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     Research Journal of Applied Sciences, Engineering and Technology


Prediction Method of Safety Mud Density in Depleted Oilfields

Zhao Kai, Deng Jin-Gen, Tan Qiang, Yu Bao-Hua and Yuan Jun-Liang
State Key Laboratory of Petroleum Resource and Prospecting (China University of Petroleum, Beijing), Beijing 102249, China
Research Journal of Applied Sciences, Engineering and Technology  2013  11:3230-3238
http://dx.doi.org/10.19026/rjaset.5.4562  |  © The Author(s) 2013
Received: November 08, 2012  |  Accepted: January 01, 2013  |  Published: April 05, 2013

Abstract

At present, many oilfields were placed in the middle and late development period and the reservoir pressure depleted usually, resulting in more serious differential pressure sticking and drilling mud leakage both in the reservoir and cap rock. In view of this situation, a systematic prediction method of safety mud density in depleted oilfields was established. The influence of reservoir depletion on stress and strength in reservoir and cap formation were both studied and taken into the prediction of safety mud density. The research showed that the risk of differential pressure sticking and drilling mud leakage in reservoir and cap formation were both increased and they were the main prevention object in depleted oilfields drilling. The research results were used to guide the practice drilling work, the whole progress gone smoothly.

Keywords:

Cap, depleted oilfields, prediction method, reservoir, safety mud density,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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