Recent advancements in data-driven methodologies for the fault diagnosis and prognosis of marine systems: A systematic review

C Velasco-Gallego, BN De Maya, CM Molina… - Ocean …, 2023 - Elsevier
In recent years, there has been an interest increase in smart maintenance within the
ship** sector due to the benefits and opportunities associated with its implementation …

Towards drilling rate of penetration prediction: Bayesian neural networks for uncertainty quantification

M Bizhani, E Kuru - Journal of Petroleum Science and Engineering, 2022 - Elsevier
Drilling rate of penetration (ROP) prediction has long been a part of any drilling activity. By
accurate prediction of ROP, optimization can be done that maximizes ROP and reduces …

Depression prediction based on LassoNet-RNN model: A longitudinal study

J Han, H Li, H Lin, P Wu, S Wang, J Tu, J Lu - Heliyon, 2023 - cell.com
Depression has become a widespread health concern today. Understanding the influencing
factors can promote human mental health as well as provide a basis for exploring preventive …

Real-time prediction of logging parameters during the drilling process using an attention-based Seq2Seq model

R Zhang, C Zhang, X Song, Z Li, Y Su, G Li… - Geoenergy Science and …, 2024 - Elsevier
In recent years, there has been a notable upsurge within the drilling industry regarding the
construction of machine learning models that leverage logging parameters to augment …

Real-time and multi-objective optimization of rate-of-penetration using machine learning methods

C Zhang, X Song, Z Liu, B Ma, Z Lv, Y Su, G Li… - Geoenergy Science and …, 2023 - Elsevier
Rate of penetration and mechanical specific energy are two widely used objectives when
optimizing the drilling process, yet a simultaneous optimization of both is still a challenge …

Pragmatic degradation learning for scene text image super-resolution with data-training strategy

S Yang, L **e, X Ran, J Lei, X Qian - Knowledge-Based Systems, 2024 - Elsevier
Super-resolution of scene text images represents a formidable computational problem,
marred by a myriad of intricate challenges. This paper focuses on the specific hurdles that …

[HTML][HTML] Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications

R Onciul, CI Tataru, AV Dumitru, C Crivoi… - Journal of Clinical …, 2025 - mdpi.com
The convergence of Artificial Intelligence (AI) and neuroscience is redefining our
understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy …

Quantitative evaluation of imputation methods using bounds estimation of the coefficient of determination for data-driven models with an application to drilling logs

J Cao, AT Tunkiel, Ø Arild, D Sui - SPE Journal, 2023 - onepetro.org
With the constantly increasing quantity of data recorded in the oil and gas industry, data
analytics and data-driven algorithms are gaining popularity. Meanwhile, they are highly …

Deep sequence model-based approach to well log data imputation and petrophysical analysis: A case study on the West Natuna Basin, Indonesia

G Antariksa, R Muammar, A Nugraha, J Lee - Journal of Applied …, 2023 - Elsevier
Well log data imputation is crucial for subsurface geology interpretation, which helps identify
the most productive areas for drilling, minimize exploration risks, and maximize hydrocarbon …

Global Optimization Workflow for Offshore Drilling Rate of Penetration With Dynamic Drilling Log Data

J Cao, H Ren, D Sui - International Conference on …, 2022 - asmedigitalcollection.asme.org
The prediction and optimization of drilling rate of penetration (ROP) are among the most
effective approaches in improving drilling efficiency. To achieve that, it calls for a well …