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SPE WVPS-655 Applied Methodology for managing a Heavy
Oil Mature Field with High Water Production E. Alvarado and M. Eggenschwiler,
SPE, Statoil International Venezuela AS, and K. Uleberg, SPE, Statoil Norway Section: Water and Gas Management
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Copyright 2015, Society of Petroleum Engineers
This paper was prepared for
presentation at the 2015 SPE WVPS 3er South American Oil and Gas Congress held
in Maracaibo, Zulia State, Venezuela, 27–30 October 2015.
This paper was selected for presentation by the SPE Western Venezuelan
Petroleum Section Program Committee, following review of information contained
in an abstract submitted by the author(s). Contents of the paper have not been
reviewed by the SPE Western Venezuelan Petroleum Section Program Committee and
are subject to correction by the author(s). The material does not necessarily
reflect any position of SPE Western Venezuelan Petroleum Section, its officers,
or members. Electronic reproduction, distribution, or storage of any part of
this paper without written consent of the SPE Western Venezuelan Petroleum
Section is prohibited. Permission to reproduce in print is restricted to an
abstract of not more than 350 words; illustrations may not be copied.
Abstract
Managing an oil field requires reliable forecasting tools to allow flexibility in running sensitivities and simulating changes in field operations, in order to produce responses that can support and streamline decision making in a short time frame.
In this paper we present a methodological approach to be
applied to a mature heavy oil field, examining cold production project
execution, field optimization practices, drilling activities (new wells,
re-entries, infill pads) and potential EOR projects such as thermal and
chemical floods.
The methodology was used to characterize water production
trends and provide guidelines for possible expansion of water handling
facilities to cope with high field water production levels encountered in a
mature field. This approach is based on a superposition of production
performance type curves, reflecting oil, water, and gas producing trends for
different areas and sand units. These are based on decline analysis,
representing well optimization performance and derived from specific dynamic
models.
This paper also stresses how inconvenient could be the use
of full field dynamic models in heavy oil fields for forecasting purposes since
near wellbore resolution is not captured. Large full field static models should
be constructed with sufficient detail, so that partial dynamic sector
simulations models can be derived.
The production performance type curves are summed up or
superimposed, following a phase field development plan which includes rig
availability, connecting time, and facility fluid handling capacities. The
Forecasting tool could be also used in resizing the existing field water
handling facilities. The tool also includes simplified probabilistic approach
to handle uncertainties.
Results of our field forecasting sensitivities are
presented in full detail herein.
Periodical updates with new historical data and new dynamic
sector models should be applied as these become available.
Introduction
A reservoir management plan is the result of the
coordination of various disciplines and teamwork between reservoir and
production engineers, geosciences, drilling, operations, facilities, and
planning. As a result of this coordinated work, an asset management tool or
methodology is developed with the purpose of maximizing the net present value
of the asset, maximizing the reservoir recovery factor, minimizing capital
investment and operating costs1.
An organizational approach to help solve some of the
reservoir management problems and challenges that lie ahead in the next decades
is the use of a variety of specialists in the task-force approach, in a
synergistic team. A good reservoir description designed to answer key reservoir
performance questions is a fundamental tool for appraisal, development,
planning and reservoir management. An optimum reservoir management requires
teamwork and close coordination among all participants through all stages of the
life of reservoir2 and an adequacy of its representation in
forecasting tools is the key upon which field development plans rest; the degree
of accuracy in forecasted production rates, the degree to which selected
facilities will fit, and the level of reality in economic projections are all
intimately related to the reservoir description3.
The ultimate tool for managing a field asset is a reliable
production forecast methodology that includes the input from the various
disciplines, field history, performance trends, reliable reservoir models, and
guidelines and best practices dictated by the company. The forecasting tool
should be flexible enough as to allow running sensitivities and changes in
field operations and still produce prompt responses that can be used for
reliable decision making in a short time frame.
In this paper we present a methodology as applied to a
mature, heavy oil field that examined cold production projects, field
optimization practices, drilling activities (new wells, re-entries, and infill
pads), and Enhanced Oil Recovery (EOR) projects including thermal and chemical
floods.
With this methodology we looked first at base line
production forecasts (cold production only), then later included field
optimization for short term asset value improvement, and lastly, we considered
the implementation of Enhanced Oil Recovery (EOR) pilots with subsequent field
expansions. We also looked at water production trends and gave guide lines for
possible expansion of water handling facilities to manage the unavoidable high
field water production levels encountered in a mature field.
Methodology
The field global production forecasting methodology
presented herein is based on a type-curve superposition approach where the type
curves from different field elements are assumed independent from each other.
The way type-curves are superimposed must satisfy the field constraints
depending on the field development strategies. The methodology can be viewed as
a semi-analytical approach that includes a combination of type-curves based on
decline analysis4 and production forecasts generated from dynamic
simulation sector models. The use of analytical decline models or dynamic
simulations from sector models can be interchanged as more information is made
available from the field and more accurate simulation results are available.
Even though superimposing regional or individual type
curves is not strictly correct because of field interference effects, it still
gives a good approximation particularly in heavy oil reservoirs when full field
reservoir models are not available and when quick answers are required for
analyzing different field development strategies. Furthermore, in heavy and
extra-heavy reservoirs high modeling resolution is required around the wells to
capture near wellbore effects which tend to dominate field production
performance. This would render full field simulation unpractical for this kind
of reservoirs. A type-curve simulation approach would make it possible to
analyze full field production performance and generate reasonable forecasts
contemplating different development options.
The individual type curves are represented as a function of
cumulative oil production, but the superposition is carried out as a function
of time, using a discretized time approach. This would allow inclusion of
drilling schedules incorporating well connections and shut-in times. The
superposition approach was constructed in Excel spreadsheets which allowed
inclusion of the type curves and field parameters describing the field operating
constraints.
The production forecast elements include a) the baseline
production and b) production potential based on type curves.
The production potential type curves should include
productivity estimates for primary production projects as new wells, sector
model simulation forecasts, re-entries performance forecast, infill pads, and
secondary production projects as Enhance Oil Recovery (EOR) thermal or chemical
projects.
The production forecasts should also include the field
regularity that take into account planned well services and stimulations,
unplanned electrical and mechanical field hardware failures, and measure
adjustments when incorporating future third party partnership inputs.
The source of historical production data (oil, water, gas)
came from a matured Orinoco Extra Heavy Oil (Faja) field with high water
production and highly viscous oil. This was the motivation to develop the
proposed production forecast methodology, based on individual components (area,
sand unit, individual wells, etc.) for a more accurate analysis.
The forecasting methodology and analytical procedures
included in our forecasting tool were developed in accordance with “Statoil`s
Guidelines for Production forecasting”5, and with a "QA/QC
Business Plan Methodology" best practices document developed by a team of
experts from various disciplines (geologists, geophysicists, reservoir and
production engineers, and specialists in thermal and chemical flood EOR projects).
Production
Forecast Elements
The forecasting elements which are the base of our
production forecasting methodology are as follows:
a) Base line production forecast from currently active
wells (based on historical production data broken down by individual sand
units)
b) Production forecast based on production data statistics
analysis of drilling activities (new wells, re-entries, and new in-fill pads)
c) Field optimization activities (well stimulations)
d) Chemical and thermal EOR projects (forecast based on
type curves).
Baseline production analysis
The historical field production data was analyzed to
extract the production decline factor and governing production mechanism, i.e.,
by pressure depletion (without water), and by production decline due to water
breakthrough and subsequent production with high water cuts.
Field Production performance periods were identified for
wells with stable production, and not affected by new drills or well shut-ins.
This was done to assess the prevailing production decline factor during each of
these time periods. In the Orinoco Extra Heavy Oil Belt (Faja) typical
production decline rates are in the order of 26% for stable wells, and 34% for
wells experiencing water breakthrough and subsequent high water cuts, as
depicted in Figure 1 and Figure 2.
The production decline factors thus identified were the
basis of our well production type curves, considering stable decline rate
during the first period, and steeper decline at the onset of water
breakthrough.
In wells exhibiting high water cuts, we recommend using the
WOR approach, looking at the WOR (water-oil ratios) versus cumulative oil
production to determine a production decline factor more appropriate for this
production regime.
In the WOR analysis, a semilog straight line model is
fitted via a semilog plot depicting the historical WOR versus cumulative oil
production, as shown in Figure 3. The semilog straight line model is fitted
considering only the period with the linear trend.
The WOR approach combines the standard production decline
analysis previously mentioned with the WOR curve in the following manner:
·
for a fixed time interval
·
The oil rate at the end of the time interval is
estimated with the production decline rate factor and the initial oil rate at
the start of the time interval
·
With this oil rate, a new value for cumulative
oil is computed during the time interval
·
The semilog straight line model is used with
the newly computed cumulative oil value, to estimate a value of WOR at the end
of the time interval
·
With the new WOR value and the computed oil
rate at the end of the time interval, a water production rate is determined
using the WOR value.
With the oil and water production rates estimated by using
the WOR approach, the oil and water production profiles are constructed for
each individual area. The production profile trend is then scaled up for each
of the wells belonging to that area.
Production Potential based on type curves
The production potential forecast was constructed based on
type curves for all primary depletion projects with known historical
information about field activities and production performance, and based on
simulation model results for EOR pilot projects such as steam injection or
polymer injection.
Type Curve
Construction based on Production data statistical analysis
Type curve construction for primary depletion relies on
statistical data analysis from all drilled and active wells, grouped by area
and reservoir. From these analyses, trends are established with regards to
initial production rates (productivity) and decline factors. These will then
allow assigning a prevailing type curve according to the field production
characteristics and with future projects.
By superimposing in time (and controlling with cumulative
oil) the oil and water production rates described by the individual type
curves, it is possible to generate the oil and water field production trends
taking into account the current field water handling capacities.
In order to honor the field production constraints, the
type curves are constructed as a function of cumulative oil and not as a
function of time.
Primary production projects:
The forecast for primary production projects will involve
the use of type curves for new wells, well stimulations, re-entries or in-fill
pads. These type curves are described in detail in the following paragraphs:
Type curves for New Wells
Type curves are defined according to the understanding on
field performance behavior, and are grouped by area and by reservoir. From the
statistical analysis of well production performance, initial flow potentials,
the type of production decline, and decline factor are estimated.
According to prevailing production mechanisms of individual
reservoirs or areas, they are discriminated by pressure drop and/or absence of
water production due to aquifer activity, and by well potential. This allows
defining a type curve methodology by natural decline and by WOR (water/oil)
analysis. A typical type curve by area and by reservoir is depicted in Figure
4.
Firstly, a detailed field catalog must be established, with
well activities and planned field development objectives for a period of five
years. The different type curves are assigned, which superimposed allow
generating the long term production profile. During the first production year,
it was observed that wells would exhibit a 32% production decline until their
drainage area expand and connect with the rest of the reservoir. At this time,
wells decline at lower rates because they have pressure support from the larger
areas.
Type curves were defined for those reservoir producing at
high water cuts, by establishing three production periods: a first period
reflecting the well start-up and ramp-up (from liq0 to liq1), a second period
characterizing the well liquid production plateau (from liq1 to liq2, the two
being equal), and a third period reflecting a production decline. Examples of
such type curves are shown in Figure 5 and Figure 6.
Type curves for well stimulation activities
If in the field development plan there are planned
activities to accelerate production, such as well stimulations; these may also
be represented by type curves, derived from historical well stimulation
performance results in different reservoirs.
Capturing of these activities by means of type curves
implies a thorough knowledge of the well candidates, establishing the base
production line prior to stimulation by applying the well cleaning chemical
treatment, and assessing the bump in production and the duration cycles, as
depicted in Figure 7.
It is considered as best practice to characterize the well
stimulations by area and reservoir, identifying its initial flow potential and
subsequent decline, and then include them in the field development plan by
means of an activity chronogram.
Type curves for re-entries and in-fill pads
Well re-entries are common field practices to re-direct a
well to a different reservoir or zone target in order to drain areas with
inactive or abandoned wells. The existing and intermediate sections already
drilled (in the inactive well) are re-used, and from there the well trajectory
is re-directed to tap into the new target, as it is exemplified in Figure 8.
Based on historical re-entry well data, an exponential
decline type curve could be representative with a decline rate of 35%. The
re-entry considers targets that might still be connected to water bearing zones
from neighboring areas and might still exhibit high water cuts, as seen in Figure
9.
The in-fill pads, as development activities for cold
primary depletion, will depend on the field development strategy under
consideration, and if there are still un-drain areas available. In order to
evaluate possible in-fill pad locations, one must count on field maps, well
logs, and the neighboring well production histories. These in-fills should be
placed between existing wells, targeting different objectives, and can be re-grouped
in at least four-well packages per pad for a parallel well layout. In the
Orinoco Oil Belt there are many concessions which have adopted a parallel well
layout as the prevailing field development strategy, such as in Petro Carabobo
(previously Cerro Negro) as shown in the SPE 69694 technical reference6.
The number of wells in a parallel in-fill well package must
be determined from specific studies to address optimal well spacing. The
in-fill pads must also be associated with existing production facilities
nearby.
The type curve constructed to represent the in-fill well
should consider areas which are connected to the rest of the field. The in-fill
wells should tap into areas of remaining high oil saturations such as the ones
prevailing in radial well layouts, where the remaining oil is trapped towards
the toe of the existing horizontal wells.
Looking at field analogs, an exponential decline analysis
yielded typical 35% decline rate and initial well potential of 350 bopd for a
partially depleted area, as depicted in Figure 10.
Secondary production projects:
Primary, cold production is the main field
development strategy at the start of production. Primary production forecasting
has been described in detail in the previous sections.
As the field pressure depletes, other means of
field development must be considered to reenergize the reservoir and recover
the vast reserves still remaining but inaccessible by primary production
schemes. Secondary or EOR projects are widely deployed or under consideration
in the Heavy Oil Belt (Faja), which involve thermal recovery methods (to
decrease the in-situ oil viscosity), or the use of chemical flooding.These
involve the use of chemicals, such as polymer, or solvents, aiming at improving
the mobility ratio between the displacing and displaced fluid phases.
A heavy oil field development plan must
consider at some point the deployment of EOR schemes to enhance the
productivity of the field.
In our reservoir production forecasting
methodology, provisions were made to include type curves representative of the
EOR schemes just mentioned.
Type Curve Construction based on simulation
models for areas designated as EOR pilots
Dynamic simulation models for EOR projects such
as steam injection or polymer injection can be used to generate production
performance results from which type curves could be derived and included in the
business plan.
Type Curves built to mimic observed (analog)
steam stimulation performance (cyclic steam injection pilot)
Cyclic steam injection is an alternate option to the
chemical well stimulation. This can be used as a preamble of a much larger
scale thermal project involving continuous steam injection, and also to modify
the existing well completion designs for cold production to handle hot fluids.
A typical production profile corresponding to the cyclic
steam injection was generated by means of a sector simulation model considering
a well subject to five injection cycles, and initial well potential of around
280 to 300 bopd, exhibiting a high production rate decline. The resulting
simulated production profile was used as a typical type curve to represent
cyclic steam injection. The type curve is depicted in Figure 11.
Type Curves to represent a thermal pilot
project
Even though steam thermal projects have been deployed in
the Orinoco Extra Heavy Oil Belt (Faja) and in other parts of Venezuela, a
pilot project has to be considered when deploying a different well layout,
involving a combination of horizontal and vertical wells. This type of scheme
has not been used yet in Venezuela. The pilot (or first thermal production pad)
will help to properly position the wells, with the optimal well layout, for
further field expansion.
The field forecasting tool considers thermal injection by
means of a type curve representing a thermal pilot. A sector simulation model
was constructed representing a typical Extra Heavy Oil Belt reservoir. The
model was used to generate production profiles and corresponding type curves,
looking at different field areas, far from producing zones, with reservoir
thickness larger than 30 feet, no stratigraphic barriers, and away from high
water producing zones. The simulation model was used to look at different well
configurations such as SAGD (steam assisted gravity drainage), HASD (horizontal
alternating steam drive), or SD (steam drainage). Typical curves representing
these thermal recovery schemes are shown in Figures 12, 13 and 14 respectivily.
Type Curve to represent a polymer pilot
project
A hypothetical sector model with a radial well
configuration and radial in-fill injectors was constructed to look at a
possible polymer pilot for the Extra Heavy Oil Belt (Faja). The radial well
configuration is widely used in one of the fields. For this reason the sector
model was constructed with a radial well layout. Several cases were looked at,
to contemplate polymer injection in an area away from water, and deploying one
or more polymer injectors. Figure 15 describes the simulation results for a
typical polymer scheme, as compared to a primary depletion option.
Based on the sector model simulation results, polymer flood
could be an attractive EOR field development strategy for the Faja. For this
reason, a type curve conforming to a radial well configuration was generated to
represent the performance of the polymer injection pilot.
Type Curve
Superposition Approach
The type curve approach to forecast field performance is
widely used in industry. An example of such methodology is described in Reference
SPE 69694.
The type curves described in this reference represent
horizontal well performance based on reservoir thickness, well length and depth
in a mature field that produces extra heavy oil crude. The field was
inventoried in terms of the number of well types present in each of the pads in
the field. Production performance was generated by varying net sand thickness,
reservoir depth, and open horizontal well section. Different parameter
combinations could yield similar production results and could be grouped together
as production type profiles. The type curves were superimposed according to the
inventory available well types for each of the pads being developed in the
field, thus yielding a base line production profile.
The type curve superposition used in our study is analogous
to the one just described.
The well type curves in our forecasting tool are
constructed in such a way that they may reflect different periods: 1.- well
ramp-up; 2.- well plateau and 3.- production decline at the onset of water
breakthrough, or at the start of pressure depletion as described in a later
section of the paper and shown in Figure 5. The forecasting tool uses a type
curve superposition approach that adds up the individual components, honoring
the facilities constrains and including time delays into rig availability and
well connections. This can be done since the type curves are expressed in terms
of fluids rates versus cumulative oil, which allow imposing the curves in time
taking into account individual shut-in intervals. A discretized time approach
is used, such that during each time interval, the incremental oil production is
computed assuming a constant rate, and the rate at the end of the time interval
is updated using the rate versus cumulative oil production curve.
As the field is developed, additional wells are included or
(superimposed) to the field profile. Different type curves are superimposed in
time, honoring field timing and operations constraints.
Field
Development and pilot expansions
In establishing a field development strategy and subsequent
business plan, one should look at field maps and count potential well
locations. Based on these, one should assemble type curves reflecting the
current field characteristics in the areas of the proposed locations.
Looking at
field maps and counting possible well locations
The field engineers must prepare a field strategy to
develop the field, looking at extended primary (cold) production, and also
including areas where EOR projects could be viable.
The number of development wells and associated pads could
then be assessed by looking at field maps and counting the potential well
locations. From field and specific EOR studies (including simulation), adequate
type curves could be generated based on individual well results, or based on
the complete EOR project simulated response. The EOR simulated response could
then be properly allocated to individual wells in the project. The global field forecast would then take
into account these developments by summing up the individual type curves and
according to the assessed number of wells.
Assembling the
type curves
By superimposing the type curves developed for each of the
elements in the whole field development strategy, it is possible to construct a
full field production forecast that considers timing constraints. These include
field regularities and efficiencies in implementing the proposed project
expansions. An example of such full field production forecast is depicted in Figure
16.
Expanding
existing water management facilities
The production forecasting tool, in actual reservoir
management, allows sizing the facilities, turning the knobs controlling the
facilities expansion design.
The type curves can be based on decline analysis or based
on sector simulation models which give high flexibility to the forecasting
tool. As an example, with the water-oil ratio (WOR) approach described
previously, it was possible to generate a full field water production forecast alerting management about the need to expand the
water handling facilities, and the urgency to implement the facilities
expansion, as shown in Figure 17.
In Figure17, the dark curve shows the expected field water
production, and the other curves depict the scaled up water production as new
phases of the project are implemented (new well drilling campaigns, re-entries,
stimulations, infill pads). The light blue curve (infill pads) represents the
total field water production due to all the planned field activities. From this
curve, in our example, it can be seen that by the 8th year of production, the
forecasted field water production would exceed the facilities water handling
capacity of 325 kbwd. At this point the facilities should have had implemented
the higher handling capacity of 400 kbwd as depicted in the figure. Later, by
year 15, the facilities would no longer be adequate and should be expanded to
handle further water production. A plot such as the one depicted in Figure 17,
would alert management as to when the expansions would be required, thus
allowing a phased facilities design.
Uncertainty
analysis
An uncertainty analysis implies the use of an experimental
design approach, widely used in industry and in accordance with “Statoil’s
Guidelines for production forecasting”5. The uncertainty approach
utilized in our forecast is simplistic in that it does not include subsurface
parameters. It is an in-house development which uses a simple Monte Carlo
approach considering that each of the variables (operational) is assigned a
triangular probability distribution function with low, mean and high possible
outcomes. In running the Monte Carlo simulations (based on the type curves and
triangular distribution functions) several production forecasts are obtained
with P10, P90 and P50 profiles. The profiles will contain the corresponding
values for the operations uncertainty variables and would allow management to
prepare contingency plans for each the outcomes.
The forecasting tool we have developed is Excel based and
has the uncertainty analysis kit imbedded in it, allowing to carry out
deterministic as well as probabilistic forecasts. Table 1 shows the variables
(operations in nature) with a prescribed uncertainty as low, mean and high
cases. The outcomes imbedded in the Monte Carlo simulation results are values
of objective functions (such as cumulative oil produced), such as shown in
Table 2. The variables could be items such as well connecting time, field regularity,
etc.
Figure 18 shows P10, P50, and P90 production forecasts for
our example. The uncertainties in these forecasts are all based on operations
variables.
The uncertainty analysis included in the forecasting tool
could be further enhanced by including simulation type curves representing the
subsurface uncertainties from P10, P90 and P50 simulation models (EOR, special
projects sector models).
Validating the
field management forecast tool
The production forecasting tool based on type curves should
be used to generate a two-year near term forecast. The resulting forecast
should be compared against actual observed trends during the first year. The
near term forecast is expected to yield results close to reality, since the
forecasting elements are tied to statistical data and field behavior trends.
The near term accuracy of the production forecasting tool
allows management to take actions and make field decisions improving the
reservoir management. As more data is made available during coming years, these
should be utilized to reaffirm the statistical analysis thus improving the
forecasting ability for the field.
Managing a field requires extensive reservoir analysis,
dynamic synthesis, field simulations studies. Engineers should develop an
understanding of the reservoir and current field behavior. The results of these
field studies can be casted in the form of type curves and annexed to the
forecasting tool described in this paper.
Conclusions
A methodology applicable to a mature heavy oil field was
presented, examining cold production project execution, field optimization
practices, drilling activities (new wells, re-entries, infill pads) and
potential EOR projects such as thermal and chemical floods.
The methodology looked first at base line production
forecasts (cold production only), then later included field optimization for
short term asset value improvement, and lastly, considered the implementation
of Enhanced Oil Recovery (EOR) pilots with subsequent field expansions.
The forecasting approach looked at water production trends
and gave guide lines for possible expansion of water handling facilities to
manage the increasing water cut levels encountered in a mature field.
We
have developed a forecasting tool (Excel based) with an embedded simplified
uncertainty analysis kit allowing carrying out deterministic as well as
probabilistic forecasts.
The forecasting tool was validated by comparing a two-year
forecast with actual production data (one year) starting from the two-year
forecast period.
Updating the database on which the forecasting tool is
based allows maintaining or improving the business forecast. The near term
accuracy of the forecasting tool allows management to take actions and make
field decisions improving the reservoir management.
Managing a field requires extensive reservoir analysis,
dynamic synthesis, field simulations studies. Engineers should develop a good
understanding of the reservoir and current field behavior prior to developing a
forecasted business plan.
Nomenclature
WOR = water/oil ratio
QA/QC = quality assurance/quality control
EOR = enhanced oil recovery
qLiq0 = initial liquid production rate
qLiq1 = liquid production rate at time 1
qLiq2 = liquid production rate at time 2
BOPD = barrels of oil per day
MBWPD = thousand barrels of water per day
MBOPD = thousand barrels of oil per day
SAGD = steam assisted gravity drainage
HASD = horizontal alternating steam drive
SD = steam drainage
P10 = high probabilistic curve
P50 = mean probabilistic curve
P90 = low probabilistic curve
Cum oil = cumulative oil
Cum water = cumulative water
Yearly prod = yearly production
References
1. Thakur G. and Satter, A.: “Integrated Petroleum
Reservoir Management – A team approach”, Reservoir Management Concepts, Charter
2, (1994), 7.
2. Richardson J. and Sneider R.: “Synergism in Reservoir
Management”, SEG (1990), 9-11.
3. Haldorsen, H. and Theodorvan Golf-Racht.: “Reservoir
Management into the next century”, SEG, (1989), 23.
4. Arps, J.J.: “Analysis of Decline Curves”, Trans., AIME
(1945), 228-47.
5. Statoil’s Guidelines for production forecasting:
“Document GL096 version 2”, (2012) 4-24.
6. Garcia, R and Eggenschwiler, M.: “How fluid and rock
properties affect production rates in a heavy-oil reservoir, Cerro Negro,
Venezuela”, SPE 69694 (2012), 4—5.
Figure 1 - Oil production
history with depletion
Figure 2 - Oil Production history with water
breakthrough
Figure 3 - Historical Water-Oil ratio
versus cumulative oil
Figure 4 - Production Type curve for
area and reservoir unit – new wells
qliq0 qliq1 qliq2
Figure 5 - Production Type
Figure 6 - WOR ratio versus cumulative
oil
Figure 7 - Production Type curve for
stimulations in area and reservoir units
Figure
8 - Re-entries scheme
Figure 9 - Production Type curve for
re-entries
Figure 10 - Production Type curve for
infill wells
Figure 11 - Production Type curve for
cyclic steam injection
Figure 12 - Production Type curve for SAGD
Figure 13 - Production Type
curve for HASD
Figure 14 - Production Type curve for SD
Polymer
injection
Figure 15 - Production profile for
Polymer injection
Figure 16 - Production profile
Figure 17 - Production profile for water
management
Figure 18 - P10, P50 and P90 production
profiles
Table1
Uncertainty parameters
Table 2 Result
functions
