CN109339771B - Shale hydrocarbon reservoir pore pressure prediction method and system - Google Patents
Shale hydrocarbon reservoir pore pressure prediction method and system Download PDFInfo
- Publication number
- CN109339771B CN109339771B CN201710648490.XA CN201710648490A CN109339771B CN 109339771 B CN109339771 B CN 109339771B CN 201710648490 A CN201710648490 A CN 201710648490A CN 109339771 B CN109339771 B CN 109339771B
- Authority
- CN
- China
- Prior art keywords
- pore pressure
- elastic
- pressure
- shale
- elastic parameters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
- E21B47/06—Measuring temperature or pressure
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
- E21B49/005—Testing the nature of borehole walls or the formation by using drilling mud or cutting data
Abstract
A method and system for predicting the pore pressure of shale oil-gas layer are disclosed. The method comprises the following steps: 1) extracting a common reflection point gather based on the seismic data; 2) performing pre-stack elastic inversion based on the common reflection point gather to obtain N elastic parameters at a drilling fluid density test point; 3) fitting N elastic parameter values at the drilling fluid density test point and the pore pressure value at the test point respectively; 4) selecting M elastic parameters with the highest fitting goodness from the N elastic parameters, and establishing an M-element fitting formula of pressure prediction, wherein M is less than N; 5) and calculating the pore pressure of the shale oil-gas layer by using the selected M elastic parameter values based on the M-element fitting formula. Compared with the traditional API prediction method, the shale hydrocarbon reservoir pore pressure prediction method has the advantages that the pressure error value is small in whole, the distribution is relatively stable, and the reliability is high.
Description
Technical Field
The invention relates to the field of oil and gas geophysical, in particular to a method and a system for predicting pore pressure of a shale oil-gas reservoir.
Background
Shale gas resource enrichment firstly needs abundant material bases, such as being located in a sedimentary facies zone of a deep water upland shelter, shale is developed in a large scale, the shale is rich in organic matters, the content of brittle minerals is high, kerogen is in a mature and over mature stage, and the like. However, for shale deposited in the sea phase in the Fuling area, the storage condition of the shale reservoir determines the yield of shale gas under the condition that the material base is relatively stable. These preservation conditions include: good top and bottom plates, moderate burial depth, fracture far away from opening, gentle construction style, high formation pressure coefficient and the like. In the storage conditions, weak structural movement modification and failure in fracture opening are important factors of the shale gas storage conditions, the formation pressure coefficient is a comprehensive judgment index of the shale gas storage conditions, and the high pressure and ultrahigh pressure of a reservoir often indicate that the storage conditions are good and the formation energy is sufficient.
Formation pore pressure is essentially the pressure that the fluid (oil, gas, water) has in the formation pores or fractures, while abnormally high or low pressure means that the pore pressure is above or below hydrostatic pressure. Formation of formation pressure anomalies can be due to a variety of factors such as unbalanced compaction (rapid sedimentation), formation extrusion (e.g., faults), hydrothermal pressurization, hydrocarbon formation, montmorillonite dehydration, concentration versus retrograde concentration, gypsum/anhydrite conversion, fluid density differences, irregularities in water potential (mountain drilling), deep gas-filled containment tanks separation and lifting, and the like. For shale formation pressure abnormality of the Longmaxi-Wufeng group in the coke dam area, the large hydrocarbon generation effect is mainly caused by shale organic matter enrichment, so that large-scale gas is enriched in high-quality shale layer sections and areas, and an abnormal high-pressure mechanism is formed. Laboratory measurements of the core also confirmed that: for shale samples with different TOC contents, the content of adsorbed gas is gradually increased along with the increase of the formation pressure. The actual exploration effect of the southeast part of the Sichuan basin and the periphery of the southeast part of the Sichuan basin also shows that the shale gas yield is always higher when the formation fluid pressure coefficient is higher no matter in the range of a Chinese petroleum block or a Chinese petrochemical block, for example, the average pressure coefficient of a first-stage block of a Job's rock dam with the fracture in the west in the Qiyueshan mountain is 1.5, the corresponding test gas yield is 11-50 ten thousand squares per day, the average pressure coefficient of a penge block with the fracture in the east in the Qiyueshan mountain is only about 1.0, and the actual test gas yield is only 2 thousand squares per day. And the actual test yield results of the main body and the peripheral multiple wells in the first stage of the shale dam and the formation pressure coefficient of the main body have good positive correlation, namely the higher the formation pressure coefficient of the shale is, the larger the corresponding final shale gas yield is. It can be said that abnormally high pressure is a key factor for high yield of marine phase shale gas enrichment.
In addition, the prediction of formation pressure can reduce drilling risk, improve efficiency, reduce cost (Gutierrez, 2006), and is vital to shale dessert quality prediction and engineering layout construction.
Stratum pressure prediction methods generally include three types, namely pre-drilling seismic data prediction, formation pressure monitoring while drilling and post-drilling logging detection, and mainly pay attention to how seismic data are used for predicting distribution characteristics of abnormal stratum pressure in an exploration stage. Research on formation pressure prediction methods at home and abroad has been carried out for decades, and although there are various methods and models for describing underground pressure information, from the idea principle of each method, a formula for really characterizing underground pressure balance is a Terzaghi formula in 1926:
PP＝Po-Pe (1)
wherein, PpRepresenting the formation pore pressure; poRepresentative of overburden pressure, PeRepresenting the vertical effective stress. Overburden pressure refers to the pressure caused by the overall weight of the overburden rock framework and pore space fluid, and is related to the thickness of the overburden, the framework density, and the void fluid density. The vertical effective stress refers to the compaction effect in the vertical direction borne by the stratum framework or rock stratumThe resultant stress, also known as the skeletal stress, is not directly measurable.
There are several models or methods for predicting the pore pressure or effective stress in equation 3.6.1, either directly or indirectly, such as the equivalent depth method (1965), the Eaton method (1976), the Bowers method (1995), the philippone method (1982), the Eberhart-Phillips method (1985), etc. The selection of the methods, particularly the establishment of parameters, has extremely strong regional characteristics, and the operation is relatively complicated in the overall view. Correspondingly, the pressure prediction models are all established after regional laboratory or wellbore test parameter screening, and have strong corresponding relation with certain sensitive parameters, such as parameters of formation velocity, acoustic wave time difference, density and the like. Therefore, when the pressure data is rich, it is possible to perform elastic parameter screening and create a specific model suitable for regional pressure prediction, such as API prediction method (2015). However, the accuracy of such methods still has a large correlation with the correlation of the elastic parameters, and has great regionality. Therefore, it is necessary to develop a method and a system for predicting pore pressure of a shale hydrocarbon reservoir, which have smooth distribution and strong reliability.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
With the deep exploration and development of shale gas resources, particularly aiming at the productivity building area of the Fuling shale gas field in China, the pressure prediction precision of a shale oil-gas layer directly determines the final yield of shale gas. The Longmaxi-Wufeng group shale stratum in the south of the Joker dam undergoes severe structural change, the surface topography is complex and changeable, the existing drilling and pressure testing data are rare, the effective prediction of the formation pressure based on a pressure balance method is difficult to carry out, and the effective pressure prediction elastic parameter and the combination form are difficult to determine. In order to solve the problems, the invention is based on the relationship between the pore pressure value and elastic parameters such as corresponding longitudinal wave impedance, transverse wave impedance, longitudinal wave velocity, transverse wave velocity, density, Young modulus, Poisson ratio, longitudinal wave velocity ratio and transverse wave velocity ratio calculated at the drilling fluid density test point, tests the goodness of fit under different combination index fitting, and finally screens and establishes a multivariate index prediction method for shale oil and gas reservoir pore pressure earthquake to predict the shale rock stratum pore pressure in the south area of the rock ballast dam.
According to one aspect of the invention, a method for predicting pore pressure of a shale hydrocarbon reservoir is provided. The method mainly comprises the following steps:
1) extracting a common reflection point gather based on the seismic data;
2) performing pre-stack elastic inversion based on the common reflection point gather to obtain N elastic parameters at a drilling fluid density test point;
3) fitting N elastic parameter values at the drilling fluid density test point and the pore pressure value at the test point respectively;
4) selecting M elastic parameters with the highest fitting goodness from the N elastic parameters, and establishing an M-element fitting formula of pressure prediction, wherein M is less than N;
5) and calculating the pore pressure of the shale oil-gas layer by using the selected M elastic parameter values based on the M-element fitting formula.
Preferably, the N elastic parameters include longitudinal wave impedance, transverse wave impedance, longitudinal wave velocity, transverse wave velocity, density, young's modulus, poisson's ratio, and longitudinal-transverse wave velocity ratio.
Preferably, the N elastic parameter values at the drilling fluid density test point and the pore pressure value at the test point are each fitted exponentially in step 3).
Preferably, the M-ary fitting formula is:
wherein, PpIs the shale oil-gas layer pore pressure, XiFor the corresponding elastic parameters, one of the M elastic parameters with the highest goodness of fit, A, is selectediAnd BiFor different elastic parameters corresponding to coefficients of an exponential fit formula, RiIs notGoodness of fit corresponding to the elastic parameters.
Preferably, step 1) includes performing amplitude preserving processing on the seismic data to obtain the common reflection point gather.
According to another aspect of the invention, a shale oil and gas layer pore pressure prediction system is provided. The system comprises a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the following steps when executing the program:
1) extracting a common reflection point gather based on the seismic data;
2) performing pre-stack elastic inversion based on the common reflection point gather to obtain N elastic parameters at a drilling fluid density test point;
3) fitting N elastic parameter values at the drilling fluid density test point and the pore pressure value at the test point respectively;
4) selecting M elastic parameters with the highest fitting goodness from the N elastic parameters, and establishing an M-element fitting formula of pressure prediction, wherein M is less than N;
5) and calculating the pore pressure of the shale oil-gas layer by using the selected M elastic parameter values based on the M-element fitting formula.
Preferably, the N elastic parameters include longitudinal wave impedance, transverse wave impedance, longitudinal wave velocity, transverse wave velocity, density, young's modulus, poisson's ratio, and longitudinal-transverse wave velocity ratio.
Preferably, the N elastic parameter values at the drilling fluid density test point and the pore pressure value at the test point are each fitted exponentially in step 3).
Preferably, the M-ary fitting formula is:
wherein, PpIs the shale oil-gas layer pore pressure, XiFor the corresponding elastic parameters, one of the M elastic parameters with the highest goodness of fit, A, is selectediAnd BiFitting corresponding indexes for different elastic parametersCoefficient of the formula, RiThe goodness of fit corresponding to different elastic parameters.
Preferably, step 1) includes performing amplitude preserving processing on the seismic data to obtain the common reflection point gather.
Compared with the traditional API prediction method, the shale hydrocarbon reservoir pore pressure prediction method has the advantages that the pressure error value is small in whole, the distribution is relatively stable, and the reliability is high.
The method and apparatus of the present invention have other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts.
FIG. 1 is a flow chart of a shale reservoir pore pressure prediction method according to an exemplary embodiment of the present invention;
FIGS. 2a-2h are graphs of intersection and exponential fit of different elastic parameters with pressure, where the elastic parameter in FIG. 2a Is the longitudinal wave impedance Ip, the elastic parameter in FIG. 2b Is the transverse wave impedance Is, the elastic parameter in FIG. 2c Is the longitudinal wave velocity Vp, the elastic parameter in FIG. 2d Is the transverse wave velocity Vs, the elastic parameter in FIG. 2e Is the density ρ, the elastic parameter in FIG. 2f Is the Young's modulus Ymod, the elastic parameter in FIG. 2g Is the Poisson's ratio σ, and the elastic parameter in FIG. 2h Is the longitudinal and transverse wave velocity ratio Vp/Vs;
fig. 3 is a graph comparing an actual pressure at an actual sampling point with a predicted pressure of two methods, wherein a dotted line is a theoretical value,
for the purpose of the prediction method of the present invention,
a traditional API prediction method;
fig. 4 is a graph of absolute error versus predicted pressure for two methods at actual sampling points, wherein,
for the purpose of the prediction method of the present invention,
a traditional API prediction method;
FIG. 5 is a diagram of a formation pressure profile for a high quality shale section in the south of a coke dam predicted according to an API prediction method;
FIG. 6 is a plot of predicted formation pressure for a high quality shale section from a Joule dam according to the method of the present invention.
Detailed Description
The invention will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
According to the pressure balance principle (formula 1), the mainstream pressure prediction method is to calculate the overburden pressure according to the density information, and then select a proper model and parameters to simulate the pore pressure or the effective stress, so as to represent the pore pressure of the stratum. The prediction precision of the formation pressure depends on the accuracy and the detailed degree of data, and the prediction means has complex operation flow and more influence factors, and must be carried out in stages in actual work.
In fact, formation pressure often has a strong correspondence with certain sensitive parameters, such as formation velocity, acoustic moveout, and density. Therefore, under the condition of abundant pressure data, elastic parameter screening can be carried out, and a special model suitable for regional pressure prediction is established. Such as the API method:
wherein σ is Poisson's ratio, Ip represents longitudinal wave impedance, and C is a pressure coefficient factor.
However, different elastic parameters reflect pressure information to different degrees in different regions, and an Eaton model (1976) for representing the relation between seismic velocity and pore pressure, a Bowers model (1976) for representing the relation between seismic velocity and vertical effective stress, a Filippone model (1982) and the like are established based on a large amount of data statistics and laboratory measurement, and the contribution form of each parameter to the pressure information is strictly demonstrated. In contrast, empirical formulas based on elastic parameter fitting tend to lack laboratory demonstrations and have limited application.
Shale layers in the south of the flint dam experience severe structural changes, the surface topography is complex and changeable, the existing drilling and pressure testing data are rare, effective prediction of the formation pressure based on a pressure balance method is difficult to carry out, and effective pressure prediction elastic parameters and combination forms are difficult to determine. Therefore, the method carries out different types of fitting on the pore pressure value calculated at the drilling fluid density test point and different elastic parameters, screens sensitive parameters and establishes a multivariate prediction model, thereby predicting the formation pressure of the shale layer.
A method for predicting pore pressure of a shale hydrocarbon reservoir according to an exemplary embodiment of the present invention is described in detail below with reference to fig. 1.
The method mainly comprises the following steps:
step 1: based on the seismic data, a common reflection point gather is extracted.
In one example, seismic data is amplitude preserved and high quality Common Reflection Point (CRP) gathers are extracted.
Step 2: and performing pre-stack elastic inversion based on the common reflection point gather to obtain N elastic parameters at the drilling fluid density test point.
In one example, the N elastic parameters include compressional impedance, shear impedance, compressional velocity, shear velocity, density, young's modulus, poisson's ratio, and compressional-shear velocity ratio. It will be appreciated by those skilled in the art that the N elasticity parameters may be any suitable type of elasticity parameter.
And step 3: and respectively fitting the N elastic parameter values at the drilling fluid density test point and the pore pressure value at the test point.
Shale drilling is typically balanced drilling, with the drilling fluid density versus gravity being substantially similar to the formation pressure. The pressure data generated by the drilling fluid density at a certain depth point is:
wherein H is the vertical height of the overburden, rho is the drilling fluid density, and g is the acceleration of gravity. For example, corresponding pressure data can be calculated according to the drilling fluid density information of the straight well section of the coke page 5-7 wells in the south working area of the coke-rock dam.
In actual work, all variables are not necessarily in a linear relationship, such as the relationship between blood concentration and time after taking medicine; the relationship between the curative effect and the length of the course of treatment; the relationship between the dose of poison and the lethality is usually in a curve. Curve fitting refers to selecting an appropriate curve type to fit the observed data and analyzing the relationship between two variables by using a fitted curve equation. The function relation between the coordinates of the discrete points is approximately described or compared by a continuous curve, and nonlinear data and problems can be directly represented by simple variables.
FIGS. 2a-2h are cross-sectional views of pressure data at 42 measured drilling fluid density points and elastic parameters at corresponding locations in a vertical well section of a 5-7 well in a NanoFocus of a Joule dam, wherein the elastic parameter in FIG. 2a Is a longitudinal wave impedance Ip, the elastic parameter in FIG. 2b Is a shear wave impedance Is, the elastic parameter in FIG. 2c Is a longitudinal wave velocity Vp, the elastic parameter in FIG. 2d Is a shear wave velocity Vs, the elastic parameter in FIG. 2e Is a density ρ, the elastic parameter in FIG. 2f Is a Young's modulus Ymod, the elastic parameter in FIG. 2g Is a Poisson's ratio σ, and the elastic parameter in FIG. 2h Is a shear wave velocity ratio Vp/Vs, P/M, and P/MpIs the shale oil and gas layer pore pressure. It can be seen that the use of the exponential form has a fit of different degrees of agreement for different intersection combinations.
The form of fitting needs to be evaluated according to the fitting relation with the actual nonlinear point, and is generally expressed by goodness of fit. The goodness of fit refers to the degree of fit of the regression line to the observed value, and is the overall relationship between the expression dependent variable and all independent variables. The statistic for measuring goodness of fit is a coefficient of probability (also called deterministic coefficient) R2 with a value range of [0, 1%]. The specific R is equal to the ratio of the sum of the squares of the regression to the sum of the squares of the total, i.e., the percentage of variability in the dependent variable that the regression equation can interpret. The closer the value of R2 is to 1, the better the fitting degree of the regression straight line to the observed value is; conversely, a value of R2 closer to 0 indicates a worse fit of the regression line to the observed value. The following table is an exponential fit formula and corresponding goodness of fit for the 8 melds of FIGS. 2a-2h, where PpIs the formation pressure, i.e., pore pressure.
TABLE 1 exponential fitting forms and goodness of fit for different elastic parameters and pressures
As can be seen from table 1, the pressure-longitudinal wave impedance and the pressure-longitudinal wave velocity have the highest goodness of fit, which is higher than the pressure-shear wave impedance and the pressure-shear wave velocity, because a large amount of hydrocarbons are generated in the pores of the shale organic matter, the gas content is increased and the formation pressure is increased, the corresponding longitudinal wave velocity tends to be reduced, and the shear wave is a shear wave propagating along the rock skeleton, and the sensitivity is not high. In addition, the pressure-Young modulus and the pressure-Poisson ratio have stronger fitting goodness.
And 4, step 4: and selecting M elastic parameters with the highest fitting goodness from the N elastic parameters, and establishing an M-element fitting formula of pressure prediction, wherein M is less than N.
And selecting the elastic parameters with the highest fitting goodness, and developing the construction of a multivariate fitting formula of pressure prediction. For example, because the pressure-density has the lowest goodness of fit, and the longitudinal wave impedance and the longitudinal wave velocity have strong correlation, only the pressure-longitudinal wave impedance combination with the highest goodness of fit can be selected, and a ternary exponential prediction model can be constructed by combining two combinations with higher goodness of fit, such as pressure-Young modulus, pressure-Poisson ratio and the like.
The general form of exponential fit for different elastic parameters is:
wherein, PpIs the shale oil-gas layer pore pressure, XiAs an elastic parameter, AiAnd BiThe elastic parameters are corresponding to the coefficients of the exponential fit formula.
Further introducing goodness of fit to construct a shale hydrocarbon reservoir pore pressure earthquake multivariate index prediction model as follows:
wherein, PpIs the shale oil-gas layer pore pressure, XiFor the corresponding elastic parameters, one of the M elastic parameters with the highest goodness of fit, A, is selectediAnd BiFor different elastic parameters corresponding to coefficients of an exponential fit formula, RiThe goodness of fit corresponding to different elastic parameters.
For example, when three elastic parameters, i.e., the longitudinal wave impedance, the young's modulus, and the poisson ratio, with the highest goodness of fit in table 1 are selected, the ternary exponential prediction formula is established as follows:
PP＝180.61·e-2E-7·Ip+30.03·e-2E-11·Ymod+43.27·e-6.059·σ (5)
where Ip is the longitudinal wave impedance, Ymod is the young's modulus, and σ is the poisson's ratio.
And 5: and calculating the pore pressure of the shale oil-gas layer by using the selected M elastic parameter values based on the M-element fitting formula.
For example, when the three elastic parameters of longitudinal wave impedance, young's modulus, and poisson's ratio in table 1 having the highest goodness of fit are selected, the pore pressure at the drilling fluid density test points can be calculated using the values of the three elastic parameters of longitudinal wave impedance, young's modulus, and poisson's ratio at these test points based on equation (5).
Application example
To facilitate understanding of the solution of the embodiments of the present invention and the effects thereof, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
The traditional API prediction method and the seismic multivariate index pressure prediction method provided by the invention (in the example, the seismic multivariate index pressure prediction method is selected, and three elastic parameters including longitudinal wave impedance, Young modulus and Poisson ratio with the highest fitting goodness are selected) are respectively selected to predict the pressure data at 42 actually measured drilling fluid density points of the vertical well section of the focusing page 5-7 wells, and the pressure data is compared with the pressure data calculated according to the drilling fluid density, as shown in FIG. 3. The parameter C of the API prediction method is selected according to the principle of minimum error from the actual pressure, specifically, C is 2.37E 16.
Obviously, the results predicted by the two prediction methods are generally consistent in trend and basically identical to theoretical values, because both are formulas established according to longitudinal wave impedance and poisson ratio. However, due to the introduction of the young's modulus information, it can be roughly seen that the error of the prediction method of the present invention is smaller, especially in the first 18 sampling point positions, and the prediction accuracy based on the API prediction method is significantly lower than the result of the prediction of the present invention. A more detailed comparison can be observed from the absolute error comparison of fig. 4, and although the conventional API prediction method can adjust the coefficient parameters so that the main distribution of the predicted values is close to the theoretical value, the discontinuity has a great error at the first 12 samples, the 20 th sample and the 31 st sample. Compared with the method, the method has the advantages of small overall predicted pressure error value, relatively more stable distribution and strong reliability.
Fig. 5 and 6 show the stratum pressure distribution of the high-quality shale layer sections of the south Longmaxi-Wufeng group of the coke dam respectively predicted according to the API prediction method and the seismic ternary index prediction method provided by the invention. Elastic parameters such as longitudinal wave impedance, Young modulus, Poisson ratio and the like adopted by the two methods are obtained according to the pre-stack elastic inversion of the region, the trace gather quality is improved by means of removing multiple waves and linear noise, attenuating random noise, remaining static correction of earth surface consistency and the like before pre-stack inversion, and inversion accuracy is improved by adopting a strict quality control strategy, particularly by increasing the constraint degree of a density model in the inversion process.
It can be seen that the stratum abnormal high-pressure areas predicted by the two methods are located in the dominant part of the horizontal bridge broken anticline, the pongamy syncline, the back inclined zone of the Wujiang river and the middle and north part of the sand tuo broken nose, and the large area in the northwest of the horizontal bridge broken anticline. However, the two methods are different in details, and the stratum pressure change predicted by the shale oil-gas layer pore pressure earthquake ternary index prediction method is more continuous, and is wider in the range of the target zones and more consistent with the geological condition. Particularly, the pressure value of the prediction result of the traditional API method at the position of the coke page 8 well is lower and is not consistent with the actual production, while the prediction result of the method disclosed by the invention shows that the coke page 8 well is positioned at the edge of the flat-bridge broken anticline abnormal high-pressure zone, corresponds to high shale gas production and is consistent with the actual production condition.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (8)
1. A method for predicting pore pressure of a shale hydrocarbon reservoir is characterized by comprising the following steps:
1) extracting a common reflection point gather based on the seismic data;
2) performing pre-stack elastic inversion based on the common reflection point gather to obtain N elastic parameters at a drilling fluid density test point;
3) fitting N elastic parameter values at the drilling fluid density test point and the pore pressure value at the test point respectively;
4) selecting M elastic parameters with the highest fitting goodness from the N elastic parameters, and establishing an M-element fitting formula of pressure prediction, wherein M is less than N;
5) calculating the pore pressure of the shale oil-gas layer by using the selected M elastic parameter values based on the M-element fitting formula;
the M-ary fitting formula is:
wherein, PpIs the shale oil-gas layer pore pressure, XiFor the corresponding elastic parameters, one of the M elastic parameters with the highest goodness of fit, A, is selectediAnd BiFor different elastic parameters corresponding to coefficients of an exponential fit formula, RiThe goodness of fit corresponding to different elastic parameters.
2. The shale hydrocarbon reservoir pore pressure prediction method of claim 1, wherein the N elastic parameters comprise compressional wave impedance, shear wave impedance, compressional wave velocity, shear wave velocity, density, young's modulus, poisson's ratio, and compressional-shear wave velocity ratio.
3. The shale hydrocarbon reservoir pore pressure prediction method of claim 1, wherein N elastic parameter values at the drilling fluid density test point and the pore pressure value at the test point are respectively fitted in an exponential form in step 3).
4. The method for predicting the pore pressure of the shale oil and gas reservoir according to claim 1, wherein the step 1) comprises amplitude preserving processing of seismic data to obtain a common reflection point gather.
5. A shale hydrocarbon reservoir pore pressure prediction system, the system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of:
1) extracting a common reflection point gather based on the seismic data;
2) performing pre-stack elastic inversion based on the common reflection point gather to obtain N elastic parameters at a drilling fluid density test point;
3) fitting N elastic parameter values at the drilling fluid density test point and the pore pressure value at the test point respectively;
4) selecting M elastic parameters with the highest fitting goodness from the N elastic parameters, and establishing an M-element fitting formula of pressure prediction, wherein M is less than N;
5) calculating the pore pressure of the shale oil-gas layer by using the selected M elastic parameter values based on the M-element fitting formula;
the M-ary fitting formula is:
wherein, PpIs the shale oil-gas layer pore pressure, XiFor the corresponding elastic parameters, one of the M elastic parameters with the highest goodness of fit, A, is selectediAnd BiFor different elastic parameters corresponding to coefficients of an exponential fit formula, RiThe goodness of fit corresponding to different elastic parameters.
6. The shale hydrocarbon reservoir pore pressure prediction system of claim 5, wherein the N elastic parameters comprise compressional wave impedance, shear wave impedance, compressional wave velocity, shear wave velocity, density, Young's modulus, Poisson's ratio, and compressional-shear velocity ratio.
7. The shale hydrocarbon reservoir pore pressure prediction system of claim 5, wherein the N elastic parameter values at the drilling fluid density test point and the pore pressure value at the test point are each exponentially fitted in step 3).
8. The shale hydrocarbon reservoir pore pressure prediction system of claim 5, wherein step 1) comprises amplitude preserving seismic data to obtain a common reflection point gather.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710648490.XA CN109339771B (en) | 2017-08-01 | 2017-08-01 | Shale hydrocarbon reservoir pore pressure prediction method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710648490.XA CN109339771B (en) | 2017-08-01 | 2017-08-01 | Shale hydrocarbon reservoir pore pressure prediction method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109339771A CN109339771A (en) | 2019-02-15 |
| CN109339771B true CN109339771B (en) | 2022-04-12 |
Family
ID=65291230
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710648490.XA Active CN109339771B (en) | 2017-08-01 | 2017-08-01 | Shale hydrocarbon reservoir pore pressure prediction method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109339771B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113514890B (en) * | 2021-03-18 | 2022-12-09 | 中国石油大学(华东) | Method, device and equipment for predicting formation pressure coefficient by using seismic data |
| CN113625364B (en) * | 2021-08-18 | 2022-08-02 | 西南石油大学 | Shale formation pore pressure calculation method based on double correction |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101163990A (en) * | 2005-03-08 | 2008-04-16 | 地质力学国际公司 | Quantitative risk assessment applied to pore pressure prediction |
| CN102640018A (en) * | 2009-12-18 | 2012-08-15 | 雪佛龙美国公司 | Workflow for petrophysical and geophysical formation evaluation of wireline and LWD log data |
| CN103628866A (en) * | 2013-11-22 | 2014-03-12 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Method for obtaining stratum pressure coefficients of stratum |
| CN104267429A (en) * | 2014-09-30 | 2015-01-07 | 中国石油天然气股份有限公司 | Method and device for determining formation pressure |
| CN104698492A (en) * | 2013-12-09 | 2015-06-10 | 中国石油天然气股份有限公司 | Abnormal formation pressure calculation method |
| CN105445791A (en) * | 2015-11-25 | 2016-03-30 | 成都理工大学 | Stratum aperture pressure prediction method based on variety earthquake attributes |
| CN106285642A (en) * | 2015-05-14 | 2017-01-04 | 中国石油化工股份有限公司 | A kind of formation pore pressure Forecasting Methodology based on seismic data |
| CN106368691A (en) * | 2015-07-24 | 2017-02-01 | 中国石油化工股份有限公司 | Method for predicting three-dimensional abnormal pore pressure based on rock physical seismic information |
| CN106845086A (en) * | 2016-12-30 | 2017-06-13 | 中国石油天然气集团公司 | formation pressure calculation method and device |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6751558B2 (en) * | 2001-03-13 | 2004-06-15 | Conoco Inc. | Method and process for prediction of subsurface fluid and rock pressures in the earth |
| GB2404988B (en) * | 2002-04-10 | 2006-04-12 | Schlumberger Technology Corp | Method,apparatus and system for pore pressure prediction in presence of dipping formations |
| US7493241B2 (en) * | 2003-07-23 | 2009-02-17 | Lee Wook B | 3D velocity modeling, with calibration and trend fitting using geostatistical techniques, particularly advantageous for curved for curved-ray prestack time migration and for such migration followed by prestack depth migration |
| FR2893421B1 (en) * | 2005-11-14 | 2007-12-21 | Inst Francais Du Petrole | METHOD FOR QUANTITATIVE EVALUATION OF FLUID PRESSURES AND DETECTION OF SURPRESSIONS OF A SUBTERRANEAN MEDIUM. |
| BRPI0810840A2 (en) * | 2007-05-09 | 2014-10-29 | Exxonmobil Upstream Res Co | METHODS FOR INVISION OF TIME LAPSE SEISMIC DATA AND TO PRODUCE HYDROCARBONS FROM A TRAINING IN A SUB SURFACE REGION |
| CN105242307B (en) * | 2015-09-22 | 2016-08-31 | 中国石油大学(北京) | Carbonate complex seismic reservoir porosity acquisition methods and device |
-
2017
- 2017-08-01 CN CN201710648490.XA patent/CN109339771B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101163990A (en) * | 2005-03-08 | 2008-04-16 | 地质力学国际公司 | Quantitative risk assessment applied to pore pressure prediction |
| CN102640018A (en) * | 2009-12-18 | 2012-08-15 | 雪佛龙美国公司 | Workflow for petrophysical and geophysical formation evaluation of wireline and LWD log data |
| CN103628866A (en) * | 2013-11-22 | 2014-03-12 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Method for obtaining stratum pressure coefficients of stratum |
| CN104698492A (en) * | 2013-12-09 | 2015-06-10 | 中国石油天然气股份有限公司 | Abnormal formation pressure calculation method |
| CN104267429A (en) * | 2014-09-30 | 2015-01-07 | 中国石油天然气股份有限公司 | Method and device for determining formation pressure |
| CN106285642A (en) * | 2015-05-14 | 2017-01-04 | 中国石油化工股份有限公司 | A kind of formation pore pressure Forecasting Methodology based on seismic data |
| CN106368691A (en) * | 2015-07-24 | 2017-02-01 | 中国石油化工股份有限公司 | Method for predicting three-dimensional abnormal pore pressure based on rock physical seismic information |
| CN105445791A (en) * | 2015-11-25 | 2016-03-30 | 成都理工大学 | Stratum aperture pressure prediction method based on variety earthquake attributes |
| CN106845086A (en) * | 2016-12-30 | 2017-06-13 | 中国石油天然气集团公司 | formation pressure calculation method and device |
Non-Patent Citations (3)
| Title |
|---|
| Use of reflection tomography to predict pore pressure in overpressured reservoir sands;C.M. Sayers等;《the 2003 SEG Annual Meeting》;20031031;第1-4页 * |
| 地震地层压力预测综述;孙武亮,等;《勘探地球物理进展》;20071231;第30卷(第6期);第428-432页 * |
| 基于三维地震数据的地层压力预测方法研究;谭峰;《中国优秀硕士学位论文全文数据库 基础科学辑》;20170315;第12-32页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109339771A (en) | 2019-02-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106951660B (en) | Sea facies clastic rock horizontal well reservoir logging interpretation method and device | |
| Close et al. | Integrated workflows for shale gas and case study results for the Horn River Basin, British Columbia, Canada | |
| Ameen et al. | Diverse fracture properties and their impact on performance in conventional and tight-gas reservoirs, Saudi Arabia: The Unayzah, South Haradh case study | |
| CN110321595B (en) | Fault sealing evaluation method for extracting static quality coefficient by logging | |
| Liu et al. | Asymmetric propagation mechanism of hydraulic fracture networks in continental reservoirs | |
| Alfataierge et al. | 3D hydraulic-fracture simulation integrated with 4D time-lapse multicomponent seismic and microseismic interpretations, Wattenberg Field, Colorado | |
| CN109339771B (en) | Shale hydrocarbon reservoir pore pressure prediction method and system | |
| Shoemaker et al. | Calculating far-field anisotropic stress from 3D seismic in the Permian Basin | |
| Buijs et al. | An engineered approach to hydraulically fracture the Vaca Muerta shale | |
| Alimahomed* et al. | Impact of pore pressure on modeled hydraulic fracture geometry and well spacing in the East Duvernay Shale Basin, Canada | |
| Soltanzadeh* et al. | Application of mechanical and mineralogical rock properties to identify fracture fabrics in the Devonian Duvernay formation in Alberta | |
| Cherian et al. | Understanding development drivers in horizontal wellbores in the Midland Basin | |
| Soltanzadeh et al. | A regional review of geomechanical drilling experience and problems in the Duvernay Formation in Alberta | |
| Sabea et al. | Geological model of the Khabour Reservoir for studying the gas condensate blockage effect on gas production, Akkas Gas Field, Western Iraq | |
| Abass et al. | Integration of mud logging and wire-line logging to detect overpressure zones: a case study of middle Miocene Kareem Formation in Ashrafi oil field, Gulf of Suez, Egypt | |
| Peza et al. | 3-D Integrated Workflow for Understanding the Fracture Interference and Its Impact into the Gas Production of the Woodford Shale | |
| Grazulis | Analysis of stress and geomechanical properties in the Niobrara Formation of Wattenberg Field, Colorado, USA | |
| Martemyanov et al. | Analytic modelling for wellbore stability analysis | |
| Wüst et al. | Interbedded Carbonate and Calcareous Shales of the Devonian Duvernay Formation of Alberta, Canada: Implications for Completion Due to High Variability of Geomechanical Properties | |
| Zeng et al. | A new method for assessing stage-based hydraulic fracturing quality in Wolfcamp formation of Permian Basin | |
| Hai et al. | The Characteristics of Hydraulic Fracture Growth in Woodford Shale, the Anadarko Basin, Oklahoma | |
| Altaf et al. | 3D Geomechanical Modelling for Unconventional Sweet Spot Identification and Optimized Reservoir Development | |
| CN110297280A (en) | A kind of prediction technique of carbonate rock superpressure spatial distribution characteristic | |
| Ezati et al. | A new workflow to quantitative 3D screening of suitable zones for hydraulic fracturing | |
| Chatterjee et al. | Stress modeling for the oil and gas fields of Krishna-Godavari and Cauvery basins, India, using finite element technique |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |