Experimental investigation and intelligent modeling of pore structure changes in type III kerogen-rich shale artificially matured by hydrous and anhydrous pyrolysis

B Liu, MR Mohammadi, Z Ma, L Bai, L Wang, Z Wen… - Energy, 2023 - Elsevier
The occurrence and enrichment of shale plays are highly controlled by pore characteristics
of the formation. In this study, an immature sample rich in kerogen type III from the …

Hybrid physics-machine learning models for predicting rate of penetration in the Halahatang oil field, Tarim Basin

S Jiao, W Li, Z Li, J Gai, L Zou, Y Su - Scientific Reports, 2024 - nature.com
Rate of penetration (ROP) is a key factor in drilling optimization, cost reduction and drilling
cycle shortening. Due to the systematicity, complexity and uncertainty of drilling operations …

Applying machine learning to predict the rate of penetration for geothermal drilling located in the Utah FORGE site

MA Ben Aoun, T Madarász - Energies, 2022 - mdpi.com
Well planning for every drilling project includes cost estimation. Maximizing the rate of
penetration (ROP) reduces the time required for drilling, resulting in reducing the expenses …

An advanced long short-term memory (LSTM) neural network method for predicting rate of penetration (ROP)

H Ji, Y Lou, S Cheng, Z Xie, L Zhu - ACS omega, 2022 - ACS Publications
Rate of penetration (ROP) is an essential factor in drilling optimization and reducing the
drilling cycle. Most of the traditional ROP prediction methods are based on building physical …

[HTML][HTML] Crop selection: A survey on factors and techniques

RK Nde, JLEK Fendji, BO Yenke, J Schöning - Smart Agricultural …, 2024 - Elsevier
Agriculture has proven to be the most effective and efficient economic activity in many
developing countries, contributing to economic growth. However, it faces numerous …

[HTML][HTML] A systematic review of machine learning modeling processes and applications in ROP prediction in the past decade

Q Li, JP Li, LL Xie - Petroleum Science, 2024 - Elsevier
Fossil fuels are undoubtedly important, and drilling technology plays an important role in
realizing fossil fuel exploration; therefore, the prediction and evaluation of drilling efficiency …

Developing GAN-boosted artificial neural networks to model the rate of drilling bit penetration

MH Sharifinasab, ME Niri, M Masroor - Applied Soft Computing, 2023 - Elsevier
The goal of achieving a single model for estimating the rate of drilling bit penetration (ROP)
with high accuracy has been the subject of many efforts. Analytical methods and, later, data …

Predicting rate of penetration of horizontal drilling by combining physical model with machine learning method in the China Jimusar oil field

C Ren, W Huang, D Gao - SPE Journal, 2023 - onepetro.org
Rate of penetration (ROP) is one of the important indicators for evaluating drilling efficiency,
which provides the basis for drilling parameter optimization. ROP prediction methods can be …

Establishment of data-driven multi-objective model to optimize drilling performance

F Qu, H Liao, J Liu, M Lu, H Wang, B Zhou… - Geoenergy Science and …, 2023 - Elsevier
Drilling parameters optimization has consistently generated research interest over the years
because of the cost-saving benefits associated to improve drilling efficiency. However …

Capillary pressure correction of cuttings

S Alessa, A Sakhaee-Pour, FN Sadooni… - Journal of Petroleum …, 2022 - Elsevier
The accurate characterization of capillary pressure is essential in determining multiphase
flow behavior in subsurface conditions. It is also essential in quantifying reservoir rock …