Advances in nano research

  • 제9권1호
  • /
  • Pages.59-67
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  • 2020
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  • 2287-237X(pISSN)
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  • 2287-2388(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

A new model for curbing filtrate loss in dynamic application of nano-treated aqueous mud systems

  • Okoro, Emmanuel E. (Petroleum Engineering Department, Covenant University) ;
  • Oladejo, Bukola R. (Petroleum Engineering Department, Covenant University) ;
  • Sanni, Samuel E. (Chemical Engineering Department, Covenant University) ;
  • Obomanu, Tamunotonjo (Petroleum Engineering Department, Federal Polytechnic of Oil and Gas, Bonny Island) ;
  • Ibe, Amarachukwu A. (Physics Department, Nigeria Maritime University) ;
  • Orodu, Oyinkepreye D. (Petroleum Engineering Department, Covenant University) ;
  • Olawole, Olukunle C. (Physics Department, Covenant University)
  • 투고 : 2019.08.10
  • 심사 : 2020.07.05
  • 발행 : 2020.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

Filter cake formation during rotary drilling operation is an unavoidable scenario, hence there is need for constant improvement in the approaches used in monitoring the cake thickness growth in order to prevent drill-string sticking. This study proposes an improved model that predicts the growth of mud cake thickness overtime with the consideration of the addition of nanoparticles in the formulated drilling fluid system. Ferric oxide, titanium dioxide and copper oxide nanoparticles were used in varying amounts (2 g, 4 g and 6 g), and filtration data were obtained from the HPHT filtration test. The filter cakes formed were further analyzed with scanning electron microscope to obtain the morphological characteristics. The data obtained was used to validate the new filtrate loss model. This model specifically presents the concept of time variation in filter cake formation as against the previous works of constant and definite time. Regression coefficient which is a statistical measure was used to validate the new model and the predicted results were compared with the API model. The new model showed R2 values of 99.9%, and the predictions from the proposed filtration model can be said to be more closely related to the experimental data than that predicted from the API model from the SSE and RMSE results.

키워드

과제정보

The authors would like to appreciate the management of Covenant University for providing an enabling environment to carry out this research, and assistance in publication.

참고문헌

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