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Selection of the best well control system by using fuzzy multiple-attribute decision-making methods

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  • S. Mostafa Mokhtari
  • Hamid Alinejad-Rokny
  • Hossein Jalalifar

Abstract

There are numerous difficulties involved in drilling operations of an oil well, one of the most important of them being well control. Well control systems are applied when we have irruption of liquids or unwanted intrusion of the reservoir's liquid (oil, gas or brine) into the well, during drilling when the pressure of well fluid column is less than formation pressure, and the permeability of the reservoir has a value that is able to pass the liquid through. For this purpose, a variety of methods including Driller, wait and weight, and the concurrent methods were used to control the well at different drilling sites. In this study, we investigate the optimum method for well control using a fussy method based on many parameters, including technical factors (mud weight, drilling rate, blockage of pipes, sensitivity to drilling network changes, etc.) and security factors (existence of effervescent mud, drilling circuit control, etc.), and cost of selection, which is one of the most important decisions that are made under critical conditions such as irruption. Till now, these methods were selected based on the experience of field personnel in drilling sites. The technical criteria and standards were influenced by experience, so the soft computerizing system (fuzzy method) was used. Thus, both these criteria and standards would be of greater importance and indicate whether the optimum numerical method is the same one that is expressed by human experience. The concurrent method was selected as the best for well control, using the fuzzy method at the end of the evaluation, while field personnel experience suggests the Driller method.

Suggested Citation

  • S. Mostafa Mokhtari & Hamid Alinejad-Rokny & Hossein Jalalifar, 2014. "Selection of the best well control system by using fuzzy multiple-attribute decision-making methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(5), pages 1105-1121, May.
  • Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:1105-1121
    DOI: 10.1080/02664763.2013.862218
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    References listed on IDEAS

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    4. Büyüközkan, Gülçin & Feyzioglu, Orhan & Nebol, Erdal, 2008. "Selection of the strategic alliance partner in logistics value chain," International Journal of Production Economics, Elsevier, vol. 113(1), pages 148-158, May.
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    Cited by:

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    2. Tonghui Lian & Caihua Yu & Zhongqun Wang & Zhiping Hou, 2017. "The evaluation study on tourism websites: from the perspective of triangular intuitionistic fuzzy multiple attribute group decision making," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(16), pages 2877-2889, December.
    3. Oguzhan Ece & Ahmet Serhat Uludag, 2017. "Applicability of Fuzzy TOPSIS Method in Optimal Portfolio Selection and an Application in BIST," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(10), pages 107-127, October.

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