Reservoir Simulation: Keeping Pace with
Oilfield Complexity
David A. Edwards
Dayal Gunasekera
Jonathan Morris
Gareth Shaw
Kevin Shaw
Dominic Walsh
Abingdon, England
Paul A. Fjerstad
Jitendra Kikani
Chevron Energy Technology Company
Houston, Texas, USA
Jessica Franco
Total SA
Luanda, Angola
Geologic complexity and the high cost of resource development continue to push
reservoir simulation technology. Next-generation simulators employ multimillion cell
models with unstructured grids to handle geologies with high-permeability contrasts.
Through the use of more-realistic models, these new simulators will aid in increasing
ultimate recovery from both new and existing fields.
Viet Hoang
Chevron Energy Technology Company
San Ramon, California, USA
Lisette Quettier
Total SA
Pau, France
Oilfield Review Winter 2011/2012: 23, no. 4.
Copyright © 2012 Schlumberger.
ECLIPSE and INTERSECT are marks of Schlumberger.
Intel®, Intel386™, Intel486™, Itanium® and Pentium® are
registered trademarks of Intel Corporation.
Linux® is a registered trademark of Linus Torvalds.
Windows® is a registered trademark of Microsoft
Corporation.
1950
4
Interest in simulators is not new. People have
long used simulators to model complex activities.
Simulation can be categorized into three
periods—precomputer, formative and expansion.1 The Buffon needle experiment in 1777 was
the first recorded simulation in the precomputer
era (1777 to 1945). In this experiment, needles
were thrown onto a flat surface to estimate the
value of π.2 In the formative simulation period
(1945 to 1970), people used the first electronic
computers to solve problems for military applications. These ranged from artillery firing solutions
to the development of the hydrogen bomb. The
expansion simulation period (1970 to the present) is distinguished by a profusion of simulation
applications. These applications range from
2000
Oilfield Review
Surface Network Simulator
Process Simulator
Economic Simulator
Static and Dynamic Data
Reservoir Simulator
> Production simulation. A reservoir engineer takes static and dynamic data (bottom right ) and develops input for a reservoir simulator (bottom left ). The
reservoir simulator, whose primary task is to analyze flow through porous media, calculates production profiles as a function of time for the wells in the
reservoir. These data are passed to a production engineer to develop well models and a surface network simulator (top left ). A facilities engineer uses the
production and composition data to build a process plant model with the help of a process simulator (top right ). Finally, data from all the simulators are
passed to an economic simulator (right ).
games to disaster preparedness and simulation of
artificial life forms.3 Industry and government
interest in computer simulation is increasing in
areas that are computationally difficult, potentially dangerous or expensive. Oilfield simulations fit all of these criteria.
Oil and gas simulations model activities that
extend from deep within the reservoir to process
plants on the surface and ultimately include final
economic evaluation (above). Numerous factors
are driving current production simulation planning to produce accurate results in the shortest
possible time. These include remote locations,
geologic complexity, complex well trajectories,
enhanced recovery schemes, heavy-oil recovery
and unconventional gas. Today, operators must
make investment decisions quickly and can no lon-
ger base field development decisions solely on
data from early well performance. Operators now
want accurate simulation of the field from formation discovery through secondary recovery and
final abandonment. Nowhere do these factors
come into sharper focus than in the reservoir.
This article describes the tools and processes
involved in reservoir simulation and discusses
how a next-generation simulator is helping operators in Australia, Canada and Kazakhstan.
Visualizing the Reservoir
Oilfield Review
The earliest reservoir
date from the
WINTER simulators
11/12
1930s and were
physical
models;
the
interaction
Intersect Fig. 1
ORWNT11/12-INT
1 viewed—
of sand, oil and
water could be directly
often in vessels with clear sides.4 Early physical
simulators were employed when reservoir behav-
ior during waterfloods surprised operators. In
addition to physical simulators, scientists used
electrical simulators that relied on the analogy
between flow of electrical current and flow of reservoir fluids.
In the early 1950s, although physical simulators were still in use, researchers were starting
to think about how a reservoir might be described
analytically. Understanding what happens in a
reservoir during production is similar in some
respects to diagnosing a disease. Data from various laboratory tests are available but the complete disease process cannot be viewed directly.
Physicians must deduce what is happening from
laboratory results. Reservoir engineers are in a
similar position—they cannot actually view the
subject of their interest, but must rely on data to
1. Goldsman D, Nance RE and Wilson JR: “A Brief History
of Simulation,” in Rossetti MD, Hill RR, Johansson B,
Dunkin A and Ingalls RG (eds): Proceedings of the 2009
Winter Simulation Conference. Austin, Texas, USA
(December 13–16, 2009): 310–313.
2. Buffon’s needle experiment is one of the oldest known
problems in geometric probability. Needles are dropped
on a sheet of paper with grid lines, and the probability of
the needle crossing one of the lines is calculated. This
probability is related directly to the value of π. For more
information: Weisstein FW: “Buffon’s Needle Problem,”
WolframMathWorld, http://mathworld.wolfram.com/
BuffonsNeedleProblem.html (accessed July 25, 2011).
3. Freddolino PL, Arkhipov AS, Larson SB, McPherson A
and Schulten K: “Molecular Dynamics of the Complete
Satellite Tobacco Mosaic Virus,” Structure 14, no. 3
(March 2006): 437–449.
4. Peaceman DW: “A Personal Retrospection of Reservoir
Simulation,” in Proceedings of the First and Second
International Forum on Reservoir Simulation. Alpbach,
Austria (September 12–16, 1988 and September 4–8,
1989): 12–23.
Adamson G, Crick M, Gane B, Gurpinar O, Hardiman J
and Ponting D: “Simulation Throughout the Life of a
Reservoir,” Oilfield Review 8, no. 2 (Summer 1996): 16–27.
Winter 2011/2012
5
tell them what is happening deep below the
Earth’s surface. Production and other data are
used to build analytical models to describe flow
and other reservoir characteristics. In a reservoir model, the equations that describe fluid
behavior arise from fundamental principles that
have been understood for more than a hundred
years. These principles are the conservation of
mass, fluid dynamics and thermodynamic equilibrium between phases.5
When these principles are applied to a reservoir, the resulting partial differential equations
are complex, numerous and nonlinear. Early analytical derivations for describing flow behavior in
the reservoir were constrained to simple models,
whereas current formulations show a more complex picture (below).6 While formulation of the
equations for the reservoir has always been
straightforward, they cannot be solved exactly
and must be solved by finite-difference methods.7
In reservoir simulation, there is a trade-off
between model complexity and ability to converge to a solution. Advances in computing capability have helped enhance reservoir simulator
capability—especially when complex models and
large numbers of cells are involved (next page).8
Computing hardware advances over the past
decades have led to a steady progression in simulation capabilities.9 Between the early 1950s and
1970, reservoir simulators progressed from two
dimensions and simple geometry to three dimensions, realistic geometry and a black oil fluid
model.10 In the 1970s, researchers introduced
compositional models and placed a heavy emphasis on enhanced oil recovery. In the 1980s, simulator development emphasized complex well
management and fractured reservoirs; during the
1990s, graphical user interfaces brought enhanced
1951
1D flow of a
compressible fluid
∂ 2p
x
∂x
=
2
cφµ
∂p
k
∂t
.
2011
3D flow of n
components in a
complex reservoir
z
x
y
V
∆t
δ (φ
Np,c
Σρ S χ
p
p p
cp
W
) + qc
–
Faces
Np,c
ΣT Σ
k
k
p
ρp
k rp
χ ∆p – ∆Pc p – ρp g ∆h )
µp cp (
= Rc .
> Reservoir simulation evolution. One of the first attempts to analytically describe reservoir flow
occurred in the early 1950s. Researchers developed a partial differential equation to describe 1D flow
of a compressible fluid in a reservoir (top). This equation is derived from Darcy’s law for flow in porous
media plus the law of conservation of mass; it describes pressure as a function of time and position.
(For details: McCarty DG and Peaceman DW: “Application of Large Computers to Reservoir
Engineering Problems,” paper SPE 844, presented at a Joint Meeting of University of Texas and Texas
A&M Student Chapters of AIME, Austin, Texas, February 14–15, 1957.) Recent models developed for
reservoir simulation consider the flow of multiple components in a reservoir that is divided into a large
number of 3D components known as grid cells (bottom). Darcy’s law and conservation of mass, plus
thermodynamic equilibrium of components between phases, govern equations that describe flow in
and out of these cells. In addition to flow rates, the models describe other variables including
pressure, temperature and phase saturation. (For details: Cao et al, reference 6.)
6
ease of use. Nearing the end of the 20th century,
reservoir simulators added features such as local
grid refinement and the ability to handle complex
geometry as well as integration with surface facilities. Now, simulators can handle complex reservoirs while offering integrated full-field management. These models—known as next-generation
simulators—have taken advantage of several
recently developed technologies, including parallel computing.
Parallel Computing—Divide and Conquer
One of the hallmarks of current reservoir simulators is the use of parallel computing systems.
Parallel computing operates on the principle that
large problems like reservoir simulation can be
broken down into smaller ones that are then solved
concurrently—or in parallel. The shift from serial
processing to parallel systems is a direct result of
the drive for improved computational performance.
In the 1980s and 1990s, computer hardware
designers relied on increases in microprocessor
speed to improve computational performance.
This technique, called frequency scaling, became
the dominant force in processor performance for
personal computers until about 2004.11 Frequency
scaling came to an end because of the increasing
power consumption necessary to achieve higher
frequencies. Hardware designers for personal
computers then turned to multicore processors—
one form of parallel computing.
The kind of thinking that would eventually
lead to parallel processing for reservoir simulators, however, began around 1990. In an early
experiment, oilfield researchers demonstrated
that an Intel computer with 16 processors could
efficiently handle an oil-water simulation
model.12 Since then, the use of parallel computer
systems for reservoir simulation has become
more commonplace.
As prices for computing equipment have
decreased, it has become standard practice to
operate parallel computing systems as clusters
of single machines connected by a network.
These multiple machines, operating in parallel,
act as a single entity. The goal in parallel computing has always been to solve large problems
more quickly by going n times faster on n processors.13 For a host of reasons, this ideal performance is rarely achieved.
To understand the limitations in parallel networks, it is instructive to visualize a typical system used by a modern reservoir simulator. This
system might have several stand-alone computers networked through a hub and a switch to a
Oilfield Review
The Next Generation
Since 2000, a petroleum engineer could choose
from a number of reservoir simulators. Simulators
were numerous enough that the SPE supported
frequent projects to compare them.17 Although the
simulators differ from one another, their structures have common roots, which lie in serial computing and reliance on simple grids. An example of
this type of reservoir simulator is the ECLIPSE
simulator.18 The ECLIPSE simulator has been a
benchmark for 25 years and has been continually
updated to handle a variety of reservoir features.
Like microprocessors, however, reservoir simulators have reached a point at which the familiar
tools of the past may not be appropriate for some
current field development challenges.
Scientists have developed new reservoir tools—
the next-generation simulators—to broaden the
Winter 2011/2012
108
109
Intel Itanium 2 microprocessor
Reservoir cells
Intel microprocessor
108
Intel Pentium 4 microprocessor
Int
106
107
Intel Pentium II microprocessor
Intel Pentium microprocessor
Intel486 microprocessor
105
106
Intel386 microprocessor
104
Intel286 microprocessor
Number of transistors on microprocessor
107
Number of reservoir cells employed
controller computer and a network server.14 As
each of the individual computers works on its
portion of the reservoir, messages are passed
between them to the controller computer and
over the network to other systems. In parallel terminology, the individual processors are the parallel portions of the system, while the work of the
controllers is the serial part.15 The overall effect
of communications is the primary reason why
ideal performance in parallel systems can be only
approached but not realized. All computing systems, even parallel systems, have limitations.
The maximum expected improvement that a
parallel system can deliver is embodied in
Amdahl’s law.16 Consider a simulator that requires
10 hours on a single processor. The 10-hour total
time can be broken down into a 9-hour part that is
amenable to parallel processing and a 1-hour part
that is serial in nature. For this example, Amdahl’s
law states that no matter how many processors are
assigned to the parallel part of the calculation, the
minimum execution time cannot be less than
one hour.
Because of the effect of serial communications, in reservoir simulation there is often an optimal number of processors for a given problem.
Although the data management and housekeeping
parts of the system are the primary reasons for a
departure from the ideal state, there are others.
These include load balancing between processors,
bandwidth and issues related to congestion and
delays within various parts of the system. Reservoir
simulation problems destined for parallel solution
must use software and hardware that are designed
specifically for parallel operation.
105
Intel8086
microprocessor
103
1970
1980
1990
2000
2010
104
Year
> Computing capability and reservoir simulation. During the past four decades, computing capability
and reservoir simulation evolved along similar paths. From the 1970s until 2004, computer
microprocessors followed Moore’s law, which states that transistor density on a microprocessor
(red circles), doubles about every two years. Reservoir simulation paralleled this growth in computing
capability with the growth in number of grid cells (blue bars) that could be accommodated. In the last
decade, computing architecture has focused on parallel processing rather than simple increases to
transistor count or frequency. Similarly, reservoir simulation has moved toward parallel solution of the
reservoir equations.
5.Brown G: “Darcy’s Law Basics and More,” http://bioen.
white_papers/FromDeadEndToOpenRoad.pdf (accessed
okstate.edu/Darcy/LaLoi/ basics.htm (accessed
September 13, 2011).
August 23, 2011).
Flynn LJ: “Intel Halts Development of 2 New
Smith JM and Van Ness HC: Introduction to Chemical
Microprocessors,” The New York Times (May 8, 2004),
Engineering Thermodynamics, 7th Edition. New York
http://www.nytimes.com/2004/05/08/business/
City: McGraw Hill Company, 2005.
intel-halts-development-of-2-new-microprocessors.html
(accessed Sept 13, 2011).
6.Cao H, Crumpton PI and Schrader ML: “Efficient General
Formulation Approach for Modeling Complex Physics,”
12.Wheeler JA and Smith RA: “Reservoir Simulation on a
paper SPE 119165, presented at the SPE Reservoir
Hypercube,” SPE Reservoir Engineering 5, no. 4
Simulation Symposium, The Woodlands, Texas, February
(November 1, 1990): 544–548.
2–4, 2009.
13.Speedup, a common measure of parallel computing
7.Finite-difference equations are used to approximate
effectiveness, is defined as the time taken on one
solutions for differential equations. This method obtains
processor divided by the time taken on n processors.
Parallel effectiveness can also be stated in terms of
an approximation of a derivative by using small,
Oilfield
Review
efficiency—the speedup divided by the number of
incremental steps from a base value.
WINTER
8.Intel Corporation: “Moore’s Law: Raising the Bar,”
Santa 11/12processors.
Intersect
Fig.14.Baker
3
Clara, California, USA: Intel Corporation (2005),
ftp://
M: “Cluster Computer White Paper,” Portsmouth,
download.intel.com/museum/Moores_law/Printed_
England:
ORWNT11/12-INT
3 University of Portsmouth (December 28, 2000),
Materials/ Moores_Law_Backgrounder.pdf (accessed
http://arxiv.org/ftp/cs/00040004014.pdf (accessed
October 17, 2011).
July 16, 2011).
Fjerstad PA, Sikandar AS, Cao H, Liu J and Da Sie W:
Each of these computers in the parallel configuration
“Next Generation Parallel Computing for Large-Scale
may have either a single core or multiple core
Reservoir Simulation,” paper SPE 97358, presented at
microprocessors. Each individual core is termed a
the SPE International Improved Oil Recovery Conference
parallel processor and can act as an independent part
in Asia Pacific, Kuala Lumpur, December 5–6, 2005.
of the system.
9.Watts JW: “Reservoir Simulation: Past, Present, and
15.The serial portion is often called data management
Future,” paper SPE 38441, presented at the SPE
and housekeeping.
Reservoir Simulation Symposium, Dallas, June 8–11, 1997.
16.Barney B: “Introduction to Parallel Computing,”
10.In the black oil fluid model, composition does not
https//computing.llnl.gov/tutorials/parallel_comp/
change as fluids are produced. For more information
(accessed September 13, 2011).
see: Fevang Ø, Singh K and Whitsun CH: “Guidelines for
17.Christie MA and Blunt MJ: “Tenth SPE Comparative
Choosing Compositional and Black-Oil Models for
Solution Project: A Comparison of Upscaling
Volatile Oil and Gas-Condensate Reservoirs,” paper SPE
Techniques,” paper SPE 66599, presented at the
63087, presented at the SPE Annual Technical
SPE Reservoir Simulation Symposium, Houston,
Conference and Exhibition, Dallas, October 1–4, 2000.
February 11–14, 2001.
11.Scaling, or scalability, is the characteristic of a system
18.Pettersen Ø: “Basics of Reservoir Simulation with the
or process to handle greater or growing amounts of
Eclipse Reservoir Simulator,” Bergen, Norway:
work without difficulty. For more information: Shalom N:
University of Bergen, Department of Mathematics,
“The Scalability Revolution: From Dead End to Open
Lecture Notes (2006), http://www.scribd.com/
Road,” GigaSpaces (February 2007), http://www.
doc/36455888/Basics-of-Reservoir-Simulation
gigaspaces.com/files /main/Presentations/ByCustomers/
(accessed September 13, 2011).
7
Wellbore
Segment
Node
Nodes at branch junction
Reservoir
Σ
FIN
Σ
FOUT
ΣF
OUT
ΣF
IN
Nodes at well connections
with grid cells
> Multisegment well model. For each segment node in a wellbore, the new well model calculates the
total flow in (ΣFIN) and total flow out (ΣFOUT), including any flow between the wellbore and the
connected grid cell in the reservoir. Assuming a three-phase black oil simulation, there are three mass
conservation equations and a pressure drop equation associated with each well segment. During the
simulation, the well equations are solved, along with the other reservoir equations, to give pressure,
flow rates and composition in each segment.
One of the next-generation tools available
technology to handle the greater complexity now
present in the oil field. These simulators take now, the INTERSECT reservoir simulator, is
advantage of several new technologies that include the result of a collaborative effort between
parallel computing, advanced gridding techniques, Schlumberger and Chevron that was initiated in
modern software engineering and high-perfor- late 2000.19 Total, which also collaborated on the
mance computing hardware. The choice between project from 2004 to 2011, assisted researchers in
the next-generation simulators and the older ver- developing the thermal capabilities of the softsions is determined by field complexity and busi- ware. Following the research phase and a subseness needs. Next-generation tools should be quent development phase, Schlumberger released
considered if the reservoir needs a high cell count the INTERSECT simulator in late 2009. This systo capture complex geologic features, has exten- tem integrates several new technologies in one
sive local grid refinements or has a high permea- package. These include a new well model,
bility contrast.
advanced gridding, a scalable parallel computing
In addition to handling fields of greater com- foundation, an efficient linear solver and effective
plexity, the next-generation simulator gives the field management. To fully understand this simuoperator an important advantage—speed. Many lator, it is instructive to examine each of these
reservoir simulations involve difficult calcula- parts, starting with the new model for wells.
Oilfield
tions that can take hours or days to reach
com- Review
WINTER 11/12
pletion using older tools. The next-generation
Multisegment Well Model
Intersect Fig. 4_2
simulators can reduce calculation times on com- The INTERSECT simulator uses a new multisegORWNT11/12-INT 4
plex reservoirs by an order of magnitude or ment well model to describe fluid flow in the wellgreater. This allows operators to make field devel- bore.20 Wells have become more complex through
opment decisions in time and with confidence, the years, and models that describe them must
thus maximizing value and reducing risk. Shorter reflect their actual design and be able to handle a
runs lead to more runs, which in turn leads to variety of different situations and equipment. These
operators having a better understanding of the include multilateral wells, inflow control devices,
reservoir and the impact of geologic uncertain- horizontal sections, deviated wells and annular
ties. Shorter run times also allow the simulator to flow. Older, conventional well models treated the
be used more dynamically—it can evaluate well as a mixing tank that had a uniform fluid comdevelopment scenarios and optimize designs as position, and the models thus reflected total inflow
new data and information become available.
to the well. The new multisegment model overcomes this method of approximation, allowing each
branch to produce a different mix of fluids.
8
This well model provides a detailed description of wellbore fluid conditions by discretizing
the well into a number of 1D segments. Each segment consists of a segment node and a segment
pipe and may have zero, one or more connections
with the reservoir grid cells (left). A segment’s
node is positioned at the end farthest away from
the wellhead, and its pipe represents the flow
path from the segment’s node to the node of the
next segment toward the wellhead. The number
of segment pipes and nodes defined for a given
well is limited only by the complexity of the particular well being modeled. It is possible to position segment nodes at intermediate points along
the wellbore where tubing geometry or inclination angle changes. Additional segments can be
defined to represent valves or inflow control
devices. The optimal number of segments for a
given well depends on a compromise between
speed and accuracy in the numerical simulation.
An advantage of the multisegment model is
its flexibility in handling a variety of well configurations, including laterals and extended-reach
wells. The model also handles different types of
inflow control devices, packers and annular flow.
The new multisegment well model is, however,
only the beginning of the story on the INTERSECT
simulator and others like it. The next step splits
the reservoir into smaller areas, called domains.
19.DeBaun D, Byer T, Childs P, Chen J, Saaf F, Wells M,
Liu J, Cao H, Pianelo L, Tilakraj V, Crumpton P, Walsh D,
Yardumian H, Zorzynski R, Lim K-T, Schrader M,
Zapata V, Nolen J and Tchelepi H: “An Extensible
Architecture for Next Generation Scalable Parallel
Reservoir Simulation,” paper SPE 93274, presented at
the SPE Reservation Simulation Symposium, Houston,
January 31–February 2, 2005.
For another example of a next-generation simulator:
Dogru AH, Fung LSK, Middya U, Al-Shaalan TM, Pita JA,
HemanthKumar K, Su HJ, Tan JCT, Hoy H, Dreiman WT,
Hahn WA, Al-Harbi R, Al-Youbi A, Al-Zamel NM,
Mezghani M and Al-Mani T: “A Next-Generation Parallel
Reservoir Simulator for Giant Reservoirs,” paper SPE
119272, presented at the SPE Reservoir Simulation
Symposium, The Woodlands, Texas, February 2–4, 2009.
20.Youngs B, Neylon K and Holmes J: “Multisegment
Well Modeling Optimizes Inflow Control Devices,”
World Oil 231, no. 5 (May 1, 2010): 37–42.
Holmes JA, Byer T, Edwards DA, Stone TW, Pallister I,
Shaw G and Walsh D: “A Unified Wellbore Model for
Reservoir Simulation,” paper SPE 134928, presented at
the SPE Annual Technical Conference and Exhibition,
Florence, Italy, September 19–22, 2010.
21.DeBaun et al, reference 19.
22.Weisstein FW: “Traveling Salesman Problem,” Wolfram
MathWorld, http://mathworld.wolfram.com/Traveling
SalesmanProblem.html (accessed October 12, 2011).
23.Karypis G, Schloegel K and Kumar V: “ParMETIS—
Parallel Graph Partitioning and Sparse Matrix Ordering
Library,” http://mpc.uci.edu/ParMetis/manual.pdf
(accessed July 7, 2011).
Karypis G and Kumar V: “Parallel Multilevel k-way
Partitioning Scheme for Irregular Graphs,” SIAM
Review 41, no. 2 (June 1999): 278–300.
24.Fjerstad et al, reference 8.
25.Hesjedal A: “Introduction to the Gullfaks Field,” http://
www.ipt.ntnu.no/~tpg5200/intro/gullfaks_introduksjon.
html (accessed September 26, 2011).
Oilfield Review
Domains and a Parallel, Scalable Solver
The calculation of flow within the reservoir is the
most difficult part of the simulation—even for
simulators using parallel computing hardware. The
number of potential reservoir cells is many times
larger than the number of processors available. It
is natural to parallelize this calculation by dividing
the reservoir grid into areas called domains and
assigning each one to a separate processor.
Partitioning a structured Cartesian grid into segments containing equal numbers of cells while
minimizing their surface area may be a straightforward process; partitioning realistic unstructured
grids, however, is more difficult (right). Realistic
grids must be used to model the heterogeneous
nature of a reservoir that has complex faults and
horizons. The grids must also have sufficient detail
to delineate irregularities such as water fronts, gas
breakthroughs, thermal fronts and coning near
wells. These irregularities are usually captured by
the use of local grid refinements. Partitioning
unstructured grids with these complex features
and numerous local refinements is challenging; to
address this, next-generation simulators typically
use partitioning algorithms.21
The objective of partitioning the unstructured
grid is to divide the grid into a number of segments, or domains, that represent equal computational loads on each of the parallel processors.
Calculating the optimal partitioning for unstructured grids is difficult, and the solution is far
from intuitive. Reservoir partitioning is similar to
the “traveling salesman problem” in combinatorial mathematics that seeks to determine the
shortest route that permits only one visit to each
of a set of cities.22 Unlike the traveling salesman
who is concerned only about minimizing his time
in transit, partitioning of the reservoir must be
guided by the physics of the problem. To this end,
the INTERSECT simulator employs the ParMETIS
reservoir partitioning algorithm.23 The advantages of partitioning a complex reservoir grid to
balance the parallel workload become obvious by
considering simulation of the Gullfaks field in the
Norwegian sector of the North Sea.24
Gullfaks, discovered in 1979 and operated
by Statoil, is a complex offshore field that
has 106 wells producing about 30,000 m3/d
[189,000 bbl/d] of oil.25 This field is highly faulted
with deviated and horizontal wells crossing the
faults. An INTERSECT simulation of this field
developed several domain splits so that different
numbers of parallel processors could be evaluated for load balancing (right). When compared
Winter 2011/2012
Structured Reservoir Grid
Well
Unstructured Reservoir Grid
> Reservoir grids. Reservoir simulators may lay out the grid in either a structured pattern (upper left )
or as an unstructured pattern (lower right ). Structured grids have hexahedral (cubic) cells laid out in
a uniform, repeatable order. Unstructured grids consist of polyhedral cells having any number of
faces and may have no discernable ordering. Both grid types partition the reservoir space without
gaps or overlaps. Structured grids with many local grid refinements around wells are usually treated
as unstructured. Similarly, when a large number of faults are present in a structured grid, it becomes
unstructured as a result of the nonneighbor connections created.
Gullfaks Field Unstructured Grid
Gullfaks Field Domain Split
Oilfield Review
WINTER 11/12
Intersect Fig. 5
ORWNT11/12-INT 5
> Gullfaks domain decomposition. The highly faulted nature of the Gullfaks field and the number of
wells and their complexity result in complicated reservoir communication and drainage patterns. The
simulator takes these factors into account and develops a complex, unstructured grid in preparation
for partitioning (left ). Fine black lines define individual cell boundaries; vertical lines (magenta)
represent wells. Different colors denote varying levels of oil saturation from high (red) to low (blue).
This unstructured grid is split into eight domains using a partitioning algorithm for an eight-processor
simulation (right ). In the partitioned reservoir, different colors denote the individual domains. Only
seven colors appear in the figure—one domain is on the underside of the reservoir and cannot be
viewed from this angle. The primary criterion for the domain partition is an equal computational load
on each of the parallel processors.
9
0
Equations for cellnearest neighbors
2,550
Row number
2,000
Row number
4,000
6,000
2,560
2,570
2,580
2,590
Equations for other
reservoir connections
2,550
2,570
2,590
Column number
8,000
Equations for wells
10,000
0
2,000
4,000
6,000
8,000
10,000
Column number
> Matrix structure. A matrix of the linearized reservoir simulation equations is typically sparse and
asymmetrical (left ). The unmarked spaces represent matrix positions with no equation, while each dot
represents the derivative of one equation with respect to one variable (right ). The nine points inside
the red square (right ) represent mass conservation equations for gas, water and oil phases. The
points on the off-diagonal (left ) represent equations for connections between cells and their
neighboring cells in adjacent layers. Points near the vertical and horizontal axes (left ) represent the
well equations.
V
Δ φ ( ρo So xi + ρg Sgyi + ρw SwWi ( = _ (qo + qg + qw ) +
Δt r
kr
Δxyz T ρo xi μo (Δp _ ΔPcgo _ γo ΔZ ( +
o
krg
Δxyz T ρgyi μ (Δp _ γg ΔZ ( +
g
Residual, R(x)
krw
Δxyz T ρwwi μ (Δp _ ΔPcgo _ ΔPcwo _ γw ΔZ (
w
Tolerance
0
xn
x2
Oilfield Review
WINTER 11/12
Intersect Fig. 7
ORWNT11/12-INT
7
x1
x0
Solution variable x
> Numerical solution. The complete set of fundamental reservoir equations can be written in finitedifference form (top). These equations describe how the values of the dependent variables in each
grid cell—pressure, temperature, saturation and mole fractions—change with time. The equations
also include a number of property-related terms including porosity, pore volume, viscosity, density and
permeability (see DeBaun et al, reference 19). Numerical solution of this large set of equations is
carried out by the Newton-Raphson method illustrated on the graph. A residual function R(x) that is
some function of the dependent variables is calculated at x0 (dashed black line marks coordinate
position) and x 0 plus a small increment (not shown). This allows a derivative or tangent line (black) to
be calculated, that when extrapolated, predicts the residual going to zero at x1. Another derivative is
calculated at x1 that predicts the residual going to zero at x2. This procedure is carried out iteratively
until successive calculated values of R(x) agree within some specified tolerance. The locus of points
at the intersection of the derivative line and its corresponding value of x describe the path of the
residual as it changes with each successive iteration (red).
10
with an ECLIPSE simulation on Gullfaks using
eight processors, the INTERSECT approach
decreased computational time by more than a
factor of five. Runs with higher numbers of processors showed similar improvements and confirmed the scalability of the simulation.
Proper domain partitioning is only part of the
next-generation simulation story. Once the reservoir cells are split to balance the workload on the
parallel processors, the model must numerically
solve a large set of reservoir and well equations.
These equations for the reservoir and wells form
a large, sparsely populated matrix (left).
Although the equations generated in the simulator are amenable to parallel computation,
they are often difficult to solve. Several factors
contribute to this difficulty, including large system sizes, discontinuous anisotropic coefficients,
nonsymmetry, coupled wells and unstructured
grids. The resultant simulation equations exhibit
mixed characteristics. The pressure field equations have long-range coupling and tend to be
elliptic, while the saturation or mass balance
equations tend to have more local dependency
and are hyperbolic. The INTERSECT simulator
uses a computationally efficient solver to achieve
scalable solution of these equation systems.26 It is
based on preconditioning the equations to make
them easier to solve numerically. Preconditioning
algebraically decomposes the system into subsystems that are then manipulated based on their
particular characteristics to facilitate solution.
The resulting reservoir equations are solved
numerically by iterative techniques until convergence is reached for the entire system including
wells and surface facilities (left).27
The solver provides significant improvements
in scalability and performance when compared
with current simulators. A major advantage of
this highly scalable solver is its ability to handle
both structured and unstructured grids in a general framework for a variety of field situations
(next page, top).
26.Cao H, Tchelepi HA, Wallis J and Yardumian H: “Parallel
Scalable Unstructured CPR-Type Linear Solver for
Reservoir Simulation,” paper SPE 96809, presented at
the SPE Annual Technical Conference and Exhibition,
Dallas, October 9–12, 2005.
27.The linear solver consumes a significant share of system
resources. In a typical INTERSECT case, the solver may
use 60% of the central processing unit (CPU) time.
28.Güyagüler B, Zapata VJ, Cao H, Stamati HF and
Holmes JA: “Near-Well Subdomain Simulations for
Accurate Inflow Performance Relationship Calculation
to Improve Stability of Reservoir-Network Coupling,”
paper SPE 141207, presented at the SPE Reservoir
Simulation Symposium, The Woodlands, Texas,
February 21–23, 2011.
Oilfield Review
Winter 2011/2012
Ideal scaling
Fractured carbonate oil field
Highly faulted supergiant field
Offshore supergiant field
Massive gas condensate field
Large onshore oil field
Highly faulted oil field
18
16
14
Run time on 16 processors
Run time on n processors
Field Management Workflow
An improved field management workflow is one of
the components of the INTERSECT simulator
package. Field management tasks include design
of and modifications to surface facilities, sen­
sitivity analyses and economic evaluations.
Traditionally, field management tasks have been
distributed among the various simulators—includ­
ing a reservoir simulator, process facilities simula­
tor and economic simulator. The isolation of the
simulators in the traditional workflow tends to
produce suboptimal field management plans.
The field management (FM) module in the
INTERSECT simulator addresses the weaknesses
of traditional methods with a collection of tools,
algorithms, logic and workflows that allow all of
the different simulators to be coupled and run in
concert. This provides a great deal of flexibility;
for example, the module would allow two iso­
lated, offshore gas reservoirs to be linked to a
single surface processing facility for modeling
and evaluation.28
At the top level, the module executes one or
more strategies that are the focal point of the
whole framework. Strategies, which consist of a
list of instructions and an optional balancing
action, can encompass a wide variety of scenarios
that might affect production. These strategies
may include factors that affect subsurface deliv­
erability such as reservoir performance, well per­
formance and recovery methods. Other strategies
affecting production may include surface capa­
bility and economic viability. After the strategy is
selected, the FM module employs tools to create
a complete topological representation of the field
including wells, completions and inflow control
devices. Once the strategy has been set and the
field topology is defined, the module uses operat­
ing targets and limits to set well balancing
actions and potential field topology changes. An
important feature of the FM workflow is the abil­
ity to control multiple simulators running on dif­
ferent machines and operating systems and in
different locations (right).
Chevron and its partners used the INTERSECT
simulator in their field development of a major
gas project off the coast of Australia. The large
capital outlays envisioned for this project
required a next-generation simulator that could
run cases quickly on large, unstructured grids
characterized by highly heterogeneous geology.
12
10
8
6
4
2
16
50
100
150
200
250
300
Number of processors, n
> INTERSECT simulation scalability. This simulation system has been used in
a variety of offshore and onshore field scenarios including large gas
condensate fields and fields with significant faulting. Scalability—measured
as the run time on 16 processors divided by the run time on n processors, or
speedup—is calculated as a function of the number of processors. The
diagonal straight line (dashed) represents ideal scaling.
INTERSECT
field management
Surface network
simulator
Reservoir A, using
the ECLIPSE reservoir
simulator
Oilfield Review
WINTER 11/12
Intersect Fig. 9
ORWNT11/12-INT 9
Reservoir B, using the
INTERSECT reservoir
simulator
>Multiple reservoir coupling. The field management module can link independent Reservoirs A
and B and surface facilities (center ) via network links. In this example, Reservoir A (lower left ) is
using the ECLIPSE simulator on a Microsoft Windows desktop computer while Reservoir B (lower
right ) is using the INTERSECT system on a Linux parallel cluster. The surface network simulator,
running on a Microsoft Windows desktop, is handling surface facilities for this network (upper
right ). The FM module (top) that controls all of these simulators may be a desktop or local
mainframe computer.
11
AUSTRALIA
IO/Jansz field
Gorgon field
Barrow
Island
Dampier to Bunbury
natural gas pipeline
Pipeline junction
LNG plant
Gorgon pipeline
Existing pipeline
0
0
km
50
mi
50
> Gorgon project, offshore Australia. The Gorgon project includes the Gorgon and IO/Jansz subsea gas
fields that lie 150 to 220 km [93 to 137 mi] off the mainland. Gas is moved from the fields by deep
underwater pipelines (black) to Barrow Island, about 50 km off the coast. There, the raw gas is stripped
to remove CO2 and then either liquefied to LNG for export by tanker or moved to the mainland by
pipeline for domestic use. On the mainland, gas from Barrow Island is transported through an existing
pipeline (blue) that gathers gas from other producing areas nearby.
Better Decisions—Reduced Uncertainty
The Gorgon project—a joint venture of Chevron,
Royal Dutch Shell and ExxonMobil—will produce LNG for export from large fields off the
coast of northwest Australia.29 This project will
take subsea gas from the Gorgon and IO/Jansz
fields and move it by underwater pipeline to
Barrow Island about 50 km [31 mi] off the oast
(above). Chevron—the operator—is building a
15 million–tonUK [15.2 million–metric ton] LNG
plant on Barrow Island to prepare the gas for
export to customers in Japan and Korea.
Engineers at Chevron knew that one of the challenges would be to dispose of the high levels of
CO2 separated from the raw gas.30 Chevron will
meet this challenge by removing the CO2 at the
LNG plant and burying it deep beneath the surface of Barrow Island (below). Gorgon will be
capable of injecting 6.2 million m3/d [220 MMcf/d]
of CO2 using nine injection wells spread over
three drill centers on Barrow Island.
Oilfield Review
WINTER 11/12
Intersect Fig. 11
ORWNT11/12-INT
11
CO2 stripping
Gorgon project
gas fields
LNG plant
With billions of dollars of capital and LNG revenues at stake, Chevron and its partners understood from the start that the engineers developing
the business case would need to know how much
the project would yield and for how long.
Extensive reservoir modeling and simulation
were the solutions to this challenge. Some of
Chevron’s simulations on an internal serial simulator with fine grid models of individual Gorgon
formations were taking 13 to 17 hours per run.
Early in the project, Chevron decided that migration to the INTERSECT simulator would be
required for timely project development.
Although some computer models require a
minimal amount of input data, that cannot be
said for reservoir simulators. These simulators
employ large datasets and typically use purposebuilt migrators to move the data from one simulator to another. For Gorgon, Chevron used internal
migrating software to transform input from
their internal simulator to the corresponding
INTERSECT input dataset. These data were used
to develop history-matching cases at data centers
in Houston and in San Ramon, California, USA.31
The results from these cases showed that both
simulators were producing equivalent results—
although taking very different amounts of CPU
time to do it. This process was repeated on highperformance, parallel computing clusters at the
Chevron operations center in Perth, Western
Australia, Australia.
As Chevron project teams in Australia began
advanced project planning, the INTERSECT simulator reduced simulation times by more than an
order of magnitude. In one Gorgon gas field simulation with 15 wells and 287,000 grid cells, serial
run times with the internal simulator were six to
eight hours, while the INTERSECT system
reduced run times to under 10 minutes with
LNG product
Seismic
surveys
Surveillance
wells
CO2
CO2 disposal
> CO2 disposal. As natural gas is produced from the various reservoirs in the Gorgon project (left ), it is fed to a CO2 stripping facility located near the LNG
plant (middle). Stripped natural gas (orange) flows to liquefaction and an associated domestic gas plant (not shown), while the extracted CO2 (blue) is
compressed and injected into an unused saline aquifer 2.5 km [1.6 mi] beneath the surface for disposal. Conditions in the CO2 storage formation are
monitored by seismic surveys and surveillance wells (right ).
12
Oilfield Review
excellent scalability. In addition to this simulation, Chevron has used the INTERSECT simulator
on other fields in the Gorgon area including
Wheatstone, IO/Jansz and West Tryal Rocks. Both
black oil and compositional models have been
used with grids ranging from 45,000 to 1.4 million
cells. INTERSECT simulation times on these
cases using the Perth cluster ranged from 2 minutes to 20 minutes depending on the case.
Next-generation reservoir simulation on geologic scale models with fast run times has
enhanced decision analysis and uncertainty
management at Gorgon.
Reducing Simulation Time
Reduction of reservoir simulation execution
time was also a key factor for Total at their
Surmont oil sands project in Canada. Surmont,
located in the Athabasca oil sands area about
60 km [37 mi] southeast of Fort McMurray,
Alberta, Canada, is a joint venture between
ConocoPhillips Canada and Total E&P Canada
(right).32 The project was initiated in 2007 with
a production of 4,293 m3/d [27,000 bbl/d] of
heavy oil; it is expected to reach full capacity of
16,536 m3/d [104,000 bbl/d] in 2012.
At Surmont, the highly viscous bitumen in the
unconsolidated reservoir is produced by steamassisted gravity drainage (SAGD). In this process, steam is injected through a horizontal well,
and heated bitumen is produced by gravity from a
parallel, horizontal producing well below the
injector. Typically, one steam chamber is associated with each injector and producing well, and a
SAGD development consists of several adjacent
well pairs.
From a simulations point of view, at the start
of SAGD operations, the individual steam chambers are independent of each other and simulations can be performed on individual SAGD pairs.
As the heating and drainage proceed, this independence between well pairs ceases because of
pressure communications, gas channeling and
aquifer interactions. Including all well pairs in a
typical SAGD development quickly leads to multimillion-cell models that could not be run in a reasonable time frame with commercial thermal
simulators.33 Total turned to the INTERSECT
new-generation simulator to model the full-field,
nine-pair SAGD operation at Surmont.
The model describes an oil sands reservoir with
an oil viscosity of 1.5 million mPa.s [1.5 million cp]
and 1.7 million grid blocks with heterogeneous cell
properties.34 The model includes external heat
sources and sinks to describe the interaction with
Winter 2011/2012
C A N A D A
0
0
km
U N I T E D
S T A T E S
200
mi
200
Fort McMurray
Surmont
Athabasca
oil sands
A l b e r t a
Edmonton
Calgary
> Surmont project. The Surmont oil sands project is located
southeast of Fort McMurray, Alberta, Canada, within the greater
Athabasca oil sands area. Depending on the topography and the
depth of the overburden, oil sands at Athabasca may be produced
by surface mining or steam-assisted processes such as SAGD.
over- and underburden material. The producers are on a parallel computer cluster. To test its speed
controlled by maximum steam rate, maximum liq- and scalability, the software was used on different
uid rate and minimum bottomhole pressure (BHP). parallel hardware configurations ranging from 1
The injectors are controlled by maximum injection to 32 processors. These tests proved the ability of
this application to handle this large heterogerate and maximum BHP.
The INTERSECT system was used to model the neous model quickly enough to support operational decisions. For example, using 16 processors,
first three years of SAGD operations atOilfield
SurmontReview
WINTER 11/12
29.Flett M, Beacher G, Brantjes J, Burt A, Dauth Intersect
C,
TC, Nations T and Noonan SG: “SAGD Gas Lift
Fig.32.Handfield
13Completions
Koelmeyer F, Lawrence R, Leigh S, McKenna J, Gurton R,
and Optimization: A Field Case Study at
ORWNT11/12-INT
13 paper SPE/PS/CHOA 117489, presented at the
Robinson WF and Tankersley T: “Gorgon Project:
Surmont,”
Subsurface Evaluation of Carbon Dioxide Disposal Under
SPE International Thermal Operations and Heavy Oil
Barrow Island,” paper SPE 116372, presented at the
Symposium, Calgary, October 20–23, 2008.
SPE Asia Pacific Oil and Gas Conference and Exhibition,
33.Total initially tried a commercial, thermal simulator to
Perth, Western Australia, Australia, October 20–22, 2008.
model operations at Surmont. Case run times were very
30.Raw gas from the Gorgon fields has about 14% CO2.
long—45 hours—making this approach impractical.
31.To ensure that two reservoir simulators are producing
34.The oil is modeled using two pseudocomponents—
equivalent results, a user may employ a technique called
one light and one heavy.
history-matching. Each simulator will run the same case,
and the oil or gas production rates, as a function of time,
will be compared. If they match, the two cases are
deemed equivalent. This technique can also be used to
calibrate a simulator to a field where long-term
production data are available.
13
Conserving Resources
As operators continue to push into remote areas
in search of resources, next-generation simulators will be there to aid in planning and development. A case in point is the new Chevron Tengiz
field in the Republic of Kazakhstan, at the shore
of the Caspian Sea. Tengiz is a deep, supergiant,
naturally fractured carbonate oil and gas field
with an oil column of about 1,600 m [5,250 ft] and
a production rate of 79,500 m3/d [500,000 bbl/d].36
The Tengiz field is expansive, covering an area of
the INTERSECT simulator executed the Surmont
case in 2.6 hours.35 Parallel scalability is also
good—10 times faster on 16 processors compared
with a serial run. In addition to predicting flow
performance from SAGD operations, the system
can also give information on profiles of important
variables such as pressure and temperature in the
steam chambers (below). In preparation for fully
deploying the INTERSECT technology at Surmont,
Total is confirming these results on the most current version of the simulator.
Temperature, °C
10
66
122
178
234
> Steam chambers. The nine steam chambers at Surmont are located at a depth of 300 m [984 ft] near
the bottom of the oil sands reservoir. These chambers have a lateral spacing of about 100 m [328 ft] and
a length of nearly 1,000 m [3,281 ft]. Each chamber has a pair of wells—one steam injector (magenta)
and a parallel producing well (not shown). INTERSECT simulation of a thermal process such as SAGD
also yields information on temperature profiles in the steam chambers. At Surmont, the temperature
varies from more than 230°C [446°F] (red areas) at the core of the chamber to ambient temperature at
the periphery (blue areas). Gaps along the length of the chambers reflect permeability differences in
the oil sands. The operator monitors temperature in the steam chambers during production. While the
steam chambers are relatively small, the SAGD process is efficient. Once the chamber growth reaches
the rock at the top of the reservoir, thermal efficiency drops because of heat transfer to the overburden.
35.This case used 16 processors—four multicore
Lim K-T and Hoang V: “A Next-Generation Reservoir
processors each having four cores built into the chip.
Simulator as an Enabling Tool for Routine Analyses of
Heavy Oil and Thermal Recovery Process,” WHOC paper
36.Tankersley T, Narr W, King, G, Camerlo R,
2009-403, presented at the World Heavy Oil Congress,
Zhumagulova A, Skalinski M and Pan Y: “Reservoir
Puerto La Cruz, Venezuela, November 3–5, 2009.
Modeling to Characterize Dual Porosity, Tengiz Field,
Republic of Kazakhstan,” paper SPE 139836, presented
39.Afifi AM: “Ghawar: The Anatomy of the World’s Largest
at the SPE Caspian Carbonates Technology Conference,
Oil Field,” Search and Discovery (January 25, 2005),
Atyrau, Kazakhstan, November 8–10, 2010.
http://searchanddiscovery.com/documents/2004/afifi01/
(accessed September 29, 2011).
37.The Tengiz simulation also couples the reservoir and
well models to surface separation facilities to maximize
40.Dogru et al, reference 19.
plant capacities as part of development planning.
41.Dogru AH, Fung LS, Middya U, Al-Shaalan TM, Byer T,
Oilfield Review
38.Chevron Corporation: “Envisioning Perfect Oil Fields,
Hoy H, Hahn WA, Al-Zamel N, Pita J, Hemanthkumar K,
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Growing Future Energy Streams,” Next*, no. 4
Mezghani M, Al-Mana A, Tan J, Dreiman W, Fugl A and
(November 2010): 2–3.
Al-Baiz A: “New Frontiers in Large Scale Reservoir
Intersect Fig. 14
Simulation,”
Chevron is also using the INTERSECT system to
reduce
ORWNT11/12-INT
14 paper SPE 142297, presented at the SPE
Reservoir Simulation Symposium, The Woodlands,
run time in field scale models for thermal recovery
Texas, February 21–23, 2011.
processes. For more information, see:
14
about 440 km2 [170 mi2], and contains an estimated 4.1 billion m3 [26 billion bbl] in place.
The challenge for Chevron in modeling Tengiz
was the field’s geologic complexity coupled with
the need to reinject large quantities of H2S recovered from the production stream. This required
combining detailed geologic information with
information on the distinctly different flow
behaviors between fractured and nonfractured
areas of the field. To assist in current field management and support future growth, Chevron
developed an INTERSECT case that encompassed the 116 producing wells. The model contained 3.7 million grid blocks in an unstructured
grid that included more than 12,000 fractures.37
Chevron has experienced improved efficiency
using the new simulator at Tengiz; simulations
that once took eight days now take eight hours.38
More-realistic geologic input leads to more-accurate production forecasts that allow engineers to
make better field development decisions. In addition to their use in the development of new fields,
next-generation simulators may also aid recovery
of additional oil and gas from older fields.
The world energy markets rely heavily on the
giant reservoirs of the Middle East. The largest of
these reservoirs—Ghawar—was discovered in
1948 and has been producing for 60 years.39
Ghawar is a large field, measuring 250 km
[155 mi] long by 30 km [19 mi] wide. Simulation
of a reservoir the size of Ghawar is challenging
because of the fine grid size that must be
employed to capture the heterogeneities seen in
high-resolution seismic data. Using fine grid sizes
can reduce errors in upscaling (next page).
To handle reservoirs the size of Ghawar and
the other giant fields that it owns, Saudi Aramco
has developed a next-generation reservoir simulator.40 In one Ghawar black oil simulation, the
model used more than a billion cells with a 42-m
[138-ft] grid and 51 layers with 1.5-m [5-ft] spacing.41 Using a large parallel computing system,
42.Dogru AH: “Giga-Cell Simulation,” The Saudi Aramco
Journal of Technology (Spring 2011): 2–7.
43.Farber D: “Microsoft’s Mundie Outlines the Future of
Computing,” CNET News (September 25, 2008) http://
news.cnet.com/830113953_3-10050826-80.html
(accessed August 4, 2011).
44.Dogru et al, reference 41.
45.Bridger T: “Cloud Computing Can Be Applied for
Reservoir Modeling,” Hart Energy E&P (March 1, 2011),
http://www.epmag.com/Production-Drilling/CloudComputing-Be-Applied-Reservoir-Modeling_78380
(accessed August 11, 2011).
Oilfield Review
50 m
250 m
© 2011 Google-Imagery © 2011 Digital Globe, GOIEYE
© 2011 Google-Imagery © 2011 Digital Globe, GOIEYE
> Grid resolution. Areal grid size plays an important role in capturing reservoir heterogeneity and eliminating errors caused by upscaling. Overhead photos
of the Colosseum in Rome illustrate this concept. If the area of interest is the Colosseum floor (dashed box, upper left), then a 50 m x 50 m [164 ft x 164 ft]
grid is appropriate to capture what is required. Choice of a larger 250 m x 250 m [820 ft x 820 ft] grid (dashed box, right) includes driveways, streets,
landscaping and other features not associated with the focal point of interest. In the case of the Colosseum, use of the larger grid to capture properties
associated with the floor would introduce errors.
this model simulated 60 years of production history in 21 hours.42 The results were compared
with an older simulation run using a 250-m [820ft] grid and a given production plan. The older
simulator predicted no oil left behind after secondary recovery; the new simulator revealed oil
pockets that could be produced using infill drilling or other methods. This example shows how
next-generation simulators may facilitate additional resource recovery.
Although a primary goal of next-generation
simulators has been to more completely
describe reservoirs through reduced grid size
and upscaling, scientists are also pursuing other
technology innovations. Improved user interfaces and new hardware options for reservoir
simulation are imminent.
These improved user interfaces embody a concept known as spatial computing. Spatial computing relies on multiple core processors, parallel
programming and cloud services to produce a virtual world controlled by speech and gestures.43
This concept is being tested for controlling large
Winter 2011/2012
reservoir simulations with hand gestures and verbal commands rather than with a computer
mouse.44 To test this concept, a room is equipped
with cameras and sensors connected to large
screens on the walls and a visual display on a table.
Using hand gestures and speech, engineers manage the simulator input and output. If needed, the
system can be used in a collaborative manner via a
network with engineers and scientists at other
locations. This kind of system has vast possibilities—it tends to mask computing system complexOilfield Review
ity and allows the
engineers
and scientists to freely
WINTER
11/12
interact with the
reservoir
simulation.
Intersect Fig. 15
ORWNT11/12-INT
15
Just as ideas
such as spatial computing
will
enhance the user interface, new hardware utilization concepts that go beyond onsite parallel
computing clusters will add to reservoir simulation capability. Clusters of parallel computers are
expensive and the associated infrastructure is
complex and difficult to maintain. Some operators are discovering that it may be useful to use
cloud computing to communicate with multiple
clusters in many locations.45 Using this approach,
the operator can add system capacity as the
situation dictates rather than depending on a
fixed set of hardware. This approach allows the
user to communicate with the cloud system via a
“thin client” such as a laptop or a tablet. Reservoir
modeling tools using this technology have already
been developed, and more will follow.
New technology for reservoir simulation is
emerging on several fronts. Foremost are nextgeneration reservoir simulators that produce
more-accurate simulations on complex fields
with reduced execution time. Other technologies
such as spatial and cloud computing are on the
near horizon and will allow scientists and engineers to interact more naturally with the simulations and potentially add hardware capability at
will. These developments will give operators
more-accurate forecasts, and those improved
forecasts will lead to better field development
decisions. —DA
15