Emerging Technologies and Workflows in Digital Oil Field

Author:

Chanana P.1,Soni T. M.1,Bhakne U.1

Affiliation:

1. Wipro Limited

Abstract

Abstract The need for the petroleum industry to understand more about subsurface and surface operations has driven it to gather greater data volumes of various types at high frequency. However, searching relevant information from this data is quite complex and time consuming, complicating the decision making process. With emerging technologies, business processes are improving and the industry experts are able to design more intelligent, advanced and automated workflows. These advanced workflows are required to bridge the gap between various inter-linking operations and to reduce time lag. This results in reducing the time involved in decision making and improving the quality of decision. In the same way, managing data has advanced to a great extent with evolving technology. Data management, which includes data gathering from various sources, data cleansing, validating and analyzing, is required to manage huge amounts of data and take quick actions for better business benefits. The need for integration of data management and improved business processes in the oil and gas industry has given rise to several Digital Oil Field (DOF) implementations over the decade. Digital Oil Field, an evolving technology, has transformed from a reactive approach to a proactive approach. In the past decade, DOF involved installing devices such as sensors on the field to gather data. Now, it has moved to visualizing data on dashboards, monitoring, and making operations effective. Soon we will have smarter solutions where systems will make decisions and indicate potential means to overcome issues with the help of best practices and predictive analysis. This paper aims to review the work done in the sphere of DOF over the decade. Basic DOF architecture and data flow in business processes from data capturing to visualization are also explained briefly in this paper. The benefits of DOF technologies to the petroleum industry, the success factors of DOF and its future direction are also discussed.

Publisher

OTC

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. UAV-vision based Oilfield Anomaly Detection via Optimized LBAM-YOLO;2023 9th International Conference on Computer and Communications (ICCC);2023-12-08

2. Introduction;Pipeline Spatial Data Modeling and Pipeline WebGIS;2020

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