Felix James Cardano Pacis
Ekstern tilknyttet UiS
Kontakt
E-post: felix.j.pacis@uis.no
Organisasjonsenhet
Det teknisk-naturvitenskapelige fakultet
Institutt for data- og elektroteknologi
Publikasjoner
Vitenskapelige publikasjoner
Pacis, Felix James Cardano; Alyaev, Sergey; Pelfrene, Gilles; Wiktorski, Tomasz
(2024)
Enhancing information retrieval in the drilling domain : Zero-shot learning with large language models for question-answering. I: IADC/SPE International Drilling Conference and Exhibition: March 5–7, 2024, Galveston, Texas, USA.Society of Petroleum Engineers.
ISBN 978-1-959025-10-8.
DOI: 10.2118/217671-MS
Pacis, Felix James Cardano; Ambrus, Adrian; Alyaev, Sergey; Khosravanian, Rasool; Kristiansen, Tron Golder; Wiktorski, Tomasz
(2023)
Improving predictive models for rate of penetration in real drilling operations through transfer learning.Journal of Computational Science.
ISSN 1877-7503.
Volum 72.
Hefte 102100.
Pacis, Felix James Cardano; Wiktorski, Tomasz; Ambrus, Adrian; Alyaev, Sergey
(2023)
Exploration of strategies to improve continual learning from irregular sequential drilling data. I: ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering: Volume 9: Offshore Geotechnics; Petroleum Technology.The American Society of Mechanical Engineers (ASME).
ISBN 978-0-7918-8691-5.
Pacis, Felix James Cardano; Alyaev, Sergey; Ambrus, Adrian; Wiktorski, Tomasz
(2022)
Transfer Learning Approach to Prediction of Rate of Penetration in Drilling.Lecture Notes in Computer Science (LNCS).
ISSN 0302-9743.
Volum 13351.
s.358-371.
Ambrus, Adrian; Alyaev, Sergey; Jahani, Nazanin; Pacis, Felix James Cardano; Wiktorski, Tomasz
(2022)
Rate of Penetration Prediction Using Quantile Regression Deep Neural Networks. I: Proceedings of ASME 2022 41st International Conference on Ocean, Offshore & Arctic Engineering Volume 10 : Petroleum technology.The American Society of Mechanical Engineers (ASME).
ISBN 978-0-7918-8595-6.
Formidling
Pacis, Felix James Cardano
(2024)
Enhancing Information Retrieval in the Drilling Domain: Zero-Shot Learning with Large Language Models for Question-Answering.
SPE/IADC Drilling Conference;
2024-03-05 - 2024-03-07.
Alyaev, Sergey; Pacis, Felix James Cardano; Wiktorski, Tomasz
(2024)
Domain-adapted Embeddings Model Using Contrastive Learning for Drilling Text Data.
FAIEMA 2024;
2024-10-01 - 2024-10-02.
Alyaev, Sergey; Pacis, Felix James Cardano; Wiktorski, Tomasz
(2024)
Cloud-Free Question Answering from Internal Knowledge Bases: Building an AI for Drilling Applications.
DigiWells Seminar 2024;
2024-11-26 - 2024-11-27.
Pacis, Felix James Cardano; Wiktorski, Tomasz; Ambrus, Adrian; Alyaev, Sergey
(2023)
Exploration of Strategies to Improve Continual Learning From Irregular Sequential Drilling Data.
OMAE 42nd International Conference on Ocean, Offshore & Arctic Engineering;
2023-06-11 - 2023-06-16.
Pacis, Felix James Cardano; Alyaev, Sergey; Wiktorski, Tomasz
(2023)
Demo: Chatbot for oil and gas data.
DigiWells Seminar;
2023-11-28 - 2023-11-29.
Ambrus, Adrian; Alyaev, Sergey; Jahani, Nazanin; Pacis, Felix James Cardano; Wiktorski, Tomasz
(2022)
Rate of Penetration Prediction Using Quantile Regression Deep Neural Networks.
ASME 41st International Conference on Ocean, Offshore and Arctic Engineering;
2022-06-05 - 2022-06-10.
Pacis, Felix James Cardano; Alyaev, Sergey; Ambrus, Adrian; Wiktorski, Tomasz
(2022)
Transfer Learning Approach to Prediction of Rate of Penetration in Drilling.
INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE (ICCS) 2022;
2022-06-21 - 2022-06-23.
Pacis, Felix James Cardano; Wiktorski, Tomasz; Alyaev, Sergey; Ambrus, Adrian; Khosravanian, Rasool; Kristiansen, Tron Golder
(2022)
Exploring transfer learning for ROP prediction on the NCS.
Annual seminar SFI DigiWells;
2022-12-01 - 2022-12-02.
Pacis, Felix James Cardano; Alyaev, Sergey; Wiktorski, Tomasz
(2022)
Demo: Chatbot for NCS well data.
Annual DigiWells Seminar 2022;
2022-12-01 - 2022-12-02.
Alyaev, Sergey; Ambrus, Adrian; Pacis, Felix James Cardano; Jahani, Nazanin; Wiktorski, Tomasz; Elsheikh, Ahmed
(2021)
Drilling Process Modelling with Deep Learning: Towards ROP Prediction.
DigiWells seminar;
2021-11-23 - 2021-11-24.