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SPE 51893
Reservoir Characterization of Ekofisk Field: A Giant, Fractured Chalk Reservoir in the
Norwegian North Sea -- History Match
B. Agarwal, SPE, Phillips Petroleum Company UK Ltd, H. Hermansen, J. E. Sylte, SPE, Phillips Petroleum Company
Norway, L. K. Thomas, SPE, Phillips Petroleum Company
Copyright 1999, Society of Petroleum Engineers Inc.
This paper was prepared for presentation at the 1999 SPE Reservoir Simulation Symposium
held in Houston, Texas, 14–17 February 1999.
This paper was selected for presentation by an SPE Program Committee following review of
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The history match of the 3-D fluid flow model constructed on
the Ekofisk Field is the focus of this study. The Ekofisk
reservoir is a high porosity, low matrix permeability naturally
fractured chalk. Fluid flow is largely governed by the
distribution, orientation and interconnectivity of the natural
fracture system associated with complex structure and
reservoir distribution. Because of the impact heterogeneity has
on preferential fluid flow direction, significant attention was
given to capturing as much of the intrinsic heterogeneity as
possible, both laterally and vertically, in the new 3-D
geological model. A fairly high-resolution simulation mesh
was defined for the fluid flow model, and a flow-based
upscaling technique was then applied to preserve the
heterogeneity from the geological to the fluid flow model.
Because of the complexity of the Ekofisk Field, with its
numerous faults and fracture networks, anisotropy was one of
the primary attributes manipulated to achieve an individual
well and field history match. However, faults and fault sealing
factors, vertical permeability, pseudo relative permeability
curves, bubble point pressure correlations, local permeability,
and rock compressibility were also key parameters in the
history match, and are presented in this paper. A brief
discussion on the preliminary implementation of waterinduced compaction is included.
The Ekofisk Field is a prolific field discovered in 1969 and
located in the Norwegian Sector of the North Sea. The
reservoir consists of two fine-grained limestone producing
formations, the Ekofisk formation (Danian Age) and the Tor
formation (Maastrichtian Age), separated by a thin,
impermeable Tight Zone. The reservoir was initially
overpressured and contained an undersaturated oil at 50 Mpa
(7120 psi) and 131 oC (268 oF) at a datum elevation of 3170 m
(10,400 ft) subsea. The bubble point pressure was
approximately 38 Mpa (5545 psig). Production started from
the chalks in 1971. Current estimates from the reservoir
characterization project indicate about 7 billion barrels of oil
originally in place. Current production from 76 deviated and
horizontal wells is 310,000 barrels of oil per day and 510
MMCFD of gas. A pilot water injection project was initiated
in 1981 in the highly fractured Tor formation1 and in the
Lower Ekofisk in 19862. Fieldwide water injection began in
19873. Current water injection rates are 800,000 bwpd into 37
active injection wells. Fig. 1 shows a structure map of the
Ekofisk Field drawn on the top Ekofisk formation.
Reservoir characterization on Ekofisk was directed at
gaining a detailed understanding of reservoir hydrocarbon
volumes, the architecture of the reservoir and at fully
describing the heterogeneity and anisotropy of reservoir
parameters4,5. Because water breakthrough has been observed
in areas of the field not consistent with expectations, it is
important that the highest degree of heterogeneity be
represented in the flow model. This is especially significant
given that the Ekofisk Field is currently undergoing a major
field re-development in which 45 new wells will be drilled
before the end of 2003. To date a total of 19 new wells have
already been drilled and are currently on production.
The history match of the Ekofisk Reservoir
Characterization fluid flow model was completed in
September 1997 after a period of approximately twelve (12)
months of intense work. The complexity of the Ekofisk field,
with its numerous faults and fracture networks, provided quite
a challenge in matching the twenty-five (25) years worth of
production and performance data. The heterogeneity that was
captured in the 3-D geological model, and preserved in the
upscaling process to the fluid flow model, proved to be the key
to being able to match individual well performance. In
general, a very good history match was achieved on both a
field and platform basis, and on an individual well basis.
History Match
Basic Data. The basic data used in the history matching of the
flow model includes the following: (1) Data from 160
wellbores; (2) twenty-five years of production history; (3)
RFT and FMT data representing formation pressures for a
majority of the wells; (4) production log data for most wells,
showing relative contributions from sub-layers; (5) logs
representing water saturation from later wells to help model
the degree of water influx from the flank; (6) time-lapse
neutron study to match vertical gas distribution in
approximately 25 wells with data; (7) historical bottomhole
pressure data.
History Matching Parameters. The main parameters used in
the history match of the fluid flow model were the following:
(1) Anisotropy in the x-y direction to control gas and water
pattern away from the gas and water injection wells; (2) fault
factors to control pressure support and flow direction; (3) nonneighbor connections across faults to manage fluid movement
and direct communication between sub-layers; (4) vertical
permeability, kv/kh to control gas migration and pressure
support; (5) flank permeability to control aquifer pressure
support and water influx; (6) pseudo relative permeability
curves and rock region definitions to control fluid movement;
(7) endpoint krw and permeability around water injectors to
match bottomhole injection pressures; (8) permeability and
skin in cells with completions to adjust well productivity to
match bottomhole pressure data; (9) well productivity index to
match PLT contributions; (10) bubble point pressure
correlation to match initial undersaturated gas-oil ratios; (11)
rock compressibility to provide pressure support from the
flank and to match compaction volumes.
In general, the principal factor in the history match was
modeling the high GOR and watercut responses in specific
wells. Anisotropy was the key to achieving this and was based
on the fault and fracture networks which exist in the Ekofisk
field. In the following sections, we describe the various
parameters and their influence on the history match.
Anisotropy/Faults. Fluid flow in the Ekofisk field is
largely governed by the distribution, orientation and
interconnectivity of the natural fracture system associated with
complex structure and reservoir distribution. There are over
300 fault planes mapped in the Ekofisk field, with a majority
of the major faults defined in the fluid flow model.
Non-Neighbor Connection Faults. The faults with the
largest throws, primarily the NE-SW normal faults, have been
designated as non-neighbor connection faults. These are faults
that allow communication across different bed boundaries; for
example, Layer EA2 might be feeding directly into Layer EB,
or more importantly, Layer TAU1 communicates directly with
Layer ED2, thus allowing a “window” through the Tight Zone.
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Fig. 2 is a schematic of a typical non-neighbor connection
As is represented in this figure, non-neighbor connection
faults are modeled as vertical boundaries. For this reason, in
specific instances where the dip of the fault plane is large,
separate definitions have been specified for the Ekofisk and
Tor formations. There are a total of nine (9) non-neighbor
connection faults defined in the fluid flow model. Once the
fault is defined, the sealing factor across the fault can be
adjusted during the history match to model the correct
pressure and saturation responses.
Simple Faults. Numerous faults are defined in the reservoir
simulation model to control pressure and/or fluid flow
direction. These faults represent, in general, the medium-sized
strike-slip faults and reverse faults, whose throws are typically
less than 50 feet. The fault map is “rasterized” onto the
simulation mesh to determine the grid cell boundaries
corresponding to the fault. Simple faults are used primarily to
affect fluid flow direction in the history match, and to
constrain the effects of waterflood response.
Lateral. The remainder of the faults, not mapped directly
within the flow model as either non-neighbor connection or
simple faults, were defined based on anisotropy. Performance
data in the field suggested that many of the fault networks act
as conduits to flow, and can, therefore, be considered as
enhanced transmissibility. To correctly integrate this into the
reservoir simulation model, a detailed analysis was performed
on the available data in which response times between
injector-injector, injector-producer, and producer-producer
combinations were evaluated. Based on the relative time
observed to see a response in a particular well, an initial value
of anisotropy was assigned to this specific area. For example,
if a water injector was put on stream, and a definite response
was observed in GOR in an offset producer in one to two
months, this was a clear indication of direct communication
between the two wells. Therefore, a relatively high anisotropy,
say 10:1, was assigned to this area. This procedure was
followed for all well combinations for which data existed.
Fracture density was used as an additional basis for
determining the value of anisotropy to apply to a given area.
Areas with high fracture intensity were assigned higher values
of anisotropy, and values with lower fracture intensity were
assigned lower values of anisotropy. In this fashion, a template
of x-y anisotropy that was directly linked to the fault and
fracture network was created as initial input to the flow model.
A study based on the stress regimes which exist in the field
showed that the regional trend in anisotropy was
approximately 3:1. This value was assigned to all areas with
minimal fracturing, such as most of the flanks and portions of
the crest of the field. Fig. 3 illustrates the anisotropy template
created for the reservoir simulation model. The history match
itself required tuning of these anisotropy values, but the
relationship to fracture density was maintained throughout.
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Vertical. Similar to the lateral anisotropy defined, vertical
communication was also based on the fault network. Typical
input values of kv/kh range from 0.0 to 0.1, with the majority of
the grid cells assigned values in the 0.025 to 0.033 range. The
Ekofisk is considered a tighter formation than the Tor so lower
values were assigned to the top seven (7) simulation layers,
and lateral variation of kv/kh was greater than in the Tor. The
Tight Zone is generally a complete seal throughout the
majority of the field. There are, however, areas in the field in
which communication between the Ekofisk and Tor
formations does occur. In previous models built on the Ekofisk
field, a “window” was described in the Tight Zone where a
block of cells were assigned non-zero vertical permeability.
For the ERC model, however, a more geological basis was
the objective of defining the “window” in the Tight Zone. This
was done by initially assigning vertical permeability to cells in
the flow model that contained the major faults. For the most
part, these cells corresponded to the cells to which nonneighbor connection faults were defined. Although the nonneighbor connection faults allow some vertical communication
through the Tight Zone in areas of large throw, the amount of
fluid movement was not sufficient with non-neighbor
connections alone. The values assigned to the kv/kh grids in the
flow model were tuned based on the history match to control
gas migration, and to match RFT pressures from individual
wells. Fig. 4 is a representation of the Z-direction modifying
factor grid, defined in the simulation model, for the Tight
Zone. In this figure, the blue areas represent zero vertical
communication and the colored areas reflect non-zero
transmissibility due to the fault network.
Relative Permeability/Rock Regions
Mechanistic studies were performed to generate water-oil
pseudo-relative permeability curves for the 3-D reservoir flow
model6. The purpose of this work was to account for the fluid
transfer between the high permeability fractures and low
permeability matrix blocks, minimize numerical dispersion,
and accurately model physical dispersion due to layering,
fracturing, and matrix block geometries. Although the matrix
blocks contain the bulk of the pore volume, it is the natural
fracture system that dominates overall permeability.
For strongly water wet areas such as the Tor and Lower
Ekofisk formations at Ekofisk, the bulk of waterflood
displacement is a capillary dominated process and the rate of
recovery is strongly dependent on matrix block size and the
amount of matrix surface area exposed to fractures, with
ultimate recovery determined by the capillary, gravity, and
viscous equilibrium. For less water wet areas, such as the
Upper Ekofisk formation at Ekofisk, viscous and gravity
forces as well as capillary forces are important.
A typical water-oil pseudo relative permeability curve for
the Lower Ekofisk formation is presented in Fig. 5 for a high
fracture intensity region. The curves in general indicate a low
relative permeability to water and high relative permeability to
oil for the lower water saturation range to reflect the very
favorable imbibition properties for the Tor and Lower Ekofisk
chalk. A fairly steep slope exists on the water and oil relative
permeabilities at higher water saturations to reflect the rapid
movement of water through the natural fracture system once
the imbibition process is approaching completion.
For the gas oil system with very low viscous gradients, the
mechanistic studies have focused primarily on gravity
drainage, with capillary continuity between matrix blocks and
oil phase reimbibition being two important phenomena. This
results in ultimate oil recoveries similar to a non-fractured
block of the same height, and oil drained from the higher
matrix blocks re-enters lower matrix blocks, slowing the
drainage process.
A two stage upscaling process was used to develop pseudo
relative permeability curves for the single porosity model.
The first involved detailed modelling of stacks of individual
matrix blocks, which included gravitational, viscous, and
capillary effects in addition to block to block reinfiltration.
Then, the upscaled properties from the detailed model were
applied in the field scaled single porosity cross-sections to
account for intra-layer heterogeneities.
History matching the GOR for Ekofisk is quite challenging
primarily due to the extensive system of fractures and faults in
the reservoir. Natural gas injection from early in the life of the
field as well as the evolution of gas in the field during
depletion below bubble point results in rapidly increasing
GOR's in highly fractured areas. Also, as waterflood was
initiated after the field was well below bubble point, gas
collapse due to pressure support from the water injectors has
resulted in significant drops in producing GOR’s. Achieving
the correct balance between putting gas back into solution and
maintaining a gas saturation for the higher GOR wells was a
priority in this work.
Bubble Point Pressure
Bubble point pressure as a function of depth for the
Ekofisk field was determined based on production GOR data
from thirty-four (34) pre-bubble point producers. The
procedure involved establishing the average pre-bubble point
GOR for each producer, determining the bubble point pressure
from the Rs versus pressure curve implemented in the full field
model, then relating the pressure to depth based on perforation
depths and PLT contributions. Fine-tuning of the correlation
was required to match early-time GOR behavior. Fig. 6
represents the correlation implemented in the flow model.
Results and Observations. The history match of the ERC
fluid flow model was completed in September 1997 and was
subsequently used in predicting new well locations for the redevelopment campaign on the Ekofisk field. Even though a
good history match was achieved, it is certainly non-unique,
and the ultimate test of any reservoir simulation model is its
ability to predict into the future. Data gathered for new wells
drilled in the field toward the later stages of the history match
process were valuable in validating the performance of the
reservoir model. For several of the long horizontal wells, the
model proved to be quite accurate in its predictions in that it
showed high water saturation in areas not previously
indicated. This lends confidence to the history match of the
Pressure. History matching pressure in Ekofisk is
complicated by the layered nature of the field as well as the
extensive faulting in the reservoir. The presence of the Tight
Zone providing a complete seal throughout the majority of the
field, a combination of different rock properties, and aquifer
support results in significant differential depletion in many
areas of the field. In general, Ekofisk formation pressures are
lower than those of the Tor formation. This can be observed in
numerous wells around the field, such as the A-09A, C-14A
and B-23A. However, a reverse trend has been observed in
some wells, particularly on the eastern flank. The C-21 and
C-22 are good examples of this. Fig. 7 represents Ekofisk and
Tor formation pressures through time, as recorded through
RFT data.
Extreme rate changes due to workovers, new producers
and the introduction of water flooding in a staged manner has
resulted in pressure swings on the order of 1000-2000 psi
periodically throughout the field. As a result, a match to within
150 psi of the measured pressure was considered good. A
majority of the wells were matched to this tolerance. A good
example of the RFT match is well A-05A, drilled in May,
1986, which shows a good match of both the Tor and Ekofisk
formation pressures, as well as the significant differential
depletion, Fig. 8. Flank well pressures often proved more
difficult to match as they were much more sensitive to
permeability. Too high a permeability resulted in greater water
influx than actual so that a match to the measured water
saturations could not be achieved. Too low a permeability
resulted in severe pressure depletion causing the pressures to
be much lower than those measured in RFT data. The
objective was to try to achieve a balance using absolute
permeability, pseudo-relative permeability and rock
compressibility. Rock compressibility in the Tor formation
was increased to 6.0E-06 psi-1 based on available lab data.
This change had a positive impact on the overall Tor
pressures, allowing a better match to be achieved.
In summary, Tor flank pressure was controlled primarily
by the following:
1. Vertical permeability (kv/kh) was increased in the far flank
to provide more aquifer support.
2. Flank absolute permeability was increased to provide
more lateral support. The constraint of water influx was
rigorously enforced, however.
3. Bottom aquifer support was increased by adjusting
permeability and kv/kh in the bottom layer of the flow
model. This was a sensitive parameter and required
careful tuning to match water production.
4. Tor rock compressibility was increased to a higher value
based on laboratory data.
Ekofisk Formation pressures initially tended to be a little on
the high side. Pseudo-relative permeability (kro) was used in
conjunction with flank permeabilities to bring pressures back
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in line with measured data. Also, the fact that Tor pressures
were low and Ekofisk were high, suggested either downward
fluid/pressure migration or limited upward fluid/pressure
migration through the Tight Zone. Tuning of the kv/kh grids
helped to control this.
Gas-Oil Ratio. Gas-oil ratio (GOR) was one of the major
matching parameters in the fluid flow model. The intent was
to match measured and simulated GOR on a field and platform
basis, and on an individual well basis, to the best extent
possible. Given the high GOR’s in some of the early wells and
some of the later Tor wells, this was not a trivial task. One of
the main challenges was to maintain a reasonable gas
saturation in the Tor formation past commencement of the
waterflood. The natural result was for the gas to be rapidly
pushed back into solution with an increase in pressure from
water injection. A good example of this can be seen with the
GOR behavior of well A-14. Initially, the well produced at
solution GOR with initial pressures considerably above the
bubble point. With pressure depletion and possibly some
impact of the natural gas injection, the GOR,s rose steadily
through the years and peaked at a GOR of 12,230 SCF/STB in
1990. With the start up of the offset water injection well K-03,
a sharp and dramatic drop in producting GOR is seen as
displayed in Fig. 9. The model produces a very reasonable
match of this behavior.
An excellent match was obtained for both the full field and
individual platforms, Figs. 10 through 13. In these figures, the
solid line represents the response calculated from the flow
model, and the dashed line reflects the actual historical values.
Watercut. Watercut was another important matching
parameter for the Ekofisk field. With the re-development of
the field ongoing, and approximately 45 new wells planned,
determining the waterflood pattern development away from
injectors was key. Ekofisk is a very complex field with the
fault network acting as a major conduit to flow. Given the
heterogeneity in the field, water does not move radially away
from the water injection wells. Instead, fingers and channels
exist which are normally very difficult to duplicate in a
reservoir simulation model. By directly linking permeability
and anisotropy to the fracture system in the field, and by
defining a fairly high grid resolution, the ERC flow model is
better able to model the water pattern development.
An excellent match was achieved for the full field and
individual platforms as presented in Figs. 14-17. Good
examples of individual well water cut matches can be seen on
the match of well C-06 as presented in Fig. 18. This well had
direct communication to the offset water injection well K-19.
Nearly instantaneous breakthrough was seen with the start of
water injection at K-19. This well was subsequently
sidetracked a short distance away and came in essentially
water free producing from the same intervals as the original
well. The excellent match of this water cut performance could
only be achieved with extreme flow channels resulting from
the fracture and fault system. Another good example can be
seen with the results of the C-23 well, which was drilled close
to a fault system connecting the well with the offset water
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injection well K-04. Watercut has increased steadily in this
well and is currently at approximately 90 percent, Fig. 19.
Tracer tests, performed in 1994 by injecting tracer into the
K-04 well and monitoring offset producing wells, confirmed a
strong interaction with the injection well. This match could
only be achieved with a high quality reservoir model.
One area of difficulty was in the vicinity of the Charlie
platform itself, the C-11 well. This well is located in a vicinity
of the field where seismic imaging is very poor, and as a result
the fault system poorly defined. This well has historically been
used as a compaction monitoring well. In this regard, time
lapse data on water saturation in the wellbore existed. The data
indicates that the thin zone just beneath the Tight Zone
showed high water saturation very early, followed by the zone
just above the Tight Zone. These two zones are typically
considered the high compaction zones. Given the timing and
completion schedule of the water injectors surrounding this
well, the only way to match the water saturation profile in the
wellbore was to channel water directly to the area by way of
defining two additional faults with very tight sealing factors.
Implementation of this extreme measure helped but did not
provide an exact match. The complex nature of the fracture
and fault system in this area in combination with poor seismic
imaging appears to be the reason for not being able to match
the watercut precisely.
These above examples and others are considered as
validation of the Ekofisk reservoir model. The drilling of the
new re-development wells has been an overwhelming success
with production rates now being seen at Ekofisk that have not
been achieved since the late 1970’s. The confidence in the
model’s history match and prediction capabilities has been an
important part of this success.
Permeability. The permeability algorithm4 and upscaled
permeabilities5 developed for the ERC model performed very
well in that major changes to the distribution of permeability
were not necessary. Some local refinement around wells, in
particular water injectors, was required possibly due to in situ
fracturing. Also flank permeability in certain geologic layers
were adjusted to control pressure support from the aquifer.
Water Induced Compaction. For the Ekofisk field,
laboratory data has been used to establish a relationship
between the amount of water contacting the chalk and the
degree of compaction observed, Fig. 20. That is, increased
compaction is observed as the water saturation in a particular
block or cell increases, ie, the rock is weakened as a function
of increasing water saturation. Logic was developed to
incorporate these relationships in the model and the history
match was re-run.
Implementation of this water weakening logic in the fluid
flow model had a significant impact on both the Tor formation
pressure and the late GOR behavior of the wells. Furthermore,
the compaction volumes of the fluid flow model much better
matched the actual volumes. The increased compaction
resulting from water weakening, provided more energy in the
system. This increase in energy resulted in higher pressures,
especially in the Tor formation. This, in turn, mitigated the
pressure depletion in specific compartments in the field, which
prevented excess gas coming out of solution and, thus, too
high GOR’s late in history.
The fluid flow model with the water weakening logic
implemented will be used in the future for the Ekofisk field
and will be the basis for further history matching. Refinement
of the relationships established from laboratory data still need
to be performed and the history match subsequently finetuned.
Concluding Remarks
An integrated, high-resolution geological model and a fairly
high-resolution reservoir simulation flow model have been
developed for the detailed planning of the redevelopment of
the Ekofisk Field. A number of innovative techniques have
been applied within the disciplines of geoscience, petrophysics
and reservoir engineering resulting in integrated models which
enable team-driven decisions to be made regarding reserves
The models have been developed using all data,
information and knowledge available during the time span of
the project. Processes have been put in place to ensure the
continuous and rapid updating of the geological and fluid flow
models as new data, information and knowledge becomes
In this study, the history match of the fluid flow model is
presented. Here the challenges of matching such a complex
field and the approach taken to achieve the final history match
are discussed.
The following observations are warranted:
1. Additional reservoir characterization is obtained through
the history matching process. Flow across faults, vertical
permeabilities, flank characteristics, etc, are solved for in the
history match.
2. The geological model is not an exact representation of
the reservoir; it reflects the best interpretation possible based
on the limited data available. The history match solves a
number of characterization issues and the process of
downscaling will ultimately reflect this back into the
geological model.
3. Generally, a good pressure match was achieved in the
fluid flow model.
4. GOR was well matched on both a field and platform
basis and an individual well basis.
5. A good watercut match was obtained with some
exception, for example around the Charlie platform where thin
zones of high water production exist.
6. Further development of the relationships for the water
weakening logic will enhance the history match, and allow
more accurate GOR trends to be achieved late in history.
Rs = solution gas dissolved in oil phase, scf/stb
GOR = gas-oil-ration, scf/stb
PLT = production logging tool
cf = rock compressibility, psi-1
The authors acknowledge permission to publish the above
paper from Phillips Petroleum Company Norway and Coventurers, including Fina Exploration Norway SCA, Norsk
Agip A/S, Elf Petroleum Norge AS, Norsk Hydro A.S., Den
norske stats oljeselskap a.s., TOTAL Norge A.S. and Saga
Petroleum A.S.
Thomas, L. K., Dixon, T. N., Evans, C. E., and Vienot, M. E.:
“Ekofisk Waterflood Pilot,” JPT,February, 1987, pp. 221-232.
Sylte, J. E., Hallenbeck, L. D., and Thomas, L. K.: “Ekofisk
Formation Pilot Waterflood,” Paper SPE 18276 presented at the
1988 SPE Annual Technical Conference, Houston, Texas,
October 2-5, 1988.
Hallenbeck, L. D., Sylte, J. E., Ebbs, D. J., and Thomas, L. K.:
“Implementation of the Ekofisk Full Field Waterflood,” SPE
19838 presented at the 1989 SPE Annual Technical Conference,
San Antonio, Texas, October 8-11, 1989.
Agarwal, B., Allen, L. R., and Farrell, H. E.: “Ekofisk Field
Reservoir Characterization: Mapping Permeability Through
Facies and Fracture Intensity,” SPE Formation Evaluation,
December 1997, pp. 227-233.
Agarwal, B., Thomas, L. K., Sylte, J. E., and O’Meara, D.:
“Reservoir Characterization of Ekofisk Field: A Giant,
Fractured Chalk Reservoir in the Norwegian North Sea:
Upscaling,” SPE 38875 presented at the 1997 SPE Annual Fall
Meeting, San Antonio, Texas, October 5-8, 1997.
Hermansen, H. and Thomas, L. K.: “Reservoir Simulation
Challenges at the Ekofisk Field,” Conference on
Characterization and Interpretation of Fluid Flow in Fractured
Reservoirs, Society of Core Analysts (SPWLA), Hamburg,
Germany, October, 1998.
SPE 51893
0 4
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0 0
1 0
Depth: 2890 m (9480 ft.)
Size: 27 sq. km. (16.8 sq. mi.)
76 producing wells, 37 injectors
0 1-
Fig. 1.--Top structure map of the Ekofisk field.
Fig. 2 - Schematic representing typical non-neighbor connection
fault in the Ekofisk Field.
Fig. 3 - Lateral anisotropy map of the Ekofisk field fluid flow model.
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Fig. 4 - Vertical anisotropy map of the Tight Zone of the
Ekofisk field fluid flow model.
W a t e r- O il P s e u d o R e l a t i v e P e r m e a b i l i t y
Ppb vs. Depth Based on Pre-Pbp GOR Data
Hi g h F r a c t u r e I n t e n s i t y R e g i o n
0 .8
k ro
Depth, ft
0 .6
0 .4
k rw
0 .2
0 .2
0 .4
0 .6
0 .8
W a t e r S a t ura t io n , f r a c t i o n
Fig. 5 - Typical water-oil pseudo relative permeability curve
from a high fracture intensity region of the Ekofisk field.
Bubble Point Pressure, psi
Fig. 6 - Ekofisk field bubble point as a function of depth
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D a n i a n R F T D a ta
C r e ta c e o u s R F T D a ta
Fig. 7 - Ekofisk andTtor RFT and simulation model pressures.
Fig. 8 - RFT match for well A-05A from history match.
Fig. 9 - Well A-14 GOR match.
Fig. 11 - Bravo Platform GOR match. Dashed line reflects actual
and solid line reflects simulated response
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Fig. 10 - Alpha Platform GOR match. Dashed line reflects actual
and solid line reflects simulated response
Fig. 12 - Charlie Platform GOR match. Dashed line reflects actual
and solid line reflects simulated response
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Fig. 13 - Total Field GOR match. Dashed line reflects actual
and solid line reflects simulated response
Fig. 15 - Bravo Platform watercut match. Dashed line reflects
actual and solid line reflects simulated response
Fig. 14 - Alpha Platform watercut match. Dashed line reflects
actual and solid line reflects simulated response
Fig. 16 - Charlie Platform watercut match. Dashed line reflects
actual and solid line reflects simulated response
Fig. 17 - Total Field watercut match. Dashed line reflects
actual and solid line reflects simulated response
SPE 51893
Fig. 18 - Well C-06 watercut match. Dashed line reflects
actual and solid line reflects simulated response
Ekofisk Compaction Data - 40% Initial Porosity
Model Porosity, percent
Sw - 0%
Sw - 5%
Sw - 10%
Sw - 15%
Sw - 20%
Sw - 25%
Effective Axial Stress, thousands
Fig. 19 - Well C23 watercut match. Dashed line reflects
actual and solid line reflects simulated response
Fig. 20 - Ekofisk uniaxial stress laboratory data for a 40%
porosity chalk sample converted to model porosity.