Summary
Downhole pressure and temperature data are important information that helps
us understand the bottomhole flow condition. Today, the data are readily
available from permanent monitoring systems such as downhole gauges or
fiber-optic sensors. In a previous study, we showed that using temperature and
pressure data, water entry along a horizontal wellbore can be detected by a
semianalytical model. Flow in the wellbore is well-defined, but flow in the
reservoir is described by a single-phase, 1D model. The assumptions limited
application of the model to mostly a single-phase condition.
In this paper, we present an improved model that is more flexible. We use a
streamline-simulation method to solve the flow problem in the reservoir for
fast tracking of reservoir flow. We developed a transient, 3D, multiphase
reservoir thermal model to calculate reservoir temperature. We integrated the
reservoir flow model and thermal model with a horizontal-well temperature model
to predict the pressure and temperature distribution in a horizontal-well
system. We apply the model to a synthetic example. The example is an infinite
waterdrive case. The results of simulation show that the temperature features
in a horizontal well can detect the location and amount of water breakthrough
successfully. Meanwhile, even the pressure trend does not reflect the water
entrance as clearly as the temperature curve, the capability of which to
indentify the reservoir permeability distribution is very helpful in
temperature calculation. We apply the model to a field case: a horizontal well
in the Sincor field for heavy-oil production. The results showed that we can
successfully identify where and how much water enters the horizontal well in
this field example.
We use an inversion method to interpret the pressure and temperature data to
obtain a flow-rate profile along horizontal wells. The inversion method is the
traditional Markov Chain Monte Carlo (MCMC) method. This stochastic method
searches for the possible solution in the parameter space and uses the
Metropolis-Hastings algorithm to judge the acceptance. We discuss how to reduce
the parameters to make the inversion method work more efficiently according to
the downhole pressure and temperature data.
© 2010. Society of Petroleum Engineers
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History
- Original manuscript received:
19 September 2009
- Meeting paper published:
5 October 2009
- Revised manuscript received:
1 February 2010
- Manuscript approved:
19 March 2010
- Published online:
14 July 2010
- Version of record:
11 August 2010
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