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March 25-26, 2015 | Houston | Texas
> reservoir pressure. Other
data is possible to collect,
but cost-prohibitive - such
as bottom hole pressure
data and lab analysis of
fluid samples. There is
still more data that we
can collect but has a high
measure of imperfectness
- such as core samples.
Geologic heterogeneity &
natural fractures violate the
assumptions in some of our
most sophisticated models
used for production data
analysis. Pressure/stress
dependent properties and
multi-phase flow should also
get an honorable mention
when discussing complexities
in modeling. We often don’t
know whether wells can be
treated as if they are isolated
within the reservoir until
months after we’ve had to
make a decision regarding
well spacing.
An understanding of first
principles can elucidate some
of the possible interpretations
involved in analysis, but there
is no single answer - or in
some cases, not enough data to
provide any answer at all. And
then we must base a business
decision based upon it.
What are the greatest
advances in developing
reservoir modeling
technologies specific to
unconventional oil & gas?
I would have to say
computational power,
because it allows for an
acceleration of the scientific
method. The ability to test
multiple hypothesis in
simulation and determine
which do and do not lead to
plausible explanations of
the behavior we empirically
observe in production data
accelerates our ability to
learn and gain knowledge
of unconventional well
performance.
What will you be
presenting at the upcoming
Reservoir Engineering for
Unconventional Oil & Gas
congress?
The Transient Hyperbolic
Model (THM) is a decline
curve model that is
equivalent in form to the
analytic solution for a closed
linear reservoir. It has three
primary advantages: 1) The
parameters of the model have
physical significance and are
well correlated to behavior of
the reservoir - meaning they
can be estimated a priority,
not simply through empirical
curve fitting; 2) Because
the model is equivalent in
form to an analytic solution,
it is possible to compute
the THM analytically as
well - from any analytic or
numerical history match, the
parameters of the model can
be calculated, and rapidly
applied to other wells that
do not have data sets that
make analytic or numerical
modeling possible; and 3)
The THM represents the
behavior of transient linear,
transitionary, and boundarydominated flow, allowing
modeling of full-life behavior
of unconventional wells. If it
can be fit to production data
that is still within a transient
or transitionary regime, given
the evaluator can accurately
express a belief about longterm behavior of the reservoir
(i.e. not just the model).
What are the reservoir
modeling analysis
methodologies that will
be key in estimating EUR
accurately and predicting
production performance?
One common feature of
numeric, analytic, and
empirical models, has been
a recent trend of increasing
complexity. For example, the
industry observed complex
geo-mechanical behavior
in the Haynesville during
the development boom
circa 2007. This spurred the
development of simulators
that could model how
reservoir properties change
during the depletion process.
Analytic models led to semianalytic models that combine
some of the power of
numerical simulators. There
are numerous empirical
models proposed to describe
transient flow. This can be
overwhelming to a reservoir
engineer deciding which
combination to use under
what conditions.
This circles back to having
computational power
available to analyze these
different models within a
reasonable period of time.
One step further, which
would improve the abilit y to
make inferences about future
performance from all models,
is the addition of statistical
uncertainty evaluation to any
model. If we know our models
are not accounting for every
complexity (because we do
not know every complexity),
then it is important we
generate a range of possible
interpretations for any given
model. Discrete model fits
do not tell us the probability
of achieving the model’s
predictions, nor assist in
tuning the model over the
long term - how is one to
judge what was a correct or
incorrect, or good or bad,
forecast, in an objective
manner? If we focus on the
outcomes of various possible
model fits, and how likely
those outcomes are, we would
gain much confidence in our
ability to estimate future
performance in all cases, by
our new found ability to judge
and learn from how well we
forecast in the past.
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