Hasil untuk "Oils, fats, and waxes"

Zhe Ban, Carlos Pfeiffer

Abstract Data reconciliation techniques have been the subject of many classic studies in the data conditioning process. By reconciling the measurements, accurate estimation of the system output and unmeasured variables is provided. However, accurately determining measurement noise and parameter uncertainty in real time remains a significant challenge. How to simultaneously estimate parameters in the system has been attracting considerable interest. So far, very little attention has been paid to time-varying parameter estimation in oil production systems. In particular, estimation of parameter dynamics and the corresponding uncertainty without prior knowledge remains challenging. This work extends a previous study on dynamic parameter estimation by considering scenarios where parameters change both gradually and abruptly. To address these dynamics, nonlinear filtering methods are employed and compared. A comparative analysis was conducted using both quantitative metrics and visualization plots to evaluate the performance of various approaches. Under the same abrupt parameter change scenario, nonlinear filter-based methods demonstrated superior performance in parameter estimation, achieving a root mean square error of $$6.56 \times 10^{-11}$$ , compared to $$7.84 \times 10^{-11}$$ for the MCMC-based method-even without the use of prior information. Additionally, nonlinear filters showed a significant advantage in simultaneous state estimation, with a root mean square error of $$1.94 \times 10^{4}$$ , markedly lower than the $$1.47 \times 10^{6}$$ observed with the MCMC-based approach. The effectiveness of nonlinear filtering methods was further validated in scenarios involving gradual parameter changes, again without relying on prior knowledge. This work provides an important opportunity to advance the understanding of dynamic parameter estimation in the gas and oil industry, and the improved model can possibly be applied to real-time optimization and model-based control. Graphical abstract

Jianli Qiang, YanChi Yang, Mingjin Cai et al.

Abstract Due to the fluid leak-off effect in the reservoir, the bottomhole pressure decreases after the pumps are shut down. Analyzing the shut-in pressure decline curve of a fractured well is a common method for determining fracturing parameters. Although the G-function pressure decline analysis method briefly explains the pressure decline process, it is not accurate for calculating fracture parameters in fractured low-porosity gas reservoirs. This paper considers the influence of natural fractures on the leak-off coefficient and proposes an approach to evaluate fracture complexity by using the fluctuation characteristics of the construction pressure curve and the G-function characteristics during fracture closure. In this study, the pressure decline curve was segmented to determine fracture parameters, and a shut-in pressure decline analysis model for fractured low-porosity gas reservoirs was established. The fracture complexity is characterized by the fluctuation of the superposition derivative curve, and the approximate series is calculated to quantitatively evaluate fracture complexity. Field data from multiple wells were used to calculate the approximate series, thereby verifying its practicality. Results from actual case data show a positive correlation between fracture complexity and the approximate series. Additionally, this paper adopts a comprehensive filtering model to remove the noise caused by water hammer effects during the shut-in process, improving data quality and analysis accuracy. The feasibility and reliability of the model are validated using actual data from fractured wells in the Dabei Oilfield.

LI Chunlei, YANG Heshan, ZHANG Hongxia et al.

Reservoir’s static pressure is an essential basic data in the development and research of oil and gas fields. Its acquisition conditions are strict， and the sample number is extremely small. Static pressures are estimated with empirical methods based on dynamic pressure data during the production process； however， data errors are significant. To address the above issues， a static pressure prediction method for CO2 flooding oil reservoirs based on the time series partitioning Transformer model was proposed， utilizing deep learning theory. Model parameters were selected based on correlation analysis， and iterative interpolation was used to fill in samples to construct a static pressure prediction sample set. According to the principle of channel independence， the multivariate time series was divided into univariate time series， and a time series partitioning mechanism was introduced to divide the time series into subsequential blocks to capture local features. Based on the Transformer model architecture， a multi-head self-attention mechanism was utilized to extract features， and a self-supervised learning mechanism was employed to enhance the ability to capture complex dynamic characteristics， achieving the prediction of reservoirs’ static pressure. The research results indicate that the proposed model can accurately predict the static pressures at the middle of the oil reservoir of each well in the active well group， significantly improving prediction accuracy.

XUE Yifan, WEN Zhigang, HUANG Yahao et al.

The study of the filling veins in deep reservoirs within the strike-slip fault zone in the north of Fuman Oilfield utilizes a range of methods including petrographic characteristics, analysis of rare earth elements andSr（strontium） isotopes, fluorescence spectra of oil inclusions, microscopic thermodynamics, and U-Pb isotopic dating of carbonate rocks. The findings reveal two stages of calcite vein formation in this area. These veins originate from the formation water of the middle and Lower Ordovician sources, with no evidence of oxidizing fluid intrusion, suggesting that the deep to ultra-deep oil and gas reserves have maintained good sealing properties in later stages. Furthermore, based on the burial history deduced from inclusions and low U-Pb isotope dates from carbonate rocks, it has been determined that there are three distinct stages of hydrocarbon charging in the deep Ordovician strata of the northern strike-slip fault zone in the Tarim Basin. These stages correspond to （459±7.2） Ma（middle Caledonian）, （348±18） Ma（early Hercynian）, and 268 Ma（late Hercynian）. It is noted that the early Hercynian period was the key phase for hydrocarbon accumulation in the deep and ultra-deep carbonate rocks in the north of Fuman Oilfield, with a significant correlation observed between oil and gas charging and fault activity.

Abdul Rehman Baig, Sulaiman A. Alarifi, Mobeen Murtaza et al.

Abstract Sand production in oil and gas wells is a significant concern, leading to equipment erosion, reduced well productivity, and safety hazards. Researchers have developed an eco-friendly solution to consolidate sand via an Enzyme-induced Carbonate Precipitation (EICP) process. It fortifies loose sand in wells, preventing it from resurfacing. This study addresses this challenge by developing a novel EICP solution effective at high temperatures (120 °C). This advancement goes beyond previous formulations, which often exhibited low strength at elevated temperatures. In this study, we developed six different solutions to consolidate sand at different temperatures with various bio- and synthetic polymers, the resulted sand consolidation has been tested by obtaining the precipitation composition after consolidation, visualizing consolidated sand structures, assessing strength and measuring permeability of the consolidated sand. AN 125, a synthetic copolymer based on Acrylamide and 2-Acrylamido-2-Methylpropane Sulfonic Acid (AM-AMPS), emerged as the most effective additive. It fostered the strongest consolidated sand at both temperatures (2,175 psi at 70 °C and 2,155 psi at 120 °C). It also exhibited superior thermal stability compared to bio-polymers like xanthan gum, which degraded at 120 °C. The EICP solution with AN 125 led to a moderate permeability decrease of around 30% during simulated sand pack flooding, indicating minimal impact on well flow. The developed formulation offers a robust and environmentally friendly approach to sand consolidation in oil and gas wells, enhancing well integrity and production efficiency. Furthermore, this work emphasizes the significance of a proper methodology towards evaluating the suitability of bio-polymers and synthetic copolymers for sand consolidation using EICP formulations.

Suparit Tangparitkul, Thakheru Akamine, David Harbottle et al.

This study examines discrepancies in oil displacement mechanisms at equivalent interfacial tensions, focusing on the distinct contributions of surfactants and nanoparticles. It was hypothesized that similar interfacial activities would result in consistent displacement outcomes, while differences would reflect unique interfacial behaviors. Micromodel experiments revealed that at high interfacial tension (~20 mN/m), surfactants outperformed nanofluids in efficiency and ultimate oil recovery by reinforcing capillary forces. Conversely, nanofluids showed limited ability to modify interfacial forces. At lower interfacial tensions (6.5 mN/m for surfactants, 15.6 mN/m for nanofluids), both systems displayed similar displacement efficiencies and fingering patterns, driven by distinct mechanisms: capillary instability for surfactants and expansive layer flow for nanofluids. These findings challenge the assumption that nanofluids rely primarily on interfacial tension reduction for enhanced oil recovery (EOR) and highlight the need to refine our understanding of nanoparticle interfacial activities. Future studies should extend these insights to core-scale experiments for a more comprehensive evaluation of two-phase flow dynamics.

Mohammed Alruqimi, Luca Di Persio

Despite numerous research efforts in applying deep learning to time series forecasting, achieving high accuracy in multi-step predictions for volatile time series like crude oil prices remains a significant challenge. Moreover, most existing approaches primarily focus on one-step forecasting, and the performance often varies depending on the dataset and specific case study. In this paper, we introduce an ensemble model to capture Brent oil price volatility and enhance the multi-step prediction. Our methodology employs a two-pronged approach. First, we assess popular deep-learning models and the impact of various external factors on forecasting accuracy. Then, we introduce an ensemble multi-step forecasting model for Brent oil prices. Our approach generates accurate forecasts by employing ensemble techniques across multiple forecasting scenarios using three BI-GRU networks.Extensive experiments were conducted on a dataset encompassing the COVID-19 pandemic period, which had a significant impact on energy markets. The proposed model's performance was evaluated using the standard evaluation metrics of MAE, MSE, and RMSE. The results demonstrate that the proposed model outperforms benchmark and established models.

Matija Šuković, Igor Jovančević

This paper proposes an innovative approach to improving quality control of olive oil manufacturing and preventing damage to the machinery caused by foreign objects. We developed a computer-vision-based system that monitors the input of an olive grinder and promptly alerts operators if a foreign object is detected, indicating it by using guided lasers, audio, and visual cues.

Chen Wang, Sarah Erfani, Tansu Alpcan et al.

Anomaly detection in decision-making sequences is a challenging problem due to the complexity of normality representation learning and the sequential nature of the task. Most existing methods based on Reinforcement Learning (RL) are difficult to implement in the real world due to unrealistic assumptions, such as having access to environment dynamics, reward signals, and online interactions with the environment. To address these limitations, we propose an unsupervised method named Offline Imitation Learning based Anomaly Detection (OIL-AD), which detects anomalies in decision-making sequences using two extracted behaviour features: action optimality and sequential association. Our offline learning model is an adaptation of behavioural cloning with a transformer policy network, where we modify the training process to learn a Q function and a state value function from normal trajectories. We propose that the Q function and the state value function can provide sufficient information about agents' behavioural data, from which we derive two features for anomaly detection. The intuition behind our method is that the action optimality feature derived from the Q function can differentiate the optimal action from others at each local state, and the sequential association feature derived from the state value function has the potential to maintain the temporal correlations between decisions (state-action pairs). Our experiments show that OIL-AD can achieve outstanding online anomaly detection performance with up to 34.8% improvement in F1 score over comparable baselines.

Yilmaz Emin, Karatas Burak

The aims of this study were to cold-press mahaleb seed, and then fully characterize the oil to extent its food and functional food applications. The novelty of this study relies upon the first data provided by the thermal analysis, sensory analysis, and volatile aroma compounds composition. The seeds were pressed with a screw-type single-head press with a maximum oil exit temperature of 40 °C. Most common physico-chemical properties, composition analyses, volatile aroma compounds profile, sensory descriptive analysis and consumer tests were completed. The main properties were appropriate and the composition (fatty acids, phytosterols and tocopherols) data concurred with the available literature. Thermal data were provided, and the oil peak crystallization and melting temperatures were −44.45 °C and −8.41 °C, respectively. There were 38 volatile aroma compounds quantified mostly with almond, green, vanillin, woody, and fermented aroma definitions. The panel described the oil with 5 sensory descriptive (almond, vanillin, dough, green, cooling) terms. Consumers liked appearance the most (4.49) with a general acceptance score of 3.70 on a 5-point hedonic scale. Overall, the mahaleb seed oil is a conjugated linolenic, oleic and linoleic fatty acids, β-sitosterol and γ-tocopherol rich, very aromatic, and consumer-liked sample. Further studies with various food applications are foreseen.

Sergio Garcia-Vega, Andreas G. F. Hoepner, Joeri Rogelj et al.

The Paris Agreement aims to reach net zero greenhouse gas (GHG) emissions in the second half of the 21st century, and the Oil & Gas sector plays a key role in achieving this transition. Understanding progress in emission reductions in the private sector relies on the disclosure of corporate climate-related data, and the Carbon Disclosure Project (CDP) is considered a leader in this area. Companies report voluntarily to CDP, providing total emissions and breakdowns into categories. How reliable are these accounts? Here, we show that their reliability is likely very poor. A significant proportion of Oil & Gas companies' emission reports between 2010 and 2019 fail a 'simple summation' mathematical test that identifies if the breakdowns add up to the totals. Companies' reports reflect unbalanced internal bookkeeping in 38.9% of cases, which suggests worryingly low quality standards for data guiding the private sector's contribution to halting climate change.

Ivan Nekrasov, Aleksandr Zagulyaev, Vladimir Bukhtoyarov et al.

The article considers the issue of increasing the structural reliability of a biofuel production plant. A review of the existing basic technological schemes of the biofuel production plant has been carried out. The main structural elements are determined and a functional diagram is constructed. Processed cooking oil was chosen as the input raw material. A structural analysis of the reliability of each element and the entire system as a whole was carried out. The least reliable elements are determined, options for improving the overall reliability of the installation are proposed.

Francesca Venturini, Silvan Fluri, Manas Mejari et al.

This work systematically investigates the oxidation of extra virgin olive oil (EVOO) under accelerated storage conditions with UV absorption and total fluorescence spectroscopy. With the large amount of data collected, it proposes a method to monitor the oil's quality based on machine learning applied to highly-aggregated data. EVOO is a high-quality vegetable oil that has earned worldwide reputation for its numerous health benefits and excellent taste. Despite its outstanding quality, EVOO degrades over time owing to oxidation, which can affect both its health qualities and flavour. Therefore, it is highly relevant to quantify the effects of oxidation on EVOO and develop methods to assess it that can be easily implemented under field conditions, rather than in specialized laboratories. The following study demonstrates that fluorescence spectroscopy has the capability to monitor the effect of oxidation and assess the quality of EVOO, even when the data are highly aggregated. It shows that complex laboratory equipment is not necessary to exploit fluorescence spectroscopy using the proposed method and that cost-effective solutions, which can be used in-field by non-scientists, could provide an easily-accessible assessment of the quality of EVOO.

Shuai Jian, Zhang Yi

The analysis on the operating pressure cycle characteristics of gas pipeline is an important research basis for predicting the fatigue life of the pipeline. Based on the rain flow counting method, the pressure cycle in the random load spectrum was counted, the outlet pressure change history of two stations in a pipeline was counted, and the pressure cycle amplitude, mean value, frequency proportion and damage proportion of the pipeline in the stations were analyzed. The analysis results show that the Nmber of pressure cycles with pressure ratio greater than 0.8 in the gas pipeline accounts for more than 90% of the total cycles; the acquisition cycle has little effect on the counting of large pressure cycles, but has great effect on the counting of small pressure cycles; the acquisition cycle is selected for pipeline pressure cycle based on the principle of no loss of large cycle; the higher the frequency proportion of large cycle, the greater the proportion of damage to the pipeline is, but when the Nmber of cycles is large and the cycle amplitude is small, a large Nmber of small cycles are the main causes of pipeline damage. The analysis conclusions provide a theoretical support for the fatigue life prediction of oil and gas pipelines.

Abhishek Mund, Amit K Nayse, Arindam Das

Clathrate hydrate is a naturally occurring ice-like solid which forms in water phase under suitable temperature and pressure conditions, in the presence of one or more hydrophobic molecules. It also forms inside the oil and gas pipes leading to higher pumping cost, flow blockage and even catastrophic accidents. Engineered surfaces with low hydrate adhesion can provide an effective solution to this problem. Liquid impregnated surfaces is one such example of engineered surfaces which has already shown tremendous potential in reducing the nucleation and adhesion of solids. Here we report the design and synthesis of liquid impregnated surfaces with extremely low hydrate adhesion under the mixed environment of oil and water. The most challenging aspect of designing these surfaces was to stabilize a lubricant layer simultaneously under the water and oil. A detailed methodology to make such lubricant stable surfaces from theoretical perspective was described and experimentally validated for lubricant stability. Experimental measurements on such surfaces showed extremely low hydrate accumulation and one order of magnitude or more reduction in hydrate adhesion force.

Kaikai CHENG, Jitao YAO, Zhengjie CHENG et al.

The number of samples for detecting corrosion characteristic value is difficult to reach a large enough size in practical engineering, which leads to the pipeline corrosion evaluation results tend to be aggressive. For this reason, the influence of sample size on the inference results was analyzed, and based on the Bayesian theory and the uncertainty of measurement, the Bayesian inference method for the pipeline corrosion depth under the condition of small sample size was proposed. Then, the correlation between the corrosion depth and the length was considered, and the prediction method of corrosion depth based on the correlation and Bayesian inference was developed. Thereby, the corrosion depths under different defect lengths was inferred with the pipeline corrosion detection data, and further the effectiveness of the method was verified. The results indicate that: the new method could better reflect the influence of sample size on the inference results, the prediction results are more conservative and consistent with the engineering experience, and so it is safer and more favorable to the engineering application. The research results could provide more accurate information to the prediction of pipeline corrosion depth, as well as theoretical reference to the prediction of the characteristic value of other corroded pipelines with consideration given to the correlation of random variables.

Sadegh Dahim, Behrouz Harimi, Mohammad Hossein Ghazanfari et al.

The liquid bridge is considered a good means to maintain capillary continuity between overlying matrix blocks if its stability in fractures is preserved. Despite several studies focusing on the liquid bridge in different environments, little attention is paid to dig through a single liquid bridge between thin sections of minerals found in fractured reservoirs. In this study, a set of experiments was conducted to investigate liquid bridge stability and surface profile for different values of liquid volume and surface wettability conditions. It is found that critical fracture aperture is linearly proportional to the contact angle and to the third root of liquid volume, which is depicted by a newly developed expression. An accurate method for computation of capillary pressure of liquid bridge (known as fracture capillary pressure) from the experimentally determined interface profiles, based on the numerical solution of the Young-Laplace equation, is proposed. Following the Plateau sequence, both nodoid and unduloid shape bridges are observed with an increase in fracture aperture, corresponding to positive and negative fracture capillary pressure, respectively. It is interesting to note that instability of liquid bridges occurs at small negative values of capillary force where some attraction force exists between fracture faces. By applying a 1D mathematical model of liquid dripping, a modified expression for the prediction of critical fracture aperture is proposed, including fluid and flow-related parameters. The findings of this study help to better incorporate the role of liquid bridge and corresponding fracture capillary pressure in capillary continuity in fractured porous media.

Rida Ait El Manssour, Gleb Pogudin

The equation $x^m = 0$ defines a fat point on a line. The algebra of regular functions on the arc space of this scheme is the quotient of $k[x, x', x^{(2)}, \ldots]$ by all differential consequences of $x^m = 0$. This infinite-dimensional algebra admits a natural filtration by finite dimensional algebras corresponding to the truncations of arcs. We show that the generating series for their dimensions equals $\frac{m}{1 - mt}$. We also determine the lexicographic initial ideal of the defining ideal of the arc space. These results are motivated by nonreduced version of the geometric motivic Poincaré series, multiplicities in differential algebra, and connections between arc spaces and the Rogers-Ramanujan identities. We also prove a recent conjecture put forth by Afsharijoo in the latter context.

Mohammadreza Mahmoudi, Hana Ghaneei

This study aims to analyze the impact of the crude oil market on the Toronto Stock Exchange Index (TSX)c based on monthly data from 1970 to 2021 using Markov-switching vector autoregressive (MSI-VAR) model. The results indicate that TSX return contains two regimes, including: positive return (regime 1), when growth rate of stock index is positive; and negative return (regime 2), when growth rate of stock index is negative. Moreover, regime 1 is more volatile than regime 2. The findings also show the crude oil market has negative effect on the stock market in regime 1, while it has positive effect on the stock market in regime 2. In addition, we can see this effect in regime 1 more significantly in comparison to regime 2. Furthermore, two period lag of oil price decreases stock return in regime 1, while it increases stock return in regime 2.

Babacar Seck, Robert J. Elliott

This paper introduces a new type of risk measures, namely regime switching entropic risk measures, and study their applicability through simulations. The state of the economy is incorporated into the entropic risk formulation by using a Markov chain. Closed formulae of the risk measure are obtained for futures on crude oil derivatives. The applicability of these new types of risk measures is based on the study of the risk aversion parameter and the convenience yield. The numerical results show a term structure and a mean-reverting behavior of the convenience yield.