Reservoir management strategies traditionally attempt to combine and balance
complex geophysical, petrophysical, thermodynamic and economic factors to determine
an optimal method to recover hydrocarbons from a given reservoir. Reservoir simulators
have traditionally been too large and run times too long to allow for rigorous solution in
conjunction with an optimization algorithm. It has also proven very difficult to marry an
optimizer with the large set of nonlinear partial differential equations required for
accurate reservoir simulation.
A simple capacitance-resistance model (CRM) that characterizes the connectivity
between injection and production wells can determine an injection scheme maximizes the
value of the reservoir asset. Model parameters are identified using linear and nonlinear
regression. The model is then used together with a nonlinear optimization algorithm to
compute a set of future injection rates which maximize discounted net profit. This
research demonstrates that this simple dynamic model provides an excellent match to
historic data. Based on three case studies examining actual reservoirs, the optimal injection schemes based on the capacitance-resistive model yield a predicted increase in
hydrocarbon recovery of up to 60% over the extrapolated exponential historic decline.
An advantage of using a simple model is its ability to describe large reservoirs in
a straightforward way with computation times that are short to moderate. However,
applying the CRM to large reservoirs with many wells presents several new challenges.
Reservoirs with hundreds of wells have longer production histories – new wells are
created, wells are shut in for varying periods of time and production wells are converted
to injection wells. Additionally, ensuring that the production data to which the CRM is
fit are free from contamination or corruption is important. Several modeling techniques
and heuristics are presented that provide a simple, accurate reservoir model that can be
used to optimize the value of the reservoir over future time periods.
In addition to optimizing reservoir performance by allocating injection, this
research presents a few methods that use the CRM to find optimal well locations for new
injectors. These algorithms are still in their infancy and represent the best ideas for future research. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/6648 |
| Date | 23 October 2009 |
| Creators | Weber, Daniel Brent |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Format | electronic |
| Rights | Copyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works. |
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