Hasil untuk "Petroleum refining. Petroleum products"

Barima Money, Rayan Hassan Modather, David Abutu et al.

Geopolymers are promising alternatives to Portland cement due to their lower carbon footprint, superior mechanical strength, durability, and reduced shrinkage. However, inconsistent data and significant variations in experimental results highlight the uncertainty surrounding the reaction mechanisms of clay-based geopolymers (CBGs) in high-temperature oil wells. This review provides critical insights into these mechanisms and summarizes CBG synthesis methods. It explores the reaction processes of CBGs under high-temperature oil well conditions and analyzes the variables influencing these mechanisms. This review also examines the application of CBGs in oil wells, focusing on the challenges and potential solutions. Laboratory studies show that Portland cement degrades at temperatures above 300 °F (150 °C), while CBGs maintain their integrity and strength at temperatures up to 1500 °F (815 °C). This makes them particularly suitable for high-temperature geothermal wells and high-pressure, high-temperature oil well cementing. The dominant reaction mechanisms of CBGs include chemical reactivity, dissolution, gelation, polymerization, and growth. These findings highlight the potential of CBGs in addressing the challenges of high-temperature oil wells, thereby paving the way for further research in this area.

Hichem Horra, Ahmed Hadjadj, Elfakeur Abidi Saad et al.

Abstract This study aims to investigate the limitations of geostatistical prediction models outside the observed data range for estimating rock strength in nonreservoir formations in large geological fields with limited wireline data. To address this gap, this method explores alternative approaches to estimate rock strength using minimum data. A novel 3D rock strength prediction model that integrates geostatistic with deep learning algorithms is proposed. Initially, the deep learning model is trained using the available dataset to capture the complex nonlinear relationships within the data. The developed model is used to increase the dataset size by focusing on nearby data points to mitigate geological variability. geostatistic methods are then applied to establish spatial correlations of rock strength across an extended range compared with those of the actual dataset. The results reveal marked improvements in both the prediction range and spatial resolution of rock strength through the proposed methodology. The developed deep learning models achieved coefficient of determination values ranging from 0.9 to 0.99, demonstrating excellent predictive capability. Cross-validation confirms the model effectively captures local variations. The prediction range in the field expanded by 250% compared to the initial dataset, successfully addressing areas that previously exhibited flat readings when the model was applied to the initial data. This study advances petroleum industry knowledge by integrating deep learning and geostatistical methods to overcome rock strength prediction limitations in nonreservoir formations. The novel 3D model enhances the prediction range and spatial resolution, addresses data gaps and enables better decision-making for areas with limited wireline data.

Sebastian Stock, Michael Leuschel, Atif Mashkoor

When validating formal models, sizable effort goes into ensuring two types of properties: safety properties (nothing bad happens) and liveness properties (something good occurs eventually. Event-B supports checking safety properties all through the refinement chain. The same is not valid for liveness properties. Liveness properties are commonly validated with additional techniques like animation, and results do not transfer quickly, leading to re-doing the validation process at every refinement stage. This paper promotes early validation by providing failure divergence refinement semantics for Event-B. We show that failure divergence refinement preserves trace properties, which comprise many liveness properties, under certain natural conditions. Consequently, re-validation of those properties becomes unnecessary. Our result benefits data refinements, where no abstract behavior should be removed during refinement. Furthermore, we lay out an algorithm and provide a tool for automatic failure divergence refinement checking, significantly decreasing the modeler's workload. The tool is compared and evaluated in the context of sizable case studies.

Qi Guo, Xiaofei Xie, Shangqing Liu et al.

Code refinement aims to enhance existing code by addressing issues, refactoring, and optimizing to improve quality and meet specific requirements. As software projects scale in size and complexity, the traditional iterative exchange between reviewers and developers becomes increasingly burdensome. While recent deep learning techniques have been explored to accelerate this process, their performance remains limited, primarily due to challenges in accurately understanding reviewers' intents. This paper proposes an intention-based code refinement technique that enhances the conventional comment-to-code process by explicitly extracting reviewer intentions from the comments. Our approach consists of two key phases: Intention Extraction and Intention Guided Revision Generation. Intention Extraction categorizes comments using predefined templates, while Intention Guided Revision Generation employs large language models (LLMs) to generate revised code based on these defined intentions. Three categories with eight subcategories are designed for comment transformation, which is followed by a hybrid approach that combines rule-based and LLM-based classifiers for accurate classification. Extensive experiments with five LLMs (GPT4o, GPT3.5, DeepSeekV2, DeepSeek7B, CodeQwen7B) under different prompting settings demonstrate that our approach achieves 79% accuracy in intention extraction and up to 66% in code refinement generation. Our results highlight the potential of our approach in enhancing data quality and improving the efficiency of code refinement.

Wu-Yen Chuang

A formula for the full nonperturbative topological string free energy was recently proposed by Hattab and Palti \cite{HP24a}. In this work, we extend their result to the refined topological string theory. We demonstrate that the proposed formula for the full nonperturbative refined topological string free energy correctly reproduces the trans-series structure of the refined topological string and captures the Stokes automorphisms associated with its resurgent properties.

Mingming Zhang, Jinglin Wen, Zhiming Xu et al.

Abstract Borehole instability in igneous rock formation has attracted more and more attention in recent ten years. In order to understand the mechanism of wellbore instability in igneous formation, a borehole stability model is established by applying the thermal, seepage and stress coupling model combined with a true triaxial rock strength criterion, which can reveal evolution of borehole collapse pressure with time. The effect of drilling cycle on borehole collapse pressure considering the coupling effects of temperature, seepage, and stress is quantitatively analyzed. Results show that, compared with only considering the effect of stress, wellbore collapse pressure increases with the coupling effect of temperature, seepage, and stress. Meanwhile, the stability of wellbore can be enhanced by reducing drilling fluid temperature; with the increase of formation porosity, the borehole collapse pressure increases rapidly, and then remains unchanged or decreases; while with the increase of formation permeability, borehole collapse pressure decreases rapidly, i.e. the shear failure of wellbore is mitigated. Besides, compared with vertical well, the horizontal well is more sensitive to the change of rock permeability. The investigation of drilling cycle shows that, the borehole collapse pressure increased sharply when the formation was drilled instantaneously. However, the subsequent growth trend slows down, which suggests that during the early stages of drilling operation, it is advisable to appropriately increase the mud weight to enhance the wellbore’s support capability by the bottomhole pressure. The research findings can enhance the understanding of the instability mechanism of igneous rock formations and reduce the risk of wellbore instability in igneous rock formations.

Pedro Frejlich, David Martínez Torres

We discuss in this note two dual canonical operations on Dirac structures $L$ and $R$ -- the \emph{tangent product} $L \star R$ and the \emph{cotangent product} $L \circledast R$. Our first result gives an explicit description of the leaves of $L \star R$ in terms of those of $L$ and $R$, surprisingly ruling out the pathologies which plague general ``induced Dirac structures''. In contrast to the tangent product, the more novel contangent product $L \circledast R$ need not be Dirac even if smooth. When it is, we say that $L$ and $R$ \emph{concur}. Concurrence captures commuting Poison structures, refines the \emph{Dirac pairs} of Dorfman and Kosmann-Schwarzbach, and it is our proposal as the natural notion of ``compatibility'' between Dirac structures. The rest of the paper is devoted to illustrating the usefulness of tangent- and cotangent products in general, and the notion of concurrence in particular. Dirac products clarify old constructions in Poisson geometry, characterize Dirac structures which can be pushed forward by a smooth map, and mandate a version of a local normal form. Magri and Morosi's $PΩ$-condition and Vaisman's notion of two-forms complementary to a Poisson structures are found to be instances of concurrence, as is the setting for the Frobenius-Nirenberg theorem. We conclude the paper with an interpretation in the style of Magri and Morosi of generalized complex structures which concur with their conjugates.

Damian Rovara, Lukas Burgholzer, Robert Wille

As new advancements in the field of quantum computing lead to the development of increasingly complex programs, approaches to validate and debug these programs are becoming more important. To this end, methods employed in classical debugging, such as assertions for testing specific properties of a program's state, have been adapted for quantum programs. However, to efficiently debug quantum programs, it is key to properly place these assertions. This usually requires a deep understanding of the program's underlying mathematical properties, constituting a time-consuming manual task for developers. To address this problem, this work proposes methods for automatically refining assertions in quantum programs by moving them to more favorable positions in the program or by placing new assertions that help to further narrow down potential error locations. This allows developers to take advantage of rich and expressive assertions that greatly improve the debugging experience without requiring them to place these assertions manually in an otherwise tedious manner. An open-source implementation of the proposed methods is available at https://github.com/cdatum/mqt-debugger.

A. Kaukin, V. Kosarev, E. Miller

The sanctions imposed on Russia in February 2022 have affected the current and future revenues of the domestic energy sector as well as the tax revenues derived from it, while they have also made public welfare losses due to accumulated imbalances in the fuel and energy sector more sensitive to the Russian economy (for example, through subsidies for oil refining). Developing recommendations for adjusting the way the Russian oil refining sector is subsidized is now an urgent matter. This paper estimates the factor payment for the use of oil rent, considers the structure of its distribution in the Russian economy, and provides a scenario analysis of the consequences of imposing sanctions, which include a “price ceiling,” changes in tax regulation, and an increase in the processing depth of refineries. Based on this analysis, reforms in the taxation of the Russian oil refining sector are proposed. The results of the scenario analysis show that, under the current conditions, it is extremely important to continue modernizing oil refineries. A potential reduction in the production of petroleum products would result in the smallest losses industry-wide, provided that it is achieved by suspending the least efficient refineries (those with low GVA in the absence of subsidies) and by ending subsidies for friendly economies

Over the past decades, the balance of hydrocarbon production shows a rising trend of production heavy, high-viscosity oil. Their transportation and refining involves higher energy costs, which can be reduced by using the innovative oil treatment method based on the creation of the cavitation phenomenon. It should be noted that the information about the change in oil characteristics as a result of cavitation treatment is contradictory. This also applies to the nature of the change in the properties and the stability of these changes over time. In some cases, the different nature of the changes is associated with the group composition of the samples. This paper is devoted to the study of the influence of cavitation oil treatment of different nature on their fractional composition and characteristics of individual fractions. Here was an attempt to determine the optimal time of fractionation of oil (after processing) in order to obtain the greatest output of light fractions. Heavy and light oil from the Ilsk field (Krasnodar Krai) were selected as the research subjects. The study of the influence of the treatment conditions on the viscosity of oil products was carried out on the sample of straight-run fuel oil (SRFO) provided by AO «Gazpromneft - MNPZ», obtained at the ELOU-AVT-6 unit. Cavitation processing of samples was carried out in the apparatus of HPH «Donor-2». The density of the samples and their fractions were determined according to GOST 3900-85 «Oil and petroleum products. Methods for the determination of the density». Fractional composition of petroleum in accordance with GOST 2177-99 «Petroleum products. Methods of determination of fractional composition». Vacuum distillation was used to determine the fractional composition of dark petroleum products. Kinematic viscosity was determined by GOST 33-2016. The conducted researches have shown the prospect of using the cavitation phenomenon in oil refining processes. It is possible to change (reduce) the viscosity of the oil product, to increase the yield of fractions with lower boiling points by using this phenomenon. The efficiency of the treatment increases with the increase in the density of the oil product. The results do not confirm information about the post-effect of cavitation treatment of petroleum products, when changes in the characteristics of the sample continue for some time after refining. By measuring the viscosity of the fuel oil sample, it is shown that the relaxation of the characteristics begins after treatment and it is most noticeable during the first 5 days after treatment. This allows us to recommend to treatment the sample immediately prior to fractionation. It should also be noted that as a result of cavitation treatment, not only dark but also light petroleum fractions are affected, and in all of these fractions along destruction and isomerization reactions, there are also processes of compaction.

Ryzhkova S.V., Deshin A.A.

The results of one-dimensional modeling of oil and gas generation by organic matter of the non-oil and oil-bearing Bazhenov Formation in a well network within the northern part of Nyurol megadepression are presented. Modeling considering the mixed lithology of the Bazhenov Formation has no significant effect on the resource assessment, but refines the most important parameter of the hydrocarbon system, which is porosity. Based on the obtained data and taking into account the tectonic history, the history of liquid hydrocarbon generation has been reconstructed. The intensity of generation in different parts of the megadepression and adjacent structures depends on their spatial association with the zones of development of Triassic graben-rifts, as well as the relative growth of paleo-uplifts with the bending of the Western Siberian geosyncline. Recent tectonic movements have had a significant impact on the dynamics of oil generation.

Chuanxiang SUN, Haikuan NIE, Haikun SU et al.

To investigate the porosity, permeability and rock mechanics of deep shale under temperature-pressure coupling, we selected the core samples of deep shale from the Lower Silurian Longmaxi Formation in the Weirong and Yongchuan areas of the Sichuan Basin for porosity and permeability experiments and a triaxial compression and sound wave integration experiment at the maximum temperature and pressure of 120 °C and 70 MPa. The results show that the microscopic porosity and permeability change and the macroscopic rock deformation are mutually constrained, both showing the trend of steep and then gentle variation. At the maximum temperature and pressure, the porosity reduces by 34%–71%, and the permeability decreases by 85%–97%. With the rising temperature and pressure, deep shale undergoes plastic deformation in which organic pores and clay mineral pores are compressed and microfractures are closed, and elastic deformation in which brittle mineral pores and rock skeleton particles are compacted. Compared with previous experiments under high confining pressure and normal temperature, the experiment under high temperature and high pressure coupling reveals the effect of high temperature on stress sensitivity of porosity and permeability. High temperature can increase the plasticity of the rock, intensify the compression of pores due to high confining pressure, and induce thermal stress between the rock skeleton particles, allowing the reopening of shale bedding or the creation of new fractures along weak planes such as bedding, which inhibits the decrease of permeability with the increase of temperature and confining pressure. Compared with the triaxial mechanical experiment at normal temperature, the triaxial compression experiment at high temperature and high pressure demonstrates that the compressive strength and peak strain of deep shale increase significantly due to the coupling of temperature and pressure. The compressive strength is up to 435 MPa and the peak strain exceeds 2%, indicating that high temperature is not conducive to fracture initiation and expansion by increasing rock plasticity. Lithofacies and mineral composition have great impacts on the porosity, permeability and rock mechanics of deep shale. Shales with different lithologies are different in the difficulty and extent of brittle failure. The stress-strain characteristics of rocks under actual geological conditions are key support to the optimization of reservoir stimulation program.

Puja Trivedi, Ryan Rossi, David Arbour et al.

Most real-world networks are noisy and incomplete samples from an unknown target distribution. Refining them by correcting corruptions or inferring unobserved regions typically improves downstream performance. Inspired by the impressive generative capabilities that have been used to correct corruptions in images, and the similarities between "in-painting" and filling in missing nodes and edges conditioned on the observed graph, we propose a novel graph generative framework, SGDM, which is based on subgraph diffusion. Our framework not only improves the scalability and fidelity of graph diffusion models, but also leverages the reverse process to perform novel, conditional generation tasks. In particular, through extensive empirical analysis and a set of novel metrics, we demonstrate that our proposed model effectively supports the following refinement tasks for partially observable networks: T1: denoising extraneous subgraphs, T2: expanding existing subgraphs and T3: performing "style" transfer by regenerating a particular subgraph to match the characteristics of a different node or subgraph.

Nadia Nahar, Haoran Zhang, Grace Lewis et al.

Machine learning (ML) components are increasingly incorporated into software products for end-users, but developers face challenges in transitioning from ML prototypes to products. Academics have limited access to the source of commercial ML products, hindering research progress to address these challenges. In this study, first and foremost, we contribute a dataset of 262 open-source ML products for end users (not just models), identified among more than half a million ML-related projects on GitHub. Then, we qualitatively and quantitatively analyze 30 open-source ML products to answer six broad research questions about development practices and system architecture. We find that the majority of the ML products in our sample represent more startup-style development than reported in past interview studies. We report 21 findings, including limited involvement of data scientists in many open-source ML products, unusually low modularity between ML and non-ML code, diverse architectural choices on incorporating models into products, and limited prevalence of industry best practices such as model testing, pipeline automation, and monitoring. Additionally, we discuss seven implications of this study on research, development, and education, including the need for tools to assist teams without data scientists, education opportunities, and open-source-specific research for privacy-preserving telemetry.

Boning Zhang, Eric B. Herbold, Richard A. Regueiro

Granular material is showing very often in geotechnical engineering, petroleum engineering, material science and physics. The packings of the granular material play a very important role in their mechanical behaviors, such as stress-strain response, stability, permeability and so on. Although packing is such an important research topic that its generation has been attracted lots of attentions for a long time in theoretical, experimental, and numerical aspects, packing of granular material is still a difficult and active research topic, especially the generation of random packing of non-spherical particles. To this end, we will generate packings of same particles with same shapes, numbers, and same size distribution using geometry method and dynamic method, separately. Specifically, we will extend one of Monte Carlo models for spheres to ellipsoids and poly-ellipsoids.

R. Matsumoto, T. Tabata

This study aimed to identify the impact of achieving the 1.5 °C target on the petroleum supply chain in Japan, and discuss the feasibility and challenges of decarbonization. First, a national material flow was established for the petroleum supply chain in Japan, including processes for crude petroleum refining, petroleum product manufacturing, plastic resin and product manufacturing, and by-product manufacturing. In particular, by-product manufacturing processes, such as hydrogen, gaseous carbon dioxide, and sulfur, were selected because they are utilized in other industries. Next, the outlook for the production of plastic resin, hydrogen, dry ice produced from carbon dioxide gas, and sulfur until 2050 was estimated for reducing petroleum consumption required to achieve the 1.5 °C target. As a result, national petroleum treatment is expected to reduce from 177,048.00 thousand kl in 2019 to 126,643.00 thousand kl in 2030 if the reduction in petroleum consumption is established. Along with this decrease, plastic resin production is expected to decrease from 10,500.00 thousand ton in 2019 to 7511.00 thousand ton by 2030. Conversely, the plastic market is expected to grow steadily, and the estimated plastic resin production in 2030 is expected to be 20,079.00 thousand ton. This result indicates that there is a large output gap between plastic supply and demand. To mitigate this gap, strongly promoting the recycling of waste plastics and making the price competitiveness of biomass plastics equal to that of petroleum-derived plastics are necessary.

Yueyi Xia, Xiaoshan Sun, Xinjie zhao et al.

With the increased attention to processing heavy crude oils, a detailed description of chemical composition is critical for the petroleum refining industry. The current analytical technique such as ultrahigh resolution mass spectrometry has been successfully applied for the molecular level characterization of complex petroleum fractions. But the structural characterization of heavy petroleum feedstock is still a great challenge. In this study, a novel in-depth characterization method of nitrogen heterocycles (N-heterocycles) in heavy petroleum mixtures was proposed by online liquid chromatography coupled with electrospray ionization high resolution energy-resolved mass spectrometry. A series of typical basic aromatic, neutral aromatic and naphtheno-aromatic nitrogen heterocyclic model compounds were synthesized to investigate energy-resolved fragmentation behaviors in high energy collision-induced dissociation at 10-100 eV. Energy-dependent fragmentation pathways were elucidated. Notably, characteristic double bond equivalent (DBE) versus carbon number distributions of N1 ions and all CH ions were discovered, which were closely related to their core structure. Then a workflow to assign core structures of alkyl-substituted N-heterocycles in petroleum was proposed and validated. The developed method was applied to investigate the structural isomers in feed and product vacuum gas oil (VGO) fractions. Core structural differences in feed VGO and subtle structural variations between feed and product VGOs were recognized. This work can distinguish structural isomers of N-heterocycles with the subtle difference in their core structure in heavy petroleum fractions based on global energy dimensional fragmentation characteristics.

Xingang Bu, Ming Han, Abdulkareem M. AlSofi

This work numerically investigates surfactant effects on spontaneous water imbibition in oil-wet carbonates. An open boundary core-scale imbibition model with 9 × 9 × 10 gridblocks was used in UTCHEM to simulate carbonate core plug exposure to a vast water body. The simulation models were developed based on surfactant-assisted imbibition tests that were conducted in secondary and tertiary oil production modes using Amott cells at 75°C. Capillary and gravity forces were captured by history matching the experiments. Through history matching, the inputs for surfactant adsorption and diffusion, capillary pressure and relative permeability were calibrated. In tertiary mode, the surfactants-assisted imbibition process presents the performance in mixed-wet state rather than oil-wet state, which is governed by wettability alteration. A simulation model for surfactant-assisted imbibition in secondary mode was used to investigate the effects of various factors including interfacial tension (IFT) reduction, wettability alteration, adsorption, volume of surrounding water and capillary force. The simulation results suggest that surfactant-assisted water imbibition in secondary mode is gravity dominant, which is facilitated by both IFT reduction and wettability alteration caused by addition of proper surfactants. Different from water imbibition in water-wet core, it presents vertically dominant oil flow with a hemispherical oil-rich area and uneven remaining oil saturation. It is obvious that sufficient surfactant supply in vast water is required to make effective imbibition, in consideration of surfactant consumption and changes in concentration gradients. This core-scale modeling provides insights of surfactant-assisted imbibition in initially oil-wet carbonates and helps scale up the application in a cost-effective way.

Massimiliano Fessina, Giambattista Albora, Andrea Tacchella et al.

Tree-based machine learning algorithms provide the most precise assessment of the feasibility for a country to export a target product given its export basket. However, the high number of parameters involved prevents a straightforward interpretation of the results and, in turn, the explainability of policy indications. In this paper, we propose a procedure to statistically validate the importance of the products used in the feasibility assessment. In this way, we are able to identify which products, called explainers, significantly increase the probability to export a target product in the near future. The explainers naturally identify a low dimensional representation, the Feature Importance Product Space, that enhances the interpretability of the recommendations and provides out-of-sample forecasts of the export baskets of countries. Interestingly, we detect a positive correlation between the complexity of a product and the complexity of its explainers.

Youngju Song, Minki Cho, Dongjae Lee et al.

Contextual refinement (CR) is one of the standard notions of specifying open programs. CR has two main advantages: (i) (horizontal and vertical) compositionality that allows us to decompose a large contextual refinement into many smaller ones enabling modular and incremental verification, and (ii) no restriction on programming features thereby allowing, e.g., mutually recursive, pointer-value passing, and higher-order functions. However, CR has a downside that it cannot impose conditions on the context since it quantifies over all contexts, which indeed plays a key role in support of full compositionality and programming features. In this paper, we address the problem of finding a notion of refinement that satisfies all three requirements: support of full compositionality, full (sequential) programming features, and rich conditions on the context. As a solution, we propose a new theory of refinement, called CCR (Conditional Contextual Refinement), and develop a verification framework based on it, which allows us to modularly and incrementally verify a concrete module against an abstract module under separation-logic-style pre and post conditions about external modules. It is fully formalized in Coq and provides a proof mode that combines (i) simulation reasoning about preservation of sideffects such as IO events and termination and (ii) propositional reasoning about pre and post conditions. Also, the verification results are combined with CompCert, so that we formally establish behavioral refinement from top-level abstract programs, all the way down to their assembly code.