What's new in production

“…and pretty soon,” U.S. Senator Everett Dirksen famously remarked, “it adds up to real money.”

These days, descriptions of swing-for-the-fences industry transformations are frequently preceded by “digital.” To those of you who wistfully recall the days when the word referred to your fingers, get used to it.

In pursuit of billions saved or earned, digital technologies are the most powerful levers in the corporate conex box. Richard Ward, senior expert, McKinsey & Company, presents examples in three areas where they could have a billion-dollar impact.

Finding $1 billion in the supply chain. Historically, fictional AB Oil Co. has made a vast number of design, procurement and operational choices. Valves have been sized and ordered, casing-team contracts awarded, and orders for cement placed, pretty much with the same vendors, in the same way. In the meantime, some vendors were charging less in one field than another; some crews had fewer failures than others; one supplier had lowered the cost of an entire class of suitable products. But AB Oil’s engineers never took advantage of any of these opportunities. Why? Because there is too much of this type of information: there are too many dynamic variables, in too many places, for any single person to know everything, or enough to make optimal decisions. It is too much even for a team of professionals dedicated to the task.

But not too much for its computers. Advanced-analytics programs can perform a massive analysis of all these data, normalize them, and identify opportunities for cost savings that can be leveraged across future operations.

In one case, a super-major, drilling horizontal shale wells in North America, found that its costs, as well as those of its competitors, varied highly across plays. The company assembled a data team and collected information from finance, operations, competitor investor presentations and industry news stories. A software program did bottom-up analysis, churning through millions of records, normalizing, correlating and seeking high-probability maximums and minimums. They were guided by an experienced team of engineers and procurement staff. At the end of this multi-week process, the team could confidently propose critical changes to casing design, procurement and casing crew selection.

The savings came to $700,000/well. As this company had about 1,300 future wells to drill, the total potential was roughly $1 billion.

Saving $1 billion in engineering time. AB Oil Co. employs tens of thousands of engineers and technicians working on thousands of projects. Not all of those projects can be successful, or even efficient in how they operate. How can they be identified?

A new analytical method comes from Formula One racing. Big-league racing teams have hundreds of engineers pursuing thousands of technical projects, in parallel. Researchers gathered communications data, interim work products, time sheets, staff locations and travel expenses. Using analytical tools, they were able to gain comprehensive views of the efficiency and effectiveness of the different teams. Without the bias of any top-down assumptions, the tools processed millions of correlations and hypotheses. Each step in the analysis highlighted high correlations with and predictions of high performance, while eliminating low-value insights.

Teams that communicated more frequently than would be expected, turned out to be struggling to schedule basic review meetings, or problem-solving sessions across time zones. This resulted in wasted hours of effort, and hours wasted waiting on others.

Once the data were in, remedial actions included canceling projects, reassigning staff and establishing better working norms. In documented cases, teams saw a 15% to 20% reduction in charged engineering hours across their portfolios of projects.

Similarly, given current fully-loaded engineering costs, AB Oil Co. would only need to find about 1,500 surplus engineering positions to capture $1 billion in value over three years.

Increasing production by $1 billion. The Pareto Principle says 80% of potential production increases in existing fields, comes from roughly 20% of the wells. But, which 20%? Until now, cumbersome analytical tools resulted in workover programs focused on the easiest rather than the best targets.

Ward offers the example of ConocoPhillips, and its 2013 implementation of the Plunger Lift Optimization Tool (PLOT) project. Taking what was already there—analytic tools for wells and plunger lift systems, multiple data sources, and engineers’ knowledge—the project simplified it through the use of software workflow automation, or robotic process automation. This technology sits next to existing tools and data sets, copies the processes executed by the engineering team, and then improves upon them by using automation and knowledge of the hidden commands, contained in every major software tool.

PLOT integrated 43 different charts and graphs into a single flow that told the performance story of a well from beginning to end. Nothing new needed to be created to integrate the workflow. No new databases or data models were required, as the tools function at arm’s length.

ConocoPhillips reportedly increased production by 30% in the field where this capability was implemented. Over three years, assuming a net price of $40/bbl, this increase will yield an additional $1 billion in revenue—by using digital technology available to anyone.

Billions. You can almost hear Carl Sagan saying it. In this case, it’s dollars, not stars, but unlike the stars, this is a destination smart companies can reach.