Hasil untuk "Petroleum refining. Petroleum products"

Yaoli Liu, Ziheng Ouyang, Shengtao Lou et al.

Reference-guided image generation has progressed rapidly, yet current diffusion models still struggle to preserve fine-grained visual details when refining a generated image using a reference. This limitation arises because VAE-based latent compression inherently discards subtle texture information, causing identity- and attribute-specific cues to vanish. Moreover, post-editing approaches that amplify local details based on existing methods often produce results inconsistent with the original image in terms of lighting, texture, or shape. To address this, we introduce \ourMthd{}, a detail-aware refinement framework that performs two consecutive stages of reference-driven correction to enhance pixel-level consistency. We first adapt a single-image diffusion editor by fine-tuning it to jointly ingest the draft image and the reference image, enabling globally coherent refinement while maintaining structural fidelity. We then apply reinforcement learning to further strengthen localized editing capability, explicitly optimizing for detail accuracy and semantic consistency. Extensive experiments demonstrate that \ourMthd{} significantly improves reference alignment and fine-grained detail preservation, producing faithful and visually coherent edits that surpass both open-source and commercial models on challenging reference-guided restoration benchmarks.

Beatriz Lemos, Alessandra Winter, Erika Blini et al.

The enhanced oil recovery method by low-salinity water flooding in sandstones has had promising results. When two immiscible phases are in contact with a solid surface, one is generally more strongly attracted by the solid than the other, called the wetting phase. The ability of different polar compounds to change the rock wettability depends on the rock type. In sandstone reservoirs, the electrostatic attraction between the positively charged surface of the oil and the negatively charged basal plans of the rock controls the oil adhesion on the rock surface. It is well known that typically lowering the injection brine salinity can enhance oil recovery, however, the effects of low-salinity water injection in sandstones are probably the result of several mechanisms acting in conjunction, highlighting the need to execute experimental tests. Moreover, this study aimed to evaluate the effect of brines with different compositions and salinity on the oil recovery factor of reservoir sandstone cores by carrying out core flooding experiments. In addition, reservoir cores were very friable, so sandpacks were produced to facilitate manipulation and make it possible to carry out the water flooding tests. Furthermore, they were used in four core flooding tests. Also, results indicated a potential low-salinity water effect, with an average incremental oil recovery of around 5.8%. The injectivity was analyzed using differential pressure during the experiments, and significant alterations were not observed due to the change in salinity of injected brines. Ultimately, the mineralogical analysis suggests that even sandstones with no clay content might show additional oil recovery due to low-salinity water injection, bespeaking the need to conduct more experiments for further investigation of the impact of the injected brine, the mineralogical composition of the rocks and the acting mechanisms.

Qianyu Zhang, Bolun Zheng, Xinying Chen et al.

Video compression artifacts arise due to the quantization operation in the frequency domain. The goal of video quality enhancement is to reduce compression artifacts and reconstruct a visually-pleasant result. In this work, we propose a hierarchical frequency-based upsampling and refining neural network (HFUR) for compressed video quality enhancement. HFUR consists of two modules: implicit frequency upsampling module (ImpFreqUp) and hierarchical and iterative refinement module (HIR). ImpFreqUp exploits DCT-domain prior derived through implicit DCT transform, and accurately reconstructs the DCT-domain loss via a coarse-to-fine transfer. Consequently, HIR is introduced to facilitate cross-collaboration and information compensation between the scales, thus further refine the feature maps and promote the visual quality of the final output. We demonstrate the effectiveness of the proposed modules via ablation experiments and visualized results. Extensive experiments on public benchmarks show that HFUR achieves state-of-the-art performance for both constant bit rate and constant QP modes.

Yongzheng Zhu, Shiji Zhao, Yuanye Zhou et al.

Mesh generation is essential for accurate and efficient computational fluid dynamics simulations. To resolve critical features in the flow, adaptive mesh refinement (AMR) is routinely employed in certain regions of the computational domain, where gradients or error estimates of the solution are often considered as the refining criteria. In many scenarios, however, these indicators can lead to unnecessary refinement over a large region, making the process a matter of trial and error and resulting in slow convergence of the computation. To this end, we propose a heuristic strategy that employs the residuals of the governing partial differential equations (PDEs) as a novel criterion to adaptively guide the mesh refining process. In particular, we leverage on the physics-informed neural networks (PINNs) to integrate imprecise data obtained on a coarse mesh and the governing PDEs. Once trained, PINNs are capable of identifying regions of highest residuals of the Navier-Stokes/Euler equations and suggesting new potential vertices for the coarse mesh cells. Moreover, we put forth two schemes to maintain the quality of the refined mesh through the strategic insertion of vertices and the implementation of Delaunay triangulation. By applying the residuals-guided AMR to address a multitude of typical incompressible/compressible flow problems and comparing the outcomes with those of gradient-based methods, we illustrate that the former effectively attains a favorable balance between the computational accuracy and cost.

Zhijie Wang, Yuheng Huang, Da Song et al.

The recent advancements in Generative AI have significantly advanced the field of text-to-image generation. The state-of-the-art text-to-image model, Stable Diffusion, is now capable of synthesizing high-quality images with a strong sense of aesthetics. Crafting text prompts that align with the model's interpretation and the user's intent thus becomes crucial. However, prompting remains challenging for novice users due to the complexity of the stable diffusion model and the non-trivial efforts required for iteratively editing and refining the text prompts. To address these challenges, we propose PromptCharm, a mixed-initiative system that facilitates text-to-image creation through multi-modal prompt engineering and refinement. To assist novice users in prompting, PromptCharm first automatically refines and optimizes the user's initial prompt. Furthermore, PromptCharm supports the user in exploring and selecting different image styles within a large database. To assist users in effectively refining their prompts and images, PromptCharm renders model explanations by visualizing the model's attention values. If the user notices any unsatisfactory areas in the generated images, they can further refine the images through model attention adjustment or image inpainting within the rich feedback loop of PromptCharm. To evaluate the effectiveness and usability of PromptCharm, we conducted a controlled user study with 12 participants and an exploratory user study with another 12 participants. These two studies show that participants using PromptCharm were able to create images with higher quality and better aligned with the user's expectations compared with using two variants of PromptCharm that lacked interaction or visualization support.

Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter et al.

We introduce MIM (Masked Image Modeling)-Refiner, a contrastive learning boost for pre-trained MIM models. MIM-Refiner is motivated by the insight that strong representations within MIM models generally reside in intermediate layers. Accordingly, MIM-Refiner leverages multiple contrastive heads that are connected to different intermediate layers. In each head, a modified nearest neighbor objective constructs semantic clusters that capture semantic information which improves performance on downstream tasks, including off-the-shelf and fine-tuning settings. The refinement process is short and simple - yet highly effective. Within a few epochs, we refine the features of MIM models from subpar to state-of-the-art, off-the-shelf features. Refining a ViT-H, pre-trained with data2vec 2.0 on ImageNet-1K, sets a new state-of-the-art in linear probing (84.7%) and low-shot classification among models that are pre-trained on ImageNet-1K. MIM-Refiner efficiently combines the advantages of MIM and ID objectives and compares favorably against previous state-of-the-art SSL models on a variety of benchmarks such as low-shot classification, long-tailed classification, clustering and semantic segmentation.

LI Jingsong, WANG Tao, WANG Jinwei et al.

The inversion of production performance after fracturing of coal-bed methane well is the key technology to realize the efficient development of gas reservoir. In order to improve the inversion efficiency of traditional numerical simulation methods, with the help of machine learning modeling technology and intelligent algorithm, this paper studies the automatic inversion and programmed design of key parameters such as coal-bed methane reservoir matrix permeability, gas saturation, fracture half length, fracture number and fracture conductivity. The multi-layer perceptron model is constructed with the training data generated by the nested discrete fracture coal-bed methane numerical simulator, and the collaborative inversion of reservoir-fracture parameters is realized by combining the intelligent algorithm. The results show that: (1) Using a small number of training samples (only 100 simulated samples are required for this case study), the machine learning model can accurately simulate the relationship between fracture/reservoir parameters and daily and cumulative gas production of shale gas wells; (2) The intelligent inversion algorithm based on machine learning agent assistance has high convergence efficiency and can quickly obtain a reasonable reservoir fracture parameter combination model with high inversion accuracy. It is concluded that the combination of machine learning modeling technology and intelligent inversion algorithm is helpful to promote the application and development of intelligent optimization technology of tight gas reservoirs, and provide theoretical guidance and technical support for accelerating the intelligent development process of unconventional oil and gas reservoirs in China.

Mengyu Wang, Henghui Ding, Jun Hao Liew et al.

In this paper, we explore a principal way to enhance the quality of object masks produced by different segmentation models. We propose a model-agnostic solution called SegRefiner, which offers a novel perspective on this problem by interpreting segmentation refinement as a data generation process. As a result, the refinement process can be smoothly implemented through a series of denoising diffusion steps. Specifically, SegRefiner takes coarse masks as inputs and refines them using a discrete diffusion process. By predicting the label and corresponding states-transition probabilities for each pixel, SegRefiner progressively refines the noisy masks in a conditional denoising manner. To assess the effectiveness of SegRefiner, we conduct comprehensive experiments on various segmentation tasks, including semantic segmentation, instance segmentation, and dichotomous image segmentation. The results demonstrate the superiority of our SegRefiner from multiple aspects. Firstly, it consistently improves both the segmentation metrics and boundary metrics across different types of coarse masks. Secondly, it outperforms previous model-agnostic refinement methods by a significant margin. Lastly, it exhibits a strong capability to capture extremely fine details when refining high-resolution images. The source code and trained models are available at https://github.com/MengyuWang826/SegRefiner.

Qinrou Wen, Jirui Yang, Xue Yang et al.

High-quality instance segmentation has shown emerging importance in computer vision. Without any refinement, DCT-Mask directly generates high-resolution masks by compressed vectors. To further refine masks obtained by compressed vectors, we propose for the first time a compressed vector based multi-stage refinement framework. However, the vanilla combination does not bring significant gains, because changes in some elements of the DCT vector will affect the prediction of the entire mask. Thus, we propose a simple and novel method named PatchDCT, which separates the mask decoded from a DCT vector into several patches and refines each patch by the designed classifier and regressor. Specifically, the classifier is used to distinguish mixed patches from all patches, and to correct previously mispredicted foreground and background patches. In contrast, the regressor is used for DCT vector prediction of mixed patches, further refining the segmentation quality at boundary locations. Experiments on COCO show that our method achieves 2.0%, 3.2%, 4.5% AP and 3.4%, 5.3%, 7.0% Boundary AP improvements over Mask-RCNN on COCO, LVIS, and Cityscapes, respectively. It also surpasses DCT-Mask by 0.7%, 1.1%, 1.3% AP and 0.9%, 1.7%, 4.2% Boundary AP on COCO, LVIS and Cityscapes. Besides, the performance of PatchDCT is also competitive with other state-of-the-art methods.

C. Herce, Chiara Martini, Marcello Salvio et al.

Petroleum products account for the 32.3% of worldwide primary energy. There are more than 100 oil refineries in Europe that directly employ 119,000 people with a turnover of EUR 600 billion and around 1.2% to the total value added in manufacturing. Therefore, the petroleum refining sector is very important in the European economy, and its decarbonization is crucial in the energy transition. Refineries present a high degree of complexity and integration, and the continuous increase of their energy efficiency is a key topic for the sector. In this work an analysis of the energy efficiency in ten Italian refineries based on mandatory energy audits and public data is presented. The primary (0.0963 ± 0.0341 toe/t), thermal (3421.71 ± 1316.84 MJ/t), and electrical (68.20 ± 19.34 kWh/t) specific energy consumptions have been evaluated. Some insights about the impact of refined products mix (mainly driven by production of diesel fuel) and Nelson Complexity Index in energy consumption are presented. Lastly, an overview of energy performance improvement actions (EPIAs) information extracted from energy audits is presented. This work presents a first step for the benchmark of Italian refineries that should be subsequently improved.

V. Özkan, A. Özkan

Crude oil; is a fossil fuel containing carbon, hydrogen, sulfur and many other components and is one of the world's largest and most widely used energy sources. However, in order for crude oil to be used as an energy source, it must be refined. With the use of petroleum products obtained as a result of refining, very high amounts of SOx gas are released into the atmosphere. These gases seriously harm both the environment and human health. This study aimed to reduce the amount of sulfur in crude oil and reduce its possible damages by using clinoptilolite zeolite (CZ). For this purpose, first of all, CZ; was characterized by SEM and XRF. Then, 0.1 g, 0.5 g, 1 g, 2 g and 5 g of the characterized CZ were weighed and added to the 50 mL crude oil samples separately. The mixture was mixed with a magnetic stirrer at 400 rpm for 60 and 120 minutes at room temperature before going through with an adsorptive desulfurization step. Afterwards, it was separated from the adsorbent by centrifugation and the residual sulfur amount was determined by ASTM D 1552-03 method. As a result of this study, which was carried out in an experimental laboratory environment; it has been observed that the desulfurization efficiency varies between 0.75 and 5.76 % (w/v) with the use of CZ adsorbent. Moreover; it was determined that the highest sulfur removal was obtained by using 5 g CZ.

Wang Hongjiang, Liu Lianqiang, Zhang Jiaxiang et al.

End face cavitation easily occurs iNmechanical seal of centrifugal pump during operation, which affects the reliability and stability of the equipment operation. In order to study the influence of end face cavitation of spiral groove on the mechanical seal performance, taking the leakage Q and opening force F as the optimization objectives, and the groove depth h, groove diameter ratio β, spiral angle θ, groove Nmber n and groove position coefficient λ as variables, an orthogonal test table was established, and the CFD method was used to conduct Nmerical simulation on the flow field of the sealing film. The study results show that the influence of λ on opening force and leakage is remarkable, followed by n; when λ≠0, the cavitation inside the liquid film is intense, which affects the sealing stability; when λ=0, based on orthogonal test method, the optimal working condition curve method of multiple objective parameters is put forward for the first time; then, taking P(leakage increment ΔQ/opening force increment ΔF)as the optimization evaluation index, 6 groups of optimal working condition points are obtained, and the optimal working condition curve is obtained by fitting, which provides a theoretical basis for the selection and optimization of spiral groove structure. The study based on the optimal working condition curve method solves the multi-objective optimization problem of orthogonal test and is universal.

G. Nazarova, E. Ivashkina, E. Ivanchina et al.

Abstract One way to improve and predict unsteady processes of petroleum fuel production is to develop a mathematical model, that considers the feedstock composition. A study of various feedstock deep refining processes is particularly important. In this paper, we present the prediction of the catalytic cracking unit under feedstock base expansion by using oil fractions with a higher boiling point. The zeolite-containing catalyst with ZSM-5/Y ratio = 0.11 was used in this work. A new kinetic model involving the high molecular weight of C13–C40 hydrocarbons, gasoline groups, gas individual hydrocarbons and coke formation reactions was developed. The feed comprehensive studies, the development and application of a mathematical model allow assessing the feasibility of various feedstock types involvement. The impact of four feedstock types on the yield of catalytic cracking products, catalyst deactivation degree, gasoline and gas composition, and octane number were determined. Among the feedstocks under study are West Siberian oil vacuum gas oil, a mixture of Kazakhstan and West Siberian oil, a mixture of vacuum and atmospheric gas oil with residual feedstock (extract, slack waxes, petrolatum, deasphalting agent, raffinate), a mixture of vacuum distillate and residual feedstock (extracts, slack waxes).

Longde SUN, Zihui FENG, Hang JIANG et al.

Some unusual events happened in petroleum industry in 2020, such as the negative WTI oil price, price soaring of melt-blown nonwoven fabric, Exxon Mobil Corp.(NYSE:XOM) removed from Dow Jones Industrial Average, and the oil demand peak theory proposed by BP Energy Outlook 2020 Edition. These events have made profound impact on petroleum exploration. Prospecting is at the forefront of petroleum industry chain, and prospectors have great influence on petroleum industry. The responsibility of petroleum prospectors is to find oil, which calls for the correct way of thinking as well as scientific and technical means, both of which are indispensable. When it comes to the cognition of petroleum exploration, we should draw lessons from predecessors' philosophy of finding oil from a development perspective. It is necessary to define the relationship between subject activity and objective structure, as there is an inherent tension between the two and a dialectical relationship that complements each other. It is also essential to illustrate the logic of initiative and decisiveness, as between the two is the dual logic of active logic that changes the world and deterministic logic based on science and technology. The strategic breakthrough in the Gulong shale oil exploration in Daqing is a typical example. Our knowledge and practice of oil exploration has overthrown the Hubbert Curve. The new curve may have more than one peak, which means hopes are always there for finding oil. Climbing to the top of a mountain must start from the foot. A journey of a thousand miles must begin with a single step. Looking forward to the future, prospectors have the wisdom, ability, and methods to find more, cleaner, and more affordable oil to drive the progress of human civilization. This is the duty of petroleum prospectors.

Xiaodong Wei

We present a novel method named truncated hierarchical unstructured splines (THU-splines) that supports both local $h$-refinement and unstructured quadrilateral meshes. In a THU-spline construction, an unstructured quadrilateral mesh is taken as the input control mesh, where the degenerated-patch method [18] is adopted in irregular regions to define $C^1$-continuous bicubic splines, whereas regular regions only involve $C^2$ B-splines. Irregular regions are then smoothly joined with regular regions through the truncation mechanism [29], leading to a globally smooth spline construction. Subsequently, local refinement is performed following the truncated hierarchical B-spline construction [10] to achieve a flexible refinement without propagating to unanticipated regions. Challenges lie in refining transition regions where a mixed types of splines play a role. THU-spline basis functions are globally $C^1$-continuous and are non-negative everywhere except near extraordinary vertices, where slight negativity is inevitable to retain refinability of the spline functions defined using the degenerated-patch method. Such functions also have a finite representation that can be easily integrated with existing finite element or isogeometric codes through Bézier extraction.

Rama Cont, Purba Das

We present several constructions of paths and processes with finite quadratic variation along a refining sequence of partitions, extending previous constructions to the non-uniform case. We study in particular the dependence of quadratic variation with respect to the sequence of partitions for these constructions. We identify a class of paths whose quadratic variation along a partition sequence is invariant under {\it coarsening}. This class is shown to include typical sample paths of Brownian motion, but also paths which are $\frac{1}{2}$-Hölder continuous. Finally, we show how to extend these constructions to higher dimensions.

Kayode Sanni, Promise Longe, Sylvester Okotie

An accurate prediction of production rate for wellhead choke is highly vital in petroleum production engineering applications. It is deployed in the control of surface production, prevention of water and gas coning, and optimization of the entire production systems. Although there are several choke correlations in literature to estimate production rate; however, most of the published correlations were derived with datasets outside Niger Delta fields. Thus, this study presents a new empirical-based model, which is a derivative from Choubineh et al. model, to predict the liquid production rate of chokes for Niger Delta oil wells. The new model was developed and optimized using multivariate regression and the Generalized Reduced Gradient (GRG) optimization algorithm. Furthermore, a total of 283 production data points from 21 oil wells in 7 fields in the Niger Delta region, with a randomly generated ratio of 70: 30 of the datasets, was used to develop and validate the developed model. The developed Model 2 predicted the choke production rate with a fitting accuracy of average absolute percentage error (AAPE) of 23.73% and coefficient of determination (R2) of 0.973; in addition, the model predicted validating accuracy of AAPE of 9.33% while the coefficient of determination (R2) stands at 0.982. Consequently, this model can be relied on as a quick and robust tool for estimating the choke production rate of producing oil wells. Moreover, the sensitivity analysis results show that the choke size has the most significant impact on the predicted liquid rate. In contrast, gas gravity has the least impact.

Ruth Johnson, Andrada Fiscutean, Serghei Mangul

With the ever-increasing carbon footprint associated with conferences, scientists can learn to refine their conference experiences when they do need to travel. We offer insight on how to optimize the conference experience through attending speaker sessions, giving presentations, and networking.

Ali Y. Jirjees, Abdulaziz M. Abdulaziz

In the present study, well log and well test data of 3 wells are investigated in details using Interactive Petrophysics (IP 3.5) software and KAPPA Workstation 2016 (Saphir) to examine the reservoir properties and to evaluate the effect of uncertainty in well logs interpretation on well test results. This involves the variations in the output value induced by random, systematic and model-based errors of the petrophysical input data. Results of well test uncertainty analysis indicated that the effective permeability calculations are strongly affected by the pay thickness uncertainty in Mishrif Formation with changes between 14.5% and 47%. Significant influences are related to pressure (∼10%), µo and Bo (∼9%), ?? (∼6%), and Sw (∼5%). Alternatively, radius of investigation calculations reported slight influences attributed to Sw (∼9%) and porosity (5%) uncertainties, with minimal influence by Bo, µo, and pressure. The skin factor calculations, however, showed a great sensitivity towards the pressure measurements up to 35%. Compared to fluid data, petrophysics uncertainty analysis showed marked influences on the effective permeability, skin factor and radius of investigation. These results show the importance of accurate reservoir petrophysics on well test interpretation that may change the calculated values between 10 and 50% that strongly influence the expected well performance. Keywords: Uncertainty analysis, Well log petrophysics, Well test, West Al Qurna Oil Field, Iraq

Bao Zefu, Liu Jiang

In view of the complicated configuration process of the dual-acting injection-type hydraulic impactor in the field application, the internal flow channel model and mathematical model of the hydraulic impactor are established by using FLUENT software. The working condition parameters of the test environment are used. Using the ICEM software, simulations are conducted to obtain the pressure distribution of the upper and lower chambers of the impactor, the velocity distribution and the velocity vector streamline of the fluid through the sucking element. FLUENT is used to carry out the application simulation of hydraulic impactor. By identifying the input parameters the same as the actual application and determining the flow state, the prediction of the tool operation can be realized. By changing the size of the suction element and adjusting the input and output parameters, the pre-analysis of the actual situation of the impactor with different specifications can be performed. By replacing the equipment with different specifications and the sucking element, the output parameters can be calculated according to the internal pressure and the distribution of the velocity, thereby providing references for the hydraulic impactor options identification in drilling different formations with different lithologies.