How New Analytics Developments Will Change the Oil & Gas Industry

Posted on: September 6, 2016 by Dr. Erwin Dijkstra

Robotization and remote operations offer a new solution to some of the sector’s most challenging issues and will be a game-changing development in the Oil & Gas industry. Creating collaborative work environments, where onshore and offshore, central and decentral teams can work closely together – bringing multiple disciplines into one collaborative team – will enable organizations to make better decisions, use less staff and reduce the number of in-field risks.

Drilling robotization with remote control is a relatively young advancement. It was initially developed for fields that require a large grid of wells to be drilled, usually used in cases of tight gas reservoirs. Importantly, cost per well has to be kept very low, while multiple drilling rigs are required. Staffing this many rigs would be near impossible, since there are not enough experienced drill workers available – not to mention that costs would go through the roof.

With remote drilling, the rig floor crew are replaced by advanced drilling robots. A small but powerful computer platform is required to control the robot and pre-analyse the data before transmission by satellite to the Remote Operation Centre (ROC). There can be multiple ROCs across the world, with multi-skill teams looking after drilling, logging, geology and steering.

Now imagine that we document these decisions in a knowledge base that can be used by humans, and also by cyber-physical systems such as the on-site drilling robot with its compute platform. Such systems have been described in the Atos white paper on Connected Robots. The key aspects are shown in following diagram.

key aspects

This definition of a connected robot is one that consists of physical actuators and sensors, but is able to process the data collected in a separate location. In this case part of the processing is done on site, but the more advanced processing is done centrally at the ROC. The above image also shows why we refer to this as a ‘cyber-physical’ system, because it operates in the physical world – on the well site and in the ROC with the activities as indicated on the left – as well as in the digital world with typical processes as indicated on the right. This is similar to the key role of cyber-physical systems in Smart Factories so typical for driving the latest industrial revolution, labelled Industry 4.0.

In the next few years, we will see further advances in big data analytics, with the following developments being incorporated in the oil and gas market’s cyber-physical systems:

Distributed Analytics for remote operations and IoT:

The development of analytical models and knowledge graphs will take place centrally, using all data available from all sites for training and testing. The execution of the resulting models will be distributed to the on-site platform to provide automated analysis and control the drilling robot and other remotely controlled wellsite equipment. This is what we call closing the prescriptive loop: it takes out the need for human (remote) decision where quick action is required; for instance when a well control risk is detected the automated prescriptive loop will control action to bring the well to safe state.

Virtual Assistants for operations, engineering, and maintenance:

A wide range of tools has been developed to extract information from structured and unstructured data and will be integrated into large knowledge database. From the combined information, consistency will be checked and potentially new knowledge inferred. The digital virtual assistant will be human interface to provide accessibility via simple intuitive query language, and most likely, speech recognition.

Structuring handover of enormous sets of documentation:

Large capital projects produce enormous amounts of documents, which provide a nearly unmanageable amount of information. Particularly in handing over from EPC (Engineering, Procurement and Construction) to Operations this is extremely challenging. This information is effectively considered to be unstructured and therefore inaccessible. Building knowledge graphs with semantic document analysis can provide a superior solution to provide insights in the documentation and relations within and between the documents.

The Industrial Data Platform:

The insights and knowledge are developed by and used in collaborations between multiple companies. Clients, partners, suppliers need to share data and knowledge about their equipment, services, assets, and products; each having its own vocabulary, its own ontology. These need to be integrated for which semantic graphs will be created to translate & link the vocabularies & ontologies.

Clients, partners and suppliers also need access to knowledge and information. These needs will lead to industrial data platform, which require a refined model to manage and control access, functional usage through micro-services, billing, security, privacy.

Cognitive computing and semantic web for Oil & Gas

Cognitive science brings together things like perception, intelligence, calculation, reasoning and, in the end, conscience. They articulate many disciplines of science and technology: linguistics, anthropology, psychology, neurosciences, philosophy and artificial intelligence.

Cognitive systems are complex information processing ones, capable of acquiring information, putting it into action and transmitting knowledge. These will include deep learning capabilities, where multi-layer complex neural networks, will discover information out of large sets of data without any human supervision.

The industry will benefit from developing Semantic Data Ecosystem for the Oil and Gas Sector. EPIM, the Exploration & Production Information Management Association, is implementing its vision of a shared suite of knowledge based-applications for the Norwegian Oil & Gas industry using semantic web standards and the domain concepts from ISO 15926 for industrial automation systems and integration.

In my next blog post I will be considering the typical roadmap for organisations in the oil and gas sector.

For further information on digital technologies are changing the industry, take a look at my previous blog here

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About Dr. Erwin Dijkstra
Head of Atos Codex Offers for IoT, analytics and AI, Distinguished Expert and member of the Scientific Community
Erwin Dijkstra is responsible for Atos Codex Offers in IoT, analytics and Artificial Intelligence. Erwin has a PhD in Solid State Physics, has worked almost a decade for a leading Oil company in drilling, petroleum engineering and field development planning. Erwin is a member of the Atos Scientific Community that is the best 135 scientific people from within the group who are “creators of change”, making sure that whenever our clients choose Atos they always get the best solutions available in their journey to digital transformation. In Erwin’s view, as a business expert, he has a duty to keep learning with its customers, to keep actively engaging with disruptive innovative ideas, to have regular exchange with his peers and expert communities, and to be open to investigating new developments. He is fully committed to help Atos and Atos’ clients anticipate and craft their vision regarding upcoming technology disruptions and the new challenges facing our industry.

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