Summary
The permanent downhole gauge (PDG) is a promising tool for reservoir testing
but has yet to reach its full potential. Generally, conventional well-testing
methods are most able to use small sections of constant-flow-rate data.
However, data mining, a newly developed technique in computer science, is a
tool that can reveal the relationship among variables from large volumes of
data. The application of data-mining algorithms to synthetic and field data has
been successful in extracting the reservoir model from variable-flow-rate and
pressure-transient data. In fact, because of uncertainty in flow-rate
measurements, this technique is one of the few ways to make use of the
flow-rate data that are now available with some modern PDG tools.
The application is conducted in two steps. First, the pressure and flow-rate
data from the PDG are used to train a nonparametric data-mining algorithm. The
reservoir model is obtained implicitly in the form of polynomials in a
high-dimensional Hilbert space defined by kernel functions when the algorithm
converges after being trained to the data. Next, a specific flow-rate input
(for example, constant rate) is fed into the data-mining algorithm. The
datamining algorithm will make a pressure prediction subject to the input flow
rate. Because the data-mining algorithm has already obtained the reservoir
model, the pressure prediction is expected to be the reservoir response given
the constant flow rate. Therefore, the proposed constant flow rate and the
predicted pressure reveal the reservoir model underlying the variable PDG data,
without needing to specify in advance which reservoir model is to be used.
Three methods (Methods A, B, and C), differing by input vectors, kernel
functions, and presence of breakpoints detection, are proposed in the
paper.
Synthetic noisy data and real field data were used to test this approach.
Methods B and C were able to satisfactorily recover the wellbore/reservoir
model in most considered cases. Even in extreme cases when the flow-rate data
are noisy and changing frequently, and in the absence of any shut-ins, the
method was still able to extract the reservoir models.
© 2012. Society of Petroleum Engineers
View full textPDF
(
1,443 KB
)
History
- Original manuscript received:
1 July 2011
- Meeting paper published:
30 October 2011
- Revised manuscript received:
30 July 2012
- Manuscript approved:
3 August 2012
- Published online:
28 December 2012
- Version of record:
27 February 2013
View Cart
