CN108121013A - Method for identifying microbial carbonate rock lithofacies - Google Patents
Method for identifying microbial carbonate rock lithofacies Download PDFInfo
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- CN108121013A CN108121013A CN201711112999.9A CN201711112999A CN108121013A CN 108121013 A CN108121013 A CN 108121013A CN 201711112999 A CN201711112999 A CN 201711112999A CN 108121013 A CN108121013 A CN 108121013A
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- 238000000034 method Methods 0.000 title claims abstract description 64
- 239000011435 rock Substances 0.000 title claims abstract description 27
- 230000000813 microbial effect Effects 0.000 title claims abstract description 12
- BVKZGUZCCUSVTD-UHFFFAOYSA-L Carbonate Chemical compound [O-]C([O-])=O BVKZGUZCCUSVTD-UHFFFAOYSA-L 0.000 title abstract description 7
- 238000012545 processing Methods 0.000 claims abstract description 5
- 241000195493 Cryptophyta Species 0.000 claims description 35
- 230000015572 biosynthetic process Effects 0.000 claims description 16
- 150000004649 carbonic acid derivatives Chemical class 0.000 claims description 8
- 238000005520 cutting process Methods 0.000 claims description 7
- 239000004576 sand Substances 0.000 claims description 7
- FFBHFFJDDLITSX-UHFFFAOYSA-N benzyl N-[2-hydroxy-4-(3-oxomorpholin-4-yl)phenyl]carbamate Chemical compound OC1=C(NC(=O)OCC2=CC=CC=C2)C=CC(=C1)N1CCOCC1=O FFBHFFJDDLITSX-UHFFFAOYSA-N 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 5
- 208000035126 Facies Diseases 0.000 abstract 2
- 239000010459 dolomite Substances 0.000 description 15
- 229910000514 dolomite Inorganic materials 0.000 description 15
- 235000019738 Limestone Nutrition 0.000 description 11
- 239000006028 limestone Substances 0.000 description 11
- 229910003460 diamond Inorganic materials 0.000 description 7
- 239000010432 diamond Substances 0.000 description 7
- 238000003384 imaging method Methods 0.000 description 6
- 244000005700 microbiome Species 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 238000003475 lamination Methods 0.000 description 2
- 230000000704 physical effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000004575 stone Substances 0.000 description 2
- 241000894006 Bacteria Species 0.000 description 1
- 241001070941 Castanea Species 0.000 description 1
- 235000014036 Castanea Nutrition 0.000 description 1
- 241001424413 Lucia Species 0.000 description 1
- 241001637516 Polygonia c-album Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- -1 micrite Substances 0.000 description 1
- 230000007935 neutral effect Effects 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- 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
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- 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
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Abstract
The invention provides a method for identifying microbial carbonate rock facies. The method comprises the following steps: acquiring logging data and core description data of a reference well; determining a lithofacies type according to the core description data; processing the logging data and constructing corresponding identification marks; establishing a lithofacies identification model based on the lithofacies type and the identification mark; and identifying the lithofacies of the target well based on the identification model. The technical scheme provided by the invention can effectively identify the carbonate rock facies of microbial causes, and has the advantages of high identification accuracy, good application effect and good popularization.
Description
Technical field
The present invention relates to a kind of recognition methods of Microbial Carbonates lithofacies, belong to petroleum geology exploration field.
Background technology
Carbonate rock lithofacies Logging Identification Method at present is broadly divided into qualitative and quantitative two class methods, including qualitative intersection
Plate method of identification (L.Stowe etc., 1988), qualitative Electrical imaging chart method (B.Tanwi, 2002；Da-Li Wang etc., 2008),
Quantitative neutral net (Wang Shuoru etc., 1996；Tanwi Basu etc., 2002；Zhang Zhi state etc., 2005；Michael Stundner
Deng 2004；Qi etc., 2006；Christian Perrin etc., 2007；Hong Tang etc., 2009), support vector machines (Zhang Xiang
Deng 2010) and fuzzy theory (S.J.Cuddy, 2000；Lim etc., 2004；Fan Xiangyu etc., 2005) the methods of.On this basis,
By the improvement of mathematical algorithm (Luo Wei equalitys, 2008；Zhong Yihua etc., 2009；Kiatichai etc., 2013) or by establishing conjunction
Into log parameter (Wong etc., 1998；Dull etc., 2004；Lucia etc., 2005；Hong Tang etc., 2009；Wang Rui etc., 2012；
Li Chang etc., 2017), further improve coincidence rate.
For Microbial Carbonates well logging recognition research, since algae limestone develops lamination and laminated structure feature,
Feature substantially easily identifies that (2006) Chen Zhi bravely waits 2005, Li Chaoliu etc. on electric imaging logging image.Due to Electrical imaging data
Relatively fewer, conventional logging identification algal limestone becomes main means, and algae ash cannot be preferably identified for single crossplot chart version
Rock situation proposes combination crossplot method, first distinguishes mud stone class and algal limestone class with Δ GR and Δ RT, then with Δ RT and
M parameter regions divide sandstones and micrite class, and in the Caidamu Basin, small Liangshan area Shizigou group and upper Youshashan group stratum should
With and effectively distinguished algal limestone, micrite, mud stone and sandstone (Peng Xiaoqun etc., 2012).The lower Youshashan group in flower soil ditch area
With upper dried firewood ditch group stratum algal limestone on Logging Curves there is the feature for being different from other lithology, determined therefrom that 7 kinds
Log parameter, and the preferred sample of F-Means quick clustering iterative algorithms is utilized, using discriminant analysis method to patterns noise
Well section carries out algal limestone identification (Sun Zhencheng etc., 2005).For more than the algal limestone layer of Nan Yi Mountain areas and thin, siltstone and algal limestone
Mixed deposit causes be difficult to identify algal limestone in well logging the problem of, more using Electrical imaging plate, Cyclic Analysis, cluster analysis etc.
Kind method synthesis identification thin layer algal limestone (Li Chang etc., 2013).The above method obtains better effects in practical applications.
Different from Caidamu Basin algal limestone, Sichuan Basin Denying Formation algae dolomite is transformed strongly, especially by diagenesis
It is dolomitization, algae lamina and stack feature in electric imaging logging unobvious, causing electric imaging logging cannot be effective
Identification.In addition microorganism dolomite rock type reaches 8 kinds or more, wherein algae origin cause of formation dolomite rock type reach 5 kinds with
On.Since rock type is various, and rock-electricity relation is complicated, and different lithofacies logging characters is caused to be difficult to quantification.
Therefore there is an urgent need to new technical method, microorganism dolomite rock type can be efficiently identified, it is especially right
The identification of algae dolomite lithofacies.
The content of the invention
In order to solve the above technical problems, a kind of plate knowledge it is an object of the invention to provide Microbial Carbonates lithofacies
Other method.Technical solution provided by the invention can effectively identify microbiogenic carbonate rock, and identify accurately
Degree is high.
In order to achieve the above objectives, the present invention provides a kind of recognition methods of Microbial Carbonates lithofacies, this method bags
Include following steps：
It obtains with reference to the log data of well and core description data；
Data are described according to the core, determine Lithofacies Types；
The log data is handled, and builds corresponding identification mark；
It is marked based on the Lithofacies Types and the identification, establishes the identification model of lithofacies；
Based on the identification model, the lithofacies of target well are identified.
In the above-mentioned methods, it is preferable that the log data is handled, and build corresponding identification mark include with
Lower process：
The log data is normalized, so that the value of the log data is located in the range of 0-1；
Based on the log data after the normalized, two-dimensional array is built；
The two-dimensional array is divided into different numberical ranges, and set different numberical ranges correspond to respectively it is different
Identification mark.
In the above-mentioned methods, it is preferable that the two-dimensional array is divided into different numberical ranges, and sets different numbers
Value scope corresponds to different identification marks respectively includes procedure below：
Process 1：The two-dimensional array is divided into 4 different numberical ranges, is respectively [0-0.15], [0.15-
0.5], [0.5-0.8] and [0.8-1.0]；
Process 2：It sets 4 different numberical ranges and corresponds to 4 kinds of different identification marks respectively.
In the above-mentioned methods, it is preferable that marked based on the Lithofacies Types and the identification, establish the identification model of lithofacies
Including procedure below：Calibration is compared with the identification mark in the Lithofacies Types, determines that different Lithofacies Types are corresponding
Identification model.
In the above-mentioned methods, it can be marked using pattern as identification, different numberical ranges is corresponded to different patterns, example
Such as, [0-0.15] is triangle, [0.15-0.5] is circle, [0.5-0.8] is square, [0.8-1.0] is diamond shape；It can also
It is marked using color as identification, different numberical ranges is corresponded to different colors, for example, [0-0.15] is blueness, [0.15-
0.5] it is yellow, [0.5-0.8] is red, and [0.8-1.0] is green；But not limited to this.
Present invention research is found：(four equalizations are divided into if divided using equal offshoot program to two-dimensional array
Numberical range), then algae cloud rock and shale micrite cloud rock cannot be distinguished, and use technical solution provided by the invention, will be above-mentioned
Two-dimensional array is divided into 4 different numberical ranges, can effectively distinguish different Lithofacies Types.
Fig. 6 illustrates the pattern after core hole 108 well of mill small stream is divided equally and the pattern after adjustment, wherein, divide equally
Pattern afterwards is that two-dimensional array is divided into 4 groups of different numberical ranges, sets [0-0.25] as triangle, [0.25-
0.5] it is circle, [0.5-0.75] is square, and [0.75-1.0] is diamond shape；And the pattern after adjusting is then using this Shen
The dividing mode that please be provided sets [0-0.15] as triangle, [0.15-0.5] is circle, and [0.5-0.8] is square,
[0.8-1.0] is diamond shape；If from figure it can be found that using equal offshoot program, for a sections of 108 well of core hole mill small stream and b
Algae cloud rock and shale micrite cloud rock cannot be distinguished in section, their pattern is all triangle-triangle-triangle, and for the side after adjustment
Case can then be distinguished completely.
In a detailed embodiment, by taking depth of stratum 5312-5314m in Fig. 2 as an example, based on the Lithofacies Types and
The identification mark, establishing the identification model of lithofacies includes procedure below：
It obtains with reference to log data and core description data of the well at depth of stratum 5312-5314m；
According to the core description data, it is algae cloud lithofacies to determine the Lithofacies Types at this；
Two-dimensional array is built after the log data is normalized；
The two-dimensional array is divided into 4 different numberical ranges, each numberical range corresponds to an identification icon,
In [0-0.15] be triangle, [0.15-0.5] be it is circular, [0.5-0.8] be square, [0.8-1.0] is diamond shape；
Calibration is compared with identification mark in Lithofacies Types, determine algae cloud lithofacies at the depth of stratum have 3 kinds it is different
Identification model is triangle-circle-triangle, triangle-square-trigonometric sum triangle-diamond shape-triangle respectively；Wherein, each is identified
Model is made of 3 identification icons.
In technical solution provided by the invention, the corresponding identification model of each lithofacies is not limited to one kind, for example, algae cloud
There is triangle-square-triangle or triangle-circle-triangle etc. on pattern figure in lithofacies, then this 2 kinds of moulds
Formula is exactly that algae cloud lithofacies obtain pattern plate, and Lithofacies Types of being specifically subject to compare calibrated result with identification mark, each knowledge
Other model can include multiple identifications and mark.
In the above-mentioned methods, it is preferable that the log data is normalized according to the formula shown in formula 1：
In formula 1, XnFor the log data after normalized, X is the log data before normalized, XminFor normalizing
Change the minimum value in before processing log data, XmaxFor the maximum in log data before normalized.
In the above-mentioned methods, it is preferable that based on the log data after the normalized, structure two-dimensional array include with
Lower process：
Natural gamma, interval transit time and deep lateral resistivity after normalized is denoted as b1, b2 and b3 respectively；
Described b1, b2 and b3 are combined in the horizontal, obtain two-dimensional array B
B=[b1 b2 b3]。
In the above-mentioned methods, it is preferable that the log data includes natural gamma, interval transit time and deep lateral resistivity.
In the above-mentioned methods, it is preferable that the Lithofacies Types include 7 classes, respectively algae cloud lithofacies, micrite algae cloud lithofacies,
Algae sand formation cuttings cloud lithofacies, sand formation cuttings cloud lithofacies, algal gel crystalline substance cloud lithofacies, micrite cloud lithofacies and shale micrite cloud lithofacies.
In the above-mentioned methods, it is preferable that based on the identification model, the lithofacies of target well are identified including following mistake
Journey：
Obtain the log data of target well；
Corresponding identification mark is built after the log data of the target well is normalized；
The identification mark for building obtained target well with the identification model of the lithofacies is compared, obtains target well
Lithofacies Identification result.
In the above-mentioned methods, corresponding identification mark is built after the log data of the target well being normalized
It is referred to the embodiment progress that the above-mentioned log data to reference to well is handled and builds corresponding identification mark.
In the above-mentioned methods, it is preferable that before the log data is normalized, this method further includes pair
The log data is pre-processed, the step of to remove exceptional value.
In the above-mentioned methods, it is preferable that this method further includes the step of correspondence for building lithofacies and lithology.
Beneficial effects of the present invention：
1) algae cloud lithofacies are favourable High-quality Reservoirs, and main gas pay is distributed in algae cloud rock reservoir, provided by the invention
Technical solution can effectively identify microbiogenic carbonate rock, and recognition accuracy is high, this is surveyed for gas-bearing formation
Visiting exploitation has extremely important directive significance.
2) compared with the method for conventional view curve qualitative recognition, technical solution provided by the invention realizes a plurality of well logging
Curve is shown in an image, can be used not only for the carbonate rock lithofacies well logging recognition of microorganism (algae etc.) origin cause of formation, right
Equally applicable in other kinds of lithofacies well logging recognition, key is that rock core lithofacies are compared with pattern plate, can establish differentiation
The pattern plate of different lithofacies, it is possible to obtain very good effect.
Description of the drawings
Fig. 1 is the flow chart of the recognition methods of Microbial Carbonates lithofacies provided in an embodiment of the present invention；
Fig. 2 is mill 105 well part coring section of small stream and pattern plate figure；
Fig. 3 is mill 51 well part coring section of small stream and pattern plate figure；
Fig. 4 is the corresponding well logging pattern plate of different lithofacies；
Fig. 5 cores a section lithofacies well logging recognition result figure for mill small stream 108 well part；
Fig. 6 is the effect contrast figure of the pattern after 108 well of mill small stream is divided equally and the pattern after adjustment.
Specific embodiment
In order to which technical characteristic, purpose and the advantageous effect to the present invention are more clearly understood, now to the skill of the present invention
Art scheme carry out it is described further below, but it is not intended that the present invention can practical range restriction.
Following embodiment is by taking Sichuan Basin basin Mo Xi-Gao Shi terrace lands area Sinian Dengying ditch group stratum as an example, to coring
Well 108 well of mill small stream carries out lithofacies well logging pattern plate identification.Wherein, the Basic Geological situation of this area and regional parameters situation be such as
Under：
Sichuan Basin Sinian Dengying group High-quality Reservoir is apparent by phased feature, and sand formation cuttings beach phase mutually preserves object with algal head in platform
Property best, " the mound beach complex " both being especially built up, therefore well logging recognition lithofacies are for the pre- measuring tool of High-quality Reservoir
It is significant.
Embodiment 1
Present embodiments provide a kind of recognition methods of Microbial Carbonates lithofacies.The flow of this method as shown in Figure 1,
It comprises the following steps：
Step S101：It obtains with reference to the log data of well and core description data
Utilize CLS-5700 logging program apparatus measures stratum natural gammas GR, interval transit time DT and deep lateral resistivity
RD。
Step S102：Data are described according to the core, determine Lithofacies Types
The type of lithology is especially more, probably there is more than 10 kinds, can not all identify, can be carried out according to its physical property
Sort out, be divided into 7 major classes in total：
Table 1 divides lithofacies and lithology correspondence
Sichuan Basin Denying Formation Microbialites rock type is various, and argillaceous dolomite, powder-fine grain dolomite, mud crystallite are white
Yun Yan, dolarenite and with the relevant dolomite of alga microbial etc., more than rock type reaches at least eight kinds of.It is (blue with algae
Bacterium) dolomite that participates in includes algae lamination dolomite, algae lamina dolomite, algae oncolite dolomite, algae grumeleuse dolomite etc.
Deng.Algae origin cause of formation dolomite rock type complexity is various, logs well and the algae white clouds of each species cannot be distinguished, therefore various types algae
Class origin cause of formation dolomite is uniformly classified as algae cloud lithofacies.
According to the core description data (physical property characteristic of different type lithology) of acquisition, with reference to the lithology classification of forefathers
Scheme is divided into 7 major class lithofacies to the white rock of Denying Formation microorganism, specifically refers to table 1.
Step S103：The log data is handled, and builds corresponding identification mark
Process 1：Log data is pre-processed, to remove exceptional value；
Process 2：Pretreated single log is normalized respectively, normalized process is as follows：
In formula, GRmaxFor natural gamma maximum, API；
GRminFor natural gamma minimum value, API；
DTmaxFor interval transit time maximum, μ s/f
DTminFor interval transit time minimum value, μ s/f；
RDmaxFor deep lateral resistivity maximum, ohm meter；
RDminFor deep lateral resistivity minimum value, ohm meter；
For the logging character of Sichuan Basin Mo Xi-Gao Shi terrace lands area lithofacies, log parameter determines as follows：
Interval transit time maximum DTmax=55
Interval transit time minimum value DTmin=43
Natural gamma maximum GRmax=60
Natural gamma minimum value GRmin=6
Deep lateral resistivity maximum RDmax=99990
Deep lateral resistivity minimum value RDmin=200
Process 3：B1, b2, b3 are combined in the horizontal, it is as follows to form two-dimensional array B：
B=[b1 b2 b3]。
Process 4：Two-dimensional array is divided into 4 different numberical ranges, be respectively [0-0.15], [0.15-0.5],
[0.5-0.8] and [0.8-1.0], the corresponding identification of setting [0-0.15] is labeled as triangle, [0.15-0.5] corresponding identification
Labeled as circular, [0.5-0.8] corresponding identification labeled as square, [0.8-1.0] corresponding identification labeled as diamond shape.
Step S104：It is marked based on the Lithofacies Types and the identification, establishes the identification model of lithofacies
Exemplified by grinding 105 well depth of stratum 5310-5317m of small stream, the core description data at the depth of stratum, thin section identification
As a result, identification mark (i.e. pattern figure in Fig. 2) and definite Lithofacies Types are as shown in Fig. 2, the Lithofacies Types at this are algae
Cloud lithofacies, the mark of the identification at this that makes discovery from observation can be divided into 3 classes, be triangle-circle-triangle, triangle respectively
Shape-square-triangle, triangle-diamond shape-triangle, accordingly, it is determined that the corresponding identification mould of algae cloud lithofacies at the depth of stratum
Type (i.e. pattern plate in Fig. 2) is triangle-circle-triangle, triangle-square-triangle and triangle-water chestnut
Shape-triangle.
The rest may be inferred, and the Lithofacies Types of other Different Strata depths and the corresponding identification model of different Lithofacies Types are as schemed
Shown in 2 and Fig. 3.Fig. 2 sections of coring to mill small stream 105 well part carry out log data processing and are depicted as pattern figure, according to core rock
It mutually demarcates, algae cloud lithofacies, algae sand formation cuttings cloud lithofacies, the pattern plate of shale micrite cloud lithofacies is labelled on Fig. 2；Fig. 3 is to grinding small stream
51 well parts section of coring carries out log data processing and the figure that draws a design, and is demarcated also according to rock core lithofacies, establishes sand formation cuttings cloud
Lithofacies and algae cloud lithofacies, shale micrite cloud lithofacies pattern plate.Entirely establishing pattern plate process is namely based on core hole lithofacies
Pattern plate corresponding with its is summarized.
For mill small stream area, beyond above-mentioned 2 mouthfuls of core holes, the coring data of other 5 mouthfuls of core holes, root are also used
According to these data, compare rock core lithofacies and for pattern figure, summarize 47 kinds of pattern figures of the 7 type lithofacies in the work area
Version, i.e., the identification model of different lithofacies, as shown in Figure 4.It is can be found that from Fig. 4：There is corresponding knowledge per one kind Lithofacies Types
Other model, and multiple and different identification models is corresponded to per a kind of Lithofacies Types, each identification model marks structure by 3 identifications
Into.
Step S105：Based on the identification model, the Lithofacies Types of target well are identified
The log data of target well (Sichuan Basin Mo Xi-Gao Shi terrace lands area 108 well of mill small stream i.e.) is obtained, with reference to step
S103 handles the log data of target well, and builds corresponding identification mark；
Identification of the above-mentioned structure on target well is marked, with 7 class lithofacies, the 48 kinds of well logging recognitions established in step S104
Pattern plate (Fig. 4) is compared, and determines the corresponding Lithofacies Types of each identification model, as shown in Figure 5.
In order to verify the reliability of above-mentioned recognition result, the core description data of target well and thin section identification result are obtained
(as shown in Figure 5) compared with definite recognition result, finds coincidence rate more than 80%, it is seen that skill provided by the invention
Art scheme recognition accuracy is high.
Claims (10)
1. a kind of recognition methods of Microbial Carbonates lithofacies, this method comprise the following steps：
It obtains with reference to the log data of well and core description data；
Data are described according to the core, determine Lithofacies Types；
The log data is handled, and builds corresponding identification mark；
It is marked based on the Lithofacies Types and the identification, establishes the identification model of lithofacies；
Based on the identification model, the lithofacies of target well are identified.
2. according to the method described in claim 1, wherein, handling the log data, and build corresponding identification mark
Note includes procedure below：
The log data is normalized, so that the value of the log data is located in the range of 0-1；
Based on the log data after the normalized, two-dimensional array is built；
The two-dimensional array is divided into different numberical ranges, and sets different numberical ranges and corresponds to different identification respectively
Mark.
3. according to the method described in claim 2, wherein, the two-dimensional array is divided into different numberical ranges, and is set
Different numberical ranges corresponds to different identification marks respectively includes procedure below：
Process 1：The two-dimensional array is divided into 4 different numberical ranges, be respectively [0-0.15], [0.15-0.5],
[0.5-0.8] and [0.8-1.0]；
Process 2：It sets 4 different numberical ranges and corresponds to 4 kinds of different identification marks respectively.
4. according to claim 1-3 any one of them methods, wherein, based on the Lithofacies Types and the identification mark, build
The identification model of vertical lithofacies includes procedure below：
Calibration is compared with the identification mark in the Lithofacies Types, determines the corresponding identification model of different Lithofacies Types.
5. according to the method described in claim 2, wherein, the log data is normalized according to the formula shown in formula 1
Processing：
In formula 1, XnFor the log data after normalized, X is the log data before normalized, XminFor normalized
Minimum value in preceding log data, XmaxFor the maximum in log data before normalized.
6. according to claim 2-5 any one of them methods, wherein, based on the log data after the normalized, structure
Building two-dimensional array includes procedure below：
Natural gamma, interval transit time and deep lateral resistivity after normalized is denoted as b1, b2 and b3 respectively；
Described b1, b2 and b3 are combined in the horizontal, obtain two-dimensional array B
B=[b1 b2 b3].
7. according to claim 1-6 any one of them methods, wherein, the log data includes natural gamma, interval transit time
And deep lateral resistivity.
8. according to claim 1-7 any one of them methods, wherein, the Lithofacies Types include 7 classes, are respectively algae cloud rock
Phase, micrite algae cloud lithofacies, algae sand formation cuttings cloud lithofacies, sand formation cuttings cloud lithofacies, algal gel crystalline substance cloud lithofacies, micrite cloud lithofacies and shale micrite cloud rock
Phase.
9. according to claim 1-7 any one of them methods, wherein, based on the identification model, to the lithofacies of target well into
Row identification includes procedure below：
Obtain the log data of target well；
The log data of the target well is handled, and builds corresponding identification mark；
The identification mark for building obtained target well with the identification model of the lithofacies is compared, obtains the lithofacies of target well
Recognition result.
10. according to the method described in claim 2, wherein, before the log data is normalized, this method
It further includes and the log data is pre-processed, the step of to remove exceptional value.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109447944A (en) * | 2018-09-21 | 2019-03-08 | 中国石油天然气股份有限公司 | Lithofacies identification method and system for carbonate rock |
| CN111626377A (en) * | 2020-06-10 | 2020-09-04 | 中国石油大学(北京) | Lithofacies identification method, device, equipment and storage medium |
| CN111753958A (en) * | 2020-06-22 | 2020-10-09 | 成都理工大学 | Lamp shade group microorganism rock microphase identification method based on logging data deep learning |
| CN112709568A (en) * | 2020-12-08 | 2021-04-27 | 中国石油天然气股份有限公司 | Method and device for identifying dolomite stratum algae dolomite |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109447944A (en) * | 2018-09-21 | 2019-03-08 | 中国石油天然气股份有限公司 | Lithofacies identification method and system for carbonate rock |
| CN109447944B (en) * | 2018-09-21 | 2020-08-11 | 中国石油天然气股份有限公司 | Lithofacies identification method and system for carbonate rock |
| CN111626377A (en) * | 2020-06-10 | 2020-09-04 | 中国石油大学(北京) | Lithofacies identification method, device, equipment and storage medium |
| CN111626377B (en) * | 2020-06-10 | 2023-12-26 | 中国石油大学(北京) | Lithology recognition method, device, equipment and storage medium |
| CN111753958A (en) * | 2020-06-22 | 2020-10-09 | 成都理工大学 | Lamp shade group microorganism rock microphase identification method based on logging data deep learning |
| CN112709568A (en) * | 2020-12-08 | 2021-04-27 | 中国石油天然气股份有限公司 | Method and device for identifying dolomite stratum algae dolomite |
| CN112709568B (en) * | 2020-12-08 | 2023-12-26 | 中国石油天然气股份有限公司 | Method and device for identifying dolomite formation algae dolomite |
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