Modelling and forecasting the trends of life cycle curves in the production of non-renewable resources

Semenychev, V. K.; Kurkin, E. I.; Semenychev, E.V.

doi:10.1016/j.energy.2014.07.063

Cited by 15 publications

(7 citation statements)

References 21 publications

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“…= " + " exp (− ( − " ) . ); -new model of bellshaped trend as rational function multiplied by cumulative Verhulst logist for bell asymmetry (Semenychev, Kurkin, & Semenychev, 2014):…”

Section: Methodsmentioning

confidence: 99%

Cyclicity In Russian Regional Economy Sectors: Models And Results

Korobetskaya¹,

Semenychev²,

Semenychev³

2020

European Proceedings of Social and Behavioural Sciences

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“…= " + " exp (− ( − " ) . ); -new model of bellshaped trend as rational function multiplied by cumulative Verhulst logist for bell asymmetry (Semenychev, Kurkin, & Semenychev, 2014):…”

Section: Methodsmentioning

confidence: 99%

Cyclicity In Russian Regional Economy Sectors: Models And Results

Korobetskaya¹,

Semenychev²,

Semenychev³

2020

European Proceedings of Social and Behavioural Sciences

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“…There are several curve-fitting methods in this field for small sample data, such as genetic algorithms [22]. The use of artificial neural networks combined with statistical methods to compensate drawbacks of the separate approaches in trend forecasting leads to better classification and approximation results.…”

Section: Production Trend Forecast Methodsmentioning

confidence: 99%

Production trend identification and forecast for shop-floor business intelligence

Viharos

Csanaki²,

Nacsa

et al. 2016

ACTA IMEKO

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<p>The paper introduces a methodology to define production trend classes and also the results to serve with trend prognosis in a given manufacturing situation. The prognosis is valid for one, selected production measure (e.g. a quality dimension of one product, like diameters, angles, surface roughness, pressure, basis position, etc.) but the applied model takes into account the past values of many other, related production data collected typically on the shop-floor, too. Consequently, it is useful in batch or (customized) mass production environments. The proposed solution is applicable to realize production control inside the tolerance limits to proactively avoid the production process going outside from the given upper and lower tolerance limits.</p><p>The solution was developed and validated on real data collected on the shop-floor; the paper also summarizes the validated application results of the proposed methodology.</p>

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“…Geological and technical factors are taken into account. [Hubbert, 1956;Semenychev et. al., 2014;Malanichev, 2017a;Kozlov, 2018] 5 Solving a differential equation with a lagging argument Analysis of conditions leading to economic fluctuations.…”

Section: No Approach Description Sourcesmentioning

confidence: 99%

“…The flaws of this approach include its insufficient consideration of major oil production limitations (such as the need to maintain material balance and natural production decline). Production profiles are set a priori, while the quality of empirical data approximations is verified using mathematical procedures [Semenychev et al, 2014]. To deal with these shortcomings, [Malanichev, 2017a] proposed an ordinary differential equation that describes oil production growth taking into account the need to maintain material balance in oil reservoirs and the natural production decline.…”

mentioning

confidence: 99%

Limits of Technological Efficiency of Shale Oil Production in the USA

Malanichev¹

2018

Foresight and STI Governance

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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.

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Modelling and forecasting the trends of life cycle curves in the production of non-renewable resources

Cited by 15 publications

References 21 publications

Cyclicity In Russian Regional Economy Sectors: Models And Results

Cyclicity In Russian Regional Economy Sectors: Models And Results

Production trend identification and forecast for shop-floor business intelligence

Limits of Technological Efficiency of Shale Oil Production in the USA

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