CN106908856B - Earthquake prediction method for lake facies thin-layer dolomite reservoir - Google Patents
Earthquake prediction method for lake facies thin-layer dolomite reservoir Download PDFInfo
- Publication number
- CN106908856B CN106908856B CN201710038743.1A CN201710038743A CN106908856B CN 106908856 B CN106908856 B CN 106908856B CN 201710038743 A CN201710038743 A CN 201710038743A CN 106908856 B CN106908856 B CN 106908856B
- Authority
- CN
- China
- Prior art keywords
- reservoir
- individual well
- curve
- well reservoir
- dolomite
- 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.)
- Active
Links
- 229910000514 dolomite Inorganic materials 0.000 title claims abstract description 39
- 239000010459 dolomite Substances 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 28
- 208000035126 Facies Diseases 0.000 title claims abstract description 16
- 238000009826 distribution Methods 0.000 claims abstract description 24
- 239000011435 rock Substances 0.000 claims abstract description 12
- 238000004452 microanalysis Methods 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 8
- 239000004744 fabric Substances 0.000 claims description 4
- 238000013178 mathematical model Methods 0.000 claims description 4
- 238000005266 casting Methods 0.000 claims description 3
- 238000005136 cathodoluminescence Methods 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 239000010410 layer Substances 0.000 abstract description 27
- 238000005553 drilling Methods 0.000 abstract description 6
- 239000002356 single layer Substances 0.000 abstract description 4
- 239000003208 petroleum Substances 0.000 abstract description 3
- 238000007431 microscopic evaluation Methods 0.000 abstract 1
- 239000004568 cement Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- -1 content Substances 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000002687 intercalation Effects 0.000 description 1
- 238000009830 intercalation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004062 sedimentation Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
Landscapes
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a seismic prediction method for a lake facies thin-layer dolomite reservoir, and relates to the field of geological engineering in the petroleum industry. The method comprises the following steps: acquiring a seismic data body of a target reservoir region and a rock slice of a single-well reservoir; performing microscopic analysis on the rock slices to obtain the dolomite reservoir distribution of the single-well reservoir; obtaining a logging curve of the single-well reservoir through a logging method, and obtaining a logging curve sensitive to the dolomite reservoir reaction of the single-well reservoir; establishing a logging identification template according to the logging curve sensitive to the dolomite reservoir reaction of the single-well reservoir; and loading the logging identification template into the seismic data body, and predicting the distribution of the dolomite reservoir in the target reservoir region. The method provided by the embodiment of the invention can predict the lake facies thin-layer dolomite reservoir with the single-layer thickness of about 0.5-1.5 m, overcome the limitation of the thin-layer dolomite seismic resolution, accurately predict the thin-layer dolomite reservoir seismic and improve the drilling success rate.
Description
Technical field
The present invention relates to petroleum industry Geological Engineering field, in particular to a kind of earthquake of lacustrine facies thin layer dolostone reservoirs is pre-
Survey method.
Background technique
Lacustrine facies thin layer dolomite large area in Continental Basins In China is distributed, since thickness in monolayer is at 0.5-1.5 meters or so,
Thickness is very thin, and Lithology Discrimination is difficult with earthquake prediction.Petroleum geology expert is a kind of not yet both at home and abroad at present is used to know
Not and prediction the dolomitic effective ways of lacustrine facies thin layer, only qualitatively identify and predict, the factor theoretically considered compared with
The shake of more, not excessive consideration actual well drilled situations, i.e. well does not combine well, thus causes certain application office
Limit.For example, 10 meters of earthquake prediction dolomite thickness, but actually drilling well dolomite thickness is by 0.5-1.5 meters of multi-thin-layer
What dolomite and mud stone alternating layers formed.
Summary of the invention
In order to which preferably lacustrine facies thin layer dolostone reservoirs are identified and are predicted, the present invention provide it is a kind of using it is qualitative with
Quantitative approach combines the earthquake prediction method for carrying out lacustrine facies thin layer dolostone reservoirs.
Specifically, including technical solution below：
A kind of earthquake prediction method of lacustrine facies thin layer dolostone reservoirs, the method includes：
Obtain the seismic data cube in target reservoir region and the petrographic thin section of individual well reservoir；
Micro-analysis is carried out to the petrographic thin section, obtains the dolostone reservoirs distribution of the individual well reservoir；
The log that the individual well reservoir is obtained by logging method, in conjunction with the dolostone reservoirs point of the individual well reservoir
Cloth obtains the sensitive log of the dolostone reservoirs reaction to the individual well reservoir；
According to the sensitive log of the dolostone reservoirs reaction to the individual well reservoir, well logging recognition mould is established
Plate；
The well logging recognition template is loaded into the seismic data cube, predicts the dolomite in the target reservoir region
The distribution of reservoir.
Preferably, described that micro-analysis is carried out to the petrographic thin section, obtain the dolostone reservoirs point of the individual well reservoir
Cloth includes：Description mineralogical composition, content, cement type are observed according to drill cores, the lithology of the individual well reservoir is differentiated, knows
Not Chu the individual well reservoir dolostone reservoirs distribution.
Preferably, the means of the drill cores observation include：Petrographic thin section under mirror, casting body flake, fluorescence thin section analysis,
Scanning electron microscope, cathodoluminescence analysis.
Further, the sensitive log of the dolostone reservoirs reaction to the individual well reservoir includes：From
Right gamma curve, interval transit time curve, compensated neutron curve, density curve and resistivity curve.
Further, the dolostone reservoirs to the individual well reservoir react corresponding to for the sensitive log
The individual well reservoir dolostone reservoirs distribution value range be：The natural gamma value of the natural gamma curve 30~
Between 60API, the sound wave value of the interval transit time curve is between 210~270 μ s/m, in the compensation of the compensated neutron curve
Subvalue is between 20~34PU, and the density value of the density curve is in 2.35~2.65g/cm3Between, the resistivity curve
Resistivity value is greater than 6 Ω m.
Preferably, the log for establishing the well logging recognition template includes：When natural gamma curve, sound wave
Poor curve, density curve.
Preferably, the mathematical model for establishing the well logging recognition template is：Z=DEN/ (△ GR* (AC/ACmax) *
4) , wherein , ⊿ GR=(GR-GRmin)/(GRmax-GRmin)；
Z indicates reconstruct curve values；
DEN refers to the density value under a certain predetermined depth of the individual well reservoir；
AC refers to the sound wave value under a certain predetermined depth of the individual well reservoir；
GR refers to the natural gamma value under a certain predetermined depth of the individual well reservoir；
ACmax refers to the maximum value of the dolomite section sound wave of the individual well reservoir；
GRmax refers to the maximum value of the dolomite section natural gamma of the individual well reservoir；
GRmin refers to the minimum value of the dolomite section natural gamma of the individual well reservoir.
Further, described that the well logging recognition template is loaded into the seismic data cube, predict the target storage
The distribution of the dolostone reservoirs of layer region includes：
After the well logging recognition template is loaded into the seismic data cube, in the seismic data cube described in explanation
The top bottom interface of the dolostone reservoirs in target reservoir region；
Extract the seismic properties in the top bottom interface, point of the dolostone reservoirs in target reservoir region described in qualitative forecasting
Cloth range；
Application of Logging-constrained Inversion is carried out in the distribution, the dolostone reservoirs in target reservoir region described in quantitative forecast
Distribution.
The beneficial effect of technical solution provided in an embodiment of the present invention：It provides a kind of shake by well and combines prediction dolomite
The earthquake prediction method of the lacustrine facies thin layer dolostone reservoirs of reservoir distribution.Specifically, first by using drill cores, thin slice number
According to judging the type of individual well reservoir, then combined with the well-log information of individual well reservoir, it is regular that foundation meets " lithology-electrical property "
Well logging recognition template, then carry out well shake and combine, by seismic properties and Application of Logging-constrained Inversion carry out dolostone reservoirs it is qualitative with
Quantitative forecast.Thickness in monolayer can be predicted in 0.5-1.5 meters or so lacustrine facies thin layer white clouds in prediction technique provided in an embodiment of the present invention
Rock reservoir overcomes the limitation of thin layer dolomite seismic resolution, can accurately carry out thin layer dolostone reservoirs earthquake prediction, improves and bores
Well success rate.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for
For those of ordinary skill in the art, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing.
Fig. 1 is a kind of method flow of lacustrine facies thin layer dolostone reservoirs earthquake prediction method provided in an embodiment of the present invention
Figure.
Specific embodiment
To keep technical solution of the present invention and advantage clearer, below in conjunction with attached drawing to embodiment of the present invention make into
One step it is described in detail.Unless otherwise defined, all technical terms used in the embodiment of the present invention all have and art technology
The normally understood identical meaning of personnel.
The present invention provides a kind of earthquake prediction method of lacustrine facies thin layer dolostone reservoirs, and referring to Fig. 1, this method includes as follows
Step：
Step 101：Obtain the seismic data cube in target reservoir region and the petrographic thin section of individual well reservoir；
Specifically, it can be blown out by field, receiver receives to obtain seismic data, and the heart is carried out with computer in processes
The processing of seismic data obtains corresponding seismic data cube.Obtain ground at the same time it can also which data volume is added in interpretation software
Shake is explained.
The rock core or landwaste of target reservoir section can be collected by drilling well, petrographic thin section is polished by specialty analysis chemical examination,
Obtain the petrographic thin section of individual well reservoir.
Step 102：Micro-analysis is carried out to the petrographic thin section, obtains the dolostone reservoirs distribution of the individual well reservoir；
Micro-analysis under mirror is carried out to individual well petrographic thin section using microscope.It can be thin by petrographic thin section, the body of casting under mirror
The data that piece, fluorescence thin section analysis, scanning electron microscope, cathodoluminescence analysis etc. obtain, to describe the mineralogical composition of petrographic thin section, contain
Amount, cement type differentiate lithology, structure and hole, the fractured situation of individual well reservoir, and then by different depth
The petrographic thin section at place carries out micro-analysis, identifies the dolostone reservoirs distribution of individual well reservoir.
Further, micro-analysis, available target reservoir area are carried out to the individual wells that bored several in target reservoir region
The dolostone reservoirs of several individual well reservoirs in domain are distributed.It should be noted that workload is very big since drilling well is very more for the whole district,
So several mouthfuls of representational wells can also be selected to carry out micro-analysis under construction and sedimentation setting different in target area respectively.
Step 103：The log that the individual well reservoir is obtained by logging method, in conjunction with the white clouds of the individual well reservoir
Rock reservoir distribution obtains the sensitive log of the dolostone reservoirs reaction to the individual well reservoir；
Specifically, after planned well depth depth is got into drilling well, it can use the manufacture of the physical principles such as electricity, magnetic, sound, heat, core
Various loggers record ground electrical measuring instrument can continuously along pit shaft with change in depth by logging cable G.I.H
Various parameters, obtain the log of individual well reservoir, and the log of obtained all kinds of individual well reservoirs can be used for identifying underground
Rock stratum, such as oil, gas and water layer, coal seam, metalliferous deposit.
By the log of the individual well reservoir obtained by logging method, it is distributed and ties with the dolostone reservoirs of individual well reservoir
It closes, it is bent by the way that the sensitive well logging of the dolostone reservoirs reaction to individual well reservoir in such a way that same depth is compared, can be obtained
Line.The curve sensitive to dolostone reservoirs reaction, can significantly reflect dolomitic characteristic on well-log information.Wherein, right
The curve that the dolostone reservoirs reaction of individual well reservoir is sensitive includes：(Gamma Ray curve, i.e. GR are bent for natural gamma curve
Line), interval transit time curve (Acoustic time curve, i.e. AC curve), compensated neutron curve (Compensation
Neutron curve, i.e. CN curve), density curve (Densimentric curve, i.e. DEN curve) and resistivity curve
(Resistivity curve, i.e. RT curve).The feature of the curve sensitive to the reaction of the dolostone reservoirs of individual well reservoir is：From
Low state (30~60API) is presented in right gamma GR curve, and interval transit time AC curve is in low state (210~270 μ s/m), mends
Neutron CN curve is repaid in low state (20~34PU), density DEN curve shows (2.35~2.65g/cm3), resistance in high level
Rate RT curve shows (R in high level>6 Ω m), i.e. " three low two is high " state.
Step 104：According to the sensitive log of the dolostone reservoirs reaction to the individual well reservoir, well logging is established
Recognition template；
AC, DEN, GR, RT, CN any one curve all can not accurately, clearly identify dolomite, therefore, it is necessary to combine
A variety of curve matchings go out a dolomite indicatrix to identify to dolomite.Normally, may be selected AC, DEN, GR, RT,
Any 2~4 curves in CN establish template by different mathematical models, obtain different reconstruct Z value curves, pass through ratio
To Z value curve and dolomitic corresponding relationship, most reasonable template is selected, as reflects dolomite " lithology-electrical property " rule
Well logging recognition template.
It should be noted that during establishing template, by will sensitively reflect dolomitic signature logging
Curve values are amplified as far as possible, fit a white clouds for reflecting that insensitive log value reduces as far as possible to dolomite lithology
Rock feature indicative curve, for identification dolomite stratigraph position.
In the present embodiment, selected mathematical model includes：
Model one：Z=10000*RT*DEN/ (AC*GR)；
Model two：Z=DEN*RT/ ((AC/AC mud) * △ GR)；
Model three：Z=DEN*/((AC/AC mud) * △ GR)；
Model four：Z=DEN/ (△ GR* (AC/ACmax) * 4).
Wherein , ⊿ GR=(GR-GRmin)/(GRmax-GRmin)
Z indicates reconstruct curve values；
AC refers to the sound wave value under a certain predetermined depth of individual well reservoir；
DEN refers to the density value under a certain predetermined depth of individual well reservoir；
RT refers to the resistivity value under a certain predetermined depth of individual well reservoir；
GR refers to the natural gamma value under a certain predetermined depth of individual well reservoir；
AC mud refers to the sound wave value of the muddy intercalation of individual well reservoir；
ACmax refers to the maximum value of the dolomite section sound wave of individual well reservoir；
GRmax refers to the maximum value of the dolomite section natural gamma of individual well reservoir；
GRmin refers to the minimum value of the dolomite section natural gamma of individual well reservoir；
By calculating the Z value curve of above four kinds of models, different Z value curves are compared with dolomitic relationship respectively
It is right, verified, model four：The reconstruct curve Z value of Z=DEN/ (△ GR* (AC/ACmax) * 4) and dolomitic corresponding relationship are most
To be reasonable, therefore select the well logging recognition template of model foundation " lithology-electrical property " rule.It is somebody's turn to do the survey of " lithology-electrical property " rule
Well recognition template is by increasing AC value weight, opposite reduction GR value weight, solve thin dolomite stratigraph GR value by shoulder effect compared with
Greatly, the problem of causing GR excessive, being unable to respond dolomite lithology.The modelling effect is preferable, can preferably separate transition rock with
Dolomite separation, meanwhile, the dolomitic number of plies of Z Curves Recognition, thickness are consistent with fixed well result, have well solved dolomite
The problem of thin layer identifies.
Reconstruct curve has uniform range, compares convenient for the whole district.And reconstruct curve codomain is as small as possible, is convenient for later period earthquake solution
Release application.
In addition, should be noted during the well logging recognition template of foundation " lithology-electrical property " rule：
(1) model foundation element：Using model validation as first choice, based on petrophysical well logging reaction, build
Vertical pure mathematics model, does not consider the geological Significance of model temporarily.Link closely geological research analysis results, sufficiently applies and embodies white clouds
The characteristics of rock " three low two is high ".It is preferential to choose the log more sensitive to dolomite reaction, and (lithology is removed to ground environment
Insensitive log outside)；Reduce the weight of log easily affected by environment.Amplify dolomitic high-value signal (RT,
DEN), dolomite lower value signals (GR, AC) is reduced, it is therefore an objective to increase the electrical property difference of dolomite Yu other lithology.
(2) model optimum principle：Well logs are made full use of as far as possible.Improve the discrimination to thin layer.Subtract as far as possible
The non-rock character interference of few country rock interference and reduction stratum.Convenient to carry out, reducing treatment people subjective factor influences.
Step 105：The well logging recognition template is loaded into the seismic data cube, predicts the target reservoir region
Dolostone reservoirs distribution.
Specifically, after well logging recognition template being loaded into seismic data cube, the objective of interpretation reservoir in seismic data cube
The top bottom interface of the dolostone reservoirs in region；
Extract the seismic properties in the bottom interface of top, the distribution of the dolostone reservoirs in qualitative forecasting target reservoir region；
Application of Logging-constrained Inversion, the distribution model of the dolostone reservoirs in quantitative forecast target reservoir region are carried out in distribution
It encloses.
Method provided in an embodiment of the present invention can be predicted thickness in monolayer and store up in 0.5-1.5 meters or so lacustrine facies thin layer dolomites
Layer, overcomes the limitation of thin layer dolomite seismic resolution, can accurately carry out thin layer dolostone reservoirs earthquake prediction, improve drilling well at
Power.
The above is merely for convenience of it will be understood by those skilled in the art that technical solution of the present invention, not to limit
The present invention.All within the spirits and principles of the present invention, any modification, equivalent replacement, improvement and so on should be included in this
Within the protection scope of invention.
Claims (4)
1. a kind of earthquake prediction method of lacustrine facies thin layer dolostone reservoirs, which is characterized in that the method includes：
Obtain the seismic data cube in target reservoir region and the petrographic thin section of individual well reservoir；
Micro-analysis is carried out to the petrographic thin section, obtains the dolostone reservoirs distribution of the individual well reservoir；
The log that the individual well reservoir is obtained by logging method is distributed in conjunction with the dolostone reservoirs of the individual well reservoir,
Obtain the sensitive log of the dolostone reservoirs reaction to the individual well reservoir；
According to the sensitive log of the dolostone reservoirs reaction to the individual well reservoir, well logging recognition template is established；
The well logging recognition template is loaded into the seismic data cube, predicts the dolostone reservoirs in the target reservoir region
Distribution；
Wherein, the sensitive log of the dolostone reservoirs reaction to the individual well reservoir includes：Natural gamma is bent
Line, interval transit time curve, compensated neutron curve, density curve and resistivity curve；
The sensitive log of the dolostone reservoirs reaction to the individual well reservoir corresponds to the individual well reservoir
Dolostone reservoirs distribution value range be：The natural gamma value of the natural gamma curve is described between 30~60API
The sound wave value of interval transit time curve is between 210~270 μ s/m, and the compensated neutron value of the compensated neutron curve is in 20~34PU
Between, the density value of the density curve is in 2.35~2.65g/cm3Between, the resistivity value of the resistivity curve is greater than 6
Ω·m；
The log for establishing the well logging recognition template includes：Natural gamma curve, interval transit time curve, density
Curve；
Mathematical model for establishing the well logging recognition template is：Z=DEN/ (△ GR* (AC/ACmax) * 4) , wherein , ⊿ GR
=(GR-GRmin)/(GRmax-GRmin)；
Z indicates reconstruct curve values；
DEN refers to the density value under a certain predetermined depth of the individual well reservoir；
AC refers to the sound wave value under a certain predetermined depth of the individual well reservoir；
GR refers to the natural gamma value under a certain predetermined depth of the individual well reservoir；
ACmax refers to the maximum value of the dolomite section sound wave of the individual well reservoir；
GRmax refers to the maximum value of the dolomite section natural gamma of the individual well reservoir；
GRmin refers to the minimum value of the dolomite section natural gamma of the individual well reservoir.
2. being obtained the method according to claim 1, wherein described carry out micro-analysis to the petrographic thin section
The dolostone reservoirs of the individual well reservoir are distributed：Description mineralogical composition, content, cementing species are observed according to drill cores
Type differentiates the lithology of the individual well reservoir, identifies the dolostone reservoirs distribution of the individual well reservoir.
3. according to the method described in claim 2, it is characterized in that, the means of drill cores observation include：Rock under mirror
Thin slice, casting body flake, fluorescence thin section analysis, scanning electron microscope, cathodoluminescence analysis.
4. the method according to claim 1, wherein described be loaded into the earthquake for the well logging recognition template
In data volume, predict that the distribution of the dolostone reservoirs in the target reservoir region includes：
After the well logging recognition template is loaded into the seismic data cube, the target is explained in the seismic data cube
The top bottom interface of the dolostone reservoirs of reservoir area；
Extract the seismic properties in the top bottom interface, the distribution model of the dolostone reservoirs in target reservoir region described in qualitative forecasting
It encloses；
Application of Logging-constrained Inversion is carried out in the distribution, point of the dolostone reservoirs in target reservoir region described in quantitative forecast
Cloth range.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710038743.1A CN106908856B (en) | 2017-01-19 | 2017-01-19 | Earthquake prediction method for lake facies thin-layer dolomite reservoir |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710038743.1A CN106908856B (en) | 2017-01-19 | 2017-01-19 | Earthquake prediction method for lake facies thin-layer dolomite reservoir |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106908856A CN106908856A (en) | 2017-06-30 |
| CN106908856B true CN106908856B (en) | 2018-11-16 |
Family
ID=59207161
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710038743.1A Active CN106908856B (en) | 2017-01-19 | 2017-01-19 | Earthquake prediction method for lake facies thin-layer dolomite reservoir |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106908856B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107402411A (en) * | 2017-08-17 | 2017-11-28 | 中国石油天然气股份有限公司 | Quantitative identification method for microbial carbonate rock stratum algae dolomite |
| CN108227036A (en) * | 2018-01-22 | 2018-06-29 | 中国石油大港油田勘探开发研究院 | A kind of method of pulveryte core Location |
| CN110687588A (en) * | 2019-10-23 | 2020-01-14 | 成都创源油气技术开发有限公司 | Method and system for seismic identification and prediction based on dolomite |
| CN112925019A (en) * | 2019-12-06 | 2021-06-08 | 中国石油天然气股份有限公司 | Method and device for identifying pore type dolomite |
| CN111766637B (en) * | 2020-07-09 | 2021-10-01 | 中国地质大学（北京） | A Lithology Quantitative Spectrum Method for Lithology Identification of Tight Reservoirs |
| CN112415596B (en) * | 2020-12-09 | 2022-09-06 | 大庆油田有限责任公司 | Dolomite structure type identification method based on logging information |
| CN115614029A (en) * | 2021-07-14 | 2023-01-17 | 中国石油化工股份有限公司 | A method and computer-readable storage medium for identifying dolomite reservoirs |
| CN116774285B (en) * | 2022-03-09 | 2024-05-07 | 中国石油化工股份有限公司 | Thin interbed prediction method, device, equipment and medium based on characteristic curve reconstruction |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5835883A (en) * | 1997-01-31 | 1998-11-10 | Phillips Petroleum Company | Method for determining distribution of reservoir permeability, porosity and pseudo relative permeability |
| CN102759757A (en) * | 2012-07-06 | 2012-10-31 | 中国石油天然气股份有限公司 | Diabase alteration zone reservoir prediction method based on well-seismic combination |
| CN103901478A (en) * | 2012-12-28 | 2014-07-02 | 中国石油天然气集团公司 | Method for determining deposition characteristics and distribution of reservoirs by combining logging and seismic information |
| CN105863628A (en) * | 2016-03-23 | 2016-08-17 | 中国海洋石油总公司 | Fine reservoir prediction method of oilfield development phase |
| CN106094052A (en) * | 2016-06-01 | 2016-11-09 | 中国地质大学（武汉） | A kind of development degree of micro cracks in oil recognition methods of dense dolomite reservoir |
-
2017
- 2017-01-19 CN CN201710038743.1A patent/CN106908856B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5835883A (en) * | 1997-01-31 | 1998-11-10 | Phillips Petroleum Company | Method for determining distribution of reservoir permeability, porosity and pseudo relative permeability |
| CN102759757A (en) * | 2012-07-06 | 2012-10-31 | 中国石油天然气股份有限公司 | Diabase alteration zone reservoir prediction method based on well-seismic combination |
| CN103901478A (en) * | 2012-12-28 | 2014-07-02 | 中国石油天然气集团公司 | Method for determining deposition characteristics and distribution of reservoirs by combining logging and seismic information |
| CN105863628A (en) * | 2016-03-23 | 2016-08-17 | 中国海洋石油总公司 | Fine reservoir prediction method of oilfield development phase |
| CN106094052A (en) * | 2016-06-01 | 2016-11-09 | 中国地质大学（武汉） | A kind of development degree of micro cracks in oil recognition methods of dense dolomite reservoir |
Non-Patent Citations (1)
| Title |
|---|
| 井约束反演技术在白云岩缝洞型储集层预测中的应用;张永华 等;《新疆石油地质》;20060430;第27卷(第2期);第219-222页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106908856A (en) | 2017-06-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106908856B (en) | Earthquake prediction method for lake facies thin-layer dolomite reservoir | |
| Wang et al. | Quantitative characterization of reservoir space in the Lower Silurian Longmaxi Shale, southern Sichuan, China | |
| WO2017024700A1 (en) | Device for calculating content of organic carbon in source rock | |
| CN109085663A (en) | A kind of tight sandstone reservoir stratification seam recognition methods | |
| Gong et al. | Quantitative prediction of natural fractures in shale oil reservoirs | |
| Kendall et al. | Seismic anisotropy as an indicator of reservoir quality in siliciclastic rocks | |
| CN111220522A (en) | A core scale logging calculation method for hydrate saturation in high argillaceous fine-grained sediments | |
| Sharifi et al. | Investigation of static and dynamic bulk moduli in a carbonate field | |
| Bagheri et al. | Fracture permeability estimation utilizing conventional well logs and flow zone indicator | |
| Fu et al. | Investigation of multistage hydraulic fracture optimization design methods in horizontal shale oil wells in the Ordos Basin | |
| CN109826623A (en) | Knowledge method is sentenced in a kind of geophysical log of tight sandstone reservoir stratification seam | |
| Li et al. | Characteristics, logging identification and major controlling factors of bedding-parallel fractures in tight sandstones | |
| Liu et al. | Multiparameter Logging Evaluation of Chang 73 Shale Oil in the Jiyuan Area, Ordos Basin | |
| Sneider et al. | Integrated rock-log calibration in the Elmworth field-Alberta, Canada: Reservoir rock detection and characterization: Part i | |
| Deng et al. | A new index used to characterize the near-wellbore fracture network in naturally fractured gas reservoirs | |
| Hurst et al. | Sandstone reservoir description: An overview of the role of geology and mineralogy | |
| Li et al. | Application of nuclear magnetic resonance logging in the low-resistivity reservoir—Taking the XP area as an example | |
| Kaura et al. | Geophysics, Geology, Formation Evaluation, and Reservoir Characterization in Unconventional Resources | |
| Xue et al. | The Fractured Model Study of Low-Permeability Reservoir in Xinli Oil Field | |
| CN113917554A (en) | A method and electronic device for predicting the density of lamination fractures in shale reservoirs | |
| CN118818597B (en) | 'Double desserts' seismic inversion method for unconventional fracture type shale reservoir | |
| WO2023184214A1 (en) | Parameterization of nuclear magnetic resonance transverse relaxation time distribution | |
| Standen | Tips for analyzing fractures on electrical wellbore images | |
| CN115263282B (en) | Deep compact sandstone reservoir type identification method and electronic equipment | |
| WO2023230838A1 (en) | A method and system for modeling carbonate cementation in forward depositional models |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |