Oil and Gas

Paradigm shift in Oil and Gas industry

There’s disruption all around us. Over the past three decades, the rise of high-speed internet, exponential growth of computational power, and ever-decreasing costs of data storage have drastically changed how we live, what we interact with, and how businesses operate. The democratization of data and technology provides easier access to things that we have never imagined. It helped organizations to push boundaries and disrupt the status quo. Not only are technology trends shaping business models, but ever-evolving behaviors and expectations from consumers are impacting organizations’ current practices. The advent of the Information Age has been transformative – industry after industry, ranging from law, media, and manufacturing have evolved value propositions, company missions, business models and operating practices to adapt to the paradigm shift.

Traditionally, the Oil & Gas industry has been on the cutting edge of technology, rapidly adopting new technologies to enable optimal economic recovery of oilfields. However, the “Age of Data” adoption by Oil & Gas industry was delayed due to multiple reasons, including the oil price crash of 2014.

In the industry, we all understand the physical complexities associated with the Oil & Gas; from reservoirs, deep wells, cross country pipeline networks, complex refining processes and widely distributed terminals. Such disparate infrastructure has always been the cause of information lags, silos and operational inefficiencies.

Transformation is a paradigm shift for organizations. It significantly alters their pace and rhythm, while improving key business drivers. The shift often results in creating, or significantly modifying, business processes and the value it brings for its customers.

The industry has now started embracing digital transformation and there are several disruptive technologies which are helping improve efficiencies, safety of personnel and assets while creating higher value for all stake holders. A few of such transformational technologies are discussed below.

Digital twin: is a digital replica of a process or asset which has all the properties of the physical unit. Such digital twins are very useful while designing a new unit or while de-bottlenecking an existing unit. The digital twin can be simulated to work and react in the exact manner as the physical unit would when installed. Optimization of existing or new installation can be effectively achieved with digital twin.

Digital twin becomes a must have technology as it helps in engineering and testing systems, saving significant man-hours of work onsite. A digital twin created with process design can be used for complete engineering and then simulated testing of control system.

The other must have use of digital twin is for operator training. Training new operators on existing installations is time consuming and risky as there could be safety issue if any operation is incorrectly performed. A digital twin based immersive training prepares such operators without them stepping into the field. And if created during design stages, saves significant cost of building an operator training platform once a plant or asset is commissioned.

Then of course, the Digital Twin can also be used during the Asset Performance management operations, by calling back as-built plans, history of previous operations, etc.

Artificial Intelligence: Artificial intelligence (AI) algorithms are designed to make decisions, often using real-time data. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the data instantly, and act on the insights derived from that data. As such, they are designed to reach conclusions based on their instant analysis.

AI has several use cases in Oil & Gas, from operations to virtual assistants. ExxonMobil is working with MIT to design AI robots for ocean exploration, Shell believes the future of AI in its industry will see a significant increase in unmanned and automated facilities.

Leaders in the Oil & Gas industry are integrating AI in multiple areas. Reducing the carbon footprint, deep sea exploration of hydrocarbons and implementation of innovative, sustainable energy strategies are driving the pace of evolution in the field.

Machine learning: is a means of data analysis and it is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning is primarily used for analysis of large data generated by various systems to provide information on similar sequences or operations.

The most commonly applied machine learning use case for the Oil & Gas industry is in predictive maintenance to predict failure of equipment or wells, ahead of expected failure . These applications are saving millions due to reduced unscheduled downtime and improved safety.

Machine learning is being investigated in almost all the aspects of operations, from drilling to process optimization.

Edge analytics: is the collection, processing, and analysis of data at the edge of a network either at or close to a sensor. It is an integrated AI, machine learning and computing solution which is installed near the process.

A common issue in Oil & Gas installations is that some installations are in very remote areas and rely on communication using radio, satellite or private networks. Sending huge volumes of data, which can often be sensitive information, needs low latency and high bandwidth networks. The advantage of such a solution is that it reduces time lag from analysis which is done on the cloud or on premise, and that it reduces the computing and volume of data required at the premise or on cloud. The deployment of Edge Computing is widely seen as a game changer, giving Oil & Gas companies an opportunity to achieve end-to-end informational, workforce and commercial transformation.

Edge analytics are used for predictive analytics, measurement and optimization in Oil & Gas industry.

Autonomous control: has been the goal of the Oil & Gas sector for many years, but despite many false starts, the autonomous future was never as promising as it seems today.

Autonomous vehicles and drones are being increasingly utilized for subsea and aerial inspections as well as repairs. In some cases, the goal of the industry is to completely control the operations autonomously with minimum human intervention.

With AI, machine learning and edge analytics, the combination of new technologies is making it possible to develop architectures which can be used for safe operations of Oil & Gas installations.

Integrated Power and Process system: As we speak of transformation and breaking silos, integrating power and process system is one such paradigm shift in the industry.

Unified engineering using 3D models and digital twins to design complete units, while creating P&IDs, PFDs and single line diagrams automatically from design saves an enormous number of manhours otherwise needed for such effort. However, the bigger benefit comes from error free design and immediate availability of data for all the inter-discipline teams. Digital twins also help in testing the control systems, thereby reducing the time to install and commission the system.

Improving efficiencies with integrated solutions brings improved results as complete data of an installation is available in a single window highlighting and unlocking the hidden efficiencies of overall operations.

Blockchain: is a secure transaction ledger database that is shared by all parties participating in an established, distributed network of computers. It records and stores every transaction that occurs in the network, essentially eliminating the need for “trusted” third parties such as payment processors.

Deloitte has noted “Oil & Gas companies that leverage blockchain can improve trade accuracy, increase scheduling and back-office (e.g., invoicing and settlements) efficiency, accelerate access to trade data, and shorten the working capital cycle.”

3D Printing: The 3D printing process builds a three-dimensional object from a computer-aided design (CAD) model, usually by successively adding material layer by layer, which is why it is also called additive manufacturing, unlike conventional machining, casting and forging processes, where material is removed from a stock item (subtractive manufacturing) or poured into a mold and shaped by means of dies, presses and hammers.

One increasingly crucial application of 3D printing in the Oil & Gas industry is seen in the spare parts market. The high cost of downtime and logistical challenges of distribution to wide-spread, remote locations often lead to overstocking of spare parts. 3D printing provides a solution through fast, on-demand printing of legacy parts from an on-site system or a 3D printing service provider.

As Oil and Gas companies continue to explore the benefits of 3D printing utilized efficiently in their supply chains, there could be significant growth seen in the industry.

Discover our comprehensive portfolio that empowers customers to make data-driven decisions that reduce risk, improve operational performance and optimize production throughout the entire project or asset lifecycle. With our strategic partner, AVEVA, Schneider leverages unmatched, end-to-end, integrated, Power, Process and Digital Solutions unlocking the full expertise of two industry leaders to deliver improved profitability and operational excellence to maximize return on capital.

 


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