Self-attention mechanism for dynamic multi-step ROP prediction under continuous learning structure
The evaluation and prediction of the rate of penetration have been long-term challenging in
real-time drilling operations due to, for example, the complexity of influence parameters and …
real-time drilling operations due to, for example, the complexity of influence parameters and …
Analysis of missing data and comparing the accuracy of imputation methods using wheat crop data
P Saini, B Nagpal - Multimedia Tools and Applications, 2024 - Springer
In a realistic scenario, the dataset has missing values encountered during the data
collection. To effectively build the prediction model, the missingness of the attributes that …
collection. To effectively build the prediction model, the missingness of the attributes that …
Drilling Advisory Automation with Digital Twin and AI Technologies
J Cao, J Nabavi, SI Oedegaard - SPE/IADC Drilling Conference and …, 2024 - onepetro.org
This paper presents an autonomous drilling advisory system powered by digital twins and AI
solutions. Such an advisory system aims to automate real-time monitoring and parameter …
solutions. Such an advisory system aims to automate real-time monitoring and parameter …
Wide and deep cross network for the rate of penetration prediction
Z Pei, X Song, Y Ji, T Yin, S Tian, G Li - Geoenergy Science and …, 2023 - Elsevier
Rate of penetration (ROP) prediction is of great significance for drilling optimization.
However, most existing intelligent ROP prediction models are entirely data-driven, and the …
However, most existing intelligent ROP prediction models are entirely data-driven, and the …
A Sequential Feature-Based Rate of Penetration Representation Prediction Method by Attention Long Short-Term Memory Network
Z Cheng, F Zhang, L Zhang, S Yang, J Wu, T Li, Y Liu - SPE Journal, 2024 - onepetro.org
In the petroleum and gas industry, optimizing cost-effectiveness remains a paramount
objective. One of the key challenges is enhancing predictive models for the rate of …
objective. One of the key challenges is enhancing predictive models for the rate of …
Cross-Domain Knowledge Discovery and Sharing in Digital Subsurface Based on Federated Learning
The total volume of data in the subsurface is tremendous and grows exponentially each
year. Sufficiently and effectively utilizing the subsurface data from multiple sites will advance …
year. Sufficiently and effectively utilizing the subsurface data from multiple sites will advance …
Remediation of LWD data lag with hybrid real-time data using self-attention-based encoder-decoder model
J Zhang, Y Liu, J Cao, T Yang - Geoenergy Science and Engineering, 2025 - Elsevier
This study aims to address the lag issue in Logging While Drilling (LWD) data, which is
crucial for real-time decision-making in subsurface resource exploration. The primary …
crucial for real-time decision-making in subsurface resource exploration. The primary …
Decision Tree Regression Method Applied in Well Clustering: A Supervised Approach
IGP Vianna, IF Valladares, CEP Pacheco… - Offshore Technology …, 2023 - onepetro.org
Drilling rigs are an expensive resource in the oil and gas industry; hence, planning their time
properly is necessary. Estimating activity durations plays a crucial role in the rigs' planning …
properly is necessary. Estimating activity durations plays a crucial role in the rigs' planning …