Hasil untuk "Petroleum refining. Petroleum products"

M. A. Fernandes, E. Gildin, M. A. Sampaio

Monitoring bottom-hole variables in petroleum wells is essential for production optimization, safety, and emissions reduction. Permanent Downhole Gauges (PDGs) provide real-time pressure data but face reliability and cost issues. We propose a machine learning-based soft sensor to estimate flowing Bottom-Hole Pressure (BHP) using wellhead and topside measurements. A Long Short-Term Memory (LSTM) model is introduced and compared with Multi-Layer Perceptron (MLP) and Ridge Regression. We also pioneer Transfer Learning for adapting models across operational environments. Tested on real offshore datasets from Brazil's Pre-salt basin, the methodology achieved Mean Absolute Percentage Error (MAPE) consistently below 2\%, outperforming benchmarks. This work offers a cost-effective, accurate alternative to physical sensors, with broad applicability across diverse reservoir and flow conditions.

GUAN Wenjie, PENG Xiaolong, ZHU Suyang, YANG Chen, PENG Zhen, MA Xiaoran

During the development of middle and deep gas reservoirs in South Sichuan, conventional reservoir engineering methods—such as fracture propagation, stress-induced analysis, and numerical simulation—render productivity prediction of infilling wells laborious and ineffective in addressing variations in production capacity across different production stages, with stringent application conditions. In order to quickly and accurately predict the production capacity of infilling wells, this study classifies the “three-stage” declining trend observed in the production pressure curves of existing wells into: (1) A drastic decline period, regarded as the initial water production stage; (2) a rapid decline period; and (3) a slow decline period, both considered part of the later gas production stage. The Grey Wolf Optimizer(GWO) algorithm, a fast optimization algorithm with adaptive capabilities and an information feedback mechanism, is applied for hyperparameter optimization of the Long Short-term Memory (LSTM) neural network. Two stage-specific models were constructed, with the number of hidden layer neurons, dropout rate, and batch size determined by the optimal solutions obtained via GWO. The number of iterations was selected based on the loss curve and performance metric curve, while a linear warm-up strategy was used to dynamically adjust the learning rate, facilitating high-speed training and the formation of a staged productivity prediction model. Example studies show that the GWO-optimised LSTM neural network model achieves rapid convergence with a preset learning rate of 0.002 and 450 iterations, ultimately reaching a performance index of 0.923. Compared to the conventional LSTM neural network model, the average absolute errors during the early and later stages are reduced by 1.290 m3/d and 0.213 × 104 m3/d, respectively. Compared with numerical simulation fitting results, the average absolute error in gas production prediction is reduced by 0.24 × 104 m3/d. Therefore, the improved LSTM neural network model demonstrates excellent performance in capacity prediction across different production stages, and the stage-specific productivity variations in infilling wells within middle and deep shale gas reservoirs in South Sichuan. This provides a theoretical foundation for productivity prediction methods of infilling wells.

T. A. Ganiev, T. B. Minniakhmetov, S. L. Sabanov et al.

The oil and oil refining industries commonly uses vertical steel tanks for the safe storage of crude oil and petroleum products. However, the operation of these tanks comes with risks such as: corrosion, mechanical damage, and non-uniform deformation can result in significant failures with serious environmental and economic consequences. For this reason, regular monitoring of tank technical state is especially important. Early detection of deviations from design parameters, which is possible thanks to monitoring, can help prevent accidents. This paper examines the use of surface laser scanning technology is a method for inspecting tank walls. This method involves creating a 3D digital model of the tank, analyzing its stress-strain state, and identifying deviations from its original geometry. The use of a 3D scanner ensures high measurement accuracy and automates the data collection process. The study's results indicate that surface laser scanning is an effective tool for detecting deformations in tank walls and for monitoring their progression over time. Compared to traditional visual and dimensional inspection methods, this technology provides a more comprehensive assessment of the technical condition of the tanks. This approach reduces diagnostic costs and improves the safety and reliability of tank operations. The practical value of this study lies in the potential integration of surface laser scanning technology into the routine monitoring system of tank farms. This integration can enhance safety and extend the service life of oil storage facilities.

Seyed Kourosh Mahjour, Seyed Saman Mahjour

The petroleum industry faces unprecedented challenges in reservoir management, requiring rapid integration of complex multimodal datasets for real-time decision support. This study presents a novel integrated framework combining state-of-the-art large language models (GPT-4o, Claude 4 Sonnet, Gemini 2.5 Pro) with advanced prompt engineering techniques and multimodal data fusion for comprehensive reservoir analysis. The framework implements domain-specific retrieval-augmented generation (RAG) with over 50,000 petroleum engineering documents, chain-of-thought reasoning, and few-shot learning for rapid field adaptation. Multimodal integration processes seismic interpretations, well logs, and production data through specialized AI models with vision transformers. Field validation across 15 diverse reservoir environments demonstrates exceptional performance: 94.2% reservoir characterization accuracy, 87.6% production forecasting precision, and 91.4% well placement optimization success rate. The system achieves sub-second response times while maintaining 96.2% safety reliability with no high-risk incidents during evaluation. Economic analysis reveals 62-78% cost reductions (mean 72%) relative to traditional methods with 8-month payback period. Few-shot learning reduces field adaptation time by 72%, while automated prompt optimization achieves 89% improvement in reasoning quality. The framework processed real-time data streams with 96.2% anomaly detection accuracy and reduced environmental incidents by 45%. We provide detailed experimental protocols, baseline comparisons, ablation studies, and statistical significance testing to ensure reproducibility. This research demonstrates practical integration of cutting-edge AI technologies with petroleum domain expertise for enhanced operational efficiency, safety, and economic performance.

Jan M. Nordbotten, Martin A. Fernø, Bernd Flemisch et al.

The 11th Society of Petroleum Engineers Comparative Solution Project (shortened SPE11 herein) benchmarked simulation tools for geological carbon dioxide (CO$_2$) storage. A total of 45 groups from leading research institutions and industry across the globe signed up to participate, with 18 ultimately contributing valid results that were included in the comparative study reported here. This paper summarizes the SPE11. A comprehensive introduction and qualitative discussion of the submitted data are provided, together with an overview of online resources for accessing the full depth of data. A global metric for analyzing the relative distance between submissions is proposed and used to conduct a quantitative analysis of the submissions. This analysis attempts to statistically resolve the key aspects influencing the variability between submissions. The study shows that the major qualitative variation between the submitted results is related to thermal effects, dissolution-driven convective mixing, and resolution of facies discontinuities. Moreover, a strong dependence on grid resolution is observed across all three versions of the SPE11. However, our quantitative analysis suggests that the observed variations are predominantly influenced by factors not documented in the technical responses provided by the participants. We therefore identify that unreported variations due to human choices within the process of setting up, conducting, and reporting on the simulations underlying each SPE11 submission are at least as impactful as the computational choices reported.

M. N. Siddiquee

Substantial amounts of low‐value light petroleum fractions and low‐value heavy petroleum fractions, such as light naphtha, HVGO, and vacuum residue, are generated during the upgrading and refining of conventional and unconventional petroleum resources. The oil industry emphasizes economic diversification, aiming to produce high‐value products from these low petroleum fractions through cost‐effective and sustainable methods. Controlled autoxidation (oxidation with air) has the potential to produce industrially important oxygenates, including alcohols, and ketones, from the low‐value light petroleum fractions. The produced alcohols can also be converted to olefin through catalytic dehydration. Following controlled autoxidation, the low‐value heavy petroleum fractions can be utilized to produce value‐added products, including carbon fiber precursors. It would reduce the production cost of a highly demandable product, carbon fiber. This review highlights the prospect of developing an alternative, sustainable, and economic method to produce value‐added products from the low‐value petroleum fractions following a controlled autoxidation approach.

XUE Gang,GUO Tao,ZHANG Ye,XU Xiangyang,WANG Wei,HAN Kening,GUO Dongxin,JIN Xiaobo

The exploration and development of coalbed methane（CBM）in the Permian Longtan Formation in south Chongqing is in the initial stage. In order to reveal the general geological conditions of coalbed methane in the coal seam C25, by the experimental and geological data obtained from the coal mines and coalbed methane drilling, the geological characteristics such as coal rock and coal quality, coal pore penetration and gas content have been analyzed. The results show that the C25 coalbed methane of Longtan Formation of Permian in the study area is characterized by “stable development, relatively large thickness, low pore permeability, high metamorphism and high gas content”. The development of the coal seam C25 is stable throughout the whole area, showing the trend of “thick in the north and thin in the south”, and the thickness in the northern part of the coalbed is generally larger than 1.5 m. The coal quality belongs to semi-bright coal. The content of vitrinite in the organic component is 51.7%~72.2%; the vitrinite reflectance ranges from 1.8%~2.2%, and the metamorphism degree is high-over maturity. The porosity and permeability of the coal rock are relatively low, with the porosity ranging from 3.46%~8.46% and the permeability of mostly lower than 0.01×10-3 μm2. The gas content of the coal bed is high, generally more than 10.0 m3/t; meanwhile, the top and bottom plates of the coal bed are good sealing layers. Based on the production of Q1 and Y2, it is believed that the coal seam C25 of Permian Longtan Formation in south Chongqing area has good geological conditions for CBM exploration and development.

WANG Xiang, ZHANG Guicai, JIANG Ping et al.

The volume sweep coefficient is essential for evaluating the development effect and formulating development adjustment plans for oil fields. This paper aims to study the variation law of volume sweep coefficient in different stages of water injection development. From the perspective of the injection pore volume multiple， a calculation model is built of displacement efficiency and injection pore volume multiple， and a calculation method of volume sweep coefficient is proposed based on oil-water relative flow theory and reservoir engineering principle. In addition， three test areas of Shengli Oilfield are taken as examples for calculation and analysis. The results show that the relationship between the displacement efficiency and the injection pore volume multiple satisfies an exponential equation， and the relationship curve between the two is upward convex. As the injection pore volume multiple increases， the displacement efficiency gradually increases from the minimum displacement efficiency and approaches the maximum displacement efficiency. The displacement efficiency calculation model is verified， and the average relative error between the predicted and measured values is only 1.90%. During the water flooding development， the relationship curve between the volume sweep coefficient and the injection pore volume multiple shows an evolution trend of fast rising， slow rising， and near platform. The calculation results can guide the effect evaluation of development adjustment measures. At present， the volume sweep coefficient of the three test areas is about 90%. There is a large amount of remaining oil in the swept area. It is urgent to study the description and start-up method of the main remaining oil in the swept area.

Mohamed Hosin ElNeiri, Abdel Sattar Abdel Hamid Dahab, Abdulaziz Mohamed Abdulaziz et al.

The application of laser in the drilling and perforation of oil wells can achieve great benefits such as reduced drilling costs and time with a higher rate of penetration (ROP) and elimination of casing necessity in oil and gas well drilling. This paper presents an original experimental investigation of Laser cutting through Hashma sandstone (a common quarry rock in Egypt) to develop a good understanding of the laser cutting process in sandstone. Five blocks of Hashma sandstone with dimensions of 35 cm × 35 cm × 10 cm were utilized to study the effects of the various parameters involved in the lasing (cutting) process in order to evaluate the cutting process through sandstone, investigate the effect of laser parameters on the process and the cutting mechanisms. The experimental results showed that the laser drilling can provide lower specific energy (SE) compared to conventional drilling methods, revealed the effect of various laser and rock parameters (such as beam power, intensity, duration, sample size, and orientation) on the cutting process, and demonstrated the laser cutting mechanisms through sandstone such as thermal spallation and melting mechanisms. Several parameters must be optimized for an optimum laser cutting process with the lowest SE, such as using the optimum beam power, beam duration (or Lasig time), and beam mode (continuous or pulsed). The optimum parameters may change from one case to another and depend on the overall interactions among the various variables such as thermal dissipation rate and purging system efficiency.

Mohammed Farfour, Rachid Hedjam, Douglas Foster et al.

The growing number of seismic and elastic attributes poses a challenge, making the full benefit from each attribute in characterizing geological formation very difficult, if not impossible. Various approaches are routinely employed to select the best attributes for specific purposes. Machine learning (ML) algorithms have demonstrated good capabilities in combining appropriate attributes to address reservoir characterization problems. This study aims to use and combine seismic and elastic attributes to detect hydrocarbon-saturated reservoirs, source rock, and seal rocks in the Poseidon field, Offshore Australia. A large number of attributes are extracted from seismic data and from impedance data. Artificial Neural Networks (ANN) are implemented to combine the extracted attributes and convert them into Resistivity volume, and Gamma Ray volume from which Shale probability volume, Sand volume probability volume, Effective Porosity volume, and Gas Chimney Probability Cubes. The cubes are deployed for a detailed analysis of the petroleum system in the area. The produced Shale volume and Resistivity cube helped delineate the seal rock and source rock in the area. Next, the reservoir intervals were identified using Porosity, Shale, and Resistivity volumes. A pre-trained Convolutional Neural Network (CNN) is trained using another carefully selected attribute set to detect subtle faults that hydrocarbons might migrated through from source rock to trap. The integration of all the extracted cubes contributed to find new prospects in the area and assess their geological probability of success. The proposed approach stands out for its multi-physical attribute integration, Machine Learning and Human expertise incorporation, possible applicability to other fields.

Zhang Tao, Liu Daixuan, Liu Wei et al.

During the drilling process,abnormal vibration of bottomhole drilling tool is often caused by factors such as improper drilling parameters and mismatch between bottomhole assembly and formation,leading to damage of drilling tool,reduction of drilling efficiency and unacceptable wellbore quality.First,an abnormal downhole vibration warning model based on Informer time series was built.Second,based on the time-frequency domain characteristics of near-bit vibration data,the normal and abnormal vibration data sets were labeled,and the mean and root mean square values of downhole vibration after wavelet conversion were used as input values to conduct warning model training.Finally,the test set data were used to test the effectiveness of the warning model.The research results show that compared with LSTM model,this model reduces E_MS by 70% and has higher prediction accuracy in terms of long series prediction results; meanwhile,aimed at long series prediction of downhole vibration mean,the occurrence of stick-slip vibration can be judged 90 s in advance.This warning model can effectively identify and warn abnormal downhole vibration,reduce drilling risk,and provide a certain technical basis for further establishing advanced intelligent drilling system.

Suyun HU, Shizhen TAO, Min WANG et al.

Based on the typical dissection of various onshore tight oil fields in China, the tight oil migration and accumulation mechanism and enrichment-controlling factors in continental lake basins are analyzed through nuclear magnetic resonance (NMR) displacement physical simulation and Lattice Boltzmann numerical simulation by using the samples of source rock, reservoir rock and crude oil. In continental lake basins, the dynamic forces driving hydrocarbon generation and expulsion of high-quality source rocks are the foundational power that determines the charging efficiency and accumulation effect of tight oil, the oil migration resistance is a key element that influences the charging efficiency and accumulation effect of tight oil, and the coupling of charging force with pore-throat resistance in tight reservoir controls the tight oil accumulation and sweet spot enrichment. The degree of tight oil enrichment in continental lake basins is controlled by four factors: source rock, reservoir pore-throat size, anisotropy of reservoir structure, and fractures. The high-quality source rocks control the near-source distribution of tight oil, reservoir physical properties and pore-throat size are positively correlated with the degree of tight oil enrichment, the anisotropy of reservoir structure reveals that the parallel migration rate is the highest, and intralayer fractures can improve the migration and accumulation efficiency and the oil saturation.

Azizollah Khormali, Soroush Ahmadi

Abstract Scale precipitation is one of the major problems in the petroleum industry during waterflooding. The possibility of salt formation and precipitation should be monitored and analyzed under dynamic conditions to improve production performance. Scale precipitation and its dependence on production parameters should be investigated before using scale inhibitors. In this study, the precipitation of barium sulfate salt was investigated through dynamic tube blocking tests at different injection rates and times. For this purpose, the pressure drop caused by salt deposition was evaluated at injection rates of 1, 2, 3, 4, and 5 mL/min. The software determined the worst conditions (temperature, pressure, and water mixing ratio) for barium sulfate precipitation. Moreover, during the experiments, the pressure drop caused by barium sulfate precipitation was measured without using scale inhibitors. The pressure drop data were evaluated by the response surface method and analysis of variance to develop a new model for predicting the pressure drop depending on the injection rate and time. The novelty of this study lies in the development of a new high-precision correlation to predict barium sulfate precipitation under dynamic conditions using the response surface methodology that evaluates the effect of injection rate and time on the possibility of salt precipitation. The accuracy and adequacy of the obtained model were confirmed by using R2 statistics (including R2-coefficient of determination, adjusted R2, and predicted R2), adequate precision, and diagnostic charts. The results showed that the proposed model could fully and accurately predict the pressure drop. Increasing the time and decreasing the injection rate caused an increase in pressure drop and precipitation of barium sulfate salt, which was related to the formation of more salt due to the contact of ions. In addition, in a short period of the injection process, the pressure drop due to salt deposition increased sharply, which confirms the need to use a suitable scale inhibitor to control salt deposition. Finally, the dynamic tube blocking tests were repeated in the presence of two well-known scale inhibitors, which prevented salt deposition in the tubes. At the same time, no pressure drop was observed in the presence of scale inhibitors at all injection rates during a long period of injection. The obtained results can be used for the evaluation of salt precipitation during oil production in the reservoirs, in which barium sulfate is precipitated during waterflooding. For this purpose, knowing the flow rate and injection time, it is possible to determine the amount of pressure drop caused by salt deposition.

F. Adebiyi

Abstract Petroleum sector is vital in socio-economic enhancement of any petroleum producing nation as it is the leading energy producer in industrial sectors because various refined petroleum products are globally used. In process of petroleum refining, flue gases that can cause environmental degradation and eventual ill-healths are constantly been generated. This review work aims at enhancing petroleum refinery operations, alleviating flue gas emission and encouraging low-carbon perception. It condenses scattered previous studies on composition, sources, environmental impacts and benefits of flue gas in petroleum refinery. It also offers insight into handling techniques of flue gases in the industry coupled with recommendations.

Nada Salman Nikkeh, Suhair Muafaq Abdulhussein, M. A. Mohammed

In this study, the investigation of the decision-making strategy was used to select the alternative that was finally adopted in the crude oil refining process. This strategy was used to select the option that was ultimately implemented in the process. The Doura industrial refinery was the source of the information that was acquired for the analysis. The super decision software was applied in order to carry out an examination of the PDS components. After going through the process of refining, one can get the items on the following list: There are five main types of petroleum products, and they are: gasoline, gas oil, liquid gas, black oil, and white oil. Gasoline is the most common type of petroleum product. In order for the parameters to be optimally accommodated by the solution that is finally decided to be the most practical one, the analytic hierarchy process, also known as AHP, technique has been applied. This has been done in conjunction with the parameter determination system, or PDS. This has been done in order to reach the maximum potential level of productivity in the most efficient manner. As a result of the fact that this was the circumstance, a probe into the preliminary phase of the project was carried out, which in the end resulted in the expenditure of a grand total of 3969463 USD. This was determined by taking into account the costs of running the firm in addition to the prices of the raw materials that were utilized in the production process. In addition, the output of the refining process was not only dependent on the price and quantity of the product, but also on the amount of product that was actually sold. This meant that the cost and quantity of the product were not the only factors that determined the output. In order to determine what should be done during the process of making an estimate of what should be done in order to arrive at the response that was going to be the most advantageous taking everything into consideration, a mathematical model was applied as part of the process.

Z. Litvintseva, Yuriy Litvincev

The article reflects the features of the Russian oil refining industry, as well as the peculiarity of export-import relations in Russia and the competitiveness of Russian petroleum products on the world market

Akshansh Mishra, Rakesh Morisetty, Rajat Sarawagi

Oil production forecasting is an important step in controlling the cost-effect and monitoring the functioning of petroleum reservoirs. As a result, oil production forecasting makes it easier for reservoir engineers to develop feasible projects, which helps to avoid risky investments and achieve long-term growth. As a result, reliable petroleum reservoir forecasting is critical for controlling and managing the effective cost of oil reservoirs. Oil production is influenced by reservoir qualities such as porosity, permeability, compressibility, fluid saturation, and other well operational parameters. Three-time series algorithms i.e., Seasonal Naive method, Exponential Smoothening and ARIMA to forecast the Distillate Fuel Oil Refinery and Propane Blender net production for the next two years.

Paola Sandra Elenga-Wilson, C. Kayath, N. Mokémiabeka et al.

Petroleum is, up to this date, an inimitable nonrenewable energy resource. Petroleum leakage, which arises during transport, storage, and refining, is the most important contaminant in the environment, as it produces harm to the surrounding ecosystem. Bioremediation is an efficient method used to treat petroleum hydrocarbon-contaminated soil using indigenous microorganisms. The degradation characteristics for a variety of hydrocarbons (hexane, benzene, gasoline, and diesel) were qualitatively and quantitatively investigated using Bacillus isolates. Microbiological and biochemical methods have been used including isolation of oil-degrading bacteria, enzymatic activities, the determination of physicochemical parameters, biosurfactant production and extraction assay, oil displacement assay, antimicrobial assay of the biosurfactants, and bioremediation kinetics. Consequently, of the 60 isolates capable of degrading different hydrocarbons at fast rates, 34 were suspected to be Bacillus isolates capable of growing in 24 h or 48 h on BH medium supplemented with 2% of hexane, benzene, gasoline, diesel, and olive oil, respectively. Among the 34 isolates, 61% (21/34) are capable of producing biosurfactant-like molecules by using gasoline, 70% (24/34) with diesel oil, 85% (29/34) with hexane, and 82% (28/34) with benzene. It was found that biosurfactant-producing isolates are extractable with HCl (100%), ammonium sulphate (95%), chloroform (95%), and ethanol (100%). Biosurfactants showed stability at 20°C, 37°C, 40°C, and 60°C. Biosurfactant secreted by Bacillus strains has shown an antagonistic effect in Escherichia coli, Shigella flexneri 5a M90T, and Bacillus cereus. The selected isolates could therefore be safely used for biodegradation. Substrate biodegradation patterns by individual isolates were found to significantly differ. The study shows that benzene was degraded faster, followed by hexane, gasoline, and finally diesel. The Bacillus consortium used can decrease hydrocarbon content from 195 to 112 (g/kg) in 15 days.

Zachary Byrum, H. Pilorgé, J. Wilcox

Petroleum refining is among the largest industrial greenhouse gas emission sources in the U.S., producing approximately 13% of U.S. industrial emissions and approximately 3% of all U.S. emissions. While the U.S. must rapidly reduce its reliance on fossil fuels, some demand will remain for petroleum refinery products in the coming decades, and so it is critical that refineries deeply decarbonize. For the U.S. to meet its climate target of net-zero emissions economy-wide by 2050, petroleum use must dramatically decline and refineries must transform to reduce their substantial emissions. This analysis finds that using current and novel technologies – like fuel switching to clean hydrogen; electrification; and carbon capture, utilization and storage – can deeply decarbonize refineries, delivering climate benefits and improving local air quality as the U.S. transitions away from fossil fuels in the coming decades. It shows how, in the long-term, refineries could shift to processing renewable feedstocks to produce low-carbon fuels for aviation, shipping and trucking – our toughest to abate transportation sectors – ultimately reducing fuel carbon intensities by up to 80%. By leveraging technologies and adapting to low-carbon demands, refineries could provide lower-carbon products for our economy while helping meet U.S. climate goals. The paper provides policymakers and stakeholders with an overview of refinery emissions today and the possibilities for and barriers to mitigating them. To deeply decarbonize refineries, the paper calls for ambitious expansion of existing and novel technologies, supported by further independent research and supportive policies.