CN105629327B - One kind is directed to weak cementing, deep layer tight sandstone reservoir Diagenetic Facies quantitatively characterizing method - Google Patents
One kind is directed to weak cementing, deep layer tight sandstone reservoir Diagenetic Facies quantitatively characterizing method Download PDFInfo
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Abstract
The present invention be it is a kind of be directed to weak cementing, deep layer tight sandstone reservoir Diagenetic Facies quantitatively characterizing method, belong to oil exploration and development fields.Comprise the following steps：1) quantitatively calculated by casting body flake regarding corrosion rate, regarding compacting rate；2) by rock core, ordinary sheet, ESEM, with reference to determining individual well diagenesis facies type depending on corrosion rate, depending on compacting rate；3) filter out well logging sensitive parameter and utilize BP neural network technology, establish regarding corrosion rate, regarding the corresponding relation of compacting rate and well logging sensitive parameter, obtain the Well log quantitative explanation result of Diagenetic Facies；4) filter out seismic inversion attribute sensitive parameter and combine seismic inversion data volume, establish regarding corrosion rate, regarding the corresponding relation of compacting rate and seismic inversion attribute sensitive parameter, obtain the planar prediction result of Diagenetic Facies.The method that the present invention is combined using rock core, well logging and seismic data, realizes Diagenetic Facies quantitatively characterizing and prediction, is to determine the important foundation of High-quality Reservoir in deep layer tight sand, has a extensive future.
Description
Technical field
The present invention relates to petroleum exploration ＆ development geology field, be it is a kind of for weak cementing, deep layer tight sandstone reservoir into
Petrofacies quantitatively characterizing method.
Background technology
For tight sandstone reservoir research, Diagenetic Facies are more finer than sedimentary facies.In certain construction and sedimentation setting
Under, the origin cause of formation and distribution especially for Favorable Reservoir in tight sand are studied, and diagenesis influence is more notable, so diagenesis
The quantitative study of phase and its spread have very important significance.
The one Diagenetic Facies quantitatively characterizing method packaged should have specific aim, science and practicality.Diagenetic Facies at present
Qualitative point of individual well Diagenetic Facies is mainly carried out in research using technology the methods of rock core, thin slice observation, ESEM, cathodoluminescence
Analysis, lateral prediction turns into a great problem that High-quality Reservoir is predicted in deep layer tight sand between Diagenetic Facies quantitative study and well.Diagenesis
The quantitative study of phase and lateral prediction research are primarily present problems with：(1) diagenesis is realized using which kind of integrated approach and technology
The quantitative study of phase；(2) Rock thin section analysis data is discontinuous, how to select different Diagenetic Facies mutually to be tied with well logging sensitive parameter
Close, realize the continuous Diagenetic Facies quantitative interpretation of individual well；(3) it is how sharp because sandstone reservoir cross directional variations are fast, diagenesis is complicated
The lateral prediction of Diagenetic Facies between well is realized with the integrated informations such as rock core, well logging, earthquake (well shake combines)Therefore, pin is lacked at present
To weak cementing, the quantitative comprehensive characterization method of deep layer tight sandstone reservoir Diagenetic Facies.
There are forefathers to propose depending on corrosion rate, depending on compacting rate and its there is no corresponding relation to reservoir properties contribution, it is unsuitable
The foundation quantitatively divided as Diagenetic Facies.But reservoir it is weak it is cementing on the premise of, reservoir phase is represented depending on corrosion rate, depending on compacting rate
To the degree of corrosion and compacting, regarding in the case that compacting rate is certain, the height depending on corrosion rate represents that secondary pore is relative to be sent out
Educate degree, you can the foundation quantitatively divided as Diagenetic Facies, and the basis of comparative good-quality reservoir prediction.
Because rock composition, pore structure etc. caused by diagenesis difference change on well-log information with significantly different
Response characteristic, and well-log information has continuous recording feature, therefore filters out the log parameter related to Diagenetic Facies, will
Diagenetic Facies and log parameter are corresponding, establish continuous Diagenetic Facies log interpretation model.Mainly joined both at home and abroad by logging well at present
Number and mathematical method establish Diagenetic Facies well logging discrimination model, and such as bad brocade is by Diagenetic Facies and log parameter by spider diagram come straight
See reaction；The linear multiple that Haitao Zhang etc. establishes log parameter and Diagenetic Facies by Bayesian Decision differentiates relation, so as to carry out
The continuous and quantitative identification of Diagenetic Facies；Turner R identify the cementing Diagenetic Facies of clay film by gamma curve；Jing Cheng, Song Zi are neat etc.
With reference to a variety of diagenesis quantitative parameters and well logging information, using gray theory overall merit quantitative analysis tight gas reservoir individual well into
Petrofacies.The present invention is directed to weak cementing tight sandstone reservoir, filters out the well logging sensitive parameter related to corrosion, compaction,
Such as natural gamma, neutron, interval transit time, density and resistivity, established using BP neural network technology regarding corrosion rate, regarding compacting rate
With the relation for sensitive parameter of logging well, and then the Well log quantitative explanation of Diagenetic Facies is realized.
Due to the Explanation Accuracy problem of seismic data, earthquake means, the planar prediction to Diagenetic Facies are utilized both at home and abroad at present
Achievement is less.As Mathisen M E (1997) utilize seismic stratigraphy and the petrologic petrological means of earthquake, rough distinguishing is more
Hole diagenetic facies zones and fine and close diagenetic facies zones, establish earthquake-stratigraphic model of Diagenetic Facies；Zeng Hongliu (2013) etc. analyzes calcite
Content and the relation of shale relative amount and wave impedance, it have identified two kinds of earthquake Diagenetic Facies (calcite cementation phase and argillaceous agglutinations
Phase), and combine the prediction that Distribution of Sedimentary Facies carries out earthquake Diagenetic Facies.The method that the present invention is combined using " well-shake ", establish regarding molten
Erosion rate, realize the planar prediction of Diagenetic Facies depending on the relation of compacting rate and seismic inversion attribute.
The content of the invention
The present invention " one kind is directed to weak cementing, deep layer tight sandstone reservoir Diagenetic Facies quantitatively characterizing method " combines grinding for forefathers
Study carefully achievement, it is proposed that the method being combined using rock core, well logging and seismic data, it is fixed as centre to regard corrosion rate and regard compacting rate
Parameter is measured, establishes rock core Diagenetic Facies, the contact logged well between sensitive parameter and seismic inversion attribute, realizes the quantitative table of Diagenetic Facies
Seek peace planar prediction.It is characterized in that：
The present invention is realized by following steps：
1) determination is quantitatively calculated by casting body flake and regards corrosion rate, regarding compacting rate；
2) analyzed by core description, ordinary sheet, scanning electron microscopic observation, drawn with reference to regarding corrosion rate, regarding compacting rate intensity
The accurate order well diagenesis facies type of minute mark；
3) filter out the log parameter sensitive to corrosion, compaction and utilize BP neural network technology, foundation regards corrosion
Rate, the corresponding relation regarding compacting rate and well logging sensitive parameter, obtain Diagenetic Facies Well log quantitative explanation and recognition result；
The well logging sensitive parameter that step 3) filters out is：DT (sound wave), DEN (density), GR (natural gamma), CNCF (in
Son), RT (resistivity)；
4) filter out the seismic inversion attribute sensitive parameter sensitive to corrosion, compaction and combine seismic inversion data
Body, establish regarding corrosion rate, regarding the corresponding relation of compacting rate and seismic inversion attribute sensitive parameter, obtain the planar prediction of Diagenetic Facies
As a result；
In step 4), the seismic inversion attribute sensitive parameter that filters out is p-wave impedance (Ip), S-wave impedance (Is), in length and breadth
Wave velocity ratio (Vp/Vs), do depending on corrosion rate, cut into slices depending on the seismic inversion attribute of compacting rate to predict the planar distribution of Diagenetic Facies.
Brief description of the drawings：
Fig. 1 is all kinds of Diagenetic Facies and the graph of a relation for each parameter of logging well
Fig. 2 is that BP neural network method differentiates well logging Diagenetic Facies flow chart
Fig. 3 is seismic inversion parameter and diagenesis intensity dependence on parameter figure
Fig. 4 is rock core Diagenetic Facies and well log interpretation Diagenetic Facies comparison diagram
Embodiment
The present invention be by rock core, well logging and seismic data, establish it is a kind of for weak cementing, deep layer tight sandstone reservoir into
Petrofacies quantitatively characterizing method, a set of effective characterization scheme is provided for unconventionaloil pool reservoir study.
The present invention is realized by following steps：
1) determination is quantitatively calculated by casting body flake and regards corrosion rate, regarding compacting rate；
What step 1) proposed
Initial pore volume=20.91+22.9/ (δ1/δ2) (δ 1, δ 2 are represented 25% and 75% on granularity accumulation curve respectively
Particle diameter corresponding to place)
2) analyzed by core description, ordinary sheet, scanning electron microscopic observation, drawn with reference to regarding corrosion rate, regarding compacting rate intensity
The accurate order well diagenesis facies type of minute mark；
3) filter out the log parameter sensitive to corrosion, compaction and utilize BP neural network technology, foundation regards corrosion
Rate, the corresponding relation regarding compacting rate and well logging sensitive parameter, obtain Diagenetic Facies Well log quantitative explanation and recognition result；
The well logging sensitive parameter that step 3) filters out is：DT (sound wave), DEN (density), GR (natural gamma), CNCF (in
Son), RT (resistivity), the wherein increase effect of sound wave and density to the porosity caused by corrosion is more sensitive, natural gamma
More sensitive to compaction with neutron, resistivity can reflect the micropore structure of reservoir to a certain extent, and certainly
Right gamma can react the lithology of rock, shale content etc. in stratum；
What step 3) proposed carries out Diagenetic Facies Well log quantitative explanation, the first survey to filtering out using BP neural network technology
Well sensitive parameter and it is corresponding be normalized depending on corrosion rate, depending on compacting rate, to remove variable unit disunity between each well
That brings is unfavorable, random deviation and systematic error caused by also eliminating logger, ensures that log data can more accurately reflect
Influence between stratum characteristic, and each well caused by logging program disunity.Secondly choose and quantitatively calculated by thin slice
Regard corrosion rate, regarding compacting rate and it is corresponding well logging sensitive parameter be used as train samples, will well logging sensitive parameter work
To input neuron, to regard corrosion rate, regard compacting rate as decision attribute, as output neuron, when training error is less than 0.1%
When network establish and achieve the goal, and then carry out full well section Diagenetic Facies Well log quantitative explanation；
4) filter out the seismic inversion attribute sensitive parameter sensitive to corrosion, compaction and combine seismic inversion data
Body, establish regarding corrosion rate, regarding the corresponding relation of compacting rate and seismic inversion attribute sensitive parameter, obtain the planar prediction of Diagenetic Facies
As a result；
In step 4), the seismic inversion attribute sensitive parameter that filters out is p-wave impedance (Ip), S-wave impedance (Is), in length and breadth
Wave velocity ratio (Vp/Vs)；Multiple linear regression will be done depending on corrosion rate, depending on compacting rate and Ip, Is, Vp/Vs parameter respectively, will returned
Equation imports seismic inversion data volume and done regarding corrosion rate, regarding the inverting attribute of compacting rate, with reference to the diagenesis intensity criteria for classifying, overlapping
The distributions of Diagenetic Facies is drawn a circle to approve depending on corrosion rate, depending on the attribute section of compacting rate, the final plane exhibition for predicting Diagenetic Facies
Cloth.
Embodiment
West Lake Depression flower port group is a set of cementing weak deep layer tight sandstone reservoir, and reservoir has strong anisotropism, work area
Interior construction of stable, the type of sedimentary micro is clear and definite, but is influenceed by corrosion, compaction, and the type complexity of Diagenetic Facies is various, and
, can using the method for the present invention and under conditions of Shao Jing, the vertical and Diagenetic Facies of plane are explained and prediction work difficulty is larger
Different type Diagenetic Facies are quantitatively described and predicted.
All coring section core wafers, wall heart thin slice more than 600 are counted first, carry out microstructure and the hole of casting body flake
The observation and quantitative description work that gap develops, are calculated regarding corrosion rate and regarding compacting rate.
Initial pore volume=20.91+22.9/ (δ1/δ2) (δ 1, δ 2 are represented 25% and 75% on granularity accumulation curve respectively
Particle diameter corresponding to place)
Second step, by core description, ordinary sheet analyze, scanning electron microscopic observation, with reference to work area overview determine reservoir into
The rock intensity criteria for classifying, it is as shown in the table, mark off four kinds of individual well diagenesis facies types：Middle corrosion-in be compacted into petrofacies, in it is molten
Erosion-in suppress real Diagenetic Facies, strong corrosion-suppress real Diagenetic Facies, in strong corrosion-suppress real Diagenetic Facies.
| Compaction intensity | Depending on compacting rate/% | Corrosion intensity | Depending on corrosion rate/% |
| Suppress reality | >75 | Strong corrosion | >75 |
| In suppress reality | 65~75 | In strong corrosion | 60~75 |
| Middle compacting | 30~65 | Middle corrosion | 30~60 |
| Weak compacting | <30 | Weak corrosion | >30 |
3rd step, filter out the log parameter sensitive to corrosion, compaction：DT (sound wave), DEN (density), GR are (natural
Gamma), CNCF (neutron), RT (resistivity), by different Diagenetic Facies with well logging sensitive parameter analysis (Fig. 1) understand have compared with
Good corresponding relation.
4th step, to the well logging sensitive parameter that filters out and it is corresponding be normalized depending on corrosion rate, depending on compacting rate,
Choose and be used as neural metwork training sample depending on corrosion rate, depending on compacting rate and corresponding well logging sensitive parameter by what thin slice quantitatively calculated
This, will well logging sensitive parameter as neuron is inputted, to regard corrosion rate, regard compacting rate as decision attribute, as output neuron,
When training error is less than 0.1%, network, which is established, achieves the goal (Fig. 2), and then the well logging for carrying out full well section Diagenetic Facies quantitatively solves
Release, continuous Diagenetic Facies evolution Feature is portrayed on vertical.
5th step, because drilling well is few, Diagenetic Facies are difficult to the research of planar distribution, by regarding corrosion rate, regarding compacting rate
(Fig. 3) is found with seismic inversion property parameters (p-wave impedance (Ip), S-wave impedance (Is), P-S wave velocity ratio (Vp/Vs))：
Ip, Is, Vp/Vs depending on corrosion rate with having negative correlativing relation, with having positive correlation depending on compacting rate, it follows that Areal porosity is smaller,
Rock contact degree is closer, and spread speed is faster.
6th step, from geological knowledge, it will be done respectively depending on corrosion rate, depending on compacting rate and Ip, Is, Vp/Vs parameter polynary
Linear regression, regression equation importing seismic inversion data volume is done regarding corrosion rate, regarding the inverting attribute of compacting rate, it is strong with reference to diagenesis
The criteria for classifying is spent, overlaps depending on corrosion rate, the distributions of Diagenetic Facies is drawn a circle to approve depending on the attribute section of compacting rate, finally predict
The planar distribution of Diagenetic Facies.
Depending on corrosion rate=- 1.68126e-6Ip+3.83944e-5Is-204.3903488Vp/Vs+103.14788
Depending on compacting rate=4.07672e-6Ip+3.84935e-5Is-244.484517Vp/Vs+80.16936878
7th step, from analysis result, the above method can be accomplished by putting the Diagenetic Facies quantitatively characterizing to line to face, and
Diagenetic Facies result of log interpretation matches (Fig. 4) with the diagenesis facies type of core description, between the Diagenetic Facies and well of planar prediction
Can also accomplish between Diagenetic Facies section it is preferably corresponding, it is weak it is cementing under conditions of accomplished quantification Diagenetic Facies vertical explanation
And planar prediction, the exploration and development to tight gas reservoir have important directive significance.
Claims (3)
1. one kind is directed to weak cementing, deep layer tight sandstone reservoir Diagenetic Facies quantitatively characterizing method, it is characterised in that including following step
Suddenly：
(1) quantitatively calculated by casting body flake regarding corrosion rate, regarding compacting rate；
(2) analyzed by core description, ordinary sheet, scanning electron microscopic observation, mark is divided with reference to regarding corrosion rate, regarding compacting rate intensity
Accurate order well diagenesis facies type；
(3) log parameter sound wave, density, natural gamma, neutron and the resistivity sensitive to corrosion, compaction are filtered out, and
Using BP neural network technology, establish regarding corrosion rate, regarding the corresponding relation of compacting rate and well logging sensitive parameter, obtain Diagenetic Facies
Well log quantitative explanation and recognition result；
(4) filter out the seismic inversion attribute sensitive parameter sensitive to corrosion, compaction and combine seismic inversion data volume, build
It is vertical to regard corrosion rate, the corresponding relation regarding compacting rate and seismic inversion attribute sensitive parameter, obtain the planar prediction result of Diagenetic Facies.
2. one kind according to claim 1 is directed to weak cementing, deep layer tight sandstone reservoir Diagenetic Facies quantitatively characterizing method, its
It is characterised by described step (3), first to the well logging sensitive parameter that filters out and corresponding regards corrosion rate, regarding compacting
Rate is normalized, with remove variable unit disunity between each well bring it is unfavorable, also eliminate caused by logger it is random partially
Difference and systematic error, ensure that log data can more accurately reflect between stratum characteristic, and each well because logging program is not united
Influence caused by one；Secondly choose and regard corrosion rate, regarding compacting rate and the sensitive ginseng of corresponding well logging by what thin slice quantitatively calculated
Number is used as train samples, using well logging sensitive parameter as input neuron, to regard corrosion rate, regard compacting rate as decision-making
Attribute, as output neuron, when training error is less than 0.1%, network, which is established, achieves the goal, and then carries out full well section diagenesis
Phase Well log quantitative explanation.
3. one kind according to claim 1 is directed to weak cementing, deep layer tight sandstone reservoir Diagenetic Facies quantitatively characterizing method, its
Be characterised by described step (4), the seismic inversion attribute sensitive parameter filtered out is p-wave impedance Ip, S-wave impedance Is,
P-S wave velocity ratio Vp/Vs, multiple linear regression will be done depending on corrosion rate, depending on compacting rate and Ip, Is, Vp/Vs parameter respectively, will returned
Return equation to import seismic inversion data volume to do regarding corrosion rate, regarding the inverting attribute of compacting rate, with reference to the diagenesis intensity criteria for classifying, fold
Close depending on corrosion rate, the distributions of Diagenetic Facies drawn a circle to approve depending on the attribute section of compacting rate, the plane of final prediction Diagenetic Facies
Spread.
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| CN108090278B (en) * | 2017-12-15 | 2021-06-01 | 长江大学 | Clastic rock reservoir lithogenic phase dividing method |
| CN109031424B (en) * | 2018-08-06 | 2019-09-03 | 中国石油大学(华东) | A method of based on well logging Multiparameter low permeability reservoir Diagenetic Facies |
| CN109884696A (en) * | 2019-03-20 | 2019-06-14 | 中国石油化工股份有限公司 | Sandstone Computational approach of S-wave velocity based on calcite cementation function influence |
| CN112347670B (en) * | 2020-10-26 | 2022-09-27 | 青海大学 | Rockfill material creep parameter prediction method based on neural network response surface |
| CN112578475B (en) * | 2020-11-23 | 2024-06-21 | 中海石油(中国)有限公司 | Data mining-based dense reservoir dual dessert identification method |
| CN113820754B (en) * | 2021-09-10 | 2023-06-06 | 中国石油大学(华东) | Deep tight sandstone reservoir evaluation method based on artificial intelligence identification of reservoir lithofacies |
| CN114441405B (en) * | 2021-12-22 | 2023-06-06 | 中国地质大学（北京） | Quantitative evaluation method for secondary hole increasing amplitude based on compaction and cementation hole decreasing trend |
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