“No other industry begins to offer the data problems that are presented by petroleum,” – John Blair
The production of basic statistics and forecasts about oil reserves, production, consumption and stocks ought to be a matter of routine. You just stick a gauge at the end of a pipe and measure the amount of liquid flowing through (in the case of oil) right? Unfortunately, it’s not that simple, and the problem isn’t limited to just oil, but all commodities.
Doubts about the reliability of energy statistics were a major part of the “energy crisis” that erupted during the 1970s. As late as 1968, the United States had an estimated 4 million barrels per day of spare crude production capacity and thousands of wells across Texas and Louisiana were being operated for fewer than 10 days per month. But just four years later spare capacity had suddenly dropped to zero, every well was at maximum production, domestic output was falling, and politicians began to speak of an energy crisis.
The OPEC oil embargo, announced in October 1973, intensified the sense that something had gone badly awry, leaving the USA unprepared. Politicians and the media blamed a conspiracy between domestic producers and OPEC for engineering the crisis to drive up prices and profits.
Congress held hearings amid a sense that the statistics and forecasts prepared by oil and gas producers and the U.S. Department of the Interior had been either inaccurate or deliberately manipulated. One outcome of the crisis was the creation of a new U.S. Department of Energy and within it a new Energy Information Administration (EIA) in 1977 to produce more accurate and independent data. Another was the creation of the International Energy Agency (IEA) in 1974 to gather better statistics and bring greater transparency to the international energy markets.
Prior to the energy crisis, most data and forecasts were confidential and under the control of oil and gas producers themselves. After the energy crisis, data collection and forecasting would be led by impartial civil servants at national and international levels.
Improvements in data collection and forecasting in the United States, led by the EIA, have largely quelled controversy about domestic US oil production, consumption and stocks. But that doesn’t mean they are free from error or revision. Indeed, information on international markets remains much less comprehensive and accurate, mostly as a result of data collection problems in emerging markets and the deliberate secrecy of the major oil producing countries, particularly those who are members of OPEC.
According to a recent study by the Wall Street Journal, annual estimates of global crude demand by the IEA have been revised up for the past seven years (up until 2016) by an average of 880,000 barrels per day. And there is little evidence that demand estimates from other institutions are any more accurate. The EIA have also underestimated consumption over the past seven years, with the annual figures being revised up by an average of 2.3 million barrels a day.
Revisions to oil supply estimates are typically much smaller than for demand and are often about correcting overestimates for crude production. The IEA’s supply data has been revised down 60,000 barrels a day on average over the last seven years, according to the Journal analysis. That means the oversupply usually ends up being smaller than initially thought.
Demand is much harder to estimate than supply. Unlike supply which can be estimated from the pre-announced expansion plans of a relatively small number of companies, estimating demand involves billions of consumers worldwide and many millions of companies of all shapes and sizes.
The history of discrepancies underscores how commodity markets often trade based on, and pundits provide price forecasts using incomplete and often significantly revised data. And remember, the oil market is by far the largest, most liquid, most important commodity market in the world. If such big revisions are made in the oil market, then imagine how difficult it becomes to estimate demand and supply and then forecast prices in much smaller markets like lead, live cattle or lithium.