Calculating the Cost of Downtime

Emergency ‘shut-ins’ in the oil & gas sector are extremely expensive in both lost revenues and unplanned repair expenditure.

With a lack of productivity resulting in US$7.2 billion per year of lost revenues in the North Sea alone, the business case for production optimisation is clear. There are an estimated US$200 billion of annual losses globally.

No one company stands to increase revenues by these sorts of figures, but with oil price volatility continuing and day-rates for North Sea rigs reaching US$300,000 last year, margins are tight and a 25% production loss increasingly unacceptable.

Let’s take the example of a field producing 50,000 barrels per day. If we assume a Brent price of US$65 per barrel and a rig day-rate of US$250,000, over a three-week shut-in, the field’s operators will lose gross revenues of US$68.25 million, while also paying out US$5.25 million for the idle rig.

Downstream

The downstream sector is somewhat different, but the results are equally stark.

In May last year, the Nigeria National Petroleum Corp. (NNPC) announced that refinery utilisation rates at its facilities at Kaduna, Port Harcourt (two) and Warri averaged just 16.6% capacity in 2017, with none of the units achieving even 50% usage in any month during that year.

Despite having a throughput capacity of 445,000 bpd, the poor condition of the existing state refineries has seen the Nigerian government spend US$36.3 billion on fuel imports in the last five years. Meanwhile, nearly all the country’s 1.5 million bpd of oil production is exported!

Closer to home, Norway’s Equinor has closed various units of its 226,000 bpd Mongstad refinery in the past 18 months because of LPG and naphtha leaks and a power outage.

With such significant costs involved, it is no surprise that the industry is turning to IT systems, specifically machine learning and Predictive Maintenance, to help eliminate these issues.

In most cases, losses could be mitigated or avoided by utilising sensor data and historical fault records to predict critical plant failures.

Building these models into maintenance schedules would increase production capacity, improve site safety and reduce operational costs.

To find out more about how Spartan can help you prevent revenue leakage, please contact us.