CN104153768A - Granite reservoir stratum reservoir performance evaluation method - Google Patents

Abstract

The invention discloses a granite reservoir stratum reservoir performance evaluation method. The method includes the following steps that comprehensive collection is conducted on conventional logging information, cores, image logging and comprehensive stratum evaluation results of drilled wells in a work area; a work index calculation model is established on the basis of logging engineering parameters, and a work index trend line is fit out; a work index ratio model is established, a calculation result of the work index ratio is obtained, and qualitative identification is conducted on granite reservoir stratums according to the monitored logging engineering parameters; three reservoir stratum development characterization coefficient calculation models of the rock integrity, the reservoir stratum development degree and the rock stability are established; regional granite reservoir stratum recognition and stratum reservoir performance quantitative evaluation standards are established; quantitative evaluation of reservoir stratum reservoir performance of a new exploratory area or a mature exploratory area is completed. According to the method, the granite reservoir stratum recognition accuracy is improved, quantitative optimization evaluation of the granite reservoir stratum reservoir performance is achieved, and requirements in actual geological application are met.

Description

A kind of method of evaluating Granite Reservoir storage and collection performance

Technical field

The present invention relates to petroleum natural gas exploration technical field, relate in particular to a kind of method of evaluating Granite Reservoir storage and collection performance.

Background technology

Reservoir evaluation is an important step in oil-gas exploration and development, and for granitic formation, the Fast Evaluation of the identification of its reservoir and reservoir storage and collection performance all has important directive significance for drilling safety, development plan establishment etc.To in the evaluation method of reservoir storage and collection performance, well logging, well logging have adopted various ways, but have all had the limitation of self in the past:

The current evaluation for granite reservoir, the method of best results mainly contains electromagnetism direction finder (EMG-200), X-ray computer chromatography (CT) scanner, micro-Lambda well logging, circumferential acoustilog, full hole stratum micro-resistivity imaging (FMI), a series of new instruments and the new technologies such as DSI dipole shear wave imager and under-mine TV instrument (BHTV), these method and apparatus can be measured the inclination angle of Reservoir Fracture, trend, width, length, and crack, the filling operation of hole and apparent porosity etc., even can also identify microcrack and submicroscopic crack, evaluation precision meets requirement of engineering completely.But be subject to the impact of cost control, such technology is often only for work area emphasis well, key well, once and instrument break down, tend to consume a large amount of activity durations, have a strong impact on the control of operating cost.

For conventional reservoir evaluation method, with regard to well logging, the current independent method of log data judgement Granite Reservoir that relies on is less, and research and level of application are lower, and be all the evaluation method of using for reference carbonate rock mostly, as analysed in depth the work index computation model of being set up based on well logging engineering parameter after rock bit rock breaking mechanism, according to the variation tendency between the result of calculation of this model and work index Trendline, can in drilling process, to Granite Reservoir, grow section in real time and carry out qualitative identification, its reservoir discrimination can reach 80%, but be subject to drilling well, the impact of geologic(al) factor, the method is poor for reservoir quantitative assessment effect, for the Assessment Rate of reservoir development degree lower than 50%, therefore also need further improvement.

For well logging reservoir evaluation method, conventional is mainly that the response characteristic of growing section by analytic routines log in reservoir is identified reservoir at present, and by setting up the computation model of reservoir physical parameter (formation porosity, fracture opening etc.), stratum storage and collection performance is carried out to thoroughly evaluating.These class methods have a distinct increment compared with logging method with regard to the qualitative identification aspect of reservoir, can reach 90% left and right, but more to its required calculating parameter of the quantitative assessment of reservoir, it is larger that evaluation model is affected by reservoir developmental morphology, and evaluation result is often larger with prior art imaging logging evaluation result error.

Summary of the invention

The invention provides a kind of method of evaluating Granite Reservoir storage and collection performance, the present invention sets up corresponding granite reservoir evaluation model on the basis of conventional logging, log data, and introduce fuzzy mathematics theory by updating of model analyzed and processed and improve reservoir evaluation level, described below:

A method of evaluating Granite Reservoir storage and collection performance, said method comprising the steps of:

The conventional logging in work area drilling well, well-log information, rock core, imaging logging and stratum comprehensive evaluation result are carried out to comprehensive collection;

Based on well logging engineering parameter, set up work index computation model, simulate work index Trendline; Set up work index than value model, obtain the result of calculation of work index ratio, in conjunction with the well logging engineering parameter of monitoring, qualitative identification is carried out in granite reservoir;

Build rock integrality, reservoir development degree and 3 reservoirs of rock stability and grow sign coefficient calculations model;

Set up the identification of regional granites reservoir and stratum storage and collection performance Quantitative assessment;

Complete the quantitative assessment to the reservoir storage and collection performance in the exploratory area of new exploratory area or maturation.

Described work index computation model is specially:

$W_m=[Y_J+Y_J\·{(\frac{Y_J}{a}\·\frac{R}{b})}^{\frac{1}{c}}+\frac{\mathrm{\π}}{4}\·N\·D]R\·Z$

Wherein, W _mfor work index calculated value, Y _jfor the pressure of the drill, R is rotary speed, and when Z is brill, N is moment of torsion, and D is bit diameter, and a, b are respectively the maximum the pressure of the drill of range statistics and statistics maximum (top) speed, and c is test adjustment data.

Described work index is specially than value model:

W _B＝W _m/W _N

In formula, W _bfor work index ratio, W _nfor the value of work index Trendline, W _mfor work index calculated value, ratio is less than 1 and is indicated as Reservoir Section.

Described rock integrality, reservoir development degree and 3 reservoirs of rock stability are grown sign coefficient calculations model and are specially:

$K_v={\left(\frac{V_p}{V_r}\right)}^2$

$R_F=\frac{E_{\mathrm{ma}}-E}{E_{\mathrm{ma}}}$

$R_g=K_b\·G=8.836{\mathrm{\ρ}}_b^2(V_P^2{V}_S^2-\frac{4}{3}{V}_S^4)$

Wherein, K _vfor rock integrity factor, V _pfor the longitudinal wave velocity that well logging obtains, V _rfor the theoretical velocity of sound of rock matrix, R _ffor reservoir development degree coefficient, E _mafor the theoretical young's modulus of elasticity of rock matrix, E is for calculating the young's modulus of elasticity of gained, R _gfor Rock Mass Stability property coefficient, V _sfor well logging sound wave shear wave velocity; K _bbulk modulus for rock; G is the modulus of shearing of rock; ρ _bfor density of earth formations.

Describedly set up regional granites reservoir identification and be specially with stratum storage and collection performance Quantitative assessment:

1) to the well logging of work area key well, Logging estimation model result carry out data normalization processing;

2) set up fuzzy evaluation matrix;

3) calculate the weights of each evaluating;

4) set up assessment parameter matrix;

5) assessment parameter and the stratum final appraisal results of surveying well logging are analyzed, statistics obtains being applicable to the identification of granite reservoir and stratum storage and collection performance Fast Evaluation standard in work area.

The beneficial effect of technical scheme provided by the invention is:

1) the present invention evaluates reservoir for Granite Reservoir introducing work index evaluation model first, thereby realizes in drilling process the qualitative identification in the reservoir on such stratum.

2) the present invention grows the impact on rock self mechanical property from granite reservoir, the reservoir development that grow reservoir that utilizes rock mechanics parameters to build can to characterize of novelty characterizes coefficient, has reduced Using Conventional Logs and has evaluated the granite reservoir problems of separating more.

3) the present invention proposes to utilize the means of fuzzy mathematics effectively in conjunction with well logging, well logging information first, improved the accuracy of identification of granite reservoir, and realized the quantitative optimization evaluation to Granite Reservoir storage and collection performance, met the needs in actual GEOLOGICAL APPLICATION.

4) the present invention saves time and cost more compared with high-precision formation evaluation instrument, can be after Xin Jing stops boring the very first time utilize well site conventional logging, well-log information to evaluate Granite Reservoir.

Accompanying drawing explanation

Fig. 1 evaluates the flow chart of Granite Reservoir storage and collection performance based on conventional logging, log data;

Fig. 2 is well logging work index RESERVOIR RECOGNITION result map;

Fig. 3 is that logging Reservoir Evaluation model is processed result map;

Fig. 4 is for surveying well logging overall merit Granite Reservoir storage and collection performance result map.

The specific embodiment

For making the object, technical solutions and advantages of the present invention clearer, below embodiment of the present invention is described further in detail.

It is basis that the embodiment of the present invention be take conventional logging, well-log information, and relying on fuzzy mathematics is that enforcement means have been carried out RESERVOIR RECOGNITION and evaluation to Bohai Bay Oil work area Granite Reservoir, and idiographic flow is shown in Fig. 1, described below:

101: work area analysis;

Before new well is carried out to granite reservoir evaluation, first need the conventional logging in work area drilling well, well-log information, rock core, imaging logging and stratum comprehensive evaluation result to carry out comprehensive collection.

102: based on well logging engineering parameter, set up work index computation model, simulate work index Trendline; Set up work index than value model, obtain the result of calculation of work index ratio, in conjunction with the well logging engineering parameter of monitoring, qualitative identification is carried out in granite reservoir;

After having analysed in depth the acting principle of drill bit when Granite Reservoir creeps into, based on well logging engineering parameter, can set up work index computation model, as shown in Equation 1.According to the work index value of the Granite Reservoir section having calculated, can simulate work index Trendline.

$W_m=[Y_J+Y_J\·{(\frac{Y_J}{a}\·\frac{R}{b})}^{\frac{1}{c}}+\frac{\mathrm{\π}}{4}\·N\·D]R\·Z$ Formula 1

Wherein, W _mfor work index calculated value, Y _jfor the pressure of the drill, R is rotary speed, and when Z is brill, N is moment of torsion, and D is bit diameter, and a, b are respectively the maximum the pressure of the drill of range statistics and statistics maximum (top) speed.C is test adjustment data, concrete adjustment method is for first the good well of chosen area hole condition is as target well, and consideration is under identical drilling technology condition, when rock strength is identical, the rock institute work of every broken unit volume is close, and its work index value also should be close.Therefore can in target well, choose two sections of drilling wells, the relatively stable and similar well section of geological condition, suppose that respectively these two well sections of value substitution of a series of coefficient c calculate its work index value, ultimate analysis two well section work index ratios, ratio approaches the true value that 1 o'clock corresponding c value is coefficient c most.

Utilization formula 1 is calculated the work index value of gained and the relation between work index Trendline, well logging engineering parameter in conjunction with monitoring can carry out fast qualitative identification to granite reservoir, as shown in Figure 2, the well section of work index value on the Trendline left side is the region that grow reservoir, and it is far away that work index value departs from Trendline, and educate all the more reservoir.

For eliminating the work index difference between different wells, therefore introducing work index is as shown in Equation 2 than value model, and this model has not only been eliminated the influence factor of environment, also makes to differentiate result more directly perceived, thinks by statistics, and this value is less than 1 and is indicated as Reservoir Section.

W _b=W _m/ W _nformula 2

In formula, W _bfor work index ratio, W _nfor the value of work index Trendline, W _mfor work index calculated value.

According to formula 1 and formula 2, can obtain the result of calculation of work index ratio, the reservoir evaluation data of this result and region key well is contrasted to statistics, obtain work index reservoir evaluation standard as shown in table 1.Table 1 is stratum, the granite buried hill storage and collection performance work index evaluation criterion in oil field, the Bohai Sea, different regions, and its excursion is different.

Table 1 work index reservoir evaluation standard

In drilling course, can utilize formula 2 acquired results, reservoir is identified fast, and it is preliminary with boring semi-quantitative assessment to utilize standard shown in table 1 to carry out corresponding reservoir.

In Fig. 2, No. 1, No. 3, No. 5 floor position work index values are all on the work index Trendline left side, and work index ratio is all less than 1, are designated as Reservoir Section, comparatively identical with the contrast of finishing drilling well log interpretation; No. 2, No. 4, No. 6 floor position work index ratio is greater than 1, and work index obviously keeps right compared with work index Trendline, is obvious compacted zone feature, consistent with result of log interpretation.

103: build rock integrality, reservoir development degree and 3 reservoirs of rock stability and grow sign coefficient calculations model;

Based on Using Conventional Logs, according to the well logging rock mechanics parameters empirical formula shown in formula 3～formula 6, calculate and form the required rock mechanics parameters of reservoir development characteristic parameter:

The poisson's ratio of rock (μ):

$\mathrm{\μ}=\frac{\mathrm{\Δ}{t_s}^2-2\mathrm{\Δ}{t_c}^2}{2(\mathrm{\Δ}{t_s}^2-\mathrm{\Δ}{t_c}^2)}$ Formula 3

The modulus of shearing of rock (G):

$G=({\mathrm{\ρ}}_b/{\mathrm{\Δt}}_s^2)\×\mathrm{\β}$ Formula 4

The young's modulus of elasticity of rock (E):

$\begin{array}{c}E=2G(1+\mathrm{\μ})\\ =\frac{{\mathrm{\ρ}}_b}{{\mathrm{\Δt}}_s^2}\left(\frac{{3\mathrm{\Δt}}_s^2-4{\mathrm{\Δt}}_c^2}{{\mathrm{\Δt}}_s^2-{\mathrm{\Δt}}_c^2}\right)\×\mathrm{\β}\end{array}$ Formula 5

Bulk modulus (the K of rock _b):

$K_b={\mathrm{\ρ}}_b(\frac{1}{{\mathrm{\Δt}}_c^2}-\frac{4}{3{\mathrm{\Δt}}_s^2})\×\mathrm{\β}$ Formula 6

In above formula, Δ t _c, Δ t _sbe respectively the P-wave And S time difference on stratum, ρ _bfor density of earth formations, β is the unit conversion factor, β=9.290304 * 10 ⁷, μ is the poisson's ratio of rock, characterizes the ratio of rock lateral strain and longitudinal strain, is the elastic constants of reflection material lateral deformation.

After having calculated Related Rocks mechanics parameter, the reservoir development that can use formula 7～formula 9 structures can better react reservoir development characterizes coefficient, be rock integrality, reservoir development degree and 3 coefficients of rock stability, wherein in reservoir development section, rock integrity factor step-down, reservoir development degree coefficient uprises, Rock Mass Stability property coefficient step-down, and in the response characteristic of granite reservoir growth place as shown in Figure 3.

$K_v={\left(\frac{V_p}{V_r}\right)}^2$ Formula 7

$R_F=\frac{E_{\mathrm{ma}}-E}{E_{\mathrm{ma}}}$ Formula 8

$R_g=K_b\·G=8.836{\mathrm{\ρ}}_b^2(V_P^2{V}_S^2-\frac{4}{3}{V}_S^4)$ Formula 9

Wherein, K _vfor rock integrity factor, V _pfor the longitudinal wave velocity that well logging obtains, V _rfor the theoretical velocity of sound of rock matrix, R _ffor reservoir development degree coefficient, E _mafor the theoretical young's modulus of elasticity E of rock matrix is for calculating the young's modulus of elasticity of gained, R _gfor Rock Mass Stability property coefficient, V _sfor well logging sound wave shear wave velocity.

In Fig. 3, No. 1 floor position rock integrality, stability coefficient is obviously on the low side, and reservoir development degree coefficient is higher, shows that this section is better Reservoir Section, by result of log interpretation, proves that this section is fracture development section, has good storage and collection performance; No. 2～No. 4 intervals, rock integrality, stability coefficient increase, and reservoir development degree coefficient reduces obviously, indicates this type of interval Reservoir not good, and rear well log interpretation proves, and these 3 layer positions are compacted zone.

104: set up the identification of regional granites reservoir and stratum storage and collection performance Quantitative assessment;

After having set up the well logging of granite reservoir, well logging evaluation model (formula 1～formula 2, formula 7～formula 8), use suc as formula the fuzzy mathematics computational methods shown in 10～formula 13, can obtain 1 and survey well logging assessment parameter.

1) normalization of parameter

Because geology, the physical responses mechanism of well logging, logging method is different, and also there is certain difference in the measuring state separately of different instruments, thereby cause each parameter on dimension, the order of magnitude, also to have comparatively significantly difference.Therefore, to well logging, before logging parameters analyzes, must it be normalized according to reservoir to the response characteristic of curve separately, normalization principle is better with reservoir Reservoir, and its normalized value levels off to 1 for principle, that is:

$\left\{\begin{array}{c}{X}^{\′}=(X-X_{\min })/(X_{\max }-X_{\min })\\ X^{\′\′}=1-(X-X_{\min })/(X_{\max }-X_{\min })\end{array}\right.$ Formula 10

In formula, X ' is R _fnormalized value, X " is respectively K _v, R _gand W _bnormalized value, dimensionless; X is sample point curve values; X _minfor curve minimum; X _maxfor curve maximum value.

2) weights coefficient calculations

According to principles of fuzzy mathematics, first need to form a Judgement Matrix before calculating each parameter weights, what select due to the present invention is four parameter evaluation models, if therefore hypothesis has n sample, can set up the Judgement Matrix that 4 row n are listed as:

$R=\left[\begin{array}{cccc}{X}_{11}& X_{12}& \·\·\·& X_{1n}\\ X_{21}& X_{22}& \·\·\·\·& X_{2n}\\ X_{31}& X_{32}& \·\·\·& X_{3n}\\ X_{41}& X_{42}& \·\·\·& X_{4n}\end{array}\right]$ Formula 11

Above-mentioned four kinds of parameters are when comprehensive judgement reservoir, and because its sensitivity to reservoir is incomplete same, responsiveness there are differences, therefore when quantitatively sentencing knowledge reservoir, need to carry out rational weight allocation according to its importance, the weight calculation formula of each curve is:

$\left\{\begin{array}{c}{\mathrm{\ω}}_{\mathrm{ij}}=X_{\mathrm{ij}}/\underset{i=1}{\overset{4}{\mathrm{\Σ}}}{X}_{\mathrm{ij}}\\ W_i=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\ω}}_{\mathrm{ij}}/n\end{array}\right.$ Formula 12

In formula, ω _ijit is the weights coefficient (i=1～4, j=1～n) on i bar curve, a j sample point; X _ijfor the value in Judgement Matrix; N is total number of a certain curve sample point; W _iit is the weights coefficient of i bar curve.

3) survey the foundation of record assessment parameter

Utilize formula 12 can construct four reservoirs and sentence the weights coefficient set A=(W that knows curve ₁, W ₂, W ₃, W ₄), according to product summation algorithm, can obtain the stratum of buried hill each sample point of granitic formation and survey record assessment parameter matrix B:

$\begin{array}{c}B=A\⊗R\\ =(W_1,W_2,W_3,W_4)\⊗\left[\begin{array}{cccc}{X}_{11}& X_{12}& \·\·\·& X_{1n}\\ X_{21}& X_{22}& \·\·\·& X_{2n}\\ X_{31}& X_{32}& \·\·\·& X_{3n}\\ X_{41}& X_{42}& \·\·\·& X_{4n}\end{array}\right]\\ =\left(\begin{array}{c}{W}_1\·X_{11}+W_2\·X_{21}+W_3\·X_{31}+W_4\·X_{41}\\ W_1\·X_{12}+W_2\·X_{22}+W_3\·X_{32}+W_4\·X_{42}\\ \·\·\·\·\·\·\\ W_1\·X_{1n}+W_2\·X_{2n}+W_3\·X_{3n}+W_4\·X_{4n}\end{array}\right)=\left(\begin{array}{c}{B}_1\\ B_2\\ \·\·\·\\ B_n\end{array}\right)\end{array}$ Formula 13

B in formula _i(i=1,2 ..., record assessment parameter is surveyed on stratum n) being on each sample point.

According to the analysis to parameter normalization process, known this assessment parameter value is larger, and reservoir is educated all the more, otherwise reservoir is got over agensis.By observing the situation of change of Comprehensive Evaluation of Well Logging parameter, can effectively to reservoir, carry out qualitative evaluation.

105: complete the quantitative assessment to the reservoir storage and collection performance in the exploratory area of new exploratory area or maturation.

For new exploratory area, can utilize said method to carry out qualitative identification to reservoir, but in comparatively ripe exploratory area, can to region key well, survey respectively the calculating of record assessment parameter, again the comprehensive evaluation results such as the imaging of result and each artesian well, rock core are contrasted to statistics, obtain Granite Reservoir storage and collection performance Quantitative assessment as shown in table 2, and then reservoir is carried out to further quantitative assessment.Table 2 is stratum, the granite buried hill storage and collection performance Quantitative assessment in oil field, the Bohai Sea, different regions, and its excursion is different.

Table 2 regional granites reservoir storage and collection performance Quantitative assessment

Use above-mentioned calculation process to calculate the survey well logging assessment parameter of new well, accomplish that the very first time carries out qualitative evaluation to reservoir, recycling work area standard is carried out the quantitative assessment of reservoir storage and collection performance to it, finally completes evaluation.

To the well of newly opening, use work index computational methods, first in drilling process, preliminary identification is fast carried out in granite reservoir, stopping boring after well logging finishes, using above-mentioned computational methods to obtain the survey well logging assessment parameter of this well, utilize evaluation criterion shown in table 2, Granite Reservoir is carried out to further qualitative identification and quantitative assessment, result as shown in Figure 4.Road can be used for the qualitative evaluation to reservoir in figure, " to survey record overall merit ", and the overall merit coefficient in this road is larger, shows that stratum storage and collection performance is better, otherwise fine and close." reservoir development degree " road is mainly used in the quantitative assessment to stratum storage and collection performance, and this road is divided into 1～4 four grade by storage and collection performance, and the Reservoir in corresponding table 2 is very good, better, general and poor respectively.Can find out, survey result and the imaging logging result of well logging overall merit reservoir and coincide, compared with conventional logging explanation results precision, be significantly improved.As at 1653m～1658m, conventional logging explains that this section, for compacted zone, can find out that this section of storage and collection performance is better but record overall merit coefficient by surveys, really consistent at the high electrical conductive joint of this section with imaging logging.

It will be appreciated by those skilled in the art that accompanying drawing is the schematic diagram of a preferred embodiment, the invention described above embodiment sequence number, just to describing, does not represent the quality of embodiment.

The foregoing is only preferred embodiment of the present invention, in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.

Claims (5)

1. a method of evaluating Granite Reservoir storage and collection performance, is characterized in that, said method comprising the steps of:

The conventional logging in work area drilling well, well-log information, rock core, imaging logging and stratum comprehensive evaluation result are carried out to comprehensive collection;

Based on well logging engineering parameter, set up work index computation model, simulate work index Trendline; Set up work index than value model, obtain the result of calculation of work index ratio, in conjunction with the well logging engineering parameter of monitoring, qualitative identification is carried out in granite reservoir;

Build rock integrality, reservoir development degree and 3 reservoirs of rock stability and grow sign coefficient calculations model;

Set up the identification of regional granites reservoir and stratum storage and collection performance Quantitative assessment;

Complete the quantitative assessment to the reservoir storage and collection performance in the exploratory area of new exploratory area or maturation.

2. a kind of method of evaluating Granite Reservoir storage and collection performance according to claim 1, is characterized in that, described work index computation model is specially:

$W_m=[Y_J+Y_J\·{(\frac{Y_J}{a}\·\frac{R}{b})}^{\frac{1}{c}}+\frac{\mathrm{\π}}{4}\·N\·D]R\·Z$

Wherein, W _mfor work index calculated value, Y _jfor the pressure of the drill, R is rotary speed, and when Z is brill, N is moment of torsion, and D is bit diameter, and a, b are respectively the maximum the pressure of the drill of range statistics and statistics maximum (top) speed, and c is test adjustment data.

3. a kind of method of evaluating Granite Reservoir storage and collection performance according to claim 1, is characterized in that, described work index is specially than value model:

W _B＝W _m/W _N

In formula, W _bfor work index ratio, W _nfor the value of work index Trendline, W _mfor work index calculated value, ratio is less than 1 and is indicated as Reservoir Section.

4. a kind of method of evaluating Granite Reservoir storage and collection performance according to claim 1, is characterized in that, described rock integrality, reservoir development degree and 3 reservoirs of rock stability are grown sign coefficient calculations model and are specially:

$K_v={\left(\frac{V_p}{V_r}\right)}^2$

$R_F=\frac{E_{\mathrm{ma}}-E}{E_{\mathrm{ma}}}$

$R_g=K_b\·G=8.836{\mathrm{\ρ}}_b^2(V_P^2{V}_S^2-\frac{4}{3}{V}_S^4)$

Wherein, K _vfor rock integrity factor, V _pfor the longitudinal wave velocity that well logging obtains, V _rfor the theoretical velocity of sound of rock matrix, R _ffor reservoir development degree coefficient, E _mafor the theoretical young's modulus of elasticity of rock matrix, E is for calculating the young's modulus of elasticity of gained, R _gfor Rock Mass Stability property coefficient, V _sfor well logging sound wave shear wave velocity; K _bbulk modulus for rock; G is the modulus of shearing of rock; ρ _bfor density of earth formations.

5. a kind of method of evaluating Granite Reservoir storage and collection performance according to claim 1, is characterized in that, describedly sets up regional granites reservoir identification and is specially with stratum storage and collection performance Quantitative assessment:

1) to the well logging of work area key well, Logging estimation model result carry out data normalization processing;

2) set up fuzzy evaluation matrix;

3) calculate the weights of each evaluating;

4) set up assessment parameter matrix;

5) assessment parameter and the stratum final appraisal results of surveying well logging are analyzed, statistics obtains being applicable to the identification of granite reservoir and stratum storage and collection performance Fast Evaluation standard in work area.