Data Quality and Digital Twins in Decision Support Systems of Oil and Gas Companies

DOI

10.2991/aisr.k.201029.028How to use a DOI?

Keywords

data quality management, Smart fields, Smart wells, Digital twins, Digital transformation

Abstract

In the modern world, most enterprises implement digital technologies to improve business performance and enhance their competitive advantages. All digital technologies are based on data. Global digitalization leads to huge amounts of data. It is impossible to talk about IT development without understanding the nature of data and its quality management technologies. The article presents the approach to data quality management in the Digital Twins design and use. Digital Twins or Smart Fields are decision support systems to manage field development. Such decision support systems are designed to address the issues of maximizing oil recovery ratio with optimal CAPEX and OPEX. Moreover, decision support systems should include data quality management algorithms. The lack of data quality control mechanisms can lead to incorrect decisions and large losses for the business. The approach presented in the article contains 7 steps of the field management cycle using Digital Twins. Some of these seven steps are related to data quality verification. It is also shown that data with minuses or disadvantages allow to identify problematic areas of field development. For the identified problematic areas, additional data is collected to eliminate disadvantages or minuses. As a result, it is possible to clarify the real “Why” or the reasons of the critical deviations from the target situation and KPIs. The work done showed that designing decision support systems such as Digital Doubles, Digital Fields and Smart Wells without data quality management is tantamount to creating an engine without taking into account the fuel on which it runs. The application of the developed approaches to data quality management in Digital Doubles almost by a third increased their economic efficiency in the development of oil births and, as a result, their investment attractiveness.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tatyana Pavlovich
AU  - Elena Dron
PY  - 2020
DA  - 2020/11/10
TI  - Data Quality and Digital Twins in Decision Support Systems of Oil and Gas Companies
BT  - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)
PB  - Atlantis Press
SP  - 143
EP  - 149
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.201029.028
DO  - 10.2991/aisr.k.201029.028
ID  - Pavlovich2020
ER  -