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Description:
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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 . |