AI -Powered Predictive Model for Enhanced Drilling Control Performance using Invert Emulsion Fish Oil- based Drilling Mud

Author:

Ashadevi U,Sutha S,Ramaswamy A

Funder

Oil and Natural Gas Corporation

Publisher

Elsevier BV

Reference19 articles.

1. Modeling the effect of temperature on network algorithm;Adesina;Petroleum & Coal,2015

2. Selecting the most appropriate model for rheological characterization of synthetic based drilling mud;Adewale;Int. J. Appl. Eng. Res,2017

3. A critical review of drilling mud rheological models;Agwu;Journal of petroleum science and engineering,2021

4. Examination of the relationship between rate of penetration and mud weight based on unconfined compressive strength of the rock;Alkinani;Journal of King Saud University-Science,2019

5. Improved tracking of the rheological properties of max-bridge oil-based mud using artificial neural networks;Alsabaa;ACS omega,2021

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