Application of machine learning in wellbore stability prediction: A review

K Xu, Z Liu, Q Chen, Q Zhang, X Ling, X Cai… - Geoenergy Science and …, 2023 - Elsevier
Ensuring wellbore stability is vital for safe drilling and production in the process of oil
exploration and development. The current empirical formula and numerical simulation …

Real-time prediction of rheological properties of invert emulsion mud using adaptive neuro-fuzzy inference system

A Alsabaa, H Gamal, S Elkatatny, A Abdulraheem - Sensors, 2020 - mdpi.com
Tracking the rheological properties of the drilling fluid is a key factor for the success of the
drilling operation. The main objective of this paper is to relate the most frequent mud …

Improving soil stability with alum sludge: An AI-enabled approach for accurate prediction of California Bearing Ratio

A Baghbani, MD Nguyen, A Alnedawi, N Milne… - Applied Sciences, 2023 - mdpi.com
Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has
gained increasing attention due to its economic and environmental benefits. Its application …

Data driven model for sonic well log prediction

D Onalo, S Adedigba, F Khan, LA James… - Journal of Petroleum …, 2018 - Elsevier
Near wellbore failure during the exploration of hydrocarbon reservoirs presents a serious
concern to the oil and gas industry. To predict the probability of these undesirable …

New correlations for better monitoring the all-oil mud rheology by employing artificial neural networks

A Alsabaa, H Gamal, S Elkatatny… - Flow Measurement and …, 2021 - Elsevier
The rheological properties of the drilling fluid are crucial to the success of the drilling project.
The traditional mud experiments normally performed by the mud engineers provide …

Application of artificial intelligence techniques to estimate the static Poisson's ratio based on wireline log data

S Elkatatny - Journal of Energy Resources …, 2018 - asmedigitalcollection.asme.org
Static Poisson's ratio (ν static) is a key factor in determine the in-situ stresses in the reservoir
section. ν static is used to calculate the minimum horizontal stress which will affect the …

Real-time prediction of Poisson's ratio from drilling parameters using machine learning tools

O Siddig, H Gamal, S Elkatatny, A Abdulraheem - Scientific Reports, 2021 - nature.com
Rock elastic properties such as Poisson's ratio influence wellbore stability, in-situ stresses
estimation, drilling performance, and hydraulic fracturing design. Conventionally, Poisson's …

Estimation of static young's modulus for sandstone formation using artificial neural networks

AA Mahmoud, S Elkatatny, A Ali, T Moussa - Energies, 2019 - mdpi.com
In this study, we used artificial neural networks (ANN) to estimate static Young's modulus
(Estatic) for sandstone formation from conventional well logs. ANN design parameters were …

Application of machine learning in evaluation of the static young's modulus for sandstone formations

AA Mahmoud, S Elkatatny, D Al Shehri - Sustainability, 2020 - mdpi.com
Prediction of the mechanical characteristics of the reservoir formations, such as static
Young's modulus (Estatic), is very important for the evaluation of the wellbore stability and …

Applying different artificial intelligence techniques in dynamic Poisson's ratio prediction using drilling parameters

O Siddig, H Gamal, S Elkatatny… - Journal of Energy …, 2022 - asmedigitalcollection.asme.org
Rock geomechanical properties impact wellbore stability, drilling performance, estimation of
in situ stresses, and design of hydraulic fracturing. One of these properties is Poisson's ratio …