Benefits of Workflow Automation in the oil and gas industry

The ground realities are changing rapidly for the oil and gas industry, which has traditionally been susceptible to the highs and lows of economic and commodity price cycles. As 2020 draws to a close, oil and gas companies are realigning themselves for a challenging 2021 by leveraging digital technology.

The digital transformation in the oil and gas industry mostly focuses on emerging technologies such as Artificial Intelligence (AI), Robotic Process Automation (RPA), and the Industrial Internet of Things (IIoT). Workflow automation in oil & gas driven by technology is rapidly gaining traction in the industry. It helps companies striving to keep the costs down by mimicking users in carrying out mundane, routine tasks with speed and accuracy.

The scope and potential of workflow automation in oil & gas extends way beyond just deploying digital tools. It is more about how organizations can constantly discover, learn, and evolve as they strive to meet business objectives. Especially if you consider upstream oil and gas operations, the pursuit of efficiency is endless. Manual processes result in wastage of time and miscommunication. However, with an automated workflow, you can streamline most processes and eliminate time challenges.

Oil and gas industry outlook 2020

According to the Oil 2020 report published by International Energy Agency (IEA), the outbreak of COVID-19 had added a significant layer of uncertainty to the oil market outlook within the forecast period of 2020 to 2025. In 2020, the worldwide oil demand dropped for the first time since the global recession in 2009.

As per the report, the global oil demand dropped by 2.5 million barrels a day in the first quarter of 2020. As progressive recovery occurs in the second half of 2020, the global oil demand would decline by around 90,000 barrels a day compared to 2019. In the projected period of 2019-2025, the worldwide oil demand will grow by 5.7 mb/d at an average annual growth rate of 950 kb/d.

However, an expected sharp rebound in 2021 will lead to an annual growth rate of around 1 million barrels per day till 2025. The global oil production capacity is expected to grow by 5.9 million barrels per day during the forecast period on the upstream oil and gas operations.

While the oil production capacity of non-OPEC countries will rise by 4.5 million barrels per day, OPEC nations will build a capacity of another 1.4 million barrels per day of crude oil and natural gas.

Defining the right strategy for Workflow Automation in oil and gas

Today’s oil and gas industry has the opportunity to tackle the challenges by redefining its boundaries through digitalization. Workflow automation is a continuous process that amalgamates various technologies such as social, mobile, analytics, cloud (SMAC), and intelligence-driven automation, integrating with the human workforce to deliver outcomes across the entire business value chain.

A well-defined strategy for workflow automation in oil & gas is the key to long-term success as it helps in:

• Compliance burden reduction
• End-to-end optimization
• Data management with AI
• Documentation and back-end simplification
• Offering an ideal solution to counter the challenges posed by fluctuating oil and gas prices and reducing margins

Bridging the digital-human divide

Artificial Intelligence and neural networks help in improved decision-making by processing large amounts of data. Human workers leverage big data analytics to monitor which equipment shows wear and tear and need replacement. Automation enables employees to track wells effectively, anticipate issues, and respond quickly to changing scenarios.

Field workers inspecting wells need to make notes on the leaks or other issues by driving back to the trailer and logging on to a system to enter the data. However, with workflow automation, the worker needs to tell their smart device, which automatically detects the equipment type and location, and generates a work order with RPA software. Paperless processes help reduce the risk of incorrect data transfer, along with saving time.

Workflow Automation in exploration

Exploration is a costly operation involving both tangible and intangible risks. While tangible risks include environmental impacts, intangible include geopolitical instability. Furthermore, another added layer of complexities is the uncertainties of finding oil and gas, starting with the means of exploration, including drilling wells and seismic processing.

Seismic processing is required to discern the subsurface geological formations for identifying potential drill sites. The process involves gathering seismic data by sending impulses of acoustic vibrations into the subsurface onshore or subsea offshore. Creating a visual representation of this data is the conventional way of exploring sub-features such as faults and salt domes. However, it is a computationally-intensive and time-consuming process.

Geoscientists can leverage AI-driven algorithms to interpret raw seismic data running into petabytes, reducing the need for visual representation. Alternatively, AI and machine learning algorithms can be applied to visual representations of the seismic data for further analysis. Machine learning algorithms can also be trained to classify rock formations to determine how much oil can be extracted from the reservoir.

Automation of upstream production

The profitability of upstream oil and gas operations is mostly dependent on production and operational efficiency. Targeted automation can help cut costs and improve equipment reliability leading to an extended lifecycle of the asset and increased profitability.

Process Control Automation, reliability and preventive maintenance, and production optimization rank among the highest-impact automation opportunities in oil and gas. In upstream operations, automation, sensors, and analytics play a vital role. The sensors powered by the Industrial Internet of Things (IIoT) installed in oil field services trucks and other equipment can help facilitate automation of logs and invoicing processes.

A central dashboard can enable oil and gas companies to manage multiple sites for allocating routine and periodic maintenance tasks such as automatic work order creation to mitigate safety risks. Other use cases of workflow automation in oil and gas include back-office and supply chain automation to manage procurement and invoicing.

Shell, a multinational oil and gas company, predicts that robotics and artificial intelligence advances will drive even more significant change in the industry over the next 25 years, turning around societies and economies. Right from the way the industry operates to the way companies are organized will be wholly transformed. The following trends would dominate the oil and gas industry in the coming time:

• Setting up global data standards and policies for security
• Creation of a healthy ecosystem for innovation
• Clear regulatory frameworks based on the tier system for green future
• Dynamically stress test the business to ensure agility
• Seamless operations with the help of sensors
• Technology will help solve regulatory concerns

Conclusion: A new era of automation in oil and gas

Lowering demand is hampering margins, forcing the oil and gas companies to increase operational efficiencies. Since 2014, capital expenditure on oil exploration has dropped significantly by about 25% due to maximizing production and throughput by squeezing existing assets. In a volatile market, oil and gas companies that leverage digital technologies to automate and optimize operations will be at the forefront of transforming the industry. Due to faulty equipment, accidents and spills are a clear and present danger that threatens an operator’s profitability and survival.

The new era of automation in oil and gas, built on technologies such as IIoT and RPA, provides the operators with capabilities to perform autonomous and remote operations. Leveraging the right mix of technologies enables companies in making real-time data-driven decisions and connect end-to-end processes across a well’s lifecycle.