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Reservoir Characterization—Recent Advances

Reservoir Characterization—Recent Advances

Abstract

Abstract Optimum reservoir recovery and profitability result from guidance by an effective reservoir management plan. Success in developing the most appropriate reservoir management plan requires knowledge and consideration of (1) the reservoir system, including rocks, fluids, and rock-fluid interactions, as well as wellbores and associated equipment and surface facilities; (2) the technologies available to describe, analyze, and exploit the reservoir; and (3) the business environment under which the plan will be developed and implemented. Reservoir management plans de-optimize with time as technology and the business environment change or as new reservoir information becomes available. Reservoir characterization is the process of creating an interdisciplinary high-resolution geoscience model that incorporates, integrates, and reconciles various types of geological and engineering information from pore to basin scale. The reservoir data are then conceptually and quantitatively modeled and compared to the historical production data and fluid flow distribution patterns within and beyond the limits of the reservoir to match well production histories and predict their behavior. The goals of reservoir characterization are to simultaneously (1) maintain high displacement efficiency, (2) optimize high sweep efficiency, (3) provide reliable reservoir performance predictions, and (4) reduce risk and maximize profits. Notice that in addition to the technical concepts that we normally associate with "characterization," maximizing profits is an essential element of this process. Papers from the Fourth International Reservoir Characterization Technical Conference (1997), sponsored by the U.S. Department of Energy, this publication is a unique compilation of 27 papers covering every aspect of reservoir characterization and has been a popular AAPG publication since that time. Using an interdisciplinary approach, the papers address qualitative information as well as integrated quantified data and culminate in a fully integrated study.

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    6
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Top 10%
Average
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