CN105093313A - Predicting method and apparatus for production capacity of single well in Karst oil-gas reservoir - Google Patents

Abstract

The invention discloses a predicting method and apparatus for production capacity of a single well in a karst oil-gas reservoir. The method comprises the steps of acquiring three-dimension earthquake files concerning a karst oil gas area, dividing the karst oil gas area into a plurality of geological units, taking the geological units as research units and extracting M geological parameters of various sampling wells, selecting N geological parameters that are mostly relevant to early period production capacity from the M geological parameters to serve as preferred geological parameters, building a correlation between the early period production capacity and the property of an important storing layer, setting a linear relationship between the preferred geological parameters and the important storing layer, building a non-linear relation equation between early period production data and the preferred geological parameters, using the Marquardt's algorithm to determine the undetermined coefficients of the non-linear relation equation so as to determine the non-linear relation equation, and predicting the production capacity of all sampling wells based on the non-linear relation equation and the preferred geological parameter values.

Description

A kind of Karst-type oil reservoir individual well oil and gas productivity prediction method and device

Technical field

The present invention relates to oil-gas exploration and development technology, particularly, relate to a kind of Karst-type oil reservoir individual well oil and gas productivity prediction method and device.

Background technology

The hydrocarbon resources richness of nearly 1/3rd is stored in carbonatite in the world, and in recent years, carbonatite becomes the key areas of oil-gas exploration gradually.Along with the development of geophysical exploration technology, 3-d seismic exploration technology, as a kind of means, plays a part more and more important in oil-gas exploration, and the application of geologic parameter in reservoir prediction is also more and more extensive.

Carbonate Rocks Karst-type reservoir has very strong nonuniformity, this nonuniformity causes using traditional Productivity Formulae to carry out capability forecasting, the productivity prediction model of petroclastic rock cannot be applied directly in Karst-type reservoir, causes certain difficulty to Karst-type reservoir prediction.

In Karst-type oil reservoir, crack and solution cavity add the porosity and permeability of reservoir rock, cause Karst-type reservoir hole to be oozed and change greatly.In the metamorphosis of rock and crack, the inertia resistance of fluid flowing adds the difficulty of oil and gas productivity prediction.When tradition oil gas PRODUCTION FORECASTING METHODS is applied to Karst-type oil reservoir, high-precision forecast difficulty is larger.

Geologic parameter refers to by prestack or post-stack seismic data, through the geometric shape of relevant seismic event, kinematics character, dynamic characteristic and statistics feature that mathematic(al) manipulation is derived.Based on different application purposes, geologic parameter has different sorting techniques.At present, simple extraction amplitude, frequency, the geologic parameter such as relevant, can only qualitative or sxemiquantitative Study In Reservoir information, the output doping reservoir that can not be meticulous.

In sum, under current research conditions, when utilizing geologic parameter to predict Karst-type oil reservoir oil-production capacity, higher precision of prediction can not be reached.

Summary of the invention

The object of the invention is to overcome above shortcomings in prior art, and the method and apparatus that a kind of Karst-type oil reservoir individual well initial productivity is predicted is provided, cannot the problem of prognostic reserves accurately to solve in prior art.

The invention provides a kind of Karst-type oil reservoir individual well oil and gas productivity prediction method, described method comprises:

Step 1, gathers three dimensional seismic data for Karst-type oil reservoir target area; The objective interval of described target area is determined according to described three dimensional seismic data;

Step 2, extracts the coherent body of each objective interval, dessert attribute; Determine the fracture river development situation in described target area according to described coherent body and dessert attribute, according to described fracture river development situation, described target area is divided into multiple geologic unit;

Step 3, with M geologic parameter of described geologic unit each sample well for research unit extracts; Described geologic parameter comprises seismic properties, beading area, bottom hole location apart from Dominated Factors distance;

Step 4, respectively data normalization pre-service is carried out to the initial potential of each sample well and the value of each geologic parameter, respectively correlation analysis is carried out to the value being normalized pretreated initial potential and M geologic parameter, from a described M geologic parameter, selects the N number of geologic parameter the highest with described initial potential degree of correlation as preferred geologic parameter;

Step 5, treble medium physical model and output mathematical model is set up, the corresponding relation of initial potential and important reservoir attribute according to described treble medium physical model and described output Mathematical Models according to the oilwell performance data in described target area, static data and crude oil property data; Described important reservoir attribute comprises elastic storativity ratio and interporosity flow coefficient;

Step 6, sets described preferred geologic parameter and described important reservoir attribute is linear relationship; The nonlinear relationship equation between described initial potential data and described preferred geologic parameter is set up according to the corresponding relation of this linear relationship and described initial potential and important reservoir attribute;

Step 7, use Marquardt method determines the undetermined coefficient in described relation equation, thus determines described nonlinear relationship equation; Value prediction according to this relation equation and preferred geologic parameter goes out each sample well capacity.

Said method can also have following characteristics:

The value of described M is more than or equal to 4; The value of described N is 2,3 or 4.

Said method can also have following characteristics:

Described seismic properties comprises: frequency decay number percent, RMS amplitude, amplitude change rate, dessert maximal value, dessert minimum value, dessert geometrical mean.

Said method can also have following characteristics:

Correlation analysis in described step 4 comprises use Pearson came correlation analysis and Si Baiman correlation analysis carries out correlation analysis.

Said method can also have following characteristics:

The physical model that described treble medium physical model is is standard with dielectric-dielectric-Fractured reservoir.

Said method can also have following characteristics:

Also comprise in described step 1 and high s/n ratio, high resolving power and high fidelity process are carried out to the three dimensional seismic data collected.

Present invention also offers a kind of Karst-type oil reservoir individual well oil and gas productivity prediction device, comprising:

Seismic data acquisition unit, for gathering three dimensional seismic data for Karst-type oil reservoir target area; The objective interval of described target area is determined according to described three dimensional seismic data;

Seismic data interpretation unit, extracts coherent body, the dessert attribute of each objective interval for frequency division; Determine the fracture river development situation in described target area according to described coherent body and dessert attribute, according to described fracture river development situation, described target area is divided into multiple geologic unit;

Geologic parameter extraction unit, for M geologic parameter of described geologic unit each sample well for research unit extracts; Described geologic parameter comprises seismic properties, beading area, bottom hole location apart from Dominated Factors distance;

Geologic parameter selection unit, for carrying out data normalization pre-service respectively to the initial potential of each sample well and the value of each geologic parameter, respectively correlation analysis is carried out to the value being normalized pretreated initial potential and M geologic parameter, from a described M geologic parameter, selects the N number of geologic parameter the highest with described initial potential degree of correlation as preferred geologic parameter;

Modeling unit, for setting up treble medium physical model and output mathematical model according to the oilwell performance data in described target area, static data and crude oil property data, the corresponding relation of initial potential and important reservoir attribute according to described treble medium physical model and described output Mathematical Models; Described important reservoir attribute comprises elastic storativity ratio and interporosity flow coefficient;

Predicting unit, for setting described preferred geologic parameter and described important reservoir attribute is linear relationship; The nonlinear relationship equation between described initial potential data and described preferred geologic parameter is set up according to the corresponding relation of this linear relationship and described initial potential and important reservoir attribute; Use Marquardt method determines the undetermined coefficient in described relation equation, thus determines described nonlinear relationship equation; Value prediction according to this relation equation and preferred geologic parameter goes out each sample well capacity.

Said apparatus can also have following characteristics:

The value of described M is more than or equal to 4, and the value of described N is 2,3 or 4.

Said apparatus can also have following characteristics:

Described seismic properties comprises: frequency decay number percent attribute, RMS amplitude attribute, amplitude change rate attribute, amplitude change rate, dessert maximal value, dessert minimum value, dessert geometrical mean.

Said apparatus can also have following characteristics:

Described geologic parameter selection unit, also for using Pearson came correlation analysis and Si Baiman correlation analysis to carry out correlation analysis.

Compared with prior art, the present invention can single well capacity in accurately predicting Karst-type oil reservoir, effectively promotes the reasonable development of Karst-type oil reservoir in the present invention.

Accompanying drawing explanation

Fig. 1 is Karst-type oil reservoir individual well RECOVERABLE RESERVE PREDICTION method flow diagram.

Specific embodiment

For making the object of the embodiment of the present invention, technical scheme and advantage clearly, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.It should be noted that, when not conflicting, the embodiment in the application and the feature in embodiment can combination in any mutually.

Have foregoing description known, in Karst-type oil reservoir, crack and solution cavity add the porosity and permeability of reservoir rock, cause Karst-type reservoir hole to be oozed and change greatly.In the metamorphosis of rock and crack, the inertia resistance of fluid flowing adds the difficulty of oil and gas productivity prediction.When tradition oil gas PRODUCTION FORECASTING METHODS is applied to Karst-type oil reservoir, high-precision forecast difficulty is larger.Based on this, the present invention proposes a kind of method and device of Karst-type oil reservoir individual well oil and gas productivity prediction, preferred geologic parameter, and according to the initial potential data set up and the relation equation preferably between geologic parameter, accurately predicting production capacity.

Below in conjunction with accompanying drawing, the present invention is described in detail.

Embodiment one

The embodiment of the present invention provides a kind of method of Karst-type oil reservoir individual well oil and gas productivity prediction, and as shown in Figure 1, the method comprises:

Step 1, gathers three dimensional seismic data for Karst-type oil reservoir target area; According to the objective interval of above-mentioned three dimensional seismic data determination target area.

Step 2, frequency division extracts coherent body, the dessert attribute of each objective interval; According to the fracture river development situation in coherent body and dessert attribute determination target area, according to this fracture river development situation, target area is divided into multiple geologic unit.

Step 3 take geologic unit as M the geologic parameter that research unit extracts each sample well; Geologic parameter comprises seismic properties, beading area, bottom hole location apart from Dominated Factors distance;

Step 4, respectively data normalization pre-service is carried out to the initial potential of sample well and the value of each geologic parameter, respectively correlation analysis is carried out to the value being normalized pretreated initial potential and M geologic parameter, from M geologic parameter, selects the N number of geologic parameter the highest with initial potential degree of correlation as preferred geologic parameter.

Step 5, treble medium physical model and output mathematical model is set up, according to the corresponding relation of above-mentioned treble medium physical model and the initial potential of output Mathematical Models and important reservoir attribute according to the oilwell performance data in target area, static data and crude oil property data; Above-mentioned important reservoir attribute comprises elastic storativity ratio and interporosity flow coefficient.

Step 6, sets preferred geologic parameter and important reservoir attribute is linear relationship; Corresponding relation according to this linear relationship and initial potential and important reservoir attribute sets up initial potential data and the relation equation preferably between geologic parameter.

Step 7, use Marquardt method determines the undetermined coefficient in above-mentioned relation equation, thus determines relation equation; Value prediction according to this relation equation and preferred geologic parameter goes out sample well capacity.

The following detailed description of each step of said method.

In the step 1 of said method, the method gathering three dimensional seismic data comprises: burying explosive (when using explosive source) by measurement, brill shallow well hole, burying wave detector, arrange that cable to a few procedure of instrument truck receives and transmitting seismic signal obtains three dimensional seismic data.After gathering three dimensional seismic data, also high s/n ratio, high resolving power and high fidelity process are carried out to the three dimensional seismic data collected.

Comprise according to the method for the objective interval of three dimensional seismic data determination target area in step 1: use seismic interpretation software, adopt wiggle variable area Faxian to show seismic section, carry out seismic interpretation; Utilize well logging sound wave, densimetric curve combines with seismic data, make composite traces, complete the demarcation to earthquake interval.

Explanation about interval comprises following content:

By browse line, road, etc. time the seismic data such as section, understand the reflectance signature of earthquake reflected wave in work area, fundamental purpose layer stratal configuration, the time domain section of local structure, plane configuration, determine concrete thought and the method for Fine structural interpretation.

Continuity according to earthquake reflected wave carries out comparative interpretation respectively, for the reflection wave that continuity is poor, according to principle descending, from coarse to fine, from the well profile excessively demarcated, set up the general explanation framework of the whole district, for the good reflection wave of continuity, the Seed Points method for tracing utilizing three-dimensional visualization to explain, carries out automatic tracing contrast.

The consistance of contrast is followed the trail of in order to strengthen earthquake reflected wave, when carrying out manual comparative interpretation, use automatic tracing technology as far as possible, ensure that the feature such as crest, trough, zero phase that seismic event is followed the trail of is consistent in the whole district, for well-shooting joint inversion, reservoir parameter extracts and reservoir prediction, provides accurate seismic interpretation layer position.

The achievement of comparative interpretation, utilizes three-dimensional visualization means to carry out checking and contrasting.

In the step 2 of said method, frequency division extracts coherent body, the dessert attribute of each objective interval, observes the distribution situation in tomography, river course in the plane.According to the distribution situation of river course, fracture in plane, divide the geologic unit of target area.

Coherent technique is a kind of method predicting similarity, can export the attributes such as the similarity between seismic trace, structure dip and position angle.The change of similarity often with tomography, to deposit equal geological phenomenon relevant, the general corresponding higher similar value of continuous print geologic body, there will be exceptional value when stratum is discontinuous.3-D seismics amplitude data body is converted into related coefficient data volume, unexpected incoherent abnormal occurrence.

Dessert is obtained by instantaneous frequency and reflection strength (being also referred to as instantaneous amplitude or amplitude envelope).From the ratio that the angle dessert of mathematics is reflection strength and root mean square instantaneous frequency.

Reflection strength, also known as instantaneous amplitude, amplitude envelope, is the square root of a certain moment seismic signal gross energy.Its value is always positive, is the amplitude information irrelevant with phase place, can provides the attributional analysis of information for amplitude anomaly of acoustic impedance difference, is used for detecting tomography, river course, subterranean deposit, thin layer tuning effect and from complex wave, differentiating thick layer reflection.Reflection strength is identification to bright spot and dim spot and effective instrument, and the horizontal change of reflection strength is usually relevant with the gathering of lithology and hydrocarbon, and the acute variation of reflection strength also changes relevant with the deposition characteristics such as river course.Its mathematical definition is:

$A\left(t\right)=\sqrt{f^2\left(t\right)+h^2\left(t\right)}$

Instantaneous frequency is the rate of change of instantaneous phase to the time, and instantaneous phase is the arc tangent of empty seismic trace and real seismic trace ratio.Computing formula is:

$\mathrm{\ω}\left(t\right)=\frac{d\mathrm{\θ}\left(t\right)}{dt}$ Or $\mathrm{\ω}\left(t\right)=\frac{1}{f^2\left(t\right)+h^2\left(t\right)}\left(f\right(t)\frac{h\left(t\right)}{dt}-h(t\left)\frac{f\left(t\right)}{dt}\right)$

Its codomain is (-f ,+f).

But most of instantaneous phase is just all.Instantaneous frequency can provide the effective frequency absorption effect of lineups and the information of crack impact and reservoir thickness.For air accumulation band and low-frequency band identification, determine deposit thickness, jumping phenomenon such as display pinching, hydrocarbon water termination etc.

Seismic volume has high amplitude concurrently and low-frequency part can make dessert property value larger, and other combinations of this two attribute can make dessert property value diminish.

In the step 3 of said method, the value of M is more than or equal to 4.Wherein, seismic properties comprises: frequency decay number percent, RMS amplitude, amplitude change rate, dessert maximal value, dessert minimum value, dessert geometrical mean.

Wherein, frequency decay refers to seismic event loss of gross energy in underground medium is propagated, and is the factor that the attribute of medium inherence causes seismic wave frequency spectrum to decay, and is solid and solid, solid and fluid in medium, energy dissipation between fluid and fluid interface.Theoretical research and practical application show, if in geologic body pores'growth, and filling is oily, gas time, seismic reflection absorb strengthen, high-frequency absorption decay aggravation, low frequency energy increase.

RMS amplitude is extracted square root by the mean value of Amplitude-squared again.Because amplitude is before average square, therefore, it is very responsive to king-sized amplitude.Be suitable for the analysis of karst reservoir solution cavity, also for formation lithology Phase Transition Analysis, calculate thin sand thickness, identify bright spot, dim spot, instruction hydro carbons indicating characteristic.Its computing formula is

wherein, a _iit is the amplitude of i-th sampled point.

Amplitude change rate and RMS amplitude similar, can to highlight in work area amplitude sudden change in stratum, can identify size and the scale of solution cavity in carbonate reservoir, be the representative attribute identifying karst Reservoir Body.

In the step 4 of said method, the value of N is less than the value of M.Through many experiments and checking, the value of N is preferably 2,3 or 4.Concrete correlation analysis comprises use Pearson came (Pearson) correlation analysis and Si Baiman (Spearman) correlation analysis carries out correlation analysis, obtains N number of preferred geologic parameter.

In statistics, Pearson product moment correlation coefficlent is used to measure the mutual relationship between Two Variables and X and Y, and span is between [-1 ,+1].Pearson product-moment correlation coefficient is widely used and measures the power of Two Variables linear dependence in academic research.

Pearson correlation coefficient is a kind of situation, because must tentation data obtain from normal distribution in couples in Pearson correlation coefficient computation process, requirement for data is higher, and therefore we have employed Spearman rank correlation coefficient simultaneously and analyze.Spearman rank correlation coefficient is the order statistical parameter of a nonparametric character (irrelevant with distribution), as long as have dull funtcional relationship at X and Y, so X with Y is exactly that complete Spearman is relevant.

In the step 5 of said method, step 6 and step 7, set up treble medium physical model according to the oilwell performance data in target area, static data and crude oil property data.This model is that basement rock sillar is divided into two classes according to its factor of porosity and perviousness: the connectedness between a class and Fracture System is good; Another kind of then poor, using this two media as two independently liquid supply sources, fluid respectively from these two independently pore media flow into crack, flowing to shaft bottom.Fracture medium containing isolated cave can be summarized as the treble medium oil pool of a class matrix-matrix-slit formation according to this kind of sorting technique.

When setting up the mathematical model of treble medium oil pool, three kinds of media meet the respective equation of motion, state equation and continuity equation respectively, and the channelling item between medium represents with the source sink term of in continuity equation.Dielectric-dielectric-the Fractured reservoir proposed for people such as Ge Jiali sets up the mathematical model of treble medium.

Because crack is the passage of oil-gas migration, in prediction, only consider that two kinds of matrix are as oil bearing reservoir.For Fracture Systems:

$\frac{K_3}{\mathrm{\μ}}{\▿}^2{p}_3={\mathrm{\φ}}_3{C}_3\frac{\∂p_3}{\∂t}+q_1^{*}+q_2^{*}---\left(1\right)$

Wherein, K ₃for the permeability in crack, md; μ is fluid viscosity, μm ²; P ₃for crack initial formation pressure, MPa; φ is factor of porosity; C is well storage coefficient, m ³/ MPa; for basement rock 1,2 flow into crack flow.

For basement rock 1: $\frac{K_1}{\mathrm{\μ}}{\▿}^2{p}_1={\mathrm{\φ}}_1{C}_1\frac{\∂p_1}{\∂t}-q_1^{*}---\left(2\right)$

Wherein, K ₁for basement rock 1 permeability, md; P ₁for basement rock 1 initial formation pressure, MPa.

For basement rock 2: $\frac{K_2}{\mathrm{\μ}}{\▿}^2{p}_2={\mathrm{\φ}}_2{C}_2\frac{\∂p_2}{\∂t}-q_2^{*}---\left(3\right)$

Wherein, K ₂for basement rock 2 permeability, md; P ₂for basement rock 2 initial formation pressure, MPa.

Suppose that basement rock permeability is lower, ignore left right-hand vector, abbreviation can obtain

$\frac{K_3}{\mathrm{\μ}}{\▿}^2{p}_3={\mathrm{\φ}}_3{C}_3\frac{\∂p_3}{\∂t}+{\mathrm{\φ}}_1{C}_1\frac{\∂p_1}{\∂t}+{\mathrm{\φ}}_2{C}_2\frac{\∂p_2}{\∂t}---\left(4\right)$

In quasi-stable state channelling situation, with provided by formula below:

$q_1^{*}=\frac{{\mathrm{\α}}_1{K}_1}{\mathrm{\μ}}(p_3-p_1)---\left(5\right)$

$q_2^{*}=\frac{{\mathrm{\α}}_2{K}_2}{\mathrm{\μ}}(p_3-p_2)---\left(6\right)$

Definition dimensionless variable:

$r_D=\frac{r}{r_w};t_D=-\frac{K_{3}t}{{\mathrm{\μr}}_w^2({\mathrm{\φ}}_3{C}_3+{\mathrm{\φ}}_1{C}_1+{\mathrm{\φ}}_2{C}_2)}$

$p_{Dj}(r_D,t_D)=\frac{2{\mathrm{\πK}}_{3}h}{\mathrm{\μ}}\[p_i-p_j(r,t)\],(j=1,2,3)$

Zero dimension is carried out to equation (4) (5) (6), after arrangement:

$\left\{\begin{array}{c}\frac{1}{r_D}\frac{\∂}{\∂r_D}\left(r_D\frac{\∂p_{D3}}{\∂r_D}\right)-{\mathrm{\ω}}_1\frac{\∂p_{D1}}{\∂t_D}-{\mathrm{\ω}}_2\frac{\∂p_{D2}}{\∂t_D}=(1-{\mathrm{\ω}}_1-{\mathrm{\ω}}_2)\frac{\∂p_{D3}}{\∂t_D}\\ {\mathrm{\ω}}_1\frac{\∂p_{D1}}{\∂t_D}={\mathrm{\λ}}_1(p_{D3}-p_{D1})\\ {\mathrm{\ω}}_2\frac{\∂p_{D2}}{\∂t_D}={\mathrm{\λ}}_2(p_{D3}-p_{D2})\end{array}\right.$

Wherein, ${\mathrm{\ω}}_j=\frac{{\mathrm{\φ}}_j{C}_j}{{\mathrm{\φ}}_3{C}_3+{\mathrm{\φ}}_1{C}_1+{\mathrm{\φ}}_2{C}_2};{\mathrm{\λ}}_j=\frac{{\mathrm{\α}}_j{K}_j{r}_w^2}{K_3}$

Zero dimension boundary condition: $\frac{\∂p_{D3}}{\∂r_D}{|}_{r_D=1}=-1,(t_D>0)$

$\underset{t_D\→\mathrm{\∞}}{\lim }{p}_{D3}(r_D,t_D)=0,(t_D>0)$

Zero dimension starting condition: $p_{Dj}(r_D,t_D){|}_{t_D=0}=0,(j=1,2,3;1\≤r_D\≤+\mathrm{\∞})$ To t _dbe Laplace to convert

$\frac{{\∂}^2{\tilde{p}}_{D3}}{\∂r_D^2}+\frac{1}{r_D}\frac{\∂{\tilde{p}}_{D3}}{\∂r_D}-{\mathrm{\ω}}_{1}s{\tilde{p}}_{D1}-{\mathrm{\ω}}_{2}s{\tilde{p}}_{D2}=(1-{\mathrm{\ω}}_1-{\mathrm{\ω}}_2)s{\tilde{p}}_{D3}$

${\mathrm{\ω}}_{1}s{\tilde{p}}_{D1}={\mathrm{\λ}}_1({\tilde{p}}_{D3}-{\tilde{p}}_{D1})$

${\mathrm{\ω}}_{2}s{\tilde{p}}_{D1}={\mathrm{\λ}}_2({\tilde{p}}_{D3}-{\tilde{p}}_{D2})$

p _Dj(r _D,s)| _s＝0＝0(j＝1,2,3；1≤r _D≤+∞)

Arrange,

$\frac{{\∂}^2{\tilde{p}}_{\text{D}3}}{\∂r_D^2}+\frac{1}{r_D}\frac{\∂{\tilde{p}}_{D3}}{\∂r_D}-\[\frac{{\mathrm{\ω}}_1{\mathrm{s\λ}}_1}{{\mathrm{\ω}}_{1}s+{\mathrm{\λ}}_1}+\frac{{\mathrm{\ω}}_2{\mathrm{s\λ}}_2}{{\mathrm{\ω}}_{2}s+{\mathrm{\λ}}_2}+(1-{\mathrm{\ω}}_1-{\mathrm{\ω}}_2)s\]{\tilde{p}}_{D3}=0$

Can be in the hope of according to the solution procedure of empty argument 0 rank and 1 rank Bessel function:

${\tilde{p}}_{D3}=\frac{K_0\[\sqrt{sf\left(s\right)}{r}_D\]}{s\sqrt{sf\left(s\right)}{K}_1\[\sqrt{sf\left(s\right)}\]}$

And oil well output q _daccording to Duhamel's principle, the product of the output solution namely under level pressure and the pressure solution under fixed output quota is output: ${\tilde{q}}_D=\frac{1}{s^2{\tilde{P}}_{wD}};$

Therefore ${\tilde{q}}_D=\frac{\sqrt{sf\left(s\right)}{K}_1\[\sqrt{sf\left(s\right)}\]}{{\mathrm{sK}}_0\[\sqrt{sf\left(s\right)}\]}$

Use the inverting of Stehfest algorithm that the formula in Laplace space is transformed into the real space, inversion principle is:

$f\left(t\right)=\frac{\ln \left(2\right)}{t}\underset{i=1}{\overset{N}{\Σ}}V\left(i\right)-\tilde{f}\left(s_i\right)$

$V\left(i\right)={(-1)}^{N/2+1}\underset{k=(i+1)/2}{\overset{\min (i,N/2)}{\Σ}}\frac{k^{N/2}\left(2k\right)!}{(N/2-k)!k!(k-1)!(i-k)!(2k-i)!}$

$s_i=i\frac{ln\left(2\right)}{t}$

Relation equation q=f (the ω between output and matrix 1, the elastic storativity ratio of matrix 2, interporosity flow coefficient is obtained by inverting ₁, ω ₂, λ ₁, λ ₂), the elastic storativity ratio of matrix 1 is ω ₁, interporosity flow coefficient is λ ₁; The elastic storativity ratio of matrix 2 is ω ₂, interporosity flow coefficient is λ ₂.

Set preferred geologic parameter and important reservoir attribute is linear relationship:

ω ₁＝a ₁x ₁+a ₂x ₂+…+a _nx _n

ω ₂＝b ₁x ₁+b ₂x ₂+…+b _nx _n

λ ₁＝c ₁x ₁+c ₂x ₂+…+c _nx _n

λ ₂＝d ₁x ₁+d ₂x ₂+…+d _nx _n

Wherein x ₁, x ₂x _nfor preferred geologic parameter.ω ₁for the elastic storativity ratio of matrix 1 is, λ ₁for the interporosity flow coefficient of matrix 1 is; ω ₂elastic storativity ratio for matrix 2 is ω ₂, λ ₂for the interporosity flow coefficient of matrix 2.

Bring four equations in hypothesis into q=f (ω ₁, ω ₂, λ ₁, λ ₂), obtain initial potential and the nonlinear equation preferably between geologic parameter: q=f (x ₁, x ₂x _n, a ₁, a ₂a _n, b ₁, b ₂b _n, c ₁, c ₂c _n, d ₁, d ₂... d _n); ; a ₁, a ₂a _n, b ₁, b ₂b _n, c ₁, c ₂c _n, d ₁, d ₂d _nfor the undetermined coefficient in equation.

According to the geologic parameter of extraction and the single well productivity of correspondence, under least square meaning, the undetermined coefficient in nonlinear equation can be determined with Marquardt method, thus determine the nonlinear multivariable equation of production capacity about geologic parameter.Value prediction according to this relation equation and preferred geologic parameter goes out each sample well capacity.

Embodiment two

The invention provides a kind of device of Karst-type oil reservoir individual well oil and gas productivity prediction, this device comprises:

Seismic data acquisition unit, for gathering three dimensional seismic data for Karst-type oil reservoir target area; According to the objective interval of three dimensional seismic data determination target area;

Seismic data interpretation unit, extracts coherent body, the dessert attribute of each objective interval for frequency division; According to the fracture river development situation in coherent body and dessert attribute determination target area, according to fracture river development situation, target area is divided into multiple geologic unit;

Geologic parameter extraction unit, for taking geologic unit as M the geologic parameter that research unit extracts each sample well; Geologic parameter comprises seismic properties, beading area, bottom hole location apart from Dominated Factors distance;

Geologic parameter selection unit, for carrying out data normalization pre-service respectively to the initial potential of each sample well and the value of each geologic parameter, respectively correlation analysis is carried out to the value being normalized pretreated initial potential and M geologic parameter, from M geologic parameter, selects the N number of geologic parameter the highest with initial potential degree of correlation as preferred geologic parameter;

Modeling unit, for setting up treble medium physical model and output mathematical model according to the oilwell performance data in target area, static data and crude oil property data, according to the corresponding relation of treble medium physical model and the initial potential of output Mathematical Models and important reservoir attribute; Important reservoir attribute comprises elastic storativity ratio and interporosity flow coefficient;

Predicting unit, for setting preferred geologic parameter and important reservoir attribute is linear relationship; Corresponding relation according to this linear relationship and initial potential and important reservoir attribute sets up initial potential data and the nonlinear relationship equation preferably between geologic parameter; Use Marquardt method determines the undetermined coefficient in relation equation, thus determines nonlinear relationship equation; Value prediction according to this relation equation and preferred geologic parameter goes out each sample well capacity.

Wherein, the value of M is more than or equal to the value of 4, N is 2,3 or 4.

Seismic properties comprises: frequency decay number percent attribute, RMS amplitude attribute, amplitude change rate attribute, amplitude change rate, dessert maximal value, dessert minimum value, dessert geometrical mean.

Geologic parameter selection unit, also for using Pearson came correlation analysis and Si Baiman correlation analysis to carry out correlation analysis.

In this device, the function of each unit is corresponding respectively with the technical characteristic in said method, repeats no more herein.

Embody rule example

Comprise the following steps in this application example:

1, have 48 mouthfuls of sample mouth wells in certain work area western, burying explosive (when using explosive source) in certain work area western by measuring, boring shallow well hole, burying wave detector, arranging that cable to a few procedure of instrument truck receives and launches the three dimensional seismic data that seismic signal obtains Karst-type oil reservoir target area.

2, high s/n ratio, high resolving power and high fidelity process are carried out to the three dimensional seismic data collected.

3, in seismic interpretation software, adopt wiggle variable area Faxian to show seismic section, carry out seismic interpretation.Utilize well logging sound wave, densimetric curve combines with seismic data, make composite traces, complete and namely objective interval is determined to the demarcation of seismic horizon.

4, frequency division extracts coherent body, the dessert attribute of each objective interval, observes the distribution situation in fracture, river course according to coherent body and dessert attribute in the plane, according to fracture, river course distribution situation Further Division geologic unit.

5, take geologic unit as research unit, extract the value of 6 geologic parameters at 48 mouthfuls of sample well shaft bottoms place, these 6 geologic parameters comprise: RMS amplitude attribute, interval frequency decay number percent attribute, beading area, amplitude change rate, dessert minimum value, dessert maximal value.

6, utilize Pearson correlation analysis and Spearman correlation analysis to carry out correlation analysis to the geologic parameter value extracted and initial potential data, the correlation coefficient charts obtained is as follows:

7, according to actual conditions and the basic data of geologic unit each in work area, treble medium yield model is set up.

8, the initial productivity data of 48 mouthfuls of wells and geologic parameter data are corrected and standardization.

9, the data after mode input end input standardization, record the undetermined coefficient value in the predicted data and equation exported after forecast model convergence, the result calculated is as follows:

Undetermined coefficient	Solve value	Undetermined coefficient	Solve value
				a1	0.26	c1	-0.03
a2	0.63	c2	-0.10
				a3	0.64	c3	-0.10
b1	-1.24	d1	-0.23
				b2	-2.74	d2	-15.30
b3	1.65	d3	4.88

Through actual contrast, predicting the outcome, it is better to coincide with record of production data, and consensus forecast precision reaches 86.8%.

The present invention can single well capacity in accurately predicting Karst-type oil reservoir, effectively promotes the reasonable development of Karst-type oil reservoir.

In addition, it should be noted that, the specific embodiment described in this instructions, the shape, institute's title of being named etc. of its parts and components can be different, and the above content described in this instructions is only to structure example of the present invention explanation.

Above-described content can combine enforcement individually or in every way, and these variant are all within protection scope of the present invention.

In this article, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thus make to comprise the article of a series of key element or equipment not only comprises those key elements, but also comprise other key elements clearly do not listed, or also comprise by this article or the intrinsic key element of equipment.When not more restrictions, the key element limited by statement " comprising ... ", and be not precluded within the article or equipment comprising described key element and also there is other identical element.

Above embodiment only in order to technical scheme of the present invention and unrestricted to be described, only with reference to preferred embodiment to invention has been detailed description.Those of ordinary skill in the art should be appreciated that and can modify to technical scheme of the present invention or equivalent replacement, and does not depart from the spirit and scope of technical solution of the present invention, all should be encompassed in the middle of right of the present invention.

Claims (10)

1. a Karst-type oil reservoir individual well oil and gas productivity prediction method, is characterized in that, described method comprises:

Step 1, gathers three dimensional seismic data for Karst-type oil reservoir target area; The objective interval of described target area is determined according to described three dimensional seismic data;

Step 2, extracts the coherent body of each objective interval, dessert attribute; Determine the fracture river development situation in described target area according to described coherent body and dessert attribute, according to described fracture river development situation, described target area is divided into multiple geologic unit;

Step 3, with M geologic parameter of described geologic unit each sample well for research unit extracts; Described geologic parameter comprises seismic properties, beading area, bottom hole location apart from Dominated Factors distance;

Step 4, respectively data normalization pre-service is carried out to the initial potential of each sample well and the value of each geologic parameter, respectively correlation analysis is carried out to the value being normalized pretreated initial potential and M geologic parameter, from a described M geologic parameter, selects the N number of geologic parameter the highest with described initial potential degree of correlation as preferred geologic parameter;

Step 5, treble medium physical model and output mathematical model is set up, the corresponding relation of initial potential and important reservoir attribute according to described treble medium physical model and described output Mathematical Models according to the oilwell performance data in described target area, static data and crude oil property data; Described important reservoir attribute comprises elastic storativity ratio and interporosity flow coefficient;

Step 6, sets described preferred geologic parameter and described important reservoir attribute is linear relationship; The nonlinear relationship equation between described initial potential data and described preferred geologic parameter is set up according to the corresponding relation of this linear relationship and described initial potential and important reservoir attribute;

Step 7, use Marquardt method determines the undetermined coefficient in described relation equation, thus determines described nonlinear relationship equation; Value prediction according to this relation equation and preferred geologic parameter goes out each sample well capacity.

2. a kind of Karst-type oil reservoir individual well oil and gas productivity prediction method according to claim 1, is characterized in that,

The value of described M is more than or equal to 4; The value of described N is 2,3 or 4.

3. a kind of Karst-type oil reservoir individual well oil and gas productivity prediction method according to claim 1, it is characterized in that, described seismic properties comprises: frequency decay number percent, RMS amplitude, amplitude change rate, dessert maximal value, dessert minimum value, dessert geometrical mean.

4. a kind of Karst-type oil reservoir individual well oil and gas productivity prediction method according to claim 1, is characterized in that, the correlation analysis in described step 4 comprises use Pearson came correlation analysis and Si Baiman correlation analysis carries out correlation analysis.

5. a kind of Karst-type oil reservoir individual well oil and gas productivity prediction method according to claim 1, is characterized in that, the physical model that described treble medium physical model is is standard with dielectric-dielectric-Fractured reservoir.

6. a kind of Karst-type oil reservoir individual well oil and gas productivity prediction method according to claim 1, is characterized in that, also comprise and carry out high s/n ratio, high resolving power and high fidelity process to the three dimensional seismic data collected in described step 1.

7. a Karst-type oil reservoir individual well oil and gas productivity prediction device, is characterized in that, comprising:

Seismic data acquisition unit, for gathering three dimensional seismic data for Karst-type oil reservoir target area; The objective interval of described target area is determined according to described three dimensional seismic data;

Seismic data interpretation unit, extracts coherent body, the dessert attribute of each objective interval for frequency division; Determine the fracture river development situation in described target area according to described coherent body and dessert attribute, according to described fracture river development situation, described target area is divided into multiple geologic unit;

Geologic parameter extraction unit, for M geologic parameter of described geologic unit each sample well for research unit extracts; Described geologic parameter comprises seismic properties, beading area, bottom hole location apart from Dominated Factors distance;

Geologic parameter selection unit, for carrying out data normalization pre-service respectively to the initial potential of each sample well and the value of each geologic parameter, respectively correlation analysis is carried out to the value being normalized pretreated initial potential and M geologic parameter, from a described M geologic parameter, selects the N number of geologic parameter the highest with described initial potential degree of correlation as preferred geologic parameter;

Modeling unit, for setting up treble medium physical model and output mathematical model according to the oilwell performance data in described target area, static data and crude oil property data, the corresponding relation of initial potential and important reservoir attribute according to described treble medium physical model and described output Mathematical Models; Described important reservoir attribute comprises elastic storativity ratio and interporosity flow coefficient;

Predicting unit, for setting described preferred geologic parameter and described important reservoir attribute is linear relationship; The nonlinear relationship equation between described initial potential data and described preferred geologic parameter is set up according to the corresponding relation of this linear relationship and described initial potential and important reservoir attribute; Use Marquardt method determines the undetermined coefficient in described relation equation, thus determines described nonlinear relationship equation; Value prediction according to this relation equation and preferred geologic parameter goes out each sample well capacity.

8. a kind of Karst-type oil reservoir individual well oil and gas productivity prediction device as claimed in claim 7, it is characterized in that, the value of described M is more than or equal to 4, and the value of described N is 2,3 or 4.

9. a kind of Karst-type oil reservoir individual well oil and gas productivity prediction device as claimed in claim 7, it is characterized in that, described seismic properties comprises: frequency decay number percent attribute, RMS amplitude attribute, amplitude change rate attribute, amplitude change rate, dessert maximal value, dessert minimum value, dessert geometrical mean.

10. a kind of Karst-type oil reservoir individual well oil and gas productivity prediction device as claimed in claim 7, is characterized in that, described geologic parameter selection unit, also for using Pearson came correlation analysis and Si Baiman correlation analysis to carry out correlation analysis.