How Technology is changing the Oil and Gas Landscape – for the better By Andreea Ene, EMEIA Oil & Gas Advisory Leader, EY And Terry Jost, Principal, US Oil & Gas Risk Practice, EY

Today’s global oil and gas sector is operating in an environment of unprecedented opportunity coupled with a high degree of volatility and risk. Diminishing returns, combined with forecasts for continued lower oil prices, have heightened the urgent need for business transformation.

“In today’s world of compressed margins, companies are actively seeking ways to transform their existing operating models to create sustainable cost efficiency in their operations”

Companies must evolve their business models in order to remain competitive and satisfy shareholder expectations in today’s complex landscape. Challenges, including increasing cost and margin pressures, supply-chain issues, decreasing asset reliability and performance, evolving technology and global competition for labor, have renewed the focus on operational excellence across the industry and with Exploration and Production (E&P) companies, traditionally driven by production, in particular.

While lower oil price levels have made the sector’s structural weaknesses more visible, they did not cause them. The industry has a history of cost overruns and project delays. In today’s world of compressed margins, companies are actively seeking ways to transform their existing operating models to create sustainable cost efficiency in their operations.

To date, the majority of companies have yet to seriously tackle the cost issue and have not fully realized their improvement potential. That’s where technology comes into play. New advancements have the potential to improve business functions, including supply chains where potential benefits include cost savings with procurement and contracts. Technology has caught up with the industry’s needs —but the industry still lags behind in leveraging these advancements.

At present, there are a variety of technological trends and drivers in the oil and gas industry-

• Big data and analytics: new business and technology architectures are enabling companies to better collect insights and uncover value.

• Digital oilfield: volume and availability of internal and external data from an ever-increasing number of sensors, including pumps and well heads, is defining a digitally integrated oil value chain.

• Industrial Internet of Things and new Supervisory Control And Data Acquisition (SCADA) architectures, which include the convergence of operational technology and information technology, provide broadened operational insights that can directly impact manufacturing costs and well-head production.

• Secure cloud computing: cloud computing provides seamless availability of data across organizations and enables cost-efficient, scalable computing power on demand.

Digital continues to disrupt existing (or new) business models, products, services, systems or experiences, enabled by data and technology across the enterprise. Oil and gas companies must make the leap from the digital oilfield as it was designed and implemented almost 10 years ago and adopt practices to position themselves for the future. This includes addressing the entire E&P value chain, focusing on innovation and complete automation.

Traditionally, oil and gas companies only analyze information on a well-by-well basis and let 90 percent of it ‘go dark’. And for an oil rig that has 30,000 sensors, only 1 percent of the data is examined. The data is then used mostly to detect and control anomalies, not for optimization and prediction. There is an immense opportunity for companies to keep all data alive and available across the organization for future access — thereby delivering additional value for the business.

While connecting data silos and partial use of limited real-time data have helped to see new patterns in E&P, companies that deploy analytical software can recover relevant business insights to drive profitability from the first to the last barrel extracted. Digital solutions are a combination of Social, Mobility, Analytics and Cloud (SMAC) technologies.

Applying big data and analytics in the digital oilfield is about consolidating data and making it available for real-time analysis. Analysis can help maintain operational integrity, optimize performance, anticipate breakdowns, streamline maintenance, inform decision-making, manage HSEQ and monitor environmental impact. The benefits are undeniable- reduced operational expenditure, improved operation margins, increased production and uptime, as well as continuous HSEQ improvements. What’s more, these technologies are sustainable over the long term and can be implemented without disrupting business.

Some of the application areas for big data and analytics include-

Pre-emptive maintenance on critical components (e.g., electric submersible pumps subject to high temperatures) allows for real-time flow of operational data into a system that leverages machine learning to develop baseline, detect anomalies and alert operations. Remediation can occur before actually touching the system, resulting in significantly reduced maintenance costs and fewer production interruptions. Maximum value comes from drawing data from the entire organization to centralized control where it all can be monitored in real time.

Decision support gives companies the ability to make strategic and tangible decisions from massive sets of structured and unstructured data. Corporate headquarters and field stakeholders are able to leverage consolidated enterprise-wide data to improve timeliness and enhance the quality of business decisions.

Effective drilling strategies leverage historic data and real-time data from the well site to answer questions on ‘how many wells?’ and ‘where to drill?’ Historic data from wells in similar formations can be used to quickly discover best-performing wells with similar characteristics — allowing recommendations for optimal drilling parameters to maximize the rate of penetration, minimize the chance of drilling issues and optimize well completion.

Supply chain optimization, operating theater logistics, and efficient distribution of oilfield material can be based on cross-organization consolidated information on a variety of factors.

Balance between production and downstream stages can be optimized by adapting upstream production levels to account for expected future demand shifts in downstream retail.

Asset reliability and integrity are improved by intelligent recommendations based on complete knowledge about an asset. This minimizes the risk of catastrophic failures and process disruptions while maximizing reliability and production efficiency. It also improves the overall asset performance and profitability by managing consolidated real-time data metrics across different sub-systems. Lastly, data helps identify trends and highlight complex performance relationships that are lost in day-to-day tactical reports.

The development of new facilities and well pads has created even greater complexities in operational business structures. Frequent transitions from capital project execution to operations often lead to delays in production performance and operability due to legal constraints, inadequate documentation, unclear roles and authorities, and lack of defined operating processes. The industry’s asset-intense ventures rely heavily on facilities and equipment to perform and extract the full value of land leases. Operators’ inconsistency in developing and deploying asset life-cycle management and reliability-centered maintenance strategies remains a barrier to ensuring their desired asset integrity and uptime.

Oil and gas companies can’t afford to leave money on the table in today’s world of compressed commodity prices. Advancements in technology are helping oil and gas companies to improve efficiencies and save costs, but the uptake is still slow. Now, with growing pressure from shareholders to strengthen returns, companies must focus on the value that digital disruptions can deliver. Maintaining a competitive advantage in the evolving oil and gas industry depends on it.


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