It's your job to visit the "booths" and to evaluate and assess their abstracts (see below). It is your job to rank the abstracts and to give out the awards for First Place, Second Place, Third Place, and Honorable Mention. You will also give a "Secondary Recovery Award." You will rewrite the abstract that is the most in need of clarification. Your revision should have 125 words or less.
Instructions:
1--Please write a two-sentence evaluation of each of the abstracts. Include a description of the usefulness, clarity, and general feel of the abstract.
2--Then, pick out the one you believe was the most confusing of the abstracts. That is your "Secondary Recovery Award" winner. Please rewrite it to make it clearer and more understandable.
Integrated Reservoir
Description, Modeling and Management
Location: The Corral Room
Booth #: P27, K. J. Keogh, F. K. Berg, G. A. Team: Quantifying Geological Uncertainties
for Assessing Remaining Oil Targets: A Case Study from the Glitne Field, North
Sea
Booth #: P26, T. Duan, J. Hamman, D. Caldwell, M. Petersen: Integrating Stochastic
and Deterministic Methodologies in Geological Reservoir Modeling
Booth #: P25, E. Gomez, F. O. Iwere, W. J. Clark, Y. Z. Ma, O. Apaydin, J. Moreno,
L. J. Marquez, G. Rondon, L. E. Goyeneche, J. Falla, J. Pavas, D. Richards,
M. F. Doe: Construction and Implementation of a Field Development Plan, Cretaceous
Tetuán Member, Villeta Formation, San Francisco Field, Colombia
Booth #: P21, S. C. Ruppel, R. H. Jones, F. J. Lucia, F. P. Wang, H. Zeng, J.
Kane: Multidisciplinary Reservoir Characterization of a Giant Permian Carbonate
Platform Reservoir: Insights for Recovering Remaining Oil in a Mature U.S. Basin
Booth #: P22, E. Eslinger,
R. V. Everett: A Petrophysical Study of Reservoir Quality and Flow Unit Continuity
in a Lower Clearfork Oil Field, Lower Permian Dolostones, West Texas, Using
Ten Wells of Variable Age and Data Quality
Booth #: P28, M. J. Pranter, Z. A. Reza, P. Weimer: A Novel Integrated Approach
to Stochastic Deepwater Reservoir Modeling Using Sequence-Stratigraphic and
Geomorphic Constraints
Booth #: P23, W. H. Asyee, G. Warrlich, M. Boya-Ferrero, J. H. Van Konijnenburg,
P. Cassidy: Data Integration to Optimize Reservoir Characterization-an Example
from the Far East: Malampaya Mid-Tertiary Carbonate Build-Up
Here are the Abstracts
Booth #: P27, K. J. Keogh, F. K. Berg, G. A. Team: Quantifying Geological Uncertainties
for Assessing Remaining Oil Targets: A Case Study from the Glitne Field, North
Sea
Quantifying Geological Uncertainties for Assessing Remaining Oil Targets: A
Case Study from the Glitne Field, North Sea
Kevin J. Keogh1, Frode K. Berg1, and Glitne Asset Team2. (1) TEK F&T UTV,
Statoil ASA, Stavanger, 4035, Norway, phone: +47 90983823, keke@statoil.com,
(2) Statoil ASA, Stavanger, 4035, Norway
A sixth production well on the Glitne Field would target remaining oil in two
areas on the flanks of the field where little data was available to fully understand
the uncertainty and potentials risks that are required for making such a decision.
A geological sensitivity/uncertainty study, based around the deterministic base-case
reservoir model, was initiated to quantify the factors most contributing to
the static volumetric uncertainty in the various field segments to identify
potential upsides or downsides that will strongly affect the economics of the
potential areas.
The study has identified key geological factors that all contribute to the overall
uncertainty in estimating static volumes in the defined segments of the field
that are not captured in the base case geomodel. For each factor a best-case
and worst-case scenario is established to capture the end members (approximating
to p90-p10) in that factor uncertainty. IRAP RMS is used in combination with
an in-house ProReg Excel macro together with @Risk to produce a full range in
possible STOIIP outcomes for each of the geological factors and scenarios, a
ranking of the importance of the factors and scenarios and the effects of the
interaction of dependent factors.
The results have provided an invaluable quantitative resource that have been
used to better asses the feasibility for drilling a sixth production well on
the Glitne Field and increasing ultimate recovery and field life further. The
workflow used has its, but this study shows that a geological sensitivity study
can be set up and performed relatively simply using IRAP RMS. Furthermore, the
combination of this tool with a Monte Carlo simulation package gives a simple
and quick yet powerful statistical and visual assessment of the potential range
in STOIIP and which geological factors are contributing the most to the overall
geological uncertainty in the reservoir.
Booth #: P26, T. Duan, J. Hamman, D. Caldwell, M. Petersen: Integrating Stochastic
and Deterministic Methodologies in Geological Reservoir Modeling
Integrating Stochastic and Deterministic Methodologies in Geological Reservoir
Modeling
Taizhong Duan, Jeff Hamman, Don Caldwell, and Mark Petersen. Reservoir Characterization,
Marathon Oil Company, 5555 San Felipe, Houston, TX 77056-2725, phone: 713-2963353,
fax: 713-2963396, tduan@marathonoil.com
A workflow was implemented to build reservoir-scale geological models based
on Roxar and Marathon proprietary software that takes advantage of both deterministic
and stochastic approaches. Workflow includes two major sub-workflows, Roxar-based
stochastic modeling, and multiple rock-type and Gassmann equation-based rock
physics inversion. Input data includes well data, seismic acoustic impedance
volume (AI) and their derived data types such as mapped sedimentary bodies,
net-to-gross ratio or sand thickness map. Object and/or pixel-based stochastic
modeling simulate facies, porosity, and permeability distributions in 3D and
at reservoir scale. Simulated results are used as a pre-conditioned input or
constraint for further processing. Rock physics inversion uses the pre-conditioned
stochastically distributed porosity and facies to generate a synthetic AI volume
at well-data scale. Simulated annealing algorithm is used to repeatedly update
porosity, permeability, and water saturation until an upscaled effective medium
AI response matches the seismic acoustic impedance. Output includes multiple
realizations of facies, porosity, permeability, and water-saturation from multiple
geological scenarios, which are consistent with seismic AI and can reasonably
predict the same properties at well locations. Output results can be ranked
based on their impact on reserves calculation and dynamic reservoir simulation,
or analyzed stochastically if enough realizations implemented. This workflow
captures the core value of a well-understood deterministic model (Gassmann rock
physics) and also provides a tool to evaluate uncertainty of geological models.
This uncertainty is inherited from poor data quality or data incompleteness,
the non-unique solutions in inversion, and the lack of understanding in geological
processes and their modeling.
Booth #: P25, E. Gomez, F. O. Iwere, W. J. Clark, Y. Z. Ma, O. Apaydin, J. Moreno,
L. J. Marquez, G. Rondon, L. E. Goyeneche, J. Falla, J. Pavas, D. Richards,
M. F. Doe: Construction and Implementation of a Field Development Plan, Cretaceous
Tetuán Member, Villeta Formation, San Francisco Field, Colombia
Construction and Implementation of a Field Development Plan, Cretaceous Tetuán
Member, Villeta Formation, San Francisco Field, Colombia
Ernest Gomez1, Fabian O. Iwere1, William J. Clark1, Yuan Z. Ma1, Osman Apaydin1,
Jaime Moreno1, Leonardo J. Marquez2, German Rondon2, Luis E. Goyeneche3, Jorge
Falla3, Jaime Pavas3, David Richards4, and Michael F. Doe4. (1) Schlumberger
Data and Consulting Services, 6501 S. Fiddler's Green Circle, Ste. 400, Greenwood
Village, CO 80111, phone: 303-218-3109, fax: 303-218-3152, egomez@denver.oilfield.slb.com,
(2) Schlumberger Data and Consulting Services, Bogota, Colombia, (3) Hocol S.
A, Bogota, Colombia, (4) Midland Valley Inc, Golden, CO
The Cretaceous Tetuán Member of the Villeta Formation and the Caballos
Formation have yielded significant production at San Francisco Field. As the
primary Caballos is now in a mature stage, assessment of additional producible
reserves from the fractured carbonates in the Tetuan is of interest. To optimize
production, a field development plan was constructed for this reservoir by integrating
the available engineering, geological, geophysical and petrophysical data. The
reservoir matrix porosity and permeability is low in the Tetuán and production
is fracture controlled. Image logs from eighteen (18) wells along with the structural
and deformation history were used to develop a discrete fracture network (DFN).
The matrix and fracture properties were incorporated to construct a 3-D geocellular
model. This model was then upscaled for reservoir simulation taking into consideration
layer thickness, vertical communication, fracture distribution and completions.
The resulting reservoir model was history-matched to the pressure and production.
The calibrated model indicates that a maximum of 7% of the stock tank oil initially
in place (STOIIP) was produced as of July 31, 2003. Operating scenarios including
recompletions, additional drilling and waterflooding were examined using the
model. An incremental 11.5% of the STOIIP can be produced depending on the scenario
employed. Economics are currently being reviewed to determine which scenario
or combination of scenarios should be implemented in the Tetuán reservoir.
Booth #: P21, S. C. Ruppel, R. H. Jones, F. J. Lucia, F. P. Wang, H. Zeng, J.
Kane: Multidisciplinary Reservoir Characterization of a Giant Permian Carbonate
Platform Reservoir: Insights for Recovering Remaining Oil in a Mature U.S. Basin
Multidisciplinary Reservoir Characterization of a Giant Permian Carbonate Platform
Reservoir: Insights for Recovering Remaining Oil in a Mature U.S. Basin
Stephen C. Ruppel1, Rebecca H. Jones2, F. Jerry Lucia2, Fred P. Wang3, Hongliu
Zeng4, and Jeff Kane5. (1) Bureau of Economic Geology, The Jackson School of
Geosciences, The University of Texas at Austin, Austin, TX 78713-8924, phone:
5124712965, fax: 5124710140, Stephen.Ruppel@beg.utexas.edu, (2) Bureau of Economic
Geology, The Jackson School of Geosciences, The University of Texas at Austin,
University Station Box X, Austin, TX 78713-8924, (3) Bureau of Economic Geology,
Jackson School of Geosciences, The University of Texas at Austin, University
Station, Box X, Austin, TX 78713-1534, (4) Bureau of Economic Geology, Jackson
School of Geosciences, The University of Texas at Austin, University Station,
Box X, Austin, TX 78713, (5) Bureau of Economic Geology, Jackson School of Geosciences,
The University of Texas at Austin, University Station, Box X, Austin, 78713-1534
Despite more than 60 years of production history, recovery of the 1.5 Billion
barrels of oil in Fullerton field, a shallow water platform carbonate reservoir
of Early Permian age in the Permian Basin of West Texas, has proven difficult.
To develop a better understanding of the distribution of the original hydrocarbon
resource and to devise strategies to recover the huge volume that still remains,
we undertook a comprehensive, multidisciplinary study of the reservoir. Crucial
elements of the study include (1) geological models of analogous outcrops, (2)
description of more than 14,000 feet of core, (3) new core data for rock fabric
analysis, (4) analysis and correlation of more than 850 wireline log suites,
(5) a 3-D seismic inversion porosity model, (6) a 35,000 acre (12,000 hectare)
reservoir model, and (7) a 2,000 acre (750 hectare) flow simulation.
Important results of the study include the following. The study demonstrates
clearly the necessity of robust outcrop models for proper interpretation of
geological, petrophysical, and geophysical subsurface data sets. It also illustrates
the fundamental value of a geologically-constrained reservoir framework in realistic
reservoir modeling and simulation. It shows the tremendous potential of iterative
3-D seismic porosity inversion models in defining porosity distribution. It
reveals the importance of a rock fabric based approach for defining porosity/permeability
relationships. Finally, the study offers critical guides to the distribution
of original and remaining oil volumes and insights to how these resources may
best be recovered.
Booth #: P22, E. Eslinger, R. V. Everett: A Petrophysical Study of Reservoir Quality and Flow Unit Continuity in a Lower Clearfork Oil Field, Lower Permian Dolostones, West Texas, Using Ten Wells of Variable Age and Data Quality
A Petrophysical Study of Reservoir Quality and Flow Unit Continuity in a Lower
Clearfork Oil Field, Lower Permian Dolostones, West Texas, Using Ten Wells of
Variable Age and Data Quality
Eric Eslinger, Eric Geoscience, Inc. and The College of Saint Rose, 10 Sussex
Road, Glenmont, NY 12077, phone: 518-439-8447, fax: 518-439-8582, mulchone@albany.net
and R. V. Everett, Consultant, 430 Sparton Road, Victoria, BC V9E 2H4, Canada.
A petrophysical study was made on ten wells from an oil field in dolostones
of the Lower Clearfork (Lower Permian, Ector County, West Texas) with the goals
of identifying the best horizons to perforate and of evaluating continuity between
injector and producer wells for infill drilling planning. A major challenge
was how to integrate sparse "older" (1950s-60s) well logs with only
gamma ray and neutron curves with "newer" (1980s) more complete well
logs into a coherent model for generating credible porosity and permeability
profiles. A 10-well model was developed that included three non-cored wells
which had only gamma ray and neutron logs. Three of the wells contained useable
core plug data. A unique clustering procedure estimated missing data and lithology.
Well samples with valid core data for porosity, permeability, and grain density
were used to calibrate a mineralogy-based forward modeling procedure resulting
in profiles for porosity, permeability, and water saturation for all ten wells.
Interwell comparison of permeability profiles indicated generally poor horizontal
flow continuity. Porosity-ft and permeability-ft were computed for all ten wells.
Although the percentage of total porosity-feet (all wells total) attributed
to each well varied within a small range (5 to 13% per well), the percentage
of total permeability-feet (all wells total) attributed to each well varied
widely (1 to 53% per well) with four wells containing less than 2% each of the
total permeability-feet. The major challenge of integrating sparse well logs
and useable core was successfully met.
Booth #: P28, M. J. Pranter, Z. A. Reza, P. Weimer: A Novel Integrated Approach
to Stochastic Deepwater Reservoir Modeling Using Sequence-Stratigraphic and
Geomorphic Constraints
A Novel Integrated Approach to Stochastic Deepwater Reservoir Modeling Using
Sequence-Stratigraphic and Geomorphic Constraints
Matthew J. Pranter, Department of Geological Sciences, University of Colorado,
399 UCB, Boulder, CO 80309-0309, phone: 303-492-1461, fax: 303-492-2606, matthew.pranter@colorado.edu,
Zulfiquar A. Reza, Department of Geological Sciences, University of Colorado,
UCB 399, Boulder, CO 80309-0399, and Paul Weimer, Department of Geological Sciences,
University of Colorado, Campus Box 399, Boulder, CO 80309-0399.
In deepwater reservoir modeling, it is important to properly represent the spatial
distribution of architectural elements to account for pore-volume distribution
and the connectivity of reservoir sand bodies. This is especially critical for
rock and fluid-volume estimates, reservoir performance predictions, and development-well
planning.
This new integrated stochastic reservoir modeling approach accounts for stratigraphic
and geomorphic controls to generate the reservoir architecture and is conditioned
to seismic and well data. Information on stratal-package evolution and sediment
provenance can be integrated into the reservoir modeling process. A slope-area
analytical approach is implemented to account for topographical constraints
on channel and sheet-form reservoir architectures. A sediment source curve is
simulated based on inferred paleo-channel direction statistics (from outcrop
and stratigraphic studies) and simulated high-frequency eustatic sea-level curve.
Based on these geomorphic and sedimentological constraints, architectural elements
(channels, lobes, sheets) are built into the model sequentially (in stratigraphic
order). Channel avulsion is modeled based on its dependence on sediment source
and local topography. Information from detailed stratigraphic studies and other
data sources is accounted for during different stages of the modeling process.
Integration of realistic geological and engineering attributes into numerical
reservoir models is vital for optimal reservoir management. This approach is
unique in that it is constrained more directly to geomorphic and sedimentological
parameters than traditional object-based or surface-based techniques for stochastic
deepwater reservoir modeling.
Booth #: P23, W. H. Asyee, G. Warrlich, M. Boya-Ferrero, J. H. Van Konijnenburg,
P. Cassidy: Data Integration to Optimize Reservoir Characterization-an Example
from the Far East: Malampaya Mid-Tertiary Carbonate Build-Up
Data Integration to Optimize Reservoir Characterization-an Example from the
Far East: Malampaya Mid-Tertiary Carbonate Build-Up
Wenche H. Asyee1, Georg Warrlich2, Maria Boya-Ferrero2, Jan-Henk Van Konijnenburg3,
and Phil Cassidy2. (1) Carbonate Team, Shell International E&P, BO Box 60,
2280 AB Rijswijk, Netherlands, phone: +31 70 447 2788, wenche.asyee@shell.com,
(2) Carbonate Team, Shell International E&P, PO Box 60, 2280 AB Rijswijk,
Netherlands, (3) Sarawak Shell Bhd
The Malampaya Mid Tertiary Carbonate build up is located offshore the Philippines
in the Pacific Ocean in deep water (850 meters +). Malampaya-1 well was drilled
in 1991 and discovered a gas column of 650 meters and an uncertain oil rim.
The aim of the asset study, performed by the Shell Carbonate Development team
in Rijswijk (the Netherlands), was to produce reservoir models with a thorough
quantification of uncertainty to guide the phase II drilling campaign.
This poster tries to emphasize the importance investigating all types of new
and all data by performing small "sub" studies before static model
generation. The multi-disciplinary team worked on: seismic sparse spike inversion,
time to depth conversion, stratigraphic forward modeling, diagenesis, fracture
characterization and thorough petrophysical research to define (the number of)
reservoir rock types. The prominent subject of this poster is the integration
of seismic sparse spike inversion, geological correlation and definition of
reservoir rock types.
It is essential that these partly stand-alone studies be integrated to result
in a better geological understanding and subsequently in an optimized static
and dynamic reservoir model.