The Samarang oil field offshore Malaysia was initially developed by Shell in 1975 but was facing decline by 2003. PCSB took over operations and conducted a major redevelopment evaluation from 2004-2006 involving subsurface experts who created full-field static and dynamic models. This integrated plan identified infill wells and enhanced oil recovery opportunities to double production and extend the field life by 15 more years. The field was also transformed into a producing hub for nearby fields, further improving economics.

1. Summary
Samarang field is a 35 year-old oilfield offshore Malaysia that
was initially developed by Shell beginning in 1975. The field
was relinquished to Petronas Carigali Sdn Bhd (PCSB) in
1995, which continued field operations and were able to
significantly reduce the production decline rates. PCSB also
transformed the field into a producing hub, allowing
development of two small adjacent fields by reducing the
required capital costs. However by 2003, the field was in
trouble with production declining and reserves dwindling,
suggesting field abandonment was on the horizon.
Beginning in 2004, PCSB outsourced a major redevelopment
evaluation to a multi-disciplinary, international consulting
team. They maintained control by using internal expertise to
peer review and assist the team’s progress throughout the
evaluation and at key milestone meetings. This process
allowed PCSB to leverage their own organization’s skills
while using new technology tools employed by the team.
The evaluation included a complete review of all static data,
literally a seismic to simulation study approach, employing
virtually every subsurface discipline in an attempt to unlock
the field’s remaining value. The results were full-field static
and dynamic models covering the entire field and allowing an
integrated redevelopment plan. This plan consists of
numerous infill producers and selective application of
enhanced oil recovery. In addition, several near-field
exploratory targets were identified.
Field redevelopment is currently being implemented in a
phased approach including 1) ongoing production
enhancement, 2) sidetracking of idle wells to updip positions,
3) addition of new well jackets for additional development
wells, and 4) selective injection into the larger reservoirs with
relatively lower primary recovery. The evaluation will
provide dividends for years to come with expected doubling of
current production and extension of field life for another 15
years.
Field Development History and Background
Figure 1 shows that the Samarang Field is located offshore
Sabah, East Malaysia, about 45 miles (72 km) northwest of the
Labuan Gas terminal. The field surrounds a shallow reef with
an water depth of 30 feet (9m).
Figure 2 summarizes the field’s production and
development history. Shell was the initial operator and
relinquished the concession to PCSB in April 1995. The field
was developed in phases with the initial phase including the
larger “A” and “B” drilling platforms, separate producing
platforms at A, B, and C, and well jackets at C, D, and E.
Subsequent development included well jackets at F and G. An
additional well jacket “H” was planned by Shell for the east
flank development, but was not implemented in 1986 because
of low reserves potential and low oil prices. Key dates in the
field’s development history are as follows:-
• Discovery: 1972 By Sm-1
• Field Development: 62 Wells (1975-1979) In Smdp-
A,B; Smjt-C,D,E with first oil in June 1975
• Revisit I (86/87): 12 Wells (Smjt-F,G) And 20 S/T on
Smdp-A, B and Smjt-C,D,E
• Revisit II (91/93): 27 Wells S/T, Three Wo and Two
New In Smdp-A, B; Smjt-C, D, F
• PCSB Begins Operating: 1st April 1995
• Revisit III (97/98): 3 S/T, 1 Hhp Gas and Five New
Wells In Smjt-D, F And G.
• B Revisit (2002): 2 S/T (Sm 52 And Sm 57) and One
Recompletion (Sm 42)
Two useful industry publications were published in the
mid-1990’s, each one based around one of the major
reservoirs in the field. Using reservoir simulation, Lee1
IPTC 13162
Samarang Field – Seismic To Simulation Redevelopment Evaluation Brings New
Life to an Old Oilfield, Offshore Sabah, Malaysia
J. K. Forrest, SPE, Schlumberger, A. Hussain and M. Orozco, SPE, Petronas Carigali Shd Bhd (PCSB), J. P. Bourge,
T. Bui, R. Henson, and J. Jalaludin, SPE, Schlumberger.
Copyright 2009, International Petroleum Technology Conference
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2. 2 IPTC 13162
studied the K reservoir and concluded 1) high primary
recoveries of 65+%, and 2) the need for more crestal
producers. Using a similar dynamic model, Baxendale2
studied the large M4/7 layer and recommended additional
infill wells as well as injection to supplement the moderate
water-drive and gas-cap expansion effects. He noted some
difficulties with the history match, partially as a result of
missing some layer interactions with the M1 and M3 layers.
Both of these studies are useful, but the simulators would be
considered very coarse by today’s standards.
During the course of this study and an expanded
exploration-focused study to the area surrounding the
Samarang field, Carrillat et al3 wrote about an integrated
geological and geophysical classification analysis. All of these
papers are useful if one wants to get a more detailed
background on the Samarang field or area.
Field Conversion to Producing Hub
Originally, the Samarang field was produced by itself
through an export line to Labuan Crude Oil Terminal (LCOT).
As such, the fiscal metering was installed at LCOT. All
produced fluids from the Samarang field are transferred to the
LCOT and Labuan Gas Terminal (LGAST). Beginning in
1997, several offset fields were tied into the Samarang
producing Hub and exported via the shared export line to
Labuan.
In December 1997, the Shell-operated Kinabalu field was
developed in December 1997 using a single platform and 20
directional wells. The production is metered at the platform
and is piped 33-km to the Samarang “A” producing facilities
(SMP-A), where it is separated together with the Samarang
field production from platforms A, E, and G. Since this tie-in,
the Samarang field production has been determined using a
Force-Balance Equation for production allocation.
Following the tie-in of the Kinabalu field, several smaller
fields have been developed and tied into the Samarang
producing Hub. In Q3 2002, the Samarang Kecil gas field,
located southwest of the field was tied into the SMPA
platform. This field has been generally curtailed due to lack
of higher gas demand, but it produces gas and some
condensate. It has also been used operationally to supply
high-pressure gas for gaslift purposes to the Samarang field.
Also in Q3 2002, the Alab field was developed by drilling two
directional wells from the Samarang B drilling platform, i.e.
the northernmost platform in Samarang and located in the
deepest water..
The last and most recent tie-in to the Samarang Hub is
from the nearby Sumandak field. This tie-in is only of a
temporary nature, beginning in October 2006 and due to end
sometime in 2009.
Geological Setting
The Samarang field (7 km x 2 km) is located in the eastern
part of the Baram delta province. Structurally, the field is
situated at the culmination of a large roll-over anticline
bounded to the southeast by a major west hading growth fault
(see Figure 3). The gently dipping west flank contrasts
sharply with the structurally complex northern plunge, as well
as the dissected collapsed crest and the faulted eastern flank.
The field is divided vertically into shallow, intermediate,
and deep sequences. The shallow sequence consists
inclusively of reservoirs from C to I markers. The
intermediate includes reservoirs from J and K markers, and the
deep consists of reservoirs below K reservoirs. The largest
hydrocarbons accumulations are located within the J, K and M
reservoirs, which account for roughly 70% of the field’s
original oil-in-place. Figure 4 shows the structure map for the
M reservoir and includes the major reservoir faults and
discontinuities such as small faults identified using ant-
tracking interpretation.
Samarang reservoirs are located between 1500 to 8000 ft
(TVDSS). They consist of a series of alternating sand, silts
and clays of Late Miocene to Early Pliocene age deposited in a
sub-tidal shallow marine environment. Figure 5 shows the
conceptual depositional environment for the field. Figure 6
shows a field cross-section (along structural strike) that
highlights the location of the hydrocarbon-bearing intervals in
relation to the existing well jackets.
Formation Evaluation and Characterization
A 1984 3-D seismic survey is available and was
interpreted, but the best data and control are provided by the
open-hole logs from 144 wells. Within the field, log
correlation is good in general, and Figure 7 shows an example
type-log of the M reservoir from well SM-3, which was one of
the key cored wells for formation evaluation. All of the
producing reservoirs in the field are normally pressured, and
the field has normal temperature gradient for the area of about
1.05 DegF/100ft.
The cored interval comprises three main facies types, i.e.
sandstones, heterolithic sandstones, and shales. These facies
types were then sub-classified into nine lithofacies to cover the
broad spectrum of sand/shale content, primary sedimentary
structures, intensity of bioturbation, and unique
sedimentologic character that were identified and described
from the core.
Sandstones are prefixed with S and were classified into
three sub groups as:
1. Massive Sandstone 1 (Sm1)
2. Massive Sandstone 2 (Sm2) and
3. Laminated sandstone (Sl/c)
Sandstone dominated heterolithics were classified into
three subgroups as:
1. Bioturbated sandstone (Sb1)
2. Intensely bioturbated sandstone (Sb2) and
3. Heterolithic Sandstone (Sm/Ms)
Mudstones/Shales were classified into two subgroups as
1. Bioturbated shale (Mb) and
2. Stratified shale (Ms)
The ninth lithofacies in the cored intervals represents the
presence of carbonates in the form of patchy carbonate
cement, broken/intact shell hash layers, dolomite patches and
layered or nodular siderites. This last lithofacies, though
negligible in occurrence in the whole cored section (0.4 % of

3. IPTC 13162 3
the total cored interval), was important as a local vertical flow
baffle.
Following the identification of these lithofacies, geologists
used a Neural Network procedure to extend the lithofacies to
most of the non-cored wells. Some of the wells had wellbore
problems including missing logs or cork-screwed boreholes,
which did not allow their inclusion in this work. But in all
cases, there was sufficient well coverage to allow the
estimation of lithofacies throughout the reservoir sequences.
The final neural network model involved used the sand, silt,
and clay volumes (example shown in Figure 7) for each
individual well as calculated by petrophysical methods.
Reservoir Fluid Properties
The historical results suggest that oil properties in
Samarang field vary from about 20 to 37 API, increasing with
depth. The results also indicate that most reservoirs had an
initial gas cap, meaning that the oil was saturated at discovery
and that the bubblepoint pressure could be estimated at the
observed gas-oil contact for these reservoirs. Based on PVT
matching, industry correlations have been applied to the field
based upon individual reservoir characteristics, and these
correlations proved sufficient.
Building the Static Model
The structural framework for the model was built and
validated by integrating a wide range of seismic, geological
and engineering data (see workflow shown in Figure 8). The
field’s structural complexity resides in the imbrications of
synthetic and antithetic faults of the rollover anticline. The 3D
framework was modeled using the seismic interpreted faults
and horizons. The intermediate stratigraphy was mapped using
markers from the well correlations. Whenever necessary, the
fault positions were adjusted to the well data (i.e. faults and
stratigraphic markers, logs, perforations, dipmeter). The
horizons were modeled using seismic interpretation
constrained by well markers. The structural uncertainties
reside mainly with the fault positioning and shape and in a
lesser degree in the horizon depth position as a result of depth
conversion.
In order to create manageable models, the field was
subdivided into five static models with vertical and lateral
continuity. With this subdivision, each model had a
reasonable number of cells of regular shape. Table 1
summarizes the model dimensions and sizes.
The Facies modeling was done to constrain the property
population for Porosity and Permeability. Inputs to the facies
model were Lithofacies logs and depositional
environments/facies logs. A two-step facies modeling
procedure was performed. First, the depositional facies were
modeled as the framework, and then the Lithofacies were
modeled within the depositional facies. In order to model the
transitional nature of the depositional environments/facies (eg.
reservoirs showing Shoreface deposition), the facies were
simulated using Facies Transition Simulation. The Lithofacies
were modeled using Sequential Indicator Simulation
constrained with various options namely Vertical proportion
curves, 2D trend maps, and 3D trends. The anisotropy of the
facies was analyzed by variograms based on well data, and the
parameters were guided by the conceptual geological model.
The validation controls were done with the comparison of the
vertical proportion curves for the wells versus the model.
Figure 9 is a cross-section showing the workflow consisting of
distributing the depostional facies, then populating the
lithofacies within the depositional facies, and finally,
populating the model with petrophysical properties including
total porosity, permeability, and water saturation. Porosity was
simulated with Sequential Gaussian Simulation constrained to
the Lithofacies. Variogram ranges were computed from well
data, describing the spatial correlation length for porosity.
The permeability was computed using a function based on
core porosity/permeability correlations and conditioned to the
lithofacies (see Table 2). Water saturation was populated
using a J function based on porosity, permeability, and height
above contact. Validation steps of the porosity population
included the histogram analysis of the log versus the 3D
property.
After the models were complete, the hydrocarbon volumes
were calculated and in most cases, the volumes were similar to
previous estimates. This result was expected because of the
relatively high well density in this geological environment,
there was relatively lower uncertainty. Despite this finding,
uncertainty was addressed by creating 3 variations of the
structural framework, by running multiple stochastic
realizations on 5 property population scenarios and by varying
the porosity. Volumetric calculations were performed for all
scenarios/realizations and structural framework to evaluate the
maximum range in hydrocarbon volumes. The PCSB
technical experts continuously reviewed the work in progress,
and the static models passed a key milestone review in
September 2006.
Upgridding and Upscaling the Static Models
The geological 3D model with the very fine resolution
needed to be upgridded and upscaled to a manageable size for
dynamic modeling. The objective of upgridding and upscaling
was to reduce the number of grid cells of the geological model
while preserving the geological description of the reservoir.
This process conserved the original pore volume and the flow
characteristics of the geological model.
The static Geomodels were designed to capture the major
geological features of the field that were identified in the
geologic, geophysical, petrophysical, and engineering studies.
In particular, the grid orientation followed the major structural
and fault trend, and grid size was chosen based on the
consideration of reservoir heterogeneity, well spacing,
completion interval, and current and possible future field
development. The resulting areal resolution in grid-block
sizes for the Shallow models was about 100 ft and for other
reservoirs was about 200 ft. The average vertical resolution
for all models ranged between 2 and 4 ft to capture the
observed heterogeneity. Based on engineering considerations,
we decided to preserve the areal resolution when building the
dynamic model, so upgridding was only performed in the
vertical direction.
Pore volume weighting was used to upscale the
hydrocarbon volume from the geological model to the

4. 4 IPTC 13162
simulation model. Several upscaling algorithms for the
upscaling of the permeability were tested including geometric
average; harmonic average, and flow based upscaling. Based
on the results, we decided to use flow based upscaling
permeability as this technique best preserved the flow
characteristics of the static models.
Reservoir Engineering and Dynamic Modeling
Before beginning reservoir simulation modeling, several
tasks were conducted to assist and complement the simulation
work. First, the well completion and production data were
reviewed, improved where necessary, and converted into
formats appropriate for reservoir modeling. The production
data from 136 producers (excluding exploration and appraisal
wells) were validated and the completion events information
were prepared for dynamic modeling. These events included
zone changes and sliding sleeve changes, associating the well
production with the correct producing interval
Following the analysis of the pressure and production data,
we used two classical reservoir engineering analyses to gain
insight to the historical field performance and assist in
prediction of future performance. These classical analyses
included material balance modeling and decline curve
analysis.
Material Balance Analysis
Before using reservoir simulation models, a Material
Balance (MB) study was conducted to estimate reservoir
parameters by matching the historical performance of the
producing reservoirs using analytical methods. The purpose
of this work was to estimate original hydrocarbons-in-place,
aquifer strength, reservoir drive mechanisms and total energy
of the system, and evaluate reservoir compartmentalization.
The results of this work were as follows:
• In most cases, we were able to obtain a good match of
the pressure behavior. In these cases, the material
balance modeling indicated that the MB model and its
parameters adequately described the hydraulic regime
of the reservoir and the drive mechanisms, and no
evidence of change in fault sealing as a result of
production and pressure depletion.
• In general, the aquifer support decreased with
reservoir depth. Shallow reservoirs were
characterized by very strong aquifer support.
Intermediate reservoirs (J, K) represent intermediate
to strong aquifer support, and reservoirs below K
sequence were characterized by weak to intermediate
aquifer support.
• In the East Flank of the field, performance data
suggested that the east and west flanks were separated
by at least one sealing fault. Compared to the west
flank, east flank reservoirs were generally
characterized by weak aquifer support.
• In general, results of this MB study confirmed the
estimates of the original hydrocarbon-in-place
(HCIIP), but often, a significant range in the original
volume was possible, particularly for the strong water-
drive reservoirs.
• The analysis predicted fluid contact movements that
were in agreement with well log data. The oil residual
saturation (Sorw) used in MB analysis was 0.15 to 0.2,
which is slightly lower than the results of the relative
permeability test on core plugs (Sorw = 0.2 to 0.3).
• For reservoirs with large gas caps, the gas cap
expanded 80 feet during production before receding
back to roughly its original position. Because of this
behavior, gas-oil hysteresis and gas trapping effects
were identified as important to include in modeling
the behavior of these reservoirs.
Decline Curve Analysis
Decline Curve Analysis (DCA) was conducted to assist
with identification of candidates for production enhancement
and for estimating reserves and a production baseline for a No
Further Action case. This work was conducted using an OFM
production database.
The DCA methodology is outlined as follows:
• Exponential decline curve analyses was assumed
• Decline rate was based on historical values
• Forecast start date was Oct. 2006, end date is year
2020 (end of PSC)
• Analysis was done on reservoir basis and checked
against analyses by well and platform.
This analysis showed the estimated remaining oil recovery
to be 17.8 MMSTBO (as of 1 November 2006). An
alternative DCA using extrapolation of water-cut performance
vs. cumulative oil production resulted in an estimated
remaining oil recovery to be 17.2 MMSTBO. These two
separate analyses were in good agreement, giving confidence
to the estimates. These estimates were later used in
comparison to a simulated no further action case (discussed
next), and the forecasts were similar.
Dynamic Models – Reservoir Simulation
Reservoir simulation models were built for all of the main
reservoirs, accounting for 99% of the field’s mapped volumes.
The reservoirs were split and grouped according to size and
location. The J, K, and M/N reservoirs are the three largest
ones in the field, so were each modeled separately.
1. Shallow Interval – E to I Reservoirs
2. Intermediate Interval – J Reservoir
3. Intermediate Interval – K5/K7 Reservoirs
4. Deep Interval – M/N Reservoirs
5. Deep Interval - O1, P1, Q3/7, Q8 Reservoirs
6. East Flank Models – One static and two dynamic
models were required to cover all of the reservoirs and
the fault complexity in this area.
Calibrating Dynamic Models With Observed Data
The key objective for the history match was to calibrate
the initialized dynamic models using all the available
historical data, including oil, water, and gas production,
production ratios and bottomhole pressure measurements. The
validated reservoir models could then be reliably used for
performance predictions.

5. IPTC 13162 5
The criteria used to determine at what point a dynamic
reservoir model could be considered adequately history
matched and therefore validated, was agreed as follows:
• All models were run under oil rate control, so all wells
should match historical oil rate.
• 80% of the representative wells should have
cumulative water and gas production totals within
20% of the actual cumulative production.
• 80% of pressure points should be within 200 psi of the
historical data.
The history matching process was greatly facilitated by the
creation of an Excel-based program for post-processing
simulation results. Key features included 1) the use of a 4-
graph template (oil, water, gas, and pressure) that could be
switched between ratios, rates, and cumulative production by
completion, well, well group, or reservoir, 2) bar charts to
rank wells by various categories such as high cumulative
production or by high error as calculated as the difference
between actual and simulation production, and 3) grid display
that allowed easy viewing of grid parameters and history
match changes. An example of the 4-graph template is shown
later with predicted performance too. In addition to these
custom program, standard industry visualization software was
used to diagnose problems and monitor fluid saturation
changes with time.
Discussion of History Match Adjustments
To achieve the best possible history match, the following
global changes were applied to the reservoir models:
• The earlier material balance was useful in confirming
drive mechanism, estimating aquifer size, and
highlighting fluid contact movement over time. In the
Eclipse simulation models, the Fetkovitch analytical
aquifer models were used, which require specification
of aquifer volume and productivity index.
• Global permeability was modified within the range of
uncertainty to match oil production rates. In addition,
the horizontal permeability (Ky) was generally
increased along structural strike. This global
anisotropy was supported by two reasons: the
historical shoreline was interpreted in the north-south
direction, and the small scale north-south faults further
increase the ky to kx ratio.
• Vertical permeability between the simulation block
was reduced by a factor of 0.05 to 0.1 to better match
the water production behavior of wells. The reduction
of the vertical permeability was made to capture the
fine scale shale features observed along the wellbore
and to reflect the fact that reliable vertical
permeability data were not available for modeling.
• Intersand transmissibility was reduced significantly to
match the general water production behavior of the
model, which was edge water-drive in most cases.
• Fault transmissibilities were reduced by a factor of 0.1
to 0.001 along the faults to match fluid movement
across the faults. The results from ant tracking
analysis were used for determining possible locations
for small faults and for improving the match of
observed water and gas production.
• The pore volume was slightly adjusted within 5 to
10% of the original value to better match the oil and
gas production in localized regions of the model.
• Relative permeability curves were modified to match
observed water-cut and breakthrough. Specifically,
the critical water saturation was set equal to 5
saturation units above connate water saturation, and
the curve shape was modified slightly reduce
simulated water production.
Following the global modifications and changes to fault
transmissibilities, some local changes were necessary for
matching the production and pressure behavior of some wells.
These modifications included the following:
• Local permeability multipliers and local vertical
permeability barriers to match the gas and water
production of the wells.
• Local small seismic scale faulting identified using Ant
Tracking, which did not affect pressure but affected
the arrival of water influx or gas-cap expansion.
• In a few cases, fluid production across faults was
included by using non-neighbor connections across
the faults.
• In a few cases, the use of local permeability
multipliers and local pore volume multipliers were
used to improve individual wells or completions.
In summary of the history matching results, the models
were calibrated to the observed historical data by mainly
applying adjustments to the inter-sand transmissibility, fault
transmissibility, vertical permeability, and some local
adjustment to permeability. The simulation and the production
data suggested that gas-cap expansion and an effective water
drive resulted in good sweep efficiency and high recovery
factor for several of the models. Having obtained a good
match between historical and simulated data, the models were
used for the performance prediction and for assessing different
development scenarios.
In general, the models showed that remaining oil was
distributed structurally higher in the reservoir, along the major
faults. This result indicated the need for infill wells, targeting
these attic oil areas, would be required.
EOR Screening
The Samarang field has been on production for over 30
years. Over 40% of the original STOIIP has been recovered,
the oil rate has been declining, and the water cut has been
increasing. These facts coupled with historically high oil
prices create significant incentives to examine the application
of EOR processes to the field.
Implementing EOR in the Malaysian producing fields
presents a significant technical and economic challenge,
particularly as the majority of oil in the region is produced
offshore, and the Samarang field is a good example of these
challenges. The existing platforms have limited space and are
more than 20 years old. The well spacings are relatively large
for an EOR process, and well slots are limited. These issues
will affect the cost of implementing an EOR process.

6. 6 IPTC 13162
The field location also limits the types of processes that
can be used and puts performance constraints on the processes
that can be applied. For example, for processes such as
chemical flooding, the platform storage space can limit the
rate that chemicals can be injected, and the supply of injection
fluids such as gases to the field can limit both the processes
used and the injection rate. Despite all of these challenges, a
thorough investigation of EOR was conducted as part of this
Samarang redevelopment plan.
Historically, parameter ranges have been developed to
provide engineers a first cut-estimate of conditions favorable
for a given EOR process. In agreement with the RE CTE, the
screening criteria based on Taber et. al (1997) and later
modified in Henson et. al (2003) were used. Five EOR
methods passed the screening process and were then ranked in
terms in terms of feasibility. The most applicable process was
some combination of immiscible gas and water injection.
Future consideration of chemical flooding can be considered
subsequent to the installation of the basic injection
infrastructure. Surface facilities can be planned to allow deck
space or deck space expansions if chemical flooding is proved
feasible in the future.
Predicting Future Performance
Having obtained good matches between historical and
simulated data and approved by the internal and external
technical reviewers, the models were validated to be used for
the performance prediction and for assessing different
development scenarios. Figure 10 shows a schematic of a
decision tree with the six main areas were considered:
• No Further Action (NFA)
• Application of Production Enhancement and
Workovers
• Enhanced Artificial Lift
• Infill Drilling and East Flank Development
• Enhanced Oil Recovery
• Application of New Completion Techniques
The redevelopment team identified 64 Production
Enhancement activities to be scheduled from 2009 until 2020,
and an additional 22 activities associated with the EOR in the
M reservoir (as discussed later). The numbers of wells and
types of processes are summarized in Table 3. All candidates
were evaluated in terms of risk and were sorted into three risk
categories ranging from low to high. In all cases, the selected
PE candidates were assumed to be successful with their risk of
failure included as a cost risk. However, the implementation
must be monitored as the successes and possible failures due
to mechanical problems or other can reduce or increase the
planned infill development well program.
Historically, gaslift has been used, initially with high-
pressure gas reservoirs within the field. Subsequently, in mid-
2000, a 50 MMscfd gaslift compression facility was installed
to meet the increased gaslift requirement resulting from
reservoir pressure depletion and increasing field water cut. As
a result of the planned significant redevelopment, additional
compression is required and planned to be installed by January
2014. Until this date, the field will operate with a constrained
gaslift supply, requiring daily gaslift optimization procedures
and improved well and reservoir monitoring practices such as
a conventional SCADA system.
The next step to identify additional reserves that could be
produced the addition of new drainage points. The starting
point for this process was the identification of drainage targets
in all of the reservoir models. This was done by running the
simulator in prediction mode, using the assumed production
enhancement candidates to estimated end of field life. At this
time, remaining mobile oil thickness maps were generated (see
Figure 11), and areas with significant remaining mobile oil
were identified. From this process, two main plays were
found; attic oil trapped against the field’s major faults,
generally updip of the existing completions, and patches of oil
bypassed by water encroachment due to reservoir faults and/or
localized structural highs.
This process identified 112 drainage targets (minimum
reserves potential of 0.1 MMSTBO) that were grouped into
wellbores, using a few simple rules;
• Targets with largest cumulative oil first.
• Organized by platform/reservoir
• Each wellbore should have no more than 3
degrees/100ft dog-leg severity, and total wellbore
deviation should be less than 60 degrees
• Initial assumption of dual completions.
The well delivery team aimed to connect the maximum
amount of targets with the least amount of wellbores, across
the whole field, The well delivery team had to include and
account for three other variables including slot availability,
economic cut-offs, and commingling possibilities. Any target
that could not be drilled, either because they could not be
reached from existing and/or new platforms or because of
collision issues were added to the salvage list.
Both horizontal and multi-lateral wells were considered
during this stage, but two problems were encountered.
Samarang is a large field with multiple stacked reservoirs, 95
wells and 46 side tracks have been drilled since the field
started production in 1975, often passing though many
shallower reservoirs to reach the deeper targets. Any
horizontal well has to be threaded though the existing wells
and this creates major collision issues. In addition, many of
the new wells are designed to hit the attic oil trapped against
the major faults; this means the targets are often stacked on
top of each other. Drilling the attic oil targets in one layer
would make it difficult to hit the targets lying almost directly
underneath it in another layer. Further consideration of
horizontals or multi-laterals were deferred to detailed well
planning of individual wells.
Using a multi-disciplinary process and key milestone
review workshops including Redevelopment Concept
Identification, well optimization, and Concept Select, the
redevelopment team were able to propose a development plan
achieving all of the objectives:

7. IPTC 13162 7
• Maximize reserves recovery subject to maximizing net
present value, and employing new technology
whenever feasible.
• integrate input from all disciplines and project
stakeholders
• optimize areal and vertical drainage in each reservoir
and across all reservoirs.
• include assessment of geological risks, particularly
with respect to fault position. This is potentially an
important constraint with most reservoirs bounded by
faults on both sides.
• integrate drilling constraints (shallow reservoirs,
horizontal displacement of shallow vs. deep
reservoirs, platform limitations) and address wellbore
collision issues.
Figure 12 summarizes this process leading to an optimized
solution. Within each development concept, several iterations
were required to optimize infill well locations, mainly due to
the difficulty of combining stacked targets over large vertical
distances in a tight, given target range. Fault risk was also a
major constraint, as several wells were planned to drilled near
uncertain faults locations. Economic analysis was used as the
basis for optimizing the redevelopment plans, resulting in 4
final cases (Case A to Case D) that including complete cost
estimates from well construction and facilities and production
profiles matching to estimated project schedules. Case B and
D included the results from the EOR evaluation as discussed
next.
Results of EOR Evaluation
A comprehensive evaluation of the benefit of different
injection schemes of water and immiscible gas was conducted
using for the three largest reservoirs (see Table 4 for a
reservoir summary). The investigated EOR schemes for J and
K reservoirs could potentially increase the cumulative oil
production by a combined 6 MMSTB including both J and K
reservoirs, compared to infill drilling only. Economic analysis
suggested that these volumes were uneconomic, but future
work and data is planned to reconfirm this finding. The main
problem is that primary drive mechanisms have been very
good, leaving insufficient “target oil” for an EOR process.
The investigated EOR schemes for M reservoir could
potentially increase the cumulative oil production by 20
MMSTB compared to infill drilling only. This increase
represents ~7% of OIIP. A significant fraction of this oil
production can be produced by reactivating existing idle wells
during the application of the EOR process. An updip injection
scheme was generally more efficient than a downdip injection
scheme because the current oil column is located at or above
the current completion intervals. The updip injection scheme
pushes the oil towards current and idle completions in the M
reservoir, which can be reactivated. Numerous injection
schemes were evaluated, but four methods were selected for
economic analysis including:
1. WAGCD (Updip WI) – Water Assisted Gas-Cap
Drive. The benefit of the updip water injection can be
partially explained by the fact that injecting water into
the existing gas-cap created essentially a single-slug
IWAG process.
2. Updip IWAG – Water-Alternating Gas Injection using
Updip Injection Wells.
3. GASWAG – Gravity-Assisted Simultaneous Water
and Gas Injection.
Table 5 compares these selected EOR processes for the M
reservoir using the predicted cumulative oil recoveries, which
indicated the following:
• Including additional infill producers after the
injection starts, both downdip and updip injection
yielded similar cumulative oil recoveries. However,
the downdip injection scheme generally required
more new wells (injectors as well as producers) to
have similar cumulative oil production as the updip
injection scheme.
• For updip injection, continuous downdip injection of
gas and updip injection of water (GASWAG) gave the
highest cumulative oil recovery, followed by updip
WAG and updip water injection (WAGCD).
Figure 13 shows the excellent recovery factors by model
for the selected redevelopment plan two reservoirs achieving
60+% recovery. The poorest oil recovery was in a reservoir
with a large gas-cap, where the early field development
strategy included production of the high-pressure gas as gaslift
for the field. The good recoveries are a challenge for the East
Flank area, which has remained relatively undeveloped
because of more fault complexity and smaller reservoir
compartments.
Recommendations
Based on the results of our investigation, we recommended
a redevelopment plan with three major components:
1. Three new platforms, platform upgrades to existing
production facilities, and infill drilling from six of the seven
existing drilling jackets. Because of the benefit of injection on
the existing idle wells, the number of infill producers was
optimized to reduce the overall redevelopment costs. Figure
14 shows a schematic of the old and required additions to the
facilities infrastructure.
2. Gravity Assisted Simultaneous Water and Gas
Injection (GASWAG EOR process) from two of the above
mentions platforms. Figure 15 is a schematic of the GASWAG
development plan for the M reservoir layers, and Figure 16
shows the performance of the M reservoir with and without
the GASWAG process. This graph includes the history match
of the past performance for comparison.
3. Field, reservoir and production management. The
recommended plans include a major production enhancement
effort, gaslift optimization, and modernizing the data
The updated development plan for the field involves
drilling 33 new wells comprising 22 production wells, six gas
injection wells and five water injections wells.This solution is
anticipated to increase recoverable reserves by as much as

8. 8 IPTC 13162
78MMstb from 17MMstb to 95MMstb within 2026. Recovery
factors from the field will increase from the pre-FDP forecast
of 41%, to 51%.
Because of some data uncertainty, we recommended to
perform additional analyses to evaluate the in-situ value of
Sorg and the effect of hysteresis on WAG injection process.
Injectivity tests were also recommended to test the actual
injectivity in the field with the likely injectant quality. The
future results of geomechanical studies should also be
considered to better understand the maximum injection
pressures by reservoir. These additional data will be obtained
during an aggressive production enhancement campaign,
planned for 2009-10, and an initial round of infill drilling.
The first 5 wells are designed to further confirm simulation
results and provide useful data such as additional core and a
selective suite of modern open-hole logs.
At the end of this study, there were only a few wells on
production in M reservoir for at the end of the history
matching period. It is recommended to revisit the simulation
model once the results of the data acquisition and production
enhancement campaign are available. In addition, the
successes and any failure of the production enhancement
program must be monitored as the results may influence the
planned infill drilling program, requiring a fewer or more
wells.
Acknowledgements
The authors would like to thank Petronas Carigali Sdn Bhd
(PCSB) and Schlumberger for permission to publish this
paper. Additional thanks to Dr. Raj Deo Tewari and M Razib
A Raub (PCSB) for their review of reservoir simulation
models and EOR investigations. Lastly, thanks to the many
multi-disciplinary team members that were involved in the
multi-year full-field review (FFR) and field redevelopment
plan (FDP).
References
1. Lee, Boo-Soon: “Samarang K5/7 Reservoir Simulation Study,”
Paper SPE 26351, presented at SPE Asia Pacific Oil & Gas
Conference, Singapore, 8 – 10 February, 1993.
2. Baxendale, D: “Application of Reservoir Management and
Simulation to Monitor the Performance of the M4/7/N1
Reservoirs in the Samarang Field, Offshore Sabah, East
Malaysia,” Paper SPE 29281, presented at SPE Asia Pacific Oil
& Gas Conference, Kuala Lumpur, Malaysia, 20 – 22 March,
1995.
3. Carrillat, A., Basu, Tanwi, Ysaccis, R., Hall, J., Mohamed, F.
Mansor, A., Brewer. M. and Mahmoud, S. "Integrated Geological
and Geophysical Analysis by Hierarchical Classification:
Combining Seismic Stratigraphy and AVO Attributes", presented
at 69th EAGE Conference & Exhibition, 01 June 2007.
Table 1 – Static Model Dimensions
Model
Grid size
(ft)
Average
Layer
thickness
Rows,
Columns,
Layers
Total
Cells
million
E-I Model 100 x 100 4 ft 62x154x605 5.8
JKL Model 200 x 200 4 ft 91x142x687 8.9
M-N Model 200 x 200 3 ft 76x140x372 4
O-S Model 200 x 200 2 ft 86x128x916 10
East Flank
Model
50 x 50 2.5 ft 104x188x516 10
Table 2 – Porosity-Permeability Transform by Facies
Rock Type Lithofacies Pool Air Perm (mD)
Best Rock Sm1, Sm2, Sl/c K=8.51E06 Phi^6.4
Intermediate Rock Sb1, Sb2, Sm/Ms K=2.63E06Phi^6.1
Poor Rock Ms, Mb + Carbonate K=1.08E06 Phi^5.9
Table 3 – Production Enhancement Plans
Production Enhancement Activity
No. of
Strings
Through-Tubing Perforation Addition 15
Sand Clean-Out (Coiled-tubing or other) 5
Water Shut-off Treatments 4
Acid Stimulation Treatment 7
Slickline Fishing Operation 2
ESP Conversions 2
Through-tubing Zone Change (e.g. Sliding Sleeves) 4
Major- and minor-rig Workovers 13
Re-open closed wells (reconnect surface flow system) 13
Re-open closed zones during EOR application 22
Totals 86
Table 4 – General Summary of Largest Reservoirs
J K M
STOIIP, MMSTB 218 160 280
Np (as of Oct.2006), MMSTB 122.3 92.4 122
Current RF, % 54% 57% 44%
Average Permeability, mD 500 350 200
Oil viscosity, cp 1.7 0.6 0.5
Gas Cap ratio, m 0.03 0.12 0.4
Drive mechanism
Strong water
drive
Strong water
drive
Water
drive/Gascap
Number active wells 11 8 6
No. of Drainage Point 70 61 119
Current average rate (Oct.
2006) STB/Day
2000 1500 500
Table 5 – M Reservoir Oil Recovery by EOR Process
Injection Process
RF, % Number of
Injectors
Number of
Infill
WI facility,
kbd
GI facility,
MMscf/d
WAGCD (Updip
WI)
56.1 5 12 50
Updip IWAG* 57.3 5 12 50 50
GASWAG1 57.5 11 12 50 25
GASWAG2 58.3 11 12 50 50

9. IPTC 13162 9
Figure 1 – Location of Samarang Field, Offshore Sabah, East Malaysia.
0
2
4
6
8
10
12
14
16
18
20
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
NumberofJobs
-
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
FlowRate-(bbl/day)
New Well
Work-Over
Side-Track
Oil Rate
Liquid Rate
New wells on Platfrom A &
B and from Jacket C, D, & F
New wells on
Jacket E & G
WO & ST on
A, B, D & E
WO & ST
on A, B & C
PCSB took over as operator as from
1st of April 1995
New wells on
Jacket D, G & F ST on B,
D & F
Figure 2 – Field Production and Development History.

10. 10 IPTC 13162
Figure 3 –Samarang Field – East-West Seismic Cross-Section.
Figure 4 – M Reservoir Structure Map Showing Large and Small Faults and Original OWC.

11. IPTC 13162 11
Figure 5 – Conceptual depositional model (wave storm dominated shoreface with tide influenced estuary)
Figure 6 – Field Cross-Section Showing Disribution of Hydrocarbon-Bearing Sands and Well Jackets
SSW NNE
E
G
F
H
J
K
MN
OPQ
-
2000
-
4000
-
6000
G E BAF CD Well Jkts
Sand Layers

12. 12 IPTC 13162
Figure 7 – Type-Log from Samarang-3 Well from the M Reservoir Section (Including Core Data)
Figure 8 – Static to Dynamic Modeling Workflow

13. IPTC 13162 13
UMSF
LSF
Offshr
Depo
Facies
PhiT
Litho
Facies
SM1
SL
Shale
SM2
SB
30%
0%
Figure 9 – Geomodelling Workflow – Depositional Facies to Lithofacies to Property Distribution
Figure 10 – Redevelopment Decisions Using Schematic Decision Tree
Depositional
Facies
Lithofacies
Porosity
Distribution

14. 14 IPTC 13162
Figure 11 – Example of Remaining Mobile Oil Thickness for the M5.0 Layer
Concept
Identification and
Selection
(23 Scenarios)
Case A
Conventional PE,
Infill Drilling, EF
Waterflood
Case B
Modified Case A
with EOR
in M reservoir
Case D
Modified Case C
with EOR
in M reservoir
Case C
Minimal
Infrastructure
via slot recovery
and conductor
strap-ons
ESPs vs. Gas Lift
Additional Platforms
New Drainage Target Opportunities
Enhanced Recovery Processes
Deviated and Horizontal CompletionsProduction Enhancement Initiatives
Secondary
Recovery
Commingled, Selective or
Intelligent Completions
Concept
Identification and
Selection
(23 Scenarios)
Case A
Conventional PE,
Infill Drilling, EF
Waterflood
Case B
Modified Case A
with EOR
in M reservoir
Case D
Modified Case C
with EOR
in M reservoir
Case C
Minimal
Infrastructure
via slot recovery
and conductor
strap-ons
Concept
Identification and
Selection
(23 Scenarios)
Case A
Conventional PE,
Infill Drilling, EF
Waterflood
Case B
Modified Case A
with EOR
in M reservoir
Case D
Modified Case C
with EOR
in M reservoir
Case C
Minimal
Infrastructure
via slot recovery
and conductor
strap-ons
ESPs vs. Gas Lift
Additional Platforms
New Drainage Target Opportunities
Enhanced Recovery Processes
Deviated and Horizontal CompletionsProduction Enhancement Initiatives
Secondary
Recovery
Commingled, Selective or
Intelligent Completions
Figure 12 – Example of Remaining Mobile Oil Thickness for the M5.0 Layer

15. IPTC 13162 15
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
EFGHI J K MN OPQ East Flank
Reservoir Models
RecoveryFactor(%)
EOR
Infill
PE 1/2/3
NFA
Cumulative Production
Figure 13 – Oil Recovery Factors by Dynamic Model and Redevelopment Type
KNDP-A
SMK-A
SUPG-B
SUDP-A
LGAST52.1km x 14”
SMV-C
SMDP-B
LCOT
SMW-B
12” x 1.83 km (LP gas)
SMR-A
SMJT-C
SMQ-A
SMG-A
SMP-A
SMDP-A
SMJT-F
SMV-B
SMDP-B
SMJT-D
SMG-AA
SMP-B
SMP-C
SMJT-E
SMJT-G
SMV-A
SMV-AA
New Gas Pipeline
New Liquid/Bulk Pipeline
WI
GI
Liquid/Bulk Pipeline
Gas Pipeline
Submarine Cable
New Gas Lift Line
6” x km (WI)
SMJT-AA
KNDP-A
SMK-A
SUPG-B
SUDP-A
LGAST52.1km x 14”
SMV-CSMV-C
SMDP-B
LCOT
SMW-B
12” x 1.83 km (LP gas)
SMR-A
SMJT-C
SMQ-A
SMG-A
SMP-A
SMDP-A
SMJT-F
SMV-B
SMDP-B
SMJT-D
SMG-AA
SMP-B
SMP-C
SMJT-E
SMJT-G
SMV-A
SMV-AA
New Gas Pipeline
New Liquid/Bulk Pipeline
WI
GI
Liquid/Bulk Pipeline
Gas Pipeline
Submarine Cable
New Gas Lift Line
6” x km (WI)
SMJT-AA
Figure 14 – Field Facilities Schematic With Redevelopment Requirements

16. 16 IPTC 13162
Gravity
Assisted
Simultaneous
Water
And
Gas
Injection
Recovery mechanisms
- Re-pressurizing reservoir
- Sweeping remaining oil towards new and reopened wells
- Improved vertical sweep using gravity assistance
- Pushing attic oil back down to producers
- Reduced Sor with respect to gas in water swept layers
EORScope:
– 50MMscfdGasInj.
– 6DowndipInjectors
– 50kbpdWater Inj.
– 5updipInjectors
– 22Reactivations
– 5Infill Producers
Figure 15 –GASWAG EOR Process Schematic for M Reservoir
0
5000
10000
15000
20000
25000
30000
35000
40000
6/75 6/79 6/83 6/87 6/91 6/95 6/99 6/03 6/07 6/11 6/15 6/19 6/23 6/27 6/31 6/35
Date
OPR,STB/D
Observed ALL_MN_COMB_5.RSM MN_GIWI_5050-CASEB.RSM MN_A_ESP.RSM
0.0
5000.0
10000.0
15000.0
20000.0
25000.0
30000.0
35000.0
6/75 6/79 6/83 6/87 6/91 6/95 6/99 6/03 6/07 6/11 6/15 6/19 6/23 6/27 6/31 6/35
Date
WPR,STB/D
Observed ALL_MN_COMB_5.RSM MN_GIWI_5050-CASEB.RSM MN_A_ESP.RSM
0
10000
20000
30000
40000
50000
60000
70000
6/75 6/79 6/83 6/87 6/91 6/95 6/99 6/03 6/07 6/11 6/15 6/19 6/23 6/27 6/31 6/35
Date
GPR,Mscf/D
Observed ALL_MN_COMB_5.RSM MN_GIWI_5050-CASEB.RSM MN_A_ESP.RSM
0
500
1000
1500
2000
2500
3000
6/75 6/79 6/83 6/87 6/91 6/95 6/99 6/03 6/07 6/11 6/15 6/19 6/23 6/27 6/31 6/35
Date
PR,psia
Observed ALL_MN_COMB_5.RSM MN_GIWI_5050-CASEB.RSM MN_A_ESP.RSM
Figure 16 – M Reservoir History Match and Performance Predictions (With and Without GASWAG EOR Process)