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This study investigates the relationship between consumer sentiment (CONS), inflation expectations (INEX) and international energy prices, drawing on principles from behavioral. We focus on Brent crude oil price and Henry Hub natural gas prices as key indicators of energy market dynamics. Based on the monthly data from January 2003 to March 2023, three wavelet methods are applied to examine the time-frequency linkage, while the nonlinear distributed lag model (NARDL) is used to verify the asymmetric impact of two factors on energy prices. The results highlight a substantial connection between consumer sentiment, inflation expectations and international energy prices, with the former in the short term and the latter in the medium to long term. Especially, these correlations are particularly pronounced during the financial crisis and global health emergencies, such as the COVID-19 epidemic. Furthermore, we detect short-term asymmetric effects of consumer sentiment and inflation expectations on Brent crude oil price, with the negative shocks dominating. The positive effects of these factors on oil prices contribute to observed long-term asymmetry. In contrast, inflation expectations have short-term and long-run asymmetric effects on natural gas price, and both are dominated by reverse shocks, while the impact of consumer sentiment on natural gas prices appears to be less asymmetric. This study could enrich current theories on the interaction between the international energy market and serve as a supplement to current literature. [ABSTRACT FROM AUTHOR]

This study explores the time-varying effects of crude oil prices (OP) and gold prices (GP) on the Turkish stock market using a weekly data series from November 26, 1989 to July 10, 2022. For this purpose, we develop a new hybrid technique, the rolling window-based nonparametric quantile causality test, which allows the investigation of time-varying causality at various quantiles. The results reveal that (i) under all market conditions, there is time-varying causality from crude OP and GP to Turkish stock market returns (SMR) and volatility. (ii) The causal effects of both crude OP and GP on stock market volatility are larger than their causal effects on SMR. (iii) The crude OP have a greater impact on SMR than the GP, while the GP has a greater impact on stock market volatility than the crude OP. (iv) Both crude OP and GP have the strongest (least) causal impact on SMR and volatility under normal (bullish) market conditions. (v) Crude OP and GP have a greater impact on stock market volatility than on stock returns under all market conditions. Overall, our results highlight that OP and GP have a strong impact on the Turkish stock market, and this impact varies by returns and volatility. Therefore, financial investors should consider the volatility of crude OP and GP in the Turkish stock market. [ABSTRACT FROM AUTHOR]

Crude oil is an essential energy source that affects international trade, transportation, and manufacturing, highlighting its importance to the economy. Its future price prediction affects consumer prices and the energy markets, and it shapes the development of sustainable energy. It is essential for financial planning, economic stability, and investment decisions. However, reaching a reliable future prediction is an open issue because of its high volatility. Furthermore, many state-of-the-art methods utilize signal decomposition techniques, which can lead to increased prediction time. In this paper, a model called K-means-dense-sparse-dense long short-term memory (K-means-DSD-LSTM) is proposed, which has three main training phrases for crude oil price forecasting. In the first phase, the DSD-LSTM model is trained. Afterwards, the training part of the data is clustered using the K-means algorithm. Finally, a copy of the trained DSD-LSTM model is fine-tuned for each obtained cluster. It helps the models predict that cluster better while they are generalizing the whole dataset quite well, which diminishes overfitting. The proposed model is evaluated on two famous crude oil benchmarks: West Texas Intermediate (WTI) and Brent. Empirical evaluations demonstrated the superiority of the DSD-LSTM model over the K-means-LSTM model. Furthermore, the K-means-DSD-LSTM model exhibited even stronger performance. Notably, the proposed method yielded promising results across diverse datasets, achieving competitive performance in comparison to existing methods, even without employing signal decomposition techniques. [ABSTRACT FROM AUTHOR]

This paper investigated the use of linear models to forecast crude oil futures prices (WTI) on a monthly basis, emphasizing their importance for financial markets and the global economy. The main objective was to develop predictive models using time series analysis techniques, such as autoregressive (AR), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), as well as ARMA variants adjusted by genetic algorithms (ARMA-GA) and particle swarm optimization (ARMA-PSO). Exponential smoothing techniques, including SES, Holt, and Holt-Winters, in additive and multiplicative forms, were also covered. The models were integrated using ensemble techniques, by the mean, median, Moore-Penrose pseudo-inverse, and weighted averages with GA and PSO. The methodology adopted included pre-processing that applied techniques to ensure the stationarity of the data, which is essential for reliable modeling. The results indicated that for one-step-ahead forecasts, the weighted average ensemble with PSO outperformed traditional models in terms of error metrics. For multi-step forecasts (3, 6, 9 and 12), the ensemble with the Moore-Penrose pseudo-inverse showed better results. This study has shown the effectiveness of combining predictive models to forecast future values in WTI oil prices, offering a useful tool for analysis and applications. However, it is possible to expand the idea of applying linear models to non-linear models. [ABSTRACT FROM AUTHOR]

Recent theoretical developments in economics distinguish between risk and ambiguity (Knightian uncertainty). Using state-of-the-art methods with intraday stock market data from February 1993 to February 2021, we derive financial ambiguity and empirically examine the effect of shocks to it on the price and volatility of crude oil. We provide evidence that ambiguity carries important information about future oil returns and volatility perceived by investors. We validate these results using Granger causality and in-sample and out-of-sample forecasting tests. Our findings reveal that financial ambiguity is a possible factor that explains future drops in oil prices and their increased variability. Our findings will benefit scholars and investors interested in how financial ambiguity shapes short-term oil prices. [ABSTRACT FROM AUTHOR]

This study aims to identify the factors most likely to affect renewable energy consumption (REC) across mostly homogenous country groups worldwide. Classifying countries into a relatively homogenous group is taken from their economic and social development level measured with the Human Development Index. We delimited highly, medium-, and low-developed countries and checked whether the sets of determinants for using renewable energy sources are the same. We constructed a panel dataset as a basis for the panel Bayesian model averaging (panel BMA) as a factor selection method. The most likely factors were found and compared between the groups of countries. Then, the panel fixed-effects models for each country group were estimated. The results allowed us to confirm that CO2 per capita emissions, terms of trade, GDP, foreign direct investment, crude oil price, and energy consumption from alternative sources are the most critical drivers of REC in group I. The most important factors in group II are CO2 per capita, labor force, forest area, and gas and coal consumption. In the third group, REC consumption differs from that of the more advanced groups and strongly depends on foreign direct investment inflow. The results allow the formulation of policy recommendations on a global scale. [ABSTRACT FROM AUTHOR]

Electric scooters are increasingly gaining popularity in India owing to rising global crude oil prices and rising levels of vehicular pollution. Most of them are currently powered by expensive in-wheel (IW) permanent magnet (PM) brushless DC motors. Owing to their simplicity, and ruggedness while being cost-effective (since they do not employ PMs), switched reluctance motors (SRMs) are a viable alternative. Despite these benefits, SRMs possess drawbacks such as low torque density and inferior efficiency. Recently, a multi-teeth (MT) SRM with an improved performance was reported. However, the design of MTSRM topologies and their electromagnetic performance have not been explored sufficiently. In this paper, a design formula governing the selection of the number of MT and rotor poles for MTSRMs has been proposed. Using this, a novel four-phase 8/18 IW-MTSRM is derived and proposed for an E-scooter. The characteristics of the proposed SRM are numerically compared with a conventional 8/10 SRM based on magnetic characteristics, efficiencies and steady-state operation for the complete torque-speed range. Results indicate that the proposed 8/18 MTSRM has a higher peak torque capacity, torque density, superior drive cycle efficiency and reduced torque ripple. Further, the FEA model is validated experimentally on a downsized 8/18 MTSRM prototype. [ABSTRACT FROM AUTHOR]

We build and estimate a dynamic, structural model of the world oil market to quantify the impact of the shale revolution. We model the shale revolution as a decrease in shale production costs and find that the resultant increase in shale production lowers oil prices by 24% in the short run and 48% once the shale oil transition is complete. Current oil price volatility is lowered by 8% to 23% depending on the horizon. We also find that OPEC core acts to keep its market share constant in the face of the dramatic increase in shale production. [ABSTRACT FROM AUTHOR]

Recently, there has been an increased focus on enhancing the accuracy of machine learning techniques. However, there is the possibility to improve it by selecting the optimal tuning parameters, especially when data heterogeneity and multicollinearity exist. Therefore, this study proposed a statistical model to study the importance of changing the crude oil prices in the European Union, in which it should meet state-of-the-art developments on economic, political, environmental, and social challenges. The proposed model is Elastic-net quantile regression, which provides more accurate estimations to tackle multicollinearity, heavy-tailed distributions, heterogeneity, and selecting the most significant variables. The performance has been verified by several statistical criteria. The main findings of numerical simulation and real data application confirm the superiority of the proposed Elastic-net quantile regression at the optimal tuning parameters, as it provided significant information in detecting changes in oil prices. Accordingly, based on the significant selected variables; the exchange rate has the highest influence on oil price changes at high frequencies, followed by retail trade, interest rates, and the consumer price index. The importance of this research is that policymakers take advantage of the vital importance of developing energy policies and decisions in their planning. [ABSTRACT FROM AUTHOR]

This study investigates the extent and efficiency of global energy transition by examining the impact of renewable electricity generation on non-renewable electricity generation in 73 economies from 1990 to 2020. This is done to empirically scrutinise the assumption of perfect displacement as adopted by Intergovernmental Panel on Climate Change and other environmental and economic analysts. The estimated models also control for the effects of globalisation, economic growth, and international crude oil prices. The PMG-ARDL estimation demonstrates that renewable energy is able to partly displace the non-renewable energy and that the global energy transition is far from perfect displacement. More specifically, the coefficient of renewable energy generation is − 0.157, indicating that more than 6 units of alternative energy are necessary for displacing one unit of fossil fuel energy at the global level. However, the findings reveal that globalisation, economic growth, and crude oil prices negatively influence the energy transition by pushing up the non-renewable energy generation. To delve into finer details, the ARDL findings uncover that 1% increase in globalisation, economic growth, and crude oil prices increases the non-renewable energy generation by 0.88%, 0.75%, and 0.03%, respectively. The Driscoll–Kraay and feasible generalised least squares estimation techniques also confirm these baseline findings. Potential policy implications for enhancing the efficiency of global energy transition are also discussed. [ABSTRACT FROM AUTHOR]

The subject of predicting global crude oil prices is well recognized in academic circles. The notion of hybrid modeling suggests that the integration of several methodologies has the potential to optimize advantages while reducing limitations. Consequently, hybrid techniques are extensively used in contemporary research. In this paper, a novel decompose-ensemble prediction approach is proposed by integrating various optimization algorithms, namely biography-based optimization (BBO), backtracking search algorithm (BSA), teaching-learning-based algorithm (TLBO), cuckoo optimization algorithm (COA), multi-verse optimization (MVO), and multilayer perceptron (MLP). Furthermore, the aforementioned approaches, namely BBO-MLP, BSA-MLP, and TLBO-MLP, include the de-compose-ensemble technique into the individual artificial intelligence model in order to enhance the accuracy of predictions. In order to validate the findings, the forecast is conducted using the authoritative data on oil prices. This study will use three primary indicators, including EMA 20, EMA 60, EMA 100, ROC, and AUC assessments, to assess and evaluate the efficacy of the five methodologies under investigation. The below findings are derived from the conducted research: Based on the achieved AUC values of 0.9567 and 0.9429, it can be concluded that using a multi-verse optimization technique is considered the most suitable strategy for effectively handling the dataset pertaining to crude oil revenue. The next four approaches likewise have a significant AUC value, surpassing 0.8. The AUC values for the BBO- MLP, BSA-MLP, TLBO-MLP, and COA-MLP approaches were obtained as follows: (0.874 and 0.792) for training and testing stages, (0.809 and 0.792) for training and testing stages, (0.9353 and 0.9237) for training and testing stages, and (0.9092 and 0.8927) for training and testing stages, respectively. This model has the potential to contribute to the resolution of default probability and is very valuable to the credit card industry. Broadly speaking, this novel forecasting approach serves as a notable predictor of crude oil prices. [ABSTRACT FROM AUTHOR]

The oil market is one of the most important markets for the global economy. Often, oil prices influence the financial results of whole countries and sectors. Therefore, the modeling and prediction of crude oil prices are of high importance. Most up-to-date publications have used daily closing rates in crude oil price modeling, not considering the variability in prices during the day. The application of this kind of price representation leads to a loss of information about the range of price changes during the day, which influences the accuracy of the models and makes them useless in short-term course predictions. In this paper, we introduce the concept of a new state model in a binary-temporal representation, which uses trend analysis, which is one of the main methods used in the prediction of the direction of future changes in the course trajectory. The model described in this paper stands as the first tool that allows for predicting course changes in a given range. The presented work also summarizes the research results of modeling crude oil prices from the last six years, which prove the effectiveness of the mentioned modeling method. [ABSTRACT FROM AUTHOR]

Commodity markets, such as crude oil and precious metals, play a strategic role in the economic development of nations, with crude oil prices influencing geopolitical relations and the global economy. Moreover, gold and silver are argued to hedge the stock and cryptocurrency markets during market downsides. Therefore, accurate forecasting of crude oil and precious metals prices is critical. Nevertheless, due to the nonlinear nature, substantial fluctuations, and irregular cycles of crude oil and precious metals, predicting their prices is a challenging task. Our study contributes to the commodity market price forecasting literature by implementing and comparing advanced deep-learning models. We address this gap by including silver alongside gold in our analysis, offering a more comprehensive understanding of the precious metal markets. This research expands existing knowledge and provides valuable insights into predicting commodity prices. In this study, we implemented 16 deep- and machine-learning models to forecast the daily price of the West Texas Intermediate (WTI), Brent, gold, and silver markets. The employed deep-learning models are long short-term memory (LSTM), BiLSTM, gated recurrent unit (GRU), bidirectional gated recurrent units (BiGRU), T2V-BiLSTM, T2V-BiGRU, convolutional neural networks (CNN), CNN-BiLSTM, CNN-BiGRU, temporal convolutional network (TCN), TCN-BiLSTM, and TCN-BiGRU. We compared the forecasting performance of deep-learning models with the baseline random forest, LightGBM, support vector regression, and k-nearest neighborhood models using mean absolute error (MAE), mean absolute percentage error, and root mean squared error as evaluation criteria. By considering different sliding window lengths, we examine the forecasting performance of our models. Our results reveal that the TCN model outperforms the others for WTI, Brent, and silver, achieving the lowest MAE values of 1.444, 1.295, and 0.346, respectively. The BiGRU model performs best for gold, with an MAE of 15.188 using a 30-day input sequence. Furthermore, LightGBM exhibits comparable performance to TCN and is the best-performing machine-learning model overall. These findings are critical for investors, policymakers, mining companies, and governmental agencies to effectively anticipate market trends, mitigate risk, manage uncertainty, and make timely decisions and strategies regarding crude oil, gold, and silver markets. [ABSTRACT FROM AUTHOR]

This research explores economic growth dynamics in five ASEAN countries (1980–2020), analyzing the short and long-term impact of financial development (FD), oil prices, investment (INV), and inflation on GDP growth. Panel ARDL analysis reveals a transient positive link between FD and long-term GDP growth, necessitating a holistic approach for short-term stability. Similarly, oil prices exhibit long-term volatility, urging diversification strategies. In contrast, consistent and significant relationships exist between INV and GDP growth in both time frames, underscoring investment's pivotal role. The study emphasizes managing inflation for sustained growth, offering vital insights for policymakers, economists, and analysts in fostering ASEAN's economic stability. [ABSTRACT FROM AUTHOR]

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student's t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices. [ABSTRACT FROM AUTHOR]

During the development process of heavy oil with steam huff and puff, the wellbore undergoes alternating conditions of temperature rise and fall, leading to a significant decline in the strength of the cement sheath. This work studied the inhibition effect and mechanism of the composite material composed of mullite and basalt fiber on the strength decline of cement stone (the hardened body of cement slurry) under simulated wellbore temperature alternating conditions from 80 °C to 260 °C. The pore structure distribution of cement stone was investigated by mercury intrusion porosimetry (MIP), the microstructure and phase composition of cement stone was characterized by scanning electron microscope (SEM) and energy dispersive spectrometer (EDS), together with quantitative X-ray diffraction (XRD) analysis. After 4 rounds of curing under alternating temperature conditions, the compressive strength of cement stone containing mullite and basalt fiber composite material was 28.6 MPa at 260 °C, which was only 9.2 % lower than that at 80 °C. However, the compressive strength of the blank cement stone without composite materials decreased to 22.5 MPa at 260 °C, which was 20 % lower than that at 80 °C. After 4 rounds of alternating temperature cured, the harmful pores of the cement stone without composite materials increase, and the xonotlite flakes and stacks together. The cement stone added with mullite has a finer pore structure after 4 rounds of alternating temperature cured because mullite reacts with portlandite to form C-A-S-H gel. The xonotlite remains fibrous, and a small amount of new phase hibschite is formed. Furthermore, basalt fiber can react with C-A-S-H gel at high temperatures, and the two phases are closely combined to further improve the high-temperature resistance of cement stone. [ABSTRACT FROM AUTHOR]

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This study is the first to investigate the asymmetric effects of oil price volatility on stock returns for the Ho Chi Minh Stock Exchange (HOSE). We utilized weekly series of VN30-Index, WTI crude oil prices, geopolitical risks (GPR) index, and gold prices spanning from 6 February 2012 to 31 December 2023 as data sources. Using a nonlinear autoregressive distributed lag (NARDL) bounds testing approach, we found that, in the shortterm, oil price volatility has negative asymmetric effects on market returns. Specifically, in the shortterm, a 1 percent increase in oil price volatility immediately leads to a 2.6868 percent decrease in the market returns, while a similar magnitude decrease in oil price volatility is associated with a 6.3180 percent increase in the market returns. In addition, the results obtained from the NARDL model indicated that, in the longterm, the negative and positive changes of oil price volatility have significantly negative effects on the market returns. Finally, the findings derived from the error correction model (ECM) show that a 98.21 percent deviation from the equilibrium level in the previous week is converged and corrected back to the long-term equilibrium in the current week. [ABSTRACT FROM AUTHOR]

The objective of this research is to identify a bio-based friction modifier (FM) with tribological performance comparable to conventional FMs. Promising alternatives to conventional FMs, such as the FMs derived from natural sources, including rapeseed and salmon oil, were selected. Increasing concerns about crude oil prices, environmental impact, and the depletion of fossil resources have further fueled the search for renewable, biodegradable, and environmentally friendly raw materials for lubricants Tribological tests were conducted using a rheometer under non-conformal contact. The normal force, temperature, and sliding speed were varied to simulate conditions such as those found in a food extruder. To simulate cold extrusion applications, water and bio-based FM mixtures were used. The best-performing bio-based FMs were then mixed with a polyalphaolefin to simulate warm extrusion conditions. The results were compared to those obtained from mixtures of a polyalphaolefin and selected conventional FMs. The main finding of this study demonstrated that rapeseed and salmon oils, with a peak coefficient of friction (COF) of 0.16, are the best-performing bio-based FMs for reducing friction. When mixed with distilled water for cold extrusion (case 1) and with polyalphaolefin for warm extrusion (case 2), they performed similarly to the conventional FM, tallow amine, also with a maximum COF of 0.16, and significantly better than polyalphaolefin alone (maximum COF of 0.25). Consequently, rapeseed and salmon oils are suitable bio-based FM candidates to replace conventional FMs in food-grade lubrication. [ABSTRACT FROM AUTHOR]

The oil and gas trade is one of the main ways to promote regional economic development by improving the effectiveness of resource allocation. While regional energy cooperation could lead to growth in the energy trade, blind investment will reduce effective yields. Kazakhstan and China maintain a stable oil and gas trade, but resource exports to China are not growing as expected. The aim of this research is to analyze the competitiveness and complementarity of Kazakhstan and China in the oil and gas trade, as well as the main factors affecting the oil and gas trade between Kazakhstan and China. By creating a linear regression equation to analyze the gravity model of the oil and gas trade between Kazakhstan and China, it was revealed that a 1% growth of the gross domestic product in both countries would lead to a 1.471% increase in the oil and gas trade. However, an increase in oil and gas production in Kazakhstan will not contribute to the expansion of the oil and gas trade with China. Kazakhstan and China could improve their oil and gas trade by strengthening financial cooperation, improving energy efficiency, increasing investment in infrastructure such as oil refineries and pipelines, and developing new oil and gas fields in Kazakhstan. [ABSTRACT FROM AUTHOR]

If a financial asset's price movement impacts a firm's product demand, the firm can respond to the impact by adjusting its operational decisions. For example, in the automotive industry, automakers decrease the selling prices of fuel-inefficient cars when the oil price rises. Meanwhile, the firm can implement a risk-hedging strategy using the financial asset jointly with its operational decisions. Motivated by this, we develop and solve a general risk-management model integrating risk hedging into a price-setting newsvendor. The optimal hedging strategy is calculated analytically, which leads to an explicit objective function for optimizing price and "virtual production quantity" (VPQ). (The latter determines the service level—that is, the demand-fulfillment probability.) We find that hedging generally reduces the optimal price when the firm sets the target mean return as its production-only maximum expected profit. With the same condition on the target mean return, hedging also reduces the optimal VPQ when the asset price trend positively impacts product demand; meanwhile, it may increase the VPQ by a small margin when the impact is negative. We construct the return-risk efficient frontier that characterizes the optimal return-risk trade-off. Our numerical study using data from a prominent automotive manufacturer shows that the markdowns in price and reduction in VPQ are small under our model and that the hedging strategy substantially reduces risk without materially reducing operational profit. This paper was accepted by Kay Giesecke, finance. Funding: Financial support from the Hong Kong Research Grants Council [General Research Fund 17200521] is gratefully acknowledged. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2023.4942. [ABSTRACT FROM AUTHOR]

This paper examines the impact of oil price and quantity shocks on income inequality in the USA. Using micro income data, we construct measures of pre‐tax money income inequality at the state level for the period 1980–2017. We find that a $10 increase in oil price increases inequality, measured by the ratio of pre‐tax income of the 90th percentile to 10th percentile (p90/p10), by 3.8 percentage points in states with high levels of oil endowments per capita. The increase in capital income to households at the top of the distribution drives the observed change in inequality. In addition to price shocks, we study the impact of quantity shocks that arise from oil discoveries. Quantity shocks have no effect on the overall income distribution. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data. [ABSTRACT FROM AUTHOR]

As one of the leading oil producers in the world, Nigeria's economy is profoundly influenced by fluctuations in global oil prices, affecting the government capacity to allocate resources on national healthcare system and its ability to ensure healthy lives and promote wellbeing for all, as outlined in Sustainable Development Goal 3 (SDG-3). On this backdrop, this study has undertaken a comprehensive examination of both linear and non-linear relationship between oil price shocks and sustainable development in Nigeria, a country heavily reliant on oil revenue from 1970 to 2022 in Nigeria. Using linear Autoregressive Distributed Lag (ARDL) and nonlinear Autoregressive Distributed Lag (NARDL) models, the findings show that increasing crude oil price shock has a substantially large adverse impact on SDG-3 in the short run while decreasing crude oil price has unobserved effect in both short and longrun when asymmetries were considered. Based on the findings of this study, Nigerian policymakers should expedite efforts to diversify its economy away from oil dependency. This diversification should focus on promoting sectors with growth potentials, creating jobs, and generating stable revenues to support healthcare and sustainable development initiatives. The study highlights other actionable insights and policies to navigate the challenges posed by oil price volatility while advancing the noble aspirations of SDG 3 for the people of Nigeria. [ABSTRACT FROM AUTHOR]

This work uses the vector-valued Bernoulli equation to build a nonlinear multi-variable grey Bernoulli model, which is available to describe the nonlinear relationship between the output variables. By using P a d e ´ approximation, the proposed model can be implemented with high time efficiency. Additionally, the Sine Cosine Algorithm is employed to determine the Bernoulli exponent, thereby enhancing prediction accuracy. To evaluate the predictive performance of the proposed model, three case studies using three real-world data sets with different features of predicting per capita household income, fuel prices and crude oil prices are carried out. The results are compared with three existing grey multi-input multi-output models. Experimental results demonstrate that the proposed model excels in handling nonlinear relationships between variables and has strong robustness against noise, consistently delivering lower error values, demonstrating superior predictive performance. • A nonlinear multi-variable grey Bernoulli model is proposed based on the Hadamard product. • Linear parameter estimation is carried out using the least squares method or the least norm method. • P a d e ´ approximation is used to compute the matrix exponential to improve the time efficiency. • The model is optimized using the Sine Cosine Algorithm. • The proposed model shows superior prediction performance compared with three other typical models. [ABSTRACT FROM AUTHOR]

Dissolved air flotation (DAF) is an effective method for separating suspended oil and solid particles from wastewater by utilizing small air bubbles. This study aims to investigate the impact of key factors, such as saturating pressure and water flow rate, on the separation of fine oil droplets from a water stream. The macroscopic flow patterns within the cell were analyzed using particle image velocimetry (PIV), while Digital Image Analysis (DIA) was employed to study microscopic phenomena, including oil droplet rising velocity and oil-bubble contact mechanisms. Our findings propose a safe operating window (specifically, water flow rate and saturation pressure) for the effective separation of oil droplets without any oil escaping into the clean water stream. It was found that the oil droplet rising velocity increases with the saturation pressure up to 200 kPa. However, a further increase in the pressure of the air saturating chamber leads to a decrease in oil droplet rising velocity. Additionally, we identified a peak in rising velocity at an oil droplet size of approximately 200 µm. Below this threshold, the rising velocity increases with droplet size, while for droplet sizes exceeding 200 µm, the rising velocity decreases with size. This behavior can be explained by the conflicting effects of droplet size increment according to the Stokes law for the rising velocity of oil droplets. As the droplet size increases, the average density of the bubbles/droplet aggregate rises, reducing the ∆ρ in the Stokes law and subsequently lowering the aggregate rising rate. However, as per the Stokes law, the oil droplet rising velocity increases proportionally to the square of its size. [ABSTRACT FROM AUTHOR]

The need for renewable energy is regarded as a major component in the move towards achieving sustainable development. Using a large sample of 177 countries over the period 1990 to 2020, this research explores the impact of the most significant drivers of renewable energy. Findings from this work contribute to the literature by identifying the most significant drivers of renewable energy deployment and their different responses in developed and developing economies. Empirical results suggest that GDP, oil price, access to electricity, and CO2 and methane emissions are significant determinants of renewables both as a share in energy consumption and as a share in electricity production. Additionally, trade is found to be a significant driver for the share of renewables in total energy consumption but not for the share of renewables in the electricity production model. Finally, our findings indicate that the factors influencing the development of renewables vary significantly between developed and developing countries, necessitating distinct approaches for each group of countries. These results can play a significant role from a policy perspective in designing and implementing specific policies to increase renewable energy deployment. [ABSTRACT FROM AUTHOR]

In 2020, the world experienced several significant events, including the coronavirus (COVID-19) pandemic and the collapse of international crude oil prices. The rapid spread of this pandemic has dramatic impacts on financial markets all over the world, thereby increasing market risk aversion in an unprecedented way since the subprime financial crisis. The decline in stock markets implied volatilities of equity and oil prices, thereby heightening turmoil in global financial markets despite comprehensive and substantial financial reforms. To this end, we investigated the likely effects of this pandemic on the Saudi stock market while controlling for oil prices based on daily data for a period from 1/1/2020 to 19/9/2022. To ascertain the existence of a long-run equilibrium relationship between the variables, we applied autoregressive distributed lag (ARDL) modeling and the error correction model, with this ultimately revealing the existence of strong cointegration in the long run. The ARDL bounds test was found to be robust by combined cointegration tests, thus providing further evidence of a strong relationship in the long run. Granger causality tests also yielded evidence of causality between the variables in both directions. The total COVID-19 confirmed cases and oil prices also caused movements in stock returns in the short run. Our findings have some prominent implications for asset managers and policymakers to improve stock market efficiency and boost global economic activity. Saudi authorities can consequently remove the regulatory and legal obstacles to develop their stock market and better improve the risk management, which will allow to make quick decisions in response to any oil price volatilities. Policymakers should also adopt proactive strategies that can comfort stock investors' anxieties over the increasing oil price volatilities. Finally, the findings should be treated with some cautions because of the limited sample size and the tests' statistical inference. Nevertheless, they do open opportunities for further studies to look in more detail at how the COVID-19 pandemic affected, over the short and long run, monetary and fiscal policy coordination, financial stability, and various other macroeconomic indicators in Saudi Arabia. [ABSTRACT FROM AUTHOR]

This study utilized both single-regime GARCH and double-regime GARCH models to investigate oil price volatility, Spanish macroeconomic factors, and stock prices during major crises such as geopolitical conflicts, the global financial crisis (GFC), and COVID-19, covering the period from Q2-1995 to Q4-2023. Additionally, the impact of crude oil price volatility on these factors was examined. The empirical results confirmed the presence of the leverage effect and identified multiple volatility switches associated with remarkable events like the GFC, the European debt crisis, the COVID-19 pandemic, and the Russian war. ARDL model analysis revealed a statistically significant positive relationship between oil prices and both unemployment and inflation rates in the long term, while other factors showed a negative correlation. [ABSTRACT FROM AUTHOR]

Although the recent debate in energy economics on the importance of oil price indexation versus shale gases suggest that big data can be used in predictive analysis in energy economics, little is known particularly in the context of shale gas and oil price interlinkages. Grounding our investigations in such directions we investigate in this paper the relationship between gas and crude oil markets and the impact US shale gas by employing time-varying causality method by Shi et al. (J Time Ser Anal 39(6):966–987, 2018; J Financ Econom 18(1):158–180, 2020) and cross-quantilogram correlation approach by Han et al. (J Econom 193(1):251–270, 2016). In particular, as a representative of the crude oil market, we use OPEC oil; WTI; Crude oil Oman; Crude oil Dubai while for the gas market, we use natural gas prices of UK NBP (National Balancing Point), NYMEX HH (Henry Hub) and US shale gas prices. Data period is from 11th January 2013 to 8th September 2020. We find significant negative spillovers from crude oil markets to natural gas markets particularly during moderate market conditions. The results suggest crucial implications in energy economics literature, to diversify assets to hedge against risks. We further find strong causality association between oil markets, natural gas markets and further oil markets and shale gas markets. Our findings describe that aftermath of the shale-gas boom the predictability nexus between oil and natural gas increased. Once we condition for shale gas the significant negative spill overs from oil markets to natural gas markets increases in the long-run. We suggest important policy prescriptions which have interconnected market repercussions. [ABSTRACT FROM AUTHOR]

Over the years, oil prices and financial stock markets have always had a complex relationship. This paper analyzes the interactions and co-movements between the oil market (WTI crude oil) and two major stock markets in Europe and the US (the Euro Stoxx 50 and the SP500) for the period from 1990 to 2023. For that, I use both the time-varying and the Markov copula models. The latter one represents an extension of the former one, where the constant term of the dynamic dependence parameter is driven by a hidden two-state first-order Markov chain. It is also called the dynamic regime-switching (RS) copula model. To estimate the model, I use the inference function for margins (IFM) method together with Kim's filter for the Markov switching process. The marginals of the returns are modeled by the GARCH and GAS models. Empirical results show that the RS copula model seems adequate to measure and evaluate the time-varying and non-linear dependence structure. Two persistent regimes of high and low dependency have been detected. There was a jump in the co-movements of both pairs during high regimes associated with instability and crises. In addition, the extreme dependence between crude oil and US/European stock markets is time-varying but also asymmetric, as indicated by the SJC copula. The correlation in the lower tail is higher than that in the upper. Hence, oil and stock returns are more closely joined and tend to co-move more closely together in bullish periods than in bearish periods. Finally, the dependence between WTI crude oil and the SP500 stock index seems to be more affected by exogenous shocks and instability than the oil and European stock markets. [ABSTRACT FROM AUTHOR]

Uncertain time series models constitute a pivotal tool for analyzing phenomena evolving over time. However, most of the prevailing research centers on parameter models, which exhibit limitations in addressing intricate temporal dynamics. To address this gap, this paper introduces a non-parametric uncertain time series model. Primarily, we propose the definition of the non-parametric uncertain time series models. Subsequently, tailored to the non-parametric uncertain additive autoregressive model, we propose a non-parametric estimation method based on linear polynomial splines. Following this, the effectiveness of these estimations is verified through residual analysis and uncertain hypothesis tests. Finally, we apply the introduced non-parametric estimation approach to the Brent crude oil spot price. [ABSTRACT FROM AUTHOR]

The uncertainty caused by high volatile crude oil prices and the higher level of deregulations worldwide has significant effects on the economic growth of a country. The financial markets of many developing countries experienced a severe downturn during the oil price shocks in March-April 2020. Traditional predictive approaches, which assume linearity and stationarity of time series in the long run, fail to accurately capture short-term fluctuations. This paper presents an efficient algorithm based on ARMA denoising and taking advantage of the wavelet transformation. By decomposing the time series and extracting the intricate underlying structure, wavelet denoising minimizes distortions and enhances forecasting accuracy. The results demonstrate a substantial improvement in performance compared to conventional forecasting techniques. [ABSTRACT FROM AUTHOR]

Due to the extremely low permeability of shale formations, the combination of horizontal well and volume fracturing has been proposed as an effective technique to improve the production of Dagang continental shale oil reservoirs. Based on the flow material balance method (FMB) and straight-line analysis (SLA) method, the stimulated reservoir volume (SRV) and drainage volume are determined to identify the flow regimes of the seepage mechanism of shale oil reservoirs. To resolve the challenges of multi-scaled flow regimes and bottom hole pressure (BHP) variation before and after pumping in shale oil wells, a multi-linear analytical flow model was established to predict the future production and the final expected ultimate recoverable oil (EURo) after fitting the historical production dynamics. Based on the results, it can be concluded that the flow regime of a shale oil well in production can be divided into two stages consisting of linear flow within SRV and composite flow from the un-stimulated area to SRV. The effects of fracturing operation parameters, such as fracturing fluid volume and sand/liquid ratio, on shale oil productivity, are analyzed, and insightful suggestions are drawn for the future development of this pay zone. [ABSTRACT FROM AUTHOR]

The stability analysis of horizontal wells is essential for a successful underground coal gasification (UCG) operation. In this paper, a new 3D coupled thermo-mechanical numerical modeling is proposed for analyzing the stability of UCG horizontal wells. In this model, the effect of front abutment stresses, syngas pressure, syngas temperature and thermal stresses is considered to predict the mud weight window and drilling mud pressure during UCG process. The results show that the roof caving in UCG panel has a greatest impact on the stability of horizontal well. Moreover, when the time of coal gasification is increased, the well convergence increases and for more stability it is necessary to increase the drilling mud pressure. This research was carried out on the M2 coal seam in Mazino coal deposit (Iran). The results showed that the mud weight window for horizontal well drilling is between 0 and 33 MPa. The appropriate stress for the maximum stability of the horizontal well, taking all the thermal and mechanical parameters into account, is 28 MPa. The suggested numerical method is a comprehensive and consistent way for analyzing the stability of horizontal wells in UCG sites. [ABSTRACT FROM AUTHOR]

The present work investigates the volumetric and viscometric properties of an aqueous solution of 1,2-dimethylethylenediamine (DEEDA) over an entire concentration range and an absorber operating temperature range of 313.15K-333.15K at atmospheric pressure. The investigated volumetric properties included the density, excess molar volume, partial molar volume, and the investigated viscometric properties included the viscosity, viscosity deviation, free energy for activation of viscous flow, excess free energy for activation of viscous flow, and excess entropy for activation of viscous flow. The results indicated that there are strong intermolecular interactions and suitable molecular packing in the binary DEEDAewater mixture. Hence, the mixture was found to deviate from a real mixture according to the calculated excess properties. The DEEDA solvent's preliminary volumetric and viscometric properties revealed convincing potential as a novel amine for carbon capture. Additionally, the Redlich-Kister-based correlations showed favorable correlative performance for excess molar volume, viscosity deviation, and excess entropy for activation of viscous flow. [ABSTRACT FROM AUTHOR]

Sandstone reservoirs often contain clay particles that can cause damage and reduce permeability during low-salinity water flooding. In this study, the effect of surfactants on fine migration in clay-rich sandstones and its impact on oil recovery was investigated. First, the impact of surfactants on interparticle forces in fine-matrix, fine-fine, and oil-matrix systems was modeled. The results showed that both CTAB (cetyltrimethyl ammonium bromide) and QS (quillaja saponin) cause EDL compaction, weakening the repulsive forces. However, SDS (sodium dodecyl sulfate) and TX (triton X-100) do not affect the EDL. Next, the effect of surfactants on IFT reduction and wettability alteration was experimentally investigated. All surfactants reduced IFT due to the surface excessive concentration mechanism. The wettability alteration experiment illustrated that although QS and CTAB compact EDL around oil and matrix particles leading to attraction force augmentation, they both alter wettability through adsorption on matrix and carboxylic groups present in crude oil, respectively. Surfactant aqueous solutions were then injected into various clay-rich sandstone sanpacks, which resulted in increased oil recovery. However, the mechanisms leading to enhanced oil recovery variedby surfactant type. CTAB increased recovery by 10% through IFT reduction and wettability alteration, while SDS and TX increased recovery by 12% and 9%, respectively, through wettability alteration and extreme fine migration. In contrast, partial fine migration in the QS flooding experiment reached a recovery increase of 18%. Permeability trends through experiments were also recorded. During CTAB injection, permeability did not reduce, while QS aqueous solution reduced rock permeability to 5 mD. SDS and TX reduced the magnitude of permeability to 2 mD. In conclusion, this study demonstrates that surfactants can effectively improve oil recovery in clay-rich sandstones by altering the interparticle forces, reducing IFT, and changing wettability. The results suggest that the type of surfactant used should be carefully selected to achieve the desired recovery increase without affecting the permeability of the reservoir. [ABSTRACT FROM AUTHOR]

Cambrian in Sichuan basin developed thick black shale, varies carbonate and clastic rock, which deposited in different sedimentary environment. Sichuan basin in the upper Yangtze platform contained a record of environment during the Cambrian. Detail facies analyses of Cambrian enable us to discuss the sedimentary environment and palaeogeographic setting. Sedimentation commenced in the Early Cambrian with the deposition of shelf facies (Qiongzhusi Formation and Canglangpu Formation). At this stage, thick shale and clastic rock deposited in Sichuan basin. At the end of the Early Cambrian, a carbonate platform developed in upper Yangtze platform, and Sichuan basin was located in restricted platform (Longwangmiao Formation). In the Middle Cambrian, tidal flat and restricted platform developed in Sichuan basin (Douposi Formation), because of continuous regression. During the Late Cambrian, Sichuan basin was located in carbonate platform again (Xixiangchi Formation). There are three types of sedimentary system in the Cambrian of Sichuan basin: clastic sedimentary system, clastics-carbonate mixed sedimentary system and carbonate sedimentary system. Vertically, the basin shows the evolutionary character of clastic-carbonate sedimentary systems. The three sedimentary systems correspond to three "transgression-regression" cycles of the Cambrian. The transgression in the initial period of the Early Cambrian led to the formation of clastic sedimentary system in the Qiongzhusi Formation of Lower Cambrian. The transgression in the later period of the Early Cambrian led to the formation of clasticcarbonate mixed sedimentary system in the Middle-Lower Cambrian. The transgression in the initial period of the Late Cambrian led to the formation of carbonate sedimentary system in the Xixiangchi Formation of Upper Cambrian. With the end of Late Sinian continental rifting ended, Sichuan basin entered a stable evolutionary stage of the craton basin, while the paleo-land developed in the north and southwest. In Qiongzhusi-Canglangpu period, the basin developed onshore-shelf sedimentary facies from west to east; In Longwangmiao-Xixiangchi period, the basin developed tidal flat-platform-slope sedimentary facies from west to east. [ABSTRACT FROM AUTHOR]

Graphene is a single atom thick crystal composed of carbon atoms. It is the lightest, thinnest, strongest material that conducts heat and electricity well heretofore. In terms of application, by introducing oxygen-containing groups, graphene can be well dispersed in solvents, can be chemically modified and functionalized, or connected with other electroactive substances through covalent bond or non-covalent bond to form composite materials, which is conducive to further processing and promotion. The application of graphene in oilfield chemistry started late, but developed rapidly. Graphene has played an active role in drilling fluid, cementing fluid, fracturing fluid, displacement fluid and other oilfield working fluids. It can enhance the temperature and salt resistance of working fluid and improve the effect of working fluid. In this paper, several directions of graphene applications in oilfield chemistry, such as modified graphene, graphene copolymers and graphene nanoparticles, are reviewed in detail from the synthesis methods, action mechanisms and effects of graphene and its derivatives, and the frontier cases at this stage are given. On the basis of the existing research, suggestions for the development direction of graphene materials in oilfield chemistry are given for a variety of graphene materials, aiming to provide guidance for the application of graphene in oilfield chemistry. [ABSTRACT FROM AUTHOR]

Present drilling fluids for deep water wells have severe degenerative effect on the environment with high operational and disposal costs. Thus, making them less desirable in recent times. Ester synthetic drilling fluid provides a novel environmentally friendly alternative but conventional ester-based drilling fluids exhibit high viscosities in deep-water wells causing excessive equivalent circulating density (ECD) and increased risk of lost circulation owing to narrow mud density window. This study experimentally investigates the critical fluid properties and aerobic biodegradability potentials of two newly developed deep-water synthetic ester drilling fluids namely: iso-propyl caprylate (COIPE) and iso-propyl linolenate (LOIPE) synthetic fluids and their comparison with synthetic-paraffin (SP-SBF) and isomerized-olefin (IOSBF) synthetic hydrocarbon fluids. The esters of iso-propyl caprylate and iso-propyl linolenate were produced from the isolation of ester mixtures that were obtained from the homogeneous catalytic transesterification of coconut and linseed plant oil biomass respectively. The COIPE was isolated from the coconut oil iso-propyl ester mixture by low-pressure fractional distillation technique. While fractional distillation and crystallization were used to isolate the LOIPE ester from the linseed oil iso-propyl ester mixture. Meanwhile, the aerobic biodegradation investigation was conducted by a modified oxygen consumption respirometry technique. The GC-MS analysis of the COIPE and LOIPE showed that the former contains essentially of lower saturated carbon compounds (C8). Whereas the latter contains higher molecular weight and unsaturated carbon compounds (C18+). The COIPE and LOIPE kinematic viscosity values are in good agreement with that of the reference synthetic hydrocarbon fluid samples (SP-SBF and IO-SBF). Although, the COIPE synthetic ester has lower viscosity value owing to the presence of shorter chain and saturated carbon atoms (C8 esters). Similarly, the linolenic oil iso-propyl ester has excellent cold flow characteristics for deep-water well drilling owing to lower values of cloud and pour points as a result of higher concentration of poly-unsaturated linolenic esters. The iso-propyl caprylate and the iso-propyl linolenate ester synthetic fluids are readily biodegradable in the sea water inoculum under aerobic condition. However, the iso-propyl caprylate is inherently biodegradable because its degradation level and that of the reference chemical sample were already above 60% during the 10-day window period. The SP-SBF and the IO-SBF synthetic fluids have lower aerobic biodegradation values because they contain little quantity of poly aromatic hydrocarbons as evident in their GC-MS profiles. Finally, esters and unsaturated synthetic-based fluid are more rapidly biodegradable than paraffinic synthetic fluids and the rate of biodegradation of organic compounds decreases as molecular weight increases. [ABSTRACT FROM AUTHOR]

Shale gas reservoirs are unconventional tight gas reservoirs, in which horizontal wells and hydraulic fracturing are required to achieve commercial development. The fracture networks created by hydraulic fracturing can increase the drainage area extensively to enhance shale gas recovery. However, large volumes of fracturing fluid that is difficult to flow back to the surface and remained in the shale formation, will inevitably lead to damages of the shale formations and limit the effectiveness of stimulation. Supercritical water (SCW) treatment after hydraulic fracturing is a new method to enhance shale gas recovery by using appropriate heat treatment methods to the specific formation to convert the retained fracturing fluid into a supercritical state (at temperatures in excess of 373.946°C and pressures in excess of 22.064 MPa). An experiment was conducted to simulate the reaction between shale and SCW, and the capacity of SCW treatment to enhance the permeability of the shale was evaluated by measuring the response of the shale porosity and permeability on SCW treatment. The experimental results show that the shale porosity and permeability increase by 213.43% and 2198.37%, respectively. The pore structure alteration and permeability enhancement of the shale matrix were determined by analyzing the changes in pore structure and mineral composition after SCW treatment. The mechanisms that affect pore structure and mineral composition include oxidative catalysis decomposition of organic matters and reducing minerals, acid-catalyzed decomposition of carbonate minerals and feldspar minerals, hydrothermal catalysis induced fracture extension and cementation weakening induced fracture extension. SCW treatment converts harm into a benefit by reducing the intrusion of harmful substances into the shale formation, which will broaden the scope and scale of shale formation stimulation. [ABSTRACT FROM AUTHOR]

The paper presents the results of studies on the transformation of the organic matter of siliceous-clayey carbonate rocks of the SemilukskoeMendymsky horizon of the Romashkino oil field in a hydrothermal fluid for an hour (with a water-to-rock ratio of 33) at 340°C and 380°C and pressures of 17 and 20 MPa. As a result of hydrothermal treatment, at 340°C and 17 MPa, based on nitrogen porosimetry and electron microscopy data, transformations of rock-forming minerals in the rock are observed. They lead to an increase in the volume and average diameter of mesopores in it and the formation of micropores, as well, which improve its filtration properties. At the same time, the amount of kerogen in the composition of the organic matter decreases and the yield of the petroleum hydrocarbon extract increases, in which, according to the SARA analysis, the content of asphaltenes increases and the content of resins, aromatic and saturated hydrocarbons decreases. In the composition of aroatic hydrocarbons, the proportion of alkyltrimethylbenzenes and dibenzothiophenes increases, phenanthrene homologues appear, and in the composition of saturated hydrocarbons, the amount of iso-structure alkanes decreases relative to the content of linear alkanes. Raising the temperature and pressure of the hydrothermal fluid to 380°C and 20 MPa increases the degree of kerogen conversion from 12.4% to 23.6%. At the same time, changes occurring in the component composition of petroleum hydrocarbon extracts remains similar to the experiments carried out at 340°C and 17 MPa; the content of naphthalenes decreases, the content of dibenzothiophenes increases and C11-C17, C19-C22 alkyltrimethylbenzenes appear. According to IR spectroscopy, with increasing temperature and pressure of the hydrothermal fluid the intensity of the absorption bands of the aromatic ring, aliphatic fragments, and oxygen-containing groups increases in resins; the structural-group composition of asphaltenes changes little: aromaticity and the content of condensed structures slightly increase. The revealed distinctive features in the composition of organic matter after hydrothermal impact on siliceous-clayey carbonate rocks confirm the concept of staged destruction of kerogen, when large structural heteroatomic blocks (asphaltenes) are split off at the initial stages. Changes occurring in the composition of petroleum hydrocarbon extracts indicate their involvement in the process of hydrothermal transformation of organic matter of siliceous-clayey carmbonate rocks with the predominant reactions of dehydrogenation of naphthenic compounds and oxidative polycondensation of aromatic structures. The data of electron microscopy and nitrogen porosimetry of rocks after hydrothermal exposure at 380°C and 20 MPa indicate a deterioration in their reservoir properties. The most optimal thermobaric conditions of the hydrothermal fluid for the generation of petroleum hydrocarbons from kerogen of siliceous-clayey carbonate deposits of the SemilukskoeMendymsky horizon of the Romashkino field, which improve their reservoir properties, are 340°C and 17 MPa; with implication of hard-to-recover heavy hydrocarbon resources and wellestablished catalytic methods of in-situ conversion. [ABSTRACT FROM AUTHOR]

Soil corrosion and hydrogen embrittlement are the main factors of hydrogen pipeline failure. The gas escapes, diffuses and accumulates in the soil and enters the atmosphere when leak occurs. The mechanism of gas diffusion in buried pipelines is very complicated. Mastering the evolution law of hydrogen leakage diffusion is conducive to quickly locating the leakage point and reducing the loss. The leakage model of the underground hydrogen pipeline is established in this paper. The effect of leakage hole, soil type, pipeline pressure, pipeline diameter on hydrogen leakage diffusion were investigated. The results show that when the hydrogen pipeline leaks, the hydrogen concentration increases with the increase of leakage time, showing a symmetrical distribution trend. With the pipeline pressure increase, hydrogen leakage speed is accelerated, and longitudinal diffusion gradually becomes the dominant direction. As the leakage diameter increases, hydrogen leakage per unit of time increases sharply. Hydrogen diffuses more easily in sandy soil, and its diffusion speed, concentration, and range are higher than that in clay soil. The research content provides a reference and basis for the detection and evaluation of buried hydrogen pipeline leakage. [ABSTRACT FROM AUTHOR]

The development of low-permeability oil and gas resources presents a significant challenge to traditional development methods. To address the problem of "no injection and no production" in low -permeability reservoirs, a novel fracture-injection-production integration technology named fracturing-flooding has been proposed by oilfield sites. This technology combines the advantages of conventional fracturing, water flooding, and chemical flooding, resulting in improved reservoir physical properties, increased injection, replenished energy, and increased oil displacement efficiency. The technology is especially suitable for low-permeability reservoirs that suffer from lack of energy, and strong heterogeneity. Fracturing-flooding technology has shown significant results and broad development prospects in some oilfields in China. This paper analyzes the development status of fracturing-flooding technology from its development history, technical mechanism, technical characteristics, process flow, types of fracturing and oil displacement fluids, and field applications. Physical and numerical simulations of fracturing- flooding technology are also summarized. The results suggest that fracturing-flooding technology is more effective than conventional fracturing, water flooding, and chemical flooding in stimulating lowpermeability tight reservoirs and improving oil recovery. Moreover, it has a high input-output ratio and can be utilized for future reservoir stimulation and transformation. [ABSTRACT FROM AUTHOR]

Fluid production from unconsolidated reservoirs often leads in sand production, which poses a number of issues. Sand deposition in flowlines can result in significant pressure dips, pipe and facility damage, and obstructions that decrease productivity. More research is needed to understand the movement and deposition of sand in oil--water--sand (O--W--S) fluxes. This article focuses on O--W--S flows in a 6-meter-long horizontal pipe with an inner diameter of 0.0381 m. The study looks at the flow behavior of high viscosity oil-water (O--W), wateresand (W--S), and oil--water--sand (O--W--S) flows. Experiments were carried out at 250 psig pressure in a laboratory flow test facility using various heavy synthetic oils (viscosities ranging from 3500 cP to 7500 cP at 25°C) and tap water. The sand concentration varied from 1% to 10%, with an average sand particle diameter of 145 mm and material density of 2630 kg/m³. Water cuts ranged from 0.0 to 1.0. The experimental results revealed a minor change in pressure gradient between (O--W) and (O--W--S) flows. However, increasing the sand concentration in (O--W--S) flow resulted in higher pressure losses. The reduction factor of pressure gradient indicated that the highest decrease in pressure drop occurred at higher superficial oil velocities. Furthermore, a direct relationship was observed between the reduction factor and the decrease in water cut. The results also showed that the minimum required transportation velocity for sand slurry decreased with increasing superficial oil velocity, while the minimum transportation condition increased with higher sand concentration. The comparison between the expected pressure gradient from Bannwart and McKibben et al. and the actual experimental data demonstrated significant accuracy for the oil viscosities and superficial oil velocities used in the study. [ABSTRACT FROM AUTHOR]

Increasing world request for energy has made oil extraction from reservoirs more desirable. Many novel EOR methods have been proposed and utilized for this purpose. Using nanocomposites in chemical flooding is one of these novel methods. In this study, we investigated the impact of six injection solutions on the recovery of light and heavy oil with the presence of two different brines as formation water using a homogenous glass micromodel. All of the injection solutions were based on a 40,000 ppm NaCl synthetic seawater (SSW), one of which was additive free and the others were prepared by dispersing nanocomposite silica-based polyacrylamide (NCSP), nanocomposite alumina-based polyacrylamide (NCAP), the combination of both nanocomposites silica and alumina based on polyacrylamide (NCSAP), surfactant (CTAB) and polyacrylamide (PAM) with a concentration of 1000 ppm as additives. The Stability of nanocomposites was tested against the salinity of the brine and temperature using salinity and DSC tests which were successful. Alongside stability tests, IFT, contact angle and oil recovery measurements were made. Visual results revealed that in addition to the effect of silica and alumina nanocomposite in reducing interfacial tension and wettability alteration, control of mobility ratio caused a major improvement in sweeping efficiency and oil recovery. According to the sweeping behavior of injected fluids, it was found that the main effect of surfactant was wettability alteration, for polyacrylamide was mobility control and for nanocomposites was the reduction of interfacial tension between oil and injected fluid, which was completely analyzed and checked out. Also, NCSAP with 95.83% and 70.33% and CTAB with 84.35% and 91% have the highest light oil recoveries at 250,000 ppm and 180,000 ppm salinity, respectively which is related to the superposition effect of interactions between nanocomposites, solution and oil. Based on our results it can be concluded that the most effective mechanism in oil recovery was IFT reduction which was done by CTAB reduction also by using a polymer-based nanocomposite such as NCSAP and adding the mobility control factor, the oil recovery can be further enhanced. In the case of heavy oil recovery, it can be concluded that the mobility control played a much more effective role when the PAM performed almost similarly to the CTAB and other nanocomposites with a recovery factor of around 17%. In this study, we tried to investigate the effect of different injection solutions and their related mechanisms on oil recovery. [ABSTRACT FROM AUTHOR]

Asphaltene precipitation can result in several production, operational, and transportation problems during oil recovery. If asphaltene precipitates and deposits, it can reduce reservoir permeability, damage wellbore equipment, and plug the pipelines. It is therefore extremely important to evaluate the conditions at which asphaltene precipitation occurs; this is referred to as the asphaltene onset pressure. Asphaltene onset pressure has been measured using many different experimental techniques. There have also been many attempts along the years to predict asphaltene onset pressure using mathematical correlations and models. This research provides an up-to-date comprehensive review of the methods by which asphaltene onset pressure can be measured using laboratory experiments and mathematical models. The research explains the main mechanisms of all the laboratory experiments to measure asphaltene onset pressure under static conditions and how to conduct them and highlights the advantages and limitations of each method. The research also provides a summary of the commonly used mathematical models to quantify asphaltene onset pressure directly and indirectly. [ABSTRACT FROM AUTHOR]

Surface chokes are widely utilized equipment installed on wellheads to control hydrocarbon flow rates. Several correlations have been suggested to model the multiphase flow of oil and gas via surface chokes. However, substantial errors have been reported in empirical fitting models and correlations to estimate hydrocarbon flow because of the reservoir's heterogeneity, anisotropism, variance in reservoir fluid characteristics at diverse subsurface depths, which introduces complexity in production data. Therefore, the estimation of daily oil and gas production rates is still challenging for the petroleum industry. Recently, hybrid data-driven techniques have been reported to be effective for estimation problems in various aspects of the petroleum domain. This paper investigates hybrid ensemble data-driven approaches to forecast multiphase flow rates through the surface choke (viz. stacked generalization and voting architectures), followed by an assessment of the impact of input production control variables. Otherwise, machine learning models are also trained and tested individually on the production data of hydrocarbon wells located in North Sea. Feature engineering has been properly applied to select the most suitable contributing control variables for daily production rate forecasting. This study provides a chronological explanation of the data analytics required for the interpretation of production data. The test results reveal the estimation performance of the stacked generalization architecture has outperformed other significant paradigms considered for production forecasting. [ABSTRACT FROM AUTHOR]

This paper examines the returns and volatility of two distinct types of crude oil: Brent and West Texas Intermediate (WTI). The time series data, collected from the international market, spans from February 1, 2014, to February 1, 2024. It includes 2,537 observations for WTI and 2,542 observations for Brent. The study employs the E-Views software to analyze the secondary data and to investigate the impacts of ARCH and GARCH models at a significant level. The mean reversion of WTI is 0.993582, and Brent crude mean reversion is 0.956194. The results show that WTI has a slower mean reversion than Brent because it is closer to 1. Further half-life model analysis showed that WTI reverted to its mean position after 99 days and Brent after 16 days. This study concludes that WTI is more volatile than Brent crude oil. [ABSTRACT FROM AUTHOR]

We conduct a comparison of three portfolio investment strategies in the US stock market following the implementation of Regulation Fair Disclosure in October 2000. The strategies analyzed are analyst‐recommended, recommendation changes, and momentum portfolios. Across various time periods, company sizes, and industry sectors, the momentum portfolio consistently outperforms the other strategies. Portfolios based on analyst recommendations exhibit poor performance in industries such as consumer staples and materials, which are strongly correlated with oil prices. These industries are susceptible to external demand and supply‐side price shocks that are not adequately captured by analyst recommendations. The findings highlight firstly, the efficacy of the momentum strategy and the limitations of relying solely on analysts' recommendations, particularly in oil‐dependent sectors; and secondly, the varying dynamics and performance of different investment strategies for investors seeking to optimize their investment decisions across different sectors and market conditions. [ABSTRACT FROM AUTHOR]