Edge analytics in the oil and gas industry has become an important topic, especially with the development of Industry 4.0. This paper reviews the existing literature regarding the movement away from traditional data acquisition and towards edge analytics. A meticulous, systematic literature review was conducted in order to understand how the industry would benefit from the implementation of edge analytics. It also aims to discover any advanced technologies belonging to other industries that could be of benefit to oil and gas. The result shows that edge analytics implementation in oil and gas industry has started to build evidence for its successful implementation at the wellsite. Various innovations from other industries may be useful for further enhancement of existing edge analytics or transforming existing data aggregation of data acquisition at the wellsite.

Real-time data stream in the format of WITSML which can have frequency as low as 1 Hz is one of the best candidate to produce KPIs for the drilling operation activity. The KPIs generated from this calculation will have a relationship with other information from other data sources, known as metadata. The question is how can this KPI information be utilized for further analysis, wider/more complex analysis process which needs to be combined with metadata? An OLTP model is not the recommended model for data analytics but OLAP is. Another question is how will this data be stored in terms of the physical storage? We argue to use column-oriented for the physical storage which can perform analytical queries 10x to 30x faster than the row-oriented storage. The implementation of an OLAP model for storing KPIs data is proven to improve the performance of the analytical query significantly and combined with the implementation of column-oriented in the OLAP model improves more performance. This concludes that the implementation of OLAP with column-oriented data model can be used as the solid foundation for storing KPI data.

Data analytics using real-time data has brought many challenges, particularly in respect to security. From data generation to storage, each step in the life cycle has its own challenges. This paper identifies those challenges. There are several security options that can utilized but for the purpose of this paper we have limited ourselves to the two most common mainstream techniques already used for protecting big data: encryption and access control. The goal is to ensure the confidentiality, integrity and availability (CIA) for phases of the data life cycle, from generation to storage. This research offers a novel approach to the data security and of real-time data transfer and analytics. This paper presents metrics for each of the identified security challenges and aims to validate a real-time data transfer and analytics environment from the security perspective of encryption and access control.

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In less than a decade, there has been a tremendous evolution in the various storage solutions used to contain wellsite data in the upstream oil and gas industry. Many organizations from wellsite data management providers to the oil and gas operators themselves are moving away from traditional relational database management systems (RDBMS) and towards big data solutions. There are several reasons for this evolution. First, data flow has increased at an unprecedented rate as the number of rigs increase and so has the data frequency rate. Second, the types of data from the sensors and data acquisition systems (DAQ) have also grown. Third, for data analytics that requires high-speed processing, it needs to be received in a timely manner so insights can be drawn for better, faster decision- making. Big Data implementation known as NoSQL databases were proposed to handle conditions where: the data volumes are increasing exponentially, various types of data that are being transmitted or connected from various sources which are substantial for data analytics, and the last is the high-velocity streaming data that requires high-speed processing. Moving in this direction is not without sacrifice. For example, companies adopting NoSQL give up several important factors, for instance, how the database handles transactions from multiple applications. Implementing NoSQL means giving up the benefits of Atomic, Consistent, Isolated and Durable (ACID). NewSQL comes as a promising new solution which offers the same scalable performance as NoSQL for the Online Transaction Processing (OLTP) read-write workloads while still maintaining ACID guarantees for transactions. This paper will investigate, characterize and analyze the list of NewSQL databases in depth and develop comprehensive taxonomy for various critical aspects which come with them. We then will map the requirement of the data storage for handling upstream data to the proposed taxonomy so it can be used as a basis for analyzing each of the databases. At the end, we will predicate and highlight sorted databases which can be recommended for the implementation of next generation wellsite data storage in the upstream oil and gas industry.