model calculates each reservoir, and prediction is produced daily
Amount is used as a variable, obtains the verification letter of reservoir productivity prediction by statistical regression methods with the practical daily output of corresponding reservoir
Number relational expression, for verifying above-mentioned reservoir productivity prediction model.
6) evaluation boundary is carried out to reservoir productivity prediction model according to the classification standard of reservoir productivity；According to the to be evaluated of acquisition
The evaluation parameter of valence reservoir obtains the predictive factor and prediction daily oil production of reservoir to be evaluated, and determines commenting for reservoir to be evaluated
Valence result.
The evaluation parameter of reservoir to be evaluated is obtained, the predictive factor of reservoir to be evaluated can be calculated, by predictive factor generation
Entering reservoir productivity prediction model can be obtained the prediction daily oil production of reservoir to be evaluated.
Evaluation boundary is carried out to reservoir productivity prediction model according to the classification standard of reservoir productivity, evaluation is divided into middle height
Layer, low-productivity layer, dry poor layer etc., can judge position locating for reservoir to be evaluated according to predictive factor or prediction daily oil production
It sets to get evaluating reservoir result is arrived.
The present invention provides a specific experimental data, and data source is the oil field HN M block, the Reservoir type of the block and
High-quality Reservoir distribution is closely related with depositional environment.Geologic climate is warm arid, and in half deep lake-Vlei environment, material resource is more,
Sedimentary sand bodies are annularly distributed.Since periodical retreat of lake water forms part river channel sand and channe-mouth bar sandstone is stacked on top of each other,
Main purpose layer skeleton matching is deposited, sand factor is lower, generally based on powder, packsand for a set of sand shale interactive mode
10%-30%.Reservoir properties are low porosity and low permeability, and the general 5%-18% of porosity, be averaged 9.1%, the general 0.1-70mD of permeability,
Average 15.2mD.
The sample set of the raw data matrix obtained according to above-mentioned steps is as shown in table 1, totally 43 reservoirs, 11 evaluations ginsengs
Number:
Table 1
| Reservoir number | H | h | Qe | Re | Vsh | Md | Rt | So | F | S | P |
| A1 | 2763.6 | 0.8 | 0.08 | 0.4 | 9.0 | 0.0329 | 69.6 | 45.3 | 6.0 | 5.0 | 0.8 |
| A2 | 2765.4 | 1.6 | 0.08 | 0.5 | 14.4 | 0.0329 | 157.1 | 43.6 | 8.7 | 7.9 | 1.6 |
| A3 | 2721.2 | 0.8 | 0.24 | 0.5 | 14.0 | 0.0403 | 46.8 | 46.5 | 6.9 | 5.7 | 1.6 |
| A4 | 2722.5 | 1.0 | 0.24 | 0.747 | 13.53 | 0.0528 | 75.0 | 48.9 | 7.59 | 8.96 | 5.00 |
| A5 | 2781.8 | 1.3 | 0.03 | 0.576 | 6.59 | 0.0403 | 68.4 | 45.0 | 9.00 | 7.83 | 2.60 |
| A6 | 2783.8 | 1.2 | 0.03 | 0.535 | 15.22 | 0.0403 | 63.3 | 46.3 | 7.10 | 8.40 | 2.40 |
| … | … | … | … | … | … | … | … | … | … | … | … |
| A43 | 2704.1 | 1.8 | 0.30 | 1.1 | 6.7 | 0.0659 | 60.3 | 49.7 | 9.9 | 17.2 | 19.1 |
After z-score standardization, standardized data is formed, as shown in table 2:
Table 2
| Reservoir number | H | h | Qe | Re | Vsh | Md | Rt | So | F | S | P |
| A1 | 0.763 | -1.151 | -0.411 | -1.197 | 0.130 | -1.243 | -0.831 | -0.962 | -1.289 | -1.101 | -0.544 |
| A2 | 0.769 | -0.428 | -0.411 | -1.108 | 1.122 | -1.243 | -0.592 | -1.392 | -0.483 | -0.888 | -0.533 |
| A3 | 0.616 | -1.151 | -0.382 | -0.970 | 1.040 | -0.955 | -0.609 | -0.672 | -1.026 | -1.048 | -0.533 |
| A4 | 0.620 | -0.970 | -0.382 | -0.590 | 0.959 | -0.471 | -0.722 | -0.073 | -0.825 | -0.807 | -0.490 |
| A5 | 0.630 | 0.658 | -0.382 | -0.049 | -0.948 | 0.063 | 0.125 | 0.227 | -0.220 | 0.577 | -0.163 |
| A6 | 0.826 | -0.699 | -0.420 | -0.889 | -0.315 | -0.955 | 0.470 | -1.030 | -0.406 | -0.891 | -0.521 |
| … | … | … | … | … | … | … | … | … | … | … | … |
| A43 | 0.556 | -0.247 | -0.37 | -0.055 | -0.304 | 0.035 | -0.339 | 0.129 | -0.129 | -0.194 | -0.311 |
Relevant related coefficient two-by-two is sought in a reservoir to the numerical value after the standardization of each evaluation parameter, is formed
Correlation matrix, as shown in table 3:
Table 3
| H | h | Qe | Re | Vsh | Md | Rt | So | F | S | P | |
| H | 1.000 | 0.396 | 0.457 | 0.517 | 0.044 | 0.478 | 0.261 | 0.396 | 0.530 | 0.930 | 0.683 |
| h | 0.396 | 1.000 | 0.851 | 0.899 | -0.073 | 0.946 | 0.042 | 1.000 | 0.406 | 0.657 | 0.672 |
| Qe | 0.457 | 0.851 | 1.000 | 0.935 | -0.003 | 0.908 | 0.218 | 0.851 | 0.589 | 0.684 | 0.899 |
| Re | 0.517 | 0.899 | 0.935 | 1.000 | -0.169 | 0.988 | 0.153 | 0.899 | 0.693 | 0.734 | 0.825 |
| Vsh | 0.044 | -0.073 | -0.003 | -0.169 | 1.000 | -0.195 | 0.088 | -0.073 | -0.022 | -0.033 | 0.055 |
| Md | 0.478 | 0.946 | 0.908 | 0.988 | -0.195 | 1.000 | 0.090 | 0.946 | 0.611 | 0.714 | 0.764 |
| Rt | 0.261 | 0.042 | 0.218 | 0.153 | 0.088 | 0.090 | 1.000 | 0.042 | 0.333 | 0.211 | 0.374 |
| So | 0.396 | 1.000 | 0.851 | 0.899 | -0.073 | 0.946 | 0.042 | 1.000 | 0.406 | 0.657 | 0.672 |
| F | 0.530 | 0.406 | 0.589 | 0.693 | -0.022 | 0.611 | 0.333 | 0.406 | 1.000 | 0.544 | 0.633 |
| S | 0.930 | 0.657 | 0.684 | 0.734 | -0.033 | 0.714 | 0.211 | 0.657 | 0.544 | 1.000 | 0.833 |
| P | 0.683 | 0.672 | 0.899 | 0.825 | 0.055 | 0.764 | 0.374 | 0.672 | 0.633 | 0.833 | 1.000 |
Weight coefficient is calculated, as shown in table 4:
Table 4
| Weight coefficient | Related coefficient average value | |
| H | 0.087 | 0.469 |
| h | 0.108 | 0.580 |
| Qe | 0.119 | 0.639 |
| Re | 0.120 | 0.648 |
| Vsh | -0.007 | -0.038 |
| Md | 0.116 | 0.625 |
| Rt | 0.034 | 0.181 |
| So | 0.108 | 0.580 |
| F | 0.088 | 0.472 |
| S | 0.110 | 0.593 |
| P | 0.119 | 0.641 |
Each reservoir evaluation parameter that weight coefficient is substituted into standardized data, is calculated by the following formula each storage
The capability forecasting factor of layer, abbreviation predictive factor, formula are as follows:
Z=0.087H+0.108h+0.119Qe+0.12Re-0.007Vsh+0.116Md+0.034Rt+0.108So+0.088F
+0.11S+0.119P。
Reservoir productivity prediction model is obtained using statistical regression methods, as shown in Fig. 2, reservoir productivity prediction model is initial
Functional relation:
Y=0.7934 × e1.2952×z
Wherein, coefficient R2=0.7831, z are the capability forecasting factor, unit: side/day；Y is reservoir prediction production capacity (day
Oil production), unit: side/day.
As shown in figure 3, test function relational expression are as follows:
F=0.8721 × X1.0146
Wherein, X is the practical daily oil production of known reservoir, unit: side/day；Coefficient R2=0.8457, the degree of correlation compared with
Height, reservoir productivity prediction model is rationally, reliably.
According to the classification standard of preset reservoir productivity, the daily oil production that statistics is obtained is pre- with determining known reservoir
The factor is surveyed, as two-dimensional coordinate data point, label is successively made, forms two-dimensional coordinate system flat distribution map, as shown in Figure 4；
The line of demarcation of all kinds of boundaries is marked, the limits criteria of the comprehensive evaluation value of evaluation reservoir is read, as shown in table 5:
Table 5
In conjunction with the geological conditions in example area, according to the Reservoir Classification that the practical oil productive capacity of reservoir carries out, I class reservoir is most
It is good, higher natural production capacity can be obtained, but this kind of reservoir is less；II class reservoir is relatively preferable, when prediction production capacity is higher than 1.2 ton/days
When, have pressure break, acidification or acid fracturing condition；III class reservoir Liquid output is seldom, substantially without natural production capacity, by pressure break, acidification or
Production capacity after acid fracturing will not be too big.Known reservoir is predicted and evaluated, as shown in table 6:
Table 6
To reservoir productivity to be evaluated prediction and evaluation of classification, the evaluation parameter of the reservoir to be evaluated obtained first by statistics,
Calculate corresponding predictive factor；Reapply the estimated production capacity size that productivity prediction model calculates reservoir to be evaluated, prediction knot
Fruit is as shown in figure 5, the limits criteria referring to the predictive factor of above-mentioned known reservoir carries out evaluation of classification, evaluation result such as table 7
It is shown:
Table 7
Above-mentioned reservoir oily productivity prediction model can be according to locating geological conditions, DP technology and technological means tune
It is whole, it evaluates limits criteria of the predictive factor of reservoir in different regions and is adjustable.
Specific embodiment of the present invention is presented above, but the present invention is not limited to described embodiment.
Under the thinking that the present invention provides, to the skill in above-described embodiment by the way of being readily apparent that those skilled in the art
Art means are converted, are replaced, are modified, and play the role of with the present invention in relevant art means it is essentially identical, realize
Goal of the invention it is also essentially identical, the technical solution formed in this way is to be finely adjusted to be formed to above-described embodiment, this technology
Scheme is still fallen in protection scope of the present invention.
Claims (6)
1. a kind of sandstone reservoir oily PRODUCTION FORECASTING METHODS, which comprises the following steps:
1) porosity, permeability, shale content, the median grain diameter, reservoir middle part depth, reservoir of any reservoir of known oil reservoir are obtained
Effective thickness, formation resistivity and practical day oil production level；
2) according to depth and practical day oil production level in the middle part of the reservoir, the km well depth day oil-producing of any reservoir is determined
Amount；According to the porosity, permeability, reservoir effective thickness and formation resistivity determine any reservoir qualitative index,
Flow coefficient, packing coefficient, seepage coefficient and oil saturation；
3) with depth, reservoir effective thickness, formation resistivity, km well depth day oil-producing in the middle part of shale content, median grain diameter, reservoir
At least median grain diameter, reservoir middle part are deep in amount, qualitative index, flow coefficient, packing coefficient, seepage coefficient and oil saturation
As evaluation parameter, building is different for degree, reservoir effective thickness, formation resistivity, qualitative index, flow coefficient and oil saturation
The raw data matrix of reservoir evaluation parameter numerical value composition, obtains standardized data matrix by standardization；
4) related coefficient between any two evaluation parameter numerical value is calculated according to standardized data matrix, any one evaluation parameter
The ratio of the average value of related coefficient and the sum of the average value of related coefficient of other evaluation parameters is any evaluation parameter
Weight coefficient；
5) according to the evaluation parameter of the weight coefficient of evaluation parameter and each reservoir, the predictive factor of each reservoir is calculated；According to
The practical day oil production level and predictive factor of each reservoir, fitting obtain reservoir productivity prediction model；
6) evaluation boundary is carried out to reservoir productivity prediction model according to the classification standard of reservoir productivity；According to the storage to be evaluated of acquisition
The evaluation parameter of layer obtains the predictive factor and prediction daily oil production of reservoir to be evaluated, and determines the evaluation knot of reservoir to be evaluated
Fruit.
2. sandstone reservoir oily PRODUCTION FORECASTING METHODS according to claim 1, which is characterized in that the predictive factor
Calculation formula is as follows:
In formula, z is the predictive factor of reservoir, λjFor the weight coefficient of j-th of evaluation parameter, yjFor the standard of j-th of evaluation parameter
Numerical value after change, m are total evaluation parameter number.
3. sandstone reservoir oily PRODUCTION FORECASTING METHODS according to claim 1 or 2, which is characterized in that the phase relation
Several calculation formula are as follows:
In formula, rjkFor the related coefficient between evaluation parameter j and evaluation parameter k, rjk∈ [- 1,1],For evaluation ginseng
The average value of number j,For the average value of evaluation parameter k, n is reservoir quantity, yijIt comments for j-th for i-th of reservoir
Numerical value after the standardization of valence parameter.
4. sandstone reservoir oily PRODUCTION FORECASTING METHODS according to claim 3, which is characterized in that the weight coefficient
Calculation formula is as follows:
In formula, λjFor the weight coefficient of j-th of evaluation parameter,To be commented in correlation matrix with j-th
The weighted arithmetic average of the related coefficient of the relevant m-1 evaluation parameter of valence parameter, m are total evaluation parameter number.
5. sandstone reservoir oily PRODUCTION FORECASTING METHODS according to claim 1, which is characterized in that the standardization
Using z-score standardized method.
6. sandstone reservoir oily PRODUCTION FORECASTING METHODS according to claim 1, which is characterized in that the fitting is using system
Count the Return Law.
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