1. E-mails: anamaria_2000@yahoo.com, david.davies@pet.hw.ac.uk,
economides@houston.oilfield.slb.com
This paper was prepared for presentation at the IADC World Drilling Conference held in
Madrid, Spain, 5-6 June 2002. Copyright 2002, IADC Drilling Conference
This paper was selected for presentation by an IADC Program Committee following
review of information contained in an abstract submitted by the author(s). Contents of
the paper, as presented, have not been reviewed by the International Association of
Drilling Contractors and are subject to correction by the author(s). The material, as
presented, does not necessarily reflect any position of the IADC, their officers, or
members. Papers presented at the IADC meetings are subject to publication review by
Editorial Committees of the IADC. Electronic reproduction, distribution, or storage of any
part of this paper for commercial purposes without the written consent of the
International Association of Drilling Contractors is prohibited. Permission to reproduce in
print is restricted to an abstract of not more than 300 words; illustrations may not be
copied. The abstract must contain conspicuous acknowledgment of where and by whom
the paper was presented. Write
Librarian, SPE, P.O. Box 833836, Richardson, TX 75083- 3836, U.S.A., fax
01- 972- 952- 9435.
Abstract
Current production technologies are demanding
not only a great effort to understand their technical
engineering aspects but also novel approaches and
evaluation methodologies to justify their investment and
contribute in their implementation in a wide spectrum of
geological, production and business scenarios.
Asset teams of the main operators and services
companies working at international level need also
management tools to search reservoirs with potential
interest to apply new technologies worldwide. The
production Geology approach has shown to be an
effective tool that contributes in the implementation of
complex production technologies in multiple production
geology scenarios1
.
Different sources of information were reviewed
during the screening process to select reservoirs with
appropriate geological, reservoir and production
characteristics with potential to apply intelligent well
technology. The review of the exploitation plans of key
production areas worldwide allowed the identification of
potential reservoir types with production problems or
opportunities for which the implementation of intelligent
technology is likely to be beneficial.
This new approach will help asset teams of the
main operator companies to take technical decisions faster
following their own development plans and business
strategies. The applicability of this novel evaluation
methodology to introduce production technologies at
corporative level will be shown briefly trough a case
study in Venezuela 1, 2
.
Production Geology Approach
Current production technologies are demanding new
procedures to measure, control and monitor large amount
of engineering data. To optimize production in remote,
deeper and hostile areas novel drainage strategies are
requiring advance drilling and completion technologies
3,4,5
. Current production seismic trends are oriented in the
analysis of data at basin or perforation scale6
. Non-
conventional ways to analyze the geological information
following these trends are therefore necessary.
Petroleum engineers are constrained in the search for
production technologies applications to hardware and
software development, technology evolution, engineering
data availability/analysis and data confidentiality.
However, due to the large amount of reservoir and
geological information available in the open literature,
production geologist may contribute in the design of
exploitation plans looking in advance for reservoir
candidates and geological constrains of each production
technology.
The production geology approach goal is to reduce
knowledge gaps between production technologies and
geological information using knowledge management
tools (figure 1). The challenges are:
• To search for reservoir candidates to apply
advanced drilling & completion technologies
• to identify the geological variables that impact
economically any production technology in any
reservoir scenario at any scale (basin, reservoir,
well to well, perforation)
• to contribute in the understanding of the
geological aspects of production problems such
as sand production, scale, migration of fines,
formation damage, gas/water coning, hydrates
• to participate in production & reservoir
technology development projects
• to cooperate in drilling, completion,
development and production optimization
projects using decision support tools for
Production Geology Approach as a tool to accelerate the implementation of
advanced drilling technologies: Intelligent well evaluation methodology
Ana Maria Hernandez, SINTEF Petroleum Research, Norway; Dr. David Davies, Heriot Watt University,
Edinburgh, UK; Dr. Christine Economides, University of Houston, USA.
2. technology assessment
The following information management tools were
used in this paper to reach that goal:
• Information databases
• Historic technological charts
• Knowledge maps (figure 2)
• Decision support tools for technology
assessment
• Screening Criteria
• Economic ranking matrix
Knowledge management allows a fast understanding
of large amount of information related with a specific
technical topic in short time. It is useful to build the
technical background required for multidisciplinary teams
working on the implementation of production
technologies worldwide7
.
This information will be useful as a frame where
operators can analyze drilling and completion
technologies following their own exploitation plans and
business strategies.
Intelligent Well Evaluation Methodology
The focus of this paper is to identify the geological
variables that impact economically the intelligent well
technology and their related production problems at basin,
reservoir, well to well and perforation scale using
decision support tools for technology assessment. The
following evaluation methodology using the production
geology approach is proposed:
1. Technological background
2. Initial screening criteria: Production geology
scenarios with potential to apply Intelligent well
technology
3. Geological constrains of Intelligent well systems
at reservoir, well to well and perforation scale:
a. Down hole sensors
b. Isolated control flow zones
4. Intelligent well techno-economic options
Intelligent wells: Technological Background
To get a fast understanding of the intelligent well
technology a series of information sources were
consulted. Intelligent wells can be defined as complex
instrumented wells with downhole devices that are
connected remotely with reservoir management decision
systems 8,9,10
. Their main goal is to measure control and
monitor real-time data.
The drivers for intelligent well systems appear to
involve the following factors:
1. Rare or zero intervention
2. More completions per slot or per
penetration
3. Underground gathering system sensors
and controls
4. To reduce costs and/or risks.
The main components are isolated control flow zones,
specialized chokes and valves, down hole sensors,
intelligent artificial lift systems, specialized surface
systems and telemetry technology. Some production
scenarios where intelligent technology will increase
economic benefits are 8,9,10,11,12,13
:
• Oil rims with gas/water coning problems in
mature reservoirs
• Complex Improved Oil Recovery projects
that require monitoring of injection
/production fluids such as water alternating
gas (WAG)
• Compositional heterogeneous reservoirs that
require control of unwanted fluids (gas or
water).
• Improvement of reservoir drainage strategies
trough production optimization
• Heterogeneous reservoirs with pressure
differential
• New development plans in remote hostile
offshore environment
• To reduce well intervention cost mainly
offshore
• Intelligent wells to greatly accelerate ultimate
recovery
Initial screening criteria: production geology scenarios
with potential to apply Intelligent well technology
The search for possible reservoirs in key production
development areas worldwide for intelligent technology
has yielded screening criteria for candidate recognition.
The analysis of the open literature allowed the
identification of key areas with potential intelligent well
applications.
According to the current offshore development plans
worldwide and intelligent well technology applicability
the following screening criteria was used:
• Oil rims in heterogeneous reservoir with short-term,
high economic potential interest for the main
operators
• A set of locations where IOR projects in complex
reservoir are in progress with up to-date reservoir,
3. production, drilling and geological data
• Reservoirs with technology maturity, either with
intelligent wells or where related technology (4D,
Ocean bottom seismic, borehole seismic, down hole
sensors, complex wells among others) have been
implemented
• Remote offshore areas with economic potential
where the reduction of intervention cost and surface
facilities are necessary
• Areas with technological potential under
environmental regulations
Wide ranges of potential reservoir candidates to apply
intelligent well technology at basin scale are summarized
in the table 1 according to reservoir type, reservoir
geology and potential applications 14,15,16,17,18,19,20,21,22,23
.
In the British side of the North Sea, current efforts are
associated to extend the life of mature reservoirs with
complex IOR projects3
and the development of thin oil
rims. In Norway, the main efforts will be done in
secondary recovery projects in several reservoirs with
complex fluid column, high lateral/vertical heterogeneity
plus high internal heterogeneity4
.
In the Gulf of Mexico, complex heterogeneous
reservoirs with related technologies such as borehole and
4D seismic are evaluating the potential implementation of
intelligent well technology with the goal to accelerate its
implementation during the next five years24.
In West Africa5
many E & P development plans are in
“under way” status, however, the construction of
pipelines to connect Angola, Nigeria and Congo, the LNG
plan in Angola, the construction of refineries in Angola &
Congo and the gas to liquids projects in Nigeria are
opening a wide range of possibilities to continue the
exploitation of delta lobes, carbonates and turbidities oil
and condensates reservoirs.
In these key potential intelligent well
development areas there are some geographical
constrains:
1. Remote areas such as the Uk Atlantic Margin,
West Africa and the Norwegian continental
shelf will require specialized offshore
technology to overcome related production
problems such as water handling, sand
production and hydrates processing3
.
2. Increase in water deep are expected in the
development plans for the next 10 years in ultra
deep reservoirs (more than 5000 water deep) in
the Gulf of Mexico24
, USA; Barents sea,
Norway and in Angola, West Africa. It will be
necessary to improve the reliability of the
technology.
3. Areas under environmental regulations such as
the North Slope of Alaska, Liverpool bay 22
in
the UK and the Coral reef Barrier of Australia
are planning to develop intelligent well
technology to avoid well intervention and
environmental economic sanctions.
Geological Constrains of Intelligent well technology at
reservoir scale
To highlight the geological variables that impact
economically the technology, the reservoir candidates
were analyzed at reservoir scale (km-m). It allowed
observing the most common reservoir types where
intelligent well can apply and they are mature:
• Structural reservoirs with high lateral and
vertical heterogeneity
• Tilted reservoir associated with salt domes
• Reservoirs with several degrees of
compartmentalization and connectivity due to
changes in reservoir architecture
• Stratigraphic reservoirs with bypassed oil
zones
All of them present a critical zone for Intelligent well
technology implementation: partially connected sands
within irregular gas and water contacts in the middle part
of the reservoir, usually in tilted structures. They are the
result of changes in reservoir architecture between the
lower and the upper part of it. It’s important to identify
these zones in advance to optimize production and reduce
production problems. However, they can be the best place
for intelligent injectors/producers and sensors in
heterogeneous oil rims and complex IOR projects if they
are detected in advance.
To improve the dynamic reservoir management at Km- m
scale it is important to improve spatial target dimension
and geometrical visualization. 4D seismic techniques
combined with the new generation of sensors and Ocean
Bottom Seismic (OBS) techniques will get a more
realistic visualization of geological data. However,
4D/4C seismic allow having 3D geometry and reservoir
coverage but its resolution is limited. Borehole seismic
systems have high resolution but geometrical limitation at
km-m scale, which is needed to control and monitor,
unwanted fluids in complex reservoir flow units within
the reservoir types identified.
Reservoir continuity is the geological variable that more
impact at Km-m scale. Several potential applications to
optimize observability and controllability following the
reservoir continuity are visualized if permanent resistivity
and electromagnetic sensors are implemented combined
with 4C/4D seismic, borehole seismic, micro seismic
ocean bottoms seismic, plus isolated inflow control valves
4. 25,26,27,28,29
. It is still necessary to reduce the data gap
between seismic and core data and improve its resolution.
Permanent down hole monitoring systems will play an
important role in the new reservoir characterization “in
situ” and “virtual reservoir” trends. The need to speed up
the reservoir knowledge reducing the uncertainties
regarding to the everyday reservoir life and reservoir
spatial distribution will have an impact in the dynamic
reservoir management as a key part of the visionary
instrumented field.
A decision support tool for technology assessment based
on Monte Carlo simulation was used to evaluate the
geological variables that impact economically the
intelligent well technology at well to well and perforation
scale. It will be describe in detail in the techno-economic
section.
Geological constrains of Intelligent well technology at
well to well scale
The following variables were evaluated in the reservoir
candidates using Monte Carlo simulation at well-to-well
scale (figure 3):
• Vertical connectivity (m)
• Kv / Kh ratio
• Flow Units (m)
Reservoir flow units was the variable with more
impact at this scale, therefore, it was reviewed in
detail in the reservoir candidates because they are
the geological architectural elements between wells
that might be control and monitor using borehole
seismic and also they are strongly related with the
dimension, design and placement of the isolated
control flow zones. In this paper a reservoir flow unit
is defined in the following way:
Reservoir flow unit
• Rock types
• Rock wet ability
• Rock strength
It was noticed differences in the dimensions
reservoir flow units in each geographical area (figure
4). Reservoir candidates in West Africa and
Venezuela present high lateral and vertical
heterogeneity but more homogeneous rock types
(arenites with more than 95 % of quartz). Flow units
associated with thick fluvial channels and
submarines lobes can be found in the reservoir
types described above. Their range is between +/-
200 - 20 meters in horizontal wells.
By contrast reservoir candidates in Norway, Gulf of
Mexico and Indonesia not only present high lateral
vertical heterogeneity but also several degrees of
internal heterogeneity (sublitarenites and litarenites
with more than 95 % of rock fragments) and high
clay content. In some cases can be considered
associated to chaotic sedimentation mainly in the
Norwegian North Sea. The architectural elements
that compose channels, bars and lobes in these
geographical areas tend to be thinner with a range
between +/- 50 – 5 meters in horizontal wells.
The observations above highlight the necessity for
sensors to improve the geometrical visualization and
design of the isolated control zones in the range of
200- 0 meters. Permanent high resolution monitoring of
changes in fluid saturation in the near well area and deep
looking between wells using electromagnetic and seismic
sensors in the well bore are recommended in all the
scenarios. Reservoir under complex recovery process will
be benefit with potential saturation movies that will
reduce uncertainties in the well injector/producer location
in compartmentalized reservoir. Heterogeneous reservoir
with oil rims will increase the possibility to detect
bypassed oil highlighting changes in the saturated volume
permanently. The potential improvement in the
knowledge of the drainage patterns in complex reservoir
will optimize infill-drilling programs improving the
sweep efficiency. Permanent monitoring will also open
the feasibility for new production scenarios and new ways
to do reservoir management in mature fields decreasing
substantially drilling cost and extending their life.
Permanent reservoir monitoring system will fill the need
of many operators for reservoir uncertainty reduction in
several ways such as reducing the target location. It will
allow them work reduce the current “dimensional
problem “ up scaling core data and downscaling seismic
data, finally proposing a technology to work at the scale
reservoir engineers need to improve their reservoir
5. management understanding. All the reservoir types will
be benefit with a permanent down hole system.
Geological constrains of Intelligent well technology at
perforation scale
A decision support tool for technology assessment was
used to evaluate the geological variables that impact
economically the intelligent well technology at
perforation scale. The following variables were evaluated
in some of the reservoir candidates using Monte Carlo
simulation:
• Rock strength
• Rock type
• Rock wetability
These three variables compose a flow unit at
perforation scale and they presented +/- the same
impact. Rock type can be defined as the result of the
combination of:
• Textural attributes (grain size, shape,
roundness and sorting)
• Mineralogical variability
• Clay composition
The analysis of geological variables that impact the
isolated control zones is shown in the figure 5. Two
important concepts arrive: completion windows and
drainage points. The completion windows can be defined
as the volume of rock that composes an optimal reservoir
flow unit. Drainage points are the intervals that can be
perforated following a reservoir flow unit.
Bigger completion windows are found in West Africa and
Venezuelan reservoirs and three potential well
configurations are proposed: horizontal, high angle and
multibranch wells. In them, it is possible to isolated
reservoir flow units in intervals between +/- 200 –20 mts
in horizontal wells.
In the Furrial Field, Venezuela high angle wells
perforated following the completion windows shown
higher production (double in some cases) and less
production problems than the vertical well perforated
before in the same field 30
. Isolated control zones of more
than 200 meters are proposed in this type of well
configuration.
Smaller completion windows are found in the Norway,
Gulf of Mexico and Indonesian reservoir due to their rock
types (sublitarenites and litarenites). Heterogeneous
reservoirs usually have between 10 – 15 rock types;
however, it is possible to find reservoirs with even more
than 50 rock types in the Norwegian North Sea related
with chaotic sedimentation. These reservoirs have the
smallest completion windows (+/- 10-5 m).
Two possible well configurations will be high angle wells
following the completion windows and long horizontal
wells crossing small channels. They will be drilled in oils
rims with a vertical section between 200 –40 meters so
isolated control zones of high angle well might have +/-
100 meters in high angle wells, in horizontal wells they
might be longer. In both cases, perforation optimization
(very deep “rock type” perforations in the optimal
completion windows) and individual control zones are
suggested to get higher productivity and less production
problems.
An additional problem that might have a high economic
impact in the implementation of isolated control zones is
the presence of intervals prone to scale or sand
production. A previous study done in the North Monagas
fields, Venezuela showed in the analysis of core
information vs. perforated intervals that wells perforated
in the completion windows had more production even if
the perforated intervals were small, by contrast wells
perforated in the “layers” (optimal completion windows
plus sensitive intervals prone to sand or scale problems)
had less productivity and more production problems 31,32.
Zones with extreme porosity /permeability values usually
have the highest production of sand, and there is a
geological explanation for that (the apparently good
sands, with biggest grain size that represent reactivation
zones between cross bedding planes use to be the weakest
sand intervals with lowest geomechanical strength). They
might produce the early breakouts in the rock if they are
perforated. Intermediate intervals will be very sensitive
with any change in the flow regime if they are perforated.
Zones with high clay mineral contents will have some
plastic deformation and will tend to fail later and produce
scale problems. The most resistant intervals seem to be
cross bedding planes even if they are small, if the wells
are perforated in these zones they will act as a filter.
A software the get the optimal completion windows
during the perforation planning is suggested. Some
possible analysis to upscale this perforation analysis to
reservoir scale might be done analyzing reservoir pressure
data vs. perforated intervals or micro-seismic data vs.
flow zone indicators zones. Rock types are used together
with wetability data to identify the flow zone indicators
and the Amott wetting index to estimate the relative
permeability curves in the reservoirs, therefore the
geological perforation data might be extrapolated with
engineering data.
Intelligent well technologies and advanced business
6. models
To accelerate the identification of technical/economical
value of intelligent well technologies the current criteria,
advanced decision support tools and methodologies to
justify new technology were reviewed. Traditional
economical evaluations are:
1. - Discounted cash flow analysis33
that evaluate time
value of money and investment opportunity
2. - Life cycle cost34
related with technology reliability
evaluation
3. - Decision trees35
related with the identification of
the economic threshold
4. _ Monte Carlo Simulation 36
that allow getting a
forecasting and risk analysis and identification of
sensitivities and economic drivers
The intelligent well economic projects goal is to get the
economical impact that everyday events might have
during the reservoir life and it require to analyze
complexity, variability and uncertainty. To capture these
events there is a necessity of more flexible techno/
economic decision support tools and adaptable
methodologies to evaluate economically technology
diversity
in a more complex reservoir and business scenarios. Some
of these new petroleum economic trends are:
• Dynamic Complexity: Flexible management
models to capture complex conditions that create
uncertainties over time in petroleum projects37
.
• Multi-prospects Evaluation: Probabilistic models
that allow evaluating multiplayer prospects
building and economical correlation matrix38
.
• Multi-objective Decision Analysis: It allows
measuring technological benefits & financial
performance ranking several technological
alternatives in several scenarios39
(NPV +
technology gain)
• Techno-economic decision support tools using
new Monte Carlo simulation capabilities of the
Crystal Ball software
The last one was used in this paper. In the first place a
decision support tools for intelligent well technology
assessment was designed based on quantitative risk
analysis using Monte Carlo Simulation40.
The
methodology is described in the figure 6. The first part
was:
1. - to identify the geological variables that impact
economically the intelligent well technology
2. - to do the sensitivity analysis of each variable
3. - to search the probability distribution to model
each variable
4. - to determine the decision variables and the
assumptions that generate the variability and
uncertainty
5. - to define the forecast
The seconds step was to run the Monte Carlo Simulation
using the following advanced tools of the Crystal Ball
software:
• Correlation matrix: Defines and automates
correlations of assumptions
• Tornado Chart: Individually analyses the impact
of each model variable on a target outcome
• Two-dimensional simulation: Independently
addresses uncertainty and variability using two-
dimensional simulation
It was useful as a techno-economic tool to evaluate the
impact of the geological variables in the implementation
of intelligent well technology41
(figure 7). It can be
adaptable for the operators following their own
development plans and business strategies. As the
Intelligent well technology is the result of several
production technologies a technical/ economical ranking
matrix is proposed for the evaluation of the best
technological practices in the same reservoir identifying
the geological variables that have more technological
impact in each reservoir scenario.
A flowchart showing an overall methodology
proposed for an intelligent well project is shown in the
figure 8. It takes into account the evaluation of technical,
economical and reservoir scenarios adaptable to
operator’s needs. Two bottlenecks for the acceleration of
intelligent well technologies implementation seems to be
business and reservoir models, which don’t capture the
current complexity, variability and uncertainty, and
oversimplified models are far from what the industry
already have to date.
Case study using the Production Geology Approach:
Multilateral technology implementation in Venezuela
To accelerate the implementation of multilateral
technology in PDVSA Venezuela, a multidisciplinary
technology team investigated the technological constrains
and their potential application during 1999 and it required
a corporative domestic effort. The following corporative
steps were used in INTEVEP-PDVSA, Venezuela to
implement the complex well architecture technology at
domestic scale with success:
1. Open literature review and generation of a
bank of documents related with all the
technical aspects of the technology accessible
to the asset teams by internet
2. Discussion of all technical aspects, related
with the technology with professionals
working on the technology worldwide
7. (operator of the main oil companies, services
companies, research institutes, official
institutions, universities) trough domestic and
international forums.
3. Discussion of all case studies with the
domestic asset teams in the reservoirs
identified using the production geology
approach via domestic forums.
4. Review of the domestic exploitation plans
and candidate recognition to apply
technology in the 2000-2006 corporative
development plans. Discussion of the 20
potential cases identified with
multidisciplinary asset teams via round tables
and domestic and international forums.
5. Spread and divulgation of the technological
information obtained to the asset teams at
domestic scale by Internet using the
information management tools previously
described.
6. Pilot project to test technology reliability,
performance and economic potential in the
reservoirs with high technical-economical
potential within the corporative project
hierarchy
7. Technology massification
In this way, all the asset teams were able to get the
technological background necessary to understand the
technology and to analyze the potential application in
their own exploitation units. All this effort was useful to
implement the complex well architecture in Venezuela at
domestic scale, changing 95% of failure in previous
multilateral wells to 95% of technological implementation
success in just one year.
Conclusions
1. Intelligent well technology implementation can be
accelerated only if multidisciplinary efforts using
knowledge management tools to facilitate the
understanding are undertaken.
2. A production geology approach was used in this
paper with the goal to accelerate the understanding of
the impact of the geological variables at reservoir,
well to well and perforation scale on Intelligent well
technology.
3. Screening criteria were established to identify
reservoir candidates for intelligent well technology in
key potential production geologies scenarios
worldwide.
4. A critical zone for intelligent well technology
implementation was identify at reservoir scale
5. To improve the geometrical spatial visualization and
resolution of sensors are necessary to decrease the
current data gap between seismic and core data,
permanent seismic monitoring systems are suggest.
6. Geological variables that impact economically the
implementation of Intelligent well technology were
identified at reservoir (reservoir continuity), well to
well (flow units) and perforation scale (rock type,
rock strength and rock wetability).
7. The identification of completion windows and
optimal drainage points to increase productivity and
avoid production problems will highlight the best
well placement and configurations increasing the
performance of the isolated control zones.
8. The 0-200 meters scale is critical for measurement,
control and monitoring.
9. A decision support tools for technology assessment
based on advanced Monte Carlo simulation was
useful to analyze the impact of the geological
variables.
10. New business and reservoir models are necessary to
capture everyday events related with intelligent well
technology in complex reservoir and business
scenarios.
11. Asset teams of the main operator companies working
in offshore development plans may adapt this
intelligent well evaluation methodology to evaluate
their potential application following their own
development plans and business strategies.
12. This paper presents an innovative approach for
analyzing geological information to contribute in the
implementation of the intelligent well technology and
production technologies in general.
Acknowledgements
The author wishes to thank SINTEF Petroleum Research,
Norway for supporting publication of this paper, mainly
to the Intelligent Well Strategic Program leader Fridtjof
Nyhavn and SINTEF director David Lysne. Many thanks
to Dr. David Davies from Heriot Watt University,
Edinburgh, UK, where most of the reservoir candidates
information was compiled, to allow the publication of this
paper. Special thanks to the Well Construction
Knowledge community of PDVSA-INTEVEP, Venezuela
for their support during the MTL project and to Dr.
Christine Economides of the University of Houston, USA
for her technical remarks.
References
1. Hernandez, A.M.: “ Multilateral wells: experience
and future development in PDVSA, Venezuela”.
Presented at the SPE Forum Series on engineering
aspects of multilateral wells, Reservoir Management
section, Colorado, USA. July 1999.
2. Hernandez, A.M.; Barrios, J.C.; Saputelli, L.;
Economides, M. J: “ Multilateral wells: experience
8. and future development in PDVSA, Venezuela”.
Paper presented at the 11 International Conference on
Horizontal Technology, Houston, USA. November
1999.
3. www.dti.gov.uk
4. http://odin.no/oed/engelsk
5. www.mbendi.co.za/proj/
6. www.sintef.iku.no
7. Saputelli, L., Ungredda, A: “Knowledge
Communities help to Identify best operating
practices” paper SPE 53759 presented at the VI
LACPEC conference, Caracas, Venezuela. April
1999.
8. www.force.org/wells/wells_agenda.htm
9. S. Yu; D. Davies; D. Sherrard. “The modelling of
Advanced Intelligent wells-an application” paper
SPE 62950 presented at the SPE Annual conference,
Texas, USA. October 2000.
10. Strand, G; Ansell J; Rausand, M. Modeling of
Intelligent wells. Forecasting Life-Cycle cost.
SINTEF internal report, 1999.
11. Erlandsen, S.: “Production Experience from Smart
wells in the Oseberg Field” paper SPE 62953
presented at the SPE Annual Technical Conference,
Texas, USA. October 2000.
12. www.speoslo.no/html/mars2001.htm
13. Coull C. “Intelligent completion provides saving for
Snorre TLP. Oil & gas journal, April 2001.
14. Larsen, J; Skauge, A. “ Simulation of the Immiscible
WAG process using cycle-dependent three-phase
relative permeability’s” paper SPE 56475 presented
at the Annual Technical conference, Texas, USA.
October 1999.
15. Krol, D; Noual, V; van Maren, P. “Exploring Mature
Areas: The role of Technology” paper SPE 56893
presented at the Offshore European Conference,
Aberdeen, UK. September 1999.
16. Jensen, B; Hjelleset, E; Larsen, L. “Interference
testing to verify drainage strategy for a large offshore
development” paper SPE 56420 presented at the
Annual conference, Texas, USA, October, 1999.
17. Flolo, L; Kjoerefjord “ Revealing the petrophysical
properties of the thin-bedded rock in a Norwegian
Sea reservoir with logs, core and mini-perm data.
SPE Reservoir Evaluation & eng. Vol. 3, N# 3, June
2000.
18. King, G; David, W; Tokar, T. “ Takula field: Data
acquisition, interpretation and integration for
improved simulation and reservoir management”
paper SPE 66400 presented at the SPE Reservoir
Simulation Symposium, Texas, USA. February 2001.
19. Barting, D; Lassus-Dessus, J; Lopez, B. “Well
control guidelines for Girassol” paper SPE/IADC
52763 presented at the Drilling conference, Holland.
March 1999.
20. Mikes, D; Barzandju, O; Brining, J. “ Upscaling of
flow units for reservoir flow incorporating small
scale heterogeneities” paper SPE 68702 presented at
the Asian Pacific Oil and Gas conference, Indonesia.
April 2001.
21. Gorkhan, C; Ranler, B; Murray W. ”Design,
implementation and analysis of multilayers transient
test in White Rose Field” paper SPE 63080 presented
at the annual conference, Texas, USA. October 2000.
22. Gilliver, R. “ Conservation partnerships as part of
environmental management in a sensitive coastal
location: Liverpool Bay and gas production
operation” paper SPE 46880 presented at the
international conference on Health, Safety and
Environment, Caracas, Venezuela. June 1998.
23. Graham; Scott, C; Litllewood, J.” Development of a
downhole scale management philosophy for water
sensitive reservoirs” paper SPE 58726 at the
International Symposium on Formation Damage
control, Louisiana, USA. February 2000.
24. www.fe.doe.gov/oil-gas/reports/ostr/ostr_all.pdf
25. Seymour, R; Barr, F. “An improved seabed seismic
4D data collection method for reservoir monitoring”
paper SPE 36893 at the European Petroleum
conference, Milan, Italy. October 1996.
26. Al-Najjar, N; Brevik, I; Psalia, D. “4D seismic
modelling of the Statfjord field: Initial Results” paper
SPE 56730 presented at the Annual Technical
conference, Texas, USA. October 1999.
27. Multiwell Imaging. The leading edge, April-May
1999
28. Mjaaland, S; Wulff, A; Causse, E; Nyhavn, F.
“Integrating seismic monitoring and Intelligent
wells” paper SPE 62878 presented at the Technical
conference, Texas, USA. October 2000.
29. Shyen, J; Johnston, D. “Interpretation and modelling
of time-lapse seismic data: Lena Field, GOM” paper
SPE 56731 at the Annual conference, Texas, USA.
October 1999.
30. Smith, R; Colmenares, R; Rosas, E. “Optimised
reservoir development using high angle wells, El
Furrial Field, Venezuela” paper SPE 69436 presented
at the Latin American and Caribbean Petroleum
Engineering conference, Argentina. March 2001.
31. Guzmán, J.; Hernández, A. 1995. Diagenetic and
Depositional constrains in Reservoir Quality,
Examples from North Monagas Oil Fields of Eastern
9. Venezuela. Presented at the AAPG International
Meeting. Houston, USA.
32. Hernandez, A.; Gonzalez, O. Geological
Interpretation in the Construction of Trend Maps of
Sand Prone Regions. International Sand Control
Workshop. PDVSA-INTEVEP, Los Teques,
Venezuela, 1994.
33. Simpson; Lamb; Finch. “The application of
probabilistic and qualitative methods to assets
management decision making” paper SPE 59455
presented at the Asia Pacific conference on integrated
modelling for Assets Management, Japan. April
2000.
34. Harding, T.“ Life cycle value/cost decision making”
paper SPE 35315 present at the International
Petroleum conference, Mexico. March 1996.
35. Mudford, B. “ Valuing and comparing oil and gas
opportunities: a comparison of decision tree and
simulation methodologies” paper SPE 63201
presented at the Annual conference, Texas, USA.
October 2000.
36. Murtha, J. “Monte Carlo Simulation: Its status and
future” paper SPE 37932 presented at the Annual
Technical conference, Texas, USA. March 1999.
37. Gallant, L; Kieffel, H. “Using learning models to
capture the dynamic complexity in Petroleum
exploration”. Paper SPE 52954 presented at the
Hydrocarbon Economics and Evaluation Symposium,
Texas, USA. April 2001.
38. Faya, L. “ Probabilistic model to develop multiplayer
gas and oil prospects” paper SPE 69614 presented at
the Latin American and Caribbean Petroleum
Engineering conference, Argentina. March 2001.
39. Suslick, S; Furtado, R; Nepomuceno, R. “ Integrating
Technological and financial uncertainties for offshore
oil exploration: an application of multi-objective
decision analysis” paper SPE 68579 presented at the
Hydrocarbon Economics and Evaluation Symposium,
Texas, USA. April 2001.
40. www.decisioneering.com
41. Kengpol, K; O’Brien, C. “The development of a
decision support tools for the selection of advanced
technology to achieve rapid product development”.
Int. J. Production Economics 69 (2001) 177-199.
Table
Figures
Reservoir Type Reservoir Geology
Geographical
Area
Potential IW Production Scenarios
Oil
Distal shore to shallow
marine sandstones
North Sea
UK
Pressure maintenance. Injector using
scaling (low PI/K)
Oil
Shallow marine
Sandstones
North Sea
UK
HP/HT unconsolidated, ESP to increase
flow rates
Oil
Partially communicated
channalized sandstones
North Sea
Norway
Extended reach with smart completion to
control gas production
Oil
Distal deltaic sands
sequences
North Sea
Norway
Drainage optimization, zones with early
water influx, unsealing faults, sand
production
Oil Deltaic to shallow marine
North Sea
Norway
Improvement of drainages strategies
Oil
Bay fill thin bedded sand
sequences
North Sea
Norway
Improvement of drainage strategies
/extended reach wells
Oil Distal shore sandstones
North Sea
Norway
Water injection projects
Oil
Complex fluvial deltaic
sequence
North Monagas
Venezuela
Production optimization to reduce water
cut/ gas break thought WAG project
Oil
Fluvial to near shore
sandstones
Canada
Multiplayer reservoir with pressure
differential
Oil
Marginal marine sands/
faulted
Canada Poor quality, new recovery strategies
Oil
Unconsolidated deltaic
sands
Oman
Heavy oil underlying by strong aquifers,
water/oil separators
Oil rim
With low GOR, aquifer
Triassic Sandstone /
halite’s
North Sea
UK
High K intervals, prevent water/gas
coning, high L/V heterogeneity, selective
isolation
Oil rims
Fluvial-deltaic to shallow
marine
North Sea
UK
Depressurization, complex contact
movement
Oil rim with gas cap
Coastal deltaic/ submarine
fans
North Sea
UK
Drainage optimization
Thin Oil rim
With solution gas
Chalk sequences
North Sea
Denmark
High porosity chalk intervals, gas/water
coning
Oil/Gas to liquids
Coastal complex sand and
carbonates
West Africa
Angola
Low permeability formation, pressure
decline
Oil/ Gas to liquids Deltaic sand lobes
West Africa
Angola
High water cuts, scale inhibitors in water
injection
Oil/ Gas to liquids
Turbidities sand
sequences
West Africa
Congo
Water deeper than 1400 mts
Oil/ Gas to liquids Deltaic sand Lobes
West Africa
Angola
Potential gas condensate development
Gas
Distal shore to shallow
marine
North Sea
UK
Pressure maintenance. Injector using
scaling inhibitors to avoid formation
damage (low PI/K)
Dry Gas
Triassic Sandstone /
halite’s
North Sea
UK
High K intervals, prevent water/gas
coning, high L/V heterogeneity, selective
isolation
Dry Gas
Irregular Sands bodies in
salt tectonics
GOM
USA
Improvement of well productivity
Wet gas, condensate Upper Cretaceous Chalk
North Sea
Denmark
High porosity chalk intervals, gas/water
coning
Gas, condensate Deltaic to shallow marine
North Sea
Norway
Improvement of drainages strategies
Gas condensate
Norwegian continental
shelf
North Sea
Norway
Potential gas development
Rich gas, condensate
Complex structure/ highly
stratified
North Sea
Norway
Overpressirized, fault transmissibility’s
affecting L/V pressure distribution
Compositional Shallow marine
North Sea
Norway
Immiscible WAG injection
Cpositional
Distal deltaic tidal,
marginal marine, faulted
dome
North Sea
Norway
Improve producer injection locations,
heterogeneous K
Advance
dGeology
Productio
n
Drilling
&
Completion
Technologies
Knowledge Management
Knowledge
Gaps:
Time &
Money
10. Figure 1. Production Geology Approach
.
Figure 3. Sensitivity chart showing the geological variables that
impact the intelligent well technology at well-to-well scale
Figure 4. Reservoir flow units dimensions in
horizontal direction in reservoir candidates
Economics
Software’s
Modelling
Tools
Discounted
Cash Flow
Databases
IW State of
Art
Field
experience
IW Related
Technology
IWSt Reservoir
Management
IWS technology
Development plans
IW Economics
Field
Status
Technological
Needs
Remote
Sensors
Advanced well
Technology
Companies
working on IW
Worldwide
Sponsors
Previous
IW projects
Technical Books/
Magazines
Business
Scenarios
Previous
Technology
Reservoir
Economics
Inflow control
Valves
Reservoir
Packages
+200 150 100 50 25 15 5 m
Reservoir
Candidates
Norway
Gulf of Mexico
Indonesia
West Africa
Venezuela
Sand production prone
Scale prone
Optimal
Completion
Window
200-20 m
Reservoir flow units:
Rock types + rock wetability + rock strength
Drainage points Horizontal well
Project Economics
Flow Cash, NPV
Valves
Chokes
Control Zones
Sensors
Identification of variables that
produce economical impact
Variable
1
Variable
2
Variable
n
Search of the probability
distribution to model each
variable
Determination
of variables with
high impact
Sensitivity analysis
of each variable
Run the
Simulation
Analysis of
results
NPV
1141.17
45
5.6
11.57
100.00
40.5
11.23
6.16
1895.83
16
14.0
8.52
150.00
43.5
9.55
13.84
-200.00 0.00 200.00 400.00 600.00
STOIIP
Wells to drill
Plateau rate
Discount factor
Facility size
Recovery
Well cost
Well rate
Decision
Variables
Assumptions
Variability
Uncertainty
Forecast
Injector
Injection points
Valves/chokes
Zonal flow sensors
Producer
Drainage point
Valves/chokes producer
Permanent resistivity sensors
Interwell data
Distance between wells
Completed interval
Perforation
rock types
rock wettability
rock strenght
pore pressure
barriers
layers
Surprise handling
Water Breakout
Monte Carlo Simulation
Figure 5. Figure showing the completion windows,
drainage points, sensitive production intervals and
reservoir flow units described in this paper
Figure 6. Decision support tools for technology
assessment flowchart
Figure 7. Monte Carlo simulation to determine the
variables with more economic impact at perforation
scale
Table 1. Summary of the different reservoir types
and potential intelligent well applications
Target Forecast: Reservoir variability
Reservoir continuity (km-m) .44
Vertical connectivity (m) .50
Kv/kh
ratio
.49
Flow units (m) .75
-1 -0.5 0 0.5 1
Measured by Rank Correlation
Sensitivity Chart
Sand production prone
Scale prone
Optimal
Completion
Window
200-20 m
Reservoir flow units:
Rock types
Rock wetability
Rock strength
Optimum
Drainage points Intelligent well
Figure 5. Figure showing the completion windows,
drainage points, sensitive production intervals and
reservoir flow units described in this paper
Figure 7. Monte Carlo simulation to determine the
variables with more economic impact at perforation
scale
IW project
Brainstorming
Identify Technical
Issues related with
IW
Identify
Economical
Issues related
with IW
Identify IW
Potential
Scenarios
Initial
Options
Screening
IW production
Optimization
Solutions
Prospective IW
economic
scenarios
Identify Critical
Decision Issues
New business
models
Discounted Cash
Flow Analysis
IW Case history
selection
IW
Reservoir
Modeling
Figure 8. Intelligent well project flowchart
Figure 2. Knowledge Maps
Sensors &
controls
Decision
Variables
Assumptions
Variability
Uncertainty
Forecast
Injector
Injection points
Valves/chokes
Zonal flow sensors
Producer
Drainage point
Valves/chokes producer
Permanent resistivity sensors
Interwell data
Distance between wells
Completed interval
Perforation
rock types
rock wettability
rock strenght
pore pressure
barriers
layers
Surprise handling
Water Breakout
Monte Carlo Simulation
