Subsurface Consultancy Services

LEAP Energy

Mature Field

Water Flood

Optimization:

Workflows to

facilitate Decisions

SPE ATW: 22 to 25 July, 2011Kota Kinabalu

Typical problems faced by clients in Water Flood Management in Mature Fields

Erroneous and ambiguous dataComplex and long (>50 years)

production history

High well density, large number of wells

Very limited core and fluid property data

Only basic well logs available

Available datasetRow Labe ls Oil_Rate_m3/d Wat_Rate_m3/d Gas_Rate_m3/d WC WOR GOR Cum Oil Cum Wat

1/6/1991 2.45 7.00 100.00 0.74 2.86 40.81 18931.89 26530.8

1/7/1991 1.99 5.68 100.00 0.74 2.86 50.31 19005.41 26667.3

1/8/1991 1.08 3.09 100.00 0.74 2.86 92.50 19067.03 26781.7

1/9/1991 3.15 9.01 100.00 0.74 2.86 31.71 19096.22 26835.9

1/10/1991 1.81 5.18 96.77 0.74 2.86 53.37 19171.89 26976.4

1/11/1991 1.08 3.09 100.00 0.74 2.86 92.50 19228.11 27080.8

1/12/1991 1.08 3.09 100.00 0.74 2.86 92.50 19260.54 27141

1/1/1992 1.08 3.09 100.00 0.74 2.86 92.50 19294.05 27203.2

1/2/1992 1.08 3.09 100.00 0.74 2.86 92.50 19327.57 27265.4

1/3/1992 1.08 3.09 100.00 0.74 2.86 92.50 19358.92 27323.6

1/4/1992 1.08 3.09 100.00 0.74 2.86 92.50 19392.43 27385.8

1/5/1992 1.08 3.09 100.00 0.74 2.86 92.50 19424.86 27446

1/6/1992 1.08 3.09 100.00 0.74 2.86 92.50 19458.38 27508.2

1/7/1992 1.08 3.09 100.00 0.74 2.86 92.50 19490.81 27568.4

1/8/1992 1.08 3.09 100.00 0.74 2.86 92.50 19524.32 27630.6

1/9/1992 1.08 3.09 100.00 0.74 2.86 92.50 19557.84 27692.8

1/10/1992 1.08 3.09 100.00 0.74 2.86 92.50 19590.27 27753

1/11/1992 1.08 3.09 100.00 0.74 2.86 92.50 19623.78 27815.2

1/12/1992 1.08 3.09 100.00 0.74 2.86 92.50 19656.22 27875.4

1/1/1993 1.08 3.09 100.00 0.74 2.86 92.50 19689.73 27937.6

1/2/1993 1.08 3.09 100.00 0.74 2.86 92.50 19723.24 27999.8

1/3/1993 1.08 3.09 100.00 0.74 2.86 92.50 19753.51 28056

1/4/1993 1.08 3.09 100.00 0.74 2.86 92.50 19787.03 28118.2

1/5/1993 1.08 3.09 100.00 0.74 2.86 92.50 19819.46 28178.4

1/6/1993 2.23 6.38 96.67 0.74 2.86 43.27 19852.97 28240.6

Uncertainty in Fluid Contacts

Poor understanding of well interactions

Decision Matrix to drive the process of WF Management / Optimization

Indicators 1. Start/

increase

Water Flood

2. Drill more In-

fill

3. Do Nothing 4. Suspend/

Stop Water

Flood

Source

Average RF Low Low As expected As expected Estimated/ Drive

dependent

Average Pressure Low Pressure Undepleted/ less

depleted Pressure

Jacked up Jacked up Monitored

Variablity in average Field

Pressure

Low High Low Low Measured

Mobility Contrast Low High (use adv. Tech

like Hor Drilling)

NA High Based on PVT analysis

Lateral Permeability

variability (facies/

stratigraphic heterogeneity)

Low High NA Low Well tests, poro-perm

relatioship

H/V Perm Ratio High High (vertical well

tech), Low (use adv.

Tech like Hor Drilling)

NA NA Based on Core

measurements/ data

Voidage Compensation Low High High High Calculated

Water Cut Variability/ Extent

of WC

Low Water Cut Low Water Cut Within expected

limits

Very High Measured

Production Data Quality

Index

High High NA Low Based on DQI tool

Connectivity / Interaction

between wells

High High Low/ High Low/ High Based on various

approaches to data

analysis (Linear trend,

Average, Response, etc.)

Percentage of under-swept

area (DPI)

High (victim) High (victim) Low (thieve) Low (thieve) Estimated Drainage

Radius (EDI)

Variability in average Field

RF (BL based)

High High Low Low BL Fit

Flood Efficiency Factor

(Exponent based)

High High High/ Low Low From Cum-Cum Plot

Vertical Variability Index Low High NA Low From VVI logs

Remaining STOIIP (Reserves)

Density

High High Low Low Based on contact

movement (Petrel)

Recommendation/ Decision

Indicators used

in an integrated

toolkit- to

facilitate

analysis and

decision

making in

water-flood

management

Scope:

Create a set of workflows and tools (Excel based) to facilitate a rapid assessment of

flood and recovery performance, identifying remaining potential and providing a

decision guide for future studies and operational activities.

Deliverables:

Set of documented

techniques & workflows

Decision guides

Map, Area & WellPilot tools

xls or applications

Practical illustration with a

field example

General

Remaining Potential identification

MAP

Focussed

Well-based property range

assessment – for future detailed

studies

Illustrated Experience

Application and Advantages

3D geo-

cellular and

Streamline

simulation

Full 3D geo-

cellular &

numerical

models

THIS PROJECT

Classical

Assessment

and

Production

Forecast

-Property-match based

production forecast

(rather than an arbitrary

decline)

-Stochastic forecast for

uncertainty

management

-Analytical assessment

of flood optimization

incl. infill locations

Pre-cursor to 3D simulation-Data quality assessment to provide priority matching

intervals

-Improved understanding of average reservoir

properties within well drainage area

-Well to well correlations and connectivity from production

data, guiding stratigraphic mapping

-Insights on flood pattern with contact movement

history

- Provide focus areas for more advanced studies

Decision guide

for operational

activities or

detailed studies

Data quality

maps

Period Validity

assessment

Geological

and Structural

ATTRIBUTE

derivation

Production

and Flood

ATTRIBUTE

derivation

Well (PW-PW,

IW-PW)

Interaction

and pairing

Assessment

Data

Screening /

Quality

Assessment

Vertical

Heterogeneity

Indices

Lateral Variability

Indices

Curvature/Dip

maps

Suspicious

Water cut

changes

Anomalous rate

data

Reservoir and Flood

Characteristics

Well basic stats (EUR,

CumO, CumW)

Flood performance well

attributes (AvgO / MaxO,

CumO vs CumW)

Flood and Recovery efficiency vs. inter-well distance,

geological parameters for future optimization

Semi-Quantitative assessment for future optimization

Contact

Movement

Mapping

Stochastic

History

Matching

Buckley

Leverett

Deriving decision maps for

alternative approaches to

fields (water flooding / infill

/ do nothing / abandon or

suspend ops)

Algorithms generated to automate…

1. Data Screening / Quality Assessment

Data

Screening

Algorithm

Identify

anomalous

behavior

Event

Identification

Well Intervention

(confirm/identify)

Sweep changes

(confirm/identify)

- Anomalous

gradients and erratic

changes,

- Allocation issues,

- Lack of oil/ water/

gas production data

Data Quality Map

and discard

Separating the meaningful from the meaningless

Data Screening / Quality Assessment

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3/d

Oil_Vol_m3/d (smooth) Oil_Rate_m3/d

Liq_Rate_m3/d Liq_m3/d (smooth)

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er

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io [

m3

/m3

]

Wat

er

Cu

t [m

3/m

3]

WOR (smooth) WOR WC WC (smooth)

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Gas

Rat

e [

m3

/d]

Gas

Oil

Rat

io [

m3

/m3

]GOR Gas_Rate_m3/d

0

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10000

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25000

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35000

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45000

0 100000 200000 300000 400000 500000

Cu

m O

il [m

3]

Cum Water [m3]

derivative CumOil/CumWat10%40%70%100%

(moments in well history when

value of diffCumOil/diffCumWat

changed more than 10%)

Possible allocation problem

Missing oil / water / gas reading

Erratic behavior

Sift through

data to

understand

extent of

misreporting

or allocation

Data Quality

Maps to

visualize

extent of

impact of

misreporting

and allocation

Visual inspection and validation

Well to well interaction

Automated Correlation matrix calculations

Resulting mapping of correlation strength

2. Well interaction assessmentAssessing well to well connectivity using multiple trending algorithms

Simplified physics &

Data analysis

algorithms

Uses different methods to analyze trends

in production/ shut-in behavior between a

reference well and its `pair’

3. Geological Attribute Derivations

Vertical and Lateral Heterogeneities

V

V

I

VC

L

VC

L_

sm

oo

th

VC

L-V

CL

_sm

oo

th

Delt

aV

CL

2 *

100

Geological Attributes Derivation: Integrating well interactions and lateral facies variations

Possibility to use neural-networks to map out heterogeneity- both vertical (sand

counting) and lateral (based on log motifs).

4a. Production and Flood Attribute Derivation: Effective Drainage Radius

Focus on finding areas with un-swept oil/ high remaining potential for in-fill drilling

4b. Production and Flood Attribute Derivation: Displacement Efficiency

Well production data

Quantify desired production behavior

cumOil = Factor * cumWater Exponent

log(cumOil) = log(Factor) + Exponent * cumWater

log (cum Water)

log

(cu

m O

il)

• high initial oil production large intercept (Factor)

• low water cut steep slope (Exponent) inverse semi-log linear relationship

Map Slope

Displacement Efficiency

Map out areas where displacement has been inefficient

Parameter range

estimation-Reservoir-Flood

-Fluid-Production

Deterministic match

Using Monte Carlo

generate multiple sets

of parameters

Compare each of the

multiple BL fits with

production history

Multiple solution sets

Probability

Distribution for each

parameter based on

multiple solutions

Generation of

probability

Distributions for

production attributes

Run BL algorithm for

all the sets of

parameters to

generate multiple

solutions

Stochastic approach to

Autohistory match using Buckley Leverett

Select solutions

with Error<

tolerance

Calculation of

production

attributes

-STOIIP

-RF

-UR

-Sweep Efficiency

To be used as

a precursor to dynamic

simulation in

future

Range of possible

OIL vs Time forecast

for this well under

No Further Activity

5. Stochastic Auto-HM Single well multi-parameter fitting using a modified Buckley-Leverett formulation

Process developed to provide insights

on reservoir properties and also allow a

physics-based forecasting of NFA (and

simple ‘what if’ scenarios)

Multiple parameters can be

searched concurrently

Developing and testing

alternative global non-

convex search algorithm

options

Works well with established

waterflood (BL assumptions)

6. Contact Movement Mapping

Methodology

Fluid type log

= an Oil and water Indicator

QC

TVD oil, TVD water logs

Upscale to

allow filtering

well log data

against 3D

model Zones

and/or Blocks

Step 1Used a Resistivity Log

with two different cut

offs as an oil/ water

discriminator (R=3

ohm.m or R=2 ohm.m)

Also used a Sw

discriminator with two

different cut offs (0.3

and 0.4)

Track movement of fluid

contact with production

or time to bring out

variability in vertical

sweep

■Questions and Answers!!