msc thesis’2011
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Facies analysis and production classification of Frasnian age
reservoir
Investigator: Irina Knyazeva
Tyumen, 09’2011
Supervisors: Chris Elders
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km
Area of interestArea of interest
Location of the area
We
st S
iber
ia
Tectonic mapTectonic map
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Regional tectonic
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Target Layer: Dkt – clasticUpper FrasnianLate Devonian, PaleozoicFrasnian-Tournaisian Oil&Gas complex
Chronostratigraphy
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3D seismic cube3D seismic cube Core data – 7 wellsCore data – 7 wells Well log data – 27 wellsWell log data – 27 wells
Available data
Area of 3D seismic survey235 km2
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Seismic interpretationSeismic interpretationCore descriptionSedimentology analysisFacies determination
Core descriptionSedimentology analysisFacies determination
Project workflow
Facies analysisFacies analysis
Palaeoenvironmen reconstructionPalaeoenvironmen reconstruction
Litho-facies differentiationLitho-facies differentiation
Macro descriptionFractional compositionCement contentMineralogical compositionPhi-K relationshipWell logs characteristics
Macro descriptionFractional compositionCement contentMineralogical compositionPhi-K relationshipWell logs characteristics
Core – Well logs tieCore – Well logs tie
3D litho-facies modeling3D litho-facies modeling
Litho-facies cubePorosity cubePermeability cubeKh map
Litho-facies cubePorosity cubePermeability cubeKh map
Output. RecommendationsOutput. Recommendations
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Seismic interpretation
Well #4066Well #4066
Dkt_topDkt_top
Dkt_botDkt_bot
Fm_topFm_top
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Seismic interpretation results
Bottom of reservoirBottom of reservoir
Top of reservoirTop of reservoir
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Seismic interpretation results
Thickness of reservoirThickness of reservoir
Implication
Combination of these three maps shows that sedimentation rate was slower in South-East and much faster in central and North-West parts.
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Core description
Structural map bottom of the reservoirStructural map bottom of the reservoir
Wells with core
#4053#4053
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Facies analysis
Tid
al c
han
nel
Tid
al c
han
nel
Tid
al b
arT
idal
bar
Tid
al f
lat
Tid
al f
lat
Characteristics:- Lithology: mud, sand and less commonly conglomerate;- Cross-bedding and cross-lamination structure;- Bimodal in tidal estuaries;- Fossils content typical for shallow marine;- Fining up succession.
Tidal channel facies
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Facies analysis
Tid
al c
han
nel
Tid
al c
han
nel
Tid
al b
arT
idal
bar
Tid
al f
lat
Tid
al f
lat
Tidal bar facies
Characteristics:- Lithology: from fine grained to medium grain size sand;- Sigmoidal cross-bedding associated with the tidal deltas and inlet fills;- Bidirectional current indicators.
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Facies analysis
Tid
al c
han
nel
Tid
al c
han
nel
Tid
al b
arT
idal
bar
Tid
al f
lat
Tid
al f
lat
Tidal flat facies
Characteristics:- Lithology: mud and fine grain sand;- Tabular muds with thin sheets and lenses of sand;- Ripple cross-lamination and flaser/lenticular bedding;- Fossils content: shallow marine fauna and salt marsh vegetations.
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Facies analysis
Up
per
sh
ore
face
Up
per
sh
ore
face
Fo
resh
ore
Fo
resh
ore
Upper shoreface facies
Characteristics:- Lithology: from fine-grained to medium grained sand;- Sedimentary structure: planar cross-bedding;- Clean good reservoir with good porosity and permeability values.
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Facies analysis
Up
per
sh
ore
face
Up
per
sh
ore
face
Fo
resh
ore
Fo
resh
ore
Foreshore facies
Characteristics:- Lithology: from medium grained to very coarse grained sand;- Sedimentary structure: trough and planar cross-bedding;- Small amount of bioturbation (by Scolithos ichnofacies);- High energy deposition environment.
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Ichnofauna examples
Palaeophycus.Realted to Scolithos ichnofacies. Characterized by high and low sedimentation energy foreshore. Also typical for storm affected sandstones. Can be found in brackish water
Planolites.Realted to Scolithos ichnofacies. Can be found in any type of environments: from fresh water to deep-water settings.
Chondrites.Related to Cruziana ichnofacies. Can be found in marine settings. Specific points for Chondrites ichnofacies is low oxygen conditions.
Scolithos.Usually for brackish water and marine environments. But Scolithos burrows are result from different organism livings this can be from marine to continental environments.
Asterosoma.Related to Cruziana ichnofacies. Can be found in Upper and Lower shoreface settings.
Thalassinoids.Related to Cruziana ichnofacies. Typical for brackish water environments.
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Palaeoenvironment reconstruction
Sweet et al, Basic clastic facies
Mar
gin
al m
arin
e en
viro
nm
ent
Mar
gin
al m
arin
e en
viro
nm
ent
Sweetness seismic attribute
Tidal channels
Tidal bars
River
Open sea
Gary Nichols, Sedimentology and stratigraphy, lectures, 2011
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Litho-facies determination
Phi, %
lgK
, mD
0 5 10 15 20
1000
100
10
1
0.1
0.01
LF2
LF1
LF3LF4
Phi, fraction
lgK
, mD
Phi-K transformPhi-K transformTNK-BP interpretation My own interpretation
LF1 < 0.0625 mm
LF2 = (0.0625 - 0.25) mm
LF3 = (0.25 - 0.5) mm
LF4 = (0.5 - 2) mm
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Litho-facies determination
Litho-facies 4: lg К=8.004*lg(Phi)+9.985Litho-facies 3: lg К=7.819*lg(Phi)+9.198Litho-facies 2: lg К=5.057*lg(Phi)+5.159Litho-facies 1: lg K=3.601*lg(Phi)+2.829
Phi, %
lgK
, mD
0 5 10 15 20
1000
100
10
1
0.1
0.01
LF2
LF1
LF3LF4
Phi, fraction
lgK
, mD
Phi-K transformPhi-K transformTNK-BP interpretation My own interpretation
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Litho-facies 2 overview
Cross-bedded from VFG to FG Sandstone partly
bioturbated
Cross-bedded from VFG to FG Sandstone partly
bioturbated
Core example Fractional composition Mineralogical composition
Cement content
Litho-facies properties
5,17%
77,3%
13,9%
3,63%
0
10
20
30
40
50
60
70
80
3,0-1,0 1,0-0,1 0,1-0,01 <0,01
Coarse material,
1%Feldspar,
2% Quartz, 97%
Coarse1%
Feldspar2%
Quartz88%
Sw_ir4%
Phi_ef5%
Chlorite,49%
Kaolinite2%
Mix of K&M,13%
Mica,36%
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Litho-facies 3 overview
MG poor sorted quartzitic Sandstone
with some detrit
MG poor sorted quartzitic Sandstone
with some detrit
Core example Fractional composition Mineralogical composition
Cement content
Litho-facies properties
2,75%
70,43%
19,8%
7,02%
0
10
20
30
40
50
60
70
80
3,0-1,0 1,0-0,1 0,1-0,01 <0,01
Quartz, 93%
Coarse material,
2%Feldspar,
5%
Phi_ef9%
Quartz, 88%
Feldspar4%
Coarse2%
Sw_ir2%
Mix of K&M,18%
Kaolinite, 2% Chlorite,
36%
Mica, 44%
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Litho-facies 4 overview
33,6%
63,2%
2,75% 0,45%
0
10
20
30
40
50
60
70
3,0-1,0 1,0-0,1 0,1-0,01 <0,01
Feldspar, 2%
Coarse material,
4%Quartz,
94%
From CG to VCG poor sorted Sandstone
with pebble size quartz, often massive structure
From CG to VCG poor sorted Sandstone
with pebble size quartz, often massive structure
Core example Fractional composition Mineralogical composition
Cement content
Litho-facies properties
Quartz84%
Feldspar1%
Coarse 3%
Sw_ir 1% Phi_ef; 11%
Mix of K&M, 23%
Kaolinite, 1%
Mica, 41%
Chlorite, 35%
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Litho-facies prediction
What do we have?- 7 cored wells;- Lack of logging tools;- Poor quality well logging data;- 4 litho-facies defined base on core data;- 20 uncored wells.
ProblemLitho-facies prediction in uncored wells
SolutionStatistical technique “Fuzzy Logic”SolutionStatistical technique “Fuzzy Logic”
Donetsk anticline 35 wells
North Donetsk anticline 2 wells
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Prediction results
#4053#4053#4076#4076
Core Core Prediction Prediction
Litho-facies 1 Litho-facies 2
Litho-facies 3 Litho-facies 4
StatementLitho-facies prediction using Fuzzy logic is based on assertion that a particular litho-facies type can give any log reading although some readings are more likely than others.
ResultsIn a result of prediction we get good differentiation between litho-facies and lithology prediction in uncored wells.
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3D static modeling
Structural modelStructural modelDepth
3230
3260
Litho-facies cubeLitho-facies cubeCodeLF1LF2LF3LF4
Average porosity mapAverage porosity mapPorosity
0.13
0.6
Average permeability mapAverage permeability mapPermeability
1
100
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3D modeling results
Output from static modeling is conductivity map kh (permeability*thickness). This map is useful to define and prove most attractive spots with highest oil rate. Kh map allows to eliminate potential productive zones and localize remaining reserves.
Prospective drilling zonesProspective drilling zonesK*h
8000
0
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Conclusion
Marginal marine environment – tide dominated estuary;
5 facies and 4 petrophysical litho-facies within were defined and predicted in uncored wells using fuzzy logic technique;
Tight integration of seismic, core and well log data is realized in 3D static model that is more predictive and have lower degree of uncertainty associated with them;
Output result from static modeling is conductivity map. This map is useful to define and prove most attractive spots with highest oil potential.
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Thank you for attention!
http://2.bp.blogspot.com/_Bz97zTlEL6U/TPhI9rk8aBI/AAAAAAAABuc/dZvl28QthbU/s1600/P1020294_Estuary.JPG