Machine learning methods applied to drilling rate of penetration prediction and optimization-A review

LFFM Barbosa, A Nascimento, MH Mathias… - Journal of Petroleum …, 2019 - Elsevier
Drilling wells in challenging oil/gas environments implies in large capital expenditure on
wellbore's construction. In order to optimize the drilling related operation, real-time decisions …

Real-time determination of rheological properties of spud drilling fluids using a hybrid artificial intelligence technique

K Abdelgawad, S Elkatatny… - Journal of …, 2019 - asmedigitalcollection.asme.org
The rheological properties of the drilling fluid play a key role in controlling the drilling
operation. Knowledge of drilling fluid rheological properties is very crucial for drilling …

A new methodology for optimization and prediction of rate of penetration during drilling operations

Y Zhao, A Noorbakhsh, M Koopialipoor, A Azizi… - Engineering with …, 2020 - Springer
Predictive models have been widely used in different engineering fields, as well as in
petroleum engineering. Due to the development of high-performance computer systems, the …

Drilling rate of penetration prediction and optimization using response surface methodology and bat algorithm

MK Moraveji, M Naderi - Journal of Natural Gas Science and Engineering, 2016 - Elsevier
Rate of penetration (ROP) prediction is crucial for drilling optimization because of its role in
minimizing drilling costs. There are many factors, which determine the drilling rate of …

Integrating advanced soft computing techniques with experimental studies for pore structure analysis of Qingshankou shale in Southern Songliao Basin, NE China

B Liu, R Nakhaei-Kohani, L Bai, Z Wen, Y Gao… - International Journal of …, 2022 - Elsevier
Evaluating pore structure of unconventional shale reservoirs enables us to determine their
productivity, allowing for better operational decisions. Despite extensive studies in this field …

[HTML][HTML] Estimation of oil recovery factor for water drive sandy reservoirs through applications of artificial intelligence

AA Mahmoud, S Elkatatny, W Chen, A Abdulraheem - Energies, 2019 - mdpi.com
Hydrocarbon reserve evaluation is the major concern for all oil and gas operating
companies. Nowadays, the estimation of oil recovery factor (RF) could be achieved through …

A robust rate of penetration model for carbonate formation

A Al-AbdulJabbar, S Elkatatny… - Journal of …, 2019 - asmedigitalcollection.asme.org
During the drilling operations, optimizing the rate of penetration (ROP) is very crucial,
because it can significantly reduce the overall cost of the drilling process. ROP is defined as …

Drilling rate of penetration prediction of high-angled wells using artificial neural networks

AK Abbas, S Rushdi, M Alsaba… - Journal of Energy …, 2019 - asmedigitalcollection.asme.org
Predicting the rate of penetration (ROP) is a significant factor in drilling optimization and
minimizing expensive drilling costs. However, due to the geological uncertainty and many …

Development of new permeability formulation from well log data using artificial intelligence approaches

T Moussa, S Elkatatny… - Journal of …, 2018 - asmedigitalcollection.asme.org
Permeability is a key parameter related to any hydrocarbon reservoir characterization.
Moreover, many petroleum engineering problems cannot be precisely answered without …

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 …