Julius U. Akpabio

Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells

Okorie Ekwe Agwu, Julius U. Akpabio, Adewale Dosunmu · Journal of Petroleum Exploration and Production Technology · 2019 · 48 citations · Full text

Engineering, Computer Science, Petroleum Engineering +11

Rice husk and saw dust as filter loss control agents for water-based muds

Okorie Ekwe Agwu, Julius U. Akpabio, Glory W. Archibong · Heliyon · 2019 · 42 citations · Full text

Engineering, Petroleum Engineering, Drilling +10

Evaluating the locally sourced materials as fluid loss control additives in water-based drilling fluid

Anietie N. Okon, Julius U. Akpabio, Kilaliba Wanaemi Tugwell · Heliyon · 2020 · 38 citations · Full text

Engineering, Materials Engineering, Petroleum Engineering +14

Artificial neural network model for predicting drill cuttings settling velocity

Okorie Ekwe Agwu, Julius U. Akpabio, Adewale Dosunmu · Petroleum · 2019 · 32 citations · Full text

Engineering, Manufacturing Engineering, Mechanical Engineering +12

Potentials of waste seashells as additives in drilling muds and in oil well cements

Okorie Ekwe Agwu, Julius U. Akpabio, Moses Gideon Akpabio · Cleaner Engineering and Technology · 2020 · 21 citations · Full text

Engineering, Materials Engineering, Petroleum Engineering +11