A framework for history matching - StreamSim Technologies, Inc.

Mepo
Multipurpose Environment
for Parallel Optimisation
®
A framework for history matching

Mepo significantly reducing the turnaround time to update models
The Multipurpose Environment
for Parallel Optimisation (Mepo),
is designed to support the process of History
Matching in Reservoir Engineering.
While the turnaround time for creating new
and updating old models has been significantly
reduced, the reservoir engineer still
has to validate the model through history
matching and uncertainty assessment, before
generating production forecasts.
Most oil companies spend a significant amount
of time in creating geological models for reser-
voir simulation. Significant resources are
consumed when adapting to multidisciplinary
work and integrated reservoir modeling work-
flows. While recent software developments have
reduced the modeling time tremendously, the
history matching or model validation task is still
cumbersome and very time-consuming.
If you cannot match the observed data to your
simulation model, the history matching process
can exceed the time used to create or update the
reservoir model by a factor of 10.
Furthermore, if you do not have the capacity for
additional history matching, you will probably
pick the apparent best match so far, and use this
model for production forecasting. Bad decisions
are often taken due to the large uncertainties in
the model, e.g. drilling of wells missing reservoir
targets.
What if the history matching and
uncertainty assessment could be
performed quickly and at a low
cost?
Mepo, a new software framework, is revolutionising
history matching by reducing manpower
requirements and assessing the uncertainty of
simulation models. Recent studies show a reduction
in modeling time of more than 50%.

Mepo a framework for history matching
Mepo is an invaluable tool and framework
for the reservoir engineer performing history
matching (HM). The benefits offered by Mepo
are explained below.
I. A match will be found
A simulation model with a poor match can give misleading
results. Planning new wells with a low risk of missing the
reservoir is of paramount importance. In addition, a well
matched model will be reliable to the extent that it can be
used in decline analysis and other predictions of future field
performance.
Mepo optimises model parameter sets until convergence
is reached, i.e. until a match is obtained. Even in cases in
which convergence is not reached, Mepo offers concepts
and methods to investigate the solution space and to provide
information on changing the HM strategy. Implementing
Mepo in the HM-workflow will define a guideline and
facilitate the process to get a match.
II. Assessing the viability of the simulation model
In order to increase the acceptance of results from the
simulation model, the uncertainty of any acceptable HM
result should be quantified. When a model reproduces all
history properly, the HM is considered acceptable. However,
due to the nature of the problem, there are a number of
acceptable solutions. Each solution might generate different
predictions. Mepo investigates the model diversity, which
can give insights into the model uncertainty.
Mepo includes global optimisation methods, which have
the potential to identify various good matches. A number
of acceptable matches can be used for prediction runs.
Differences in the predicted results reflect and quantify the
model uncertainty. This approach goes far beyond the linear
perturbation of parameter values based on one acceptable
match. The method adds value by quantifying uncertainty
and increasing reliability on the reservoir model used for
reservoir predictions.
Mepo supports the identification of several acceptable matches
to assess the uncertainty of the simulation model.
Several simulations fall within an acceptable uncertainty range
in the history period, but the predictions from the same models
define an uncertainty in the simulated production forecast.

Mepo a framework for history matching
III. HM studies can be performed in considerably less
time than conventional methods
If a match can be achieved in less time while producing
better results, added value will be generated. Mepo sup-
ports the history matching process using local and global
optimisation methods. Input parameters are varied until
a match (or matches) between observed and calculated
output values is achieved. Therefore, manual editing of
input files is reduced to conceptual changes of the
optimisation strategy.
Mepo allows the user to perform
correlation analysis to determine
important parameters. The GUI offers
visualisation of various intermediate
results, parameter distributions and
weight functions.
Loaded optimisation results can
also be analysed using a Maximum
Likelihood Analysis, Pearson‘s linear
correlation coefficient or Spearman‘s
rank correlation.
Mepo has an intuitive graphical user
interface (GUI) as a front-end to the
Mepo optimisation environment. This
gives the user a structured approach
to efficient history matching.

Questions & Answers
Benefits
“What benefits does Mepo give me compared to
my existing conventional history matching (HM)
workflow?”
“Usually I use one history matched
simulation model as base case for prediction runs.”
The Mepo workflow yields several matches of
acceptable quality. Thus, the uncertainty in your
simulation model is assessed before predictions are
made.
“Manual HM is usually time consuming, and I
often find it difficult to obtain a good match.”
With the application of scalable CPU clusters, Mepo will
reduce the amount of time used for HM, and the total
turnover time to update models is reduced significantly.
Mepo utilises state-of-the-art global optimisation
methods, with the capability to search the whole solution
space looking for ways to achieve a HM. When the
simulations are running, the engineer can analyse output
data, and adjust the workflow by modifying the
optimisation strategy or selecting new parameters
(discrete or continuous) into the HM.
“I judge the quality of the match by appearance, and
don’t use any quantitative measure.”
Mepo uses a customisable objective function to
measure the difference between simulated and
measured history data. Individual weighting schemes
for measurements and/or time periods are available.
Prior information (e.g. correlations between the HM
parameters like permeability and porosity) can be
added as penalty terms to the objective function.
“I modify only one parameter at a time, and choose
the ones which appear to be the most promising.”
A one parameter at a time approach ignores correlation
effects. The Mepo workflow includes global optimisation
and experimental design with the capability to modify
several parameters at a time.
“I have to manually edit and evaluate each
simulation.”
Mepo has an integrated pre- and post-processor, which
automatically checks the quality of the HM, and
generates new input files for simulation. You can interact
with this process at any time.
“How is Mepo different from other history
matching software?”
Optimising HM projects.
Mepo is a flexible framework for HM, an optimisation
environment with an integrated pre- and postprocessor.
The Mepo workflow currently includes
Bayesian analysis, experimental design, local and global
optimisation methods, and allows the inclusion of new
optimisation methods.
The graphical user interface is user friendly, and gives
full control and overview of the simulations. Mepo
provides a structured approach to HM, and produces
comprehensiveness, transparency and reliability of
results.
Global optimisation methods and parallel
processing.
A unique feature of Mepo is the use of global
optimisation and multiple CPUs. An Evolution
Strategy is applied to find several matches within
determined acceptable quality parameters. These
matches are then applied in prediction runs assessing
the model uncertainty. Distribution of these runs to a
number of parallel CPUs significantly reduces the simu-
lation time. Mepo proposes and generates new cycles
of runs based on the previous results. A cycle of runs
typically ranges from 2 – 20, but can consist of any
number of runs.
Both discrete and continuous parameters can be
changed.
A major benefit of Mepo is the ability to change both
discrete and continuous parameters. Typical parameters
that cause difficulties in HM studies are fault locations
and relative permeability curves (discrete), and
permeability and pore volumes (continuous).
Changing parameters and optimisation methods
during study.
An advanced algorithm management integrated in Mepo
allows to steer the optimisation process, e.g. changes in
the optimisation strategy (e.g. method), and to activate
or deactivate parameters as the HM study progresses.

Questions & Answers
Technology
“How does Mepo check the quality of the
history match?”
The quality of the HM is simply quantified by the difference
between the measured and simulated values. All types of
measurement values such as pressures (BHP, RFT),
production rates, WCT and GOR, can be included. The
following definition of the objective function is used in Mepo:
Q denotes the quality, or objective function
i references an objective element, e.g. the oil rate at a
particular well
k references the time step at which an observed value exists
w is the weight of the objective element i at time step k
i,k
defines observed value of the objective element i at time step k
oi,k ci,k σi defines calculated value of the objective element i at time step k
is the standard deviation (the measurement error) of the
objective element i
n,m refers to all model parameters
x model parameter and mean value
C covariance matrix
“How does Mepo assess the uncertainty in
the production forecasts?”
Uncertainties are assessed by identifying several simulation
models that belong to different parts of the search space,
all having acceptable matches. In addition, experimental
design is used to assure a large initial variation of
parameter levels.
“Which optimisation methods are used in
Mepo?“
A pool of optimisation algorithms is implemented to assist
the engineer in the HM study. An evolution strategy is used
for global optimisation. Local search methods like a Simplex
algorithm and a gradient method are used for fine tuning
applications or small size problems. A Bayesian approach is
included to identify parameter sets with a good potential to
further improve the match.
An evolution strategy belongs to the class of evolutionary
algorithms, which use only the objective function value to
determine new search steps, and do not require any
gradient information from the optimisation problem.
They can therefore be used in cases where gradient information
is not available, and where traditional algorithms fail
because of significant non-linearities or discontinuities in the
search space. Evolutionary algorithms have proven to be
robust and easy to adopt to different engineering problems.
The nature of evolutionary algorithms is to use parallel
structures in generating parent-to-child sequences. This
principal feature can be easily transferred to parallel structures
of an optimisation program allowing parallel
computing to be used.
“Which parameters can be varied in the
history matching study?”
In principle, there are no limits to which parameters one
can alter, both discrete and continuous parameters can
be varied. Fault locations, relative permeability curves,
stochastic realisations, PVT data and grids are examples of
discrete parameters often used in an HM study. Uncertainties
in continuous parameters like permeability, porosity,
aquifer size and productivity index are also common. A
generalised pre-processing concept allows the inclusion of
basically every simulation parameter as a design or
optimisation parameter.

Questions & Answers
Technology Hardware & Software
“Which simulator output parameters can be
optimised?”
Most often the optimisation is based on pressure values
(BHP, RFT), production rates (OPR, GPR, etc.) and ratios
(GOR, WCT). Mepo includes an advanced post-processor
which reads and interprets all standard Eclipse output files
(summary files, user files, RFT files, etc.). All parameters
accessible through these files can be included in the objective
formulation. Customised post- processing scripts can
be easily linked to Mepo to include other parameters into
the objective formulation (e.g. NPV, gas volume in selected
regions).
“Is there any limitation to the size of the
simulation model?”
In principle - no, the benefits of Mepo are scalable and
speed depends on the number of CPUs and available
licenses. Regardless of the number of licenses, the uncertainties
in the simulation model can be assessed and
Mepo will assist the engineer towards a match.
To minimise the impact on your resources, Scandpower
Petroleum Technology can offer expert advice and deliver
pre-installed clusters of computers with the necessary operating
system and simulation software (e.g. Eclipse and/or
3DSL).
“Can I weight measurements or observed
values?”
Yes, measured values and time periods can be weighted.
To examine which parameters or time periods to weight, the
data can be loaded into Mepo and analysed by visualising
weight factors together with the measured data.
Visualisation of weight factors and measured data.
“Which reservoir simulators is Mepo linked
to?”
Any simulator can be launched by Mepo. Effective definition
of the objective function requires advanced post-processing
tools to read and interpret the simulator output. Postprocessing
tools are available through Mepo for the Eclipse
simulator output. Any simulator with an Eclipse compatible
output format can be used on the same level as Eclipse,
e.g. 3DSL, Frontsim, Eclipse 300, etc.
Customised post-processing scripts can easily be linked to
Mepo to read output data from any simulator to be used in
the Mepo workflow.
“What kind of hardware and software is
required?”
Mepo is scalable, and does not require a minimum or
maximum number of processors. Mepo can run on a
stand-alone one-processor machine or on a multi-processor
machine in a network. Mepo is best utilised through parallel
optimisation in a cluster of computers/CPUs, where you
have access to several simulator licenses.
Eclipse ® is a trademark of Schlumberger
Frontsim ® is a trademark of Schlumberger
3DSL ® is a trademark of Streamsim Technologies

Questions & Answers
Cost, Training & Deployment
“If my company wants to test Mepo, how
do we allocate hardware and software
resources?”
Scandpower Petroleum Technology can visit your office and
install the necessary software. We can also provide expert
advice, hardware and software, or perform the study in our
offices. Please contact Scandpower Petroleum Technology
for further information.
“What’s the price of Mepo?“
Mepo is cost efficient, and there are various Mepo licensing
alternatives.
“Can we lease Mepo?”
Yes, various lease options are available, typically starting at
the monthly level.
“How about Mepo training?”
A typical pilot project includes the offer to train the client in
using Mepo. After the pilot project, Scandpower Petroleum
Technology offers full 3-day training courses for additional
users. The official web page of Mepo, www.mepo.com,
offers downloads and tutorials.
“How can you get us started with our
first project?”
We will show you how to get started on the path to a
structured approach to history matching. Our experts will
assist you during the initial stage and when you need help
or extra capacity.

Mepo optimised history matching
IV. Mepo supports the workflow and
introduces best practices for the project
management of HM studies
Starting from an initial reservoir simulation model, several
sensitivity studies may be launched and many parameter
changes introduced before finding an acceptable match.
Mepo supports this workflow and assists the reservoir engineer
in analysing results. Mepo offers a structured concept
to document intermediate results, keeping an overview of
simulation runs. No time is lost trying to analyse and gather
information from old runs.
By full application of Mepo capabilities a HM project
becomes transparent and easily reproducible. The
experience of the reservoir engineer is indispensable for
choosing the right optimisation strategy. However, creating
a project transparency becomes important in today’s team
working environment. Using the Mepo project manager,
significant time delays for introducing new team
members can be prevented. Lost know-how due to
changing responsibilities on a HM project becomes a thing
of the past.
Best practices of project management are supported by a
structured organisation of reservoir simulation runs,
reproducibility of results generated during all optimisation
runs and creating a transparency on the entire HM project.
Transparency and reproducibility by
documenting key strategy changes and key
results of the history matching process.

Mepo short for history matching
Multipurpose
The Mepo optimisation environment has successfully been applied to
various scientific and industrial engineering problems, including air wing
design and nuclear fuel management systems. Now Mepo is applied to
history matching of reservoir simulation models, and development was
sponsored by ENI, Hydro, Statoil and Total.
environment
Mepo is a framework for history matching:
A number of optimisation methods are available and new
can be added
Any sensitivity run can be launched
The optimisation strategy can be redefined, i.e. different
optimisation schemes can be coupled sequentially keeping
results from previous optimisation cycles
Mepo can be linked to any simulator
parallel
Mepo can operate on an arbitrary number of available processors
connected in a computer network, or on multi-processor machines.
The distribution of simulations to a cluster of CPUs has the potential of
significantly reducing the total turnover time for history matching. Mepo
is a system to handle the vast amount of information from the simulations,
and directs the users to appropriate matches. This concept clearly
supports the assessment of the uncertainty in the simulation model.
optimisation
In Mepo the optimisation problem is to minimise an objective function.
This objective function is related to the difference between observed and
simulated values. In order to minimise this difference, key parameters in
the simulation model can be varied. Typical parameters to vary are fault
locations/transmissibilities, permeabilities and pore volumes.
Both global and local optimisation techniques, with complementary
features, are implemented in Mepo. Global optimisation (e.g. Evolutionary
Algorithms) can be used initially to find several acceptable matches,
and local techniques (e.g. gradient methods) can be applied towards the
end to further fine-tune the matches.
“Mepo was developed in cooperation with
several research and industry partners.
Conceptual developments and research
applications were started at the Institute
for Scientific Computing and Institute for
Spaceflight and Reactortechnology at the
University of Braunschweig, Germany in
1995. Research activities are ongoing.”

The Mepo workflow

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epo
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