Intermediate Oil Research Articles

A novel crude oil prices forecasting model based on secondary decomposition

Crude oil prices have risen sharply and plummeted in recent decades. Therefore, its effective forecasting faces great difficulties and challenges. In this paper, a novel crude oil prices forecasting model based on secondary decomposition with improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), state space correlation entropy (SSCE), improved variational mode decomposition by tunicate swarm algorithm (TVMD), and improved kernel based extreme learning machine by artificial gorilla troops optimizer (GTO-KELM), named ICEEMDAN-SSCE-TVMD-GTO-KELM, is proposed. Firstly, ICEEMDAN is applied to decompose the original data and the state space correlation entropy is applied to determine the components with higher complexity. Secondly, the component with the highest complexity is decomposed again, which can decompose the nonstationary, nonlinear and highly complex time series into multiple more regular sub-series. Finally, the KELM with parameter optimization is applied to forecast all the components, and the forecast values are reconstructed to obtain the final forecast results. The data of West Texas Intermediate (WTI) and Brent oil are used in the experiments and Diebold Mariano test. Experiments show that the proposed secondary decomposition forecasting model has high forecasting accuracy. Taking WTI as an example, RMSE, MAE, MAPE and R2 of the proposed model are 0.2947, 0.2133, 0.3665 and 0.9939, respectively, which are better than those of all 9 forecasting models including the one-time decomposition model and the direct forecasting model at the 95% confidence level, demonstrating the promising application of the proposed method in crude oil prices forecasting.

Price discovery efficiency of China's crude oil futures: Evidence from the Shanghai crude oil futures market

This study investigates the pricing efficiency of the Shanghai Crude Oil Futures (SC) as a newly emerged crude oil future market from the perspective of co-integration and estimates its contribution to price discovery in China based on the vector error correction model (VECM), permanent–transitory (PT), information share (IS), and modified information share (MIS) models. All results show a long-run equilibrium relationship exists between crude oil futures and spot prices in China, but SC contributes just approximately 50% to price discovery, which is roughly equal to that of the spot market. Compared with West Texas Intermediate (WTI) and Brent crude oil markets, the pricing efficiency of SC lags significantly, which means that SC has a certain capability of price discovery, although it is now still in its initial development stage as the price taker of the international crude oil market.

Coreflood-on-a-Chip Investigation of Oil Bank Formation in Low Interfacial Tension Chemical Floods at Favorable and Unfavorable Viscosity Ratios

Summary An efficient tertiary chemical flood involves the chemicals displacing the oil in a stable manner, so the initially disconnected trapped oil ganglia coalesce into a bank as the flood progresses. However, the location of surfactants and polymers in the core during a chemical flood is difficult or impossible to determine at very fine time and space resolutions. In this work, we bridge this gap by visualizing saturation profiles in a coreflood-on-a-chip, a micromodel that is the same length as a core, and monitoring fluorescent aqueous injectant as displacements occur. We visualize, for the first time, surfactants in and around oil banks at the pore and Darcy scales during low-tension displacements. We present 12 chemical floods in the coreflood-on-a-chip, imaged at the centimeter scale, including six surfactant and six alkali-surfactant-polymer (ASP) floods at low, intermediate, and high initial water saturations. Additionally, we present a micron-scale visualization of an ASP flood under UV light to observe the distribution of surfactant around an oil bank at the pore scale. We found that oil banks formed even during very unfavorable displacements, when surfactant solution (1 cp) displaced oil (80 cp) in a micromodel at intermediate and residual oil saturations. Although our results show saturation profiles are mostly well described by fractional flow theory, the distribution of injected aqueous phase is not. Significant aqueous injectant, including surfactant, flows within and ahead of oil banks.

Oral delivery of stem-cell-loaded hydrogel microcapsules restores gut inflammation and microbiota.

Mesenchymal stem cells (MSCs) are an attractive candidate for the treatment of inflammatory bowel disease (IBD), but their poor delivery rate to an inflamed colon is a major factor hampering the clinical potential of stem cell therapies. Moreover, there remains a formidable hurdle to overcome with regard to survival and homing in to injured sites. Here, we develop a strategy utilizing monodisperse hydrogel microcapsules with a thin intermediate oil layer prepared by a triple-emulsion drop-based microfluidic approach as an in-situ oral delivering carrier. The oral delivery of stem-cell-loaded hydrogel microcapsules (SC-HM) enhances MSC survival and retention in the hostile stomach environment due to the intermediate oil layer and low value of the overall stiffness, facilitating programmable cell release during gastrointestinal peristalsis. SC-HM is shown to induce tissue repair, reduce the colonic macrophage infiltration responsible for the secretion of the pro-inflammatory factors, and significantly mitigate the severity of IBD in a mouse model, where MSCs released by SC-HM successfully accumulate at the colonic crypt. Moreover, a metagenomics analysis reveals that SC-HM ameliorates the dysbiosis of specific bacterial genera, including Bacteroides acidifaciens, Lactobacillus (L.) gasseri, Lactobacillus reuteri, and L. intestinalis, implying optimization of the microorganism's composition and abundance. These findings demonstrate that SC-HM is a potential IBD treatment candidate.

Chemical characterization of oil and water accommodated fraction (WAF) at different temperatures

In this work, the formation and the chemical composition of Water Accommodated Fractions (WAFs) of Intermediate Fuel Oil 180, Naphthenic North Atlantic, and Marine Gas-Oil oils were studied at different temperatures (between 5 and 25 °C) with and without a dispersant (Finasol OSR52). In terms of composition, both the raw oils and the WAFs showed high levels of lighter PAHs (naphthalene related compounds). The WAFs were prepared under slow stirring rate to avoid the formation of droplets. However, the use of dispersant increased the solubility of the PAHs around two orders of magnitude through the formation of small droplets. The kinetics of the WAF formation was followed by direct measurement on the water phase, and through the accumulation on PDMS sheets and commercial stir-bars. This study showed that the water phase achieved the steady state after 48 h of continuous stirring, while the PDMS sheet and Twister required slightly longer times. It was also observed that the solubility of the PAHs increased with the temperature, almost doubling the concentrations in the studied ranged but when the dispersant was used, only Naphthenic North Atlantic crude oil followed this pattern. Finally, the stability of WAF was monitored at room temperature and it was concluded that the concentrations halved after 24–30 h, regardless the use of dispersant. Therefore, for WAF-based toxicity testing, it is important to renew these solutions at least once or twice a day to assure the stable concentrations along the bioassay.

Oil supply news shock and Chinese economy

This paper studies the effects of the oil supply news shock on the Chinese economy using a novel approach as newly proposed in Känzig (2021). Specifically, we use the changes of West Texas Intermediate oil futures prices around OPEC meeting announcements as a high-frequency instrument in a structural VAR model to identify the oil supply news shock. Our results suggest that the Chinese domestic economy is not affected significantly by the shock in terms of industrial production and CPI, two important macroeconomic indicators. However, due to the global features of the international trade, China's exchange rate and trade balance respond to the shock.

Regulation Effects of Beeswax in the Intermediate Oil Phase on the Stability, Oral Sensation and Flavor Release Properties of Pickering Double Emulsions.

Double emulsions (W/O/W) with compartmentalized structures have attracted a lot of research interests due to their diverse applications in the food industry. Herein, oil phase of double emulsions was gelled with beeswax (BW), and the effects of BW mass ratios (0–8.0%) on the stability, oral sensation, and flavor release profile of the emulsions were investigated. Rheological tests revealed that the mechanical properties of double emulsions were dependent on the mass ratio of BW. With the increase in BW content, double emulsions showed a higher resistance against deformation, and lower friction coefficient with a smoother mouthfeel. Turbiscan analysis showed that the addition of BW improved the stability of double emulsions during a 14 days’ storage, under freeze–thawed, and osmotic pressure conditions, but it did not improve the heating stability of double emulsions. The addition of BW contributed to lower air-emulsion partition coefficients of flavor (2,3-diacetyl) compared to those without the addition of BW at 20 °C and 37 °C, respectively. Furthermore, the addition of BW and its mass ratio significantly altered the flavor release behavior during the open-bottle storage of double emulsions. The response value of 0% BW dropped sharply on the first day of opening storage, showing a burst release behavior. While a slow and sustained release behavior was observed in double emulsions with 8.0% BW. In conclusion, gelation of the intermediate oil phase of double emulsions significantly enhanced the stability of double emulsions with tunable oral sensation and flavor release by varying the mass ratio of beeswax.

Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine

Crude oil market analysis has become one of the emerging financial markets and the volatility effect of the market is paramount and has been considered as an issue of utmost importance. This study examines the dynamics of this volatile market of crude oil by employing a hybrid approach based on an extreme learning machine (ELM) as a regressor and the improved grey wolf optimizer (IGWO) for prophesying the crude oil rate for West Texas Intermediate (WTI) and Brent crude oil datasets. The datasets are augmented using technical indicators (TIs) and statistical measures (SMs) to obtain better insight into the forecasting ability of this proposed model. The differential evolution (DE) strategy has been used for evolution and the survival of the fittest (SOF) principle has been used for elimination while implementing the GWO to achieve better convergence rate and accuracy. Whereas, the algorithmic simplicity, use of less parameters, and easy implementation of DE efficiently decide the evolutionary patterns of wolves in GWO and the SOF principle updates the wolf pack based on the fitness value of each wolf, thereby ensuring the algorithm does not fall into local optimum. Furthermore, the comparison and analysis of the proposed model with other models, such as ELM–DE, ELM–Particle Swarm Optimization (ELM–PSO), and ELM–GWO shows that the predictability evidence obtained substantially achieves better performance for ELM–IGWO with respect to faster error convergence rate and mean square error (MSE) during training and testing phases. The sensitivity study of the proposed ELM–IGWO provides better results in terms of the performance measures, such as Theil’s U, mean absolute error (MAE), average relative variance (ARV), mean average percentage error (MAPE), and minimal computational time.

Oil and renewable energy stock markets: Unique role of extreme shocks

Various environmental issues and destructive disasters have driven the attention of renewable energy sources to an unprecedented level. Based on extreme shocks, this paper mainly examines the causal relations of oil and renewable energy markets from the perspectives of time and frequency domains. We find the causal relations of Wilder Hill Clean Energy index (NEX) and West Texas Intermediate oil futures nearly do not exist in normal shocks but mainly exist among the extreme shocks, especially during the long-term and short-term horizons. These results are robust considering alternative oil futures benchmark and alternative renewable energy stocks. Interestingly, during the COVID-19 pandemic, we find that significant causal relations mainly exist among the extreme shocks of NEX and WTI, but the relations are weaker. Our paper aims to disclose new insights into oil and renewable energy stock markets, which are of vital importance to related enterprises, market participants, policy makers, and scholars.

Modulation of the spatial distribution of crystallizable emulsifiers in Pickering double emulsions

The unique role of the spatial distribution of crystallizable emulsifiers in regulating the structure and properties of double emulsions has been gradually recognized. Herein, we utilized crystallizable monoglycerides of different carbon chain length (GMS/GMP/GML) to “structuring” the intermediate oil phase of double emulsions during a two-stage emulsification process followed by a cooling treatment. A ternary eigenvector (I, M, E) based on the numerical processing of polarization images was invented to quantitatively characterize the distribution pattern of monoglycerides. Crystallization kinetics analysis and dissipative particle dynamic simulation were then employed to reveal the regulatory mechanism for the site-specific interface distribution behavior. Results suggested that the distribution pattern of monoglycerides could be pricesly tuned as the internal interface-, external interface- or oil-phase dominated one in double emulsions. The surface activity as well as crystallization rates of monoglycerides dominated the interfacial distribution kinetics, and the cooling gradient along the interface region further regulated their interfacial distribution potential. Specificly, shorter crystallization time (t1/2) made GMP molecules rapidly solidified in oil phase, leading to the oil phase domninate crystallization (0, M, 0), whereas, slow crystallization rate rendered GML and GMS with sufficient time to diffuse to the interface, thus forming interfacial crystals ((I, 0, 0) and (0, 0, E)). The sensitivity of GML to cooling gradient along the interface region led to its preferential external interface distribution under cooling treatment. The presented study explored novel strategies that can be used in characterizing and manipulating the distribution pattern of crystallizable emulsifiers in multi-interface emulsion systems.

Predicting the changes in the WTI crude oil price dynamics using machine learning models

This study aims to use a monthly dataset from 1991 to 2021 to predict West Texas Intermediate (WTI) oil price dynamics using U.S. macroeconomic and financial factors, as well as a global crisis and crashes. We used advanced machine learning models such as Logistic Regression, Decision Tree, Random Forest, AdaBoost, and XgBoost in this study. According to the results, the XgBoost and Random Forest models outperform traditional models. We also used DeLong statistical test procedures to accurately compare machine learning models' performance. In addition, the study used SHAP - SHapley Additive exPlanations values to support model evaluation and interpretability. This new outline highlights the critical features of the WTI crude oil price prediction and provides appropriate model explanations by utilizing the practical SHAP values. The empirical findings showed that machine learning models could successfully and accurately predict the trend of WTI crude oil price changes. Our findings are important for policymakers, companies, and investors, as well as long-term energy-based economic development.

Prediction of the removal of aqueous phase during boiling of highly stable water-hydrocarbon emulsions

The removal of water from highly stable natural and technogenic water-hydrocarbon emulsions is studied. For various crudes (oil waste blends, pyrolysis resin emulsion, lake oil sludge, intermediate oil layer) dehydration end temperatures, leading to complete stabilized evaporation of water without foaming, are defined (water content is less than 1% wt.). The end dehydration temperature varies within a wide range (109-150 °C). The parameters of hydrocarbon phase, which indirectly reflect the stability to removal of water by evaporation during thermomechanical dehydration and help to predict final dehydration temperature, such as, density, coking capacity, pour point and viscosity, are suggested.

Enhanced Biofuel Production via Catalytic Hydropyrolysis and Hydro-Coprocessing

In order to successfully integrate biomass pyrolysis oils as starting materials for conventional oil refineries, upgrading of the pyrolysis oils is needed to achieve desired properties, something which can be performed either as part of the pyrolysis process and/or by separate catalytic treatment of the pyrolysis intermediate oil products. In this study, the quality of stem wood-derived pyrolysis oil was improved via ex situ catalytic hydropyrolysis in a bench-scale pyrolyzer (stage 1), followed by catalytic hydro-coprocessing with fossil co-feed in a laboratory-scale high pressure autoclave (stage 2). The effect of pyrolysis upgrading conditions was investigated based on the quality of intermediate products and their suitability for hydro-coprocessing. HZSM-5 and Pt/TiO2 catalysts (400 °C, atmospheric pressure) were employed for ex situ pyrolysis, and the NiMoS/Al2O3 catalyst (330 °C, 100 bar H2 initial pressure) was used for hydro-coprocessing of the pyrolysis oil. The application of HZSM-5 in the pyrolysis of stem wood under a N2 atmosphere decreased the formation of acids, ketones, aldehydes, and furans and increased the production of aromatic hydrocarbons and phenolics (guaiacols and phenols). Replacing HZSM-5 with Pt/TiO2 and N2 with H2 resulted in complete conversion of guaiacols and significant production of phenols, with further indications of increased stability and reduced coking tendencies.

Stock returns, oil prices and leverage: evidence from US firms

PurposeThis paper aims to investigate how the relation between stock returns of US firms and West Texas Intermediate (WTI) oil prices is affected by leverage from 1990 to 2020.Design/methodology/approachThis paper examines how the relationship between stock returns of US firms and WTI oil prices is affected by leverage from 1990 to 2020 using a fixed-effect model estimation framework.FindingsResults from the fixed-effect regression models suggest that leverage effects on stock returns are pervasive both in aggregate and cross-industry levels, while the mining industry is more sensitive. In addition to the positive oil price effects attenuated by leverage at the aggregate level, the authors observe stronger marginal effects of leverage only for the mining sector. Being more exposed to commodity prices, the positive effects of oil prices on stock returns in the mining sector are offset by large debt ratios. Asymmetries, effects of debt maturity structure and implications are also discussed.Research limitations/implicationsThis study is grounded on the contemporary cash flow claim of leverage NOT on the long-run effect of leverage considering cash flow constraints. The oil price increase is assumed to represent an advancement of the overall economy. This study does not capture the oil prices response to some other economic forces and vice-versa.Practical implicationsMining companies should therefore reduce the stock of debt with respect to their assets to make possible the “pass-through” from oil prices to the stock market.Originality/valuePreviously undocumented and the authors show that leverage reduces the total effect of oil prices on stock returns, consistent with the hypothesis. Asymmetric and debt maturity structures effects are also discussed.

Offshore field experiments with in-situ burning of oil: Emissions and burn efficiency

In situ burning (ISB) is an oil spill response technique including ignition and burning to remove oil on the water surface. The technique rapidly and effectively removes large portions of the oil. However, the combustion process causes a large smoke plume and leaves a viscous residue in the water. During six large-scale experimental burns in the North Sea in 2018 and 2019, the smoke plume, released oil and contained residues were analysed. The objectives were to document the content of particles and gases in the smoke plume, properties of both the released oils and residues, and the effectiveness of the burns. Oseberg crude oil, Ultra Low Sulphur Fuel Oil (ULSFO), Intermediate Fuel Oil (IFO180) and Marine Gas Oil (MGO) were released into a fire-boom and ignited. Particles and gases in the smoke plume were monitored using drones with several sensors. Soot particle monitoring indicated that more than 90% of the particles produced during the burns were <1 μm. Soot fallout was mainly limited to visible smoke, and the particle concentration was highest directly under the smoke plume and declined with distance from the burn. Gas monitoring in the smoke indicated low concentrations of SO2 and NOX (<2 ppm), and the concentrations of CO2 and CO were within air quality standards. Black Carbon produced relative to the amount of oil burned was 10–18%. The burn efficiency varied and were estimated to 80–91% for Oseberg, >90% for MGO, and <60% for both ULSFO and IFO180. The present paper addresses the results of the smoke plume monitoring, properties of the ISB residues and the burn efficiency.

Effect of rare disaster risks on crude oil: evidence from El Niño from over 145 years of data

We extend the literature on the effect of rare disaster risks on commodities by examining the effect of the El Niño-Southern Oscillation (ENSO) on crude oil via the recently developed kth order nonparametric causality-in-quantile framework, utilizing a long-range historical data set spanning the period 1876:01 to 2021:04. The methodology allows us to test for the predictive role of ENSO over the entire conditional distribution of not only real oil returns but also its volatility, by controlling for misspecification due to uncaptured nonlinearity and regime changes. Empirical findings show that the Southern Oscillation Index (SOI), measuring the ENSO cycle, not only predicts real West Texas Intermediate (WTI) oil returns of the United States (US), but also volatility, over the entirety of the respective conditional distributions. The findings highlight the role of rare disaster risks over not only financial markets, but also commodities with significant implications for policymakers and investors.

The asymmetric and multi-scale volatility correlation between global oil price and economic policy uncertainty of China.

With the monthly data of WTI oil price and economic policy uncertainty (EPU) of China from January 2000 to August 2020, this paper detailedly investigates the asymmetric volatility correlations between two types of EPU of China and global oil price in different time scales. The empirical results demonstrate that the volatility correlation between EPU of China and West Texas Intermediate (WTI) oil price is mainly reflected in the monetary policy uncertainty (MPU), while that of fiscal policy uncertainty (FPU) is much weaker. Specifically speaking, the volatility correlation between MPU of China and downward WTI oil price is significantly negative in the short-middle term (4–8 months) and changes to positive in the middle-long term (8–16 months), while that of upward WTI oil price is only significantly positive in the long term (16–32 months). Our findings provide a deeper understanding of the oil price-EPU correlation in China, and can be valuable guidance for diversified market participants such as government policy-makers and global investors.

Forecasting oil futures price volatility with economic policy uncertainty: a CARR-MIDAS model

ABSTRACT In this paper, we propose a conditional autoregressive range-mixed-data sampling (CARR-MIDAS) model that incorporates economic policy uncertainty (EPU) to predict the crude oil futures price volatility (range). We apply the proposed model to West Texas Intermediate (WTI) oil futures price ranges and four EPU indices, namely the Global EPU, US EPU, China EPU and Russia EPU. Empirical results show that all the four EPU indices have a significantly negative impact on the oil futures price volatility, and the EPU indices are informative for forecasting the oil futures price volatility. Moreover, the China EPU index outperforms the other EPU indices in forecasting the oil futures price volatility.

Influence of dispersant application on the toxicity to sea urchin embryos of crude and bunker oils representative of prospective oil spill threats in Arctic and Sub-Arctic seas

This study deals with the toxicity assessment of crude and bunker oils representative of prospective oil spill threats in Arctic and Sub-Arctic seas (NNA: Naphthenic North-Atlantic crude oil; MGO: Marine Gas Oil; IFO: Intermediate Fuel Oil 180), alone or in combination with a third-generation dispersant (Finasol OSR52®). Early life stages of sea urchin, Paracentrotus lividus, were selected for toxicity testing of oil low-energy water accommodated fractions. A multi-index approach, including larval size increase and malformation, and developmental disruption as endpoints, was sensitive to discriminate from slight to severe toxicity caused by the tested aqueous fractions. IFO (heavy bunker oil) was more toxic than NNA (light crude oil), with MGO (light bunker oil) in between. The dispersant was toxic and further on it enhanced oil toxicity. Toxic units revealed that identified PAHs were not the main cause for toxicity, most likely exerted by individual or combined toxic action of non-measured compounds.

A novel multiscale forecasting model for crude oil price time series

Forecasting crude oil prices is an essential research field in the international bulk commodities market. However, price movements present more complex nonlinear behavior due to an increasingly diverse range of risk factors. To achieve better accuracy, this study explores a novel multiscale hybrid paradigm to estimate crude oil prices. The method takes advantage of the variational mode decomposition method to decompose the crude oil price into several simple models, which can be explained using regular factors, irregular factors and trends. Data characteristic analysis is conducted to identify the complexity of different components of the time series. It is important for a multiscale model to select an appropriate model to produce the optimal forecasts. Thus, the final forecasted values are generated by reconstituting all these forecasting items. By investigating the West Texas Intermediate and Brent crude oil prices, this paper presents how data characteristic identification and analysis are conducted in a multiscale paradigm. The empirical analysis proves that the proposed model can achieve superior forecasting results, which indicates the effectiveness of the multiscale model at forecasting complex time series, especially crude oil prices.