CN102109613A - Method for defining effective thickness of target reservoir bed under complex geological conditions - Google Patents

Method for defining effective thickness of target reservoir bed under complex geological conditions Download PDF

Info

Publication number
CN102109613A
CN102109613A CN2009102437568A CN200910243756A CN102109613A CN 102109613 A CN102109613 A CN 102109613A CN 2009102437568 A CN2009102437568 A CN 2009102437568A CN 200910243756 A CN200910243756 A CN 200910243756A CN 102109613 A CN102109613 A CN 102109613A
Authority
CN
China
Prior art keywords
thickness
reservoir
seismic
well
apparent
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN2009102437568A
Other languages
Chinese (zh)
Other versions
CN102109613B (en
Inventor
黄文锋
姚逢昌
李宏兵
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Petroleum and Natural Gas Co Ltd
Original Assignee
China Petroleum and Natural Gas Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Petroleum and Natural Gas Co Ltd filed Critical China Petroleum and Natural Gas Co Ltd
Priority to CN2009102437568A priority Critical patent/CN102109613B/en
Publication of CN102109613A publication Critical patent/CN102109613A/en
Application granted granted Critical
Publication of CN102109613B publication Critical patent/CN102109613B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Abstract

The invention belongs to petroleum geophysical prospecting, and relates to a method for defining effective thickness of a target reservoir bed under complex geological conditions, by which the reservoir thickness is confirmed by using seismic wave information (namely, characteristic points and amplitude information). The method comprises the following steps: selecting comprehensive seism and logging information of a target layer section in a studied work area; obtaining a stack profile section after conducting Hi-definition and high resolution process on seism data; then meticulously explaining the position of the layer; and finally performing thickness prediction on the target reservoir bed. By the adoption of the invention, the effective thickness of the target reservoir bed under complex geological conditions can be estimated; and the method is not only suitable for conditions of each thin layer, and but also suitable for conditions that thickness, net sand ratio, thickness of various single layers and distribution manner of an actual thin alternating layer are all intensely changed, is not limited by tuning thickness of the thin layer, and has stronger adaptability.

Description

Definite method of target reservoir net thickness under a kind of complex geological condition
Technical field
The invention belongs to the geophysical prospecting for oil technology, is definite method of target reservoir net thickness under a kind of complex geological condition.
Background technology
Because reservoir prediction has run into difficulty under the restriction complex geological condition of seismic resolution.Reservoir thickness prediction is current difficult point and focus under the thin interbed condition.Because thin layer tuning and sand mud alternating layers distribute, the thickness that earthquake is picked up can not reflect the net thickness of thin interbed reservoir.
Open up frequently, although can improve resolution, high-frequency information is unreliable, and can't eliminate the tuning influence of wavelet.
Seismic trace integral is to utilize the nothing constraint inversion technique that seismic data carries out, and with the wave impedance on stratum certain corresponding relation is arranged, although visually improved resolution, has distorted spectrum signature, has reduced dominant frequency, has tuning effect.
Well logging constraint inverting, well-log information longitudinal frame height, seismic section lateral resolution high two big advantages have been made full use of, under the well logging constraint, model is carried out iterative modifications, obtain high-resolution formation wave impedance data, for meticulous explanations such as reservoir thickness, rerum natura provide a kind of possibility answer, but the reliability of inversion result is subjected to the control of geologic condition and well-log information, and inverting has run into challenge under the complex geological condition frequent at mature fault, pinching, that thin interbed distributes.
The inverting of thin layer frequency spectrum is the thin bed reflections coefficient inversion method of frequency field, and without any need for constraint, the spectrum signature establishing target function inverting that utilizes thin layer is the reflection coefficient and the thickness of thin layer top bottom boundary one by one.Inversion result is a reflection coefficient sequence, has eliminated the tuning influence of wavelet, can reflect geology details such as thin layer, minor fault and pinching, has improved the resolution of earthquake greatly.But be not suitable for the stratum of wave impedance gradual change, still meeting difficulty aspect the inverting thin interbed.High-frequency information outside the earthquake effective band that inverting obtains is unreliable, and inversion result is generally used for qualitative interpretation.
Seismic properties (as instantaneous frequency and reflection amplitude) is usually used in reservoir thickness and sedimentary facies is explained.From Widess (1973) and Kallweit (1982) respectively at time and frequency domain analysis behind the thin layer earthquake tuning effect, in the ascendant about the research of thin layer tuning rule aspect.Thin layer quantitative interpretation method can be divided into time domain and frequency field two classes.Generally, by supposing that amplitude or dominant frequency with the linear thickness of thin layer of predicting of thickness, are applicable to the situation of a layer usually, and can only be similar to use in tuning thickness.Very few to the tuning rule research of thin interbed at present, under the complex geological condition reservoir thickness determine have difficulties.
Summary of the invention
The object of the invention provides a kind of restriction that is not subjected to thin layer tuning thickness, has definite method of target reservoir net thickness under the stronger adaptive complex geological condition.
The present invention realizes as follows:
1) at exploration area artificial excitation's seismic event, and receives underground reflective information, after protecting width of cloth processing, obtain the zero degree seismic section;
2) determine the reservoir thickness of target interval according to the interpretation results of known survey area geology, well-log information and seismic data;
The reservoir thickness of described target interval is according to fixed research task, and the one-tenth-value thickness 1/10 to top, the end degree of depth or the thin reservoir thickness between the time of research interval adds up and obtain is also referred to as clean thickness or true thickness.
3) sound wave of fixed well and densimetric curve multiplied each other obtain the wave impedance curve, the Acoustic Impedance Data of objective interval is compressed and stretched, and objective interval up and down the Acoustic Impedance Data on stratum remain unchanged, generate sand mud thin interbed wedge model;
4) just drilling by sand mud thin interbed wedge model and calculating theoretical theogram, seismic wavelet adopts the zero-phase wavelet that extracts and does 90 degree phase shifts from actual seismic data, on the theogram section, pick up the time apparent thickness of reservoir, reservoir thickness obtains the apparent seismic net-gross ratio with the ratio of time apparent thickness, extract average amplitude value in the reservoir time apparent thickness from the theogram section simultaneously, utilize following formula to calculate de-tuned curve c (t), to eliminate the influence of wavelet;
c(t)=N/(t*A) (1)
N is a reservoir thickness in the formula, and t is the time apparent thickness of reservoir, and A is the average amplitude value in the reservoir time apparent thickness;
5) on the geological data of study area, from 90 ° of seismic traces pick up near reservoir top, the end zero point as the top of reservoir at the bottom of the time, be respectively t 1(x, y), t 2(x, y), calculating apparent thickness: t (x, y)=t 2(x, y)-t 1(x, y) (2)
(x y) is apparent thickness to t in the formula, and (x y) is the coordinate of seismic trace correspondence;
Simultaneously, timing statistics window t from the seismic section 1(x, y) ~ t 2(x, y) the average amplitude attribute A of Nei 90 ° of phase place seismic responses (x, y);
6) carry out spatial interpolation by de-tuned curve c (t) to the reference mark fixed well, obtain seismic trace (x, the de-tuned factor c that y) locates (t (x, y)), according to following formula, spend tuning factor with amplitude attribute A (x y) changes into and looks net-gross ratio:
N2AG(x,y)=A(x,y)×c(t(x,y)), (3)
Looking net-gross ratio and apparent thickness multiplies each other and obtains the reservoir thickness attribute:
N(x,y)=N2AG(x,y)×t(x,y), (4)
(x y) is the apparent seismic net-gross ratio to N2AG in the formula, and (x y) is the reservoir thickness attribute to N;
7) select demarcation well w for use 1, w 2..., w n, after doing meticulous coupling contrast, determine layer of sand and mud layer in the target interval by the theogram of the wave impedance curve calculation of well and the other seismologic record of well, the thickness of statistics sandstone reservoir obtains reservoir thickness N 1, N 2..., N n, (x y), obtains the calibration coefficient c at each well place divided by the reservoir thickness property value N at each well place of step 6) 1, c 2..., c n
8) (x, y), (x y) determines to obtain reservoir thickness to the reservoir thickness attribute N of step 6) with this coefficient to utilize golden interpolation in the gram of geological statistics theory to obtain the well calibration coefficient c ' of each seismic trace.
The present invention can estimate the net thickness of target reservoir under the complex geological condition, can be applicable to the situation of single thin layer, can be applicable to all situations of acute variation of thin interbed thickness in the reality, net-gross ratio, each thickness in monolayer, distribution mode again, be not subjected to the restriction of thin layer tuning thickness, have stronger adaptability.
Description of drawings
Fig. 1 is the zero degree seismic section of reservoir thickness;
Fig. 2 is that the true reservoir thickness of fixed well target interval is explained synoptic diagram;
Fig. 3 is a sand mud thin interbed wedge-like wave impedance section;
Fig. 4 is just being drilled by sand mud thin interbed wedge model to calculate de-tuned curve;
Fig. 5 is the seismic section after the 90 degree phase-shift processing;
Fig. 6 is the layer bit time predicting reservoir thickness that utilizes the destination layer of 90 ° of phase place geological datas and explanation.
Embodiment
Reservoir thickness estimating techniques of the present invention utilize seismic event information (being unique point and amplitude information) to determine reservoir thickness.The target interval of choosing the research work area synthetically shakes, well logging information carries out reservoir thickness prediction.After seismic data obtained the poststack section through high-fidelity and High-resolution Processing, meticulous interpretation layer position was done the thickness prediction of target reservoir then.
The present invention realizes as follows:
1) at exploration area artificial excitation's seismic event, and receives underground reflective information, after protecting width of cloth processing, obtain the zero degree seismic section; Fig. 1 is the zero degree seismic section that is used to calculate reservoir thickness in certain work area.
2) determine the reservoir thickness of target interval according to the interpretation results of known survey area geology, well-log information and seismic data; Fig. 2 is that the well logging substratum of a bite fixed well target interval in certain work area is explained the true thickness value of adding up the thickness reservoir of each substratum.
The reservoir thickness of described target interval is according to fixed research task, and the one-tenth-value thickness 1/10 to top, the end degree of depth or the thin reservoir thickness between the time of research interval adds up and obtain is also referred to as clean thickness or true thickness.
3) sound wave of fixed well and densimetric curve multiplied each other obtain the wave impedance curve, the Acoustic Impedance Data of objective interval is compressed and stretched, and objective interval up and down the Acoustic Impedance Data on stratum remain unchanged, generate sand mud thin interbed wedge model; Fig. 3 is the sand mud thin interbed wedge-like wave impedance section that the Acoustic Impedance Data of a bite fixed well objective interval in certain work area is compressed and the back that stretches generates.
4) just drilling by sand mud thin interbed wedge model and calculating theoretical theogram, seismic wavelet adopts the zero-phase wavelet that extracts and does 90 degree phase shifts from actual seismic data, on the theogram section, pick up the time apparent thickness of reservoir, reservoir thickness obtains the apparent seismic net-gross ratio with the ratio of time apparent thickness, extract average amplitude value in the reservoir time apparent thickness from the theogram section simultaneously, utilize following formula to calculate de-tuned curve c (t), to eliminate the influence of wavelet;
c(t)=N/(t*A) (1)
N is a reservoir thickness in the formula, and t is the time apparent thickness of reservoir, and A is the average amplitude value in the reservoir time apparent thickness;
Fig. 4: just drilling the de-tuned curve of calculating by the sand mud thin interbed wedge model of Fig. 3.Fig. 4 a is for by the seismic wavelet mutually convolution theoretical theogram that obtain of the sand mud thin interbed wedge-like wave impedance section of Fig. 3 with extraction from seismologic record (Fig. 1).Fig. 4 b is to the seismic section after Fig. 4 a work 90 degree phase shifts.Fig. 4 c is the de-tuned curve that extracts from Fig. 4 b, and the shape of de-tuned correction function is a quite simple curve, the almost proportional relation of it and apparent thickness.
5) on the geological data of study area, from 90 ° of seismic traces pick up near reservoir top, the end zero point as the top of reservoir at the bottom of the time, be respectively t 1(x, y), t 2(x, y), calculating apparent thickness: t (x, y)=t 2(x, y)-t 1(x, y) (2)
(x y) is apparent thickness to t in the formula, and (x y) is the coordinate of seismic trace correspondence;
Simultaneously, timing statistics window t from the seismic section 1(x, y) ~ t 2(x, y) the average amplitude attribute A of Nei 90 ° of phase place seismic responses (x, y);
Fig. 5 makes the seismic section of 90 degree after the phase-shift processing by Fig. 1.On this seismic section, pick up zero logical some top bottom boundary reflection interval that obtains target reservoir.
6) carry out spatial interpolation by de-tuned curve c (t) to the reference mark fixed well, obtain seismic trace (x, the de-tuned factor c that y) locates (t (x, y)), according to following formula, spend tuning factor with amplitude attribute A (x y) changes into and looks net-gross ratio:
N2AG(x,y)=A(x,y)×c(t(x,y)), (3)
Looking net-gross ratio and apparent thickness multiplies each other and obtains the reservoir thickness attribute:
N(x,y)=N2AG(x,y)×t(x,y), (4)
(x y) is the apparent seismic net-gross ratio to N2AG in the formula, and (x y) is the reservoir thickness attribute to N;
Fig. 6: the layer bit time predicting reservoir thickness that utilizes the destination layer of 90 ° of phase place geological datas and explanation.(Fig. 6 a) adds up 90 ° of phase average amplitudes (Fig. 6 b) then in the end time window of top at first to calculate apparent thickness by top bottom bit time.After 90 ° of de-tuned corrections of phase average amplitude, multiplying each other with apparent thickness and demarcating through gas well B obtains reservoir thickness (Fig. 6 c).With Fig. 6 a and Fig. 6 c contrast, both are widely different.Because thin layer tuning and sand mud alternating layers distribute, apparent thickness can not be represented effective reservoir.It is approaching that the moisture meeting of sandstone makes its wave impedance and mud stone in this reservoir, with the method processing that the present invention proposes, not only can eliminate the tuning influence of wavelet, and can estimate gassiness gross sand in the mud alternating layers of shaking out to a great extent.Well logging shows that A well and B gas well gas layer thickness are respectively 7.9m, 9.1m, and the C well is a water layer.As seen, the gas pay thickness variation tendency of prediction is coincide with the well logging result, and the present invention can portray the planar distribution of delta facies river course gas-bearing sandstone.
7) select demarcation well w for use 1, w 2..., w n, after doing meticulous coupling contrast, determine layer of sand and mud layer in the target interval by the theogram of the wave impedance curve calculation of well and the other seismologic record of well, the thickness of statistics sandstone reservoir obtains reservoir thickness N 1, N 2..., N n, (x y), obtains the calibration coefficient c at each well place divided by the reservoir thickness property value N at each well place of step 6) 1, c 2..., c n
8) (x, y), (x y) determines to obtain reservoir thickness to the reservoir thickness attribute N of step 6) with this coefficient to utilize golden interpolation in the gram of geological statistics theory to obtain the well calibration coefficient c ' of each seismic trace.
The reservoir thickness attribute that the present invention calculates under the situation of no well can reflect the variation tendency of effective reservoir.The thickness that can estimate reservoir under the situation of well is being arranged.
The present invention has eliminated the influence of wavelet in the process of estimation reservoir thickness, can reflect underground medium information (comprising sand body and oil gas information).Can predict the sand body spread of sand thickness, portrayal river course and deltaic deposit; Divide in sedimentary facies, have vital role in the identification in river course, prediction of oil-gas reserve; Can improve the description of complicated reservoirs, can arrange well location for oilfield prospecting developing reliable foundation is provided.

Claims (3)

1. definite method of target reservoir net thickness under the complex geological condition is characterized in that realizing as follows:
1) at exploration area artificial excitation's seismic event, and receives underground reflective information, after protecting width of cloth processing, obtain the zero degree seismic section;
2) determine the reservoir thickness of target interval according to the interpretation results of known survey area geology, well-log information and seismic data;
3) sound wave of fixed well and densimetric curve are multiplied each other obtain the wave impedance curve, the Acoustic Impedance Data of objective interval is compressed and stretched, generate sand mud thin interbed wedge model;
4) just drilling by sand mud thin interbed wedge model and calculating theoretical theogram, seismic wavelet adopts the zero-phase wavelet that extracts and does 90 degree phase shifts from actual seismic data, on the theogram section, pick up the time apparent thickness of reservoir, reservoir thickness obtains the apparent seismic net-gross ratio with the ratio of time apparent thickness, extract average amplitude value in the reservoir time apparent thickness from the theogram section simultaneously, utilize following formula to calculate de-tuned curve c (t), to eliminate the influence of wavelet;
c(t)=N/(t*A) (1)
N is a reservoir thickness in the formula, and t is the time apparent thickness of reservoir, and A is the average amplitude value in the reservoir time apparent thickness;
5) on the geological data of study area, from 90 ° of seismic traces pick up near reservoir top, the end zero point as the top of reservoir at the bottom of the time, be respectively t 1(x, y), t 2(x, y), calculating apparent thickness: t (x, y)=t 2(x, y)-t 1(x, y) (2)
(x y) is apparent thickness to t in the formula, and (x y) is the coordinate of seismic trace correspondence;
Simultaneously, timing statistics window t from the seismic section 1(x, y) ~ t 2(x, y) the average amplitude attribute A of Nei 90 ° of phase place seismic responses (x, y);
6) carry out spatial interpolation by de-tuned curve c (t) to the reference mark fixed well, obtain seismic trace (x, the de-tuned factor c that y) locates (t (x, y)), according to following formula, spend tuning factor with amplitude attribute A (x y) changes into and looks net-gross ratio:
N2AG(x,y)=A(x,y)×c(t(x,y)), (3)
Looking net-gross ratio and apparent thickness multiplies each other and obtains the reservoir thickness attribute:
N(x,y)=N2AG(x,y)×t(x,y), (4)
(x y) is the apparent seismic net-gross ratio to N2AG in the formula, and (x y) is the reservoir thickness attribute to N;
7) select demarcation well w for use 1, w 2..., w n, after doing meticulous coupling contrast, determine layer of sand and mud layer in the target interval by the theogram of the wave impedance curve calculation of well and the other seismologic record of well, obtain reservoir thickness N by the thickness of sandstone reservoir 1, N 2..., N n, (x y), obtains the calibration coefficient c at each well place divided by the reservoir thickness property value N at each well place of step 6) 1, c 2..., c n
8) (x, y), (x y) determines to obtain reservoir thickness to the reservoir thickness attribute N of step 6) with this coefficient to utilize golden interpolation in the gram to obtain the well calibration coefficient c ' of each seismic trace.
2. method according to claim 1, feature is a step 2) reservoir thickness of described target interval is according to fixed research task, one-tenth-value thickness 1/10 to top, the end degree of depth or the thin reservoir thickness between the time of research interval adds up and obtain is also referred to as clean thickness or true thickness.
3. method according to claim 1, feature be step 3) the Acoustic Impedance Data of objective interval is compressed and when stretching objective interval up and down the Acoustic Impedance Data on stratum remain unchanged.
CN2009102437568A 2009-12-23 2009-12-23 Method for defining effective thickness of target reservoir bed under complex geological conditions Active CN102109613B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN2009102437568A CN102109613B (en) 2009-12-23 2009-12-23 Method for defining effective thickness of target reservoir bed under complex geological conditions

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN2009102437568A CN102109613B (en) 2009-12-23 2009-12-23 Method for defining effective thickness of target reservoir bed under complex geological conditions

Publications (2)

Publication Number Publication Date
CN102109613A true CN102109613A (en) 2011-06-29
CN102109613B CN102109613B (en) 2012-11-14

Family

ID=44173822

Family Applications (1)

Application Number Title Priority Date Filing Date
CN2009102437568A Active CN102109613B (en) 2009-12-23 2009-12-23 Method for defining effective thickness of target reservoir bed under complex geological conditions

Country Status (1)

Country Link
CN (1) CN102109613B (en)

Cited By (37)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102721979A (en) * 2012-06-27 2012-10-10 中国石油天然气股份有限公司 Seismic-data-based thin layer automatic interpretation and thickness prediction method and device
CN102914799A (en) * 2012-10-12 2013-02-06 中国石油天然气股份有限公司 Forward modeling method and device for nonequivalent wave field
CN103197345A (en) * 2013-03-19 2013-07-10 中国石油大学(华东) Sandstone thickness prediction method based on seismic standard arc length logarithmic attributes
CN103412332A (en) * 2013-01-22 2013-11-27 中国地质大学(北京) Method for quantitative calculation of thickness of thin reservoir layer
CN103424773A (en) * 2012-05-25 2013-12-04 中国石油化工股份有限公司 S Stratification position flattening method-based ancient landform restoration method
CN103454685A (en) * 2013-08-09 2013-12-18 中国石油天然气股份有限公司 Method and device for predicating sand body thicknesses through logging constraint wave impedance inversion
CN103558635A (en) * 2013-10-30 2014-02-05 北京诺克斯达石油科技有限公司 Method and device for estimating film thickness based on even function seismic response
CN103590827A (en) * 2013-11-22 2014-02-19 中国石油化工集团公司 Dense clastic rock natural gas well productivity prediction method based on reservoir classification
CN103615230A (en) * 2013-10-26 2014-03-05 中国石油化工集团公司 Method for establishing double-shale-indicative-factor water saturation model
CN103777243A (en) * 2012-10-25 2014-05-07 中国石油化工股份有限公司 Sand-mud stone thin interbed reservoir thickness prediction method
CN104280773A (en) * 2013-07-12 2015-01-14 中国石油天然气集团公司 Method for predicting thin layer thickness by utilization of time-frequency spectrum cross plot changing along with geophone offsets
CN104820241A (en) * 2015-03-12 2015-08-05 中国石油天然气股份有限公司 Unconventional tight reservoir stratum forecasting method under strong reflection background
CN105093306A (en) * 2014-05-15 2015-11-25 中国石油化工股份有限公司 Method for automatic interpretation and thickness calculation of reservoir in geophysical exploration
CN105093307A (en) * 2014-05-16 2015-11-25 中国石油化工股份有限公司 Lower palaeozoic tilted stratum true thickness calculation method
CN105319585A (en) * 2015-10-14 2016-02-10 中国石油化工股份有限公司 Method for utilizing thin-layer interference amplitude recovery to identify oil and gas reservoir
CN106033125A (en) * 2016-06-29 2016-10-19 中国石油化工股份有限公司 Suppression prestack large-angle gather interference based frequency increasing method
CN106483562A (en) * 2016-09-28 2017-03-08 中国海洋石油总公司 A kind of method based on seismic amplitude predictably layer thickness variation
CN106597547A (en) * 2016-12-28 2017-04-26 中国石油化工股份有限公司 Method for accurately describing earthquake in thin reservoir
CN107015289A (en) * 2016-01-28 2017-08-04 中国石油化工股份有限公司 Trap evaluation stock number determines method and device
CN107192326A (en) * 2017-05-13 2017-09-22 克拉玛依职业技术学院 The method that rock stratum vector true thickness is calculated based on gps data
RU2636821C1 (en) * 2016-05-27 2017-11-28 Шлюмберже Текнолоджи Б.В. Method for determination of mechanical properties of reservoir rock
CN107688197A (en) * 2017-07-12 2018-02-13 中国石油天然气集团公司 Thin bed prediction method and apparatus
CN107851316A (en) * 2015-05-20 2018-03-27 沙特阿拉伯石油公司 The analysis based on image of geology thin layer section
CN108594300A (en) * 2018-03-26 2018-09-28 中国石油天然气股份有限公司 Landforms imaging method, device and computer storage media
CN109188520A (en) * 2018-09-17 2019-01-11 中国石油天然气股份有限公司 Thin reservoir thickness prediction method and device
CN109283577A (en) * 2017-07-20 2019-01-29 中国石油化工股份有限公司 A kind of seismic layer labeling method
CN109441541A (en) * 2018-11-06 2019-03-08 中国矿业大学 A kind of coal mine gob filling body carrying compression ratio monitoring system and its monitoring method
CN109917455A (en) * 2019-02-19 2019-06-21 中国石油天然气集团有限公司 A kind of depicting method and system of stratigraphic trap
CN110094202A (en) * 2019-04-24 2019-08-06 中国地质大学(北京) Effective sand thickness calculation method based on lamination factor
CN111221038A (en) * 2018-11-26 2020-06-02 中国石油天然气股份有限公司 Method and device for quantitatively predicting thickness of thin reservoir
CN111305835A (en) * 2020-03-04 2020-06-19 中海石油(中国)有限公司 Characterization method of net-to-gross ratio of weak diagenetic sandstone reservoir
CN112180463A (en) * 2020-11-03 2021-01-05 中国石油化工股份有限公司 Prediction method for sandstone spread in transition layer section
CN112711067A (en) * 2019-10-24 2021-04-27 中国石油天然气股份有限公司 Thin reservoir prediction method and device
CN113514876A (en) * 2021-06-18 2021-10-19 中国石油化工股份有限公司 Designed well depth calculation method based on stratum velocity correction
CN114002743A (en) * 2021-10-20 2022-02-01 中国海洋石油集团有限公司 Method and device for calculating reservoir thickness of natural gas hydrate
CN114428322A (en) * 2020-10-29 2022-05-03 中国石油天然气股份有限公司 Method and device for predicting thickness of thin reservoir based on frequency attribute
CN116819616A (en) * 2023-08-30 2023-09-29 中国地质大学(北京) Method for determining thickness of ultrathin high-quality shale reservoir

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN100349008C (en) * 2004-12-29 2007-11-14 中国石油天然气集团公司 Method for carrying out inversion for wave impedance of earthquake wave
CN100557464C (en) * 2007-01-15 2009-11-04 中国石油大港油田勘探开发研究院 Seismic prospecting horizon calibration method based on the prestack wave-field simulation
CN101158724B (en) * 2007-09-14 2010-06-09 中国石油集团西北地质研究所 Reservoir thickness prediction method based on dipolar wavelet
CN101551466B (en) * 2008-04-03 2011-04-20 中国石油天然气集团公司 Method for improving prediction precision of oil and gas reservoir by using seismic attribute related to offset distance
CN101276001B (en) * 2008-04-25 2011-07-20 符力耘 Underground non-uniform medium seismic investigation complexity quantitative evaluating method

Cited By (60)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103424773A (en) * 2012-05-25 2013-12-04 中国石油化工股份有限公司 S Stratification position flattening method-based ancient landform restoration method
CN103424773B (en) * 2012-05-25 2016-02-03 中国石油化工股份有限公司 A kind of palaeogeomorphology based on horizon flattening method
CN102721979A (en) * 2012-06-27 2012-10-10 中国石油天然气股份有限公司 Seismic-data-based thin layer automatic interpretation and thickness prediction method and device
CN102721979B (en) * 2012-06-27 2014-09-03 中国石油天然气股份有限公司 Seismic-data-based thin layer automatic interpretation and thickness prediction method and device
CN102914799A (en) * 2012-10-12 2013-02-06 中国石油天然气股份有限公司 Forward modeling method and device for nonequivalent wave field
CN102914799B (en) * 2012-10-12 2015-05-06 中国石油天然气股份有限公司 Forward modeling method and device for nonequivalent wave field
CN103777243A (en) * 2012-10-25 2014-05-07 中国石油化工股份有限公司 Sand-mud stone thin interbed reservoir thickness prediction method
CN103412332A (en) * 2013-01-22 2013-11-27 中国地质大学(北京) Method for quantitative calculation of thickness of thin reservoir layer
CN103412332B (en) * 2013-01-22 2016-05-25 中国地质大学(北京) A kind of method of determining thin reservoir thickness
CN103197345B (en) * 2013-03-19 2014-10-29 中国石油大学(华东) Sandstone thickness prediction method based on seismic standard arc length logarithmic attributes
CN103197345A (en) * 2013-03-19 2013-07-10 中国石油大学(华东) Sandstone thickness prediction method based on seismic standard arc length logarithmic attributes
CN104280773A (en) * 2013-07-12 2015-01-14 中国石油天然气集团公司 Method for predicting thin layer thickness by utilization of time-frequency spectrum cross plot changing along with geophone offsets
CN104280773B (en) * 2013-07-12 2017-04-05 中国石油天然气集团公司 Using the time-frequency spectrum changed with geophone offset cross figure predict thickness of thin layer method
CN103454685B (en) * 2013-08-09 2016-04-06 中国石油天然气股份有限公司 Study of The Impedence Inversion Restrained By Well Log is utilized to predict the method and apparatus of sand thickness
CN103454685A (en) * 2013-08-09 2013-12-18 中国石油天然气股份有限公司 Method and device for predicating sand body thicknesses through logging constraint wave impedance inversion
CN103615230A (en) * 2013-10-26 2014-03-05 中国石油化工集团公司 Method for establishing double-shale-indicative-factor water saturation model
CN103615230B (en) * 2013-10-26 2017-03-15 中国石油化工集团公司 A kind of method for establishing double-shale-indicatwateractor wateractor saturation model
CN103558635A (en) * 2013-10-30 2014-02-05 北京诺克斯达石油科技有限公司 Method and device for estimating film thickness based on even function seismic response
CN103590827A (en) * 2013-11-22 2014-02-19 中国石油化工集团公司 Dense clastic rock natural gas well productivity prediction method based on reservoir classification
CN103590827B (en) * 2013-11-22 2016-03-02 中国石油化工集团公司 Based on the compact clastic rock natural gas well PRODUCTION FORECASTING METHODS of Reservoir Classification
CN105093306A (en) * 2014-05-15 2015-11-25 中国石油化工股份有限公司 Method for automatic interpretation and thickness calculation of reservoir in geophysical exploration
CN105093307A (en) * 2014-05-16 2015-11-25 中国石油化工股份有限公司 Lower palaeozoic tilted stratum true thickness calculation method
CN104820241A (en) * 2015-03-12 2015-08-05 中国石油天然气股份有限公司 Unconventional tight reservoir stratum forecasting method under strong reflection background
CN107851316B (en) * 2015-05-20 2022-03-18 沙特阿拉伯石油公司 Image-based analysis method and system for geological thin section
CN107851316A (en) * 2015-05-20 2018-03-27 沙特阿拉伯石油公司 The analysis based on image of geology thin layer section
CN105319585A (en) * 2015-10-14 2016-02-10 中国石油化工股份有限公司 Method for utilizing thin-layer interference amplitude recovery to identify oil and gas reservoir
CN105319585B (en) * 2015-10-14 2017-11-14 中国石油化工股份有限公司 A kind of method hidden using thin-layers interference amplitude recovery identification of hydrocarbon
CN107015289B (en) * 2016-01-28 2019-05-07 中国石油化工股份有限公司 Trap evaluation stock number determines method and device
CN107015289A (en) * 2016-01-28 2017-08-04 中国石油化工股份有限公司 Trap evaluation stock number determines method and device
RU2636821C1 (en) * 2016-05-27 2017-11-28 Шлюмберже Текнолоджи Б.В. Method for determination of mechanical properties of reservoir rock
CN106033125B (en) * 2016-06-29 2018-06-19 中国石油化工股份有限公司 The trace gather interference of compacting prestack wide-angle carries frequency method
CN106033125A (en) * 2016-06-29 2016-10-19 中国石油化工股份有限公司 Suppression prestack large-angle gather interference based frequency increasing method
CN106483562A (en) * 2016-09-28 2017-03-08 中国海洋石油总公司 A kind of method based on seismic amplitude predictably layer thickness variation
CN106597547A (en) * 2016-12-28 2017-04-26 中国石油化工股份有限公司 Method for accurately describing earthquake in thin reservoir
CN107192326B (en) * 2017-05-13 2019-05-24 克拉玛依职业技术学院 The method for calculating rock stratum vector true thickness based on GPS data
CN107192326A (en) * 2017-05-13 2017-09-22 克拉玛依职业技术学院 The method that rock stratum vector true thickness is calculated based on gps data
CN107688197A (en) * 2017-07-12 2018-02-13 中国石油天然气集团公司 Thin bed prediction method and apparatus
CN109283577B (en) * 2017-07-20 2023-02-28 中国石油化工股份有限公司 Seismic horizon calibration method
CN109283577A (en) * 2017-07-20 2019-01-29 中国石油化工股份有限公司 A kind of seismic layer labeling method
CN108594300B (en) * 2018-03-26 2020-04-10 中国石油天然气股份有限公司 Landform imaging method and device and computer storage medium
CN108594300A (en) * 2018-03-26 2018-09-28 中国石油天然气股份有限公司 Landforms imaging method, device and computer storage media
CN109188520B (en) * 2018-09-17 2020-05-08 中国石油天然气股份有限公司 Thin reservoir thickness prediction method and device
CN109188520A (en) * 2018-09-17 2019-01-11 中国石油天然气股份有限公司 Thin reservoir thickness prediction method and device
CN109441541A (en) * 2018-11-06 2019-03-08 中国矿业大学 A kind of coal mine gob filling body carrying compression ratio monitoring system and its monitoring method
CN109441541B (en) * 2018-11-06 2020-01-03 中国矿业大学 Coal mine goaf filling body bearing compression rate monitoring system and monitoring method thereof
CN111221038A (en) * 2018-11-26 2020-06-02 中国石油天然气股份有限公司 Method and device for quantitatively predicting thickness of thin reservoir
CN109917455A (en) * 2019-02-19 2019-06-21 中国石油天然气集团有限公司 A kind of depicting method and system of stratigraphic trap
CN110094202A (en) * 2019-04-24 2019-08-06 中国地质大学(北京) Effective sand thickness calculation method based on lamination factor
CN112711067B (en) * 2019-10-24 2023-10-31 中国石油天然气股份有限公司 Thin reservoir prediction method and device
CN112711067A (en) * 2019-10-24 2021-04-27 中国石油天然气股份有限公司 Thin reservoir prediction method and device
CN111305835A (en) * 2020-03-04 2020-06-19 中海石油(中国)有限公司 Characterization method of net-to-gross ratio of weak diagenetic sandstone reservoir
CN111305835B (en) * 2020-03-04 2023-05-16 中海石油(中国)有限公司 Characterization method of weak diagenetic sandstone reservoir net wool ratio
CN114428322A (en) * 2020-10-29 2022-05-03 中国石油天然气股份有限公司 Method and device for predicting thickness of thin reservoir based on frequency attribute
CN112180463A (en) * 2020-11-03 2021-01-05 中国石油化工股份有限公司 Prediction method for sandstone spread in transition layer section
CN113514876A (en) * 2021-06-18 2021-10-19 中国石油化工股份有限公司 Designed well depth calculation method based on stratum velocity correction
CN113514876B (en) * 2021-06-18 2024-01-23 中国石油化工股份有限公司 Design well depth calculation method based on stratum velocity correction
CN114002743A (en) * 2021-10-20 2022-02-01 中国海洋石油集团有限公司 Method and device for calculating reservoir thickness of natural gas hydrate
CN114002743B (en) * 2021-10-20 2022-08-09 中国海洋石油集团有限公司 Method and device for calculating reservoir thickness of natural gas hydrate
CN116819616A (en) * 2023-08-30 2023-09-29 中国地质大学(北京) Method for determining thickness of ultrathin high-quality shale reservoir
CN116819616B (en) * 2023-08-30 2023-12-15 中国地质大学(北京) Method for determining thickness of ultrathin high-quality shale reservoir

Also Published As

Publication number Publication date
CN102109613B (en) 2012-11-14

Similar Documents

Publication Publication Date Title
CN102109613B (en) Method for defining effective thickness of target reservoir bed under complex geological conditions
Chopra et al. Velocity determination for pore-pressure prediction
Anees et al. Channel identification using 3D seismic attributes and well logging in lower Shihezi Formation of Hangjinqi area, northern Ordos Basin, China
Hongliu et al. Guidelines for seismic sedimentologic study in non-marine postrift basins
Chopra et al. Volumetric curvature attributes add value to 3D seismic data interpretation
White Seismic characterization and time-lapse imaging during seven years of CO2 flood in the Weyburn field, Saskatchewan, Canada
CN104237945B (en) A kind of seismic data self adaptation high resolution processing method
Ali et al. Resource potential of gas reservoirs in South Pakistan and adjacent Indian subcontinent revealed by post-stack inversion techniques
Das et al. Post-stack seismic inversion and attribute analysis in shallow offshore of Krishna-Godavari basin, India
Koesoemadinata et al. Seismic reservoir characterization in Marcellus shale
CN104516018A (en) Porosity inversion method under lithological constraint in geophysical exploration
Azeem et al. Sweetness analysis of Lower Goru sandstone intervals of the Cretaceous age, Sawan gas field, Pakistan
Ray et al. Attribute-assisted interpretation of deltaic channel system using enhanced 3D seismic data, offshore Nova Scotia
Manzi et al. Seismic attribute analysis to enhance detection of thin gold-bearing reefs: South Deep gold mine, Witwatersrand basin, South Africa
Karim et al. Seismic reservoir characterization using model based post-stack seismic inversion: in case of Fenchuganj gas field, Bangladesh
Long et al. Integrated characterization of ultradeep reef-shoal reservoir architecture: A case study of the Upper Permian Changxing Formation in the giant Yuanba gas field, Sichuan Basin, China
CN108646290A (en) A kind of thin layer inversion method based on model quantitative compensation
von Ketelhodt et al. Post-stack denoising of legacy reflection seismic data: implications for coalbed methane exploration, Kalahari Karoo Basin, Botswana
Belina et al. Enhancing the vertical resolution of surface georadar data
Datta Gupta et al. Importance of coloured inversion technique for thin hydrocarbon sand reservoir detection–A case in mid Cambay basin
Banerjee et al. Mapping of reservoir properties using model-based seismic inversion and neural network architecture in raniganj basin, India
Ningkai et al. Stepped and detailed seismic prediction of shallow-thin reservoirs in Chunfeng oilfield of Junggar Basin, NW China
Giustiniani et al. 3D seismic data for shallow aquifers characterisation
Gargouri Multicomponent 3D seismic interpretation of the Marcellus shale Bradford county, Pennsylvania
Rossi et al. Traveltime and attenuation tomography of CO2 plume at Sleipner

Legal Events

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