With the upcoming new Gregorian year, LNRG Technology is bringing you its projection for oil prices. The projection is referring to the average oil basket pricing of OPEC countries, thus not directly relevant to Brent oil pricing, though there is of course a strong correlation between the two. 2016 had been characterized by shifts on the supply and demand sides - the oil market had experienced a bearish market until early 2016, later taking an upturn and spiking to its annual record at the end of December. With global oil supply and demand closely corresponding, the record price height towards the end of the year is a likely indication that the oil market will continue seeing a rising trend also in 2017, though several fundamental variables might of course interfere. In combination of three scenarios, one can expect pricing range fairly similar to 2016 average annual price – 30 to 50 USD per barrel. This is somewhat discrepant with fundamental predictions of stable or even rising oil prices in the range of 50-70 USD per barrel.

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The conventional crude oil sector is an enormous market, with a daily turnover of roughly 4 billion US Dollars, not including investments and indirect expenses and excluding non-conventional resources. Annual crude oil sales revenue reached over 1 trillion US Dollars throughout 2016, which is nearly 1.3% of global GDP. Despite decreasing importance of conventional crude oil, it is still making the lion share of the global energy market. Conventional crude oil is making up about 73-74 million bpd (about 76-77%) out of total 96 million bpd of liquid fuels, whereas liquid fuels compose some 30% of the primary energy market. Thus, conventional crude oil has significantly dropped from its peak primary energy market share of 50% in the 1970s, but is still critical for global economy. Conventional crude oil is primarily important for the transportation sector worldwide, though there is a wide use of oil for backup power, industrial heating and still for electricity production in non-OECD countries. Conventional crude oil is also utilized for oils and polymers in the plastics and cosmetics industries.

In regard to the previous projection for 2016 based on the 15-year price trend, the most precise result was derived from 3rd polynomial fit, which had a very good correlation with the actual average annual oil price. Despite the fact that 3rd polynomial fit was also the closest projection for actual 2015 average annual oil price, we can barely rely on it alone due to significant divergence in 2015 and highly unlikely low figure for 2017.

As of 1st and 2nd polynomial fits, with 2015 and 2016 projection correlations, there seems to be a mediocre usefulness to utilize those for
price prediction. Using the above assumptions, the same polynomial fits can be applied for average annual oil price data series, in order to produce projections for average annual oil price
in 2017. In numeric terms, the prediction accuracy with those models for 2017 is 49% for 1st polynomial, 29% for 2nd polynomial and 19% for 3rd polynomial fits. This is of course much better than
conventional "business-as-usual" (zero or first polynomial fit), but better prediction models are required. For the 1st polynomial fit, the 2017 result is annual average price of 95 USD per
barrel, with R^{2}=0.27. For 2nd polynomial fit the 2017 result is annual average price of 42 USD per barrel, with R^{2}=0.70. For 3rd polynomial fit
the 2017 result is annual average price of just 6 USD per barrel, with R^{2}=0.83. Higher polynomial fits can also be utilized, but may not sufficiently add to prediction
accuracy. However, combining additional projections based on 10-year price trend and 5-year price trend could provide us with much more precise tools for estimating future oil pricing.

*Figure 1. Annual average OPEC basket oil price during 2002-2016, with mathematical fits of first polynomial, second polynomial and third polynomial degrees to
the annual price trend.*

With such relatively simple mathematical analysis, there is a wide range of results, ranging from significant gains per first polynomial fit to stability per second polynomial fit and unreasonably radical decline per third polynomial fit. Evidently, predictive analysis cannot provide accurate answers, since we are only speaking of statistical probability. It should be mentioned that there is a better fit for the 3rd polynomial degree, thus in combination of three scenarios, one can expect pricing range fairly similar to 2016 average annual price – 30 to 50 USD per barrel. This is somewhat discrepant with fundamental predictions of stable or even rising oil prices in the range of 50-70 USD per barrel.

**Clarification: **The above presented data is a general informative survey and is not to be considered as a
consulting in any way in relevance to capital investment, securities or any other financial instrument. For the avoidance of doubt, the author of this survey is not a certified investment
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