CN104899411B - A kind of reservoir productivity prediction model method for building up and system - Google Patents
A kind of reservoir productivity prediction model method for building up and system Download PDFInfo
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Abstract
The invention discloses a kind of reservoir productivity prediction model method for building up and systems.This method is analyzed according to existing core productivity analogue data, the reservoir characteristic parameter best with correlation with yield is determined using the association attributes of reservoir unique characteristics to participate in fitting modeling, relationship model formula is established in classification, and the relationship model formula for meeting purpose reservoir actual conditions is therefrom selected as reservoir productivity prediction model then in conjunction with well logging gas testing detection data.Present invention eliminates the influence of Other Engineering, directly explore the relationship of rock attribute itself and air content so that the result of evaluation has more objectivity, be in the case where available data allows more directly and accurate objectively reservoir production prediction method.
Description
Technical field
The present invention relates to oil-gas exploration and development technical field, especially a kind of reservoir productivity prediction model method for building up and it is
System.
Background technology
Correctly predicted reservoir productivity, judge reservoir can output oil gas, can realize that economic benefit is oil-gas exploration and development
One key task of technical field.In general, the oil-gas Layer only with commercial mining value, just can put into perforation to it
It is developed.Wasted to avoid blindly putting into the invalid perforation brought with producing the fund that water phenomenon is brought, reservoir productivity prediction into
Important link for logging technique work.
Specifically, following two big classifications can be summarized as using the method for well-log information predicting reservoir production capacity：
1st, theoretical calculation.This method assumes that fluid neuron network follows darcy flow law, is oozed using well-log information calculating
Static parameters, the empirically determined percolation equationk Crude Oil such as effective thickness, producing pressure differential, oily effective permeability glue in flow equation
The dynamic parameters such as degree, crude oil and rock volume coefficient, crude output is asked for finally by percolation equationk.
2nd, empirical analysis method.This method is most widely used, and operation principle is similar：From analyzing influence production capacity because
Element sets out, and influence factor is acquired using well-log information, establishes the relationship between each factor and production capacity, is configured to reflection production capacity
Characteristic quantity (such as production capacity index) determines evaluation criterion further according to formation testing (gas) result；Or it is established using major influence factors
Interpretation chart directly judges yield rank (high yield, middle production or low yield).
These two kinds of methods ooze that condition is good mainly for hole, and the big oil reservoir of mobility, fluid neuron network, which is followed or obeyed substantially, to be reached
Western seepage flow law, capability forecasting are relatively easy and relatively more accurate；But the extra-low permeability for being unsatisfactory for darcy flow theory is stored up
Layer, particularly the sufficiently complex stratum of the seepage flow mechanisms such as tight sandy gravel and shale, relevant parameter error lead to capability forecasting
Credible result degree is low.
On the other hand, in certain documents, such as《A kind of reservoir production prediction method and device based on CT pore analysis》
(application number：CN201310088247.9 it is pre- that a kind of reservoir yield based on CT pore analysis is disclosed in Patent Application Publication)
Survey method.This method chooses at least one full-hole core in interval of interest, obtains the CT test numbers of each full-hole core
According to (gross area including slabbed core sum and each slabbed core), each full diameter rock is calculated further according to CT test datas
The porosity of the heart, the yield of interval of interest is predicted according to the average value of all target porositys, and then judges aerogenesis rank.The party
Method come estimated output, is eliminated the influence factor of engineering, is not accounted for permeability using the porosity for reflecting reservoir self-characteristic
With the influence of the other parameters to production capacity such as gas saturation, the precision of result of calculation is inadequate, using there is certain limitation.
To sum up, reservoir productivity prediction model is established by existing method at present, the main problems are as follows：
1st, the narrow application range of theoretical calculation and empirical analysis method is unsuitable for this low porosity and low permeability reservoir of tight sand
Production forecast.
2nd, the simple fitting of production capacity progress practical with corresponding reservoir of acquisition porosity parameter is tested based on core analysis to obtain
Prediction model there are contingency, the influence to other characterisitic parameters of reservoir productivity considers incomplete, leads to capability forecasting result
Precision is not high, and confidence level is relatively low.
Invention content
In view of the above-mentioned problems, the present invention propose a kind of wider array of reservoir productivity prediction model method for building up of scope of application and
System.
This method includes the following steps：
Data collection step collects the basic data of purpose reservoir, including core productivity analogue data and well logging
Gas testing detection data, and therefrom determine and the relevant reservoir characteristic parameter of yield；
Yield testing procedure chooses different-thickness Reservoir Section, tests its yield under different producing pressure differentials；
Model foundation step is established under different producing pressure differentials, the corresponding yield of different reservoir effective thickness and reservoir characteristics
Relational model between parameter；
Parameter determination is determined under different producing pressure differentials, between the parameter of the relational model and reservoir effective thickness
Functional relation；
Based on the functional relation, preliminary capability forecasting result is calculated using the relational model for AOF calculation step；
Model selecting step, by comparing gas testing detection data of logging well with preliminary capability forecasting as a result, determining and purpose
The relational model that reservoir is consistent, as final productivity prediction model.
According to an embodiment of the invention, above-mentioned reservoir characteristic parameter is at least the porosity of reservoir, permeability, containing water saturation
A kind of parameter in degree, reservoir effective thickness, organic carbon content and horizontal well test segment length.
According to an embodiment of the invention, it in above-mentioned model foundation step, is established under different producing pressure differentials by lithology breakdown, no
Relational model with reservoir effective thickness between corresponding yield and reservoir characteristic parameter.
According to an embodiment of the invention, the expression formula of above-mentioned relation model is Q=a*Xm+ b*TOC+c*L, in formula, Q is production
Amount, X are reservoir characteristic parameter, and m, b, c are constant, and TOC is organic carbon content, and L tests segment length for horizontal well, and a is and reservoir
The related reservoir productivity coefficient of effective thickness.
According to an embodiment of the invention, the parameter a of above-mentioned relation model increases with reservoir effective thickness and is increased.
According to an embodiment of the invention, it is in above-mentioned AOF calculation step, the reservoir obtained via well logging gas testing detection is special
Property parameter data substitute into the relational model, calculate preliminary capability forecasting result；In the model selecting step, pass through ratio
The yield data that more preliminary capability forecasting result is obtained with well logging gas testing detection, determines the relationship mould being consistent with purpose reservoir
Type, as final productivity prediction model.
According to an embodiment of the invention, for tight sandstone reservoir, above-mentioned reservoir characteristic parameter is porosity, permeability and
Reservoir effective thickness.
According to an embodiment of the invention, it in above-mentioned model foundation step, is established under different producing pressure differentials by lithology breakdown, no
Relationship mould with reservoir effective thickness between corresponding yield and porosity and permeability and porosity and permeability product
Type.
In addition, the present invention also provides a kind of reservoir productivity prediction models to establish system, which is characterized in that including following step
Suddenly：
Data collection module, for collecting the basic data of purpose reservoir, including core productivity analogue data and
Well logging gas testing detection data, and therefrom determine and the relevant reservoir characteristic parameter of yield；
Yield test module for choosing different-thickness Reservoir Section, tests its yield under different producing pressure differentials；
Model building module, for establishing under different producing pressure differentials, the corresponding yield of different reservoir effective thickness and reservoir
Relational model between characterisitic parameter；
Parameter determination module, for determining under different producing pressure differentials, parameter and the reservoir effective thickness of the relational model
Between functional relation；
For being based on the functional relation, preliminary capability forecasting is calculated using the relational model for AOF calculation module
As a result；
Model chooses module, for by comparing log well gas testing detection data and preliminary capability forecasting as a result, determine with
The relational model that purpose reservoir is consistent, as final productivity prediction model.
According to an embodiment of the invention, above-mentioned reservoir characteristic parameter is at least the porosity of reservoir, permeability, containing water saturation
A kind of parameter in degree, reservoir effective thickness, organic carbon content and horizontal well test segment length.
Compared with prior art, one or more embodiments of the invention can have the following advantages that：
1st, modeling method proposed by the present invention is on the basis of reservoir characteristic and factors on productivity is considered, to rock core
Productivity simulation data are analyzed and are studied, and porosity, permeability, water saturation, reservoir effective thickness, organic is established in classification
The relational model of the reservoir characteristic parameters such as carbon content and horizontal well test segment length and yield, finally preferably goes out with reference to oil testing result
Meet the productivity prediction model of purpose reservoir characteristic, so as to for development plan deployment and planning provide the reliable reference of science according to
According to.
2nd, modeling method proposed by the present invention is analyzed and is studied based on rock core information so that participates in AOF calculation
The reservoir characteristic parameter value of formula fitting more meets actual reservoir feature, and the model of foundation is more reliable to a certain extent.
3rd, modeling method proposed by the present invention is on certain basis of classification, such as according to reservoir lithology or Reservoir type
Etc. productivity model is established, specific aim is stronger, and result of calculation is more accurate.
4th, parameter caused by modeling method proposed by the present invention avoids many external engineering factors calculates inaccurate disadvantage
End, it is applied widely, conventional and unconventional compactness reservoir and shale reservoir etc. are applicable in, solve conventional method not
Meet the reservoir productivity forecasting problem of the bad ground of darcy flow theory.Especially for the seepage flow machine such as tight sandy gravel and shale
It manages for sufficiently complex stratum, whole prediction effect is better than existing Forecasting Methodology.
5th, modeling method implementation steps proposed by the present invention are simple and practicable, than it is existing by theoretical method and it is simple influence
Factor returns the method more science for carrying out predicting reservoir production capacity, reliability higher.
6th, modeling method proposed by the present invention can clearly, comprehensively recognize the reservoir output condition of different reservoir feature
And the factor of reservoir productivity is influenced, implementing transformation for the later stage provides reliable reference frame.
Other features and advantages of the present invention will be illustrated in the following description, and partly becomes from specification
It is clear that understood by implementing the present invention.The purpose of the present invention and other advantages can be by wanting in specification, right
Specifically noted structure is sought in book and attached drawing to realize and obtain.
Description of the drawings
Attached drawing is used to provide further understanding of the present invention, and a part for constitution instruction, the reality with the present invention
It applies example and is used together to explain the present invention, be not construed as limiting the invention.In the accompanying drawings：
Fig. 1 is the work flow diagram of reservoir productivity prediction model method for building up used in the embodiment of the present invention；
Fig. 2 (a)~2 (f) is the purpose reservoir of regression fit in the embodiment of the present invention in different producing pressure differentials, different reservoir
Yield and porosity, permeability and hole under effective thickness ooze the relation schematic diagram between product；
Fig. 3 (a)~3 (b) be the purpose reservoir of regression fit in the embodiment of the present invention in producing pressure differential 7MPa, in difference
The relation schematic diagram of yield and porosity under reservoir effective thickness；
Fig. 4 (a)~4 (b) be the purpose reservoir of regression fit in the embodiment of the present invention in producing pressure differential 7MPa, in difference
The relation schematic diagram of yield and permeability under reservoir effective thickness；
Fig. 5 (a)~5 (b) be the purpose reservoir of regression fit in the embodiment of the present invention in producing pressure differential 7MPa, in difference
Yield under reservoir effective thickness oozes the relation schematic diagram of product with hole；
Fig. 6 (a)~6 (f) be the purpose reservoir of regression fit in the embodiment of the present invention when producing pressure differential is 7MPa, effectively
Thickness oozes the relation schematic diagram of product with porosity, permeability and hole；
Fig. 7 (a)~7 (f) be the purpose reservoir of regression fit in the embodiment of the present invention when producing pressure differential is 9MPa, not
The relation schematic diagram of product is oozed with the yield under reservoir effective thickness and porosity, permeability and hole；
Fig. 8 (a)~8 (c) be the purpose reservoir of regression fit in the embodiment of the present invention when producing pressure differential is 11MPa,
Yield under different reservoir effective thickness oozes the relation schematic diagram of product with porosity, permeability and hole；
Fig. 9 (a)~9 (f) be the purpose reservoir of regression fit in the embodiment of the present invention when producing pressure differential is 9MPa, effectively
Thickness oozes the relation schematic diagram of product with porosity, permeability and hole；
Figure 10 (a)~10 (c) be the purpose reservoir of regression fit in the embodiment of the present invention when producing pressure differential is 11MPa,
Effective thickness oozes the relation schematic diagram of product with porosity, permeability and hole.
Specific embodiment
Technical scheme of the present invention is described in detail with reference to the accompanying drawings and examples.
Fig. 1 shows the work flow diagram of reservoir productivity prediction model method for building up provided by the invention, including following
Step.
S100, data collection step.
The basic data of purpose reservoir is collected and arranges, the data obtained including core productivity simulation and well logging gas testing
Detect the data obtained.In an embodiment of the present invention, first these data can be divided into according to the lithology of reservoir different
Data volume, then therefrom determine for example reservoir porosity, permeability, water saturation, reservoir effective thickness, organic carbon content and
Horizontal well test segment length etc. and the relevant reservoir characteristic parameter of productivity analysis.For example, in an embodiment of the present invention, for hanging down
Straight well section tight sandstone reservoir, since there is no the influence of organic carbon content TOC and horizontal section length L, therefore in productivity analysis
Only consider the characterisitic parameters such as porosity Φ, permeability K and the reservoir effective thickness H of reservoir.
S200, yield testing procedure.
Different-thickness Reservoir Section is chosen, tests its yield under different producing pressure differentials.Certainly, if received in step S100
Collection core productivity analogue data has included yield data of the different-thickness Reservoir Section under different producing pressure differentials, then this
Step can also be omitted.In general, yield mentioned here refers to the individual well daily output.
S300, model foundation step.
It establishes under different producing pressure differentials, the relationship between the corresponding yield of different reservoir effective thickness and reservoir characteristic parameter
Model.It should be noted that reservoir characteristic parameter here refers not only to reservoir characteristic parameter itself, it is special reservoir can also to be included
The intermediate result that property parameter engages one another while.Such as porosity and permeability and the product of porosity and permeability.
In an embodiment of the present invention, it is preferable that a point lithology is established under different producing pressure differentials, different reservoir effective thickness pair
Relational model between the yield and reservoir characteristic parameter answered.The expression formula of relational model is Q=a*Xm+b*TOC+c*L.Formula
In, Q is yield, and X is reservoir characteristic parameter, and m, b, c are constant, and TOC is organic carbon content, and L tests segment length, a for horizontal well
For the reservoir productivity coefficient related with reservoir effective thickness.
S400, parameter determination.
It determines under different producing pressure differentials, the functional relation between the parameter of the relational model and reservoir effective thickness.
In the embodiment of the present invention, for the reservoir of same lithology under identical producing pressure differential, the parameter a values of relational model are effective with reservoir
The increase of thickness H and increase (in certain linear relationship), therefore a=f (∑ H) can be defined as.Correspondingly, relationship model formula is
Q=f (∑ H) * Xm+b*TOC+c*L。
S500, AOF calculation step.
Based on the functional relation, preliminary capability forecasting result is calculated using the relational model.It typically will be via
The data of reservoir characteristic parameter that gas testing detection obtains of logging well substitute into above-mentioned relation model Q=f (∑ H) * Xm+ b*TOC+c*L, meter
Preliminary capability forecasting result.
S600, model selecting step.
Above-mentioned relation model is verified with reference to the data that well logging gas testing detection obtains, therefrom selects and meets purpose reservoir
The relational model of objective circumstances, as reservoir productivity prediction model.
In an embodiment of the present invention, step S100 can be collected well logging gas testing detection data in porosity Φ,
The numerical value of the reservoir characteristic parameters such as permeability K, organic carbon content TOC and horizontal well test segment length L substitutes into step S300 and obtains
Relational model Q=f (∑ H) the * X obtainedm+ b*TOC+c*L calculates corresponding production Q.By comparing what is calculated by relational model
Yield and the yield of well logging gas testing detection, are determined for compliance with the relational model of purpose reservoir objective circumstances, are predicted as reservoir productivity
Model.
Embodiment one
On being further illustrated for by Sichuan somewhere, Xu jiahe tight sandstone reservoir establishes productivity prediction model below
State the operation principle of method.
It should be noted that due to being vertical well section tight sandstone reservoir, there is no organic carbon content TOC and horizontal segment length
The influence of L is spent, therefore, only need to consider porosity Φ, permeability K and reservoir effective thickness H of reservoir etc. in productivity analysis
Reservoir characteristic parameter.In other words, X ∈ (Φ, K, Φ × K), Φ are porosity, and K is permeability, and Φ * K are porosity and permeability
Product (oozing product in abbreviation hole).
First, the formation condition (coming to the surface) of Xu jiahe gas reservoir is simulated in test, takes 12 blocks of sandstone and 12 pieces respectively
Conglomerate rock sample carry out productivity simulation, reservoir effective thickness choose 5m, 10m, 15m, 20m and 30m, test its 7Mpa, 9Mpa,
The individual well daily output under 11Mpa producing pressure differentials.
Then, it according to test yield, fits under different producing pressure differentials, reservoir effective thickness, yield and porosity, infiltration
The relationship between product is oozed in rate and hole (referring to Fig. 2 (a)~2 (f)).In Fig. 2 (a)~2 (f), ordinate is production Q, single
Position is 104m3The abscissa of/d, Fig. 2 (a)~2 (b) are porosity Φ, and unit is percentage, and the abscissa of Fig. 2 (c)~2 (d) is
The abscissa of permeability K, unit ml, Fig. 2 (e)~2 (f) are Φ * K.Figure it is seen that in identical producing pressure differential and
Under the conditions of equal porosity, in different sections, reservoir productivity has larger difference for sandstone and conglomerate data point distribution.Wherein,
Conglomerate Reservoir yield is apparently higher than sandstone reservoir.Further sandstone, Conglomerate Reservoir in 7MPa, 9MPa, 11MPa are produced respectively and pressed
The relationship that yield when poor under different reservoir effective thickness oozes product with porosity, permeability and hole is analyzed, and is found：Point
It is best that lithology carries out productivity simulation effect, and reservoir yield oozes that product dependency is best, and permeability is taken second place with hole, porosity phase
To poor (referring to Fig. 3~Fig. 5).In Fig. 3 (a)~3 (b), ordinate is production Q, unit 104m3/ d, abscissa are holes
Φ is spent, unit is percentage.In Fig. 4 (a)~3 (b), ordinate is production Q, unit 104m3/ d, abscissa are permeabilities
K, unit ml.In Fig. 5 (a)~3 (b), ordinate is production Q, unit 104m3/ d, abscissa are Φ * K.
It should be noted that in above-mentioned sunykatuib analysis reservoir effective thickness only have chosen several centrifugal pump 5m, 10m,
15m, 20m and 30m, but when actually calculating, reservoir effective thickness can be used as input quantity, and variation range is wider.In order to make
The value of the reservoir effective thickness of simulation is more in line with objective reality, and the present inventor proposes to further determine that reservoir is effective
Relationship between thickness and productivity prediction model parameter.In the present embodiment, can be seen that reservoir by observing fitting formula has
Effect thickness and fitting a straight line intercept (such as product is oozed in porosity, permeability and hole) between have apparent functional relation (referring to
Fig. 6 (a)~6 (f)).Wherein, X-axis is reservoir effective thickness, and Y-axis is the slope of fitting a straight line.Moreover, the slope of fitting a straight line
It varies less, constant can be considered as, is i.e. the slope of fitting a straight line does not change with the variation of reservoir effective thickness.Therefore in data
The average value of slope under each reservoir effective thickness can be taken during processing.As can be seen from Figure 3, it is fitted in the case where producing pressure differential is 7MPa public
When exponential function is the exponential function of sandstone porosity in formula, coefficient (namely slope) is 0.0007H-8E-05, and values of powers is
3.6657, so that it is determined that the relationship under 7MPa between reservoir productivity and porosity.Similarly, it may be determined that in 7MPa, 9MPa and
Under 11MPa producing pressure differentials, sandstone, Conglomerate Reservoir production capacity ooze product Φ * with effective thickness H, porosity Φ, permeability K, hole respectively
Relationship (Fig. 7~Figure 10) between K.In Fig. 7 (a)~7 (f), ordinate is production Q, unit 104m3/ d, Fig. 7 (a)~7
(b) abscissa is porosity Φ, and unit is percentage, and the abscissa of Fig. 7 (c)~7 (d) is permeability K, unit ml, Fig. 7
(e)~7 the abscissa of (f) is Φ * K.In Fig. 8 (a)~8 (c), ordinate is production Q, unit 104m3/ d, abscissa point
It is not porosity Φ, unit is percentage, permeability K, unit ml and Φ * K.
(1) under different producing pressure differentials, the relationship of porosity Φ, reservoir effective thickness H and reservoir productivity are：
Sandstone：7MPa：QSandstone=(0.0007*H-8E-05) * Φ3.665
9MPa：QSandstone=(0.0031*H+0.0005) * Φ3.0331
11MPa：QSandstone=(0.0059*H+4E-05) * Φ2.8118
Conglomerate：7MPa：QConglomerate=(0.1133*H-0.1446) * Φ3.1942
9MPa：QConglomerate=(0.1467*H-0.1878) * Φ3.1915
(2) under different producing pressure differentials, the relationship of permeability K, reservoir effective thickness H and reservoir productivity are：
Sandstone：7MPa：QSandstone=(1.1323*H+0.0535) * K1.0779
9MPa：QSandstone=(1.5140*H-0.0761) * K0.9026
11MPa：QSandstone=(1.7657*H+0.0015) * K0.8318
Conglomerate：7MPa：QConglomerate=(1.0838*H-1.3827) * K0.7081
9MPa：QConglomerate=(1.4097*H-1.7979) * K0.7137
(3) under different producing pressure differentials, the relationship that product Φ * K, reservoir effective thickness H and reservoir productivity are oozed in hole is：
Sandstone：7MPa：QSandstone=(0.2178*H+0.0015) (Φ * K)0.8599
9MPa：QSandstone=(0.3769*H-0.0026) (Φ * K)0.7183
11MPa：QSandstone=(0.4922*H+0.0004) (Φ * K)0.6628
Conglomerate：7MPa：QConglomerate=(0.7257*H-0.9258) (Φ * K)0.5920
9MPa：QConglomerate=(0.9404*H-1.1999) (Φ * K)0.5958
In the above-described embodiments, the productivity prediction model under different producing pressure differentials, modeling principle phase are established by lithology breakdown
Together, all it is according to the conversion between core experiment acquisition analogue data completion parameter, finds out inner link, be transitioned into from experimental state
Meet practical reservoir situation, processing procedure is simple and science is reliable, changes and is asked for influencing production capacity with conventional method in the past
The thinking of all kinds of parameters avoids the deviation accumulation in calculating process.Table 1 is that the tentative prediction result of the present embodiment is real with logging well
The comparison of border testing result understands that the application result of the present invention is preferable by date comprision.
Table 1 is shown based on the well logging gas testing detection data of test section, is oozed according to single hole porosity, single permeability and hole
It is respectively the reservoir productivity under 7MPa, 9MPa, 11MPa that product, which calculates producing pressure differential,.
Table 1 must use porosity or the output statistics table of computing permeability under three sections of reservoir difference producing pressure differentials
From table 1 it follows that by above-mentioned three mouthfuls of wells in the case that producing pressure differential be 9MPa preliminary production forecast
Value and the comparison of practical capacity data are found：The yield of three sections of X1 wells palpus predicted with the product of porosity, reservoir effective thickness
Predicted value is daily 3.6474 ten thousand side, and daily 3.5545 ten thousand side is almost consistent with actual value；X2 wells and the three sections of tests of X3 wells palpus are every
Its yield, with porosity × reservoir effective thickness, permeability × reservoir effective thickness, porosity × permeability × reservoir is used to have
It is larger to imitate the yield of the prediction difference that three kinds of models such as thickness calculate.The ground such as comprehensive analysis test section formation lithology and fracture development
Matter feature and test mode it can be found that when stratum using porosity reservoir as it is main when (X1 wells porosity reservoir is more developed), it is single
The effect that capability forecasting is solely carried out using porosity is preferable, and crack is compared with the reservoir of development, lithology using calcareous sandstone for it is main when, it is logical
Cross acid fracturing test transformation so that production capacity be better than routine test (significantly find that crack is more developed on X2 wells and X3 borehole logging tool curves,
And X1 well FRACTURE CHARACTERISTICSs unobvious).It in addition, certainly also can there are the factors of other influences reservoir productivity, such as gas-bearing property to be also
Important influence factor.
Tested by productivity simulation, on the one hand illustrate reservoir can obtain industrial production capacity (or size of production capacity) in addition to hole
Porosity, permeability also have substantial connection, separately because being known as outside substantial connection with the cumulative thickness of effective reservoir and gas-bearing property quality
On the one hand it also confirms that there is preferable improvement to permeability when reservoir fissure development, can effectively reduce the hole of effective reservoir
Scale is accurate.Therefore it based on technical scheme of the present invention, for the feature of clastic rock reservoir low porosity and low permeability, is effectively pressed
The penetrating power for splitting transformation raising reservoir is to obtain the important technique measure of preferable production capacity.
In addition, the present invention also provides a kind of system for implementing above-mentioned reservoir productivity prediction model method for building up, feature exists
In, including：
Data collection module, for collecting the basic data of purpose reservoir, including core productivity analogue data and
Well logging gas testing detection data, and therefrom determine and the relevant reservoir characteristic parameter of yield；
Yield test module for choosing different-thickness Reservoir Section, tests its yield under different producing pressure differentials；
Model building module, for establishing under different producing pressure differentials, the corresponding yield of different reservoir effective thickness and reservoir
Relational model between characterisitic parameter；
Parameter determination module, for determining under different producing pressure differentials, parameter and the reservoir effective thickness of the relational model
Between functional relation；
For being based on the functional relation, preliminary capability forecasting is calculated using the relational model for AOF calculation module
As a result；
Model chooses module, for by comparing log well gas testing detection data and preliminary capability forecasting as a result, determine with
The relational model that purpose reservoir is consistent, as final productivity prediction model.
Reservoir productivity prediction modeling method and system provided by the invention are the bases in existing core productivity simulated experiment
Related borehole logging tool gas testing situation is combined on plinth, productivity model is established to classify according to different reservoir feature.The present invention proposes
It participates in intending to classify the best reservoir characteristic parameter of exploration and correlation with yield using the association attributes of reservoir unique characteristics
It builds mould jointly, eliminates the influence of Other Engineering, directly explore the relationship of rock attribute itself and air content so that the result of evaluation
More objectivity is to allow more direct and accurate objective production prediction method in available data.
Finally it should be noted that：The above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Pipe is described in detail the present invention with reference to foregoing embodiments, it will be understood by those of ordinary skill in the art that：Its according to
Can so modify to the technical solution recorded in foregoing embodiments either to which part or all technical features into
Row equivalent replacement；And these modifications or replacement, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (7)
1. a kind of reservoir productivity prediction model method for building up, includes the following steps：
Data collection step collects the basic data of purpose reservoir, including core productivity analogue data and well logging gas testing
Detection data, and therefrom determine and the relevant reservoir characteristic parameter of yield；
Yield testing procedure chooses different-thickness Reservoir Section, tests its yield under different producing pressure differentials；
Model foundation step is established by lithology breakdown under different producing pressure differentials, the corresponding yield of different reservoir effective thickness and storage
Relational model between layer characterisitic parameter；
Parameter determination determined under different producing pressure differentials, the letter between the parameter of the relational model and reservoir effective thickness
Number relationship；
Based on the functional relation, preliminary capability forecasting result is calculated using the relational model for AOF calculation step；
Model selecting step, by comparing gas testing detection data of logging well with preliminary capability forecasting as a result, determining and purpose reservoir
The relational model being consistent, as final productivity prediction model；
Wherein, the expression formula of the relational model is Q=a*Xm+ b*TOC+c*L, in formula, Q is yield, and X is reservoir characteristic parameter,
M, b, c are constant, and TOC is organic carbon content, and L tests segment length for horizontal well, and a is that the reservoir related with reservoir effective thickness produces
Energy coefficient, and the reservoir of same lithology, under identical producing pressure differential, a increases with the increase of reservoir effective thickness.
2. the method as described in claim 1, it is characterised in that：
The reservoir characteristic parameter is at least porosity, permeability, water saturation, reservoir effective thickness, the organic carbon of reservoir
A kind of parameter in content and horizontal well test segment length.
3. method as claimed in claim 1 or 2, it is characterised in that：
In the AOF calculation step, the data of reservoir characteristic parameter obtained via well logging gas testing detection are substituted into the relationship
Model calculates preliminary capability forecasting result；
In the model selecting step, by comparing preliminary capability forecasting result and the yield number of well logging gas testing detection acquisition
According to the determining relational model being consistent with purpose reservoir, as final productivity prediction model.
4. the method as described in claim 1, it is characterised in that：
For tight sandstone reservoir, reservoir characteristic parameter is porosity, permeability and reservoir effective thickness.
5. method as claimed in claim 4, it is characterised in that：
It in the model foundation step, is established under different producing pressure differentials by lithology breakdown, the corresponding production of different reservoir effective thickness
Amount and the relational model between porosity and permeability and porosity and permeability product.
6. a kind of reservoir productivity prediction model establishes system, which is characterized in that including：
Data collection module, for collecting the basic data of purpose reservoir, including core productivity analogue data and well logging
Gas testing detection data, and therefrom determine and the relevant reservoir characteristic parameter of yield；
Yield test module for choosing different-thickness Reservoir Section, tests its yield under different producing pressure differentials；
Model building module, for being established under different producing pressure differentials by lithology breakdown, the corresponding yield of different reservoir effective thickness
Relational model between reservoir characteristic parameter；
Parameter determination module, for determining under different producing pressure differentials, between the parameter of the relational model and reservoir effective thickness
Functional relation；
For being based on the functional relation, preliminary capability forecasting result is calculated using the relational model for AOF calculation module；
Model chooses module, for gas testing detection data of logging well by comparing with preliminary capability forecasting as a result, determining and purpose
The relational model that reservoir is consistent, as final productivity prediction model；
Wherein, the expression formula of the relational model is Q=a*Xm+ b*TOC+c*L, in formula, Q is yield, and X is reservoir characteristic parameter,
M, b, c are constant, and TOC is organic carbon content, and L tests segment length for horizontal well, and a is that the reservoir related with reservoir effective thickness produces
Energy coefficient, and the reservoir of same lithology, under identical producing pressure differential, a increases with the increase of reservoir effective thickness.
7. reservoir productivity prediction model as claimed in claim 6 establishes system, it is characterised in that：
The reservoir characteristic parameter is at least porosity, permeability, water saturation, reservoir effective thickness, the organic carbon of reservoir
A kind of parameter in content and horizontal well test segment length.
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