T he development of the shale oil extracting technology revolution in the United States led to the rapid growth of its production and reduced the related costs to an acceptable level. The shale oil revolution dramatically influenced the global oil market and was a key factor in the reduction of oil prices in 2014-2016. This paper investigates the problems of long-term forecasting of shale oil production and the productivity of drilling rigs. This research applies an asymmetric bell-shaped function using the OLS approach. This function is derived as an analytical solution of the differential equation of oil production. Another contribution of this study is the asymmetric function, which correlates better with the data on the extraction of traditional and non-traditional oil resources. Кeywords: shale oil production; technological efficiency; institutional factors; bell-shaped curve fitting; rig productivity. An analysis of the empirical data with the derived asymmetrical bell-shaped curve shows that the productivity of drilling rigs would peak by 2026 at 1,200 bbl per day, which is two times higher than the current level. The peak of production would correspond to the maximum oil production of 11.3 mln bbl per day and to technically recoverable resources of 96 bln bbl. This could mean that starting from 2023, the volume of oil shale oil production in the US may not be enough to meet growing global demand for oil and other resources with even higher production costs. The theoretically grounded and practically tested asymmetrical bell-shaped curve can serve as one of the tools for assessing the long-term impact of technological innovation over the course of Foresight studies for the oil and gas complex.
304Economic forecasts rarely come true with a high degree of accuracy, however, this does not lead to the eradication of an interest in such forecasts. We can see an analogy with weather forecasts, which are frequently incorrect, but they do not express such a strong devia tion from the real weather as other forecast methods. Economic forecasts restrict the future ambiguity and thus promote more confident long term development strategies and investments. If one is to choose between a forecast and its absence, the former is explicitly prefer ential because it allows for the reduction of losses from probable wrong decisions, taking advantage of opening opportunities or escaping potential threats.Long term price forecasts have a special signifi cance. Long term investment decision making in any branch, including ferrous metallurgy, is based on the understanding of price trends during the implementa tion and pay off phases of a project. In the ferrous met allurgy industry, the time slot can exceed 10 years. It is vital that the trends of final product prices be compara ble with the expected cost dynamics. It allows for more reliable forecasts of producers' margins and, conse quently. of investment payback periods.The object of the analysis in this article are long term price tendencies in the global rolled iron market. The purpose was to propose a forecast procedure for world steel prices that would include compatible trends of steel prices and costs.As a result, we have developed a medium term fore cast algorithm for the average world steel price, which is based on a regression between metal roll prices, global steel capacities and the dynamics of steel production costs. The derived regression model provides a good description of the historical dynamics of world steel prices since 1980, including the drop in prices during the crisis of 2009. The simplicity of the model allows for use in the calculation of price targets for strategic busi ness plans in metallurgical companies.Approaches to price forecasting in the steel industry. The lack of popular intuitive prediction methods among practitioners in the metallurgical industry [1] results in a situation when experts frequently substitute forecasts by wishful thinking, i.e., instead of forecasting what is going to be, they express their anticipations. In addi tion, a considerable amount of time is spent on the coordination of different trends (the growth rate of steel consumption, steel prices, raw material prices, etc.). Formal methods allow for the elimination of the above mentioned subjective judgement and additional labor costs through the identification of regularities in the object to be forecast through the construction of eco nomic models.In the process of price forecasting (in virtually every industry), we can distinguish the following approaches to model construction:(1) decomposition of price time series: detection of the trend, autocorrelation, seasonal and random com ponents;(2) factor analysis based on a regression model: allo cation of essential price f...
Сланцевая нефть: потенциал добычи как функция ее цены Маланичев А.Г.В работе предложен новый подход к краткосрочному прогнозированию добычи нефти на сланцевых месторождениях в США. Он основан на аналитическом решении дифференциального уравнения добычи. Введено понятие потенциала добычи, которое соответствует экспоненциальной, гармонической и гиперболической кривым снижения базовой добычи из отдельной скважины. Потенциал добычи оценивается с помощью стационарного решения дифференциального уравнения. Его значение отвечает на вопрос, какой объем добычи будет достигнут с течением времени при прочих равных условиях (постоянном количестве буровых установок, их производительности и скорости снижения базовой добычи).Показано, что добыча на сланцевых месторождениях США наилучшим образом описывается гармоническим потенциалом с лагом в 6 месяцев. Использование данного потенциала позволяет свести дифференциальное уравнение добычи к уравнению Риккати и выразить решение задачи Коши в элементарных функциях.Устойчивость предложенного метода прогнозирования добычи исследована с помощью анализа чувствительности к изменению цены нефти марки WTI. Установлено, что потенциал и объем добычи сланцевой нефти пропорциональны квадратному корню из количества буровых установок и, значит, цене нефти. Это может свидетельствовать о некотором завышении ожиданий роста добычи сланцевой нефти исследователями, которые используют линейные модели и, следовательно, занижении ожиданий роста цены на нефть.Точность прогноза объемов добычи оценена на ретроспективной выборке с января 2015 г. по март 2017 г. Среднемодульная ошибка прогноза для 6-и 12-месячного горизонтов составила 1,5% и 3,3% соответственно. Эти характеристики на 45% лучше, чем точность, достигнутая c помощью прогноза на данных EIA.Ключевые слова: добыча; сланцевая нефть; прогноз; дифференциальные уравнения.
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