CN102053268A - Method and device for identifying lithology of altered volcanic rock - Google Patents

Method and device for identifying lithology of altered volcanic rock Download PDF

Info

Publication number
CN102053268A
CN102053268A CN2010105483950A CN201010548395A CN102053268A CN 102053268 A CN102053268 A CN 102053268A CN 2010105483950 A CN2010105483950 A CN 2010105483950A CN 201010548395 A CN201010548395 A CN 201010548395A CN 102053268 A CN102053268 A CN 102053268A
Authority
CN
China
Prior art keywords
volcanics
lithology
rgb
parameter
characteristic
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.)
Granted
Application number
CN2010105483950A
Other languages
Chinese (zh)
Other versions
CN102053268B (en
Inventor
陈福利
冉启全
刘运成
孙圆辉
徐青
阮宝涛
李冬梅
王拥军
袁大伟
李忠诚
刘宇
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.)
Petrochina Co Ltd
Original Assignee
Petrochina Co 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 Petrochina Co Ltd filed Critical Petrochina Co Ltd
Priority to CN2010105483950A priority Critical patent/CN102053268B/en
Publication of CN102053268A publication Critical patent/CN102053268A/en
Application granted granted Critical
Publication of CN102053268B publication Critical patent/CN102053268B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Geophysics And Detection Of Objects (AREA)

Abstract

The invention relates to the field of geological exploration, in particular to a method and a device for identifying lithology of altered volcanic rock. The method comprises the steps of establishing correlation between the lithology of the volcanic rock and corresponding drilling and logging data; extracting lithology characteristic parameters from the drilling and recording well logging data associated with the lithology of the volcanic rock; performing information fusion on the extracted lithological characteristic parameters to obtain a volcanic rock drilling record lithological characteristic model; acquiring measurement parameters of the volcanic rock to be measured in a drilling and logging well; combining the measurement parameters of the volcanic rocks to be measured with the same characteristic parameters in the information fusion, and matching the combination with the characteristic model; and if the matching is successful, outputting the volcanic lithology corresponding to the volcanic to be detected. The method and the device have the characteristics of intuition, accuracy, rapidness and high efficiency in identifying the lithology of the volcanic rock, and particularly the recognition of the altered volcanic rock is obviously improved.

Description

A kind of alteration Lithology Identification Methods for Volcanic Rocks and device
Technical field
The present invention relates to geological exploration field, particularly about a kind of alteration Lithology Identification Methods for Volcanic Rocks and device.
Background technology
Existing more than the 100 year history of volcanics research, lithology identification basis is the volcanics lithology breakdown, mineral content, chemical constitution, the place of production and structure, structure all are the foundations of pyrogenic rock classification name.Owing to the difference of each family's classification starting point, add the diversity of volcanics itself, cause existing volcanics title to reach kind more than 1000.Though this has reflected the complexity of volcanics and various fact, also show the confusion that human factor causes in the classification name.With the polarizing microscope is basic tool, the petrographic thin section that is characterized as appraisal basis with the mineral photosensitiveness identifies it is the basis of determining volcanic rocks classification, lithology, sets up the key that volcanics alteration pattern is lithology identification from lithochemistry component, origin mechanism and evolution rule.Along with going deep into of volcanics oil-gas exploration and development, the exploitation of deep volcanic rock reservoirs exploration receives publicity day by day.Because deep volcanic rock reservoir lithology, fluid composition are complicated and changeable, crystallization degree difference is big, brings great difficulty to evaluation of volcanic rock reservoir.Determine accurately that wherein the volcanics lithology is carry out the every research work of volcanic rock reservoir basic and crucial.
At volcanics lithology identification difficult point, carried out the research of a large amount of relevant volcanics lithology identification aspects both at home and abroad, structure method of identification, neural network method and combined recognising method thereof have been formed, as the three-dimensional Lithology Identification Methods that combines with ECS and electric imaging logging such as the TAS figure method of capturing well logging (ECS) based on element, electric imaging logging.To the volcanics lithology identification volcanics lithology identification thinkings that adopt the lithochemistry component to combine, determine the volcanics lithology at present with structure more.In volcanics lithology breakdown and Study of recognition, the volcanics TAS figure sorting technique that international ground section's connection (IUGS) is recommended is only applicable to fresh volcanics classification, because the ancient volcanics of deep layer all can be subjected to alteration transformation in various degree behind diagenesis, studies show that, after the volcanics alteration (comprising hypergene weathering eluviation and diagenesis later hydrothermal alteration), physicochemical property (the component of rock, structure, structure, color, hardness, electrically, drillability etc.) can take place significantly to change, though alteration volcanics lithology identification problem has many-sided research, but be confined to experimental analysis more, make slow progress generally.Current paper and patent retrieval show: still form the effective ways and the special system tool of discerning the alteration volcanics that utilize well information to carry out the identification of alteration volcanics lithology.
Summary of the invention
The embodiment of the invention provides a kind of alteration Lithology Identification Methods for Volcanic Rocks and device, is used for solving prior art alteration volcanics lithology identification complexity, and inaccurate problem.
The embodiment of the invention provides a kind of alteration Lithology Identification Methods for Volcanic Rocks in order to solve the problems of the prior art, comprising:
Set up the volcanics lithology and record the related of log data with corresponding brill;
In the brill record log data related, extract characteristic parameter with described volcanics lithology;
The characteristic parameter of described extraction is carried out information fusion, obtain the characteristic model of volcanics lithology;
Obtain the measurement parameter of volcanics to be measured in boring the record well logging;
With the measurement parameter of described volcanics to be measured form with described information fusion in the identical combination of characteristic parameter, mate with above-mentioned characteristic model;
If the match is successful, then export the volcanics lithology corresponding with described volcanics to be measured.
The embodiment of the invention also provides a kind of alteration volcanics lithology recognition device, comprising:
Associative cell, characteristic parameter extraction unit, information fusion unit, measuring unit, matching unit, output unit;
Described associative cell is used to set up the volcanics lithology and records the related of log data with corresponding brill;
Described characteristic parameter extraction unit is used for extracting characteristic parameter in the brill record log data related with described volcanics lithology;
Described information fusion unit is used for the characteristic parameter of described extraction is carried out information fusion, obtains the characteristic model of volcanics lithology;
Described measuring unit is used for obtaining volcanics to be measured at the measurement parameter that bores the record well logging;
Described matching unit is used for the measurement parameter of described volcanics to be measured is formed and the identical combination of described information fusion characteristic parameter, mates with above-mentioned characteristic model;
Described output unit is used for exporting the volcanics lithological information corresponding with described volcanics to be measured when the match is successful.
Pass through the embodiment of the invention, to volcanics lithology identification have intuitively, accurately, feature rapidly and efficiently, particularly the understanding to the alteration volcanics obtains to significantly improve, and with drilling and coring delivery, the contrast of sidewall sampling lithology qualification result, alteration volcanics lithology recognition accuracy reaches more than 93%.
Description of drawings
In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art, to do to introduce simply to the accompanying drawing of required use in embodiment or the description of the Prior Art below, apparently, accompanying drawing in describing below only is some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to these accompanying drawings.
Figure 1 shows that the process flow diagram of a kind of alteration Lithology Identification Methods for Volcanic Rocks of the embodiment of the invention;
Figure 2 shows that the SiO after the alteration of embodiment of the invention typical case volcanics 2Middle content figure;
Figure 3 shows that thorium (Th) in the fresh slag of the embodiment of the invention-potassium (K) graph of a relation;
Figure 4 shows that thorium (Th) in the fresh slag of the embodiment of the invention-uranium (U) graph of a relation;
Figure 5 shows that elemental iron in the embodiment of the invention volcanics (Fe)-elemental silicon (Si) massfraction graph of a relation;
Figure 6 shows that the synoptic diagram of the characteristic model of embodiment of the invention volcanics lithology;
Figure 7 shows that the principle schematic of the embodiment of the invention according to RGB color value calculating rgb space eigenwert;
Figure 8 shows that the structural representation of a kind of alteration volcanics of embodiment of the invention lithology recognition device;
Figure 9 shows that a kind of alteration volcanics of embodiment of the invention lithology RGB merges and fusion spatial parameter figure.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the invention, the technical scheme in the embodiment of the invention is clearly and completely described, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills belong to the scope of protection of the invention not making the every other embodiment that is obtained under the creative work prerequisite.
Be illustrated in figure 1 as the process flow diagram of a kind of alteration Lithology Identification Methods for Volcanic Rocks of the embodiment of the invention.
Step 101 is set up the volcanics lithology and is recorded the related of log data with corresponding brill.
Wherein, bore the well data in the record log data, come from some record data that in drilling process, produce, time for example, the pressure of drill bit, the rotating speed of drill bit, the density of mud; Logging data, measurement and record that the rock chip taken out of in the drilling process and rock fluid are carried out, the color of rock chip for example, size, the density of particle etc.; Log data, under the instrument lower going-into-well, physics, chemical measurement to carry out the lithostratigraphy for example carry out measurements such as radioactivity, density, sound wave, resistivity.
The described volcanics lithology of setting up is meant with corresponding brill record the related of log data, set up the corresponding relation between the data that volcanics that rock core research named and this volcanics produce in boring record well logging process, during as the corresponding low brill of ryolite, more shallow, the low-density of color, high natural gamma, high resistivity; Darker, the high neutron of alteration andesite corresponding color, low-resistivity, low uranium, low thorium etc.Here high and low produces data in boring the record well logging all have concrete data.
The degree of depth of drilling well rock core collection and the degree of depth of boring the record well log measurement are mated, read the brill record Well logging Data of the corresponding degree of depth of rock core, utilize analysis different lithology such as crossplot analysis and its method, statistical analysis method, theoretical analysis the volcanics lithology of naming and the correlativity and the degree of correlation of boring the various data of record well logging, example is as shown in table 1:
Table 1
Figure BDA0000032866200000041
Also comprise in this step, the volcanics rock core is carried out analysis of chemical elements, set up the related of volcanics lithology and element chemical analysis data.
Can know down that to mirror discerning the volcanics of adding up mineral names according to volcanic rocks credit class standard, is converted into the massfraction classification indicators with mineral volume fraction sorting parameter; For phanerocrystalline-adiagnostic or miocrystalline volcanics, need be by total rock chemical analysis means, carry out the volcanics category name according to main oxides content analysis result and TAS figure, be that full alkali-silica component is named, component is named requirement and must be selected fresh volcanics to analyze, and will provide SiO to the alteration volcanics 2Utilize TAS figure to carry out the volcanics category name again after the correction of content because of the alteration variation, directly adopt element quality (weight) mark to carry out lithology identification to alteration volcanics and nonacid volcanics, do not adopt the TAS figure method lithology breakdown on the oxides closure The model calculation basis, to reduce the lithology identification error that closed effect is brought.
Step 102 extracts characteristic parameter in the brill record log data related with described volcanics lithology.
Selection and lithology correlativity, the brill that susceptibility is good are recorded log data as the rock signature parameter, to dissimilar volcanics separately characteristic parameter can be arranged respectively, select the most characteristic monistic rock signature parameter that has, for example, acid slag has high natural gamma (GR), and curve is straight; High resistivity (RT), curve is straight; Low-density (DEN), lithology sensitive parameter such as curve is straight.Described correlativity is meant that brill is recorded log parameter and the lithology correlativity is good, and susceptibility is meant along with lithological change parameter also marked change.
Lithology according to volcanics, select to bore record WELL LITHOLOGY susceptibility rule notable attribute curve, implement forward sensitivity (significantly increasing), anti-(bearing) to responsive (reducing) as strengthening density (DEN) log value along with volcanics is acid as strengthening natural gamma (GR) along with volcanics is acid, linearity, logarithm, statistical probability accumulative total sensitivity, the perhaps variable brill record log data that is changed to quantitative sensitivity.For example conventional clastic sedimentary rock lithology sensitivity curve mainly contains natural gamma (GR), hole diameter (CAL), spontaneous potential (SP), the rerum natura sensitivity curve mainly contains interval transit time (AC), lithology and density logging (DEN), compensated neutron (CNL), electrically sensitivity curve mainly contains formation resistivity (RT), invaded zone resistivity (RI), flushed zone resistivity (RXO) etc., qualitative, quantitative examination affirmation that the susceptibility rule of various curves needs.Concrete research method can be used conventional logging crossplot analysis and its method, frequency statistics method, theoretical analysis etc.
Well data mainly reflects rock drillability, hardness, rub proofness, rerum natura etc., well logging can reflect color, density, grain size, gas bearing condition of rock etc., log data can measure the radioactivity, stress characteristics, factor of porosity of rock, electrically, constituent content, rock texture etc., different rocks survey the susceptibility difference of parameter to boring record.Characteristic parameter can be based upon on known definite lithology and the brill record log data basis and analyze the back extraction by being familiar with after the manual analysis; Also can record on the logging character parameter basis and can select automatic extraction at the brill of knowing lithology in advance, automatically extraction can be adopted the characteristic parameter that related coefficient is big, susceptibility is good, be that the measurement parameter variation that lithological change causes is the bigger the better, for example can set threshold value, when the parameter of boring the record well logging surpasses threshold value (as confidence level R to characteristic parameter 2>0.7) thinks that then this parameter meets the requirement of lithology relevant sensitization, can be used as characteristic parameter.
The characteristic parameter of alteration volcanics and non-alteration volcanics is different, for example Figure 2 shows that the SiO after the typical volcanics alteration 2Middle content figure, SiO after the acid volcanic rock alteration during this figure shows 2The content scope is bigger, compares SiO with fresh volcanics 2Content can not simply use SiO as the unique conditional of alteration volcanics lithology identification 2Content changes in the alteration process, the standard applicability variation of naming as lithology, but still the identifying feature parameter that can be used as the alteration volcanics uses, under the identical alteration environment, the lithology alteration is similar, SiO 2The content measurer is regular, and the identification of TAS figure method lithology is not suitable for the alteration volcanics.
Be illustrated in figure 3 as thorium in the fresh slag (Th)-potassium (K) graph of a relation, this figure shows along with volcanics is acid to be increased, and Th and K are the index positive correlation, if the imbalance of Th-K relation is generally alteration features, wherein R 2Correlativity (square R of coefficient R for Th and K 2Be called definite coefficient, can determine the probability of dependent variable exactly with independent variable), y is independent variable curve (Th); Figure 4 shows that thorium in the fresh slag (Th)-uranium (U) graph of a relation, this figure shows along with volcanics is acid to be increased, and Th and U are linear positive correlation, if the imbalance of U-Th relation is generally weathering leaching feature, wherein R 2Be the correlativity of Th and U, R 2Can calculate according to the Excell correlation analysis and by least square method, y is independent variable curve (Th), if depart from the intrinsic relation that this figure sets up, can be judged to be weathering, alteration, it or not fresh volcanics, two measured values that are associated keep intrinsic relevance (can be used as alteration volcanics characteristic parameter with the degree of deviation), can be used as normal volcanics criterion, depart from then as the criterion of alteration volcanics; Figure 5 shows that elemental iron in the volcanics (Fe)-elemental silicon (Si) massfraction graph of a relation, this figure shows along with volcanics is acid to be increased, the Si element increases and the minimizing of Fe element, this figure has provided volcanics component characteristics scope, can determine acid volcanic rock, neutral volcanics, basic volcanic rocks according to Fe, Si content range, wherein the content range of Fe, Si can be characteristic parameter.The susceptibility of which parameter was relatively good when above-mentioned accompanying drawing illustrated lithology identification, was the key experimental basis of selecting characteristic parameter to analyze.
Step 103 is carried out information fusion with the characteristic parameter that extracts, and obtains the characteristic model of volcanics lithology.Can also set up the mapping of boring record logging operation parameter with described characteristic model accordingly, described operating parameter can for example be drilling speed, pressure at the volcanics drill bit of certain lithology or the like.
Curve negotiating scale that above-mentioned characteristic parameter is constituted or statistical standardization are converted into the have identical qualitative sensing standardized curve of (just focusing on), give corresponding RGB color value with described standardized curve each point.
Step 1031, the curve that characteristic parameter is constituted focuses on planning formation standardized curve, that is to say, three characteristic parameter curves such as V1 (0~255) are for example arranged, V2 (1000~1255), V3 (300~810) is by becoming after the scale meritization: R (V1) (0~255), G (V2) (0~255), B (V3) (0~255), wherein R is red, G is green, B is blue, 0~255 is the interior intensity level of one 0~255 scope of RGB component distribution of each pixel in the image, characteristic parameter curve numerical value change has jointly directive property and is called and focuses on planning behind this planning scale, focus on planning and can be divided into linearity, planning such as logarithm or statistical probability is set according to the sensibility analysis rule result to characteristic parameter.Wherein, focusing planning realizes the conversion of characteristic parameter, becomes with lithological change measurement focusing parameter to have single sensing regularity.Any metrical information all not only reflects single character, and the multi-solution of information just is this, and many information fusion can be dwindled the common factor of information, reduces the multi-solution of information; It doesn't matter if characteristic parameter only has one merges, and can be separately that color development shows with the scale, improves visual; Attenuating with evaluation lithology granularity is example, evaluating has six kinds, wherein four kinds of information diminishes with granularity and increases, have only two kinds of information to diminish and reduce with granularity, focus on the fusion principle according to information, need be with these the two kinds information translation that diminish with granularity and reduce for diminishing and increase with granularity, then six kinds of parameters become to have with lithology and attenuate and the focus information that increases.Merging width calculates with color value:
LV = R 2 + G 2 + B 2 2
CLV = 3 ( R * G * B ) 2
An information fusion visualization object can have 3-4 information, and three kinds of colors, a kind of color width are promptly arranged; If characteristic parameter is many, adopt a plurality of information fusion objects, can adopt multistage information fusion method, as: (R (V1)+G (V2)+B (V3)) → LV (V1, V2, V3), CLV (V1, V2, V3), (R (V4)+G (V5)+B (V6)) → LV (V4, V5, V6), CLV (V4, V5, V6);
R(LV(V4、V5、V6))+G(LV(V4、V5、V6))+B(V7)→LV(LV(V4、V5、V6)、LV(V4、V5、V6)、V7)、CLV(LV(V4、V5、V6)、LV(V4、V5、V6)、V7)。
According to focusing on planning and design, the characteristic parameter that volcanics is bored in the record log data merges scale meritization, be converted into RGB color value (0-255), the volcanics rock signature parametric line value of setting scale meritization is that (characteristic parameter is the scope of a measured value to VL, show that boring in the record well logging is exactly the curve values scope), volcanics rock signature parameter minimum value is VMIN, volcanics rock signature parameter maximal value VMAX, be set at R respectively according to curvilinear characteristic, G, the B curve, article one, curve is through after the focus criteriaization, promptly change a scope into and be 0~255 curve, for the needs of information fusion can be set at the RGB three primary colors by assignment, concrete scale computing formula is:
When volcanics rock signature parametric line is converted into the standardized curve with identical qualitative sensing when being the linear graduation standardized curve by scale:
Positive correlation planning
Figure BDA0000032866200000071
Value after the planning increases with measured value, directly these values is shown with RGB in information fusion;
Negative correlation planning
Figure BDA0000032866200000072
Value after the planning increases with measured value and reduces, and directly these values is shown with RGB in information fusion;
When being converted into the standardized curve with identical qualitative sensing by scale, volcanics rock signature parametric line (characteristic parameter curve negotiating logarithmic scale is realized focus criteriaization) when being the logarithmic scale standardized curve:
Positive correlation planning V ( RGB ) = 255 ln ( VL ) - ln ( VMIN ) ln ( VMAX ) - ln ( VMIN ) ,
Inverse correlation planning V ( RGB ) = 255 ln ( VL ) - ln ( VMIN ) ln ( VMIN ) - ln ( VMAX ) ,
When being converted into the standardized curve with identical qualitative sensing by scale, volcanics rock signature parametric line (characteristic parameter curve negotiating statistical probability halving method is realized focus criteriaization) when being probability statistics scale merit curve:
Positive correlation planning V ( RGB ) = 255 P ( VL ) P ( VMAX ) - P ( VMIN ) ,
Inverse correlation planning V ( RGB ) = 255 P ( VL ) P ( VMIN ) - P ( VMAX ) ,
P (VL) is a volcanics rock signature parameter cumulative probability;
P (VMIN) is volcanics rock signature parametric statistics minimum value place's cumulative probability (0);
P (VMAX) is volcanics rock signature parametric statistics maximal value place's cumulative probability (100).
Step 1032, calculate the rgb space eigenwert according to the RGB color value, length that described space characteristics value is the rgb space vector and RGB solid space volume are neglected the cube eigenwert greatly, as shown in Figure 7, the RGB of a measurement point merges can form a RGB body, and body characteristics just has the length of vector and RGB solid space volume to neglect the cube eigenwert greatly after calculating.Described RGB color value calculates the rgb space eigenwert and can merge and the corresponding relation that merges spatial parameter figure with reference to figure 9 alteration volcanics lithology RGB.
In this step, scale is merged standardized rgb value carry out the reconstruct of space characteristics value, calculate the rgb space eigenwert, the space characteristics value is divided into: the big small cubes eigenwert of length of rgb space vector (LV) and RGB solid space volume (CLV).
Computing formula is:
LV = R 2 + G 2 + B 2 2
CLV = 3 ( R * G * B ) 2
In order to improve the different effects of record log data in information fusion of boring, can adopt experience weights method, degneracy method, artificial weights method, calculate the rgb space eigenwert apart from weights method etc., as select R color value weight to be (WB) for (WG), B color value weight for (WR), G color value weight, then length of rgb space vector (LV) and RGB solid space volume are neglected cube eigenwert (CLV) computing formula greatly and are:
LV = ( WR * R ) 2 + ( WG * G ) 2 + ( WB * B ) 2 2
CLV = 3 ( WR * WG * WB * R * G * B ) 2
Step 1033, described RGB color value is fused into color on the depth-logger, described depth-logger is exactly the distance of local drilling platform square kelly of well-drilling borehole, recording mode decision by measurement parameter, set the width that the corresponding degree of depth merges Show Color according to the rgb space eigenwert of correspondence again, can obtain the fusion visualization result, just the characteristic model of volcanics lithology.
In this step, the RGB color value is fused into chromatogram on the depth-logger, select the space characteristics of RGB to merge the width demonstration again, provide the synoptic diagram that fusion visualization result such as Fig. 6 have shown the characteristic model of volcanics lithology, wherein the lithology curve has comprised hole diameter (CAL) characteristic parameter curve, spontaneous potential (SP) characteristic parameter curve, natural gamma (GR) characteristic parameter curve respectively; The rerum natura curve comprises that respectively compensated neutron (CNL) characteristic parameter curve, lithology and density logging (DEN) characteristic parameter curve, rerum natura sensitivity curve mainly contain interval transit time (AC) characteristic parameter curve; Resistivity curve (because the accompanying drawing area is limit and can only be demonstrated resistivity) comprises invaded zone resistivity (RI) characteristic parameter curve respectively, electrically sensitivity curve mainly contains formation resistivity (RT) characteristic parameter curve, flushed zone resistivity (RXO) characteristic parameter curve.
In information fusion, if the lvalue of certain characteristic parameter is little, r value greatly then is positive correlation planning, r value is little, lvalue greatly then is negative correlation planning, for example, the lvalue that lithology merges CAL in the chromatogram (LC) is 15, r value is that 8 this characteristic parameter CAL are negative correlation planning, and the lvalue that merges CNL in the chromatogram (PC) in rerum natura is 0, and r value is 50, be positive correlation planning then for this characteristic parameter CNL, the lvalue that perhaps electrically merges RT in the chromatogram (RC) is 0, and r value is 1000, and then this characteristic parameter RT is the positive correlation rule; In well logging fusion chromatogram (TLC), merge above-mentioned lithology and merged chromatogram, rerum natura fusion chromatogram and electrically merge chromatogram, also just be equivalent to merge simultaneously 9 characteristic parameter curves.
FMI micro-resistivity imaging figure, it changes the variation that has shown the microresistivity parameter with colourity; The alteration features curve has comprised compensated neutron (CNL) logging character parametric line, compressional wave time difference (DTCO) logging character parametric line, the drilling time log characteristic parameter curve that bores in the record well logging process; Fusion chromatogram based on rock element (ECS) has comprised DWTI, two kinds of characteristic parameter curves of DWFD_WALK2; The component index has comprised SiO 2Weight percent content characteristic parameter curve, content (DWSI) the characteristic parameter curve of elemental silicon, natural gamma (GR) characteristic parameter curve; The alkalescence index has comprised weight percent content characteristic parameter curve, the K of CaO 2The weight percent content characteristic parameter curve of O, Na 2The weight percent content characteristic parameter curve of O; The ECS mineral comprise weight percent content, mud value content (WCLA), the long English matter mineral content (WQFM) of fosfosiderite (WPYR); Comprise the weight percent content of pyrite (WPYR), the weight percent content of siderite (WSID) in the color.Well logging has provided geologic lithology well logging result.
Fusion visualization result's color depends on the relative size of RGB, bigger chromatogram value determines main color, show that according to RGB mixed chromatogram feature space characteristics can be represented the space size after RGB focuses on, the power of the volcanics characteristic parameter that reflection is corresponding or big or small.
Step 104 is obtained the parameter of volcanics to be measured in boring the record well logging.Treat the parasitic volcano rock and detect, obtain the measurement parameter of this volcanics by physics and/or chemical means.
Step 105, with the measurement parameter of described volcanics to be measured form with described information fusion in the identical combination of characteristic parameter, mate with above-mentioned characteristic model.
Step 106 if the match is successful, is then exported this volcanics lithology to be measured, and for example the title of lithology can also be exported this volcanics geology is bored the relevant parameter that the record well logging waits operation, perhaps can also export other characteristic of this volcanics.
If comprised whether this volcanics is the alteration volcanics in the volcanics title of output, then can be according to the parameter of this alteration volcanics operation is carried out relevant work, for example to be reflected in the drilling well be the drillability significant change in the lithology alteration, improve as drillability after the identical lithology alteration, reduce during brill, be reflected on the well logging, landwaste argillization, color are fresh inadequately, alteration secondary mineral appearance etc., the alteration that is reflected in the well logging changes, significantly increase as the neutron well logging value, interval transit time increases, resistivity significantly descends, hole diameter enlarges or curve toothization etc.
Be illustrated in figure 8 as the structural representation of a kind of alteration volcanics of embodiment of the invention lithology recognition device.
Comprise: associative cell 801, characteristic parameter extraction unit 802, information fusion unit 803, measuring unit 804, matching unit 805, output unit 806;
Described associative cell 801 is used to set up the volcanics lithology and records the related of log data with corresponding brill;
Described characteristic parameter extraction unit 802 is used for extracting characteristic parameter in the brill record log data related with described volcanics lithology;
Described information fusion unit 803 is used for the characteristic parameter of described extraction is carried out information fusion, obtains the characteristic model of volcanics lithology;
Described measuring unit 804 is used for obtaining volcanics to be measured at the measurement parameter that bores the record well logging;
Described matching unit 805 is used for the measurement parameter of described volcanics to be measured is formed and the identical combination of described information fusion characteristic parameter, mates with above-mentioned characteristic model;
Described output unit 806 is used for exporting the volcanics lithological information corresponding with described volcanics to be measured when the match is successful.
As the further embodiment of the present invention, described information fusion unit 803 also comprises,
Standardized module 8031, rgb space characteristic value calculating module 8032, MBM 8033;
Described standardized module 8031, be used for described characteristic parameter generating feature parametric line, volcanics rock signature parametric line is converted into the standardized curve with identical qualitative sensing by scale, and give corresponding RGB color value with described standardized curve each point, carry out the processing of carrying out as above-mentioned step 1031.
Described rgb space characteristic value calculating module 8032, be used for calculating the rgb space eigenwert according to the RGB color value, length that described space characteristics value is the rgb space vector and RGB solid space volume are neglected the cube eigenwert greatly, carry out the processing of carrying out as above-mentioned step 1032.
Described MBM 8033, be used for described RGB color value is fused into chromatogram on the depth-logger, set RGB according to the rgb space eigenwert of correspondence again and merge the width that shows, obtain the characteristic model of described volcanics lithology, carry out the processing of carrying out as above-mentioned step 1033.
By said apparatus to volcanics lithology identification have intuitively, accurately, feature rapidly and efficiently, particularly the understanding to the alteration volcanics obtains to significantly improve, with drilling and coring delivery, the contrast of sidewall sampling lithology qualification result, alteration volcanics lithology recognition accuracy reaches more than 93%.
Above-described embodiment; purpose of the present invention, technical scheme and beneficial effect are further described; institute is understood that; the above only is the specific embodiment of the present invention; and be not intended to limit the scope of the invention; within the spirit and principles in the present invention all, any modification of being made, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (10)

1. alteration Lithology Identification Methods for Volcanic Rocks is characterized in that comprising:
Set up the volcanics lithology and record the related of log data with corresponding brill;
In the brill record log data related, extract characteristic parameter with described volcanics lithology;
The characteristic parameter of described extraction is carried out information fusion, obtain the characteristic model of volcanics lithology;
Obtain the measurement parameter of volcanics to be measured in boring the record well logging;
With the measurement parameter of described volcanics to be measured form with described information fusion in the identical combination of characteristic parameter, mate with above-mentioned characteristic model;
If the match is successful, then export the volcanics lithology corresponding with described volcanics to be measured.
2. method according to claim 1 is characterized in that, also comprises in boring record well logging process the volcanics rock core is carried out analysis of chemical elements, and set up the related of volcanics lithology and element chemical analysis data.
3. method according to claim 1 is characterized in that, carries out information fusion at the characteristic parameter with described extraction, comprises in the characteristic model of acquisition volcanics lithology:
With described characteristic parameter generating feature parametric line, volcanics rock signature parametric line is converted into by scale has identical qualitative sensing (information focuses on planning, comprise the planning of positive correlation planning and inverse correlation) standardized curve, and give corresponding RGB color value with described standardized curve each point;
Calculate the rgb space eigenwert according to the RGB color value, length that described space characteristics value is the rgb space vector and RGB solid space volume are neglected the cube eigenwert greatly;
Described RGB color value is fused into chromatogram on the depth-logger, sets RGB according to the rgb space eigenwert of correspondence again and merge the width that shows, obtain the characteristic model of described volcanics lithology.
4. method according to claim 3 is characterized in that, when volcanics rock signature parametric line is converted into the standardized curve with identical qualitative sensing when being the linear graduation standardized curve by scale:
Positive correlation planning V ( RGB ) = 255 VL - VMIN VMAX - VMIN ,
Inverse correlation planning V ( RGB ) = 255 VL - VMIN VMIN - VMAX ,
When volcanics rock signature parametric line is converted into the standardized curve with identical qualitative sensing when being the logarithmic scale standardized curve by scale:
Positive correlation planning V ( RGB ) = 255 ln ( VL ) - ln ( VMIN ) ln ( VMAX ) - ln ( VMIN ) ,
Inverse correlation planning V ( RGB ) = 255 ln ( VL ) - ln ( VMIN ) ln ( VMIN ) - ln ( VMAX ) ,
When volcanics rock signature parametric line is converted into the standardized curve with identical qualitative sensing when being probability statistics scale merit curve by scale:
Positive correlation planning V ( RGB ) = 255 P ( VL ) P ( VMAX ) - P ( VMIN ) ,
Inverse correlation planning V ( RGB ) = 255 P ( VL ) P ( VMIN ) - P ( VMAX ) ,
The volcanics rock signature parametric line value of above-mentioned setting scale meritization is VL, volcanics rock signature parameter minimum value is VMIN, volcanics rock signature parameter maximal value VMAX, P (VL) is a volcanics rock signature parameter cumulative probability, P (VMIN) is a volcanics rock signature parametric statistics minimum value place cumulative probability, and P (VMAX) is a volcanics rock signature parametric statistics maximal value place cumulative probability.
5. method according to claim 3 is characterized in that, calculates the rgb space eigenwert and is specially:
LV = R 2 + G 2 + B 2 2
CLV = 3 ( R * G * B ) 2
Wherein LV is the length of rgb space vector, and CLV is that RGB solid space volume is neglected the cube eigenwert greatly.
6. method according to claim 5, it is characterized in that, in the chromatogram that described RGB color value is fused on the depth-logger, set RGB according to the rgb space eigenwert of correspondence again and merge the width that shows, the characteristic model that obtains described volcanics lithology also comprises before, adopts multistage information fusion method that a plurality of RGB color values and a plurality of rgb space eigenwert are merged.
7. method according to claim 1 is characterized in that, obtains in the measurement parameter of volcanics to be measured in boring the record well logging, treats the survey volcanics and detects by physics and/or chemical means, obtains the lithologic parameter of this volcanics.
8. method according to claim 1 is characterized in that, when obtaining the characteristic model of volcanics lithology, also comprises and sets up the mapping of boring record logging operation parameter with described characteristic model accordingly;
In the output volcanics lithological information corresponding, also export the described brill record logging operation parameter of this volcanics correspondence to be measured with described volcanics to be measured.
9. alteration volcanics lithology recognition device is characterized in that comprising:
Associative cell, characteristic parameter extraction unit, information fusion unit, measuring unit, matching unit, output unit;
Described associative cell is used to set up the volcanics lithology and records the related of log data with corresponding brill;
Described characteristic parameter extraction unit is used for extracting characteristic parameter in the brill record log data related with described volcanics lithology;
Described information fusion unit is used for the characteristic parameter of described extraction is carried out information fusion, obtains the characteristic model of volcanics lithology;
Described measuring unit is used for obtaining volcanics to be measured at the measurement parameter that bores the record well logging;
Described matching unit is used for the measurement parameter of described volcanics to be measured is formed and the identical combination of described information fusion characteristic parameter, mates with above-mentioned characteristic model;
Described output unit is used for exporting the volcanics lithological information corresponding with described volcanics to be measured when the match is successful.
10. device according to claim 9 is characterized in that, described information fusion unit also comprises,
Standardized module, rgb space characteristic value calculating module, MBM;
Described standardized module, be used for described characteristic parameter generating feature parametric line, volcanics rock signature parametric line is converted into the standardized curve with identical qualitative sensing by scale, and gives corresponding RGB color value described standardized curve each point;
Described rgb space characteristic value calculating module is used for calculating the rgb space eigenwert according to the RGB color value, and length that described space characteristics value is the rgb space vector and RGB solid space volume are neglected the cube eigenwert greatly;
Described MBM is used for described RGB color value is fused into chromatogram on the depth-logger, sets RGB according to the rgb space eigenwert of correspondence again and merges the width that shows, obtains the characteristic model of described volcanics lithology.
CN2010105483950A 2010-11-18 2010-11-18 Method and device for identifying lithology of altered volcanic rock Active CN102053268B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN2010105483950A CN102053268B (en) 2010-11-18 2010-11-18 Method and device for identifying lithology of altered volcanic rock

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN2010105483950A CN102053268B (en) 2010-11-18 2010-11-18 Method and device for identifying lithology of altered volcanic rock

Publications (2)

Publication Number Publication Date
CN102053268A true CN102053268A (en) 2011-05-11
CN102053268B CN102053268B (en) 2012-07-25

Family

ID=43957793

Family Applications (1)

Application Number Title Priority Date Filing Date
CN2010105483950A Active CN102053268B (en) 2010-11-18 2010-11-18 Method and device for identifying lithology of altered volcanic rock

Country Status (1)

Country Link
CN (1) CN102053268B (en)

Cited By (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102606150A (en) * 2012-03-30 2012-07-25 中国海洋石油总公司 Method and system for identifying fragmental rock lithological characters
CN102854542A (en) * 2011-06-30 2013-01-02 中国石油集团长城钻探工程有限公司 Metamorphic rock lithology identification method
CN103616731A (en) * 2013-11-19 2014-03-05 中国石油天然气股份有限公司 Method and device for determining altered volcanic rock effective reservoir in oil and gas exploration
CN103645519A (en) * 2013-12-17 2014-03-19 中国石油大学(华东) Volcanic rock weathering crust identification and classification standard based oil and gas exploration method
CN103775075A (en) * 2014-01-26 2014-05-07 中国海洋石油总公司 Method for identifying lithology of whole well section
CN103867196A (en) * 2014-04-01 2014-06-18 北京师范大学 Method for recognizing petrographic rhythm change in siltstone and mudstone alternate stratum through imaging logging image
CN104112126A (en) * 2014-08-06 2014-10-22 南京大学镇江高新技术研究院 Marble microsection automatically identifying method
CN104134069A (en) * 2014-08-06 2014-11-05 南京大学 Automatic identification method for shale microsections
CN104182730A (en) * 2014-08-06 2014-12-03 南京大学镇江高新技术研究院 Automatic identification method of granite microsection
CN105114067A (en) * 2015-08-26 2015-12-02 中国石油天然气股份有限公司 Lithology logging facies method
CN105407364A (en) * 2015-10-27 2016-03-16 四川长虹电器股份有限公司 Channel comprehensive competitiveness realization method based on intelligent television rating system
CN106370812A (en) * 2016-08-19 2017-02-01 华北水利水电大学 Rock alteration zoning comprehensive quantitative discrimination method
CN106988737A (en) * 2017-04-28 2017-07-28 中国石油大港油田勘探开发研究院 A kind of method that utilization lithology combination recognizes sedimentary facies
CN108830140A (en) * 2018-04-28 2018-11-16 中国石油大学(华东) A kind of Lithology Identification Methods for Volcanic Rocks based on electric imaging logging fractal dimension
CN109541722A (en) * 2018-11-19 2019-03-29 中国地质调查局成都地质调查中心 A kind of Lithology Identification Methods for Volcanic Rocks
CN110346416A (en) * 2019-07-17 2019-10-18 北京金海能达科技有限公司 The method of characteristic parameter Curves Recognition Volcanic uranium deposit based on sound wave and resistivity
CN110805435A (en) * 2018-08-06 2020-02-18 中国石油化工股份有限公司 Method and system for identifying complex lithology based on logging information
CN111198406A (en) * 2020-02-26 2020-05-26 中国石油大学(华东) Lithology recognition method for factor analysis logging of red reservoir
CN111220616A (en) * 2020-01-21 2020-06-02 山东大学 System and method for judging weathering resistance of clastic rock in tunnel based on feldspar characteristics
CN112285058A (en) * 2020-10-15 2021-01-29 应急管理部天津消防研究所 Nondestructive testing method for fireproof glass
CN112801808A (en) * 2020-12-30 2021-05-14 核工业北京地质研究院 Abnormal superposition prediction method for iron-uranium ore
CN113128404A (en) * 2021-04-20 2021-07-16 中国地质科学院 Intelligent mineral identification method and system
CN114252935A (en) * 2020-09-25 2022-03-29 中国石油天然气股份有限公司 Wave and depth prediction method and device for volcanic weathering crust weathering leaching
CN117148433A (en) * 2023-10-30 2023-12-01 吉林大学 Microseism P wave S wave classification method based on GPT-L model

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU4063500A (en) * 1999-04-02 2000-10-23 Conoco Inc. A method for gravity and magnetic data inversion using vector and tensor data with seismic imaging and geopressure prediction for oil, gas and mineral exploration and production
WO2003023447A2 (en) * 2001-09-07 2003-03-20 Conocophillips Company A nonlinear constrained inversion method to determine base of salt interface from gravity and gravity tensor data
US20090248309A1 (en) * 2008-03-28 2009-10-01 Schlumberger Technology Corporation Evaluating a reservoir formation
NO20092089L (en) * 2008-05-30 2009-12-01 Schlumberger Technology Bv Method and system for fluid characterization of a reservoir

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU4063500A (en) * 1999-04-02 2000-10-23 Conoco Inc. A method for gravity and magnetic data inversion using vector and tensor data with seismic imaging and geopressure prediction for oil, gas and mineral exploration and production
US6502037B1 (en) * 1999-04-02 2002-12-31 Conoco Inc. Method for gravity and magnetic data inversion using vector and tensor data with seismic imaging and geopressure prediction for oil, gas and mineral exploration and production
WO2003023447A2 (en) * 2001-09-07 2003-03-20 Conocophillips Company A nonlinear constrained inversion method to determine base of salt interface from gravity and gravity tensor data
US20090248309A1 (en) * 2008-03-28 2009-10-01 Schlumberger Technology Corporation Evaluating a reservoir formation
NO20092089L (en) * 2008-05-30 2009-12-01 Schlumberger Technology Bv Method and system for fluid characterization of a reservoir

Cited By (34)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102854542A (en) * 2011-06-30 2013-01-02 中国石油集团长城钻探工程有限公司 Metamorphic rock lithology identification method
CN102606150B (en) * 2012-03-30 2015-03-25 中国海洋石油总公司 Method and system for identifying fragmental rock lithological characters
CN102606150A (en) * 2012-03-30 2012-07-25 中国海洋石油总公司 Method and system for identifying fragmental rock lithological characters
CN103616731A (en) * 2013-11-19 2014-03-05 中国石油天然气股份有限公司 Method and device for determining altered volcanic rock effective reservoir in oil and gas exploration
CN103616731B (en) * 2013-11-19 2016-04-06 中国石油天然气股份有限公司 Method and device for determining altered volcanic rock effective reservoir in oil and gas exploration
CN103645519A (en) * 2013-12-17 2014-03-19 中国石油大学(华东) Volcanic rock weathering crust identification and classification standard based oil and gas exploration method
CN103775075A (en) * 2014-01-26 2014-05-07 中国海洋石油总公司 Method for identifying lithology of whole well section
CN103867196A (en) * 2014-04-01 2014-06-18 北京师范大学 Method for recognizing petrographic rhythm change in siltstone and mudstone alternate stratum through imaging logging image
CN103867196B (en) * 2014-04-01 2019-03-22 北京师范大学 A method of replacing the lithofacies rhythm in stratum with mud stone using imaging logging image identification siltstone and changes
CN104112126A (en) * 2014-08-06 2014-10-22 南京大学镇江高新技术研究院 Marble microsection automatically identifying method
CN104134069A (en) * 2014-08-06 2014-11-05 南京大学 Automatic identification method for shale microsections
CN104182730A (en) * 2014-08-06 2014-12-03 南京大学镇江高新技术研究院 Automatic identification method of granite microsection
CN104134069B (en) * 2014-08-06 2017-09-26 南京大学 A kind of shale microsection automatic identification method
CN105114067A (en) * 2015-08-26 2015-12-02 中国石油天然气股份有限公司 Lithology logging facies method
CN105407364B (en) * 2015-10-27 2018-07-03 四川长虹电器股份有限公司 Based on channel synthesized competitiveness implementation method under smart television audience ratings system
CN105407364A (en) * 2015-10-27 2016-03-16 四川长虹电器股份有限公司 Channel comprehensive competitiveness realization method based on intelligent television rating system
CN106370812A (en) * 2016-08-19 2017-02-01 华北水利水电大学 Rock alteration zoning comprehensive quantitative discrimination method
CN106988737A (en) * 2017-04-28 2017-07-28 中国石油大港油田勘探开发研究院 A kind of method that utilization lithology combination recognizes sedimentary facies
CN108830140A (en) * 2018-04-28 2018-11-16 中国石油大学(华东) A kind of Lithology Identification Methods for Volcanic Rocks based on electric imaging logging fractal dimension
CN108830140B (en) * 2018-04-28 2020-06-16 中国石油大学(华东) Volcanic lithology identification method based on electric imaging logging fractal dimension
CN110805435A (en) * 2018-08-06 2020-02-18 中国石油化工股份有限公司 Method and system for identifying complex lithology based on logging information
CN109541722A (en) * 2018-11-19 2019-03-29 中国地质调查局成都地质调查中心 A kind of Lithology Identification Methods for Volcanic Rocks
CN110346416A (en) * 2019-07-17 2019-10-18 北京金海能达科技有限公司 The method of characteristic parameter Curves Recognition Volcanic uranium deposit based on sound wave and resistivity
CN111220616A (en) * 2020-01-21 2020-06-02 山东大学 System and method for judging weathering resistance of clastic rock in tunnel based on feldspar characteristics
CN111220616B (en) * 2020-01-21 2021-06-01 山东大学 System and method for judging weathering resistance of clastic rock in tunnel based on feldspar characteristics
US11933713B2 (en) 2020-01-21 2024-03-19 Shandong University Determining system and method for weathering resistant capability of clastic rocks in tunnel based on feldspar features
CN111198406A (en) * 2020-02-26 2020-05-26 中国石油大学(华东) Lithology recognition method for factor analysis logging of red reservoir
CN114252935A (en) * 2020-09-25 2022-03-29 中国石油天然气股份有限公司 Wave and depth prediction method and device for volcanic weathering crust weathering leaching
CN114252935B (en) * 2020-09-25 2024-05-28 中国石油天然气股份有限公司 Volcanic weathered shell weathered leaching wave and depth prediction method and device
CN112285058A (en) * 2020-10-15 2021-01-29 应急管理部天津消防研究所 Nondestructive testing method for fireproof glass
CN112801808A (en) * 2020-12-30 2021-05-14 核工业北京地质研究院 Abnormal superposition prediction method for iron-uranium ore
CN113128404A (en) * 2021-04-20 2021-07-16 中国地质科学院 Intelligent mineral identification method and system
CN117148433A (en) * 2023-10-30 2023-12-01 吉林大学 Microseism P wave S wave classification method based on GPT-L model
CN117148433B (en) * 2023-10-30 2023-12-26 吉林大学 Microseism P wave S wave classification method based on GPT-L model

Also Published As

Publication number Publication date
CN102053268B (en) 2012-07-25

Similar Documents

Publication Publication Date Title
CN102053268B (en) Method and device for identifying lithology of altered volcanic rock
CN101930082B (en) Method for discriminating reservoir fluid type by using resistivity data
Taylor et al. Relationships between soil properties and high-resolution radiometrics, central eastern Wheatbelt, Western Australia
CN101802649B (en) Method to generate numerical pseudocores using borehole images, digital rock samples, and multi-point statistics
CN106370814B (en) Based on ingredient-textural classification lacustrine "Hunji"rock class reservoir Logging Identification Method
CN101881153B (en) Conventional logging information fusion visualization method and system
CN109085663A (en) A kind of tight sandstone reservoir stratification seam recognition methods
CN102012526A (en) Method for discriminating type of reservoir fluid by using resistivity data
CN108717211B (en) A kind of prediction technique of the Effective source rocks abundance in few well area
Askari et al. A fully integrated method for dynamic rock type characterization development in one of Iranian off-shore oil reservoir
Munier et al. Geological site descriptive model. A strategy for the model development during site investigations
Mellal et al. Multiscale Formation Evaluation and Rock Types Identification in the Middle Bakken Formation
CN109826623A (en) Knowledge method is sentenced in a kind of geophysical log of tight sandstone reservoir stratification seam
CN109738955B (en) Metamorphic rock lithology comprehensive judgment method based on component-structure classification
CN112528106A (en) Volcanic lithology identification method
Gupta et al. Rock typing in the upper Devonian-lower Mississippian woodford shale formation, Oklahoma, USA
Hurst et al. Sandstone reservoir description: an overview of the role of geology and mineralogy
Milad et al. Permeability Prediction in a Complex Carbonate Reservoir in South Iraq by Combining FZI with NMR
Poursamad et al. Reservoir Quality Evaluation of Sarvak Formation in Gachsaran Oil Field, SW of Iran
CN109598049A (en) Method for drilling rock fracture development degree and regional rock fracture development rule
Rashed et al. Source Rock Characterization and Assessment Variability Utilizing Triple Combo Logs and Data Sets in the Permian Basin: An Example from the Northern Delaware Basin and Southern Midland Basin
Hietala et al. Integrated Rock-Log Calibration in the Elmworth Field-Alberta, Canada: Well Log Analysis Methods and Techniques: Part II
Alizadeh et al. How to measure the various types of geologic porosities in oil and gas reservoirs using image logs
Crampin Well log facies classification for improved regional exploration
Li et al. Check for updates Reservoir Identification and Classification Based on Principal Component Analysis and Supervised Neural Network in Carbonate Rocks

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant