Digital scientific platform “Aggregator of unstructured geological and field data”: architecture and basic models of data extraction
https://doi.org/10.18599/grs.2023.4.13
Abstract
The article describes the project being developed for the digital scientific platform “Aggregator of unstructured geological and field data”, which could potentially be important for the oil and gas industry. The use of new intelligent technologies within the framework of this project will significantly improve the efficiency of processing, storage and use of geological and field information contained in various text sources, mainly in field reports.
The main goal of developing a digital scientific platform is to integrate heterogeneous information about the objects of subsurface exploration, which is extracted from reports on deposits of the Republic of Tatarstan. This will create a consolidated database that will become the basis for making informed decisions in the oil and gas sector. The project of the digital scientific platform includes the development of architecture, algorithms and software solutions based on modern methods of text processing and data mining.
Keywords
data collection and analysis,
field reports,
database,
automation,
big data,
text data processing,
unstructured data,
information extraction
Supporting agencies: This work was supported by the Ministry of Science and Higher Education of the Russian Federation under agreement No. 075-15-2022-299 as part of the program for the creation and development of the NCMU “Rational development of liquid hydrocarbon reserves of the planet”.
About the Authors
O. A. Nevzorova
Kazan Federal University
Russian Federation
Russian Federation
Olga A. Nevzorova – Associate Professor, Cand. Sci. (Engineering), Senior Researcher
18 Kremlevskaya st., Kazan, 420008
R. R. Khakimullin
Kazan Federal University
Russian Federation
Russian Federation
Rustem R. Khakimullin – Laboratory Assistant
18 Kremlevskaya st., Kazan, 420008
I. I. Idrisov
Kazan Federal University
Russian Federation
Russian Federation
Ilyas I. Idrisov – Researcher
18 Kremlevskaya st., Kazan, 420008
References
1. Abdelhamid K., Ammar T.B., Laid K. (2022). Artificial Intelligent in Upstream Oil and Gas Industry: A Review of Applications, Challenges and Perspectives. Artificial Intelligence and Its Applications. AIAP 2021. Lecture Notes in Networks and Systems, vol. 413. Lejdel B., Clementini E., Alarabi L. (eds). Springer, Cham. https://doi.org/10.1007/978-3-030-96311-8_2424
2. Consoli B., Santos J., Gomes D., Cordeiro F., Vieira R., Moreira V. (2020). Embeddings for Named Entity Recognition in Geoscience Portuguese Literature. Proc. 12th Conference on Language Resources and Evaluation (LREC 2020), Marseille, 11–16 May 2020. https://aclanthology.org/2020.lrec-1.568.pdf
3. Chengbin Wang, Yuanjun Li, Jianguo Chen, Xiaogang Ma (2023). Named entity annotation schema for geological literature mining in the domain of porphyry copper deposits. Ore Geology Reviews, 152, 105243. https://doi.org/10.1016/j.oregeorev.2022.105243
4. Choubey S., Karmakar G.P. (2021). Artificial intelligence techniques and their application in oil and gas industry. Artif Intell Rev, 54, pp. 3665–3683. https://doi.org/10.1007/s10462-020-09935-1
5. Deloitte Analysis Report (2019). Digital transformation of oil and gas sector. https://www.petrotech.in/static/pdf/Theme-Session-Deloitte.pdf
6. Dezhina I.G., Myasnikov A.V., Koroteev D.A. et al. (2017). Current technological trends in the development and production of oil and gas: public analytical report. Moscow: BiTuBi, 220 p. (In Russ.)
7. Goodfellow I., Bengio Y., Courville A. (2016). Deep learning. Cambridge, MA: MIT Press.
8. Hoffimann Julio, Mao Youli, Wesley Avinash, Aimee Taylor (2018). Sequence Mining and Pattern Analysis in Drilling Reports with Deep Natural Language Processing. SPE Annual Technical Conference and Exhibition, Dallas, Texas, USA, September 2018. https://doi.org/10.2118/191505-MS
9. Lucas P. Cinelli, José F.L. de Oliveira, Vinicius M. de Pinho et al. (2021). Automatic event identification and extraction from daily drilling reports using an expert system and artificial intelligence. Journal of Petroleum Science and Engineering, 205. https://doi.org/10.1016/j.petrol.2021.108939
10. Nooralahzadeh F., Øvrelid L., Lønning J.T. (2018). Evaluation of domain-specific word embeddings using knowledge resources. Proc. 11th International Conference on Language Resources and Evaluation (LREC 2018), pp. 1438–1445. http://www.lrec-conf.org/proceedings/lrec2018/pdf/268.pdf
11. Qiu Q., Xie Z., Wu L., Tao L., and Li W. (2019). Bilstm-crf for geological named entity recognition from the geoscience literature. Earth Science Informatics, 12, pp. 565–579. https://doi.org/10.1007/s12145-019-00390-3
12. Technavio (2015). How oil and gas is using Big Data for better operations. http://www.technavio.com/blog/how-oil-and-gas-using-big-data-better-operations
Review
For citations:
Nevzorova O.A., Khakimullin R.R., Idrisov I.I. Digital scientific platform “Aggregator of unstructured geological and field data”: architecture and basic models of data extraction. Georesursy = Georesources. 2023;25(4):149-162. (In Russ.) https://doi.org/10.18599/grs.2023.4.13
Views:
138
.png){kind=logo}
Email this article 