F1: OVERALL EQUIPMENT EFFECTIVENESS
IS NOW THE TIME FOR MACHINE LEARNING?
EITAN VESELY, PRESENSO, HAIFA, ISRAEL
AS CAPITAL EXPENDITURES and O&M
budgets are cut in response to the precipitous fall in worldwide petroleum
prices, there is growing interest in the
unrealized economic potential of Industrial Analytics. Shrewd marketing on the
part of certain vendors has created a
perception that machine learning is the
holy grail solution for increased uptime
and higher recovery rates.
Let’s start with the bullish forecasts.
McKinsey Consulting estimates that the
potential economic benefit of using advanced analytics is a 13% reduction in
maintenance costs. According to GE,
companies using predictive, data-based
approach experience 36% less unplanned
downtime and can save an average of $17
million per year. Not to be outdone, Cisco
goes even further by audaciously claiming
that industry-wide adoption of Io T can
grow GDP by 0.8%.
Not surprisingly, these organizations
do not share their underlying assumptions and calculations.
This article provides a realistic assessment of the alternatives for Machine
Learning for Predictive Maintenance in
the oil and gas industry based the near-term outlook for O&M, infrastructure
investment, and workforce dynamics.
CURRENT STATE OF OVERALL
EQUIPMENT EFFECTIVENSS (OEE)
From an operational perspective, the
most important metric to consider is
Overall Equipment Effectiveness or OEE.
The OEE measurement includes availability (uptime), performance (speed),
and quality of output (defect rate). It is
calculated as follows: OEE = Availability
* Performance Quality (see Figure 1).
How does the oil and gas industry
rate? According to the Aberdeen Group,
the average oil and gas company has an
OEE of 73%. This compares to the best
in class of 89%. One of the largest con-
tributing factors is unscheduled down-
time. Between 2009 and 2012, there
were more than 1,700 refinery shut-
downs in the US. This is an average
of 1. 16 shutdowns a day. Significant-
ly, more than 90% of the mainte-
nance-related shutdowns were
The economic impact is well
documented. The average daily cost
of unscheduled downtime is $7 million for an onshore well and substantially more
for offshore facilities.
Furthermore, the outlook for OEE is getting worse. Since the fall in oil prices, there
has been a significant reduction in both new capital expenditures and O&M investment. In periods of instability, deep budget cuts can be random and asset maintenance
has become less of a priority. The expected result is further deterioration in medium-to long-term asset performance.
THE SHIFT TO INDUSTRIAL IOT IN THE ERA
OF LOWER PETROLEUM PRICES
The emergence of Industry 4.0 has coincided with the industry downturn.
Executives have taken note and at a strategic level there has been a shift from
maximizing revenue to optimizing production. Last year, Microsoft and Accenture
commissioned a study called the “Upstream Oil and Gas Digital Trend Survey.” The