American Oil and Gas Reporter - September 2021 - 74

SpecialReport: Enhanced Recovery
Traditional Approaches
In traditional reservoir management,
including waterflood design and optimization,
various predictive models can
be applied to enable quantitative decision-making.
These models range from
simple type curves to extremely complex
numerical reservoir simulations. The simulations
account for detailed reservoir
physics and integrate all kinds of measured
data, so they can produce accurate
predictions. However, building and calibrating
simulation models takes significant
time and effort, which makes
applying them challenging.
Also, the detail that gives simulations
their accuracy comes with the downside of
computational complexity. It takes enough
computing power and time to run the
models that engineers must carefully select
which scenarios they want to evaluate.
In contrast, machine learning algorithms
can provide answers quickly and
are easy to set up. Unfortunately, because
they rely on analyzing trends in historical
data rather than applying the laws of
physics (e.g., equations governing how
fluids move through porous media), their
predictions lack robustness, which limits
their applicability to short time periods.
To provide both speed and accuracy,
data physics melds state-of-the-art machine
learning with the physics present in industry-standard
reservoir simulators. Data
physics models can be created as efficiently
as machine learning models and evaluated
orders of magnitude faster than full-scale
simulations, but because they honor physics
and integrate all kinds of data, they can
make accurate long-term predictions.
In waterflood applications, data
physics enables users to move from data
preparation and modeling to effective decision-making
in a matter of days rather
than months or even years. In addition,
the models can be updated with new data
quickly, so they always remain operationally
useful.
Quantitative Optimization
Data physics offers a framework in
which petroleum engineers can optimize
a reservoir's oil production quantitatively.
It shows engineers which daily, weekly
or monthly actions will maximize returns
by investigating tens of thousands of scenarios
that simulate specific activities
(e.g., increasing injection or changing
74 THE AMERICAN OIL & GAS REPORTER
drilling programs). This allows engineers
to select plans that meet specific optimization
criteria, such as preserving production
rates or optimizing long-term
reserves growth.
Sometimes engineers need to optimize
multiple criteria simultaneously. To
achieve this goal, engineers must have an
accurate and statistically proven predictive
model of reservoir mechanisms.
They must be able to predict not only
well-by-well behavior, but also correctly
analyze well and pattern interdependencies.
In each case, the physics must drive
the predicted outcome, and statistical
confidence must be calculated so that relative
risk can be accounted for.
To this end, data physics merges modern
data science and the physics of reservoir
simulation. Data physics models, like
machine learning ones, require only days
to set up and can be run in real time. Because
they include the same physics as a
reservoir simulation, they offer excellent
long-term predictive capacity even when
historical data is sparse, missing or noisy.
At their core, data physics models are
physics-based models augmented by machine
learning techniques; the underlying
reservoir physics equations are solved continuously
across the reservoir, which limits
the machine learning algorithms' solutions
to those that are consistent with reservoir
physics. While one of the techniques used
to calculate coefficients in some of these
equations is inspired by neural networks,
the models always are constrained to honor
both the underlying system's physical
equations and the actual measured data.
This approach differs dramatically
from traditional machine learning, which
does not attempt to constrain models
using the governing physics. Purely datadriven
approaches such as neural networks
only utilize input-output behavior
present in training data and do not involve
any actual physical equations.
Many of these methods incorrectly assume
the neural network will " learn " the
equations. Instead, it often generates a solution
that is physically implausible or
impossible, or makes inaccurate predictions
when it is presented with a situation
its training data did not anticipate.
Data Physics' Role
Data physics models resemble traditional
simulation more than neural networks,
but they have the added capability
to adapt to cases where the physics
are poorly understood.
Also, data
physics' intent differs from traditional
reservoir simulation. Because the models
focus on predicting how specific activities
will impact production-not
characterizing the reservoir-they can be
designed to operate quickly and to explore
millions of scenarios.
This speed permits the models to be
validated against production history and
tuned constantly. Such tuning allows the
models' accuracy to be quantified statistically
across several scenarios, enabling
engineers to evaluate the risk associated
with each one.
Data physics can replace traditional
reservoir simulators for tasks that require a
geomodel with a high degree of accuracy.
Because data physics defines accuracy to
mean " does the model correctly predict oil
production in a certain scenario? " the models
it produces excel at forecasting production
and are easily re-run with real-time
field and production data.
Having said that, tasks that require a
full geomodel likely will continue to rely
on existing simulations. For example, understanding
geologic scenarios or drive
mechanisms, screening enhanced oil recovery
methods and making greenfield
development decisions generally will still
require current simulation methods because
data physics models need historical
data from the field that would not yet be
available in those situations.
Unlike traditional sequential reservoir
simulation workflows, data physics models
can integrate production data, log data
and seismic data in a single assimilation
step. These models directly incorporate
raw data such as log responses without
manual interpretation, and can assimilate
various forms of data without inconsistencies.
These capabilities are one reason
the models can be built rapidly and updated
continuously.
Implementation
To make it easy for engineers to apply
data physics to waterfloods, a Web-based
application has been created. This application
generates a Pareto Front plot in which
the x axis represents a percentage decrease
in injection over the time frame considered
and the y axis represents the percentage of
incremental oil production (Figure 1).

American Oil and Gas Reporter - September 2021

Table of Contents for the Digital Edition of American Oil and Gas Reporter - September 2021

Contents
American Oil and Gas Reporter - September 2021 - Intro
American Oil and Gas Reporter - September 2021 - Cover1
American Oil and Gas Reporter - September 2021 - Cover2
American Oil and Gas Reporter - September 2021 - Contents
American Oil and Gas Reporter - September 2021 - 4
American Oil and Gas Reporter - September 2021 - 5
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American Oil and Gas Reporter - September 2021 - Cover3
American Oil and Gas Reporter - September 2021 - Cover4
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