The ever increasing energy demand brings about widespread interest to rapidly,
profitably and efficiently develop unconventional resources, among which tight gas
sands hold a significant portion. However, optimization of development strategies in
tight gas fields is challenging, not only because of the wide range of depositional
environments and large variability in reservoir properties, but also because the
evaluation often has to deal with a multitude of wells, limited reservoir information, and
time and budget constraints. Unfortunately, classical full-scale reservoir evaluation
cannot be routinely employed by small- to medium-sized operators, given its timeconsuming
and expensive nature. In addition, the full-scale evaluation is generally built
on deterministic principles and produces a single realization of the reservoir, despite the
significant uncertainty faced by operators.
This work addresses the need for rapid and cost-efficient technologies to help
operators determine optimal well spacing in highly uncertain and risky unconventional
gas reservoirs. To achieve the research objectives, an integrated reservoir and decision
modeling tool that fully incorporates uncertainty was developed. Monte Carlo simulation
was used with a fast, approximate reservoir simulation model to match and predict
production performance in unconventional gas reservoirs. Simulation results were then
fit with decline curves to enable direct integration of the reservoir model into a Bayesian
decision model. These integrated tools were applied to the tight gas assets of
Unconventional Gas Resources Inc. in the Berland River area, Alberta, Canada.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-08-8460 |
| Date | 2010 August 1900 |
| Creators | Turkarslan, Gulcan |
| Contributors | McVay, Duane |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
Page generated in 0.0019 seconds