CN107765311A - It is a kind of to carry out mud shale air content Forecasting Methodology using geological data - Google Patents
It is a kind of to carry out mud shale air content Forecasting Methodology using geological data Download PDFInfo
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- CN107765311A CN107765311A CN201610673493.4A CN201610673493A CN107765311A CN 107765311 A CN107765311 A CN 107765311A CN 201610673493 A CN201610673493 A CN 201610673493A CN 107765311 A CN107765311 A CN 107765311A
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- 238000000034 method Methods 0.000 title claims abstract description 17
- 238000012360 testing method Methods 0.000 claims abstract description 12
- 239000011435 rock Substances 0.000 claims abstract description 9
- 238000005259 measurement Methods 0.000 claims abstract description 4
- 238000013508 migration Methods 0.000 claims abstract description 4
- 230000005012 migration Effects 0.000 claims abstract description 4
- 238000012549 training Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 2
- 230000008901 benefit Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 239000004215 Carbon black (E152) Substances 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 229930195733 hydrocarbon Natural products 0.000 description 1
- 150000002430 hydrocarbons Chemical class 0.000 description 1
- 238000011545 laboratory measurement Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/62—Physical property of subsurface
- G01V2210/624—Reservoir parameters
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- General Life Sciences & Earth Sciences (AREA)
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Abstract
Mud shale air content Forecasting Methodology is carried out using geological data the invention discloses a kind of, methods described includes：(1) measurement data, geologic data include well logging, gas testing and mud shale rock core air content test data；Log data includes interval transit time, density and the air content well log interpretation curve through rock core air content test data scale；The seismic data that geological data includes conventional poststack or migration before stack is handled.(2) using the high correlation between density log curve and mud shale air content data, the air content computation model based on density log curve is established：In VGAS=f (DEN) (1) formula, VGAS is air content, m/t；DEN is density log curve, g/cm；Functional relations of the f between air content and density.(3) carry out the more attribution inversions of poststack, obtain the density curve data volume needed for air content computation model.(4) air content is predicted：The density curve data volume that step (3) obtains is updated in formula (1), obtains mud shale air content.
Description
Technical field
The present invention relates to the invention belongs to oil and gas exploration field, and in particular to a kind of to carry out mud using geological data
Shale air content Forecasting Methodology.
Background technology
Current global shale gas stock number alreadys exceed global conventional gas stock number, turns into unconventionaloil pool field
Study hotspot.The America ＆ Canada of North America has possessed the exploration and development history of many decades, the geological knowledge of mud shale, surveys
Visit supporting technology and engineering technology aspect is all more ripe.As China expands day by day to the demand of the energy, using shale gas as
The research in the unconventionaloil pool field of representative was increasingly becoming focus at nearly 2 years, also constantly made a breakthrough in exploration practices and
New understanding.Comprehensive exploration and development experience, air content were the key indexs of shale gas reservoir evaluation in recent years both at home and abroad, were pages
Rock gas reservoir obtain commercial discovery key influence factor, this be primarily due to air content be hydrocarbon source condition (organic carbon content,
Evolution level), Reservior Conditions (hole and development degree of micro cracks in oil), preservation condition (i.e. roof and floor condition, region lid
Layer condition, later structural the intensity of rebuilding and tectonic style etc.) etc. comprehensive embodiment.At present, in laboratory measurement and
Greater advance is obtained in terms of logging evaluation air content, such as Nie Haikuan exists《Shale gas aggregation conditions and air content calculate --- with
Exemplified by the Sichuan Basin and its periphery Lower Paleozoic strata》Propose a kind of public affairs that shale air content is calculated using multiple linear regression
Formula, but the formula is only capable of calculating the air content of well point opening position, can not describe the cross directional variations of air content.
The content of the invention
The purpose of the present invention is overcome the deficiencies in the prior art, there is provided a kind of to be contained using geological data progress mud shale
Tolerance Forecasting Methodology, solve the problems, such as the cross directional variations that can not describe air content.
The present invention's is achieved through the following technical solutions：It is a kind of to carry out mud shale air content prediction side using geological data
Method, methods described include：
(1) measurement data, geologic data include well logging, gas testing and mud shale rock core air content test data；Log data
Including interval transit time, density and air content well log interpretation curve through rock core air content test data scale；Geological data
The seismic data handled including conventional poststack or migration before stack.
(2) it is based on density log using the high correlation between density log curve and mud shale air content data, foundation
The air content computation model of curve：
VGAS=f (DEN) (1)
In formula, VGAS is air content, m/t；DEN is density log curve, g/cm；F is between air content and density
Functional relation.
(3) carry out the more attribution inversions of poststack, obtain the density curve data volume needed for air content computation model.
(4) air content is predicted：The density curve data volume that step (3) obtains is updated in formula (1), obtains mud
Shale air content.
Further, the step (3) is realized in：With the density log curve in air content computation model
As training goal curve, find optimal seismic properties and combined with wave impedance inversion data volume so that utilized in well point position
The aim curve that these attributes are calculated and actual density log correlation highest, are specifically included：Input density is logged well
Seismic properties, post-stack inversion curve by curve, well.
Statistics finds the relation between seismic properties, poststack wave impedance inversion curve by density log curve and well；Carry out
Blind shaft validation-cross, it is determined that optimal seismic properties are combined with wave impedance inversion data volume and formula (2) described functional relation.
DEN (x, y, t)=f [Attribute 1 (x, y, t), Attribute2 (x, y, t) ..., Attributem
(x,y,t)] (2)
In formula, DEN (x, y, t) is that the density in step (2) needed for air content computation model surveys curve.
Attributei (x, y, t) is seismic properties or inverting data；F is DEN (x, y, t) between best attributes
Functional relation；M is attribute number.
Statistical relationship is applied to seismic data cube, obtains the density curve data volume needed for air content computation model.
The present invention compared with prior art, has the following advantages and advantages：Pass through technical method of the present invention
Implementation can complete fine description to high-quality mud shale air content cross directional variations, and then reduce drilling risk, improve shale gas
Exploration benefit.
Brief description of the drawings
Accompanying drawing described herein is used for providing further understanding the embodiment of the present invention, forms one of the application
Point, do not form the restriction to the embodiment of the present invention.In the accompanying drawings：
Fig. 1 carries out mud shale air content prediction steps schematic diagram for the present invention is a kind of using geological data；
Fig. 2 is air content sensitivity curve inverting flow process figure of the present invention.
Embodiment
For the object, technical solutions and advantages of the present invention are more clearly understood, with reference to embodiment and accompanying drawing, to this
Invention is described in further detail, and exemplary embodiment of the invention and its explanation are only used for explaining the present invention, do not make
For limitation of the invention.
As shown in Figure 1, the present invention is a kind of to carry out mud shale air content Forecasting Methodology methods described bag using geological data
Include：
(1) measurement data, geologic data include well logging, gas testing and mud shale rock core air content test data；Log data
Including interval transit time, density and air content well log interpretation curve through rock core air content test data scale；Geological data bag
Include conventional poststack or the seismic data of migration before stack processing；
(2) it is based on density log curve using the high correlation between density log curve and mud shale air content data, foundation
Air content computation model：
VGAS=f (DEN) (1)
In formula, VGAS is air content, m/t；DEN is density log curve, g/cm；F is between air content and density
Functional relation；
(3) carry out the more attribution inversions of poststack, obtain the density curve data volume needed for air content computation model；
(4) air content is predicted：The density curve data volume that step (3) obtains is updated in formula (1), obtains mud shale
Air content.
What the step (3) was realized in：Training is used as using the density log curve in air content computation model
Aim curve, find optimal seismic properties and combined with wave impedance inversion data volume so that utilize these attributes in well point position
The aim curve being calculated and actual density log correlation highest, are specifically included：Input density log, well
Other seismic properties, post-stack inversion curve；
Statistics finds the relation between seismic properties, poststack wave impedance inversion curve by density log curve and well；Carry out blind shaft
Validation-cross, it is determined that optimal seismic properties are combined with wave impedance inversion data volume and formula (2) described functional relation；
DEN (x, y, t)=f [Attribute 1 (x, y, t), Attribute2 (x, y, t) ..., Attributem (x, y,
t)] (2)
In formula, DEN (x, y, t) is the density log curve needed for air content computation model in step (2)；
Attributei (x, y, t) is seismic properties or inverting data；F is DEN (x, y, t) between best attributes
Functional relation；M is attribute number；
Statistical relationship is applied to seismic data cube, obtains the density curve data volume needed for air content computation model.
Fig. 2 is that density targets log inverting flow process, implementation method needed for air content computation model are：1. to each
Attribute (seismic properties such as instantaneous frequency, instantaneous amplitude, instantaneous phase) is calculated with mistake during its predicted density log
Difference, the minimum as attribute 1 of error, then forms attribute pair by attribute 1 and other attributes, calculates prediction error, error again
Another attribute of minimum attribute centering is attribute 2, and the rest may be inferred can select required attribute；2. due to step 1. in
Attribute pair can so have the problem of over-fitting with unconfined increase, it is therefore desirable to blind shaft validation-cross, i.e., in every increasing
Add successively to remove during an attribute and its is calculated after every mouth well predict error, the category during average forecasting error minimum of all wells
Property is combined Ji Wei best of breed with wave impedance；3. the relation between well curve and combinations of attributes is applied into whole data volume can
So that density data body is calculated.
Above-described embodiment, the purpose of the present invention, technical scheme and beneficial effect are carried out further
Describe in detail, should be understood that the embodiment that the foregoing is only the present invention, be not intended to limit the present invention
Protection domain, within the spirit and principles of the invention, any modification, equivalent substitution and improvements done etc., all should include
Within protection scope of the present invention.
Claims (2)
1. a kind of carry out mud shale air content Forecasting Methodology using geological data, it is characterised in that：Methods described includes：
(1) measurement data, geologic data include well logging, gas testing and mud shale rock core air content test data；Log data bag
Include interval transit time, density and the air content well log interpretation curve through rock core air content test data scale；Geological data bag
Include conventional poststack or the seismic data of migration before stack processing；
(2) it is based on density log curve using the high correlation between density log curve and mud shale air content data, foundation
Air content computation model：
VGAS=f (DEN) (1)
In formula, VGAS is air content, m/t；DEN is density log curve, g/cm；Letters of the f between air content and density
Number relational expression；
(3) carry out the more attribution inversions of poststack, obtain the density curve data volume needed for air content computation model；
(4) air content is predicted：The density curve data volume that step (3) obtains is updated in formula (1), mud shale is obtained and contains
Tolerance.
2. one kind according to claim 1 carries out mud shale air content Forecasting Methodology using geological data, its feature exists
In：What the step (3) was realized in：It is bent using the density log curve in air content computation model as training goal
Line, find optimal seismic properties and combined with wave impedance inversion data volume so that calculated in well point position using these attributes
The aim curve arrived and actual density log correlation highest, are specifically included：Earthquake by input density log, well
Attribute, post-stack inversion curve；
Statistics finds the relation between seismic properties, poststack wave impedance inversion curve by density log curve and well；Carry out blind shaft
Validation-cross, it is determined that optimal seismic properties are combined with wave impedance inversion data volume and formula (2) described functional relation；
DEN (x, y, t)=f [Attribute 1 (x, y, t), Attribute2 (x, y, t) ..., Attributem (x, y,
t)] (2)
In formula, DEN (x, y, t) is the density log curve needed for air content computation model in step (2)；
Attributei (x, y, t) is seismic properties or inverting data；F is DEN (x, y, t) between best attributes
Functional relation；M is attribute number；
Statistical relationship is applied to seismic data cube, obtains the density curve data volume needed for air content computation model.
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| CN201610673493.4A CN107765311A (en) | 2016-08-17 | 2016-08-17 | It is a kind of to carry out mud shale air content Forecasting Methodology using geological data |
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| CN201610673493.4A CN107765311A (en) | 2016-08-17 | 2016-08-17 | It is a kind of to carry out mud shale air content Forecasting Methodology using geological data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109118019A (en) * | 2018-09-04 | 2019-01-01 | 中国矿业大学(北京) | A kind of coal bed gas content prediction technique and device |
| CN110930020A (en) * | 2019-11-20 | 2020-03-27 | 中国地质大学（北京） | Method for determining economic recoverable resource amount of unconventional oil and gas resources |
-
2016
- 2016-08-17 CN CN201610673493.4A patent/CN107765311A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109118019A (en) * | 2018-09-04 | 2019-01-01 | 中国矿业大学(北京) | A kind of coal bed gas content prediction technique and device |
| CN110930020A (en) * | 2019-11-20 | 2020-03-27 | 中国地质大学（北京） | Method for determining economic recoverable resource amount of unconventional oil and gas resources |
| CN110930020B (en) * | 2019-11-20 | 2022-02-11 | 中国地质大学（北京） | Method for determining economic recoverable resource amount of unconventional oil and gas resources |
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Application publication date: 20180306 |