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WHOC12-363
Integrated Advanced Well Completion Design Implementation Helps To
Quantify Uncertainty and Optimize Well Performance in a Heavy Oil Field in the GOM
D., GARCÍA GAVITO A. E., FREITES PEMEX Schlumberger F., RODRIGUEZ R. J, CARVAJAL PEMEX Schlumberger
L. A., CARRILLO M. A., ROMERO PEMEX Schlumberger
This paper has been selected for presentation and/or publication in the proceedings for the 2012 World Heavy Oil Congress
[WHOC12]. The authors of this material have been cleared by all interested companies/employers/clients to authorize dmg events
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personnel.
Abstract
The independence in the design of well positions, geometries and completions in the reservoir frame has been one of the main concerns of the petroleum industry in general. Up to this moment it had been almost impossible to develop an integrated approach that led to the optimization of well performances. This kind of approach is critically needed for heavy and extra-heavy oil reservoirs, where non-conventional wells and different completion configurations have to be considered in order to overcome production problems (the mobility ratio is adverse to the oil and water cut is usually very high).
This project comes up to solve this issue and presents a
new methodology that brings the world of reservoir simulation and well completion design closer together and allows to automatically run several different combinations of well spatial positions and geometries (vertical, deviated, horizontal and multilateral) and the optimization of ICD´s and cased holes configurations.
Three wells were fully designed in a heavy oil field in the
Gulf of México (carbonates), using advanced reservoir simulation options like sector modeling (SM), local grid refinement (LGR) and well segmentation. Each well encompassed up to 9 unknown parameters for an open hole
which were systematically evaluated using sensitivity and uncertainty analysis (equal spacing and Central Composite sampler respectively). A process of "proxy" training and optimization was then carried out to completions with ICD´s and cased hole. The module of Uncertainty and Optimization of Petrel was successfully used to support the automated process.
The results were compared with a conventional well
considered in the original field development plan and showed that options of multilateral wells with open hole and horizontal wells with ICD´s dramatically increase the oil recovery and improve water production control. The time to perform the analysis was reduced in more than 70% in comparison with regular studies for well position and geometry.
Introduction
Historically, well design has been an area of continuous
discussion and research; the lack of tools and workflows to
perform a complete evaluation of all the factors involved (well
position, geometry and completions) has made of this an
inefficient and unreliable process.
Currently, in the work team, a regular well design study could
take 2-3 months and will go first to a reservoir simulation
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model to make discrete tests over a few possible location and
well geometries and then to analytical tools for completions,
where complex well-reservoir system interactions are not
considered. In other words, the process is carried out in a series
of “almost” independent steps.
The main objective of this project was to tackle down this way
to work by developing an integrated methodology that allows
an effective evaluation of multiple well geometries, positions
and completions configurations to identify the optimum way to
produce a highly complex heavy oil field located in the Gulf of
Mexico (GOM).
This field has no production wells and the information
available was generated for a simulation study directed to the
design of a development strategy. The simulation grid was built
using the information gathered for two exploration wells and
encompasses a total of 174.276 active cells (corner point
geometry) with the following dimensions: 150x150 areal and
variable size in vertical direction with approximately 10 m
through the production formation and 95 m in the cells located
below the water-oil contact (-4228 m). The average pressure
was estimated in 215.3 Kg/cm2 at a reference depth of -3800
m. The reservoir is under-saturated (Pb=55 Kg/cm2) and the
fluid density is of 11 oAPI approximately.
Directional wells (deviated 30o) with open hole were
considered in the original study and used as a comparison basis
for the ones designed in this project. The developed workflow
will be explained for one of the three well we designed in this
project. For the other two just the final results are shown.
Methodology and Results
The last releases of Petrel, where a whole range of new options
regarding well completions have been included, have opened a
window of opportunities for the development of an integrated,
statistically supported approach for well design, allowing
testing more well alternatives in less time. Figure 1 presents the
workflow developed in the “Advanced Well Completion
Design Project”, as it was called for PEMEX.
Three phases were carefully depicted, each one with specific
but closely related tasks. All processes were performed in the
same platform and using the same simulator (ECLIPSE100).
This is one of the most important differences in the
methodology we propose and regular studies for well design.
As it was stated before, the design of well geometries and
completions were done using different tools, according to the
different levels of detail required in every stage of the process.
As a consequence, the workflow tend to be divided in a series
of “almost” independent steps, while our methodology is
carried out using nothing but the combination of
Petrel/ECLIPSE100, keeping the input of one stage as the
direct result of the previous one.
Let us give a brief description of every phase of the workflow
we propose before going deeper into them. The purpose of
“Phase 1” is to prepare a model to achieve the detail needed
for the design of a well completion. Generally, operators
companies build simulation models for their fields, in order to
establish their developments plans. These models are usually
too coarse for well design and need to be refined. However,
refining could be a tricky process and it is necessary to be
aware of the changes in production profiles while doing it.
“Phase 2” represents the first major task in the workflow,
where multiple combinations of well geometries and positions
are explored simultaneously: the result of this stage will allow
advancing to the next phase with the best possible options with
open hole. In “Phase 3” completions with ICD’s and Cased
Hole are considered; the inner as an alternative to control water
production and the latter as the regular completion used in the
GOM.
Phase 1. Base Model Preparation
Definition of areas of interest
After receiving and validating the simulation model of the
field, the area of interest for new wells was defined. The
selection of this area could be constrained by many reasons:
environmental, budget, rig availability, etcetera; however, if it
is assumed that none of this are actual limitations, as it was the
case in the field we worked on, a simple set of equations
known as the Combined Rock Quality Index (ROI) could be
used to rapidly evaluate the reservoir in terms of its possible
production potential.
………..(1)
………………………….(2)
……………………(3)
By using the calculator of Petrel we can add constraints related
to the distance to the faults and WOC to the previous equations
in order to eliminate these zones from the group of candidates
to drill new wells; this is an effective technique to guarantee
that the results will not violate any physical or operational
limitation. Figure 2 shows an example of how the grid will
look after generating this property.
Sector model and refinement
The original grid was too coarse to perform a well design,
giving the fact that it is a detailed task where production
behavior and water flow need to be followed accurately; Local
Grid Refinement (LGR) had to be considered. To allow
capturing all the effects wanted to study we used a refinement
of 3x3x4, producing cells of 50x50x3 m approximately. This
produced important increases in simulation time making
impossible to refine the whole model. We then decided to
focus on sector models with Flux Boundary for every single
SorSOILPORONTGDzDyDxSOMPV
PORO
AVEPERMRQI 0314.0
3 SOMPVRQIPRESSUREROI
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well (we are designing three wells for which three sector
models will be needed).
It is well known that after a LGR the permeability distribution
is practically unaltered since the process makes a simple
splitting of the cells. To avoid that, we upscaled the refined
area based on the geologic model (see Figure 3). The results
between the refined and the refined-upscaled models were
compared showing increases in the water production for all
cases. This result was expected since the permeability
anisotropy generally contributes with the water flow in heavy
oil reservoirs. No relative permeability curves end-point
upscaling was performed.
Phase 2. Geometry and Position of Open Hole Wells
Design of “Base” Wells
Once the sector models are extracted we can continue with the
design of "Base Wells". These wells were the different base
geometries we want to evaluate (vertical, deviated 700 and
horizontal); laterals could be added to the latter two. All these
options were parameterized as a variable named "Well Type"
(TP). For each TP three versions were built, every one related
to the length of the well (long, medium and short); this was the
second variable: "Length of the Well Type" (LH).
Areal (North-South, DY- East-West, DX) and vertical (DZ)
displacements , the number (NumLaterals), length
(LongLaterals) and azimuth of the laterals and the maximum
liquid production rate completed the list of nine uncertainty
variables over which the study for open hole wells was to be
performed. Parameterizing the variables and assigning ranges
to every one of them will allow to control and narrow the study
to one we can effectively control and analyze.
The spatial positions variables (DX, DY, DZ) ranges are
constrained by the size of the sector model and the refined zone
themselves. Consider that we are recording the “Flux Boundary
Conditions” over a sector and that inside of it an LGR exists
(see Figure 4); a well cannot be placed in both the refined and
non-refined areas; this is a limitation of ECLIPSE100. So, it is
up to the engineer in charge of the study to create a sector and a
LGR big enough to cover all the locations that can be
interesting to test while designing the well.
Notice that all the rest but the liquid production rate are
geometry-related variables. The type of well and its length are
especially important for heavy oil reservoir; maximum contact
between the wells and the reservoir is usually wanted to
increase the productivity index of these and decreasing the
pressure drawdown in the system. For all well types, an
advanced option of ECLIPSE100 known as “Well
Segmentation” was used, in order to simulate accurately the
pressure drops in terms of friction, acceleration and gravitation
in the wellbore. This is a key factor when calculating flow in
comingling of branches and long horizontal production
sections.
Automatic creation of simulation cases
Using the Uncertainty and Optimization module of Petrel a
workflow was built to automate the processes of sensitivity and
uncertainty. This workflow was designed to be the key of an
efficient and standardized methodology, very simple to use and
that requires a minimum amount of information to be run.
Hundreds of data files with different alternatives of multi-
segmented wells could be generated with just one "click" and
systematic, organized and statistically supported studies could
be easily performed.
The workflow (for this stage) consists in 54 lines of code,
encompassing all the processes related to the wells spatial
positions and geometries. The first 44 lines correspond to the
well selection. In practical terms we had 9 different base wells
to test (3 TP, each one in its versions long, medium and short).
A code number was assigned to each well as the combination
of the TP and LH. Depending on the value that every variable
will take in every run of the experiment matrix (we will talk
about it in the following step) a well will be chosen to be
tested. Then that well will pass through the Laterals addition
process (if applicable). Conditional were place in the code to
avoid inconsistencies such as vertical wells with laterals.
Later on, the “Move Well” process (included in the version
2011.1 of Petrel) will displace the well from its original
position to test a given volume. The displaced well will enter
the “Well segmentation” utility to establish calculation nodes.
As it was stated before, this process is very important since it
takes into account all the components of the pressure drop,
bringing more accuracy to the results. The well is then
prepared to be included in the correspondent development
strategy and simulation case. In other words, the output of this
workflow is a data file with a determined configuration that can
be run directly from Petrel or exported and run from the
“Simulation Launcher”.
Sensitivity and Uncertainty Analysis
One of the most important goals to be achieved in this project
was to reduce the time to complete a full well design study. As
a consequence, it is necessary to determine which of the initial
group of variables we have to focus on. A sensitivity analysis
will rapidly allow the identification of the parameters having
major impact over the cumulative oil (NP) and water
production (WP) in the horizon of prediction (15 years); it
important to point out that this kind of analysis is only useful to
dismiss parameters when it is assumed that the variable will not
have a bigger effect interacting with other variables than by
itself.
We used the "Equal spacing sampler" with two sample points
to perform this part of the study. Figure 5 shows the Tornado
plot we obtained after running the process for one of the wells
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under in design. The azimuth and the displacement in the East-
West direction seemed to have a marginal effect over the NP
and were dismissed for the upcoming processes.
The reduced group of 7 variables was considered for the
Uncertainty analysis and sampled using a Fractional Central
Composite experiment. This method allows a deep exploration
of the sampling space, testing points in the extremes and center
of the uncertainty range of every variable and taking into
account 2nd factor interactions between them, even though they
can be confounded with 3rd factor interactions [1].
Hierarchy of open hole wells
Multiple alternatives of well geometries and positions with
open hole were generated and run in ECLIPSE100 (Figure 6);
this led to hierarchy the best possible combinations of
parameters for an improved NP. Figure 6 presents the results of
NP and WP (purple bars) for different well types proved in one
of the sector models we worked on. The yellow bar located to
the left of the graph represents the cumulative oil recovered
with the original well considered in the development plan of
the field and is kept as reference for comparison purposes. The
blue line defines the type of well; for example, code “1”
corresponds to vertical wells, code “3” to deviated 700 and
code “4” to horizontals. The number of laterals attached to
every well is given by the red line.
This kind of plots are very useful to understand the general
behavior of the different well alternatives, as it actually
happens in this case, where horizontal/multilateral wells report
a much better recovery of oil and water control than deviated
or vertical ones. A well with approximately 1.0 Km of
horizontal section with two laterals of 500 m of length,
displaced to the north of the field and the top of the formation
(see Figure 7) and operating at its maximum possible liquid
production rate, reported the best NP an excellent water
production control. Other interesting alternatives are given by
those which did not produce any water in the 15 years of
prediction. For instance, the same well described before will
not produce any water if the limit liquid production rate is
reduced to the minimum of its uncertainty range.
From the operational point of view it is also important to take a
look on the horizontal wells instead of multilateral, given the
complexity of the latter. The best horizontal well will produce
3 MMBls of oil less than the best multilateral and 5 MMBls
more of water. The question is: it is reasonable to assume the
risk of drilling a multilateral well considering the production
behavior shown in this study? This could be only answer by
performing an economical analysis.
Phase 3. Completions optimization
Well completion designs
After finishing the open hole hierarchy we wanted to study the
effect of different kind of completions over the production
profile. We considered two types of completions, in addition to
the open hole we deeply studied in the previous phase. The first
one was the cased hole (TCYD); this is the most common kind
of completion used in the GOM. Although, it is not expected to
act as a production optimizer, the location of the perforations
could help control water production, which is the bigger
problem in the study.
On the other hand we have the Inflow Control Devices
(ICD’s), which are thought to control the pressure profile along
the wells in order to improve its performance. Both the cased
hole and the ICD’s are options completely supported by Petrel,
program that offers the possibility of rapidly configuring,
parameterizing and exporting them to the simulator; the
accuracy of the calculations will be improved by the use of
“Well segmentation”.
At this point a question came to our minds: which wells to use
for adding this completions? The natural way to go would have
been to take the best well found in the uncertainty analysis with
open hole, but, is it really possible to add ICD´s to the full
extension of a multilateral well? And moreover, is it possible to
perforate more than 2 km of length? The inner is extremely
difficult; the experience gained at some locations worldwide
(Saudi Arabia for instance) suggests that for making
operationally possible to place ICD’s in a multilateral well it is
necessary to restrict the flow at some sections of the well,
generating a configuration like the one shown in Figure 8. As a
consequence the main advantage of this kind of well, the
increased well-reservoir contact is lost. The latter almost
impossible, having as a reference that the common length of
perforations in the GOM is approximately 60 m. The decision
then was to migrate to other well options.
Proxy generation and Optimization
For the addition of ICD´s we used the best horizontal well
found in the uncertainty analysis. The study of this completion
was made with base in three parameters, which were thought to
control the entire production process. The variables were: 1) x-
cross section multiplier: this is a very important since it has
direct influence over the flow passing through the devices. A
multiplier greater than one will decrease the pressure drop
between the reservoir and the well with respect to the original
cross area of the device (0.013525 ft2) , while a multiplier
smaller than one will increase the pressure drop and restrict
more the flow to the well. 2) Valves per compartment and 3)
space between packers, will also help to control the pressure
drop and the contribution of different sections of the reservoir
to the global production.
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In order to save time for the optimization of the ICD’s
configuration we decided to train a quadratic proxy. 15 runs
training runs were generated with a Central Composite sampler
by using a simple workflow in Petrel. 5 validation runs were
used to test the accuracy of the analytical model, obtaining an
excellent approach (less than 2% of error). The optimization
was performed over the proxy, showing that a configuration
with 277 m between packers, 3 devices per compartment and
x-cross section of 0.002705 ft2 will represent the best possible
ICD’s scenario in terms of NP and water control (Figure 9).
Figure 10 shows the comparison between the base well (the
one considered in the original field development plan), the best
horizontal well with open hole and the same well completed
with the optimized configuration of ICD’s. The NP was
increased in more than 2 MMBls and the water production
reduced in almost 3 MMBls by the addition of this devices.
For the cased hole completion we decided to use a highly
deviated 70o well and train a proxy with two variables: 1) depth
and 2) length of the perforations. 9 training runs were
generated with a “Central composite” sampler and 3 with a
“Montecarlo” sampler for validation. The error of the proxy
when trying to reproduce the simulation behavior was less than
3%, reliable enough to be used for optimization.
Figure 11 presents the NP and WP for the optimized
configuration with cased hole (length of the perforations: 200 ft
and depth: 14600 ft) and compare it with the base well and the
deviated well with open hole. The optimized scenario allowed
an important improvement of the production profile by
controlling more effectively the water inflow.
Results analysis
Figure 12 presents the best alternatives we found in the study
as a percentage of increment/decrement of water and oil
production of the base well. In practical terms, we are
comparing the final alternatives a drilling/completion engineer
would have available to decide the geometry, position and
completion of a new well.
Improvements of the NP and water production control were
reached. A multilateral well increased the recovery of oil in
more than 14 MMBls (85%); the reason of such a big change
in the production profile was that the multilateral well requires
a smaller reservoir-well pressure drop (see Figure 13) than the
base well to achieve the same liquid rate, slowing down the
advance of water from the aquifer. A simpler alternative as the
horizontal well with ICD’s also showed to be a great option for
this sector model. However, as it was stated before, only by
performing and economical analysis a final decision could be
made.
Figure 14 and 15 show the final results obtained for the other
two wells we analyzed in this study. Both of them were located
in zones with extreme water problems and, as a result, the
maximization of the production could only be achieved by
using horizontal/multilateral wells. For the first case a
horizontal well with ICD’s showed to be the best option,
followed by a multilateral and a horizontal well with open hole.
For the second case (Figure 15), the multilateral well reported
the best NP with a dramatic improvement of 495% with respect
to the base case. Notice than in this case, the ICD’s did not had
the same impact they had in the previous sectors. This was
mainly because the well was located so close to the aquifer that
there were no time see the equilibration of the pressure profile
offered for this kind of devices.
To sum up the importance of this last couple of wells it is must
be point out that they were placed in zones were marginal
production wells were expected, but thanks to this study they
are now considered as keys in the development of the field.
Significance of subject matter
For the very first time in Latin America a project of Advanced
Well Completion Design using numerical simulation and using
static and dynamic characterization models has been
successfully carried out. The methodology efficiently supports
statistic studies of well position, geometry and completions,
allowing the engineers to evaluate multiple alternatives in a
short time. The automated process is very easy to understand
and requires of a small amount of input data to be run, so, the
proposed solution could be used by engineers with minimum
knowledge of Petrel.
Conclusions
An integrated approach for the connection between the
reservoir simulation model and well completion design was
successfully generated and tested.
Automated, easy to use, workflows for sensitivity and
uncertainty analysis of well positions, geometries and
optimization of ICD´s and Cased Hole configurations were
designed.
Multiple scenarios of open hole wells and optimized ICD´s
and Cased Hole configurations were compared with the
conventional wells considered in the original field
development plan and substantial improvements on the NP
and water production controls were found.
The time required to complete the process of well design was
dramatically decrease, saving up to 70%.
Acknowledgement
We would like to thank to all the personnel working at the
"Subdirección de Gestion Recursos Técnicos" of PEMEX for
their collaboration and support at every stage of the project,
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particularly Dr. Daniel García Gavito, for his support during
the development of the work.
Nomenclature
Dx, Dy, Dz: cell dimensions.
NTG: Net to gross.
PORO: Porosity.
SOIL: Oil saturation
Sor: Residual oil saturation.
SOMPV: Pore volume saturated with mobile oil.
RQI: Reservoir quality index.
AVEPERM: Average permeability.
PRESSURE: Reservoir pressure.
ROI: Combined Rock Quality Index.
REFERENCES
1. Petrel 2011.1 Help. Uncertainty and Optimization Module.
Figure 1. Methodology for the Advanced Well Completion Design.
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Figure 2. Areas of interest in a field of the GOM.
Figure 3. Refined Vs Refined and Upscale sector model.
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Figure 4. Sector model and refinement.
Figure 5. Tornado Plot.
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Figure 6. Hierarchy of well types with open hole wells.
Figure 7. Well in its original position (blue) Vs Well in its optimized position (red)
31.25
34.7
40.67
43.83
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Np,
Wp
(MM
Bls)
Cases
Well I Hierarchy of Well Types
With base on Np, Wp
NP (MMBls) WP (MMBls) TP Number of Laterals
43.83 40.67
34.7
31.25
10
Figure 8. Configuration of ICD’s in a multilateral well.
Figure 9. Optimized ICD’s configuration.
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Figure 10. Comparison of original wells Vs ICD’s optimized configuration.
Figure 11. Comparison of original wells Vs TCYD’s optimized configuration.
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Original Horizontal Open Hole Horizontal with ICD's
16.59
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Well 1Comparison of Original Wells Vs ICD's Best
Configuration
WP (MMBls)
NP (MMBls)
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Original Deviated 70o with open hole
Deviated 70o with Cased Hole
16.59
11.38.96
31.2534.9
38.31
Well 1Comparison of Original Wells Vs TCYD's Best
Configuration
WP (MMBls)
NP (MMBls)
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Figure 12. Comparison between best alternatives for well 1.
Figure 13. Comparison of Reservoir-Well pressure drops for the base well and the best multilateral.
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Figure 14. Comparison between best alternatives for well 2.
Figure 15. Comparison of best alternatives for well 3.