Spe 93253

8/9/2019 Spe 93253

1/10

8/9/2019 Spe 93253

2/10

2 SPE 93253

Net to Gross varies from 5 to 42 % with an average of

17%.

In such shale dominated environment the sand continuity(bars extension, bars vertical and lateral connection) is a major

factor controlling the fluid distribution.

Sand definition

fig. 2. Schematic prograding mouth bar cross section4 main qualities of sands (see fig. 2) are identified on

cores: clean sandstones, laminated sandstones, thin beddedsandstones and mudstones, bioturbated sandstones and

mudstones.

3 electrofacies are defined through cut offs on porosity and

wet clay logs (see fig. 3):

fig. 3. Sand definition through Phie & WetClay logs cut-offs

• A sands: massive sands with porosity higher than 13 p.u.

and shalliness lower than 35%. It corresponds to the clean

sandstone core facies.• B sands: non A sands with porosity higher than 9 p.u. and

shalliness lower than 40%. It is associated to laminated

sandstones

• C sands: non A nor B sands, corresponding to laminated

or bioturbated sands with porosity higher than 5 p.u. and

shalliness lower than 45%. It includes both laminated and

bioturbated sandstones and mudstones core facies.

Laminated and bioturbated C sands are clearly different on

cores (see fig. 4). They are expected to have very different

flow behaviors:

• laminated C sands probably preserve a good horizontal

permeability

• bioturbated C sands are expected to have very poor flow

characteristics

Unfortunately standard logs cannot discriminate laminated

from bioturbated C sands.

fig. 4. Bioturbated vs. Laminated C sands impact onpermeability

Sand distribution

Once sands are defined at the wells, using log

interpretation calibrated with cores, sand is spatially correlated

based on a layering.

Peciko gross reservoir column is about 2000m thick.

Within this interval 7 Units based on correlation with other

existing fields and pressure regimes have been defined. EachUnit is subdivided into layers for a total of 39 layers. Each

layer is itself subdivided into deltaic cycles for a total of 97

deltaic cycles.

Until recently, modeling was done at layer scale and is

now done at deltaic cycle scale to better constrain the reservoir

flow model.

Modeli ng techniques vs. scale of work

Modeling can be done:

• either in 2D, through sand thickness mapping

• or in 3D, typically through object modeling

In the case of Peciko today modeling is done in 2D with a

change of scale from layer to deltaic cycle.The choice of the modeling methodology depends on the keyheterogeneity and the comprehension of the field according to

log spacing.

Layering Scale

With only delineation wells (well spacing from 2 to 4 km),

the reachable vertical correlation resolution did produce layerscale intervals (20 to 100m layer thickness).

This layering scale is now well constrained and validated with

available production wells (spacing 1.4 km). Therefore, it has

been possible to refine this layering to reach a homogeneous

geological scale: deltaic cycle. Within such an interval sand

muds tone

Burrowed

muds tone

to

si l ts tone

Thin

bedded

burrowed

sandstone

an d

muds tone

Laminated

sandstone

clean

sandstone

with few

clay

drappes

Non reservoir B sand A sand C sand

bioturbated

sandstone

and

muds tone muds tone

Burrowed

muds tone

to

si l ts tone

Thin

bedded

burrowed

sandstone

an d

muds tone

Laminated

sandstone

clean

sandstone

with few

clay

drappes

Non reservoir B sand A sand C sand

bioturbated

sandstone

and

muds tone

Phi 9.5%

K 0.05mD

Phi 7.7%

K 0.53mD

Laminated

C sand

bioturbated

C sand

8/9/2019 Spe 93253

3/10

SPE 93253 3

distribution follows a deposit logic related to active

distributary channels.

fig. 5. Layer vs Deltaic Cycle Scale

The layering is based on regional shally events that can be

correlated all over the field and therefore act as globalhorizontal barriers preventing vertical flow. Maximum

shalliness correlated at deltaic cycle scale is not so stable and

may not have a clear regional continuity. Therefore, even if it

is rare, flow barrier between deltaic cycles might be only

partial (see fig. 6).

fig. 6. Layer, deltaic cycles limits and their flow barrierpotential

Globally the deltaic cycle scale is a huge improvement forfluid organization (see 0). At layer scale water gas cycles stack

within an interval, at deltaic cycle scale this is seldom thecase. Deltaic cycle scale is clearly a better scale for fluid

organization understanding and therefore for reservoir

discrimination. At this scale, perched water can be

discriminated from stacked disconnected reservoirs.

fig. 7. Fluid status consistency at Deltaic Cycle Scale

3D modeling alternative

3D object modeling scale is an even thinner resolution. Itconsists in modeling sand bodies’ distribution within an

interval. The proper interval is the deltaic cycle scale as mouth

bars stack downstream from a distributary channel which is

active during the deltaic cycle phase. Lateral distribution is to

be controlled through trend maps (probability of presence ofsand bodies). At layer scale (which stacks several deltaic

cycles) trend maps would be too homogeneous due to

compensation effects between stacked deltaic cycles.

Therefore 3D modeling requires a good understanding of the

deltaic cycle scale which can be reached only through thegeneration of a 2D model at deltaic cycle scale prior to the 3Dmodel construction.

Impact of modeling scale on apparent sand continuity

Let’s consider the sketch from fig. 8. It shows a layer,

made of deltaic cycles within which individual mouth bars are

organized into stacks of mouth bars.

fig. 8. Sketch of layer, deltaic cycles, mouth barsorganization

Different modeling scales give very different images of the

field:

• At layer scale (2D model) sand appears very continuous

as shown by fig. 9

fig. 9. 2D model at layer scale

20 km

L a y e r

1 5 t o 1 2 0 m

D e l t a i c

c y c l e

20 km

L a y e r

1 5 t o 1 2 0 m

D e l t a i c

c y c l e

6 0 m

AB

C 1 c e l l

S a n d M a p p i n g

M e t h o d o l o g yCURRENT MODEL

6 0 m

AB

C 1 c e l l


M e t h o d o l o g yCURRENT MODEL

3a-0

3a-1

3a-2

3c-0

3a-3

D e l t a i c

c y c l e

D e l t a i c

c y c l e

D e l t a i c

c y c l e

D e l t a i c

c y c l e

L a y e r

Flow barrier?Flow barrier?

8/9/2019 Spe 93253

4/10

4 SPE 93253

• At deltaic cycle scale (2D model too), sand appears

disconnected, see fig. 10.

fig. 10. 2D model at deltaic cycle scaleThis model reproduces the stack of mouth bars from fig. 8.

• 3D object modeling can go down to the individual mouth

bar resolution, as shown by fig. 11 (or even thinner by

modeling the internal organization of each mouth bar).

fig. 11. 3D Object model controlled by deltaic cycle layering

With today understanding of Peciko connectivity, andconsidering the fluid is gas, one expects to find major flow

barriers between stacks of mouth bars. Therefore deltaic cyclescale is the scale of the anticipated major heterogeneity.

Furthermore today geological understanding would not allow

constraining individual mouth bars distribution well enough to

bring key reliable information regarding flow barrierdistribution.

NetSand mapping

This process, both at layer scale and deltaic cycle scale, is

a very interpretative work mainly done by hand in order toinput the field geological understanding.

fig. 12. Example of a NetSand Map at Layer Scale

If sand thickness maps appear very continuous at layerscale, mapping at deltaic cycle scale reveals that layers are

“hiding” both a vertical and a lateral heterogeneity in term of

sand continuity.

fig. 13. Examples of NetSand Maps at Deltaic Cycle Scale

fig. 13 shows two deltaic cycle maps that are very different

in term of sand distribution.

The difference between these two maps is explained by the

following key values: only 45% of the wells are sand bearingin deltaic cycle 1g-0 and the average Net To Gross (NTG) in

these sand bearing wells is 10%. On the other hand, for deltaic

cycle 2e-1, 96% of the wells are gas bearing and their average

NTG is 34%. Basically, such differences can highly impact

production.

Fluid identification & distributionTo be able to go from NetSand to NetPay (i.e. from Sand to

Gas Bearing Sand) one must be able to identify and distribute

fluids within the reservoirs. In Peciko gas field, only 2 fluids

are discriminated: water and gas.

Identification techniquesA series of techniques are available to identify fluid within

the formation.

Log in terpretation

Logs are indirect measurements of the formation content

(rock and fluid). By combining different logs information andthrough interpretation one can perform a fluid status.

In the case of Peciko, fluid interpretation leads to 4

possible statuses:

• Water

• Gas

• Possible Gas: interpretation is not clear enough and an

uncertainty remains.• Water Rise: fluid is interpreted as water but initially was

gas bearing. Water rise is the result of depletion from

surrounding production wells. Today in the case of Peciko

Water Rise is not a major issue but this is expected tochange in the future as production and therefore depletion

increase. Geological model considers fluid status prior to

production start (i.e. before water rise) allowing reservoir

engineers to history match the model and simulate the

water rise. Therefore in the geological model water rise is

interpreted as Gas.

Peciko Fluid status analysis is based on the following log

interpretation rules (fig. 14):

1 cell


M e t h o d o l o g y

1 cell


M e t h o d o l o g y

1 or more cells

O b j e c t

M o d e l i n g

1 or more cells

O b j e c t

M o d e l i n g

Layer 3a

NetSand ABC

Deltaic Cycle 1g-0 Deltaic Cycle 2e-1

Well No Sand

Well with Sand

8/9/2019 Spe 93253

5/10

SPE 93253 5

fig. 14. Fluid identification (a) gas interval; (b) water interval

Resistivity logs• A synthetic resistivity log R0 is created to discriminate

gas bearing reservoirs from water bearing reservoirs. R0is computed based on 100% water saturation in reservoirs.

• High values on Rt (measuring the uninvaded zone)

indicate hydrocarbon presence.

• Comparison between Rt and R0 gives a strong indication

of the presence or absence of hydrocarbons. In thick gas

bearing reservoir, R0 is opposite to Rt while in thick

water bearing reservoirs, R0 is superimposed or parallel to

Rt. Ambiguity for reservoir fluid determination appears in

thin sand reservoir context. Fluid change cannot be

detected by the logs resulting in a major uncertainty on

fluid identification.• Comparison between Rxo (measuring the invaded zone)

and Rt helps confirm fluid identification. High Rxo (wells

drilled with oil based mud) compared to Rt gives water

bearing reservoirs indication. On the other hand, Rxo and

Rt are superimposed in gas bearing reservoirs.

Gas While Drilling

• A high total gas reading (available on all wells) can be an

indication of hydrocarbon presence.

• A full analysis of GWD data enables to better validate the

fluid interpretation done by conventional logs. For the

time being, only few wells have been processed.

Interpretation

Different reservoir petrophysical properties result in

different log responses for the same fluid status. On the other

hand, the same resistivity response can be associated to

different fluid status as it is sensitive to other parameters.

Fluid status analysis is an interpretation which requires

deconvolving imbricated parameters (reservoir quality, fluid

status and saturation). This interpretation is particularly

difficult for thin reservoirs.

Some anomalies on logs behavior (see fig. 15) are found in

deeper stratigraphic unit. Log responses that would be

confidently interpreted as Proven Gas in upper units were

proven water. This is possibly due to lower salinity compared

to the upper stratigraphic units.

fig. 15. Log anomaly on deeper layer. Clean GR with Rtopposite to R0, Rt superimposed with Rxo, slightly gasshow but fluid analysis proves water

Flu id status correlation

To reduce uncertainty associated to fluid status a simple

technique consists in validating the well interpretation through

correlation with neighboring wells taking into account relative

structural position. The reliability of this work depends on thereliability of the layering correlation.

Inconsistent fluid correlation within a layer between 2

wells may result in fluid status adjustment, layering revision,

or reveal lateral distribution of independent reservoirs

Pressure measurement & optical f lu id analysis

The best way to locally reduce uncertainty on fluid status

is to be sure of the fluid the formation contains!

Logging contractors have tools that pump locally from the

formation. These tools allow to:

• Measure the formation pressure

• Perform fluid analysis through optical absorption

• Take fluid samples.

Only a limited number of fluid samples can be taken.

However, these samples are not mandatory to discriminate gas

from water as optical fluid analysis is sufficient for that.

Optical fluid analysis is a pinpoint measurement. One must

first define where to take measurements.

These measurement points are defined based on rush

interpretation of the logs before placing the tool in front of the

formation.

During measurement tool stands still. These measurements

are time consuming and may fail for several reasons such as

probe actual location (relatively to thin bed reservoir), tightsand, stuck tool. For key points several attempts can be done.

Measurements can be done either while tool is traveling down

or up. If measurement fails when going down and result in a

lack of key information, additional measurements in the areacan be planned when going up.

Optical Fluid Analysis is a way to reduce uncertainty on

fluid status. Therefore measurements are carefully chosen in

order to validate a full zone. Establishing an acquisition

strategy to target ambiguous response in order to deconvolve

fluid from lithology effects is key to optimize the benefit ofthese local and time consuming measurements (see program

example on fig. 16).

OFA: W

8/9/2019 Spe 93253

6/10

6 SPE 93253

fig. 16. Optical Fluid Analysis measurement program: pressureand fluid analysis are performed on measurement #1. Ifpressures are done systematically on measurements #2,#3 & #4, fluid analysis is conditioned to previous pointresult.

The detection of water rise (see fig. 17) is complex and

corresponds to confusing log responses:

• Rt and R0 are not superimposed but are not anti-

correlated.

• Rxo > Rt (movable water displaced by oil based mud

filtrate).

In such a case, gas show is not a criterion as even initial water

bearing zones can contain some trapped gas.

Therefore lateral correlation with surrounding producing

wells, good reservoir quality (usually A or B sands) and

pressure data showing significant reservoir depletion are key

qualitative criteria to interpret water rise.

fig. 17. Water rise interval

Fluid distribution issue

As explained earlier the elementary sand organization is

the mouth bar. Mouth bars have limited extension andtherefore the way mouth bars are distributed within a

stratigraphical interval controls sand continuity. This sand

continuity is a major issue as Peciko field develops in a shale

dominated environment.

Well spacing, compared to sand bodies’ extension does not

generally allow correlating sand bodies.

To assess continuity of sand and therefore connectivity

within the reservoir several indirect information is used:

• Consistent fluid status

• Gas Water Contact

• Pressure data

Fl uid distribution compatibility

Sand correlation reliability can be re-inforced when fluidsare consistent between wells and when Gas Water Contacts

are compatible. However compatible Gas Water Contact is a

relative criterion. Usually this contact is not precisely defined

but is only limited by a Gas Down To and/or a Water Up To

(see fig. 18 & fig. 19). In Peciko shale dominated environment

the uncertainty interval between Gas Down To and Water UpTo can be quite important.

fig. 18. Gas Water Contact vs. Gas Down To / Water Up To

fig. 19. Single fluid Deltaic cycle intervals

Pressure data

Within a connected reservoir initial pressures are at

equilibrium. After production starts things can be different.

Away from producing wells a pressure trend appears. When a

new well goes through already produced reservoirs, pressure

measurements show depletions. The degree of depletion

depends on parameters such as: for how long the reservoir has

been produced, reservoir size, reservoir quality (connectivity),

and distance to producing wells. Therefore, today on Peciko,

depending on when well was drilled, pressure can either be:

• Initial pressure: i.e. pressure data from well drilled before

production start. This category can be extended to layers

or reservoirs that are not yet perforated. For example in

Peciko upper reservoirs are not perforated yet. Therefore

pressure measurements in these layers, even from recent

wells, are still initial pressure data

• Depleted pressure: i.e. pressure data taken after

production start: such pressure measurements potentially

ProvenGas

Water GWC

D e l t a i c C y c l e

ProvenGas

Water GWC


Proven Gas

Water

Possible GasWater UpTo

GasDown To


Proven Gas

Water

Possible GasWater UpTo

GasDown To


#1PT - FA

#2PT – FA

if #1tight/water

#3PT – FAIf #2 gas

#4PT – FA

if #3tight/water

OFA: W

OFA: W

8/9/2019 Spe 93253

7/10

SPE 93253 7

show depletions, the range of depletion is highly variable

as explained here-above.

The main differences between these two data are that in orderto establish correlation between wells:

• Initial pressures can be used quantitatively while Depleted

pressures are used qualitatively (as geological model

models initial status). A depleted value can only belong to

a reservoir which initial pressure was higher than themeasured value (there is no injection in Peciko field!)

• On the other hand compatible initial pressure does not

prove wells are connected (especially when pressure is

hydrostatic); while depletion in a well proves that the

reservoir is already being produced.

Therefore, when considering pressure, one must be aware of

the kind of pressure one is dealing with to prevent mixing

them and possibly reach wrong conclusion.

Furthermore these 2 kinds of data do not relate to the same

connection. For initial pressure, equilibrium within a reservoir

has been reached through geological time; very poor

connectivity is required to reach this equilibrium. Such

reservoirs are called Geological Pressure Units (GPU). On

the other hand depletion is related to production start. It has

occurred over a short period (a few months or years); the time

frame is not the geological time but the reservoir production

time. The reservoirs identified through depletion information

reveal a much better connectivity; they are called ReservoirPressure Units (RPU).

GPU identification is a geological concept while RPU

definition comes from reservoir. A GPU is usually made of

several RPUs. RPUs are limited by transmissibility reduction

which act as barrier at reservoir production time scale but werenot strong enough to be barriers at geological time scale.

Classically a geological model would focus on GPUs but

after production starts the geological model is the input to the

reservoir model to be history matched and should help

constrain flows. Transmissibility reductions generating the

RPUs are linked to geology through poor facies quality zones.One key question controlling the required geological model

resolution is what these barriers are and how they correlate,

i.e.: what is the main heterogeneity controlling flow?

Sand Mapping Scale vs. Geological Pressure Units

It is clear that layer scale generates a model too coarse to provide lateral flow barrier (see fig. 9 and fig. 12).

Even if sand thickness varies laterally, layer scale NetSand

maps are continuous and sand thickness reduction does not

necessarily correspond to reduction in sand quality that could

be interpreted as flow barriers.When NetSand maps are generated at deltaic cycle scale it

might be different. Depending on the cycles, the resulting

maps might be still continuous or disconnected revealing the

major Geological Pressure Units, as shown by fig. 13.

From NetSand to NetPayAt deltaic cycle scale NetSand maps can only show the

major GPU extensions revealed by shally zones over the

whole interval and confirmed by initial pressure analysis.

Pay is the gas bearing sand. NetPay thickness is therefore

less or equal to NetSand thickness and NetPay extension is at

most equal to NetSand extension. What limits the NetPay

extension is therefore:

• The Gas Down To

• The Sand discontinuities either seen on NetSand maps or

not if it corresponds to a thin or to a non vertical flow

barrier that separates 2 GPUs not revealed by NetSand

maps (see fig. 20).

fig. 20. GPU limits visible on NetSand maps or not

NetPay mapping challenge is to reveal all GPUs either based

on sand discontinuity, on fluid status changes or on Pressuretrends.

Gas Water Contact I ssue

When analyzing fluid status, no clear flat gas water contact(GWC) can be identified (see fig. 21) as fluid status includes:

• Tilted aquifer; due to a hydrodynamism powered by water

expulsion from shales.

• Gas Down To significantly varying between Geological

Pressure Units

• Trapped or Perched Water related to discontinuous sand

deposit.

G D T

c o n s i s

t e n t

w i t h

s t r u c

t u r e

G D T

c o n s i s t e n t

w i t h

s t r u c

t u r e

G D T

c r o s

s

t h e s t r u c t u r e

G D T

c r o s

s

t h e s t r u c t u r e

Layer 1h

Hydrodynamism

shifts Gas Pool

towards NW flank

of the structure?

Layer 3g

Water bearing

Gas bearing

Wells

fig. 21. Regional Aquifer challenges: multiple GPUs,

Hydrodynamism…

This issue together with the 2D mapping approach led to the

use of Filling Ratio to model fluid status.At the well:

NetSandNetPayioFillingRat =

Filling Ratio (FR) is then mapped to deduce NetPay map:

io FillingRat NetSand NetPay ×=

Fi lli ng Ratio Mapping

Filling Ratio represents the proportion of sand that is gas

bearing regardless of the relative fluid location. It allows

mixing different fluid distributions (stacked GPUs when

layering does not allow separating them, perched water…).

Filling Ratio mapping, as well as NetSand mapping is ahighly interpretative work.

Barrier visible on NetSand MapBarrier non visible on NetSand Map

Barrier visible on NetSand MapBarrier non visible on NetSand Map

8/9/2019 Spe 93253

8/10

8 SPE 93253

Filling Ratio limit

The mapping process starts with the identification of the

GPUs and the manual drawing of the FR limit. To do soPressure data trends are identified on Water Head

(overpressure) plots and spatially validated.

fig. 22. Water Head Plot

Pressure Trends (see fig. 22) together with shally or water

bearing wells help define GPUs and their extension. Next,

their limit is drawn on structural maps using fluid depth

information at the wells (GWC, GDT, WUT).

fig. 23. GPUs limits, example of layer 3f (1P map)

On layer 3f (fig. 23), 3 main GPUs are identified. North

and Central GPUs are disconnected based on Pressure trend as

shown by Water Head Pressure plot. South and Central GPUs

are disconnected due to fluid status as a series of water bearing

wells spatially separate the two GPUs.

Filling Ratio Interpolation

The identified limit is a mix of NetSand extension limit,

flow barriers, fluid contact. These different zones of the limit

do not impact the Filling Ratio in the same way:

• Fluid contact areas are used as a 0 limit• NetSand extension does not directly affect the Filling

Ratio limit. As NetSand map is reaching 0 toward this

limit, by definition NetPay will reach 0 even if Filling

Ratio does tends toward 0. Forcing the Filling Ratio in

these zones would be equivalent to fill the limit of

NetSand with water and therefore apply a double

reduction in NetPay. In this area there is no Water

Contact, gas proportion may decrease but this would be

shown by Saturations and not by water contact.

• Flow barriers may either be modeled as:

o a 0 limit in the Filling Ratio map (this would

typically be a 1P approach as it is conservativethrough the introduction of water zones in between

wells – as shown on fig. 23 for layer 3f 1P map)

o or a flow barrier in the reservoir simulator (this

approach which does not downgrade Pay vs. Sand

would be a 2P or 3P methodology – as shown on fig.

24 for layer 3f 2P map).

fig. 24. GPUs limits, Layer 3f, 2P Map

The interpolation is done within this limit using Fluid

contact as “0 limit” and a Maximum value of 100% for Filling

Ratio map.

Saturation Estimate and Modeling

Saturation measurement

Saturation is computed from Archie law using porosity,

Vshale and resistivity logs.

G a s T r e

n d

W a t e r T

r e n d

P e r c h e d

W a t e r ?

P e r c h e d

W a t e r ?

N o r t h G

P U

N o r t h G

P U H y d

r o d y n a

m i s m ?

H y d r o d

y n a m i s

m ?

D

A

B

E

North GPUNorth GPU

FA

B

B

C

C

D

E

EE

E

FF

C

-40 -20 0 20 40

Water Head

3000

2900

2800

2700

D e p t h

WaterHead Layer 2a-a

Initial Gas

Initial Possible Gas

Initial Water

Water bearing

Gas bearing

100 200 300 400 500

Water Head

3500

3400

3300

3200

D e p t h

WaterHead Layer 3f

Initial Gas

Initial Possible Gas

Initial Water

N o r t h

G P U

N o r t h

G P U

C e n t r a l G P U

C e n t r a l G P U

Possible Gas

related to

South GPU?

A

BB

CC

DD

JJKK

II

FF EE

GG

HH

S o u t h G P U

S o u t h G P U

A

BB

CC

DD

EE FF

GG HH

IIII

JJ

KK

KK

Water bearing

Gas bearing

S o u t h G P U

S o u t h G P U

N o r t h G P U

N o r t h G P U

C e n t r a l G P U

C e n t r a l G P U

F l o w B a r r i e r

Water bearing

Gas bearing

8/9/2019 Spe 93253

9/10

SPE 93253 9

Water Resistivity computation depends on salinity. Peciko

gas column is over 2000m. Along this column salinity varies.

Specific zones, such as fresh water sands, are identified. Waterresistivity depends on salinity and temperature. Uncertainty on

salinity is a key parameter impacting saturation estimate.

Saturation modeling

Gas saturation modeling is one more fluid relatedchallenge in such a complex environment.

In term of modeling, a grid cell does not have the same

significance when considering a 3D model or a 2D model.

In a 3D model a cell is a subdivision of the layer or deltaic

cycle. The stack of cells subdividing the layer allows to model

vertical variability. Considering fluid, deeper cells might be

water bearing, upper cells gas bearing and toward uppermost

cells gas saturation might increase modeling the transition

zone.

In a 2D model it is very different as one cell represents the

whole layer thickness. Therefore parameters such as porosity

or saturation are average parameters hiding the variability

within the column.The main issue for 2D modeling saturation is the

management of the transition zone and the predictivity of the

resulting map.

Transiti on zone issue

A classical Gas saturation modeling methodology consists

in defining a gas zone, a transition zone and a water zone. In

the water zone gas saturation is equal to 0. The gas zone is the

area where saturation is not affected by the aquifer influence.

The transition zone is the area in between where saturation isdecreasing toward the GWC.

In a 2D model, defining a proper transition zone is a

challenge. In fact at this scale one does not know verticallywhere the sand is located within the layer. NetSand maps

provide thickness information, NetPay gives information

regarding the amount of sand above the water contact but onedoes not know if this sand is directly in contact with the water

or not. The only transition zone that can be defined represents

a maximum transition zone that is directly related to the layer

gross thickness and the gas down to depth as shown in fig. 25.

fig. 25. Gross Thickness based Transition Zone

However the sand body in this example (fig. 25) is not

representative of the typical sand organization. Compared to

classical sand body it is thick and it includes a GWC. Standard

cases present much thinner sand bodies and no GWC is

identified. Sand bodies are mono fluid either gas or water. Gas

Water Contact would be between a Gas Down To defined bythe bottom of a sand body and the Water Up To defined by the

top of a deeper sand body (sand bodies with possible gas may

exist in between) as shown in fig. 26.

fig. 26. Transition zone definition in a standard well

Now when looking at the Saturation logs (see fig. 27),

saturation behavior is very different between well “A” shown

in fig. 25 and well “B” used for fig. 26:

fig. 27. Transition zone analysis:Sg vs. Sand quality above contact

• On well “A”, GWC is identified and corresponds to a

thick sand body. A clear Sg trend is visible and this Sg

trend is opposite to the sand quality trend. Even if sand

quality decreases towards the upper part of the layer the

saturation increases. This Sg evolution can therefore be

quite confidently interpreted as a transition zone.

• On well “B”, only a Gas Down To and a Water Up To are

identified. This well corresponds to a more typical sand

organization and proportion for Peciko field (lower NTG

and thinner sand bodies compared to well “A”). In this

case no transition zone can be identified. Saturation trend

is basically following the sand quality.

Furthermore, when analyzing spatial organization of average

saturation per layer at the wells related to Filling Ratio limit(i.e. Gas Down To) no consistent saturation decreasing trend is

observed toward this limit.

This tends to show that no significant transition zone develops

on Peciko. Therefore Gas Saturation mapping within the Gas

bearing zone is done without forcing a transition zone nor

setting the Gas bearing limit as “0 Sg”.

Water ri se issue

On a recent well, water rise has been identified (see fig.

28).

TRANSITION ZONEAssociated to GWC

NO TRANSITION ZONE

Sand

qualitySg

Sand

qualitySgWell B Well A

TRANSITION ZONEAssociated to GWC

NO TRANSITION ZONE

Sand

qualitySg

Sand

qualitySgWell B Well A

Gas zone

Well A

L A Y E R

Water

zone

GWC

Apparent transition zoneat layer scale

Gas zone

Well B Water

zone

Apparent transition zone at layer scale

L A Y E R

GWC

8/9/2019 Spe 93253

10/10

10 SPE 93253

fig. 28. Water rise issue, example of a recent well

As explained earlier the geological model represents the

fluid status prior to production start. Basically it means that:

• fluid is gas or water,

• water rise is therefore set to gas,

• saturation is initial saturation.

If it is simple to consider the Water Rise status as Gas,saturation is another study. Gas saturation measured in Water

Rise zones is saturation at logging time. Thus, measured

saturation is not representative of the initial saturation. These

gas saturations are therefore pessimistic and a minimum for

initial saturation.Today this is not an issue as water rise are very rare.

However this could become a major one as production life

expends, well spacing decreases and drilling includes flank

wells which are potentially more sensitive to water rise.

Global modeling workflowThe Sand and Pay interpretation techniques shown here above

are part of a global modeling workflow (see fig. 29). Thisglobal workflow is as follow:

fig. 29. Global 2D Modeling Workflow

1. NetSand mapping

2. Filling Ratio mapping

a. Filling Ratio limit

b. Filling Ratio interpolation

3. NetPay map is then computed:

io FillingRat NetSand NetPay ×=

4. Porosity mapping (using NetSand limit as a 5 p.u. limit)

5. Gas Pore Thickness map is then computed:

PorosityNetPaycknessGasPoreThi ×=

6. Gas Saturation mapping (Sg)

Initial Gas In Place down hole condition is then computed:

SgcknessGasPoreThiIGIPdhc ×=

ConclusionUpdating and improving the geological model of a giant field

in such a complex shale dominated deltaic environment is a

challenge and requires a pragmatic approach to meet the

development deadlines. Peciko is modeled with a 2D mapping

methodology with increasing vertical resolution between

generations. These models are clearly flow driven and

constructed in the frame of an integrated geology reservoir

team.

Before production starts and based on exploration anddelineation wells, a 2D model at layer scale was the optimal

achievable vertical resolution.

The addition of production wells’ geological interpretation

together with production data improved the geologicalunderstanding of the field. It revealed weaknesses of the layer

scale model: specifically inadequacy between vertical scaleand fluid organization. This led to initiate a model at deltaic

cycle scale. This model is expected to provide reservoir model

with geologically constrained flow barriers.

Further down the road of the field life modeling might be

done in 3D. This would require a highly geologically

controlled object modeling approach in the frame of anintegrated uncertainty study. This is not feasible today by lack

of geological understanding at infra deltaic cycle scale to

properly constrain the stochastic distribution of mouth bars

and related flow barriers.

AcknowledgmentsThe authors would like to thank TOTAL, TOTAL E&P

INDONESIE, BP-MIGAS and INPEX for their permission to

publish this paper.

Special thanks to all Peciko asset members whose daily

work made Peciko models possible, and therefore this paper!

The authors also want to thank the petrophysics, the

reservoir transverse teams and the exploration team from

TOTAL E&P INDONESIE in Balikpapan for their valuable

collaboration.

NetSand (Sand thickness)

NetPay (Gas Bearing Sand)

Filling Ratio

(% of Gas bearing Sand)

Phie

Gas Bearing Pore Volume

HPM Gas In Place Down Hole Condition

Sg (Gas Saturation)

IGIP Initial Gas In Place Surface Condition

Bg (Gas Compression Factor)

modeledmodeled

derivedderived

G e o l o g i c a l C

o n c e p t & F l u i d i n f o r m

a t i o n

G e o l o g i c a l C

o n c e p t & F l u i d i n f o r m

a t i o n

WATER

Spe 93253

Documents

Transcript of Spe 93253