This study presents an investigation of the concept of harvesting geothermal energy that
remains in heavy oil reservoirs after abandonment when steamflooding is no longer
economics. Substantial heat that has accumulated within reservoir rock and its vicinity
can be extracted by circulating water relatively colder than reservoir temperature. We
use compositional reservoir simulation coupled with a semianalytical equation of the
wellbore heat loss approximation to estimate surface heat recovery. Additionally,
sensitivity analyses provide understanding of the effect of various parameters on heat
recovery in the artificial geothermal resources. Using the current state-of-art technology,
the cumulative electrical power generated from heat recovered is about 246 MWhr
accounting for 90percent downtime.
Characteristics of heat storage within the reservoir rock were identified. The factors with
the largest impact on the energy recovery during the water injection phase are the
duration of the steamflood (which dictates the amount of heat accumulated in the
reservoir) and the original reservoir energy in place. Outlet reservoir-fluid temperatures
are used to approximate heat loss along the wellbore and estimate surface fluid
temperature using the semianalytical approaches. For the injection well with insulation,
results indicate that differences in fluid temperature between surface and bottomhole are
negligible. However, for the conventional production well, heat loss is estimated around
13 percent resulting in the average surface temperature of 72 degrees C.
Producing heat can be used in two applications: direct uses and electricity generation.
For the electricity generation application that is used in the economic consideration, the net electrical power generated by this arrival fluid temperature is approximately 3 kW
per one producing pattern using Ener-G-Rotors.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-08-8323 |
| Date | 2010 August 1900 |
| Creators | Limpasurat, Akkharachai |
| Contributors | Falcone, Gioia, Teodoriu, Catalin |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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