To take a logical approach and to progressively
look deeper into the reservoir we start with pressure
transient analysis (PTA) usually dealing with high
resolution, high frequency pressure build-ups and
typically on a single well with the possible influence
of nearby wells. ‘Extreme near wellbore analysis’ is
a particular application of transient analysis applied
during a Formation Test (FT) that, whilst not seeing
that far into the reservoir, can provide unique vertical
description.
Conversely, still using PTA, we may see much further
into the reservoir using one or more build-ups
separated by time and consequent material balance,
by using deconvolution.
Moving yet further out into the reservoir we typically
analyze low frequency, low resolution rate data with, if
we are fortunate, corresponding pressure data. Using
identical modeling, both analytical and numerical, to
that found in PTA we use specialized plots and work
on much longer time timescales attempting to reach
the reservoir boundary whilst in pseudo-steady state.
This is production analysis (PA). The true advantage of
PA is that we model and match on what really matters,
that is the production of the well, or wells, in question.
On the scale and lifespan of the entire reservoir we
can history match (HM) production, pressure and
temperature data extending the model into true 3D.
The idea of the workflow is to keep it as simple as we
can. When the situation demands we add complexity.
The vertical dynamic profile of the field may be
analysed with production logging (PL) and formation
tests (FT). To bring all this to datum the output of a
well performance analysis (WPA) tool is required.
Whatever analysis is used, the data, objects and
models are shared seamlessly, the gridding is
coherent between the methods and an analysis
created in one module may be used, or driven, from
another saving time, repetition and frustration.
In future developments the technical aim, under
a complete .NET re-write, is to improve access to
third party workflows, increase the use of smart
automation tools and to handle increasingly massive
data and well numbers.
Originally from well tests, dynamic data analysis (DDA) comprised specialized
plots and analytical models usually based on a straightforward flow followed
by a shut-in period. Today the sources of dynamic data are many fold and
available at various scales in time, space and volume. With complex geometry
and fluids analysis is increasingly difficult. The task is to piece together a
description based on these various sources.
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