CN107917865B - compact sandstone reservoir multi-parameter permeability prediction method - Google Patents

compact sandstone reservoir multi-parameter permeability prediction method Download PDF

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CN107917865B
CN107917865B CN201610888949.9A CN201610888949A CN107917865B CN 107917865 B CN107917865 B CN 107917865B CN 201610888949 A CN201610888949 A CN 201610888949A CN 107917865 B CN107917865 B CN 107917865B
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particle size
permeability
porosity
formula
geological
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CN107917865A (en
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叶素娟
蔡李梅
付菊
蒙欣
黎青
胡元
操延辉
马森
张世华
张庄
赵迪
李强
董军
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China Petrochemical Corp
Sinopec Southwest Oil and Gas Co
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Sinopec Southwest Oil and Gas Co
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/08Investigating permeability, pore-volume, or surface area of porous materials
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/30Analysis
    • G01V1/306Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00Details of seismic processing or analysis
    • G01V2210/60Analysis
    • G01V2210/61Analysis by combining or comparing a seismic data set with other data
    • G01V2210/616Data from specific type of measurement
    • G01V2210/6161Seismic or acoustic, e.g. land or sea measurements
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00Details of seismic processing or analysis
    • G01V2210/60Analysis
    • G01V2210/61Analysis by combining or comparing a seismic data set with other data
    • G01V2210/616Data from specific type of measurement
    • G01V2210/6169Data from specific type of measurement using well-logging
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00Details of seismic processing or analysis
    • G01V2210/60Analysis
    • G01V2210/62Physical property of subsurface
    • G01V2210/624Reservoir parameters
    • G01V2210/6246Permeability

Abstract

The invention relates to the field of fine prediction and evaluation of compact heterogeneous sandstone reservoirs, and discloses a permeability multi-parameter prediction method for compact sandstone reservoirs.

Description

compact sandstone reservoir multi-parameter permeability prediction method
Technical Field
The invention relates to the field of fine prediction and evaluation of compact heterogeneous sandstone reservoirs, in particular to reservoir evaluation with complex pore structures and poor pore permeability correlation, and specifically relates to a multi-parameter permeability prediction method for compact sandstone reservoirs.
Background
In general reservoir classification evaluation, generally adopts porosity as a main evaluation index to determine an effective reservoir and perform classification evaluation on the reservoir, in areas with relatively simple pore structures and good pore permeability correlation, the evaluation result has better matching with the actual production condition, however, for reservoirs with complex pore structures and poor pore permeability correlation, the evaluation result based on the porosity is generally greatly different from the actual production condition.
The permeability Prediction method comprises the steps of establishing various permeability Prediction methods and models for compact heterogeneous sandstone, comprehensively analyzing and testing the permeability of heterogeneous sandstone reservoirs based on flow unit classification, 2013), dividing flow units of a D404 block grape oil layer of a new station oil field in Songliao basin, establishing permeability Prediction models of medium-low and medium-low permeability heterogeneous sandstone, researching the permeability of JS2 gas reservoir groups in New Sichuan province according to the permeability Prediction models of different types of sandstone pore permeability quantitative analysis based on sandstone lithology characteristics, respectively calculating the permeability by using the permeability Prediction models of pore permeability quantitative relation of different types of reservoirs, improving the permeability obtaining precision, researching the Pacifolong et al (predicting the permeability of sandstone reservoirs based on fractal theory, 2001) discussing the fractal structure of rock pores and the determination method of the fractal dimension according to the basic theory of fractal geometry compactness theory of rock, improving the Kozeny-Carriman equation, adjusting the dynamic permeability of particles and the effective pore radius of the sandstone reservoirs to be suitable for the permeability Prediction of the Safti reservoir, establishing the permeability Prediction models of the permeability of the shale rocks based on the fuzzy theory of the permeability, and the permeability Prediction models of the permeability of the Mastigjokutzian, establishing the permeability of rock pore space.
At present, a prediction method and a model for permeability of a compact heterogeneous sandstone reservoir are mainly based on flow unit classification, sandstone texture classification, fractal theory, neural network theory and the like, wherein the method mainly starts from rock physical parameters (porosity f, permeability k, reservoir quality index RQI and the like), and adopts different statistical methods to establish a permeability prediction model with multiple logging parameters by analyzing typical logging response characteristics of the parameters.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides compact sandstone reservoir multi-parameter permeability prediction methods.
Specifically, the invention provides a multi-parameter permeability prediction method for tight sandstone reservoirs, which comprises the following steps:
(1) determining geological main control factors of permeability in the tight sandstone reservoir, wherein the geological main control factors comprise porosity, granularity and crack development degree;
(2) establishing a logging prediction model and an earthquake prediction model of porosity and granularity;
(3) determining geological main control factors of crack development degree;
(4) establishing a crack development index model according to the geological main control factors obtained in the step (3);
(5) and establishing a seismic-geological constrained multi-parameter permeability comprehensive prediction model.
The seismic-geological constrained multi-parameter permeability comprehensive prediction method formed by the method can realize plane accurate prediction of permeability, lays a foundation for fine evaluation of heterogeneous compact sandstone reservoirs based on permeability, promotes development of subject construction, and has strong guidance.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and constitute a part of this specification, and together with the following detailed description , serve to explain the invention without limiting it.
Fig. 1 is a flow chart of a method of permeability prediction in tight sandstone reservoirs of the present invention.
Detailed Description
The following describes in detail specific embodiments of the present invention. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
For numerical ranges, between the endpoints of each range and the individual points, and between the individual points may be combined with each other to yield new numerical ranges or ranges, which should be considered as specifically disclosed herein.
The invention provides a multi-parameter permeability prediction method for tight sandstone reservoirs, which comprises the following steps:
(1) determining geological main control factors of permeability in the tight sandstone reservoir, wherein the geological main control factors comprise porosity, granularity and crack development degree;
(2) establishing a logging prediction model and an earthquake prediction model of porosity and granularity;
(3) determining geological main control factors of crack development degree;
(4) establishing a crack development index model according to the geological main control factors obtained in the step (3);
(5) and establishing a seismic-geological constrained multi-parameter permeability comprehensive prediction model.
The compact sandstone reservoir can be various compact sandstone reservoirs, such as kinds of compact sandstone of dwarass system in a depression river gas field in western province, compact sandstone of sanshoxus river group and dwarass system in western province, ancient sandstone in an Ordoss basin, and dwarass sandstone in a Tarriss basin.
Wherein, the Strophanthus diversicolor gas field Jurassic tight sandstone gas reservoir belongs to a low-pore and low-permeability tight sandstone reservoir, and micro-cracks in the reservoir develop and have strong heterogeneity, so that the pore and permeability correlation of sandstone is poor. The commonly used porosity-permeability correlation equation has low predicted permeability accuracy, can not well explain the problems of low porosity, poor reservoir and high yield in exploration and development practice in a research area, and is difficult to adapt to the exploration and development practice requirements of the Zhongjiang gas field.
According to the method, preferably, in the step (1), the method for determining the geological main control factor of the permeability comprises the steps of carrying out geological factor comparison analysis on tight sandstone reservoir samples with the same porosity and different permeabilities to determine the geological main control factor influencing the permeability, wherein the geological factors can comprise the porosity, the lithology characteristics and the microcrack development degree of the tight sandstone reservoir samples, the lithology characteristics preferably comprise rock component parameters, rock granularity, roundness and sorting of the tight sandstone reservoir samples, more preferably, the rock texture parameters comprise clay mineral content and clay mineral granularity, in the invention, when the geological factor comparison analysis is carried out, the geological factors can be determined by a conventional method, and the quantitative geological factors in the tight sandstone reservoir samples with the same porosity and different permeabilities are counted to determine the geological main control factor quantitatively, in the case of the geological factors which cannot be determined quantitatively, the geological factors are different in the same porosity and different permeabilities are observed by a qualitative observation method to determine the geological qualitative factor of the different permeability, so that the geological factors in the geological main control factor, the geological factors and the rock roundness are represented by rolling angle , and the rock abrasion angle is represented by rolling angle.
In the present invention, in step (1), the determined geological master factors may include porosity, granularity and fracture development degree. Wherein the particle size may be reflected by the median particle size. Specifically, the median particle size is calculated by referring to the following formula (2) or formula (4).
According to the method, the method for establishing the logging prediction model is preferably a multiple linear regression method; the specific steps preferably include: on the basis of the porosity and the granularity of an actually measured sample, the correlation between the actually measured porosity and the granularity and logging parameters including AC, GR, CNL, DEN and RD is analyzed through a multiple regression method, wherein the CNL is a neutron logging value, the DEN is a density logging value, and the RD is a resistivity logging value, so that logging prediction models of the porosity and the granularity are obtained as shown in the formula (1) and the formula (2):
porosity of 0.4853 AC-26.725 formula (1)
Median particle size 2.7943 × △ GR +1.7207 formula (2)
Wherein, AC is a sonic logging value with the unit of μ s/ft, and the median of the particle size is a particle size value corresponding to the 50% particle content on the particle size probability accumulation curve with the unit ofGR is the gamma log with API, wherein △ GR is (GR-GR)min)/(GR-GRMax)。
In the present invention, the logging parameters AC and GR can be obtained according to a conventional logging method in the art. In the present invention, the particle diameter can be expressed by the length of the long axis of the granulated chips measured by a laboratory microscope. In the present invention, a symbol indicates a product relationship.
According to the method, the method for establishing the earthquake prediction model is preferably a multivariate linear regression method; preferably, the seismic prediction model obtained in step (2) is represented by formulas (3) and (4):
porosity-0.0020 wave impedance value +30.4138 formula (3)
Median particle size of-1.1 x 10-4Wave impedance value +3.096 type (4)
The unit of the wave impedance value is g/cc m/s; the unit of porosity is%, and the median of particle size is the corresponding particle size value at 50% of particle content on the particle size probability cumulative curve, and the unit is
In the present invention, the wave impedance value may be obtained by seismic exploration techniques, the wave impedance value being the velocity of seismic waves propagating in the rock multiplied by the density of the rock.
The invention provides a method for predicting fractures, which is characterized in that microcracks do not have typical well logging and seismic response characteristics, so that the fracture prediction is the difficulty in reservoir research, and the inventor of the invention finds that the fracture development degree of the Zhongjiang gas field Juroco system is mainly controlled by sand body configuration, distance from fault, coherence, curvature and the like, so that the inventor provides parameters capable of comprehensively representing the fracture development degree, namely fracture development indexes in research, and adopts a multiple linear regression method to determine the weight coefficient of each parameter and establish a calculation model formula (5) of the fracture development indexes on the basis of completing the data standardization of the sand body configuration, the distance from fault, the coherence, the curvature, the seismic prediction results and the like by combining the seismic multiple attribute fracture prediction results, and completes the prediction model of the fracture development degree according to the formula shown in the formula (5).
Fracture Development Index (FDI) — 35.219+0.0747 sand configuration-0.010353 distance from fault +36.7045 coherence +380.245 curvature formula (5).
The unit of the distance from the fault is kilometers, the sand body configuration is divided into equal , unequal (1 represents equal , 2 represents unequal ), no unit, and both coherence and curvature values are unitless.
In the invention, the distance from the fault refers to the vertical distance between a predicted sample point and the fault closest to the predicted sample point, the coherence and the curvature are calculated by a three-dimensional data volume acquired by seismic exploration, the coherence represents the correlation between the sample point and surrounding data, and the smaller the coherence value is, the more a crack develops; the curvature is the degree of change in the underground attitude of the geologic body, and the greater the curvature, the more developed the fracture. Specifically, the coherent calculation method may be calculated according to various conventional methods in the art, for example, the coherent technique based on complex seismic traces may be adopted, and the coherent technique is to describe the lateral heterogeneity of the stratum, the rock, and the like by using the change of the coherent value of the seismic signal, so as to study the spatial distribution of faults and micro-fractures, the overall spatial distribution characteristics of geological structure anomalies and lithology. The curvature is used for predicting the development degree of the crack according to the bending degree of the seismic reflection homophase axis of the compact and brittle rock. The curvature can be mathematically represented by the inclination angle and the derivative of the inclination angle.
According to the method, in the step (5), a permeability comprehensive prediction model is established by a multi-parameter fitting method, wherein the permeability comprehensive prediction model is shown as the formula (6):
K=e(1.9412+0.25645 porosity-4.11463 median particle size +2.50071 fracture growth index)Formula (6)
Wherein K is permeability, the unit is mD, the unit of porosity is%, the median value of particle size is the corresponding particle size at 50% of particle content on the particle size probability cumulative curve, and the unit isWhereinAnd mD means millidarcy.
In the present invention, the method for obtaining the comprehensive permeability prediction model may be various conventional methods in the art, for example, various conventional multi-parameter fitting methods.
In the invention, when well drilling control exists (well drilling and well logging data exist), the data obtained by calculation of the formulas (1) and (2) and the fracture development index obtained by the formula (5) are substituted into the formula (6) to calculate permeability, and when no well drilling control exists or well drilling control exists but no single well logging data exists, the data obtained by calculation of the formulas (3) and (4) and the fracture development index obtained by the formula (5) are substituted into the formula (6) to calculate permeability, so that the aim of predicting the plane permeability is fulfilled.
The present invention will be described in detail below by way of examples.
The porosity and permeability measurements are made in the laboratory by conventional instruments using small pieces of cores or cuttings. Wave impedance values, which are the velocity of seismic waves propagating in the rock multiplied by the density of the rock, can be obtained by seismic exploration techniques. AC is the acoustic velocity log and is the time difference Δ t of the propagation of the gliding waves through the formation in μ s/ft. Particle size is measured by laboratory microscopy in mm based on the long axis of the granulated crumb.
Examples
This example illustrates the method of predicting permeability in tight sandstone reservoirs.
(1) Determining the permeability and the porosity of each sample of a dwarf sandstone reservoir of a dwarf gas field in a West China depression, and performing geological factor comparative analysis on the samples of the compact sandstone reservoir with the same porosity and different permeabilities, wherein the geological factors comprise rock composition parameters (including clay mineral content), rock granularity, roundness, sorting, porosity, microcrack development degree and other factors, and finally determining that the geological main control factors influencing the permeability of the dwarf sandstone reservoir of the dwarf gas field in the West China depression are rock porosity, granularity and microcrack development degree, wherein the granularity can be reflected by a median value of the granularity, and the calculation of the median value of the granularity is shown in the following formula (2) or formula (4);
(2) a logging prediction model of permeability influencing geological main control factors is established through a multivariate linear regression method, and the specific method comprises the following steps: on the basis of the porosity and the granularity of an actually measured sample, the correlation between the actually measured porosity and the granularity and logging parameters including AC, GR, CNL, DEN and RD is analyzed through a multiple regression method, so that a logging prediction model of the porosity and the granularity content is obtained, and the established logging prediction model is as follows:
porosity of 0.4853 AC-26.725 formula (1)
Median particle size 2.7943 × △ GR +1.7207 formula (2)
Wherein, AC is a sonic logging value with the unit of μ s/ft, and the median of the particle size is a particle size value corresponding to the 50% particle content on the particle size probability accumulation curve with the unit ofGR is the gamma log with API, wherein △ GR is (GR-GR)min)/(GR-GRMax);
The method for establishing the earthquake prediction model is a multivariate linear regression method; the obtained earthquake prediction model is shown as the formula (3) and the formula (4):
porosity-0.0020 wave impedance value +30.4138 formula (3)
Median particle size of-1.1 x 10-4Wave impedance value +3.096 type (4)
The unit of the wave impedance value is g/cc m/s; the unit of porosity is%, and the median of particle size is the corresponding particle size value at 50% of particle content on the particle size probability cumulative curve, and the unit is
(3) The geological main control factors for determining the development degree of the crack are sand body configuration, distance from a fault, coherence and curvature;
(4) measuring or calculating the sand body configuration, the distance from the sand body to the fault, the seismic coherence attribute and the curvature in the tight sandstone reservoir sample, determining the weight coefficients of the sand body configuration, the distance from the sand body to the fault, the coherence and the curvature by adopting a multiple linear regression method, establishing a fracture development index model as shown in a formula (5),
fracture growth index (FDI) ═ 35.219+0.0747 sand configuration-0.010353 from fault distance +36.7045 coherence +380.245 curvature formula (5)
Wherein, the unit of the distance from the fault is kilometer, the sand body configuration is divided into equal , unequal (1 represents equal , 2 represents unequal ), no unit of coherence and curvature values, and the unit of the distance from the fault is kilometer, but when the distance is substituted into formula (5), only numerical values are substituted, so the calculated Fracture Development Index (FDI) has no unit;
(5) a permeability comprehensive prediction model is established by a multi-parameter fitting method, and is shown as the formula (6):
K=e(1.9412+0.25645 porosity-4.11463 median particle size +2.50071 fracture growth index)Formula (6)
Wherein K is permeability, the unit is mD, the unit of porosity is%, the median value of particle size is the corresponding particle size at 50% of particle content on the particle size probability cumulative curve, and the unit is
(6) When well logging data exist in a well, the data obtained by calculation of the formulas (1) and (2) and the fracture development index obtained by the formula (5) are substituted into the formula (6) to calculate permeability, and when no well logging data exist or well logging data exist but no single well logging data exist, the data obtained by calculation of the formulas (3) and (4) and the fracture development index obtained by the formula (5) are substituted into the formula (6) to calculate permeability, so that the aim of predicting plane permeability is fulfilled.
TABLE 1
TABLE 2
It can be seen from the data in tables 1 and 2 above that the permeability comprehensive prediction model established by the invention can predict the permeability of the tight sandstone reservoir, the predicted permeability is closer to the actually measured permeability, the correlation coefficient reaches 0.88, and the correlation between the permeability predicted by the single parameter permeability prediction model and the actually measured permeability is only 0.68, so that the prediction model of the invention is more accurate than the single parameter model in predicting the permeability of the tight sandstone reservoir.
The preferred embodiments of the present invention have been described in detail, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.

Claims (7)

1, compact sandstone reservoir multi-parameter permeability prediction method, which is characterized by comprising the following steps:
(1) performing geological factor comparative analysis on tight sandstone reservoir samples with the same porosity and different permeabilities to determine geological main control factors of the permeability in the tight sandstone reservoir, wherein the geological main control factors comprise the porosity, the granularity and the development degree of cracks;
(2) establishing a logging prediction model and an earthquake prediction model of porosity and granularity;
(3) determining geological master factors of crack development degree, wherein the geological master factors of the crack development degree comprise: sand configuration, standoff distance, coherence and curvature in the tight sandstone reservoir sample;
(4) establishing a crack development index model according to the geological main control factors obtained in the step (3);
(5) and establishing a seismic-geological constrained multi-parameter permeability comprehensive prediction model.
2. The method of claim 1, wherein the method of modeling the well log prediction in step (2) is a multiple linear regression method.
3. The method of claim 1 or 2, wherein the log prediction model established in step (2) is as shown in equations (1) and (2):
porosity of 0.4853 AC-26.725 formula (1)
Median particle size 2.7943 × △ GR +1.7207 formula (2)
Wherein, AC is a sonic logging value with the unit of μ s/ft, and the median of the particle size is a particle size value corresponding to the 50% particle content on the particle size probability accumulation curve with the unit ofGR is the gamma log with API, wherein △ GR is (GR-GR)min)/(GR-GRMax)。
4. The method of claim 1, wherein the method of modeling seismic predictions in step (2) is a multiple linear regression method.
5. The method of claim 1 or 4, wherein the seismic prediction model established in step (2) is as shown in equations (3) and (4):
porosity-0.0020 wave impedance value +30.4138 formula (3)
Median particle size of-1.1 x 10-4Wave impedance value +3.096 type (4)
The unit of the wave impedance value is g/cc m/s; the unit of porosity is%, and the median of particle size is the corresponding particle size value at 50% of particle content on the particle size probability cumulative curve, and the unit is
6. The method according to claim 1, wherein in the step (4), a multiple linear regression method is adopted to determine weight coefficients of sand body configuration, distance from fault, coherence and curvature, a fracture development index model is established, as shown in formula (5),
fracture Development Index (FDI) — 35.219+0.0747 sand configuration-0.010353 distance from fault +36.7045 coherence +380.245 curvature formula (5).
7. The method according to claim 1, wherein in the step (5), the permeability comprehensive prediction model is established by a multi-parameter fitting method as shown in formula (6):
K=e(1.9412+0.25645 porosity-4.11463 median particle size +2.50071 fracture growth index)Formula (6)
Wherein K is permeability, the unit is mD, the unit of porosity is%, the median value of particle size is the corresponding particle size at 50% of particle content on the particle size probability cumulative curve, and the unit is
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