screening technologies - university of wyoming · 2020. 6. 16. · spe-78332 15 expert map: polymer...

E N H A N C E D O I L R E C O V E R Y I N S T I T U T E

Screening Technologies 3rd Annual Wyoming IOR/EOR Conference

V L A D I M I R A LV A R A D O

C H E M I C A L A N D P E T R O L E U M E N G I N E E R I N G

S E P T E M B E R 1 3 , 2 0 11


• Introduction

• Why run screening?

• Screening type

• Engineering (lookup tables)

• Data-driven (drawn from experience)

• Geological (ex: pay continuity)

• Multi-parameter projections

• Closing remarks

Outline


ValuesUncertaintiesDecisions

Objective

Function

EOR MethodNPV

Tax

CAPEX

OPEXReservoir

Properties

Oil

ProductionProduction

revenue

Oil Price

Water

Handling Costs

EOR MethodNPV

Tax

CAPEX

OPEXReservoir

Properties

Oil


revenue

Oil Price

Water

Handling Costs

CO2

Sequestration

EOR MethodNPV

Tax

CAPEX

OPEXReservoir

Properties

Oil


revenue

Oil Price

Water

Handling Costs

NPV

SAGD

Yes

No

High

Low

Base case

SF Yes

No

Yes

No

High

Low

Base case

High

Low

Base case NPVNPVISC


Objective

Function

EOR MethodNPV

Tax

CAPEX

OPEXReservoir

Properties

Oil


revenue

Oil Price

Water

Handling Costs

EOR MethodNPV

Tax

CAPEX

OPEXReservoir

Properties

Oil


revenue

Oil Price

Water

Handling Costs

CO2

Sequestration

EOR MethodNPV

Tax

CAPEX

OPEXReservoir

Properties

Oil


revenue

Oil Price

Water

Handling Costs

NPVNPV

SAGD

Yes

No

Yes

No

High

Low

Base case

High

Low

Base case

SF Yes

No

Yes

No

High

Low

Base case

High

Low

Base case NPVNPV

High

Low

Base case

High

Low

Base case NPVNPVNPVNPVISC

Conventional Economic

Evaluations

EOR Decision Making

Manrique et al., SPE 113269, 2008


Natural Depletion

Water Flooding

Water-Alternating-Gas (WAG)

Alkali-Surfactant-Polymer (ASP)

Gas Flooding

Miscible

Immiscible

Water-Alternating-Gas (WAG)

CO2 Sequestration

High Pressure Air Injection

Gas Injection Water Injection

WAG IGR by late Depressurization

Pressure Maint.

Steam Stimulation

Steamflooding

In-Situ Combustion

Steam-Assisted-Gravity-Drainage (SAGD)

Problem: Too many choices


• “Screening guides or criteria are among the first items

considered when a petroleum engineer evaluates a

candidate reservoir for enhanced oil recovery (EOR)”1

• “The choice of enhanced oil recovery processes is

based on technical and economic data”2

• “Sophisticated and complex numerical models are used

in industry to evaluate the suitability of a reservoir for

CO2 flooding,… Thus, these methods and models are

not suitable for a regional-scale, quick initial

assessment and screening of oil pools…with respect to

suitability…” 3

Screening

1Taber & Martin, SPE 12069, 1983 2Guerillot, SPE 17791, 1988 3Shaw & Bachu, JCPT, 2002


Prospective Simulations

Screening

Detailed Appraisal

Project Implementation

Stop

Stop

Stop

Goodyear & Gregory, SPE 28844, 1994

• Several methods for

feasibility

• Analytical or sector model

simulations => performance

prediction

• More data serve to build

detailed models and deal

with uncertainty

• A go-ahead is given (or not)

to deploy a development plan

and invest resources

Why run screening?

E N H A N C E D O I L R E C O V E R Y I N S T I T U T E *Taber, Martin & Seright, SPEREE, 1997 (I)

Lookup tables (Yes or No, or Maybe)

EOR Method

Gravity (oAPI)

Viscosity (cp)

Comp. So (% PV)

Formation Type

Net Thick. (ft)

(md)

Depth (ft)

T (oF)

N2 & Flue Gas

> 35↗ 48↗

40↗ 75↗

Sandstone Carbonate

Thin unless Dipping

NC >6,000 NC

HC Gas > 23↗ 41↗

30↗ 80↗

Sandstone Carbonate

Thin unless Dipping

NC >4,000 NC

CO2 > 22↗ 36↗

20↗ 55↗

Sandstone Carbonate

Wide range NC >2,500 NC

Immisc. gases

> 12 35↗ 70↗

Sandstone Carbonate

NC if dipping and/or good Kv

NC >1,800 NC

• Similar tables are available for chemical and thermals methods.

• “Screening criteria are based on a combination of the reservoir and oil characteristics of successful projects plus our understanding of the optimum conditions needed for good oil displacement by the different EOR fluids”*

Expert: having, involving, or displaying special skill or knowledge derived from training or experience

http://www.merriam-webster.com/dictionary/experience


• Each triangle represents a comfort reference interval

• A second triangle is the reservoir interval for each variable

• The overlapping area yields an index (0,1); the closer to “1” the better

Comfort intervals (Ex: f)

Softening lookup tables

Alvarado, Thyne & Murrell, SPE 115940, 2008


Out[18]=

VARIABLE MINIMUM MOST LIKELY MAXIMUM INDEX

Depth 30 2515 5000 0.760022

Thickness 3 251.5 500 0.0117616

Pressure 10 250 500 0.380952

Permeability 100 2550 5000 0.496016

Viscosity 0.2 1.35 2.5 0.0636642

Temperature 0 100 200 0.35

TOTAL 0.343736

Fuzzy lookup tables



Soft lookup tables can rank



WY Test Cases



Triangle U Field

0

5000

10000

15000

20000

25000

30000

Jan-

76

Jan-

80

Jan-

84

Jan-

88

Jan-

92

Jan-

96

Jan-

00

Jan-

04

Jan-

08

Jan-

12

Jan-

16

Jan-

20

Jan-

24

Jan-

28

Jan-

32

Jan-

36

Date

Oil r

ate

(B

bl/m

on

th)

-10

0

10

20

30

40

50

60

70

Wa

ter

Cu

t (%

)

Waterflooding

startedChemical treatment

started

WY Test Cases

• Can we use learned lessons?

• Critical conditions & parameters?

• Can we develop strategies for basins?

• Can we recommend objectively?



Violations

0

10

20

30

40

50

60

70

Dep

th

Per

mea

bility

Thickn

ess

Tem

pera

ture

Oil visc

osity

Pre

ssur

e

Oil de

nsity

Aniso

tropy

(kv/kh

)

Clay co

nten

t

Salinity

Parameters

Fre

qu

en

cy

Waterflooding

Chemical Flooding

Results for Minnelusa



0

10

20

30

40

0 0.2 0.4 0.6 0.8 1

Index

Fre

qu

en

cy

Waterflooding

Chemical Flooding

Results for Minnelusa



15 SPE-78332

Expert Map: Polymer Floods CO2 Immisc.

CO2 Misc.

N2 Immisc.

N2 Misc.

Polymer

Steam

WAG CO2 Immisc.

WAG HC Immisc.

WAG CO2 Misc.

WAG HC Misc.

Air

Water Flooding

Cluster 1

Cluster 5

Cluster 2

Cluster 3

Cluster 6

Cluster 4

Method %

Air 41.38

Steam 27.59

CO2 Immisc. 10.34

Polymer 8.62

WAG CO2 Immisc. 5.17

Water Flooding 5.17

N2 Immisc. 1.72

Method %

CO2 Immisc. 22.58Air 12.90

Water Flooding 12.90

CO2 Misc. 9.68

Polymer 9.68WAG HC Immisc. 9.68

N2 Misc. 6.45

WAG HC Misc. 6.45

N2 Immisc. 3.23Steam 3.23

WAG CO2 Misc. 3.23

Method %


CO2 Misc. 20.83

Polymer 18.75

N2 Immisc. 6.25

Steam 6.25WAG HC Misc. 6.25

CO2 Immisc. 4.17

WAG CO2 Misc. 4.17

N2 Misc. 2.08

WAG N2 Misc. 2.08

Method %Water Flooding 48.28

Polymer 25.29WAG CO2 Misc. 12.64

CO2 Misc. 10.34

N2 Immisc. 1.15WAG HC Misc. 1.15

Steam 1.15

Method %


WAG CO2 Misc. 13.46

WAG HC Misc. 13.46

N2 Misc. 9.62

CO2 Misc. 7.69

N2 Immisc. 7.69

Polymer 5.77

Air 3.85

Method %N2 Misc. 42.86

N2 Immisc. 21.43

WAG N2 Misc. 14.29Water Flooding 14.29

WAG HC Misc. 7.14

Polymer floods in shallow and low

pressure reservoirs

Alvarado et al., SPE 78332, 2002


16

Geologic Screening Criteria LOW MODERATE HIGH

LATERAL HETEROGENEITY

LO

WM

OD

ERATE

HIG

H

VE

RT

ICA

LH

ET

ER

OG

EN

EIT

Y

Wave-dominated delta Barrier core Barrier shore face

Sand-rich strand plain

Delta-front mouth bars Proximal delta front

(accretionary) Tidal Deposits Mud-rich strand plain

Meander belts*

Fluvially dominated delta* Back Barrier*

Eolian Wave-modified delta(distal)

Shelf barriers

Alluvial Fans Fan Delta Lacustrine delta

Distal delta front

Braided stream

Tide-dominated delta

Basin-flooring turbiditesCoarse-grained meander belt Braid delta

Back barrier**

Fluvially dominated delta** Fine-grained meander belt**

Submarine fans**

* Single units **Stacked Systems

(1) / [1] (3) / [1]

[1] (6)

(7) / [2] (2) (2)

(3) / [2]

Tyler and Finley clastic heterogeneity matrix showing depositional systems of 24 successful (Blue) and 7 failed [Red] CO2 injection projects


1. Conventional screening (XY Plots)

2. Screening with analytical tools

3. Geological screening

4. Advanced screening

5. Inclusion of soft variables

6. Evaluate constraints (simulation)

7. Economic evaluation

A possible methodology

Conventional Screening

Geologic Screening

Advanced Screening

Evaluation of Soft Variables

Decision Analysis

Performance Prediction

Analytical Simulation Numerical Simulation

Economics

Stop

Decision Analysis Stop

Re-e

valu

ation c

ycle

Fie

ld C

ases T

ype I

Fie

ld C

ases T

ype II


Geologic Screening

Advanced Screening


Decision Analysis



Economics

Stop


Re-e

valu

ation c

ycle


Geologic Screening

Advanced Screening


Decision Analysis



Economics

Stop



Geologic Screening

Advanced Screening


Decision Analysis



Economics

Stop


Re-e

valu

ation c

ycle

Fie

ld C

ases T

ype I

Fie

ld C

ases T

ype II

Fie

ld C

ases T

ype I

Fie

ld C

ases T

ype II

Manrique et al., SPEREE, 2009


CO2 Misc.

N2 Immisc.

N2 Misc.

Polymer

WAG-CO2 Misc.

WAG-HC Misc.

air

Water flooding

Field A - Case 1

Field A - Case 2

Field A - Case 3

Cluster 2-3

Method %

N2 Immiscible 50.0

Polymer 25.0

Water Flooding 25.0

Cluster 2-4

Method %

WAG-HC 38.5

N2 Miscible 23.1

Water Flooding 23.1

N2 Immiscible 15.4

Cluster 2-1

Method %

Water Flooding 47.6

WAG-CO2 Misc. 23.8

Air 9.5

CO2 Misc. 9.5

Polymer 9.5

Cluster 2-2

Method %

Water Flooding 42.9

CO2 Misc. 14.3

N2 Miscible 14.3

WAG-CO2 Misc. 14.3

WAG-HC 14.3

Binger

East Binger

Field “A” sensitivity cases

CO2 Misc.

N2 Immisc.

N2 Misc.

Polymer

WAG-CO2 Misc.

WAG-HC Misc.

air

Water flooding

Field A - Case 1

Field A - Case 2

Field A - Case 3

Cluster 2-3

Method %

N2 Immiscible 50.0

Polymer 25.0

Water Flooding 25.0

Cluster 2-4

Method %

WAG-HC 38.5

N2 Miscible 23.1

Water Flooding 23.1

N2 Immiscible 15.4

Cluster 2-1

Method %

Water Flooding 47.6

WAG-CO2 Misc. 23.8

Air 9.5

CO2 Misc. 9.5

Polymer 9.5

Cluster 2-2

Method %

Water Flooding 42.9

CO2 Misc. 14.3

N2 Miscible 14.3

WAG-CO2 Misc. 14.3

WAG-HC 14.3

Binger

East Binger

Field “A” sensitivity cases

A. Light-Oil Dolomitic Reservoir



0

5

10

15

20

25

30

35

40

45

50

0 20 40 60 80 100

% Pore Volume Injected

Reco

very

facto

r, %

CMG-75

CMG-150

CMG-300

Prize-75

Prize-150

Prize-300

Screening Criteria

c1 c2

cn

...

cn-1

Analytic

Numeric

Simulation

Analytical vs. Numerical

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Time, year

Cu

mu

lati

ve

Oil

Pro

du

cti

on

, m

3

SAGD NP=21m

SAGD NP=10m

STEAM NP=21m

STEAM NP=10m

IN-SITU NP=21m

IN-SITU NP=10m


Objective

Function

EOR MethodNPV

Tax

CAPEX

OPEXReservoir

Properties

Oil


revenue

Oil Price

Water

Handling Costs

EOR MethodNPV

Tax

CAPEX

OPEXReservoir

Properties

Oil


revenue

Oil Price

Water

Handling Costs

CO2

Sequestration

EOR MethodNPV

Tax

CAPEX

OPEXReservoir

Properties

Oil


revenue

Oil Price

Water

Handling Costs

NPV

SAGD

Yes

No

High

Low

Base case

SF Yes

No

Yes

No

High

Low

Base case

High

Low

Base case NPVNPVISC


Objective

Function

EOR MethodNPV

Tax

CAPEX

OPEXReservoir

Properties

Oil


revenue

Oil Price

Water

Handling Costs

EOR MethodNPV

Tax

CAPEX

OPEXReservoir

Properties

Oil


revenue

Oil Price

Water

Handling Costs

CO2

Sequestration

EOR MethodNPV

Tax

CAPEX

OPEXReservoir

Properties

Oil


revenue

Oil Price

Water

Handling Costs

NPVNPV

SAGD

Yes

No

Yes

No

High

Low

Base case

High

Low

Base case

SF Yes

No

Yes

No

High

Low

Base case

High

Low

Base case NPVNPV

High

Low

Base case

High

Low

Base case NPVNPVNPVNPVISC

CO2 Immisc.

N2 Immisc.

Polymer

Steam

Air

Water Flooding

WAG CO2 Immisc.

ASP

SAGD (Canada)

Avg. Grosmont Carbonate

Peace River, Shell

Cold Lake, IOL

Primrose, CNRL

Ikiztepe (Heavy Oil Carbonate)

Qarn Alam (Heavy Oil Carbonate)

Issaran (Heavy Oil Carbonate)

Cluster 5-1

Cluster 5-3

Cluster 5-4

Cluster 5-2

Cluster 5-6

Cluster 5-5

Method %

CO2 Immisc. 33.33

Air 22.22

WAGCO2 Immisc. 22.22


Polymer 11.11

Method %

Air

35.71

Steam

28.57

CO2 Immisc.

14.29

Polymer

7.14

Water Flooding

7.14

ASP

7.14

Method %

CO2 Immisc. 33.33

N2 Immisc. 16.67


Polymer 16.67

ASP 16.67

Method %

Air 70

Steam 30

Method %

Air 52.94

Steam 47.06

Method %

Steam 37.5

Air 25

Polymer 25

12.5Water Flooding

Reservoir “B”

Burnt Lake

Foster

Creek

CO2 Immisc.

N2 Immisc.

Polymer

Steam

Air

Water Flooding

WAG CO2 Immisc.

ASP

SAGD (Canada)


Peace River, Shell

Cold Lake, IOL

Primrose, CNRL




Cluster 5-1

Cluster 5-3

Cluster 5-4

Cluster 5-2

Cluster 5-6

Cluster 5-5

Method %

CO2 Immisc. 33.33

Air 22.22



Polymer 11.11

Method %

Air

35.71

Steam

28.57

CO2 Immisc.

14.29

Polymer

7.14

Water Flooding

7.14

ASP

7.14

Method %

CO2 Immisc. 33.33

N2 Immisc. 16.67


Polymer 16.67

ASP 16.67

Method %

Air 70

Steam 30

Method %

Air 52.94

Steam 47.06

Method %

Steam 37.5

Air 25

Polymer 25

12.5Water Flooding

Reservoir “B”

Burnt Lake

Foster

Creek

CO2 Immisc.

N2 Immisc.

Polymer

Steam

Air

Water Flooding

WAG CO2 Immisc.

ASP

SAGD (Canada)


Peace River, Shell

Cold Lake, IOL

Primrose, CNRL




Cluster 5-1

Cluster 5-3

Cluster 5-4

Cluster 5-2

Cluster 5-6

Cluster 5-5

Method %

CO2 Immisc. 33.33

Air 22.22



Polymer 11.11

Method %

Air

35.71

Steam

28.57

CO2 Immisc.

14.29

Polymer

7.14

Water Flooding

7.14

ASP

7.14

Method %

CO2 Immisc. 33.33

N2 Immisc. 16.67


Polymer 16.67

ASP 16.67

Method %

Air 70

Steam 30

Method %

Air 52.94

Steam 47.06

Method %

Steam 37.5

Air 25

Polymer 25

12.5Water Flooding

Reservoir “B”

Burnt Lake

Foster

Creek

CO2 Immisc.

N2 Immisc.

Polymer

Steam

Air

Water Flooding

WAG CO2 Immisc.

ASP

SAGD (Canada)


Peace River, Shell

Cold Lake, IOL

Primrose, CNRL




Cluster 5-1

Cluster 5-3

Cluster 5-4

Cluster 5-2

Cluster 5-6

Cluster 5-5

Method %

CO2 Immisc. 33.33

Air 22.22



Polymer 11.11

Method %

Air

35.71

Steam

28.57

CO2 Immisc.

14.29

Polymer

7.14

Water Flooding

7.14

ASP

7.14

Method %

CO2 Immisc. 33.33

N2 Immisc. 16.67


Polymer 16.67

ASP 16.67

Method %

Air 70

Steam 30

Method %

Air 52.94

Steam 47.06

Method %

Steam 37.5

Air 25

Polymer 25

12.5Water Flooding

Reservoir “B”

Burnt Lake

Foster

Creek

0

0.2

0.4

0.6

0.8

1

Porosity (%) (16-45)

Permeability (mD) (5-10500)

Depth (ft) (200-6800)

API Gravity (5.8-35)

Oil Viscosity (cp) (0.4-6000000)

Temperature (F) (10-280)

Primrose Gueheng Karamay 9-5 - 9-9McKittrick Karamay 6 Elk PointIron River Frog Lake MorganYorba Linda Maguerite Lake AthabascaPeace River Area Saner Ranch Cat CanyonKern River Tangleflags East Wolf Lake/Primrose/Burnt LakeFoster Creek Lean LindberghCaribou Lake Charlotte Lake Gregoire (Athabasca)Pikes Peak Cold Lake San Miguel-4Reservoir "A" Lost Hills Sec. 30 Schoonebeeck

0

0.2

0.4

0.6

0.8

1

Porosity (%) (16-45)

Permeability (mD) (5-10500)

Depth (ft) (200-6800)

API Gravity (5.8-35)

Oil Viscosity (cp) (0.4-6000000)

Temperature (F) (10-280)

Primrose Gueheng Karamay 9-5 - 9-9McKittrick Karamay 6 Elk PointIron River Frog Lake MorganYorba Linda Maguerite Lake AthabascaPeace River Area Saner Ranch Cat CanyonKern River Tangleflags East Wolf Lake/Primrose/Burnt LakeFoster Creek Lean LindberghCaribou Lake Charlotte Lake Gregoire (Athabasca)Pikes Peak Cold Lake San Miguel-4Reservoir "A" Lost Hills Sec. 30 Schoonebeeck

LOW MODERATE HIGH

LO

W

Wave-dominated delta

Barrier core

Barrier shore face


Delta-front mouth bars

Proximal delta front

(accretionary)

Tidal Deposits

Mud-rich strand plain

Meander belts*

Fluvially dominated delta*

Back Barrier*

MO

DER

ATE

Eolian

Wave modified delta (distal)

Shelf barriers

Alluvial Fans

Fan Delta

Lacustrine delta

Distal delta front

Braided stream


HIG

H

Basin-flooring turbiditesCoarse-grained meander belt

Braid delta

Back barrier**

Fluvially dominated delta**

Fine-grained meander belt**

Submarine fans**



VE

RT

ICA

L H

ET

ER

OG

EN

EIT

Y

(9) / [1] (9) / [2]

(9) (83) / [14] (52) / [4]

(19) / [1] (2) (4)

LOW MODERATE HIGH

LO

W

Wave-dominated delta

Barrier core

Barrier shore face


Delta-front mouth bars

Proximal delta front

(accretionary)

Tidal Deposits

Mud-rich strand plain

Meander belts*

Fluvially dominated delta*

Back Barrier*

MO

DER

ATE

Eolian

Wave modified delta (distal)

Shelf barriers

Alluvial Fans

Fan Delta

Lacustrine delta

Distal delta front

Braided stream


HIG

H

Basin-flooring turbiditesCoarse-grained meander belt

Braid delta

Back barrier**

Fluvially dominated delta**

Fine-grained meander belt**

Submarine fans**



VE

RT

ICA

L H

ET

ER

OG

EN

EIT

Y

(9) / [1] (9) / [2]

(9) (83) / [14] (52) / [4]

(19) / [1] (2) (4)

(9) / [1] (9) / [2]

(9) (83) / [14] (52) / [4]

(19) / [1] (2) (4)

Decision and Risk Analysis and EE

B. Canadian Oil Sand



• Comparison of basic geologic

properties.

• DP coefficient from log and

core data to estimate the

impact on steam chamber

development.

• Lateral and vertical

heterogeneity indexes for

feasibility of SAGD well pairs in

the same units, vertical

separation and length.

LOW MODERATE HIGH

LO

WM

OD

ERATE

HIG

H


VE

RT

ICA

L H

ET

ER

OG

EN

EIT

Y

LOW MODERATE HIGH

LO

WM

OD

ERATE

HIG

H


VE

RT

ICA

L H

ET

ER

OG

EN

EIT

Y

LOW MODERATE HIGH

LO

WM

OD

ERATE

HIG

H


VE

RT

ICA

L H

ET

ER

OG

EN

EIT

Y

LOW MODERATE HIGH

LO

WM

OD

ERATE

HIG

H


VE

RT

ICA

L H

ET

ER

OG

EN

EIT

Y

LOW MODERATE HIGH

LO

WM

OD

ERATE

HIG

H


VE

RT

ICA

L H

ET

ER

OG

EN

EIT

Y

LOW MODERATE HIGH

LO

WM

OD

ERATE

HIG

H


VE

RT

ICA

L H

ET

ER

OG

EN

EIT

Y




• Cells (120 m x 800 m)

indicates potential SAGD

well pairs locations.

• Cumulative oil and CSOR

can be estimated from

correlations for different

net pays with and w/o top

gas and bottom water.

• As expected, RF

decreases and CSOR

increases with thickness

and presence of top gas

and bottom water.

Discarded for SAGD




Ongoing efforts at EORI-PETE (UW)

• Selection of adequate clustering algorithms to data-mine EORI database and others

• Reservoir type identification

• Incorporation of reservoir, fluids and geologic data into clustering methods

• Per-basin analysis of historical data


Screening: Variables used

• 3-7 variables (median) were considered: depth, permeability, thickness, temperature, viscosity, pressure, salinity. Example:


Gran

ularity

As one goes from top to bottom by fixing a horizontal line at certain levels, the number of clusters can be identified. The numbers at the bottom represent the individual reservoirs in the database


Projections One way of visually looking at the data is projections. Here is an example of 7 parameters projected on 3 Principal Component (PCA). Colors identify clusters.


Projections A more familiar look can be seen from the 2D projection


Projections (analogs)

The cluster structure can be used to find analogs

The lines joining “+”

with points identify

the connection

between 3 “new”

reservoirs with their

analog cases.

Minimum distance

was used to find the

analogs


Closing remarks

• Screening is not a one-step process and does not replace reservoir studies, but helps to assist decisions

• Several screening methods should be considered, especially when different data types are used

• Data-mining approaches alleviate expert opinions’ biases and can lead to the concept of “reservoir type”


BACKUP SLIDES SPEREE


C. Steam flooding project

• Decreasing well spacing increases the recovery factor

• Steam quality > 0.6 is required for significant increments in recovery factor – this is a critical design variable

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Well Spacing (m)R

eco

very

Facto

r, %

SQ=1.0

SQ=0.8

SQ=0.6

SQ=0.4

SQ=0.2

SQ=0.0

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% Pore Volume Injected

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ove

ry F

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Well Spacing = 75mWell Spacing = 150 mWell Spacing = 225 mWell Spacing = 300 mWell Spacing = 75mWell Spacing = 150 mWell Spacing = 225 mWell Spacing = 300 m



• Performance predictions from simulations indicated improvement in oil recovery for smaller well spacing

• However, economics dictates an optimal well spacing below which no economic gain can be obtained

$0

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Well Spacing, (m)

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t V

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PVI=70%, Oil Price = $90

PVI=70%, Oil Price = $60


5000 100005000 10000

• Decision problem is well spacing for a steam flooding

• Framing is for optimal recovery/production

• Building petrophysical models was not feasible




Screening: Variables used

• Parameters values were rescaled (normalized)

• Once processed, the data were partitioned and clustered

• Dendograms were produced as a way to see the structure of the data and analyzed granularity of the dataset

screening technologies - university of wyoming · 2020. 6. 16. · spe-78332 15 expert map: polymer...

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