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8/10/2019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate, Case Study Singa Field, South Sumatra Basin http://slidepdf.com/reader/full/ipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 1/8  IPA11-G-033 PROCEEDING, INDONESIAN PETROLEUM ASSOCIATION Thirty Five Annual Convention & Exhibition, May 2011 RESERVOIR QUALITY AND POROSITY PREDICTION IN CARBONATE USING SEISMIC INVERSION AND ATTRIBUTES CASE STUDY: SINGA FIELD, SOUTH SUMATRA BASIN Y. Yanto* T. Iswachyono* M. Arief MMZ.* J. J. Wood* ABSTRACT Singa field has proven gas from carbonates of the Lower Miocene Baturaja Formation. The field is over 9 km long and 2.5 km wide and is located in Lematang Block, the central part of the Lematang Deep between the southern trend of Pendopo anticlinorium and Muara Enim anticlinorium. One exploration well and one delineation well have already been drilled and the plan is to drill 2 horizontal development wells. The field has a maximum gas reservoir thickness of 282 feet and contains an average of 30% CO 2  and 110 ppm H 2 S. Significant porosity (> 15%) has been observed in the upper part of the Baturaja Formation (BRF), 282 feet thick in the S1 well and 125 feet thick in the S2 well. Dominant porosity in thin sections is mouldic and micro vuggy, with some fractures. The lower BRF is carbonate mudstone to wackstone, with minor porosity (~8%) interpreted as a platform carbonate. The upper BRF is limestone with skeletal wackstone and packstone containing algal- foram-mollusc and a coral-algal-foram-mollusc intraclast floatstone, interpreted as lagoonal and  back reef facies. There are two concerns regarding this deep carbonate field: reservoir quality and porosity distribution. Because of the high CO 2  and H 2 S content within the gas target, the reservoir has to be large enough to be developed economically. Seismic inversion and attributes techniques are applied to characterize the reservoir and to quantify reservoir distribution. The objective is to predict reservoir quality and porosity distribution in order to generate an updated geological model for reservoir modelling and to optimize new well locations. * PT. Medco E&P Indonesia Cross-plotting acoustic impedance (AI) vs. porosity from well logs shows that the gas and brine values are populated in separate trends, with a good relationship (minimal scatter). This suggests that  porosity can be estimated from AI by a transform equation. Integrated with the AI method, seismic amplitude-envelope (AE) attribute is used to aid facies interpretation and to identify the carbonate depositional setting. Integration of AI and AE is useful for predicting reservoir quality and porosity distribution in this field. As a result of using updated reservoir modelling based on the seismic inversion and seismic attribute results, the field development  plan can be revised to include the drilling of two development wells. INTRODUCTION The Singa field is covered by 13 x 4.5 km 3D seismic survey of good quality. Seismic shows the carbonate build-up, which is illustrated by “on-lap” to the reservoir carbonate. The field is a north-south trending structure with steeply-dipping flanks containing the hydrocarbon-bearing carbonate limestone reservoir. The field has proven gas from carbonates of the Baturaja Formation (BRF). The reservoir in the Singa field is a large reefal  buildup of the BRF. The reef forms a large 4-way dip closure at the top of the BRF. The reef is developed over an earlier-formed high in the centre of the basin. The vertical relief has a maximum thickness of 900 feet. Regionally, the carbonate limestone is calcareous shale, which forms a basin- wide mappable seismic reflector. However, where deposition took place over a basement high, the shallower water and high-energy conditions allowed the growth of corals and calcareous algae, forming a series of coral reefs in the Singa area. The carbonate reservoir appears to be a reefal build-up with

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Page 1: Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate, Case Study Singa Field, South Sumatra Basin

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 18

IPA11-G-033

PROCEEDING INDONESIAN PETROLEUM ASSOCIATION

Thirty Five Annual Convention amp Exhibition May 2011

RESERVOIR QUALITY AND POROSITY PREDICTION IN CARBONATE

USING SEISMIC INVERSION AND ATTRIBUTES

CASE STUDY SINGA FIELD SOUTH SUMATRA BASIN

Y Yanto

T Iswachyono

M Arief MMZ

J J Wood

ABSTRACT

Singa field has proven gas from carbonates of the

Lower Miocene Baturaja Formation The field is

over 9 km long and 25 km wide and is located in

Lematang Block the central part of the Lematang

Deep between the southern trend of Pendopo

anticlinorium and Muara Enim anticlinorium One

exploration well and one delineation well have

already been drilled and the plan is to drill 2

horizontal development wells The field has a

maximum gas reservoir thickness of 282 feet and

contains an average of 30 CO2 and 110 ppm H2S

Significant porosity (gt 15) has been observed inthe upper part of the Baturaja Formation (BRF) 282

feet thick in the S1 well and 125 feet thick in the S2

well Dominant porosity in thin sections is mouldic

and micro vuggy with some fractures The lower

BRF is carbonate mudstone to wackstone with

minor porosity (~8) interpreted as a platform

carbonate The upper BRF is limestone with

skeletal wackstone and packstone containing algal-

foram-mollusc and a coral-algal-foram-mollusc

intraclast floatstone interpreted as lagoonal and

back reef facies

There are two concerns regarding this deep

carbonate field reservoir quality and porosity

distribution Because of the high CO2 and H2S

content within the gas target the reservoir has to be

large enough to be developed economically Seismic

inversion and attributes techniques are applied to

characterize the reservoir and to quantify reservoir

distribution The objective is to predict reservoir

quality and porosity distribution in order to generate

an updated geological model for reservoir modelling

and to optimize new well locations

PT Medco EampP Indonesia

Cross-plotting acoustic impedance (AI) vs porosity

from well logs shows that the gas and brine values

are populated in separate trends with a good

relationship (minimal scatter) This suggests that

porosity can be estimated from AI by a transform

equation Integrated with the AI method seismic

amplitude-envelope (AE) attribute is used to aid

facies interpretation and to identify the carbonate

depositional setting Integration of AI and AE is

useful for predicting reservoir quality and porosity

distribution in this field As a result of using updated

reservoir modelling based on the seismic inversion

and seismic attribute results the field development

plan can be revised to include the drilling of two

development wells

INTRODUCTION

The Singa field is covered by 13 x 45 km 3D

seismic survey of good quality Seismic shows the

carbonate build-up which is illustrated by ldquoon-laprdquo

to the reservoir carbonate The field is a north-south

trending structure with steeply-dipping flanks

containing the hydrocarbon-bearing carbonate

limestone reservoir The field has proven gas from

carbonates of the Baturaja Formation (BRF)

The reservoir in the Singa field is a large reefal

buildup of the BRF The reef forms a large 4-way

dip closure at the top of the BRF The reef is

developed over an earlier-formed high in the centre

of the basin The vertical relief has a maximum

thickness of 900 feet Regionally the carbonate

limestone is calcareous shale which forms a basin-

wide mappable seismic reflector However where

deposition took place over a basement high the

shallower water and high-energy conditions allowed

the growth of corals and calcareous algae forming aseries of coral reefs in the Singa area The carbonate

reservoir appears to be a reefal build-up with

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 28

porosity development in the upper part of the

carbonate

REGIONAL GEOLOGY

Singa field is located in Lematang Block South

Sumatra Basin approximately 30 km west of the

city of Prabumulih (Figure 1) Based on its present

day relative position to the subduction zone it is

categorized as a back-arc basin filled by Tertiary

sediments The South Sumatra Basin can be divided

into three different sub-basins namely the Jambi

Depression Central Palembang Deep and Lematang

Deep Singa field is located within Lematang Deep

(Figure 2)

Structural systems for the South Sumatra Basin

resulted from at least three tectonic styles ie thePre-Tertiary basement inheritance structure the

Eocene-Oligocene rifting mechanism and the most

recently the PliocenendashPleistocene compressional

regime (Figure 2) Lematang Deep is bounded by

the Lematang Fault to the north Beyond the fault is

the Limau Anticlinorium The Musi Platform is the

boundary to the west while the Kuang Platform is

the boundary to the east The Southern boundary for

the Lematang Deep is marked by the Garba

Mountain Lematang Deep itself consists of a series

of horsts and grabens as a product of Eocene-

Oligocene tectonics

Stratigraphy of the South Sumatra Basin began in

the Eocene epoch marked by the deposition of

volcanic rich material pre-rift sediments (Kamal and

Argakoesoemah 2005) (Figure 3) When the rifting

took place syn-rift sediments of the Lemat Fm

were deposited The Lemat Fm is proximal facies

alluvial fan and fan delta facies for the coarser grain

and lacustrine and marine shale for the finer grain

Subsequently the Lower and Upper Talangakar Fm

was deposited This Talangakar group is fluvio-

deltaic facies sediment In Miocene time the rate ofsubsidence decreased and contemporaneously a

global sea level rise took place known as the

Miocene transgression This transgression allowed

most of the paleo-high (horst) to become submerged

below sea level Where the environment was

suitable a carbonate factory formed and Baturaja

Fm was deposited Elsewhere the Telisa Fm was

deposited Singa field reservoir is a carbonate build-

up that formed during this process The relationship

between Telisa Fm and BRF in some places is

interfingering and as the transgression reached its

maximum the accommodation space for carbonate

increased beyond the carbonate factory productivity

As a result most of the carbonate factory failed to

keep up and no significant carbonate build-up

occurred post the Miocene Transgression maxima

In this phase only the Telisa Fm marine shale was

deposited Regression followed the transgression

and the uplift of Barisan Mountain range in the

south west of South Sumatra Basin provided a new

sediment provenance from which the Palembang

Group is the product This group consists of the

Lower Palembang Formation with transitional

sediment facies The Middle Palembang Fm has a

distinctive coal-bearing section and the Upper

Palembang is marked by its volcanic material

content During the Plio-Pleistocene tectonics some

of the formation was uplifted and eroded notably in

the Limau anticlinorium A series of Quaternary

volcanic activities in South Sumatra produced

volcanic material that was deposited over areas in

the South Sumatra Basin

Singa field is interpreted as an isolated carbonate

platform Palaeo-wind direction directly influences

the distribution of high energy wave agitated

carbonate facies within carbonate platforms The

windward side of the carbonate platform usually has

a steeper slope than compared with the leeward side

Carbonate texture in the windward margin is less

muddy The texture can range from packestone to

boundstone providing good initial porosity whilst

the leeward side usually consists of mudstone to

wackestone facies with fair to poor initial porosityThe windward side of the Singa field is interpreted

as the west side while the leeward is the east side of

Singa field

METHODOLOGY

For this study we used two wells and post-stack time

migrated 3D seismic which has relative preserved

amplitudes with low vertical seismic resolution and

15 Hz dominant frequency at the level of the BRF

This is due to the deep carbonate target at 12000 ft

subsurface An average velocity in the BRF of11155 ftsec yields a vertical seismic resolution of

approximately 186 feet equivalent to 26

milliseconds TWT in seismic data

The accuracy of the horizon interpretation of the

carbonate is crucial for seismic inversion analysis

Picking the top BRF and internal reflection

character of the carbonate in conventional

reflectivity data is difficult because of the chaotic

and dimming seismic amplitude effects related to

the low impedance of high porosity facies changes

and gas-bearing in carbonates To overcome this

problem a high frequency enhancement technique

(Young and Wild 2004) relative impedance

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 38

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 48

gasporous reservoir with associated low AI values

orange to green corresponds to medium porous

reservoir while brown to black corresponds to tight

reservoir with associated high AI values

We must use an integrated analysis between the

facies analysis (AE) and porosity maps to interpret

reservoir quality in the carbonate reservoir for

minimizing risk in porosity prediction from AI

Figure 8 shows reservoir quality map in upper zone

of carbonate reservoir An understanding of this

analysis is very helpful to support the development

team in its reservoir modelling so that the best areas

for new well locations can be defined

CONCLUSIONS

Reservoir quality and porosity prediction in

carbonates is very difficult In our study an

integration of AI and AE proved useful for

predicting reservoir quality and porosity distribution

in the Singa field Understanding the porosity

distribution as defined by reservoir quality analysis

can support the development team in its reservoir

modelling and optimize new well locations

We successfully drilled one horizontal well (S3)

based on our reservoir modelling Post-drill analysisshows that the well has high porosity carbonate and

produces 30 mmcfpd gases The next plan is to drill

a horizontal well

ACKNOWLEDGMENTS

We would like to thank the management of PT

Medco EampP Indonesia and DITJEN MIGAS for

their permission to publish this paper We also

express our gratitude to our colleagues in the

Exploration Division for their suggestions and

valuable comments during the preparation of this

paper

REFERENCES

Ginger D and Fielding K 2005 The Petroleum

Systems and Future Potential of the South Sumatra

Basin IPA August 2005

Kamal A Argakoesoemah RMI Solichin 2005

A Proposed Basin Scale Lithostratigraphy for SouthSumatra Basin Indonesian Association of

Geologists Stratigraphy of Sumatra Workshop 14 p

Lancester A and Whitcombe D 2000 Fast-track

lsquoColoredrsquo Inversion Presented at SEG 2000

Meeting Expanded Abstracts

Yanto Y and Febriwan T 2008 AVO-Inversion

for Reservoir Characterization of Baturaja

Carbonate Gunung Kembang Field South Sumatra

Basin IPA May 2008

Young P and Wild A 2004 Cosmetic

Enhancement of Seismic Data by Loop

Reconvolution PETEX Conference

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 58

Figure 1 - The Singa field is located approximately 30 km west of the city of Prabumulih

Figure 2 - Structural trend of South Sumatra Basin (after Ginger and Fielding IPA 2005)

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 68

Figure 3 - Generalized stratigraphic column of South Sumatra Basin (after Kamal et al 2005)

Figure 4 - Cross-plot analysis of AI and GR using data from wells S1 and S2 with colour-key water

saturation as hydrocarbon cut-off at 65 Red corresponds to gas and green to brine carbonate

with cut-off AI at 12000 msgrcc

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 78

Figure 5 - Crossplot AI versus Porosity in carbonate reservoir with colour key water saturation The red line

is hi-porosity (gas) trend and the green line is low-porosity (brine) trend with cut-off Porosity at

~8

Figure 6 - Compares section of Seismic AE attribute AI inversion and porosity through wells S1 and S2

The upper panel shows the seismic The second panel shows the AE attribute The third panel

shows absolute AI after inversion The lower panel shows porosity section

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 88

Figure 7 - Horizon slices of AE and Porosity and indicates significant porosity changes in upper carbonate

controlled by facies changes from AE The upper panel shows AE attribute and the second panel

shows Porosity distribution map in several horizon slices from top BRF

Figure 8 - Reservoir quality map in upper zone of carbonate reservoir It is derived from facies analysis

from AE and porosity distribution map

Page 2: Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate, Case Study Singa Field, South Sumatra Basin

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 28

porosity development in the upper part of the

carbonate

REGIONAL GEOLOGY

Singa field is located in Lematang Block South

Sumatra Basin approximately 30 km west of the

city of Prabumulih (Figure 1) Based on its present

day relative position to the subduction zone it is

categorized as a back-arc basin filled by Tertiary

sediments The South Sumatra Basin can be divided

into three different sub-basins namely the Jambi

Depression Central Palembang Deep and Lematang

Deep Singa field is located within Lematang Deep

(Figure 2)

Structural systems for the South Sumatra Basin

resulted from at least three tectonic styles ie thePre-Tertiary basement inheritance structure the

Eocene-Oligocene rifting mechanism and the most

recently the PliocenendashPleistocene compressional

regime (Figure 2) Lematang Deep is bounded by

the Lematang Fault to the north Beyond the fault is

the Limau Anticlinorium The Musi Platform is the

boundary to the west while the Kuang Platform is

the boundary to the east The Southern boundary for

the Lematang Deep is marked by the Garba

Mountain Lematang Deep itself consists of a series

of horsts and grabens as a product of Eocene-

Oligocene tectonics

Stratigraphy of the South Sumatra Basin began in

the Eocene epoch marked by the deposition of

volcanic rich material pre-rift sediments (Kamal and

Argakoesoemah 2005) (Figure 3) When the rifting

took place syn-rift sediments of the Lemat Fm

were deposited The Lemat Fm is proximal facies

alluvial fan and fan delta facies for the coarser grain

and lacustrine and marine shale for the finer grain

Subsequently the Lower and Upper Talangakar Fm

was deposited This Talangakar group is fluvio-

deltaic facies sediment In Miocene time the rate ofsubsidence decreased and contemporaneously a

global sea level rise took place known as the

Miocene transgression This transgression allowed

most of the paleo-high (horst) to become submerged

below sea level Where the environment was

suitable a carbonate factory formed and Baturaja

Fm was deposited Elsewhere the Telisa Fm was

deposited Singa field reservoir is a carbonate build-

up that formed during this process The relationship

between Telisa Fm and BRF in some places is

interfingering and as the transgression reached its

maximum the accommodation space for carbonate

increased beyond the carbonate factory productivity

As a result most of the carbonate factory failed to

keep up and no significant carbonate build-up

occurred post the Miocene Transgression maxima

In this phase only the Telisa Fm marine shale was

deposited Regression followed the transgression

and the uplift of Barisan Mountain range in the

south west of South Sumatra Basin provided a new

sediment provenance from which the Palembang

Group is the product This group consists of the

Lower Palembang Formation with transitional

sediment facies The Middle Palembang Fm has a

distinctive coal-bearing section and the Upper

Palembang is marked by its volcanic material

content During the Plio-Pleistocene tectonics some

of the formation was uplifted and eroded notably in

the Limau anticlinorium A series of Quaternary

volcanic activities in South Sumatra produced

volcanic material that was deposited over areas in

the South Sumatra Basin

Singa field is interpreted as an isolated carbonate

platform Palaeo-wind direction directly influences

the distribution of high energy wave agitated

carbonate facies within carbonate platforms The

windward side of the carbonate platform usually has

a steeper slope than compared with the leeward side

Carbonate texture in the windward margin is less

muddy The texture can range from packestone to

boundstone providing good initial porosity whilst

the leeward side usually consists of mudstone to

wackestone facies with fair to poor initial porosityThe windward side of the Singa field is interpreted

as the west side while the leeward is the east side of

Singa field

METHODOLOGY

For this study we used two wells and post-stack time

migrated 3D seismic which has relative preserved

amplitudes with low vertical seismic resolution and

15 Hz dominant frequency at the level of the BRF

This is due to the deep carbonate target at 12000 ft

subsurface An average velocity in the BRF of11155 ftsec yields a vertical seismic resolution of

approximately 186 feet equivalent to 26

milliseconds TWT in seismic data

The accuracy of the horizon interpretation of the

carbonate is crucial for seismic inversion analysis

Picking the top BRF and internal reflection

character of the carbonate in conventional

reflectivity data is difficult because of the chaotic

and dimming seismic amplitude effects related to

the low impedance of high porosity facies changes

and gas-bearing in carbonates To overcome this

problem a high frequency enhancement technique

(Young and Wild 2004) relative impedance

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 38

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 48

gasporous reservoir with associated low AI values

orange to green corresponds to medium porous

reservoir while brown to black corresponds to tight

reservoir with associated high AI values

We must use an integrated analysis between the

facies analysis (AE) and porosity maps to interpret

reservoir quality in the carbonate reservoir for

minimizing risk in porosity prediction from AI

Figure 8 shows reservoir quality map in upper zone

of carbonate reservoir An understanding of this

analysis is very helpful to support the development

team in its reservoir modelling so that the best areas

for new well locations can be defined

CONCLUSIONS

Reservoir quality and porosity prediction in

carbonates is very difficult In our study an

integration of AI and AE proved useful for

predicting reservoir quality and porosity distribution

in the Singa field Understanding the porosity

distribution as defined by reservoir quality analysis

can support the development team in its reservoir

modelling and optimize new well locations

We successfully drilled one horizontal well (S3)

based on our reservoir modelling Post-drill analysisshows that the well has high porosity carbonate and

produces 30 mmcfpd gases The next plan is to drill

a horizontal well

ACKNOWLEDGMENTS

We would like to thank the management of PT

Medco EampP Indonesia and DITJEN MIGAS for

their permission to publish this paper We also

express our gratitude to our colleagues in the

Exploration Division for their suggestions and

valuable comments during the preparation of this

paper

REFERENCES

Ginger D and Fielding K 2005 The Petroleum

Systems and Future Potential of the South Sumatra

Basin IPA August 2005

Kamal A Argakoesoemah RMI Solichin 2005

A Proposed Basin Scale Lithostratigraphy for SouthSumatra Basin Indonesian Association of

Geologists Stratigraphy of Sumatra Workshop 14 p

Lancester A and Whitcombe D 2000 Fast-track

lsquoColoredrsquo Inversion Presented at SEG 2000

Meeting Expanded Abstracts

Yanto Y and Febriwan T 2008 AVO-Inversion

for Reservoir Characterization of Baturaja

Carbonate Gunung Kembang Field South Sumatra

Basin IPA May 2008

Young P and Wild A 2004 Cosmetic

Enhancement of Seismic Data by Loop

Reconvolution PETEX Conference

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 58

Figure 1 - The Singa field is located approximately 30 km west of the city of Prabumulih

Figure 2 - Structural trend of South Sumatra Basin (after Ginger and Fielding IPA 2005)

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 68

Figure 3 - Generalized stratigraphic column of South Sumatra Basin (after Kamal et al 2005)

Figure 4 - Cross-plot analysis of AI and GR using data from wells S1 and S2 with colour-key water

saturation as hydrocarbon cut-off at 65 Red corresponds to gas and green to brine carbonate

with cut-off AI at 12000 msgrcc

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 78

Figure 5 - Crossplot AI versus Porosity in carbonate reservoir with colour key water saturation The red line

is hi-porosity (gas) trend and the green line is low-porosity (brine) trend with cut-off Porosity at

~8

Figure 6 - Compares section of Seismic AE attribute AI inversion and porosity through wells S1 and S2

The upper panel shows the seismic The second panel shows the AE attribute The third panel

shows absolute AI after inversion The lower panel shows porosity section

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 88

Figure 7 - Horizon slices of AE and Porosity and indicates significant porosity changes in upper carbonate

controlled by facies changes from AE The upper panel shows AE attribute and the second panel

shows Porosity distribution map in several horizon slices from top BRF

Figure 8 - Reservoir quality map in upper zone of carbonate reservoir It is derived from facies analysis

from AE and porosity distribution map

Page 3: Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate, Case Study Singa Field, South Sumatra Basin

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 38

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 48

gasporous reservoir with associated low AI values

orange to green corresponds to medium porous

reservoir while brown to black corresponds to tight

reservoir with associated high AI values

We must use an integrated analysis between the

facies analysis (AE) and porosity maps to interpret

reservoir quality in the carbonate reservoir for

minimizing risk in porosity prediction from AI

Figure 8 shows reservoir quality map in upper zone

of carbonate reservoir An understanding of this

analysis is very helpful to support the development

team in its reservoir modelling so that the best areas

for new well locations can be defined

CONCLUSIONS

Reservoir quality and porosity prediction in

carbonates is very difficult In our study an

integration of AI and AE proved useful for

predicting reservoir quality and porosity distribution

in the Singa field Understanding the porosity

distribution as defined by reservoir quality analysis

can support the development team in its reservoir

modelling and optimize new well locations

We successfully drilled one horizontal well (S3)

based on our reservoir modelling Post-drill analysisshows that the well has high porosity carbonate and

produces 30 mmcfpd gases The next plan is to drill

a horizontal well

ACKNOWLEDGMENTS

We would like to thank the management of PT

Medco EampP Indonesia and DITJEN MIGAS for

their permission to publish this paper We also

express our gratitude to our colleagues in the

Exploration Division for their suggestions and

valuable comments during the preparation of this

paper

REFERENCES

Ginger D and Fielding K 2005 The Petroleum

Systems and Future Potential of the South Sumatra

Basin IPA August 2005

Kamal A Argakoesoemah RMI Solichin 2005

A Proposed Basin Scale Lithostratigraphy for SouthSumatra Basin Indonesian Association of

Geologists Stratigraphy of Sumatra Workshop 14 p

Lancester A and Whitcombe D 2000 Fast-track

lsquoColoredrsquo Inversion Presented at SEG 2000

Meeting Expanded Abstracts

Yanto Y and Febriwan T 2008 AVO-Inversion

for Reservoir Characterization of Baturaja

Carbonate Gunung Kembang Field South Sumatra

Basin IPA May 2008

Young P and Wild A 2004 Cosmetic

Enhancement of Seismic Data by Loop

Reconvolution PETEX Conference

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 58

Figure 1 - The Singa field is located approximately 30 km west of the city of Prabumulih

Figure 2 - Structural trend of South Sumatra Basin (after Ginger and Fielding IPA 2005)

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 68

Figure 3 - Generalized stratigraphic column of South Sumatra Basin (after Kamal et al 2005)

Figure 4 - Cross-plot analysis of AI and GR using data from wells S1 and S2 with colour-key water

saturation as hydrocarbon cut-off at 65 Red corresponds to gas and green to brine carbonate

with cut-off AI at 12000 msgrcc

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 78

Figure 5 - Crossplot AI versus Porosity in carbonate reservoir with colour key water saturation The red line

is hi-porosity (gas) trend and the green line is low-porosity (brine) trend with cut-off Porosity at

~8

Figure 6 - Compares section of Seismic AE attribute AI inversion and porosity through wells S1 and S2

The upper panel shows the seismic The second panel shows the AE attribute The third panel

shows absolute AI after inversion The lower panel shows porosity section

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 88

Figure 7 - Horizon slices of AE and Porosity and indicates significant porosity changes in upper carbonate

controlled by facies changes from AE The upper panel shows AE attribute and the second panel

shows Porosity distribution map in several horizon slices from top BRF

Figure 8 - Reservoir quality map in upper zone of carbonate reservoir It is derived from facies analysis

from AE and porosity distribution map

Page 4: Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate, Case Study Singa Field, South Sumatra Basin

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 48

gasporous reservoir with associated low AI values

orange to green corresponds to medium porous

reservoir while brown to black corresponds to tight

reservoir with associated high AI values

We must use an integrated analysis between the

facies analysis (AE) and porosity maps to interpret

reservoir quality in the carbonate reservoir for

minimizing risk in porosity prediction from AI

Figure 8 shows reservoir quality map in upper zone

of carbonate reservoir An understanding of this

analysis is very helpful to support the development

team in its reservoir modelling so that the best areas

for new well locations can be defined

CONCLUSIONS

Reservoir quality and porosity prediction in

carbonates is very difficult In our study an

integration of AI and AE proved useful for

predicting reservoir quality and porosity distribution

in the Singa field Understanding the porosity

distribution as defined by reservoir quality analysis

can support the development team in its reservoir

modelling and optimize new well locations

We successfully drilled one horizontal well (S3)

based on our reservoir modelling Post-drill analysisshows that the well has high porosity carbonate and

produces 30 mmcfpd gases The next plan is to drill

a horizontal well

ACKNOWLEDGMENTS

We would like to thank the management of PT

Medco EampP Indonesia and DITJEN MIGAS for

their permission to publish this paper We also

express our gratitude to our colleagues in the

Exploration Division for their suggestions and

valuable comments during the preparation of this

paper

REFERENCES

Ginger D and Fielding K 2005 The Petroleum

Systems and Future Potential of the South Sumatra

Basin IPA August 2005

Kamal A Argakoesoemah RMI Solichin 2005

A Proposed Basin Scale Lithostratigraphy for SouthSumatra Basin Indonesian Association of

Geologists Stratigraphy of Sumatra Workshop 14 p

Lancester A and Whitcombe D 2000 Fast-track

lsquoColoredrsquo Inversion Presented at SEG 2000

Meeting Expanded Abstracts

Yanto Y and Febriwan T 2008 AVO-Inversion

for Reservoir Characterization of Baturaja

Carbonate Gunung Kembang Field South Sumatra

Basin IPA May 2008

Young P and Wild A 2004 Cosmetic

Enhancement of Seismic Data by Loop

Reconvolution PETEX Conference

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 58

Figure 1 - The Singa field is located approximately 30 km west of the city of Prabumulih

Figure 2 - Structural trend of South Sumatra Basin (after Ginger and Fielding IPA 2005)

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 68

Figure 3 - Generalized stratigraphic column of South Sumatra Basin (after Kamal et al 2005)

Figure 4 - Cross-plot analysis of AI and GR using data from wells S1 and S2 with colour-key water

saturation as hydrocarbon cut-off at 65 Red corresponds to gas and green to brine carbonate

with cut-off AI at 12000 msgrcc

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 78

Figure 5 - Crossplot AI versus Porosity in carbonate reservoir with colour key water saturation The red line

is hi-porosity (gas) trend and the green line is low-porosity (brine) trend with cut-off Porosity at

~8

Figure 6 - Compares section of Seismic AE attribute AI inversion and porosity through wells S1 and S2

The upper panel shows the seismic The second panel shows the AE attribute The third panel

shows absolute AI after inversion The lower panel shows porosity section

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 88

Figure 7 - Horizon slices of AE and Porosity and indicates significant porosity changes in upper carbonate

controlled by facies changes from AE The upper panel shows AE attribute and the second panel

shows Porosity distribution map in several horizon slices from top BRF

Figure 8 - Reservoir quality map in upper zone of carbonate reservoir It is derived from facies analysis

from AE and porosity distribution map

Page 5: Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate, Case Study Singa Field, South Sumatra Basin

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 58

Figure 1 - The Singa field is located approximately 30 km west of the city of Prabumulih

Figure 2 - Structural trend of South Sumatra Basin (after Ginger and Fielding IPA 2005)

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 68

Figure 3 - Generalized stratigraphic column of South Sumatra Basin (after Kamal et al 2005)

Figure 4 - Cross-plot analysis of AI and GR using data from wells S1 and S2 with colour-key water

saturation as hydrocarbon cut-off at 65 Red corresponds to gas and green to brine carbonate

with cut-off AI at 12000 msgrcc

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 78

Figure 5 - Crossplot AI versus Porosity in carbonate reservoir with colour key water saturation The red line

is hi-porosity (gas) trend and the green line is low-porosity (brine) trend with cut-off Porosity at

~8

Figure 6 - Compares section of Seismic AE attribute AI inversion and porosity through wells S1 and S2

The upper panel shows the seismic The second panel shows the AE attribute The third panel

shows absolute AI after inversion The lower panel shows porosity section

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 88

Figure 7 - Horizon slices of AE and Porosity and indicates significant porosity changes in upper carbonate

controlled by facies changes from AE The upper panel shows AE attribute and the second panel

shows Porosity distribution map in several horizon slices from top BRF

Figure 8 - Reservoir quality map in upper zone of carbonate reservoir It is derived from facies analysis

from AE and porosity distribution map

Page 6: Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate, Case Study Singa Field, South Sumatra Basin

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 68

Figure 3 - Generalized stratigraphic column of South Sumatra Basin (after Kamal et al 2005)

Figure 4 - Cross-plot analysis of AI and GR using data from wells S1 and S2 with colour-key water

saturation as hydrocarbon cut-off at 65 Red corresponds to gas and green to brine carbonate

with cut-off AI at 12000 msgrcc

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 78

Figure 5 - Crossplot AI versus Porosity in carbonate reservoir with colour key water saturation The red line

is hi-porosity (gas) trend and the green line is low-porosity (brine) trend with cut-off Porosity at

~8

Figure 6 - Compares section of Seismic AE attribute AI inversion and porosity through wells S1 and S2

The upper panel shows the seismic The second panel shows the AE attribute The third panel

shows absolute AI after inversion The lower panel shows porosity section

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 88

Figure 7 - Horizon slices of AE and Porosity and indicates significant porosity changes in upper carbonate

controlled by facies changes from AE The upper panel shows AE attribute and the second panel

shows Porosity distribution map in several horizon slices from top BRF

Figure 8 - Reservoir quality map in upper zone of carbonate reservoir It is derived from facies analysis

from AE and porosity distribution map

Page 7: Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate, Case Study Singa Field, South Sumatra Basin

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 78

Figure 5 - Crossplot AI versus Porosity in carbonate reservoir with colour key water saturation The red line

is hi-porosity (gas) trend and the green line is low-porosity (brine) trend with cut-off Porosity at

~8

Figure 6 - Compares section of Seismic AE attribute AI inversion and porosity through wells S1 and S2

The upper panel shows the seismic The second panel shows the AE attribute The third panel

shows absolute AI after inversion The lower panel shows porosity section

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 88

Figure 7 - Horizon slices of AE and Porosity and indicates significant porosity changes in upper carbonate

controlled by facies changes from AE The upper panel shows AE attribute and the second panel

shows Porosity distribution map in several horizon slices from top BRF

Figure 8 - Reservoir quality map in upper zone of carbonate reservoir It is derived from facies analysis

from AE and porosity distribution map

Page 8: Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate, Case Study Singa Field, South Sumatra Basin

8102019 Ipa11-G-033 Reservoir Quality and Porosity Prediction in Carbonate Case Study Singa Field South Sumatra Basin

httpslidepdfcomreaderfullipa11-g-033-reservoir-quality-and-porosity-prediction-in-carbonate-case-study 88

Figure 7 - Horizon slices of AE and Porosity and indicates significant porosity changes in upper carbonate

controlled by facies changes from AE The upper panel shows AE attribute and the second panel

shows Porosity distribution map in several horizon slices from top BRF

Figure 8 - Reservoir quality map in upper zone of carbonate reservoir It is derived from facies analysis

from AE and porosity distribution map