However interesting and generally appealing scenario thinking, as discussed in my previous article, may be, it only provides a broad, qualitative picture of the context within which one is considering a significant investment decision. But indeed, a very important picture, even with all its caveats. Yet, we need numbers when it comes to sinking capital. No decision executive will sign off on an investment proposal that only consists of a fascinating narrative. She or he will want to see some underpinning of the potential profitability and capital efficiency. And some what ifs. In other words: we need quantification, we need a cash flow outlook of some sort.
In fact, it is one of the more difficult, and therefore avoided, challenges: to combine qualitative with quantitative thinking. Many restrict themselves to one of the two worlds. Yet I believe one has to try to cross the border. Quantification alone can often not tell the story by itself, but only qualitative narratives have the danger of becoming arm-waving.
But this quantification, if done well, does not come easy. How can we put numbers on the future? One way is to assume that whatever was going on in the past will replicate itself going forward. For some variables that is quite doable. If you are investing in wind or solar energy, the wind speed patterns and annual numbers of sun hours at a particular location can reasonably be inferred from historic records, with some averaging over multiple years. However, for many variables such an approach would not be credible.
Nevertheless, this is exactly what was (and is) common across many sectors. In fact, it is well known that this practice was one of the main causes of the financial crisis in 2008. The notion of ‘risk’ was strongly related to the degree of stock volatility in prior periods. Risk models were used that were only driven by historic behaviour; there was no forward looking element (see NYT, 2009). In essence, the quantitative models made people stop thinking. For the calculation of the minimum coverage for Dutch pension funds (i.e. stretching out multiple decades) the use of the current interest rate is mandatory. Similarly for the oil and gas reserves calculations for the SEC (US Securities and Exchange Commission) it is required to use, for the purpose of asset valuation, the currentoil price for several decades of remaining production. Of course these last two examples have auditing and legal dimensions, but they do illustrate the dilemma when considering future cash flows: we know that the value that some important variable assumes today may not be representative for the future, but we (think we) have nothing else (that is auditable).
For investment decisions and strategy development we will often need both a scenario approach and quantitative analysis. It will depend on the investment project where the emphasis lies, but I would argue that for major capital decisions or strategies both should have equal weight. What we see in practice is that (if done at all) scenario work and quantitative investment evaluations are poorly linked, perhaps because these different approaches are serviced by different departments, they require different styles of working, different areas of expertise.
And of course, to come back to the earlier point, the quantification developed should use historic data as a basis, but needs also to include a forward looking, judgemental dimension. Scenario narratives and (probabilistic and judgementally forward looking) quantification should go hand in hand, in some way, for maximum understanding and clarity.
For quantitative analysis we require a choice of models and methods, fit for purpose, not too complicated, but yielding consistent results. And we need mathematics, to do things smartly, quickly and consistently. To deal with uncertainty in the numbers, we need probability theory and probability distributions. There is no way we can arrive at exact estimates of all future variables (costs, prices, schedules, sales quantities, tax rates, etc.). But what we can do is to try to estimate a range (under certain scenario assumptions).
One of the most important distributions for this purpose is the lognormal distribution. It is representative of multiplicative processes (as is the normal distribution for additive processes). This means that the product of variables with ranges of uncertainty will tend to be lognormally distributed. Examples are volumes (L*W*H) and revenues (number of units solds * price). The lognormal distribution is also used quite a bit to model share price behaviour. It has an elegant mathematical formulation and allows for modelling upsides and downsides (with a trick). A recent article I came across (Surovtsev, D. and Sungurov, A. “Vaguely Right or Precisely Wrong?”: Making Probabilistic Cost, Time and Performance Estimates for Bluefield Appraisal. SPE 181904. SPE Economics & Management Journal, July 2017.) confirms that most cost, schedule and production variables are best represented by such a distribution.
Rather than (only) relying on integrated systems and black-box like simulation software, for practitioners and analysts it may be useful to get into the guts of the lognormal distribution, understand its mathematical articulation and have some practical formulas to calculate things by hand. It gets a bit nerdy, but do check out a useful article for this purpose in our free knowledge base (in which more articles are to come).
A next blogpost will again be about global scenarios in a qualitative discussion: what can we learn from our predecessors?
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The global oilfield scale inhibitor market was valued at USD 509.4 Million in 2014 and is expected to witness a CAGR of 5.40% between 2015 and 2020. Factors driving the market of oilfield scale inhibitor include increasing demand from the oil and gas industry, wide availability of scale inhibitors, rising demand for biodegradable and environment-compatible scale inhibitors, and so on.
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The oilfield scale inhibitor market is experiencing strong growth and is mainly driven by regions, such as RoW, North America, Asia-Pacific, and Europe. Considerable amount of investments are made by different market players to serve the end-user applications of scale inhibitors. The global market is segmented into major geographic regions, such as North America, Europe, Asia-Pacific, and Rest of the World (RoW). The market has also been segmented on the basis of type. On the basis of type of scale inhibitors, the market is sub-divided into phosphonates, carboxylate/acrylate, sulfonates, and others.
Carboxylate/acrylic are the most common type of oilfield scale inhibitor
Among the various types of scale inhibitors, the carboxylate/acrylate type holds the largest share in the oilfield scale inhibitor market. This large share is attributed to the increasing usage of this type of scale inhibitors compared to the other types. Carboxylate/acrylate meets the legislation requirement, abiding environmental norms due to the absence of phosphorus. Carboxylate/acrylate scale inhibitors are used in artificial cooling water systems, heat exchangers, and boilers.
RoW, which includes the Middle-East, Africa, and South America, is the most dominant region in the global oilfield scale inhibitor market
The RoW oilfield scale inhibitor market accounted for the largest share of the global oilfield scale inhibitor market, in terms of value, in 2014. This dominance is expected to continue till 2020 due to increased oil and gas activities in this region. The Middle-East, Africa, and South America have abundant proven oil and gas reserves, which will enable the rapid growth of the oilfield scale inhibitor market in these regions. Among the regions in RoW, Africa’s oilfield scale inhibitor market has the highest prospect for growth. Africa has a huge amount of proven oil reserves and is one of the leading oil producing region in the World. But political unrest coupled with lack of proper infrastructures may negatively affect oil and gas activities in this region.
Major players in this market are The Dow Chemical Company (U.S.), BASF SE (Germany), AkzoNobel Oilfield (The Netherlands), Kemira OYJ (Finland), Solvay S.A. (Belgium), Halliburton Company (U.S.), Schlumberger Limited (U.S.), Baker Hughes Incorporated (U.S.), Clariant AG (Switzerland), E. I. du Pont de Nemours and Company (U.S.), Evonik Industries AG (Germany), GE Power & Water Process Technologies (U.S.), Ashland Inc. (U.S.), and Innospec Inc. (U.S.).
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Headline crude prices for the week beginning 9 December 2019 – Brent: US$64/b; WTI: US$59/b
Headlines of the week
In the U.S. Energy Information Administration’s (EIA) International Energy Outlook 2019 (IEO2019), India has the fastest-growing rate of energy consumption globally through 2050. By 2050, EIA projects in the IEO2019 Reference case that India will consume more energy than the United States by the mid-2040s, and its consumption will remain second only to China through 2050. EIA explored three alternative outcomes for India’s energy consumption in an Issue in Focus article released today and a corresponding webinar held at 9:00 a.m. Eastern Standard Time.
Long-term energy consumption projections in India are uncertain because of its rapid rate of change magnified by the size of its economy. The Issue in Focus article explores two aspects of uncertainty regarding India’s future energy consumption: economic composition by sector and industrial sector energy intensity. When these assumptions vary, it significantly increases estimates of future energy consumption.
In the IEO2019 Reference case, EIA projects the economy of India to surpass the economies of the European countries that are part of the Organization for Economic Cooperation and Development (OECD) and the United States by the late 2030s to become the second-largest economy in the world, behind only China. In EIA’s analysis, gross domestic product values for countries and regions are expressed in purchasing power parity terms.
The IEO2019 Reference case shows India’s gross domestic product (GDP) growing from $9 trillion in 2018 to $49 trillion in 2050, an average growth rate of more than 5% per year, which is higher than the global average annual growth rate of 3% in the IEO2019 Reference case.
Source: U.S. Energy Information Administration, International Energy Outlook 2019
India’s economic growth will continue to drive India’s growing energy consumption. In the IEO2019 Reference case, India’s total energy consumption increases from 35 quadrillion British thermal units (Btu) in 2018 to 120 quadrillion Btu in 2050, growing from a 6% share of the world total to 13%. However, annually, the level of GDP in India has a lower energy consumption than some other countries and regions.
Source: U.S. Energy Information Administration, International Energy Outlook 2019
In the Issue in Focus, three alternative cases explore different assumptions that affect India’s projected energy consumption:
EIA’s analysis shows that the country's industrial activity has a greater effect on India’s energy consumption than technological improvements. In the IEO2019 Composition and Combination cases, where the assumption is that economic growth is more concentrated in manufacturing, energy use in India grows at a greater rate because those industries have higher energy intensities.
In the IEO2019 Combination case, India’s industrial energy consumption grows to 38 quadrillion Btu more in 2050 than in the Reference case. This difference is equal to a more than 4% increase in 2050 global energy use.