Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning

S Davoodi, M Mehrad, DA Wood… - International Journal of …, 2023 - Elsevier
Awareness of uniaxial compressive strength (UCS) as a key rock formation parameter for the
design and development of gas and oil field plays. It plays an essential role in the selection …

Real-time prediction of formation pressure gradient while drilling

A Abdelaal, S Elkatatny, A Abdulraheem - Scientific Reports, 2022 - nature.com
Accurate real-time pore pressure prediction is crucial especially in drilling operations
technically and economically. Its prediction will save costs, time and even the right decisions …

Employing deep learning neural networks for characterizing dual-porosity reservoirs based on pressure transient tests

R Kumar Pandey, A Kumar… - Journal of Energy …, 2022 - asmedigitalcollection.asme.org
The deep learning model constituting two neural network models (ie, densely connected
and long short-term memory) has been applied for automatic characterization of dual …

Prediction of surface oil rates for volatile oil and gas condensate reservoirs using artificial intelligence techniques

R Al Dhaif, AF Ibrahim… - Journal of energy …, 2022 - asmedigitalcollection.asme.org
Allocated well production rates are crucial to evaluate the well performance. Test separators
and flowmeters were replaced with choke formulas due to economic and technical issues …

Formation resistivity prediction using decision tree and random forest

AF Ibrahim, A Abdelaal, S Elkatatny - Arabian Journal for Science and …, 2022 - Springer
Formation resistivity (R t) is a vital property for formation evaluation and calculation of water
saturation and hydrocarbon in places. R t can be estimated using core analysis and well …

A novel data-driven model for real-time prediction of static Young's modulus applying mud-logging data

S Davoodi, M Mehrad, DA Wood, M Al-Shargabi… - Earth Science …, 2024 - Springer
Effective drilling planning relies on understanding the rock mechanical properties, typically
estimated from petrophysical data. Real-time estimation of these properties, especially static …

Estimation of geomechanical rock characteristics from specific energy data using combination of wavelet transform with ANFIS-PSO algorithm

M Mohammadi Behboud, A Ramezanzadeh… - Journal of Petroleum …, 2023 - Springer
The geomechanical characteristics of a drill formation are uncontrollable factors that are
crucial to determining the optimal controllable parameters for a drilling operation. In the …

Real-time prediction of Litho-facies from drilling data using an Artificial Neural Network: A comparative field data study with optimizing algorithms

R Agrawal, A Malik, R Samuel… - Journal of Energy …, 2022 - asmedigitalcollection.asme.org
The lithology of the formation is known to affect the drilling operation. Litho-facies help in the
quantification of the formation properties, which optimizes the drilling parameters. The …

A new perspective for the conception of mechanical earth model using machine learning in the Volve Field, Norwegian North Sea

BE Berrehal, A Laalam, A Chemmakh… - ARMA US Rock …, 2022 - onepetro.org
Building a mechanical earth model (MEM) is a challenging task. It requires an extensive
collection of data such as logs and core data to build a reliable model. MEM can support …

Data-driven approach for resistivity prediction using artificial intelligence

A Abdelaal, AF Ibrahim… - Journal of Energy …, 2022 - asmedigitalcollection.asme.org
Formation resistivity is crucial for petrophysics and formation evaluation. Laboratory
measurements and/or well logging can be used to provide resistivity data. Laboratory …