CN106370817A - Quantitative identification of fractures and caverns based on core analysis and electrical imaging logging - Google Patents
Quantitative identification of fractures and caverns based on core analysis and electrical imaging logging Download PDFInfo
- Publication number
- CN106370817A CN106370817A CN201610888894.1A CN201610888894A CN106370817A CN 106370817 A CN106370817 A CN 106370817A CN 201610888894 A CN201610888894 A CN 201610888894A CN 106370817 A CN106370817 A CN 106370817A
- Authority
- CN
- China
- Prior art keywords
- hole
- fracture
- imaging logging
- seam
- parameter
- 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
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/18—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for well-logging
- G01V3/26—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for well-logging operating with magnetic or electric fields produced or modified either by the surrounding earth formation or by the detecting device
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Geophysics (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Electromagnetism (AREA)
- Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention provides a quantitative identification method of fractures and caverns based on core analysis and electrical imaging logging. The quantitative identification method comprises the following steps: calculating parameters of the original hole, caverns and fractures in the coring section according to the core and electric imaging logging data; acquiring marks to identify fractures and caverns by imaging logging through core calibration to obtain fracture and cavern parameters and empirical correction value DeltaX; correcting the parameters of fractures and caverns of the non-coring section of the single well with the empirical correction value, using the average value of the empirical correction value of the neighboring well as the empirical correction value for correcting for the non-coring single well; acquiring the parameters of fractures and caverns every meter of the target layer of each single well, and the fracture and cavern rate that is the sum of the lattice-method fracture and cavern rate and the rock plug rate and the full-diameter fracture and cavern rate, to establish a data base of the fracture and cavern parameters; comprehensively analyzing the acquired parameters of holes, fractures and caverns. Combined with three-dimensional geological modeling technology and connecting-well profile comparison method, the development and distribution law of the holes, caverns and fractures are accurately quantitatively identified and assessed.
Description
Technical field
The present invention relates to oil exploration and development fields, more particularly it relates to a kind of be based on core analysis and electricity
The fracture hole quantitatively characterizing method of imaging logging.
Background technology
, as a kind of important reservoir rock class, its diagenesis and evolutionary process are complicated and changeable for carbonate rock, and anisotropism is strong, storage
The influence factor that collection space is developed and rank change greatly, and increase the formation to carbonate reservoir, distribution and enrichment discipline
Research difficulty.Reservoir space in carbonates characterization technique mainly has rock core, thin slice observational method, conventional logging physical property solution now
Release, imaging logging, seismic attribute abstraction, avo, fractalses, the technology such as little rock pillar plug.Fracture hole is important as carbonate reservoir
Reservoir space, its development degree directly affects impact oil and gas reserves and production capacity size.These standard techniques all have certain
Limitation, as relatively low in seismic data resolution, it is only capable of substantially identifying large-scale Cave Development section；Little rock plug is in standard micron
During level-grade reservoir space, preferably, but tested person length and the strong anisotropism impact of reservoir are it is impossible to accurately to grand for effect
See reservoir space to be characterized.Carbonate rock fractured cave can be divided into the different stages such as micron-grade, Centimeter Level, meter level, and microcosmic stitches
Hole parameter (as micron-grade) can reflect its fracture hole developmental state by conventional core Physical Property Analysis, and macroscopical fracture hole ginseng
Number (as Centimeter Level, meter level), the porosity explained due to the restriction of condition and technology, the well logging physical property based on homogenizing geological model
Often less than normal, a large amount of situations about missing are runed counter to drilling fluid for this, simultaneously also cannot be to fracture hole occurrence, size, charges and filling
The parameters such as degree carry out quantitative statisticses it is difficult to reflect real fracture hole development characteristics, constrain the evaluation of fracture-cavity type carbonate
And prediction.
Crack, as the passage of the important reservoir space of Sandstone Gas Reservoir and oil-gas migration, plays to pass to oil gas output
Important effect.Deep layer Sandstone Gas Reservoir generally experienced the diagenesis of complexity, compares conventional reservoir, higher the dividing all of tool
Matter and anisotropy, thus the aspect such as fracture identification, quantitative assessment and forecast of distribution has researched and proposed higher requirement.Mesh
Front to deep layer tight sand crack description with prediction more than enter in terms of outcrop, rock core, seismic properties data, conventional logging etc.
Handss, have carried out qualitative-semidefinite quantity research with the method fracture such as Fractals and numerical simulation, but these methods are all in certain journey
On degree, by seismic data quality, degree of coring, fracture parameters error be big etc., factor is limited, and its verity is worth having consultations with, no
Method realizes the fine quantitative description in crack.
Core analysis, electric imaging logging etc. are widely used in the research of crack, and core observation is convenient, strong sense of reality, energy
The information such as the size in reaction hole directly perceived and crack, form, filling operation, but time and relatively costly, and length of coring is less,
Easily " a peephole view " is it is impossible to reflect seam hole structure comprehensively.The full well section of electric imaging logging is imaged, and can reflect different scale fracture hole comprehensively
Development degree and combination form, tool high-resolution, high sampling rate, the low advantage of cost, be current to identify and characterize crack
For reliable means, but resolution and attractive joint need to be eliminated, the impact that shale filling etc. brings.
It is limited to core hole quantity and thickness, single quantitative statisticses are carried out to rock core fracture hole parameter it is clear that cannot be fine
Quantitatively characterizing reservoir hole, hole, seam are developed and distribution characteristicss, and and then have impact on evaluation and the prediction of reservoir.Therefore, the present invention carries
Go out to comprehensively utilize the two methods of core analysis, advantage in hole and crack research for imaging logging, be aided with Production development data,
The basic datas such as conventional logging, earthquake, realize device to hole, hole, seam is developed and the regularity of distribution is by " point " (individual well section and pit shaft-well
All models) arrive " line " (Lian Jing contrast), then arrive fine quantitatively characterizing and the evaluation of " face " (planar prediction), direct study area's deep layer
The exploration and development of fracture-cavity type carbonate reservoir sum.
Content of the invention
For solving the problems, such as above-mentioned prior art, the present invention is based on core analysis and electric imaging logging, provides one kind
Hole, the quantitatively characterizing method in hole, seam growth and the regularity of distribution, thus be deep layer fracture-cavity type carbonate and Sandstone Gas Reservoir
Evaluate and prediction provides more rational foundation.
For reaching above-mentioned purpose, the invention provides a kind of fracture hole quantitatively characterizing based on core analysis and electric imaging logging
Method, comprises the following steps: to cored interval, carries out archioporus, hole, seam parameter system according to rock core and electric imaging logging data
Meter；Imaging logging is demarcated, by rock core, the mark being identified crack and hole, obtains fracture hole parameter, and obtain empirical calibration
Value δ x, is not cored interval fracture hole parameter with this empirical correction correction individual well, for the individual well do not cored, using offset well experience
The meansigma methodss of corrected value are corrected as the empirical correction of this well；Obtain the fracture hole parameter of every meter of each individual well interval of interest,
Face fracture hole rate is gridding method fracture hole rate and little rock plug or full diameter fracture hole rate sum, sets up hole seam parameter database；And it is comprehensive
Close the acquired hole of analysis, hole, seam parameter, in conjunction with three-dimensional geological modeling technology and even well profile control methods, device to hole, hole, seam
Develop and the regularity of distribution carries out fine quantitatively characterizing and evaluation.
Preferably for naked eyes it is observed that diameter or width be more than the fracture hole of 2mm, using gridding method by transparent grid
Paper bag is overlying on the surface of rock core, with the lines segment length through fracture hole divided by total line length, obtains gridding method fracture hole rate.
Preferably for microcosmic hole, hole, seam, obtain little rock plug or full diameter by little rock plug or full diameter Physical Property Analysis
Fracture hole rate.
Preferably, described archioporus, hole, seam parameter include original face hole rate, significant surface hole rate, hole size, charges
And filling operation；Original face seam rate, significant surface seam rate, the type in crack, occurrence, length, opening width, total seam bar number, effectively seam
Bar number, seam density, frac-pack rate.
Preferably, imaging logging identification fracture hole is that have different shadows based on fracture hole to formation resistivity and wave impedance interface
Ring, skin dark stain and dark sinusoidal response characteristic are shown as on log picture.
Preferably, unfilled hole shows as the dark block or skin dark stain of low-resistance on imaging logging response diagram, unfilled
Or shale filling crack performance is dark-coloured sine curve, if by dust or carbonate rock mineral-filled, show as mixed and disorderly bright
Speckle or bright band, it is not necessary to the fracture hole occurrence that " tadpole plot " in image reads is passed through in correction, need to provide with reference to rock core
Material correction Number of Fractures, opening width, face seam rate, fracture hole filling situation.
Preferably, fracture width includes observing the fracture width obtaining regarding width and actual width, imaging logging obtaining
Belong to regarding width, obtain the actual width in crack through empirical equation correction, empirical equation is: wu=2 (wsCos α)/π, wu
It is crack actual width, unit is mm；Ws is that crack regards width, and unit is mm；α is the angle list of measuring surface and fracture surface normal
Position is °.
Preferably, rock core empirical correction obtained by calibrating δ x=xq-xfg, interval fracture hole parameter xw=xfw+ of not coring
δ x, thus asks for and corrects sector hole of not coring, hole, seam parameter, wherein δ x is the experience difference of individual well parameter indirectly；Xq is
Parameter statistics on rock core in coring depth；Xfq is the parameter statistics on imaging logging in coring depth；Xw is not core
The parameter statistics of section；Xfw is the parameter statistics do not cored on section imaging logging.
Preferably, to be that individual well carries out linear fracture density, surface density, hole face in units of 1 meter close for every meter of fracture hole parameter
Spend isoparametric statistics.
Preferably, described quantitatively characterizing and evaluation are to even well contrast by individual well section and all models of pit shaft-well, then arrive flat
The fine quantitatively characterizing of face prediction and evaluation.
With other characterizing methods, the method for the present invention has the advantage that
1) method of the present invention, with conventional orifices, hole, seam characterizing method compared with, can quantitatively characterizing different stage reservoir space,
Especially there is the advantage of uniqueness to the fracture-cavity type carbonate reservoir of macro-scale (Centimeter Level-meter level), because its reservoir is non-all
Matter is strong, and rock core is broken, little rock plug and full diameter cannot its hole of accurate characterization, hole, seam law of development, and be based on rock core and become
As well logging, the hole read using gridding method, hole, seam parameter, has higher accuracy, in Tarim Oilfield fracture-cavity type carbonate
In reservoir and the actual exploration and development of Sandstone Gas Reservoir, practical application effect preferably, improves the prediction accuracy of High-quality Reservoir.
2) face the minimizing of Tarim Oilfield new well coring calculation, and the present situation such as high cost of coring, the present invention carries
The method going out, average empirical calibration, using existing rock core information, by the demarcation to Image Logging Data, is asked in maximizing ground
Value, and then correct this well and do not core section and other offset well holes of not coring, hole, seam parameter, not only ensure that reservoir hole, hole, seam ginseng
The accurate statistics of number, also reduces cost input, improves the performance of enterprises.
3) present invention, on the basis of rock core and imaging logging parameter quantitative are counted, sets up hole seam parameter database,
Comprehensive utilization dimensionally bottom modeling etc. technology it is achieved that device to hole, hole, seam develop and the regularity of distribution by " point " (individual well section and well
The all models of cylinder-well) arrive " line " (Lian Jing contrast), then arrive quantitatively characterizing and the evaluation of " face " (planar prediction), refer to from different perspectives
The evaluation of reservoir and the prediction in research area are led.
According to the drawings and Examples being described below, these and other aspects of the invention will be apparent from understanding
, and will be elucidated with reference to the embodiment being described below.
Brief description
Illustrated preferred embodiment in refer to the attached drawing is explained the present invention more fully below.
Fig. 1 be rock core hole, hole, seam parametric grid statistic law schematic diagram.
Fig. 2 stitches parametric grid statistic law schematic diagram for imaging logging hole.
Fig. 3 is core analysis and electric imaging logging fracture parameters table method schematic diagram.
Fig. 4 is ksx8 well Ba Shijiqike group crack and reservoir development profile.
Fig. 5 is lgx7 well fmi imaging logging image crack hole parametric statisticss.
Fig. 6 is ksx6 well shaft-well all cracks distribution schematic diagram.
Fig. 7 is ta7-1 Jing Yingshan group reservoir geology model and all distributed models of fracture hole space pit shaft-well.
Fig. 8 connects well comparison diagram for ksx8 block fracture development section.
Fig. 9 develops isopleth map for Dabei area fracture spacing.
Specific embodiment
Below by way of specific embodiment and combine accompanying drawing the method for the present invention and the technique effect that brought are done further
Describe in detail, but it is not intended that to the present invention can practical range restriction.
Using gridding method, rock core hole, hole, seam parameter quantitative statistics are described in detail to how below in conjunction with Fig. 1.
So-called gridding method is exactly to be coated on the surface of rock core with transparent grid paper (specification is generally 30cm × 30cm), with horizontal stroke
(typically longitudinally, laterally going up at interval of 1cm receive data to the vertical lines segment length being upward through fracture hole divided by total line length
Value), to characterize the percentage composition of fracture hole.As shown in figure 1, real segment represents the lines section through fracture hole, point line segment form shows do not have
Through the lines section of fracture hole, it is exactly fracture hole percentage composition that real segment adds real segment divided by a line segment, when grid is close enough, gained
Percentage composition almost can indicate fracture hole Areal porosity and face seam rate completely.
Gridding method hole, hole, seam statistics be limited primarily to naked eyes it is observed that diameter or width be more than the fracture hole of 2mm, and true
Real fracture hole rate not only includes the fracture hole being observed visually, and includes the hole of microcosmic, seam.For microcosmic hole, seam, little rock can be passed through
Plug or full diameter Physical Property Analysis obtain.Therefore, the final face fracture hole rate calculating should be gridding method fracture hole rate and little rock plug or entirely straight
Footpath fracture hole rate sum.
Imaging logging fracture hole parametric statistical methods, as shown in Fig. 2 being based on gridding method, to cored interval in imaging
True fracture hole parameter on log picture is counted.Imaging logging identifying hole, hole, the principle of seam, are based on different fracture holes pair
Formation resistivity and the impact of wave impedance interface, show as the response characteristics such as skin dark stain and sine curve on log picture.
The method according to the invention, fracture hole statistical parameter includes original face hole rate, significant surface hole rate, hole size, filling
Thing and filling operation；Original face seam rate, significant surface seam rate, the type in crack, occurrence, length, opening width (seam is wide), always seam bar
Number, effectively seam bar number, seam density (including line density and surface density), the parameter such as frac-pack rate.Specifically, fracture hole type carbonate
Rock reservoir needs to count crack and Pore genesis, and Sandstone Gas Reservoir, it is limited to resolution and perusal scope, only count
Its characteristics of fracture development.
The method according to the invention, demarcates imaging logging by rock core, obtains an empirical correction δ x, δ x is by formula
1 draws:
Formula 1: δ x=xq-xfg
In formula 1, the experience difference of δ x- individual well parameter；xqParameter statistics on rock core in-coring depth section；
xfqParameter statistics on imaging logging in-coring depth；
The method according to the invention, with this empirical correction δ x, this well of indirect gain is not cored section fracture hole parameter xw,
Drawn by formula 2
Formula 2:xw=xfw+ δ x,
In formula 2, xwThe parameter statistics of-section of not coring；xfw- the parameter statistics do not cored on section imaging logging；δ
The experience difference of x- individual well parameter.
If certain individual well is not cored, can according to multiple offset well holes, hole, seam parameter empirical correction meansigma methodss, as this well
Empirical correction.Empirical correction ask method, reference formula 1 and formula 2.
Wherein every meter fracture hole parameter, be individual well in units of 1 meter, carry out linear fracture density, surface density, hole surface density etc.
The statistics of parameter.
The method according to the invention, imaging logging is demarcated, by rock core, the mark being identified crack and hole, obtains
Hole, hole, seam parameter.Unfilled hole shows as the dark color of low-resistance in the unfilled cave of imaging logging on imaging logging response diagram
Bulk or skin dark stain, unfilled and shale crack performance is dark-coloured sine curve.Crack figure, can directly pass through Electrical imaging image right
" tadpole plot " of side is read out, and is not required to correct.It is limited to the resolution of imaging logging and the interference of argillaceous cave-sedimental fillings, crack bar
Number, opening width, face seam rate, fracture hole filling situation etc. need to be corrected in conjunction with data such as rock cores.
, and combine Fig. 3 taking storehouse car down warping region Cretaceous System Ba Shijiqike group sandstone as a example, describe in detail how using rock core and
Electric imaging logging image fracture parameter (including occurrence, width, length, line density, face seam rate etc.) carries out quantitatively characterizing.
Crack figure includes the parameters such as tendency, trend, inclination angle, and the crack on rock core is difficult to tendency, trend sentences knowledge, inclines
Angular data is also difficult to accurately recover to underground real conditions, and therefore crack figure data can read from electric imaging logging data.Electricity
In " tadpole plot " on the right side of imaging logging image, blue grid scale represents this fracture dip, and " Tinea Ranae " caudal directions represent to be split
Seam tendency, such as Fig. 3 d, crack f1Tendency and inclination angle be respectively 55 ° of 316 ° of ∠.Affected by resolution, in electric imaging logging image
None- identified crack f2、f3, but after being demarcated by rock core, can on the track of crack optional 3 points simulate sine curve, by people
Machine interactive mode obtains inclination angle and the orientation in crack, sees Fig. 3 a-3e.
Fracture width is included regarding width and actual width, when cannot read actual width by the core intersection from vertical fracture face
When, can only observe and obtain crack regarding width, the fracture width that imaging logging obtains belongs to regarding width, through empirical equation correction
The actual width in crack can be obtained, as shown in Figure 3 a.
Empirical equation 2 is: wu=2 (wscosα)/π；
In formula: wu- crack actual width, mm；ws- crack regards width, mm；α-measuring surface and the angle of fracture surface normal,
(°).
As shown in figure 3, on rock core crack f1, f2, f3 through the calculated width of formula 2 be respectively 0.54mm, 0.25mm,
0.11mm, imaging logging press the calculated true width of formula 2 be 0.82mm, 0.45mm, 0.34mm, by formula 1 this obtain this 3
The width calibration value of crack is respectively δ x1=0.28mm, δ x2=0.20mm, δ x3=0.23mm.By parity of reasoning, is somebody's turn to do
The average empirical correction δ x of section rock core cored by well and corresponding Depth Imaging is logged well width, accordingly can remaining be deep to imaging logging
Degree section fracture width initial value is corrected, and obtains the width of every crack in every meter layer.If certain well is not cored, application is many
The meansigma methodss of individual offset well fracture width empirical correction, as the empirical correction of this well.So, some well sections can significantly be overcome
Core less or do not draw the difficulty that reservoir fracture hole parameter evaluation brings, and then improve the accuracy of reservoir prediction.
The extended distance being only limitted in the range of pit shaft in the crack observed on rock core and imaging logging, can not be completely anti-
Should real fracture penetration.Need to be in conjunction with a large amount of outcrop cracks data, fitting empirical computing formula carries out estimating crack
Length in all scopes of well, matching length empirical equation 3 is: l=2.604e99.874wu
In formula 3: l- fracture length, mm；wu- fracture width, mm；
Linear fracture density refers to Number of Fractures and its thickness ratio, carries out individual well and longitudinally count in units of rice.Surveyed by imaging
The restriction of well resolution, on part rock core, observable microcrack cannot identify on imaging logging.As shown in figure 3, rock core
On may recognize that f1、f2、f3Deng 3 cracks, as shown in Figure 3 d, and the electric imaging logging of corresponding depth only can recognize that 1 crack
f1, as shown in Figure 3 e, the line density observed on rock core is 3/m, and imaging logging is 1/m, by formula 1, can correct
It is worth for 2/m.Method according to this, can obtain the linear fracture density average empirical correction δ x in whole coring depth section further,
By formula 1, the imaging logging parameter of section of not coring is corrected, finally obtains the linear fracture density value of this individual well.
In the same manner, if this well is not cored, the average empirical correction of multiple offset wells is can use to be corrected.
Face seam rate refers to the ratio shared by unit area internal fissure area, is overall merit fracture development trend and distribution characteristicss
Parameter.On rock core, fracture surface seam rate can be counted by previously described gridding method, electric imaging logging is first ask for list
Crack launch after area s, as shown in figures 3 b and 3 c, then with each crack area sum as molecule, statistics area as denominator,
This depth segment internal fissure face seam rate γ can be calculated, formula is as follows:
Wall scroll crack developed area formula 4:si=wu[πd+2(l-d)]；
Face seam rate formula 5 is: γ=∑ si/ π hd=∑ { wui[π+2(1/cosθi-1)]}/πh
Formula 4, in 5, θ-fracture dip, (°)；γ-face seam rate, %；D- bit diameter, mm；H- statistical thickness, takes constant
1000mm；Development length in the range of pit shaft for the l- crack, mm；S- wall scroll flaw area, mm2；wu- fracture width, mm；I- i-th
Crack, dimensionless；
After the hole of rock core and imaging logging statistics, hole, seam, parameter are corrected, set up hole, hole, seam parameter database.
As table 1, it is lg eagle mountain group hole seam parametric statisticss table；Table 2 is storehouse car down warping region Ba Shijiqike group partial fracture parametric statisticss table.
Table 1
Table 2
On the basis of fine hole stitches the quantitative statisticses of parameter, in conjunction with Production development data, three dimensional seismic data, profit
With three-dimensional geological modeling technology and Lian Jing control methods, realize hole, the quantitatively characterizing in hole, seam growth and the regularity of distribution and evaluation.
Specifically, using existing fracture parameters data base, in conjunction with the contrast correction of other means of production, depict Fig. 4 and arrive
Fig. 7, quantitative statisticses, and sketch out the space distribution rule of hole seam it is achieved that Reservoir Fracture " point " (individual well section and pit shaft-
Well week) quantitatively characterizing evaluation.
Then, as shown in figure 8, developing on profile in individual well crack and reservoir, load by every meter of fracture parameters data
The curve making, and by a large amount of offset wells between on the basis of FRACTURE CHARACTERISTICS, contrasted by even well, realize the quantitative table in crack " line "
Seek peace evaluation.
Finally, apply each individual well fracture parameters, combined structure position and the quantitative relationship in crack, make each parameter plane and divide
Cloth isogram, as shown in figure 9, can quantitative forecast crack plane distribution, be that oil and gas productivity prediction and well site deployment provide ginseng
Examine.
Above in conjunction with drawings and Examples, the present invention is described in detail.It is understood, however, that the enforcement of the present invention
Example is not limited to disclosed specific embodiment, and the modification to this embodiment and other embodiments are also intended to be comprised in institute
In the range of attached claims.Although being used here particular term, they only make in general and descriptive sense
With, rather than the purpose in order to limit.
Claims (10)
1. a kind of fracture hole quantitatively characterizing method based on core analysis and electric imaging logging is it is characterised in that comprise the following steps:
To cored interval, archioporus, hole, seam parametric statisticss are carried out according to rock core and electric imaging logging data；
Imaging logging is demarcated, by rock core, the mark being identified crack and hole, obtains fracture hole parameter, and obtain experience school
On the occasion of δ x, do not cored interval fracture hole parameter with this empirical correction correction individual well, for the individual well do not cored, using offset well warp
The meansigma methodss testing corrected value are corrected as the empirical correction of this well；
Obtain the fracture hole parameter of every meter of each individual well interval of interest, face fracture hole rate is gridding method fracture hole rate and little rock plug or full diameter seam
Hole rate sum, sets up hole seam parameter database；And
Hole acquired in comprehensive analysis, hole, seam parameter, in conjunction with three-dimensional geological modeling technology and even well profile control methods, device to hole,
Hole, seam are developed and the regularity of distribution carries out fine quantitatively characterizing and evaluation.
2. the fracture hole quantitatively characterizing method based on core analysis and electric imaging logging according to claim 1, wherein, for
Naked eyes it is observed that diameter or width be more than 2mm fracture hole, using gridding method, transparent grid paper is coated on the surface of rock core,
With the lines segment length through fracture hole divided by total line length, obtain gridding method fracture hole rate.
3. the fracture hole quantitatively characterizing method based on core analysis and electric imaging logging according to claim 1, wherein, for
Microcosmic hole, hole, seam, obtain little rock plug or full diameter fracture hole rate by little rock plug or full diameter Physical Property Analysis.
4. the fracture hole quantitatively characterizing method based on core analysis and electric imaging logging according to claim 1, wherein, described
Archioporus, hole, seam parameter include original face hole rate, significant surface hole rate, hole size, charges and filling operation；Original face seam
Rate, significant surface seam rate, the type in crack, occurrence, length, opening width, total seam bar number, effectively seam bar number, seam density, frac-pack
Rate.
5. the fracture hole quantitatively characterizing method based on core analysis and electric imaging logging according to claim 1, wherein, imaging
Well logging recognition fracture hole is that have different impacts based on fracture hole to formation resistivity and wave impedance interface, shows on log picture
For skin dark stain and dark sinusoidal response characteristic.
6. the fracture hole quantitatively characterizing method based on core analysis and electric imaging logging according to claim 1, wherein, does not fill
Filling perforation hole shows as the dark block or skin dark stain of low-resistance on imaging logging response diagram, and unfilled or shale filling crack performance is
Dark-coloured sine curve, if by dust or carbonate rock mineral-filled, shows as mixed and disorderly speck or bright band it is not necessary to school
Pass through the fracture hole occurrence that " tadpole plot " in image reads, need with reference to rock core information correction Number of Fractures, opening width,
Face seam rate, fracture hole filling situation.
7. the fracture hole quantitatively characterizing method based on core analysis and electric imaging logging according to claim 1, wherein, crack
Width include observing obtain regarding width and actual width, the fracture width that imaging logging obtains belongs to regarding width, through experience
Formula correction obtains the actual width in crack, and empirical equation is: wu=2 (wsCos α)/π, wuIt is crack actual width, unit is
mm；wsIt is that crack regards width, unit is mm；α is measuring surface and the angle unit of fracture surface normal is °.
8. the fracture hole quantitatively characterizing method based on core analysis and electric imaging logging according to claim 1, wherein, rock core
Empirical correction δ x=x obtained by calibratingq-xfg, interval fracture hole parameter x of not coringw=xfw+ δ x, thus asks for and school indirectly
Just do not coring sector hole, hole, seam parameter, wherein δ x is the experience difference of individual well parameter；xqIt is the ginseng on rock core in coring depth
Number statistical value；xfqIt is the parameter statistics on imaging logging in coring depth；xwIt is not core the parameter statistics of section；xfwIt is not
The parameter statistics cored on section imaging logging.
9. the fracture hole quantitatively characterizing method based on core analysis and electric imaging logging according to claim 1, wherein, every meter
Fracture hole parameter be that individual well carries out linear fracture density, surface density, the isoparametric statistics of hole surface density in units of 1 meter.
10. the fracture hole quantitatively characterizing method based on core analysis and electric imaging logging according to claim 1, wherein, institute
Stating quantitatively characterizing with evaluation is to even well contrast by individual well section and all models of pit shaft-well, the finer quantitation to planar prediction
Characterize and evaluate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610888894.1A CN106370817A (en) | 2016-10-12 | 2016-10-12 | Quantitative identification of fractures and caverns based on core analysis and electrical imaging logging |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610888894.1A CN106370817A (en) | 2016-10-12 | 2016-10-12 | Quantitative identification of fractures and caverns based on core analysis and electrical imaging logging |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106370817A true CN106370817A (en) | 2017-02-01 |
Family
ID=57895012
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610888894.1A Pending CN106370817A (en) | 2016-10-12 | 2016-10-12 | Quantitative identification of fractures and caverns based on core analysis and electrical imaging logging |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106370817A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105781540A (en) * | 2016-03-17 | 2016-07-20 | 成都创源油气技术开发有限公司 | Logging identification method for fracture-cavity strata |
| CN107064454A (en) * | 2017-02-16 | 2017-08-18 | 中国海洋石油总公司 | A kind of rock core of reservoir fissure development degree determines method |
| CN107167575A (en) * | 2017-05-08 | 2017-09-15 | 中国华能集团清洁能源技术研究院有限公司 | A kind of continuous characterizing method in crack based on rock core |
| CN108008467A (en) * | 2017-11-29 | 2018-05-08 | 中国科学院地质与地球物理研究所兰州油气资源研究中心 | Fractue spacing quantitatively characterizing method and its system |
| CN108008464A (en) * | 2017-11-29 | 2018-05-08 | 中国科学院地质与地球物理研究所兰州油气资源研究中心 | Crack anisotropism quantitatively characterizing method and its system |
| CN109725132A (en) * | 2019-02-21 | 2019-05-07 | 中国石油大学(华东) | Measure a kind of short-cut method of crack figure in straight well core |
| CN111507954A (en) * | 2020-04-14 | 2020-08-07 | 中国石油天然气股份有限公司大港油田分公司 | Statistical method and machine-readable storage medium for reservoir fractures |
| CN111766629A (en) * | 2020-06-30 | 2020-10-13 | 中国地质大学（北京） | Deep carbonate rock karst structure identification and description method |
| CN111897029A (en) * | 2020-07-09 | 2020-11-06 | 西安石油大学 | Method for determining spatial distribution of underground anticline cracks through core-logging interactive comparison |
| CN112213465A (en) * | 2020-09-29 | 2021-01-12 | 西安石油大学 | Method for recovering palygorskite flow direction and original form of volcanic mechanism based on electric imaging logging |
| CN113076883A (en) * | 2021-04-08 | 2021-07-06 | 西南石油大学 | Blowout gas flow velocity measuring method based on image feature recognition |
| CN113554673A (en) * | 2020-04-26 | 2021-10-26 | 中国石油化工股份有限公司 | Method and system for automatically identifying cracks based on while-drilling electrical imaging image |
| CN113689453A (en) * | 2021-08-24 | 2021-11-23 | 中石化石油工程技术服务有限公司 | Method, device and equipment for automatically identifying well logging image cracks and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102654586A (en) * | 2012-05-18 | 2012-09-05 | 北京师范大学 | Multi-scale fracture modeling method |
| CN103063687A (en) * | 2013-01-06 | 2013-04-24 | 中国石油大学(华东) | Device for acquiring and testing microcosmic distribution image of remaining oil in porous medium |
| CN103077558A (en) * | 2012-05-14 | 2013-05-01 | 中国石油化工股份有限公司 | Method for establishing large-sized karst cave reservoir body distribution model of fractured-vuggy carbonate rock reservoir |
| CN103077548A (en) * | 2012-05-14 | 2013-05-01 | 中国石油化工股份有限公司 | Method for establishing solution vug reservoir body distribution model of fractured-vuggy carbonate rock reservoir |
| CN103592690A (en) * | 2013-10-24 | 2014-02-19 | 长江大学 | Method for automatically recognizing reservoir cracks based on electric imaging logging porosity spectrum information |
-
2016
- 2016-10-12 CN CN201610888894.1A patent/CN106370817A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103077558A (en) * | 2012-05-14 | 2013-05-01 | 中国石油化工股份有限公司 | Method for establishing large-sized karst cave reservoir body distribution model of fractured-vuggy carbonate rock reservoir |
| CN103077548A (en) * | 2012-05-14 | 2013-05-01 | 中国石油化工股份有限公司 | Method for establishing solution vug reservoir body distribution model of fractured-vuggy carbonate rock reservoir |
| CN102654586A (en) * | 2012-05-18 | 2012-09-05 | 北京师范大学 | Multi-scale fracture modeling method |
| CN103063687A (en) * | 2013-01-06 | 2013-04-24 | 中国石油大学(华东) | Device for acquiring and testing microcosmic distribution image of remaining oil in porous medium |
| CN103592690A (en) * | 2013-10-24 | 2014-02-19 | 长江大学 | Method for automatically recognizing reservoir cracks based on electric imaging logging porosity spectrum information |
Non-Patent Citations (1)
| Title |
|---|
| 屈海洲等: "基于岩心-电成像测井的裂缝定量表征方法", 《石油勘探与开发》 * |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105781540A (en) * | 2016-03-17 | 2016-07-20 | 成都创源油气技术开发有限公司 | Logging identification method for fracture-cavity strata |
| CN107064454A (en) * | 2017-02-16 | 2017-08-18 | 中国海洋石油总公司 | A kind of rock core of reservoir fissure development degree determines method |
| CN107167575A (en) * | 2017-05-08 | 2017-09-15 | 中国华能集团清洁能源技术研究院有限公司 | A kind of continuous characterizing method in crack based on rock core |
| CN107167575B (en) * | 2017-05-08 | 2020-01-14 | 中国华能集团清洁能源技术研究院有限公司 | Rock core-based fracture continuous characterization method |
| CN108008467A (en) * | 2017-11-29 | 2018-05-08 | 中国科学院地质与地球物理研究所兰州油气资源研究中心 | Fractue spacing quantitatively characterizing method and its system |
| CN108008464A (en) * | 2017-11-29 | 2018-05-08 | 中国科学院地质与地球物理研究所兰州油气资源研究中心 | Crack anisotropism quantitatively characterizing method and its system |
| CN109725132B (en) * | 2019-02-21 | 2021-05-25 | 中国石油大学(华东) | Simple method for determining fracture occurrence in vertical well core |
| CN109725132A (en) * | 2019-02-21 | 2019-05-07 | 中国石油大学(华东) | Measure a kind of short-cut method of crack figure in straight well core |
| CN111507954A (en) * | 2020-04-14 | 2020-08-07 | 中国石油天然气股份有限公司大港油田分公司 | Statistical method and machine-readable storage medium for reservoir fractures |
| CN113554673A (en) * | 2020-04-26 | 2021-10-26 | 中国石油化工股份有限公司 | Method and system for automatically identifying cracks based on while-drilling electrical imaging image |
| CN111766629A (en) * | 2020-06-30 | 2020-10-13 | 中国地质大学（北京） | Deep carbonate rock karst structure identification and description method |
| CN111766629B (en) * | 2020-06-30 | 2022-07-29 | 中国地质大学（北京） | Method for identifying and describing deep carbonate karst structure |
| CN111897029A (en) * | 2020-07-09 | 2020-11-06 | 西安石油大学 | Method for determining spatial distribution of underground anticline cracks through core-logging interactive comparison |
| CN112213465A (en) * | 2020-09-29 | 2021-01-12 | 西安石油大学 | Method for recovering palygorskite flow direction and original form of volcanic mechanism based on electric imaging logging |
| CN113076883A (en) * | 2021-04-08 | 2021-07-06 | 西南石油大学 | Blowout gas flow velocity measuring method based on image feature recognition |
| CN113076883B (en) * | 2021-04-08 | 2022-05-06 | 西南石油大学 | Blowout gas flow velocity measuring method based on image feature recognition |
| CN113689453A (en) * | 2021-08-24 | 2021-11-23 | 中石化石油工程技术服务有限公司 | Method, device and equipment for automatically identifying well logging image cracks and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106370817A (en) | Quantitative identification of fractures and caverns based on core analysis and electrical imaging logging | |
| CN106526693B (en) | crack identification method and device | |
| CA2679415C (en) | Rock core logging | |
| Wilson et al. | From outcrop to flow simulation: Constructing discrete fracture models from a LIDAR survey | |
| EP3077619B1 (en) | Digital core model construction | |
| CN107250828A (en) | The chip analysis described for improved shaft bottom NMR characteristics | |
| Vazaios et al. | Integration of lidar-based structural input and discrete fracture network generation for underground applications | |
| CN106682377B (en) | A kind of the EO-1 hyperion extraction and quantitative estimation method of drilling core altered mineral | |
| US20140297186A1 (en) | Rock Classification Based on Texture and Composition | |
| CN105257284B (en) | Method and device for determining content of coagulated ash by utilizing element capture energy spectrum logging | |
| CN107402176A (en) | Method and device for determining porosity of crack | |
| Suarez-Rivera et al. | Improving geologic core descriptions and heterogeneous rock characterization via continuous profiles of core properties | |
| CN112946780B (en) | Method and device for determining running and sliding fracture activity period | |
| Ray et al. | Characterizing and modeling natural fracture networks in a tight carbonate reservoir in the Middle East: A methodology | |
| Fonta et al. | The Fracture Characterization and Fracture Modeling of a Tight Carbonate Reservoir—The Najmah-Sargelu of West Kuwait | |
| CN107064454B (en) | A kind of rock core of reservoir fissure development degree determines method | |
| CN109490266B (en) | Nondestructive rock sample sampling method | |
| Ashqar et al. | Evaluating a complex low-resistivity pay carbonate reservoir onshore Abu Dhabi: from model to implementation | |
| CN105590018A (en) | Oil-water layer identification method for sandstone and mudstone thin interbed oil reservoir | |
| US20240176042A1 (en) | Methods for diagenetic rock facies classification and nodular evaporite deposit characterization | |
| Koch et al. | Does fluvial channel-belt clustering predict net sand to gross rock volume? Architectural metrics and point-pattern analysis of a digital outcrop model | |
| CN109989746A (en) | Method and device for evaluating carbonate reservoir | |
| CN109598049B (en) | Method for drilling rock fracture development degree and regional rock fracture development rule | |
| Glad et al. | Anatomy of fractures: Quantifying fracture geometry utilizing X-ray computed tomography in a chalk-marl reservoir; the Lower Cretaceous Valdemar Field (Danish Central Graben) | |
| Bernhard et al. | Structural Analysis of Drill Core for Mineral Exploration and Mining: Review and Workflow Toward Domain-Based 3-D Interpretation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information |
Inventor after: Wang Zhenyu Inventor after: Tan Fei Inventor after: Zhang Yunfeng Inventor after: Qu Haizhou Inventor before: Wang Zhenyu Inventor before: Zhang Yunfeng Inventor before: Qu Haizhou Inventor before: Yang Haijun Inventor before: Zhang Lijuan |
|
| CB03 | Change of inventor or designer information | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20170201 |
|
| WD01 | Invention patent application deemed withdrawn after publication |