What's new in production

Collectively, the tsunami of sensors, analytics, control systems, and other hardware and software now flooding (or dribbling into) the offices of your asset managers has a compelling benefit. In the view of McKinsey&Company, the ability to use all this stuff to automate high-cost, dangerous or error-prone tasks offers large opportunities in production operations.

Big data can have big benefits. Minimizing nonproductive time, especially the unplanned variety, is a significant benefit. As the company points out in a recent paper, ever-increasing upstream capital investment makes optimizing production efficiency an imperative. Other challenges are ever-increasing as well—more complex operations; zero tolerance for health, safety, and environmental incidents; and the talent and experience gap.

Automation’s benefits to the oil field are nearly universal, McKinsey notes. With the substantial output volumes of offshore production platforms, even small improvements in production efficiency will have meaningful financial impact, as additional throughput translates directly into more revenue. In the low-volume regimes of current, unconventional mature assets, carefully targeted automation steps can cut costs and, more importantly, can also improve the reliability of production equipment, leading to higher revenues that can extend an asset’s economic life.

They offer an eyebrow-raising stat to illustrate the efficiency challenge: Research shows that average production efficiency dropped in the past decade, while the performance gap between industry leaders and other companies widened, from 22 percentage points in 2000 to around 40 percentage points in 2012.

Yikes. But there’s good news: this can be fixed. The paper points out that a contemporary intelligent oil field is flush with digitally enabled wired systems, equipment, and components. A typical offshore production platform can have more than 40,000 data tags, not all connected or used. Converting this flood of data into better business and operating decisions requires new, carefully designed capabilities for data manipulation, analysis and presentation, as well as tools to support decision-making.

The paper uses maintenance as a case in point. Unlocking the value of automation isn’t only about having lots of data. Some companies struggle to maintain data quality across their IT networks. Others are not good enough at aggregating data and conducting meaningful analyses. Still others experience challenges in turning analysis into action. That’s why many oil and gas operators need to identify the information shortfalls or data leaks that occur when capturing data from processes, systems and data stores and moving them to where operational and business decisions are made. Having identified the leaks, they must then address them by improving the automation of their data flows.

Many forms of leakage impair automation in maintenance. One example is having only isolated data available from individual equipment components, as opposed to more network-based availability. Another is having only equipment-level profiles of components at risk, as opposed to comprehensive coverage at the asset level. A third example is to only catalog critical equipment failures rather than conduct extensive root-cause analysis of them.

Predictive modeling is the magic ingredient for reducing downtime and improving asset productivity. It also allows the equipment provider to better understand customer needs and capture more value from the services provided. Potential drawbacks for operators are that these capabilities are difficult for others to replicate, thus limiting competitiveness.

So, what are the success factors? The paper identifies three. The first is multidisciplinary teams. Successful automation programs have staff with backgrounds ranging from process automation, process-domain expertise, data management and cybersecurity to interface design. These multidisciplinary teams include representatives from every aspect of the organization’s IT function. Sometimes, the teams also include equipment vendors.

Another is differentiating greenfield and brownfield automation. In greenfield automation programs, the digital processes are built in during project development to ready the technology for future advances, taking into account the five-to-seven-year life cycle of these projects. For brownfield programs, companies develop overlays (for example, upgrades of wireless and mobile equipment) that pull the required data flow out of the platform to support analytics units. This approach helps to avoid being locked in by technology choices from the past. Some companies use a library of reusable software components to achieve economies-of-scale across offshore platforms.

Finally, there is thinking big, piloting small, and scaling fast. Companies with successful programs think in terms of total life-cycle costs and economics. They build a digitization team and make automation part of a corporate digitization program. Their automation programs are integrated with all aspects of their complex organizations, work processes, and human behaviors. This level of complexity must be tested thoroughly and proved in small-scale pilot implementations. Then, rapid scaling secures the payoff.

Equipment providers are seen as central to success. They are increasingly making the shift to combining equipment manufacturing with service provisioning. This becomes possible by integrating condition- and performance-monitoring technology in their equipment, and combining this with a service offering based on big data processing and advanced analytics capabilities. A big benefit—at least for equipment manufacturers—is that revenue increasingly moves from “lumpy” equipment sales to more annuity-like service streams. But, ultimately, operators should be the beneficiaries of this paradigm shift.