Smart archiving of energy and petroleum projects utilizing big data analytics

Smart archiving of energy and petroleum projects utilizing big data analytics

Gasoline

Big data

Complex networks

Surveys

Distributed parameter control systems

Data mining

Data Analytics

Optical character recognition

Salvaging

Complexity of the construction projects vary by the domain and type of the project. Due to the interaction between different disciplines and parties, Energy and Petroleum Projects (EPP) are considered among the most complex. This complexity produces a dense network of interrelated documents which are produced to cover the various aspects and details of the project. Analyzing this network which is composed of unstructured data is required to gain insights from old data, this task traditionally requires experience, knowledge, and awareness about the existence of the required data. Accordingly, a key asset of any company is the knowledge accumulated over the time from various projects. This research proposes a framework that enables salvaging traditional archives and include its data in searchable databases to increase the efficiency of archiving such accumulated data without affecting the normal workflow of companies, and hence decrease the manhours expenditure and reduce the time of archiving while not affecting the accuracy of the outcome. Due to the large diversity of the EPP projects, the research focuses on five main commodities which are selected based on the frequency of their existence in projects in addition to their monetary value as the main data to be stored which are Tanks, Air Coolers, Pumps, Generators and Distributed Control Systems (DCS). The key attributes of each commodity are identified based on technical questionnaires with technical specialists to act as the basis for building the proposed framework. The proposed framework integrates four modules to provide a complete solution to the problem, by implementing data mining techniques while harnessing the power of big data analytics tools to transform the existent unstructured data into structured data in the form of smart archives which can then be used to support ongoing business processes. 2021 Elsevier B.V.

133

ElZahed, Mahmoud

Marzouk, Mohamed

Automation in Construction

2022

journalArticle

9265805

10.1016/j.autcon.2021.104005