Search Results (193)

Accidental oil spills are an all-too-common occurrence. In order to properly plan for and respond to oil spills, responders and planners need to understand how the oil will behave in the environment: how it will weather and how it will affect ecosystems and biota. To address this need, databases of oil properties have been developed, such as NOAA’s Automated Data Inquiry for Oil Spills (ADIOS^®) Oil Database, a publicly available database of oil properties useful for oil spill modelers, responders, and planners, currently containing over 1400 oil records. However, despite its size, when a spill occurs, the actual oil spilled is unlikely to be in the database, even if the oil’s identity is known. The challenge is even greater for planners, who cannot possibly plan for the spilling of thousands of individual different oils. When the exact product is not available, the responder must choose an oil record from the database that closely resembles the product at hand. This process can slow the responder down and may require them to have years of experience to choose the most appropriate oil record. If not done with care, a user can inadvertently select an atypical oil with a similar name, or a record with poor data quality, which can yield inappropriate results. In this work, we generated a set of “generic” oil records for the ADIOS^® Oil Database that have been developed to be a good representation of typical products of a certain type, e.g., “medium crude” or “diesel fuel”. These oil records can then be used in the early stages of a response when details about the spilled product are sparse. These generic oil records can also be very helpful for drills, training, and planning when the user does not need to work with a specific product. These generic records were developed by examining the extensive dataset available in the ADIOS Oil Database and determining which records matched a given type of oil and were of sufficient quality. Then all the records for each oil type were combined to create a “typical” or “average” oil that is representative of that oil type. In the course of this project, statistical methods were chosen that were most appropriate to the property at hand. The oil types chosen were: Light, Medium, and Heavy Crude, Condensate, Jet Fuel, Diesel, Gasoline, Intermediate Fuel Oil (IFO), and Heavy Fuel Oil (HFO). These are all oil types that are likely to be spilled, and for which sufficient data existed in the ADIOS Oil Database to compute an “average” oil. Other potential product types could be added in the future should more data become available. Full article

Developing complex fault-block low-permeability reservoirs faces great challenges in constructing efficient injection-production flow pathways, which often leads to unsatisfactory waterflooding development performance. To improve the recovery efficiency of this type of reservoir, this study first carried out laboratory extraction experiments and then conducted CO₂ extraction numerical simulations on low-permeability porous media via ANSYS to validate the experimental results. Based on investigations targeting the target reservoir, the key findings are summarized as follows: Supercritical CO₂ preferentially extracts light hydrocarbon components lighter than C₁₀, which accounts for more than 60% of the total extracted components; when the pressure exceeds 20 MPa, a small amount of heavy components C₁₅₊ can also be extracted. Under the experimental conditions adopted in this study, the optimal CO₂ reinjection pressure range is determined as 12–20 MPa. Within this range, the extraction efficiency increases by 2.0–2.5% per 1 MPa increment of pressure, and this pressure condition can effectively dissolve medium hydrocarbon components and promote the formation of uniform plug flow. However, when the reinjection pressure continues to rise beyond 20 MPa, the CO₂ extraction efficiency will gradually decrease. The numerically simulated CO₂ extraction efficiency of crude oil shows high consistency with the experimental measurements. This study confirms that CO₂ huff-n-puff can be effectively applied for crude oil extraction in the peripheral areas of tight reservoirs and fault-block reservoir units. Nevertheless, in field application, it is imperative to optimize the reinjection pressure design: accurately customizing injection pressure parameters according to different reservoir types and their specific development requirements is a core measure to improve CO₂ extraction efficiency. Full article

UAV-based remote sensing excels in rapid response, high timeliness, simple operation, and high degrees of automation, and has been widely applied for geological hazard monitoring. Deep learning methods based on unitemporal UAV images can only analyze the static appearance of a scene, while bitemporal change detection can capture the dynamic evolution of hazards; however, due to diverse geological landforms and topography, environmental noises such as vegetation cover, and dynamic weather conditions, change detection of geological hazards from UAV images based on traditional deep learning technology is not always effective. Therefore, there is an urgent need to utilize large vision-language models (LVLMs) to further improve the accuracy and robustness of the change detection model. Motivated by this, this paper proposes a novel remote sensing model for geological hazard monitoring, referred to as CLIFF (CLIP-BIT-EfficientNet), based on the multi-modal LVLM Contrastive Language–Image Pre-training (CLIP), the change detection network Bitemporal Image Transformer (BIT), and the classification network EfficientNet, along with corresponding datasets and model fine-tuning strategies. The proposed transfer fusion module bridges the CLIFF and BIT networks by aligning their feature distributions and dimensions, allowing the general knowledge of the LVLM and the task-specific knowledge of the learnable branch to reinforce each other. Furthermore, this integrated pipeline addresses the scarcity of labeled hazard data by allowing the BIT to train on larger public datasets, while fine-tuning EfficientNet on smaller hazard-classification datasets within the change area, making the approach more efficient and reliable than direct classification methods. Experimental results show that the proposed CLIFF algorithm outperforms state-of-the-art deep learning algorithms such as LightCDNet and ChangeFormer, with an IoU of 75.74% and an F1 score of 0.8689 for change detection. Meanwhile, CLIFF has an overall accuracy rate of 86.89% in identifying geological hazards along gas pipelines, such as crude oil spills, collapses, landslides, and floods, with per-class accuracies of 87.32% and 86.17% for crude oil spills and landslides, respectively. Full article

Heavy and highly viscous oils account for a significant proportion of the world’s hydrocarbon reserves. The development of these reserves in harsh climates is associated with technological risks due to paraffin deposits and equipment corrosion. Ensuring reliable transportation requires operational monitoring of the physical and chemical properties of fluids directly at the wellhead. Traditional laboratory methods such as SARA fractionation and gas chromatography (GC) are time-consuming and can yield to distortions in the sample composition during transportation. Field optical methods, such as an infrared (IR) spectroscopy are complicated by the optical heterogeneity of crude oils due to emulsified water, supramolecular associations of resins, asphaltenes, and paraffins. In this paper, ultrasonic (US) sample preparation for high-viscosity oils is justified as a method for increasing the reliability and information content of field IR spectroscopic analysis by unmasking the diagnostic extrema of absorption bands that are initially distorted by emulsified water, baseline scattering, and radiation scattering from large resin–asphaltene–paraffin aggregates. The technique is based on cavitation-induced destruction of emulsion shells and disaggregation of the structural framework without volume thermal heating. Experimental data obtained from watered high-viscosity oil has shown that 9 min of the US exposure reduces the light scattering index $I_{\mathrm{t}\mathrm{r}\mathrm{s}}$ by 92.83%, bringing the system into a less heterogeneous state. Statistical correlation analysis confirmed that emulsions and aggregates are the main scattering centers, and their destruction correlates directly with the transparency of the medium. Stability of spectral indices $I_{{\mathrm{C}\mathrm{H}}_3/{\mathrm{C}\mathrm{H}}_2}$, $I_{\mathrm{f}\mathrm{o}\mathrm{c}}$ and $I_{\mathrm{C}=\mathrm{O}}$ indicates the absence of chemical degradation or oxidation at the US exposure intensity of 0.12 W/mL, confirming the physical nature of the effect. The proposed method makes it possible to implement automated monitoring of the properties of high-viscosity oil directly at the wellhead, minimizing logistic costs and risks of the sample degradation. The practical significance of the proposed method is to improve the reliability and information content of wellhead monitoring by reducing optical heterogeneity and making diagnostic significant IR absorption extremes more distinguishable for further interpretation. Full article

Multiple biomarker datasets and compound-specific sulfur isotopic compositions (δ³⁴S) of dibenzothiophenes (DBTs) were analyzed for crude oil from Well B6 on the Maigaiti Slope, Tarim Basin. The very low concentrations of DBTs (124.9 μg/g oil), diamondoids (92.7 μg/g oil), and thiadiamondoids (0.20 μg/g oil), together with the absence of 25-norhopane, indicate that the B6 oil has not undergone significant secondary alteration, including thermochemical sulfate reduction (TSR), extensive thermal cracking, or biodegradation. No clear evidence of oil mixing was observed either. Aliphatic and aromatic biomarker distributions suggest that the parent source rocks contain type I–II₁ kerogen, with dominant algal and bacterial organic inputs deposited under low-salinity, weakly reducing conditions, broadly comparable to those of the Upper Ordovician Lianglitag Formation source rocks (UOLS). Oil–source correlation using compound-specific δ³⁴S values of DBTs indicates that B6 oil is derived from UOLS (or similar undiscovered source rocks), not from Cambrian source rocks. This is consistent with biomarker evidence. As the first identified Ordovician-derived oil showing relatively light DBT δ³⁴S values (average ~6.41‰), close to those of Ordovician kerogen (average ~5.62‰), and with minimal secondary overprinting, B6 oil has strong potential to serve as a UOLS end-member oil. This will likely open new exploration opportunities for deep hydrocarbon from previously untapped strata in the southwestern Tarim Basin. Full article

Brent crude oil prices are strategically important due to their sensitivity to geopolitical developments, financial market stress, and global monetary conditions. This study examines whether strategic risk indicators improve the forecasting performance of Brent crude oil returns within an integrated econometric and machine learning framework. Monthly data from January 2001 to December 2025 are employed, using the Global Geopolitical Risk Index (GPR), the CBOE Volatility Index (VIX), and the U.S. 10-year Treasury yield (DGS10) as key explanatory variables. Methodologically, the analysis first estimates benchmark econometric models, including ARIMAX (AutoRegressive Integrated Moving Average with Explanatory Variable) and ARIMAX-gjrGARCH (Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroscedasticity, and then implements machine learning models, namely XGBoost (eXtreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), and Random Forest, to capture potential nonlinear relationships. Using sMAPE (Symmetric Mean Absolute Percentage Error), forecast performance is assessed over multiple forecast horizons under a rolling-origin framework. Across several forecasting horizons and train-test split configurations, the empirical results consistently show that machine learning techniques, especially LightGBM, offer superior out-of-sample forecasting accuracy. These findings suggest that the dynamics of Brent crude oil returns are influenced by complex and nonlinear relationships between macro-financial conditions, financial uncertainty, and geopolitical risk. The study concludes that flexible data-driven forecasting frameworks offer stronger predictive performance than benchmark econometric models under strategic risk conditions and provide useful implications for energy market risk management and policy decision-making. Full article

This research systematically investigated the catalytic pyrolysis of Arab Heavy (AH) and Arab Light (AL) crude oils using NiO supported on Al₂O₃ or ZSM-5 in a microwave-assisted reactor, with particular emphasis on hydrogen (H₂) generation and value-added chemicals. To understand how both the catalyst and feedstock affect reaction products, gas and liquid products as well as catalyst activity were carefully examined. The production of H₂ and olefins was significantly enhanced by the NiO/Al₂O₃ catalyst, especially when using AL crude. This is most likely due to favorable metal-support interactions that increase the dehydrogenation activity. However, when paired with lighter feedstock, NiO/ZSM-5 greatly increased paraffin production and encouraged light alkane synthesis in both phases. GC-MS and FTIR spectroscopy confirmed that NiO/Al₂O₃ produced liquid products richer in aromatics while also containing a significant fraction of paraffins. Remarkably, the AL over NiO/Al₂O₃ combination showed very little liquid recovery, indicating that gas generation was higher in these reaction conditions. These results showed how H₂ selectivity and hydrocarbon routes in NiO/ZSM-5 and NiO/Al₂O₃ are controlled by various microwave-catalyst interactions. This work further highlights the importance of matching catalyst properties with feedstock type to control product selectivity, with NiO/Al₂O₃ showing particular promise for H₂-focused applications. Full article

Ultra-deep high-pour-point oil (waxy crude oil) reservoirs under high-temperature and high-pressure conditions are characterized by severe heterogeneity and poor displacement efficiency, with the crude oil exhibiting a pour point of approximately 47 °C. Using the XH block as a representative ultra-deep reservoir, this study systematically examines the displacement mechanisms of CO₂ flooding and CO₂–water-alternating-gas (WAG) flooding. This study aims to elucidate the CO₂–oil interactions between CO₂ and waxy crude oil, to compare oil recovery and CO₂ retention under different injection modes in media with varying permeability and heterogeneity, and to provide experimental support for field-scale development. Slim tube, swelling, and long-core flooding experiments were conducted under reservoir conditions (139 °C, 57 MPa). The phase behavior between CO₂ and crude oil, as well as its impact on oil volume and flow properties, was analyzed. Moreover, continuous CO₂ flooding and WAG flooding were compared in low-permeability and medium–high-permeability cores, and WAG was subsequently applied to a parallel-core system to quantify the effect of interlayer heterogeneity. Results indicate that while CO₂ achieves miscibility with the waxy crude at reservoir pressure, its contribution to swelling and viscosity reduction is moderate compared to light oils; thus, recovery relies primarily on miscible displacement. Compared with continuous CO₂ flooding, WAG effectively delays gas breakthrough and enlarges the swept volume, leading to higher oil recovery and CO₂ storage efficiency. Increasing permeability reduces flow resistance and significantly enhances the oil recovery factor. In strongly heterogeneous systems, dominant flow through high-permeability channels markedly weakens displacement in low-permeability zones, resulting in lower overall recovery and CO₂ retention. These results indicate that properly designed WAG schemes can improve the development performance of heterogeneous waxy oil reservoirs while simultaneously meeting CO₂ storage requirements. Full article

This study is devoted to a comparative analysis of modern methods for determining the wax crystallization onset temperature (WCOT) of high-paraffin crude oil from the Uzen field. The objects of investigation were crude oil samples from the 13th reservoir horizon with a paraffin mass content ranging from 22.5% to 27.5%. For the first time in the practice of the oil and gas industry of Kazakhstan, a comprehensive comparison of results obtained using two fundamentally different approaches was performed: the light transmittance method using the KING-UNNP-70 apparatus, which simulates reservoir conditions (pressure of 12 MPa), and a dynamic method using a Wax Flow Loop facility, which reproduces crude oil flow in a pipeline. The experimental results showed that the light transmittance method detects the appearance of the first microcrystals at temperatures of 38.0–41.7 °C, whereas the dynamic method yields higher WCOT values, ranging from 41.0 °C to 44.0 °C. It was also found that the temperature of bulk crystallization, characterizing intensive solid phase formation, lies within the range of 33.5–35.0 °C. The results confirm that under flow conditions, paraffin crystallization begins at higher temperatures compared to static conditions, which is of critical importance for the design of crude oil gathering and transportation systems. The obtained data allow more accurate prediction of the risks of asphaltene–resin–paraffin deposits (ARPD) formation and optimization of technological operating conditions of wells at the late stage of field development. Full article

Deep and ultra-deep petroleum resources have become major contributors to petroleum reserves. The Tarim Basin has recently witnessed discoveries of several oil reservoirs at depths exceeding 8000 m, which extend the exploration depth limit for crude oil and light oil resources. To clarify the role of overpressure during the critical stage of crude oil cracking (Easy Ro ≈ 1.0–2.0%), this study conducted low-temperature, long-duration, overpressure (150 MPa) gold tube pyrolysis experiments on marine crude oil from the Tarim Basin. Comprehensive analysis of the cracking products (C₁–C₃₀₊) revealed significant differences in the thermal stability and cracking behavior of hydrocarbon molecules with different chain lengths: long-chain hydrocarbons (C₁₂₊) were continuously consumed as the primary reactants, whereas short-chain hydrocarbons (C₆–C₁₂) initially formed as products and subsequently underwent secondary cracking as reactants. During this process, overpressure played a critical role in delaying the yield peak of light hydrocarbons and suppressing their secondary cracking. This mechanism resulted in a slower increase in gaseous hydrocarbon yield under overpressure conditions, and the carbon isotopic composition clearly recorded a shift in the cracking precursors from heavy to light hydrocarbons. Furthermore, fluorescence lifetime, as a sensitive spectroscopic indicator, exhibited delayed decay under overpressure, confirming the inhibition of aromatization and polymerization reactions by overpressure. By illuminating the sequential nature of hydrocarbon cracking and the moderating influence of overpressure at molecular and spectroscopic levels, this work offers crucial evidence for understanding multi-phase hydrocarbon coexistence and forecasting the preservation depth of discrete-phase crude oil in the Shuntuoguole area. Full article

As a pivotal global commodity, crude oil price volatility directly impacts economic stability and strategic security. Being the most widely traded asset worldwide, it also serves as a key financial barometer and a critical transition fuel in the shift towards renewable energy. Nevertheless, accurate forecasting of crude oil prices remains challenging due to three persistent challenges: (1) the lack of a systematic method to filter out redundant and noisy features for deep learning models; (2) the limited ability of existing models to simultaneously capture both local bidirectional dependencies and global periodic patterns; and (3) the non-adaptive nature of conventional attention mechanisms, which restricts their capacity to dynamically focus on the most informative historical periods. To bridge these gaps, this study introduces a novel forecasting framework with three key contributions. First, we introduce a hierarchical feature selection paradigm based on LightGBM to systematically eliminate data redundancy and noise, thereby constructing an optimal feature subset for subsequent deep modeling. Second, an improved Autoformer encoder, integrated with Bidirectional GRUs, is designed to simultaneously capture local bidirectional dependencies and global periodic patterns, enabling a more comprehensive multi-scale temporal representation. Third, a dynamic fusion mechanism is incorporated to adaptively recalibrate the significance of historical timesteps. This enables the model to focus on periods rich in information, enhancing contextual awareness in predictions. Future research aims to enhance forecasting capabilities by achieving a deeper integration of local and global temporal representations, potentially through exploring advanced gating or sparse attention mechanisms. Full article

Organic chlorine (Org-Cl) in crude oil poses continuous operational and environmental risks during production, trading, and refining processes. This article reviews the management of Org-Cl from its origin assumptions to analysis and mitigation measures and proposes a practical closed-loop framework. Quantitative merit value indicators (typical detection limit/quantitative limit, accuracy, and repeatability) and greenness indicators are used to compare standard methods and advanced methods, and to guide the selection of applicable methods. Corresponding technical maturity levels (TRLs) are assigned to mitigation measures (protective beds/adsorption, HDC, and emerging electrochemical/photochemical routes). Technical economic indicators with reference values (relative capital expenditure/operating expenditure levels) are summarized to assist decision-making. The main findings are as follows: (i) Evidence of secondary formation of organic chlorine under distillation-related conditions still relies on the matrix and requires independent verification; (ii) MWDXRF can achieve rapid screening (usually only 5 to 10 min), while CIC/D5808 supports quality balance arbitration; (iii) adsorption can remove a considerable portion of organic chlorine in light fractions under laboratory conditions, while the survival ability of HDC related to crude oil depends on the durability of the catalyst and the tail gas treatment capacity; and (iv) minimum viable implementation (MVI) combined with online total-chlorine monitoring and a physical principle-based digital twin technology can provide auditable closed-loop control. The limitations of this review include partial reliance on laboratory-scale data, inconsistent reports among studies, and the lack of standardized public datasets for model benchmarking. Prioritization should be given to analysis quality control, process durability indicators, and data governance to achieve reliable digital deployment. Full article

The extra-heavy oil in the Tahe Oilfield of China has extremely high viscosity, as it is rich in the heavy components asphaltene and resin, creating significant difficulties in its exploitation and transportation. Therefore, it is important to effectively reduce the viscosity and improve the fluidity of this extra-heavy oil. The traditional viscosity reduction method suffers from a high blending ratio and a shortage of light crude oil resources for extra-heavy oil blending. In this study, coal tar and washing oil—widely available low-cost by-products of the coal chemical industry—are used for extra-heavy oil blending and viscosity reduction. Washing oil—containing light components distilled from coal tar—was highly effective in reducing the viscosity of extra-heavy oil. When the dilution ratio of washing oil is 0.25, the viscosity of extra-heavy oil is reduced to 1214 mPa·s, and the viscosity reduction rate is 99.8%, indicating that washing oil is an efficient viscosity-reducing agent in extra-heavy oil blending. GC-MS showed that the washing oil contained abundant aromatic hydrocarbons and aromatic heterocyclic rings. A multi-component viscosity reduction system using washing oil coupled with toluene, xylene, and surfactant achieved an even better viscosity reduction effect. In conclusion, we designed a low-cost, high-efficiency, multi-component, and multi-functional synergistic system for extra-heavy oil viscosity reduction in the Tahe Oilfield. In the proposed working mechanism, aromatic hydrocarbons and aromatic heterocyclic rings in washing oil can intercalate into the layered structure of dense asphaltene aggregates, thereby dispersing and dissociating them. Full article

This study presents the development of a portable DOBI meter prototype designed for the rapid, low-cost evaluation of crude red palm oil (RPO) quality. The device employs two narrow-spectrum LEDs (UV at 269 nm and visible at 446 nm) as light sources, paired with a broadband photodiode (PD) detector to measure light absorption in a quartz cuvette containing 95% hexane-diluted oil samples. Dedicated LED driver circuits, a PD receiver module, and microcontroller-based data acquisition and display systems were integrated into a compact enclosure. Calibration procedures involved the measurement of LED emission spectra and PD responses, followed by standard curve generation using known RPO concentrations. The results from the DOBI meter were validated against a commercial spectrophotometer (Merck Prove 600), demonstrating high accuracy with less than 5% deviation. Further analysis of RPO extracted from microwave-treated mesocarps showed consistent DOBI values and carotenoid concentrations across both instruments. The developed device offers a reliable, accessible alternative for assessing palm oil quality, particularly in field or small-scale industrial settings. Full article

The delayed coking unit (DCU) is a critical technology in Mexican refineries for upgrading heavy crude oil into lighter, high-value products. Despite its economic relevance, the process is energy-intensive, generates substantial emissions, and produces significant coke, challenging its sustainability. This study proposes a multi-objective optimization framework to enhance DCU performance by integrating Aspen Plus^® v.12.1 simulations with sustainability metrics. Five key indicators were considered: Global Warming Potential (GWP), Specific Energy Intensity (SEI), Mass Intensity (MI), Reaction Mass Efficiency (RME), and Product Yield. A validated Aspen Plus^® model was combined with sensitivity analysis to identify critical decision variables, which were optimized through the $ϵ$-constraint method. Strategic adjustments in reflux flows, split ratios, and column operating conditions improved separation efficiency and reduced energy demand. Results show GWP reductions of 15–25% and SEI improvements of 5–18% for light and heavy gas oils, with smaller gains in MI and trade-offs in RME. Product yield was preserved under optimized conditions, ensuring economic feasibility. A key limitation is that this study did not model coking reactions; instead, optimization focused on the separation network, using reactor effluent as a fixed input. Despite this constraint, the methodology demonstrates a replicable path to improve refining sustainability. Full article