Permian gas growth to underpin US LNG exports

by | Oct 24, 2018 | Uncategorized

By Peter Stewart, Interfax Chief Energy Analyst
Interfax Global Energy held a breakfast briefing with consultancy Baker & O’Brien which posed the question: Permian gas: Pulling the Rug from Under Oil-Indexed Prices. The piece below explores the growth of Permian gas and how it will impact the global LNG market.
LNG exports from the United States are often touted as a cheap alternative to oil-indexed LNG, but the reality is more complex.
Although Cheniere’s model uses the Henry Hub as a price reference, LNG sold from the company’s Sabine Pass LNG plant reaches end-users mainly through oil-indexed term contracts on a delivered ex-ship basis or on the spot market. This because of the predominance of portfolio players among those taking gas from operational US LNG plants. Portfolio players typically make a margin between the Henry Hub-related free-on-board acquisition price and the oil-indexed sale.
That has created a multi-tiered market with differentiated pricing and resulted in a switch from inflexible long-term delivered contracts to shorter-term and more-flexible pricing structures, which have abandoned clauses restricting the final destination of cargoes. Even within oil-indexed deals, flexibility can be achieved by more frequent renegotiation clauses and by the flexible use of slopes and S-curves.
If US exchange operator CME Group’s proposed physically deliverable LNG futures contract at Sabine Pass becomes a reality, it would allow even more flexible price-risk management, as derivatives such as options and swaps could be tied to the price of the futures contract.
Nowadays, the consensus is that Henry Hub gas prices will remain low relative to oil – especially if geopolitical events and upstream underinvestment cause oil prices to spike higher. ‘Permania’ in the Permian Basin looks set to continue that trend. A 600,000 barrel per day rise in Permian oil production in 2019 will prompt a sharp rise in associated gas output. Dry gas production from the basin is expected to double from 2017 levels by 2025.
Almost all of that extra gas will go to the Gulf Coast for sale as LNG, according to Robert Beck, a consultant at Houston-based Baker & O’Brien. Beck was speaking at a roundtable discussion on Tuesday hosted by Interfax and Baker & O’Brien titled: ‘Permian – Pulling the rug from oil-indexed LNG prices?’
Citing public sources including the US Energy Information Administration, Beck estimated that an additional 100 mtpa of LNG could be available from 2025 as a result of the production growth from the Permian and other US shale plays.
US LNG’s competitiveness against other supply sources will depend on how much new capacity is built around the world over the next decade.
US LNG export capacity stands at around 25 mtpa. It will rise by a further 55 mtpa by around 2021 as plants come online that are either under construction or have taken FID and been approved by the Federal Energy Regulatory Commission and the Department of Energy. However, projects amounting to 100 mtpa in additional liquefaction capacity have also been approved in the US and Canada but have yet to reach FID. And double that capacity again has been proposed but not yet permitted.
Although Shell has warned that project FIDs need to be taken to ensure adequate liquefaction capacity to meet demand, the supply glut that has until recently depressed LNG prices could be extended if demand disappoints.
Global LNG imports hit 298 mt in 2017, according to a 2018 report from gas importers group GIIGNL. If the 4% annual growth in demand projected by some majors becomes reality, it would see LNG demand hit 405 mt in 2025 and around 500 mt in 2030. With global capacity at 365 mtpa at the end of 2017, shale gas production fuelled by Permian gas would create a potential surplus by 2025 if it is all liquefied and exported as LNG.
 

Forecasting

by | Oct 21, 2018 | Uncategorized

Forecasting

The Art of the Probable

Forecasting
Most people associate forecasting with quantitative methods often lumped under the umbrella designation “number crunching”. But good forecasts can be made based on expert judgement. Asking the right questions, seeing a problem clearly and thinking logically through the range of possible outcomes can all result in the right decisions being made.
It is certainly true that econometric and technical analysis use complex mathematical and statistical techniques to measure relationships and predict outcomes more precisely. But you no longer need a Kray supercomputer to do the calculations. Predictive analytics can now be done on a laptop and in Excel, although there are also good econometric packages that supplement the analysis.
Art or science?
My experience is that forecasting is as much an art as it is a science. I vividly remember my first job in a forecasting team, when the model threw up a result that clearly made no sense in terms of “gut feeling”. My colleagues tweaked the model with what they jokingly called fudge factors, to get a result that was more intuitively believable. So much for econometric rigour!
It’s important to encourage a diverse approach to forecasting, as most good forecasts are the result of teamwork and even the best “super-forecaster” has periods where he or she loses their magic touch. That’s inevitable, as there is always a probabalistic element in forecasting performance; often, the forecast is accurate, but the timing is wrong, and I prefer to keep experience within a team rather than sacking staff like football managers. That said, it is important to monitor and measure the accuracy of forecasts on a regular basis.
There are three main types of forecasting used in energy analysis, often combined in different ways depending on whether forecasts are short-, medium- or long-term:

  • Fundamental Analysis
  • Econometric Analysis
  • Technical Analysis

 
Fundamental Analysis
Fundamentals refer to the basic economic forces that drive prices: supply, demand, stocks, trade and competitors.
Fundamental analysis builds up a picture of whether the market is likely to be over- or under-supplied with a commodity in a particular time horizon, based on the various and often highly diverse economic and market drivers.
These often differ across time horizons: for example, demand may be driven in the short term by weather events, seasonal factors, trading positions, and do on; and in the longer term by GDP and population growth, wealth trends, government policy and trends affecting the price of competing commodities.
Similarly, supply may be affected in the short term by production shocks such as force majeure decisions, geopolitical events, cartel decisions, and unscheduled outages; but in the longer term, price trends, the availability of finance, investment decisions and the price of competitors may have a bigger effect.
Stocks are more difficult to predict because they depend closely on the time gradient of the market, which in turn is affected by the supply-demand fundamentals.
Competitors are also complex to incorporate in a forecast, partly because game-changing technological advances can be Black Swans, and because the price of competitors affect each other mutually.
Finally, imports and exports can be combined with the supply-demand analysis to create balances. The balance will affect prices and trade flows, defining whether a particular geographical market is long or short in certain timeframes.
Econometric Analysis
Econometrics can be used to measure the relationships between variables, and these measurements can be used predictively to make forecasts – predictive analytivcs.
Econometrics is often associated with correlation and regression analysis, but there are a panoply of more complex techniques. Many of these are very useful.
The days when R2 was the only measure that mattered are long gone. Econometricians have refined their techniques to ensure that the relationships they identify are meaningful. Rigourous tests are made for the significance of an economic relationship, including causation testing.
That said, it is important that the analytical techniques are applied appropriately, and in ways that are consistent with economic theory.
It’s not uncommon for the mathematical wizardy of the Quants to be confused with a kind of super-intelligence — the means can all too easily become an end in itself, and you find that decisions are taken based on the mystique of little understood mathematical methods, based on little more than blind trust.
Econometrics is a useful toolbox, but it is still a toolbox. Quants make good and bad calls like anyone else.
They should not be accorded more prestige than any other member of the analytics team. Certainly it is reasonable for a manager to ask questions about the techniques used, and to expect a cogent explanation for any forecast.
My rule of thumb is that if the econometric analysis can’t be explained in simply language and is not easily understood by an intelligent manager, it should be taken with a pinch of salt.
Technical Analysis
Technical analysis refers to techniques that are based on the price of a commodity, rather than the supply-demand pressures and events affecting its value. A technical analyst examines how prices have moved in the past as the basis for a prediction about how they will move in the future.
The technique has been controversial. During their heyday in the heady days of the 1980s, major financial houses and trading firms paid fortunes for chartists’ predictions of where markets were heading. A series of academic analyses typically concluded that there was little evidence that the techniques used were statistically meaningful. Nevertheless, many traders still use the tools in trading decisions, particularly but not exclusively in taking short-term trading decisions.
The commonly used technical analysis tecniques include: drawing trendlines on charts; identifying chart patterns including continuation and reversal patterns; moving average analysis; momentum and stochastic analysis; volume and liquidity indicators and cyclical and wave analysis. But there are endless more varieties, some of which ressemble astrology or tea-leaf reading rather than quantiative analysis of price movements.
One of the problems with evaluating the success of technical analysis is the sheer variety of indicators that have been proposed. Chart patterns often “work” in the sense that they result in a series of good calls, but frequently this peters out after 3-4 runs. I diagree with those who say the techniques are self-fulfilling. A more serious criticism, I beleive, is that it often difficult to pin down whether technical forecasts are made with perfect hindsight, or whether the trading signals genuinely result in taking profitable positions ahead of time.
Pure chartists often refuse to engage with market fundamentals because they say this colours their analysis of the price signals. I believe this is sheer nonsense. It stikes me as about as daft as someone refusing to use a map to reach their destination because they want to find their way by instinct.