With the rapidly increasing demand for energy and the increasing prices for oil
and gas, the role of unconventional gas reservoirs (UGRs) as energy sources is becoming
more important throughout the world. Because of high risks and uncertainties associated
with UGRs, their profitable development requires experts to be involved in the most
critical development stages, such as drilling, completion, stimulation, and production.
However, many companies operating UGRs lack this expertise. The advisory system we
developed will help them make efficient decisions by providing insight from analogous
basins that can be applied to the wells drilled in target basins.
In North America, UGRs have been in development for more than 50 years. The
petroleum literature has thousands of papers describing best practices in management of
these resources. If we can define the characteristics of the target basin anywhere in the
world and find an analogous basin in North America, we should be able to study the best
practices in the analogous basin or formation and provide the best practices to the
operators.
In this research, we have built an advisory system that we call the
Unconventional Gas Reservoir (UGR) Advisor. UGR Advisor incorporates three major
modules: BASIN, PRISE and Drilling & Completion (D&C) Advisor. BASIN is used to identify the reference basin and formations in North America that are the best analogs to
the target basin or formation. With these data, PRISE is used to estimate the technically
recoverable gas volume in the target basin. Finally, by analogy with data from the
reference formation, we use D&C Advisor to find the best practice for drilling and
producing the target reservoir.
To create this module, we reviewed the literature and interviewed experts to
gather the information required to determine best completion and stimulation practices
as a function of reservoir properties. We used these best practices to build decision trees
that allow the user to take an elementary data set and end up with a decision that honors
the best practices. From the decision trees, we developed simple computer algorithms
that streamline the process.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-05-278 |
| Date | 16 January 2010 |
| Creators | Wei, Yunan |
| Contributors | Holditch, Stephen A. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation |
| Format | application/pdf |
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