CN114720494A - XRD (X-ray diffraction) -based prediction method and device for crack opening coefficient of full-rock logging buried hill reservoir - Google Patents

XRD (X-ray diffraction) -based prediction method and device for crack opening coefficient of full-rock logging buried hill reservoir Download PDF

Info

Publication number
CN114720494A
CN114720494A CN202210199743.0A CN202210199743A CN114720494A CN 114720494 A CN114720494 A CN 114720494A CN 202210199743 A CN202210199743 A CN 202210199743A CN 114720494 A CN114720494 A CN 114720494A
Authority
CN
China
Prior art keywords
elastic
infinitesimal
mineral
rock
model
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.)
Pending
Application number
CN202210199743.0A
Other languages
Chinese (zh)
Inventor
谭伟雄
万欢
谭忠健
李辉
胡云
尚锁贵
王建立
李戈东
张磊
杜波
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
CNOOC Energy Technology and Services Ltd
Original Assignee
CNOOC Energy Technology and Services Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by CNOOC Energy Technology and Services Ltd filed Critical CNOOC Energy Technology and Services Ltd
Priority to CN202210199743.0A priority Critical patent/CN114720494A/en
Publication of CN114720494A publication Critical patent/CN114720494A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N23/00Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
    • G01N23/20Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by using diffraction of the radiation by the materials, e.g. for investigating crystal structure; by using scattering of the radiation by the materials, e.g. for investigating non-crystalline materials; by using reflection of the radiation by the materials
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V9/00Prospecting or detecting by methods not provided for in groups G01V1/00 - G01V8/00
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02Agriculture; Fishing; Forestry; Mining
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C10/00Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/20Identification of molecular entities, parts thereof or of chemical compositions

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Strategic Management (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • General Business, Economics & Management (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Marketing (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Tourism & Hospitality (AREA)
  • Primary Health Care (AREA)
  • Mining & Mineral Resources (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Animal Husbandry (AREA)
  • Agronomy & Crop Science (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geophysics (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Analysing Materials By The Use Of Radiation (AREA)

Abstract

The invention discloses a method and a device for predicting a fracture opening coefficient of a buried hill reservoir based on XRD (X-ray diffraction), wherein the device comprises the following steps: the device comprises a rock debris collecting, cleaning, drying and grinding device, a portable XRD diffractometer, a matrix rock mineral elastic infinitesimal simulator, a matrix rock elastic parameter calculator, a crack opening coefficient calculator, an explanation mapping device and a long graph printing device. Sampling, cleaning, drying and grinding the upward-returning rock debris to prepare XRD diffraction standard rock debris powder; testing an X-ray diffraction spectrogram, and determining the type and content of minerals; the matrix rock mineral elastic infinitesimal simulator performs series and parallel combination of mineral elastic infinitesimal, and calculates elastic parameters; logging and experimental calibration, and calculating elastic parameters of the matrix rock; calculating a crack opening coefficient; and the calculation result is interpreted into a graph in real time while drilling and is printed in a long graph. The method solves the problem of lack of means for predicting the underground hill reservoir fractures and evaluating the underground hill reservoir fractures while drilling in real time, and effectively solves the problems of poor continuity and low accuracy.

Description

XRD (X-ray diffraction) -based prediction method and device for crack opening coefficient of full-rock logging buried hill reservoir
Technical Field
The invention relates to the field of petroleum and gas logging, in particular to a method and a device for predicting a buried hill reservoir fracture opening coefficient based on XRD (X-ray diffraction) whole rock logging.
Background
With the deepening of oil and gas exploration, buried hill oil and gas reservoirs are broken through to a certain degree, complex buried hill fractured reservoirs are more and more urgently recognized, and reservoir fractures are predicted and evaluated, so that the method is a core work of buried hill reservoir exploration operation.
The physical information of the rock debris obtained by the rock debris logging is the most direct reflection of the stratum property, but with the use of a new drilling technology, the rock debris returned from the bottom of a well is usually very fine and even powdery, so that the property of a reservoir stratum is difficult to directly judge. The XRD diffraction whole rock logging technology is a mainstream logging technology developed in recent years, mineral components and content are obtained by carrying out X-ray diffraction whole rock analysis on a rock debris sample, and the XRD diffraction whole rock logging technology is mainly applied to formation lithology nomenclature at present. The actual mineral components contain rich stratum information, which is a main control factor of the mechanical property and the crack development difficulty of the stratum, at present, the information is not fully excavated, and the application of the information in the aspects of rock mechanical characteristics and reservoir crack development characteristics is particularly urgent and important.
Disclosure of Invention
The invention aims to provide a method and a device for predicting the opening coefficient of a buried hill reservoir fracture based on XRD (X-ray diffraction) whole rock logging.
In a first aspect, the invention provides a method for predicting a buried hill reservoir fracture opening coefficient based on XRD whole rock logging, which is carried out according to the following steps:
step 1: and designing a sampling interval according to the encryption principle of the sparse reservoir section of the non-reservoir section, and then continuously picking fresh rock debris really coming from the bottom of the well in front of the vibrating screen strictly according to the late time and the sampling interval.
Step 2: and processing the rock debris into an XRD diffraction analysis standard sample, testing an X-ray diffraction spectrogram of the sample, and determining the mineral content of rock matrixes of strata at different depths.
And 3, step 3: constructing an elastic infinitesimal model of the matrix rock mineral, idealizing various minerals into an elastic infinitesimal, enabling the elastic infinitesimal to be consistent with the self mechanical properties of each mineral, and characterizing and determining the constitutive equation and the elastic parameters of each elastic infinitesimal.
And 4, step 4: and (3) constructing a mineral elastic infinitesimal combined physical model, and establishing the mineral elastic infinitesimal combined physical model by connecting various mineral elastic infinitesimals in series and in parallel based on a rheological model theory method.
And 5: based on the principle that the total stress of the mineral elastic infinitesimal series model is equal to the stress of each mineral elastic infinitesimal, and the total strain of the mineral elastic infinitesimal series model is equal to the sum of the strains of each mineral elastic infinitesimal, the constitutive equation and the elastic parameters of the mineral elastic infinitesimal series model are established.
Step 6: and establishing an constitutive equation and an elastic parameter model of the mineral elastic infinitesimal parallel model based on the principle that the total stress of the mineral elastic infinitesimal parallel model is equal to the sum of the stresses of all mineral elastic infinitesimals and the total strain of the mineral elastic infinitesimal parallel model is equal to the strain of all mineral elastic infinitesimals.
And 7: and (3) carrying out logging and indoor experimental calibration on the elastic modulus and the Poisson ratio calculated by the mineral elastic infinitesimal series model and the mineral elastic infinitesimal parallel model, and establishing a matrix rock elastic modulus and Poisson ratio mathematical physical equation suitable for regional characteristics.
And 8: based on a classical equation of opening increment of the uniformly distributed load straight cracks under two-dimensional plane strain, a crack opening coefficient is defined, and an elastic modulus and Poisson ratio expression form of the crack opening coefficient is established.
And step 9: based on the mineral content results of the rock matrixes of different-depth strata measured in the steps 1 and 2, a mineral elastic infinitesimal model and a mineral elastic infinitesimal combined physical model of the matrix rock are constructed by using the methods in the steps 3 and 4, an constitutive equation and elastic parameters of a mineral elastic infinitesimal series model and a mineral elastic infinitesimal parallel model are deduced by combining the principles in the steps 5 and 6, logging and indoor experimental calibration are carried out by using the method in the step 7, the elastic modulus and Poisson's ratio mathematical physical equations of the matrix rock in a characteristic region are established, and the fracture opening coefficient of the matrix rock and the fracture development characteristic predicted by a reservoir are obtained by substituting the physical equations in the step 8.
The further technical scheme is that the mineral elastic element combination physical model in the step 4 comprises a mineral elastic element series model and a mineral elastic element parallel model, wherein the relationship between the load and the deformation of the mineral elastic element series model is as follows:
Fz=F1z=F2z=…Fnz
ΔLz=ΔL1+ΔL2+…ΔLn
in the formula: fzThe total load, KN, of the mineral elastic infinitesimal series model; Δ LzThe total deformation of the mineral elastic infinitesimal series model under the total load; fiz(i ═ 1,2, …, n) is the load to which a mineral elastic element is subjected; Δ Li(i-1, 2, …, n) is a mineral elastic microThe amount of deformation of the element.
The load and deformation relation of the mineral elastic infinitesimal parallel model is as follows:
Fr=F1r+F2r+…Fnr
ΔL=ΔL1=ΔL2=…ΔLn
in the formula: frThe total load, KN, borne by the mineral elastic infinitesimal parallel model; Δ LrThe total load total deformation of the mineral elastic infinitesimal parallel model is obtained; fir(i-1, 2, …, n) is the load that a mineral elastic element is subjected to.
The further technical scheme is that the constitutive equation and the elastic parameters of the mineral elastic infinitesimal series model established in the step 5 are characterized as follows:
the constitutive equation of the mineral elastic infinitesimal series model is
Figure BDA0003527065200000031
The mineral elastic infinitesimal series model has the elastic modulus of
Figure BDA0003527065200000032
The Poisson ratio of the mineral elastic infinitesimal series model is
Figure BDA0003527065200000033
In the formula: sigmazThe total stress borne by the mineral elastic infinitesimal series model is determined; ezThe mineral elastic infinitesimal series model elastic modulus; mu.szThe Poisson ratio is a mineral elastic infinitesimal series model; ei(i ═ 1,2, …, n) is the i-th mineral elastic modulus; mu.si(i ═ 1,2, …, n) for the ith mineral elastic microcell poisson's ratio;
Figure BDA0003527065200000034
the content of the ith mineral.
The further technical scheme is that the constitutive equation and the elastic parameters of the mineral elastic infinitesimal parallel model established in the step 6 are characterized as follows:
constitutive equation of mineral elastic infinitesimal parallel model
Figure BDA0003527065200000035
The mineral elastic infinitesimal parallel model has the elastic modulus of
Figure BDA0003527065200000036
The Poisson ratio of the mineral elastic infinitesimal parallel model is
Figure BDA0003527065200000037
In the formula: sigmarStress borne by the mineral elastic infinitesimal parallel model; erThe mineral elastic infinitesimal parallel model elastic modulus; mu.srThe Poisson's ratio is a mineral elastic infinitesimal series model.
The further technical scheme is that in the step 7, the elastic modulus and Poisson ratio well logging and indoor experimental calibration calculated by the serial model and the parallel model are carried out, and a matrix elastic parameter mathematical physical equation suitable for regional characteristics is constructed as follows
Figure BDA0003527065200000041
Figure BDA0003527065200000042
In the formula: eAIs the rock matrix equivalent elastic modulus; alpha is the equivalent elastic modulus correction coefficient of the rock matrix; mu.sAIs the rock matrix equivalent Poisson's ratio, beta is the rock matrix equivalentPoisson ratio correction factor.
The further technical proposal is that the expression forms of the elastic modulus and the Poisson ratio of the crack opening coefficient in the step 8 are as follows
Figure BDA0003527065200000043
In the formula: fwIs the crack opening coefficient.
In a second aspect, the invention provides a prediction device for opening coefficient of buried hill reservoir fractures based on XRD whole rock logging, which comprises: the device comprises a rock debris collecting device, a rock debris cleaning device, a rock debris drying device, a rock debris grinding device, a portable XRD diffractometer, a matrix rock mineral elasticity infinitesimal simulator, a matrix rock elasticity parameter calculating device, a crack opening coefficient calculating device, an explanation mapping device and a long graph printing device.
The working process of the device is as follows: the rock debris collecting device is arranged below the sand outlet of the vibrating screen, and rock debris falls into the sampler along the inclined surface of the screen cloth; selecting fresh rock debris, putting the fresh rock debris into a rock debris cleaning device, and cleaning the rock debris under the condition of fully stirring the rock debris until the rock debris leaks out of the natural color; putting the cleaned rock debris into a rock debris drying device, and drying in an environment with the temperature controlled to be less than 85 ℃; putting the dried rock debris into a rock debris grinding device for grinding to enable the rock debris to be powder, wherein the granularity is less than 20 um; placing the ground rock debris powder into a portable XRD diffractometer, testing an X-ray diffraction spectrogram of the powder, and explaining the mineral types and content data of rock matrixes of strata at different depths; transmitting the mineral species and content data of the rock matrix to a matrix rock mineral elastic infinitesimal simulator, constructing mineral elastic infinitesimal by the matrix rock mineral elastic infinitesimal simulator according to the mineral species and content, combining the mineral elastic infinitesimal in series and in parallel, and calculating an elastic parameter E under the combination of series and in parallelzz,Err(ii) a The calculated elastic parameter Ezz,ErrTransmitting to a matrix rock elasticity parameter calculating device for calculating the matrix rock elasticity parameter EAAnd muA(ii) a The elastic parameter E of the matrix rockAAnd muATransmitting the crack opening coefficient to a crack opening coefficient calculation device to calculate a crack opening coefficient Fw(ii) a The elastic parameter E of the matrix rock calculated by the matrix rock elastic parameter calculating device and the crack opening coefficient calculating deviceAAnd muAAnd crack opening coefficient FwThe data are transmitted and stored in an interpretation mapping device, mapping is carried out, and the development condition of reservoir fractures is forecasted in real time; and finally, printing and archiving the images by the long-image printing device.
Compared with the prior art, the invention has the advantages that:
according to the invention, XRD diffraction whole-rock mineral analysis is carried out on rock debris collected by logging while drilling to obtain the mineral content of rock matrixes of strata at different depths, and then the constitutive equation and mechanical parameters of the rock matrixes are deduced by combining a mineral combination physical model, and the fracture opening coefficient is forecasted, so that the prediction and evaluation of reservoir fractures are realized, and the method has the advantages of low cost, high data collection real-time property, section prediction continuity and the like.
Drawings
FIG. 1: a flow diagram of a stratum fracture opening coefficient prediction method based on XRD whole rock logging is shown.
FIG. 2: mineral elastic infinitesimal model.
FIG. 3: mineral elastic infinitesimal series model.
FIG. 4 is a schematic view of: mineral elastic infinitesimal parallel model.
FIG. 5: crack opening coefficient application example.
FIG. 6: schematic diagram of a stratum fracture opening coefficient prediction device based on XRD whole rock logging.
For a person skilled in the art, without inventive effort, other relevant figures can be derived from the above figures.
Detailed Description
The present invention is described below based on examples, but it should be noted that the present invention is not limited to these examples. In the following detailed description of the present invention, certain specific details are set forth. However, the present invention will be fully understood by those skilled in the art for those parts which have not been described in detail.
Furthermore, those skilled in the art will appreciate that the drawings are provided for purposes of illustrating the objects, features, and advantages of the invention and are not necessarily drawn to scale.
Examples
Fig. 1 is a flow chart of a method for predicting an opening coefficient of a formation fracture based on XRD whole rock logging, and a specific embodiment of the invention is described with reference to fig. 1.
Step 1 (101): the sampling interval is designed according to the principle that a non-reservoir section is sparse and a reservoir section is encrypted, then rock debris is continuously picked in front of a vibrating screen strictly according to late time and the sampling interval, various false rock debris, residual rock debris, collapsed matters and falling blocks are removed, and the real fresh rock debris coming from the bottom of a well, which is small in individual debris, fresh in color tone and obvious in edge angle, is picked out.
Step 2 (102): grinding fresh rock debris into powder, placing the powder into a sample pool of a radiation diffractometer, carrying out XRD diffraction experiment, testing the X-ray diffraction pattern of the sample, and determining the types and the contents of the minerals of rock matrixes of different depths of the stratum according to the characteristic peak value of each mineral.
Step 3 (103): constructing an elastic infinitesimal model of the mineral of the matrix rock, and idealized various minerals of the rock into an elastic infinitesimal, as shown in figure 2, wherein the elastic infinitesimal is consistent with the self-mechanical properties of each mineral, the self-constitutive equation and the elastic parameter of each mineral are also the constitutive equation and the elastic parameter of the elastic modulus of the elastic infinitesimal, and the elastic modulus and the poisson ratio of the elastic infinitesimal of common rock-making minerals are shown in table 1.
TABLE 1 elastic modulus and Poisson's ratio of common rock-making mineral elastic microelements
Mineral substance Modulus of elasticity (GPa) Poisson ratio
Quartz 96.4 0.09
Calcite 81.0 0.28
Plagioclase feldspar 74.9 0.28
Albite 78.5 0.29
Dolomite 121 0.28
Magnetite 230.8 0.26
Pyrite 299.9 0.16
Green curtain stone 154.2 0.26
Spinel 293.3 0.26
White mica 78.9 0.25
Biotite 69.66 0.25
Horniness amphibole 128.8 0.28
Pyroxene 143.7 0.24
Clay mineral 14.2 0.30
Step 4 (104): mineral elastic infinitesimal combination physical model construction based on rheological model theory method
Based on a rheological model theory method, various mineral elastic microelements are connected in series and in parallel to construct a mineral elastic microelement combined physical model, which comprises a mineral elastic microelement series model and a mineral elastic microelement parallel model. The mineral elastic infinitesimal series model is shown in figure 3, and the mineral elastic infinitesimal parallel model is shown in figure 4.
Wherein, the load and deformation relation of the mineral elastic infinitesimal series model follows the following criteria:
Fz=F1z=F2z=…Fnz
ΔLz=ΔL1+ΔL2+…ΔLn
the load and deformation relation of the mineral elastic infinitesimal parallel model follows the following criteria:
Fr=F1r+F2r+…Fnr
ΔL=ΔL1=ΔL2=…ΔLn
step 5 (105): based on the principle that the total stress of the mineral elastic infinitesimal series model is equal to the stress of each mineral elastic infinitesimal, and the total strain of the mineral elastic infinitesimal series model is equal to the sum of the strains of each mineral elastic infinitesimal, the constitutive equation and the elastic parameters of the mineral elastic infinitesimal series model are established.
The constitutive equation of the mineral elastic infinitesimal series model is
Figure BDA0003527065200000071
The mineral elastic infinitesimal series model has the elastic modulus of
Figure BDA0003527065200000072
The Poisson ratio of the mineral elastic infinitesimal series model is
Figure BDA0003527065200000073
Step 6 (106): and establishing an constitutive equation and an elastic parameter model of the mineral elastic infinitesimal parallel model based on the principle that the total stress of the mineral elastic infinitesimal parallel model is equal to the sum of the stresses of all mineral elastic infinitesimals and the total strain of the mineral elastic infinitesimal parallel model is equal to the strain of all mineral elastic infinitesimals.
Constitutive equation of mineral elastic infinitesimal parallel model
Figure BDA0003527065200000074
The mineral elastic infinitesimal parallel model has the elastic modulus of
Figure BDA0003527065200000075
The Poisson ratio of the mineral elastic infinitesimal parallel model is
Figure BDA0003527065200000076
Step 7 (107): and (3) logging and indoor experimental calibration are carried out on the elastic modulus and the Poisson ratio calculated by the mineral elastic infinitesimal series model and the mineral elastic infinitesimal parallel model, and a matrix rock elastic modulus and Poisson ratio mathematical physical equation suitable for a corresponding region is established, wherein the equation form is as follows:
Figure BDA0003527065200000081
Figure BDA0003527065200000082
step 8 (108): prediction of stratum fracture opening coefficient based on XRD (X-ray diffraction) whole rock logging
Based on a linear elastic fracture mechanics two-dimensional plane strain uniform load straight crack opening increment classical model, a crack opening coefficient is defined as:
Figure BDA0003527065200000083
given a change in drive stress, the higher the crack opening coefficient, the more likely the crack will open.
Step 9 (109): based on the mineral content results of the rock matrixes of different-depth strata measured in the steps 1 and 2, a mineral elastic infinitesimal model and a mineral elastic infinitesimal combined physical model of the matrix rock are constructed by using the methods in the steps 3 and 4, the constitutive equation and the elastic parameters of the mineral elastic infinitesimal combined physical model are deduced by combining the principles in the steps 5 and 6, the well logging and the indoor experiment calibration and the inverse calculation are carried out by using the method in the step 7, the rock matrix elastic modulus and the Poisson's ratio mathematical physical equation suitable for the regional characteristics are established, and the fracture opening coefficient of the matrix rock is worked out by substituting the step 8.
Step 10(110) determining the elastic parameter E of the matrix rockAAnd muAAnd crack opening coefficient FwAnd (5) explaining the diagram and printing the long diagram, and forecasting the development condition of the reservoir fracture in real time.
In a second aspect, the invention provides a prediction device for opening coefficient of buried hill reservoir fractures based on XRD whole rock logging, which comprises: the device comprises a rock debris collecting device 601, a rock debris cleaning device 602, a rock debris drying device 603, a rock debris grinding device 604, a portable XRD diffractometer 605, a matrix rock mineral elasticity infinitesimal simulator 606, a matrix rock elasticity parameter calculating device 607, a crack opening coefficient calculating device 608, an explanation drawing device 609 and a long graph printing device 610.
The working process of the device is as follows: the rock debris collecting device 601 is placed below a sand outlet of the vibrating screen, and rock debris falls into the sampler along the inclined surface of the screen cloth; selecting fresh rock debris, putting the fresh rock debris into a rock debris cleaning device 602, and cleaning the rock debris under the condition of fully stirring the rock debris until the rock debris leaks out of the natural color; putting the cleaned rock debris into a rock debris drying device 603, and drying in an environment with the temperature controlled to be less than 85 ℃; putting the dried rock debris into a rock debris grinding device 604 for grinding to enable the rock debris to be powder with the granularity smaller than 20 um; placing the ground rock debris powder into a portable XRD diffractometer 605, testing an X-ray diffraction spectrogram of the powder, and explaining the mineral types and content data of rock matrixes of strata at different depths; transmitting the mineral type and content data of the rock matrix to a matrix rock mineral elastic infinitesimal simulator 606, constructing mineral elastic infinitesimal by the matrix rock mineral elastic infinitesimal simulator according to the mineral type and content, combining the mineral elastic infinitesimal in series and in parallel, and calculating the elastic parameter E under the combination of series and in parallelzz,Err(ii) a The calculated elastic parameter Ezz,ErrTransmitted to the matrix rock elastic parameter calculation device 607 for calculating the matrix rock elastic parameter EAAnd muA(ii) a The elastic parameter E of the matrix rockAAnd muAThe crack opening coefficient is transmitted to the crack opening coefficient calculation device 608 to calculate the crack opening coefficient Fw(ii) a The elastic parameter E of the matrix rock calculated by the matrix rock elastic parameter calculating device and the crack opening coefficient calculating deviceAAnd muAAnd crack opening coefficient FwThe data is transmitted and stored in an interpretation mapping device 609, mapping is carried out, and the development condition of reservoir fractures is forecasted in real time; finally, the long-image printing device 610 prints and archives
The above description is only an exemplary embodiment of the present invention, and is not intended to limit the scope of the present invention. Any equivalent changes and modifications that can be made by one skilled in the art without departing from the spirit and principles of the invention should fall within the protection scope of the invention.

Claims (8)

1. A prediction method for opening coefficient of buried hill reservoir fractures based on XRD full-rock logging is characterized by comprising the following steps:
step 1: designing a sampling interval according to a principle of encryption of a non-reservoir section sparse reservoir section, then continuously picking fresh rock debris really coming from a well bottom in front of a vibrating screen strictly according to late time and the sampling interval, cleaning, drying and grinding the rock debris, and processing the rock debris into XRD diffraction analysis standard rock debris powder;
step 2: placing the standard rock debris powder into a sample pool of a portable XRD diffractometer, testing an X-ray diffraction spectrogram of the standard rock debris powder, and determining the types and the content of minerals of rock matrixes of strata at different depths;
and step 3: constructing a matrix rock mineral elastic infinitesimal model, idealized various minerals into an elastic infinitesimal, the elastic infinitesimal is consistent with the self mechanical properties of each mineral, and the constitutive equation and the elastic parameters of each mineral elastic infinitesimal are represented and determined;
and 4, step 4: constructing a mineral combination physical model, and establishing a mineral elastic infinitesimal combination physical model by connecting various mineral elastic infinitesimal in series and in parallel based on a rheological model theory method, wherein the mineral elastic infinitesimal combination physical model comprises a mineral elastic infinitesimal series model and a mineral elastic infinitesimal parallel model;
and 5: establishing a constitutive equation and an elastic parameter model of the mineral elastic infinitesimal series model based on the principle that the total stress of the mineral elastic infinitesimal series model is equal to the stress of each mineral elastic infinitesimal, and the total strain of the mineral elastic infinitesimal series model is equal to the sum of the stresses of each mineral elastic infinitesimal;
step 6: establishing a constitutive equation and an elastic parameter model of the mineral elastic infinitesimal parallel model based on the principle that the total stress of the mineral elastic infinitesimal parallel model is equal to the sum of the stresses of all mineral elastic infinitesimals and the total strain of the mineral elastic infinitesimal parallel model is equal to the strain of all mineral elastic infinitesimals;
and 7: carrying out logging and indoor experimental calibration on the elastic modulus and the Poisson ratio calculated by the mineral elastic infinitesimal series model and the mineral elastic infinitesimal parallel model, and establishing a matrix rock elastic modulus and Poisson ratio mathematical physical equation suitable for a characteristic region;
and 8: defining a crack opening coefficient based on a classical equation of opening increment of the uniformly distributed load straight cracks under plane strain, and establishing an elastic modulus and Poisson ratio expression form of the crack opening coefficient;
and step 9: based on the mineral content results of the rock matrixes of different-depth strata measured in the steps 1 and 2, a mineral elastic infinitesimal model and a mineral elastic infinitesimal combined physical model of the matrix rock are constructed by using the methods in the steps 3 and 4, an constitutive equation and elastic parameters of a mineral elastic infinitesimal series model and a mineral elastic infinitesimal parallel model are deduced by combining the principles in the steps 5 and 6, logging and indoor experimental calibration are carried out by using the method in the step 7, the elastic modulus and Poisson's ratio mathematical physical equations of the matrix rock in a characteristic region are established, and the fracture opening coefficient of the matrix rock and the fracture development characteristic predicted by a reservoir are obtained by substituting the physical equations in the step 8.
2. The XRD-based whole rock logging buried hill reservoir fracture opening coefficient prediction method is characterized in that: the mineral elastic infinitesimal combination physical model comprises a mineral elastic infinitesimal series model and a mineral elastic infinitesimal parallel model;
the load and deformation relation of the mineral elastic infinitesimal series model is as follows:
Fz=F1z=F2z=…Fnz
ΔLz=ΔL1+ΔL2+…ΔLn
the load and deformation relation of the mineral elastic infinitesimal parallel model is as follows:
Fr=F1r+F2r+…Fnr
ΔL=ΔL1=ΔL2=…ΔLn
3. the prediction method of the opening coefficient of the subsurface reservoir fracture based on the XRD whole rock logging is characterized in that: the constitutive equation and the elastic parameters of the mineral elastic infinitesimal series model are characterized as follows:
constitutive equation of mineral elastic infinitesimal series model
Figure FDA0003527065190000021
Mineral elastic infinitesimal series model elastic modulus
Figure FDA0003527065190000022
Poisson's ratio of mineral elastic infinitesimal series model
Figure FDA0003527065190000023
4. The prediction method of the opening coefficient of the subsurface reservoir fracture based on the XRD whole rock logging is characterized in that: the constitutive equation and the elastic parameter of the mineral elastic infinitesimal parallel model are characterized in that:
constitutive equation of mineral elastic infinitesimal parallel model
Figure FDA0003527065190000024
Elastic modulus of mineral elastic infinitesimal parallel model
Figure FDA0003527065190000025
Poisson's ratio of mineral elastic infinitesimal parallel model
Figure FDA0003527065190000031
5. The prediction method of the opening coefficient of the subsurface reservoir fracture based on the XRD whole rock logging is characterized in that: the matrix rock elastic modulus and Poisson ratio mathematical physical equation is as follows:
Figure FDA0003527065190000032
Figure FDA0003527065190000033
6. the prediction method of the opening coefficient of the subsurface reservoir fracture based on the XRD whole rock logging is characterized in that: the expression forms of the elastic modulus and the Poisson ratio of the crack opening coefficient are as follows
Figure FDA0003527065190000034
In the formula: fwIs the crack opening coefficient.
7. The utility model provides a mine reservoir crack aperture coefficient prediction device dives based on XRD whole rock logging which characterized in that: the device comprises a rock debris collecting device, a rock debris cleaning device, a rock debris drying device, a rock debris grinding device, a portable XRD diffractometer, a matrix rock mineral elastic infinitesimal simulator, a matrix rock elastic parameter calculating device, a crack opening coefficient calculating device, an explanation mapping device and a long map printing device.
8. The XRD-based whole rock logging buried hill reservoir fracture opening coefficient prediction device is characterized in that: the working process is as follows:
the rock debris collecting device is arranged below the sand outlet of the vibrating screen, and rock debris falls into the sampler along the inclined surface of the screen cloth; selecting fresh rock debris, putting the fresh rock debris into a rock debris cleaning device, and cleaning the rock debris under the condition of fully stirring the rock debris until the rock debris leaks out of the natural color; putting the cleaned rock debris into a rock debris drying device, drying the rock debris in an environment with the temperature controlled to be less than 85 ℃, and putting the dried rock debris into a rock debris grinding device for grinding to enable the rock debris to be powder, wherein the granularity is less than 20 mu m; placing the ground rock debris powder into a portable XRD diffractometer, testing an X-ray diffraction spectrogram of the powder, and explaining the mineral types and content data of rock matrixes of strata at different depths; transmitting the mineral type and content data of the rock matrix to a matrix rock mineral elastic infinitesimal simulator, constructing mineral elastic infinitesimal by the matrix rock mineral elastic infinitesimal simulator according to the mineral type and content, combining the mineral elastic infinitesimal in series and in parallel, and calculating the elastic parameter E under the combination of series and in parallelzz,Err(ii) a The calculated elastic parameter Ezz,ErrTransmitting to a matrix rock elasticity parameter calculating device for calculating the matrix rock elasticity parameter EAAnd muA(ii) a The elastic parameter E of the matrix rockAAnd muATransmitting the crack opening coefficient to a crack opening coefficient calculation device to calculate a crack opening coefficient Fw(ii) a The elastic parameter E of the matrix rock calculated by the matrix rock elastic parameter calculating device and the crack opening coefficient calculating deviceAAnd muAAnd crack opening coefficient FwTransmitted and stored in the interpreting device to performThe method comprises the following steps of (1) forecasting the development condition of reservoir fractures in real time; and finally, printing and archiving the images by the long-image printing device.
CN202210199743.0A 2022-03-01 2022-03-01 XRD (X-ray diffraction) -based prediction method and device for crack opening coefficient of full-rock logging buried hill reservoir Pending CN114720494A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210199743.0A CN114720494A (en) 2022-03-01 2022-03-01 XRD (X-ray diffraction) -based prediction method and device for crack opening coefficient of full-rock logging buried hill reservoir

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210199743.0A CN114720494A (en) 2022-03-01 2022-03-01 XRD (X-ray diffraction) -based prediction method and device for crack opening coefficient of full-rock logging buried hill reservoir

Publications (1)

Publication Number Publication Date
CN114720494A true CN114720494A (en) 2022-07-08

Family

ID=82236351

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210199743.0A Pending CN114720494A (en) 2022-03-01 2022-03-01 XRD (X-ray diffraction) -based prediction method and device for crack opening coefficient of full-rock logging buried hill reservoir

Country Status (1)

Country Link
CN (1) CN114720494A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024087867A1 (en) * 2022-10-27 2024-05-02 中国石油天然气股份有限公司 Characterization method for in-situ openability of deep tectonic fissure, and storage medium

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016041189A1 (en) * 2014-09-19 2016-03-24 杨顺伟 Method for evaluating shale gas reservoir and seeking desert area
CA2936443A1 (en) * 2015-07-20 2017-01-20 Cgg Services Sa Predicting mechanical and elastic rock properties of the subsurface
CN113138107A (en) * 2021-04-15 2021-07-20 东北石油大学 Rock brittleness evaluation method based on while-drilling rock debris logging information

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016041189A1 (en) * 2014-09-19 2016-03-24 杨顺伟 Method for evaluating shale gas reservoir and seeking desert area
CA2936443A1 (en) * 2015-07-20 2017-01-20 Cgg Services Sa Predicting mechanical and elastic rock properties of the subsurface
CN113138107A (en) * 2021-04-15 2021-07-20 东北石油大学 Rock brittleness evaluation method based on while-drilling rock debris logging information

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
赵丹云;刘修刚;秦可;杜建锋;张宏钧;申瑞臣;: "基于微观弹性模量与矿物组分页岩脆性评价方法研究", 西部探矿工程, no. 05, 15 May 2019 (2019-05-15) *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024087867A1 (en) * 2022-10-27 2024-05-02 中国石油天然气股份有限公司 Characterization method for in-situ openability of deep tectonic fissure, and storage medium

Similar Documents

Publication Publication Date Title
CN101354362B (en) Method for analyzing x-ray fluorescence shale content in petroleum well drilling
Azimian et al. An empirical correlation of uniaxial compressive strength with P-wave velocity and point load strength index on marly rocks using statistical method
CN105866835B (en) A kind of tomography three dimensional closure quantitative evaluation method based on crustal stress distribution
RU2636821C1 (en) Method for determination of mechanical properties of reservoir rock
Ceryan et al. Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks
Nefeslioglu Evaluation of geo-mechanical properties of very weak and weak rock materials by using non-destructive techniques: Ultrasonic pulse velocity measurements and reflectance spectroscopy
CN101344001B (en) Analytical method of X-ray fluorescence terrigenous clastic rock porosity in petroleum well drilling
Rastegarnia et al. Estimation of punch strength index and static properties of sedimentary rocks using neural networks in south west of Iran
CN103867198B (en) Carbonate rock natural gas layer stratum density discrimination method
CN107180302A (en) The method that the drillability of rock is evaluated using landwaste constituent content
CN109870720A (en) A kind of shale gas microcrack Logging Identification Method
Fumal Correlations between seismic wave velocities and physical properties of near-surface geologic materials in the southern San Francisco Bay region, California
Kahraman et al. The performance prediction of roadheaders from easy testing methods
CN104153768B (en) A kind of method evaluating Granite Reservoir storage and collection performance
CN104020276A (en) Determination method for mechanical parameters of transverse isotropy shale reservoir rocks
CN116593407B (en) Rare earth metal mineral rapid investigation device and method
Mahmoud et al. Development of lithology-based static Young's modulus correlations from log data based on data clustering technique
CN114720494A (en) XRD (X-ray diffraction) -based prediction method and device for crack opening coefficient of full-rock logging buried hill reservoir
Bhatawdekar et al. Building information model for drilling and blasting for tropically weathered rock.
NO20180859A1 (en) Classification and regression tree analysis of formation realizations
CN106677708A (en) Drilling bit system for petroleum exploration and with rock slice identification function and method
Köken Assessment of deformation properties of coal measure sandstones through regression analyses and artificial neural networks
CN113533104A (en) Method for acquiring elastic parameters before and after shale water-rock action
Moradizadeh et al. Utilizing geological properties for predicting cerchar abrasiveness index (CAI) in sandstones
Öge Regression analysis and neural network fitting of rock mass classification systems

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