Effective management of uncertainties and
life-cycle risks is critical to the success of oil and gas projects. The uncertainties
and risks are, in fact, the central challenge of the Exploration and Production
business.
In May of 2014 Alejandro Primera was
invited to give a talk about the relevance of capturing the uncertainties
associated with reservoir characteristics. The work included Experimental
Design (ED) and the modelling of reservoir uncertainties in a numerical
simulation. The aim of the talk was to provide the audience with examples and the
experience of making decisions under high subsurface and surface facility uncertainty
and risk associated.
The topics discussed by Alejandro Primera
emphasized on the correlation of the decision making process with the
uncertainties present in the process. Because of the mix of subsurface, surface
and economic uncertainties, the application of ED sampling to studies could be
a cumbersome process, by which multiple models have to be coupled, so a strict
process for the configuration of studies is far from been established in the
industry, on top of the large number of parameters which need to be taken into
consideration. Hence, Alejandro underlined that the complete evaluation of
subsurface and surface uncertainty usually depends on geological, economic and
technological uncertainties which can impact, in various degrees, the recovery
process and the project viability.
During the talk, ED examples were shown
to the audience to envisage the level and range of uncertainties which can be
incorporated into simulation modelling. In addition, the interaction between the
different elements affecting critical decisions making in a field development scenario
was highlighted. Furthermore, the differences between technical and
organisational complications when choosing between different development
options were also discussed.
More about ED
ED
can play an essential role when it comes to development of ranking and
uncertainty analysis studies in several industries. In a set of experiments, we
intentionally change one or more input parameters in order to observe the
effect the changes have on one or more responses or Key Performance Indicators
(KPIs). It is important to plan and develop the studies, ensuring that the
right type of data and sufficient sample size and power are available to answer
the research questions of interest as clearly and efficiently as possible.
There are several commercial and open source tools that could be
used to select experiments sets and fit linear, quadratic or more complex
models.
Figure 1 : ED sample workflow
In Oil and Gas, Numerical simulation of subsurface fluid flow coupled
with surface infrastructure is a time consuming exercise. Computer experiments with quantitative factors require
special types of ED, it is often possible to include many different levels of
the factors. Also, the experimental region is often too large to assume that a
linear or quadratic model adequately represents the phenomenon under
investigation. Consequently, it is desirable to fill the experimental space
with points as well as possible (space-filling designs) in such a way that each
run provides additional information even if some factors turn out to be
irrelevant.
Ranking Designs
In
every project the range and type of input parameters will differ. However, what
remains essential is the identification of most influential parameters. It is important to analyse the effect of each
parameters, whether positive or negative, and the magnitude of the effect on
the KPI.
Figure 2 : tornado plot indicating the significance of each input variable on the NPV at $100/bbl.
Proxys and Monte Carlo Simulation
One of the
advantages of ED, is the possibility to use linear regression models (proxies
of the more complex problem) to predict KPIs with the uncertainty input in
seconds, this has a potential use in optimisation problems and probabilistic
exercises like Monte Carlo simulation (MC).
As an example a
numerical simulation model could take 2 hours to run, if we assume capacity to
run 10 concurrent scenarios, a 10000 MC simulation would take around 2000 hours
or over 80 days. An ED sample set of 100 numerical simulation cases would take
20 hours, and a proxy generated out of the regression process would take
seconds to run. More over the MC simulation can be repeated and the proxy improved
with space filling designs (adding few more numerical simulation cases).
Figure 3 : MC simulation for NPV at $100/bbl along with P10,P50 and P90 points.
Strong focus on decision making and uncertainty analysis combined with advanced, state of the art and complex workflows.