Hasil untuk "Petroleum refining. Petroleum products"

Chao NI, Anping HU, Tianjie JIN et al.

Focusing on the geochronological issues related to the matching relationship between the strike-slip fault activity and the stages of hydrocarbon generation, reservoir formation, and hydrocarbon accumulation, this study aims to quantitatively constrain the tectonic–burial history, hydrocarbon generation history, reservoir porosity evolution history, and hydrocarbon accumulation history by determining the isotopic ages and temperatures of multiphase calcites (particularly the calcites which contain hydrocarbon-bearing fluid inclusions) and quartzs filling the fractures in the Ordovician strata within the non-foreland area of Tarim Basin. Three major findings have been obtained. (1) According to the tectonic–burial history restored under the constraint of the isotopic ages and temperatures, the non-foreland area of the Tarim Basin experienced a continuous burial process during the Cambrian–Ordovician period, with only a minor uplift at the end of the Silurian. Overall, the area was characterized by continuous hydrocarbon generation and a gradual increase in vitrinite reflectance (Ro). (2) While mechanical compaction and pressure-solution during burial progressively reduced the matrix porosity, the strike-slip fault activity during the Middle Caledonian II and III episodes induced physical fragmentation, which created extensive interbreccia pores, fault cavities, and structural fractures as seepage pathways for surface runoff, and, in conjunction with interlayer karstification, led to the development of widespread dissolution vugs. The formation of fracture-vug system in the Ordovician limestone provided effective storage space for hydrocarbons generated during the Late Caledonian and subsequent periods. (3) The Ordovician fault–karst limestone reservoirs underwent four stages of hydrocarbon accumulation: low–medium maturity liquid hydrocarbons during the Middle–Late Caledonian, medium–high maturity liquid hydrocarbons during the Middle–Late Hercynian, high maturity liquid hydrocarbons during the Indosinian, and high–over maturity gas during the Middle Yanshanian. Variations in hydrocarbon accumulation among different strike-slip faults or different segments of the same fault are controlled by differences in source rock maturity across structural units, as well as by the timing of fault activity and fault-related connectivity to hydrocarbon sources. This research also establishes a geochronological framework for investigating strike-slip fault- controlled reservoir formation and hydrocarbon accumulation, facilitating a more accurate determination of the reservoir formation and hydrocarbon accumulation stages, and providing critical insights for evaluating hydrocarbon enrichment zones in fault-controlled reservoirs.

LI Mingfei, HUANG Jingfu, JIA Hai et al.

In order to increase the effective penetration depth of perforation and establish efficient oil and gas channels, it is often required that the perforation channels should be parallel to the reservoir direction during perforation construction. However, geological structures such as folds and faults often cause the reservoir to be inclined, which results in the channels intersecting the casing at an angle rather than perpendicularly, that is, the casing is perforated at an inclined angle. Inclined perforation breaks through the limitation in existing perforation technology that requires the perforation direction to be perpendicular to the casing. However, after perforation, the perforation holes alter the casing structure. When the casing is subjected to external forces, stress concentration occurs around the holes, reducing the casing's strength. As a result, perforated casings are more prone to damage compared to regular casings. Since there is relatively limited research on the residual strength of casing perforated at inclined angles. In this paper, a three-dimensional finite element model is established to analyze the residual strength of inclined perforated casing. The analysis results show that when the perforation parameters such as hole diameter, hole density, and phase angle are constant, as the perforation inclination angle increases (from 0 to 30°), the remaining external squeeze strength of the casing gradually decreases, and the reduction in the remaining strength of the perforation casing does not exceed 13.00%. When the inclination angle is set to 30° and the phase angle varies from 30° to 180°, the change in the remaining strength of the perforation casing does not exceed 10.20%. When the phase angle is 90°, the remaining strength of the perforation casing is relatively optimal. When the inclination angle is set to 30° and the hole diameter increases from 10 mm to 18 mm, the reduction in the remaining strength of the perforation casing can reach 17.45%. The study can provide theoretical support for the analysis of the remaining strength of inclined perforation casings and practical application in perforation construction.

Anyi Wang, Xuansheng Wu, Dong Shu et al.

Steering has emerged as a promising approach in controlling large language models (LLMs) without modifying model parameters. However, most existing steering methods rely on large-scale datasets to learn clear behavioral information, which limits their applicability in many real-world scenarios. The steering vectors extracted from small dataset often contain task-irrelevant noising features, which degrades their effectiveness. To refine the steering vectors learned from limited data, we introduce Refinement of Steering Vector via Sparse Autoencoder (SAE-RSV) that leverages SAEs to semantically denoise and augment the steering vectors. In our framework, we first remove task-irrelevant features according to their semantics provided by SAEs, and then enrich task-relevant features missing from the small dataset through their semantic similarity to the identified relevant features. Extensive experiments demonstrate that the proposed SAE-RSV substantially outperforms all the baseline methods including supervised fine-tuning. Our findings show that effective steering vector can be constructed from limited training data by refining the original steering vector through SAEs.

Gavin Wang, Lynn Wu

Although AI has the potential to drive significant business innovation, many firms struggle to realize its benefits. We examine how the Lean Startup Method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1,800 Chinese startups between 2011 and 2020, alongside policy shifts by the Chinese government in encouraging AI adoption, we find that companies with strong AI capabilities produce more innovative products. Moreover, our study reveals that AI investments complement LSM in innovation, with effectiveness varying by the type of innovation and AI capability. We differentiate between discovery-oriented AI, which reduces uncertainty in novel areas of innovation, and optimization-oriented AI, which refines and optimizes existing processes. Within the framework of LSM, we further distinguish between prototyping focused on developing minimum viable products, and controlled experimentation, focused on rigorous testing such as AB testing. We find that LSM complements discovery oriented AI by utilizing AI to expand the search for market opportunities and employing prototyping to validate these opportunities, thereby reducing uncertainties and facilitating the development of the first release of products. Conversely, LSM complements optimization-oriented AI by using AB testing to experiment with the universe of input features and using AI to streamline iterative refinement processes, thereby accelerating the improvement of iterative releases of products. As a result, when firms use AI and LSM for product development, they are able to generate more high quality product in less time. These findings, applicable to both software and hardware development, underscore the importance of treating AI as a heterogeneous construct, as different AI capabilities require distinct organizational processes to achieve optimal outcomes.

Yi-Dong Shen, Thomas Eiter

Non-monotonic logic programming is the basis for a declarative problem solving paradigm known as answer set programming (ASP). Departing from the seminal definition by Gelfond and Lifschitz in 1988 for simple normal logic programs, various answer set semantics have been proposed for extensions. We consider two important questions: (1) Should the minimal model property, constraint monotonicity and foundedness as defined in the literature be mandatory conditions for an answer set semantics in general? (2) If not, what other properties could be considered as general principles for answer set semantics? We address the two questions. First, it seems that the three aforementioned conditions may sometimes be too strong, and we illustrate with examples that enforcing them may exclude expected answer sets. Second, we evolve the Gelfond answer set (GAS) principles for answer set construction by refining the Gelfond's rationality principle to well-supportedness, minimality w.r.t. negation by default and minimality w.r.t. epistemic negation. The principle of well-supportedness guarantees that every answer set is constructible from if-then rules obeying a level mapping and is thus free of circular justification, while the two minimality principles ensure that the formalism minimizes knowledge both at the level of answer sets and of world views. Third, to embody the refined GAS principles, we extend the notion of well-supportedness substantially to answer sets and world views, respectively. Fourth, we define new answer set semantics in terms of the refined GAS principles. Fifth, we use the refined GAS principles as an alternative baseline to intuitively assess the existing answer set semantics. Finally, we analyze the computational complexity.

Benjamin Mann, Ulrich Rüde

This work introduces an adaptive mesh refinement technique for hierarchical hybrid grids with the goal to reach scalability and maintain excellent performance on massively parallel computer systems. On the block structured hierarchical hybrid grids, this is accomplished by using classical, unstructured refinement only on the coarsest level of the hierarchy, while keeping the number of structured refinement levels constant on the whole domain. This leads to a compromise where the excellent performance characteristics of hierarchical hybrid grids can be maintained at the price that the flexibility of generating locally refined meshes is constrained. Furthermore, mesh adaptivity often relies on a posteriori error estimators or error indicators that tend to become computationally expensive. Again with the goal of preserving scalability and performance, a method is proposed that leverages the grid hierarchy and the full multigrid scheme that generates a natural sequence of approximations on the nested hierarchy of grids. This permits to compute a cheap error estimator that is well-suited for large-scale parallel computing. We present the theoretical foundations for both global and local error estimates and present a rigorous analysis of their effectivity. The proposed method, including error estimator and the adaptive coarse grid refinement, is implemented in the finite element framework HyTeG. Extensive numerical experiments are conducted to validate the effectiveness, as well as performance and scalability.

Kees Kok, Lin Zhou

In this paper, we generalise the construction of the functorial pullback of refined unramified cohomology between smooth schemes, by following the ideas of Fulton's intersection theory and Rost's cycle modules. We also define standard actions of algebraic cycles on the refined unramified cohomology groups of smooth proper schemes avoiding Chow's moving lemma, which coincide with Schreieder's constructions for smooth projective schemes. As applications, we prove the projective bundle and blow-up formulas for refined unramified cohomology groups and we reduce the Rost nilpotence principle in characteristic zero to a statement concerning certain refined unramified cohomology groups. Moreover, we compute the refined unramified cohomology for smooth proper linear varieties and show that Rost's nilpotence principle holds for these varieties in characteristic zero.

Chunyi Li, Haoning Wu, Hongkun Hao et al.

With the evolution of Text-to-Image (T2I) models, the quality defects of AI-Generated Images (AIGIs) pose a significant barrier to their widespread adoption. In terms of both perception and alignment, existing models cannot always guarantee high-quality results. To mitigate this limitation, we introduce G-Refine, a general image quality refiner designed to enhance low-quality images without compromising the integrity of high-quality ones. The model is composed of three interconnected modules: a perception quality indicator, an alignment quality indicator, and a general quality enhancement module. Based on the mechanisms of the Human Visual System (HVS) and syntax trees, the first two indicators can respectively identify the perception and alignment deficiencies, and the last module can apply targeted quality enhancement accordingly. Extensive experimentation reveals that when compared to alternative optimization methods, AIGIs after G-Refine outperform in 10+ quality metrics across 4 databases. This improvement significantly contributes to the practical application of contemporary T2I models, paving the way for their broader adoption. The code will be released on https://github.com/Q-Future/Q-Refine.

Shaojie Tang, Shuzhang Cai, Jing Yuan et al.

In the rapidly evolving landscape of retail, assortment planning plays a crucial role in determining the success of a business. With the rise of sponsored products and their increasing prominence in online marketplaces, retailers face new challenges in effectively managing their product assortment in the presence of sponsored products. Remarkably, previous research in assortment planning largely overlooks the existence of sponsored products and their potential impact on overall recommendation effectiveness. Instead, they commonly make the simplifying assumption that all products are either organic or non-sponsored. This research gap underscores the necessity for a more thorough investigation of the assortment planning challenge when sponsored products are in play. We formulate the assortment planning problem in the presence of sponsored products as a combinatorial optimization task. The ultimate objective is to compute an assortment plan that optimizes expected revenue while considering the specific requirements of placing sponsored products strategically.

Ahmed Farid Ibrahim, Sulaiman A. Alarifi, Salaheldin Elkatatny

Abstract This study proposes the use of different machine learning techniques to predict the estimated ultimate recovery (EUR) as a function of the hydraulic fracturing design. A set of data includes 200 well production data, and completion designs were collected from oil production wells in the Niobrara shale formation. The completion design parameters include the lateral length, the number of stages, the total injected proppant and slurry volumes, and the maximum treating pressure measured during the fracturing operations. The data set was randomly split into training and testing with a ratio of 75:25. Different machine learning methods were to predict EUR from the completion design including linear regression, random forest (RF), and decision tree (DT) in addition to gradient boosting regression (GBR). EUR prediction from the completion data showed a low accuracy. As result, an intermediate step of estimating the well IP30 (the initial well production rate for the first month) from the completion data was carried out; then, the IP30 and the completion design were used as input parameters to predict the EUR. The linear regression showed some linear relationship between the output and the inputs, where the EUR can be predicted with a linear relationship with an R-value of 0.84. In addition, a linear correlation was developed based on the linear regression model. Moreover, the other ML tools including RF, DT, and GBR presented high accuracy of EUR prediction with correlation coefficient (R) values between actual and predicted EUR from the ML model higher than 0.9. This study provides ML application with an empirical correlation to predict the EUR from the completion design parameters at an early time without the need for complex numerical simulation analysis. Unlike the available empirical DCA models that require several months of production to build a sound prediction of EUR, the main advantage of the developed models in this study is that it requires only an initial flow rate along with the completion design to predict EUR with high certainty.

XUE Gang, XIONG Wei, ZHANG Peixian

There are two types of shale gas reservoirs in the Wufeng-Longmaxi shale formation of southeast margin of Sichuan Basin: normal pressure shale gas reservoir in Wulong residual syncline and abnormal over-pressure shale gas reservoir in Fuling anticline. This study takes an integrated geology-engineering approach to analyze the genesis and formation mechanisms of normal pressure shale gas reservoirs in the Wulong area. The analysis is based on shale burial history curves, drilling data, horizontal well fracturing parameters, and the geological characteristics specific to normal pressure shale gas reservoirs. Combined with the actual production effects of two horizontal shale gas wells, the fracturing process parameters of normal pressure shale gas reservoirs are optimised. Then three main points have been obtained in this study: ①The normal pressure shale gas reservoir of the Wufeng-Longmaxi Formation in Wulong area is formed during the structural destruction adjustment of the Yanshanian and Himalayan tectonic periods. ②The shale gas escaping caused by tectonic elevation is the main reason for the formation of normal pressure shale gas. ③Compared with the Marcellus normal pressure shale gas reservoir, Wulong normal pressure shale gas reservoir has the similar geological characteristics, but the development effect varies greatly. The fracturing scale simulation shows that it is necessary to further optimise the horizontal well segmented fracturing parameters, increase the output of single wells, and reduce development costs. Only in this way can effective development be realised.

Xinjun Zhu, Yuntao Du, Yuren Mao et al.

Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing knowledge-aware recommendation methods directly perform information propagation on KG and user-item bipartite graph, ignoring the impacts of \textit{task-irrelevant knowledge propagation} and \textit{vulnerability to interaction noise}, which limits their performance. To solve these issues, we propose a robust knowledge-aware recommendation framework, called \textit{Knowledge-refined Denoising Network} (KRDN), to prune the task-irrelevant knowledge associations and noisy implicit feedback simultaneously. KRDN consists of an adaptive knowledge refining strategy and a contrastive denoising mechanism, which are able to automatically distill high-quality KG triplets for aggregation and prune noisy implicit feedback respectively. Besides, we also design the self-adapted loss function and the gradient estimator for model optimization. The experimental results on three benchmark datasets demonstrate the effectiveness and robustness of KRDN over the state-of-the-art knowledge-aware methods like KGIN, MCCLK, and KGCL, and also outperform robust recommendation models like SGL and SimGCL.

Wenxuan Tu, Bin Xiao, Xinwang Liu et al.

With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world. Unfortunately, graphs usually suffer from being absent due to privacy-protecting policies or copyright restrictions during data collection. The absence of graph data can be roughly categorized into attribute-incomplete and attribute-missing circumstances. Specifically, attribute-incomplete indicates that a part of the attribute vectors of all nodes are incomplete, while attribute-missing indicates that the whole attribute vectors of partial nodes are missing. Although many efforts have been devoted, none of them is custom-designed for a common situation where both types of graph data absence exist simultaneously. To fill this gap, we develop a novel network termed Revisiting Initializing Then Refining (RITR), where we complete both attribute-incomplete and attribute-missing samples under the guidance of a novel initializing-then-refining imputation criterion. Specifically, to complete attribute-incomplete samples, we first initialize the incomplete attributes using Gaussian noise before network learning, and then introduce a structure-attribute consistency constraint to refine incomplete values by approximating a structure-attribute correlation matrix to a high-order structural matrix. To complete attribute-missing samples, we first adopt structure embeddings of attribute-missing samples as the embedding initialization, and then refine these initial values by adaptively aggregating the reliable information of attribute-incomplete samples according to a dynamic affinity structure. To the best of our knowledge, this newly designed method is the first unsupervised framework dedicated to handling hybrid-absent graphs. Extensive experiments on four datasets have verified that our methods consistently outperform existing state-of-the-art competitors.

Haotian Bai, Yiqi Lin, Yize Chen et al.

The explicit neural radiance field (NeRF) has gained considerable interest for its efficient training and fast inference capabilities, making it a promising direction such as virtual reality and gaming. In particular, PlenOctree (POT)[1], an explicit hierarchical multi-scale octree representation, has emerged as a structural and influential framework. However, POT's fixed structure for direct optimization is sub-optimal as the scene complexity evolves continuously with updates to cached color and density, necessitating refining the sampling distribution to capture signal complexity accordingly. To address this issue, we propose the dynamic PlenOctree DOT, which adaptively refines the sample distribution to adjust to changing scene complexity. Specifically, DOT proposes a concise yet novel hierarchical feature fusion strategy during the iterative rendering process. Firstly, it identifies the regions of interest through training signals to ensure adaptive and efficient refinement. Next, rather than directly filtering out valueless nodes, DOT introduces the sampling and pruning operations for octrees to aggregate features, enabling rapid parameter learning. Compared with POT, our DOT outperforms it by enhancing visual quality, reducing over $55.15$/$68.84\%$ parameters, and providing 1.7/1.9 times FPS for NeRF-synthetic and Tanks $\&$ Temples, respectively. Project homepage:https://vlislab22.github.io/DOT. [1] Yu, Alex, et al. "Plenoctrees for real-time rendering of neural radiance fields." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.

Prokhorov V.L., Alekseeva I.B.

The article is devoted to the results of exploration activity to search for hydrocarbon accumulations, carried out in the 2009-2020 period in the Kaliningrad onshore area. The effectiveness of exploration activity and the replenishment of oil reserves are considered. Proposals are presented for changing the prospecting methodology, licensing of subsoil plots, as well as the direction of further exploration activity in the southeastern part of this region.

Xiangdong Guo, Min Lv, Hongjun Cui et al.

Abstract Based on static geology and dynamic production of typical wells in Yan'an gas field, a convenient method of the wells controlled reserves was established combining with material balance method (MB). The method was applied to 88 wells in Yan'an tight gas field. The results show that: ①Controlled by pore structure, wells are divided into three types based on the morphology of the capillary pressure curve and the analysis of the parameter characteristics, and their productivity is evaluated, respectively. ②The flow material balance method (FMB) ignores the change of natural gas compressibility, viscosity and Z in the calculation. After the theoretical calculation of 30 gas samples, the slope of the curve of the relationship between bottom hole pressure and cumulative production and the slope of the curve of the relationship between average formation pressure and cumulative production are not equal. ③Compared with the results of the MB, the result of the FMB is smaller, and the maximum error is 34.66%. The consequence of the modified FMB is more accurate, and the average error is 2.45%, which has good applicability. The established method is simple, only requiring production data with high precision, providing a new method to evaluate well-controlled reserves of tight gas sandstone. This method with significant application value can also offer reference values for other evaluating methods of well-controlled reserves.

Li Zhao, Chen-Song Zhang, Chun-Sheng Feng et al.

The compositional model is often used to describe multicomponent multiphase porous media flows in the petroleum industry. The fully implicit method with strong stability and weak constraints on time-step sizes is commonly used in the mainstream commercial reservoir simulators. In this paper, we develop an efficient multi-stage preconditioner for the fully implicit compositional flow simulation. The method employs an adaptive setup phase to improve the parallel efficiency on GPUs. Furthermore, a multi-color Gauss-Seidel algorithm based on the adjacency matrix is applied in the algebraic multigrid methods for the pressure part. Numerical results demonstrate that the proposed algorithm achieves good parallel speedup while yields the same convergence behavior as the corresponding sequential version.

Wang Jiangshuai, Li Jun, Liu Gonghui et al.

Managed pressure drilling technology is an effective method to solve the problem of drilling in narrow safe density window formation. In order to accurately design the managed pressure drilling parameters and improve the rationality of the design method, based on the wellbore hydraulics and heat transfer theory, after having fully considered the influence of wellbore temperature and back pressure control range, the design method of drilling fluid density and back pressure was established, the design principle of managed pressure drilling was formulated, and the optimization design of managed pressure drilling parameters was carried out for a well in Ledong block. The study results show that after having considered the influence of wellbore temperature, the design value of drilling fluid density is lower; after having considered the influence of back pressure control range, the optimal drilling fluid density can be obtained, which effectively increases the design depth of managed pressure drilling and simplifies the casing layers; and the increase of displacement will lead to the increase of bottomhole pressure, thus narrowing the optional range of drilling fluid density in the design of managed pressure drilling parameters. The study results provide theoretical guidance for the application of managed pressure drilling technology in narrow safe density window formation.

WEI Jiaxin, ZHANG Yan, SHANG Jiaohui et al.

In order to clarify the main principal factors that affect the initial productivity during the development of shale oil reservoirs, a comprehensive data analysis method involved both the hierarchical cluster analysis and the principal component analysis in data statistics is presented; and then the deta of the static formation parameters, fracturing operation parameters and the oil productivity of 51 wells in Block Li-151 are analyzed quantitatively. At first, the wells in the block are divided automatically into two types, Type A and Type B, by the hierarchical cluster analysis method. Then, a principal component analysis method is used to analyze the principal productivity factors for different types of wells. Analysis results show that, when the well shut-in time is less than 125 days, the oil production decline rate can be reduced effectively by the well shut-in measures; however, when it is greater than 125 days, the effect of well shut-in measures on oil production decline rate becomes negative. The production decline rate of Type A wells is highly negative with the amount of injected fracturing water; the main principal factors for the production decline rate of Type B wells are the moving liquid level and the porosity of shale matrix. The principal factors for the production rate of Type B wells are the number of fracturing sections. All in all, for the production optimization of shale oil development in Block Li-151, the differences of principal production factors between Type A wells and Type B wells should be considered and the different analysis results of the principal factors that affect the initial shale oil productivity under different well types should be fully utilized. Some guidance can be provided specifically for the formulation of a reasonable shale oil efficient development plan.

Jun Peng, Haodong Han, Qingsong Xia et al.

Using the fractal geometry method, the microscopic pore structures of tight sandstone reservoirs in Kalpintag Formation of Shuntuoguole area in Tarim Basin were conducted fractal characterization on the base of test analysis data such as physical property, cast thin section, scanning electron microscope and mercury injection, and the genetic mechanism of pore structure heterogeneity was investigated. The storage spaces are dominated by intergranular dissolved pore, intragranular dissolved pore and residual intergranular pore, and the throat type consists of the necking throat, lamellar throat, curved lamellar throat and tube-shaped throat. The microscopic structure type includes Type I (fractal dimension≤2.350), Type II (2.350<fractal dimension<2.580), Type III (fractal dimension≥2.580) and fracture type. The most favorable reservoirs with Type-I microscopic pore structure are mainly distributed in the Upper Member of Kalpintag Formation, while the reservoirs with Type-II and Type-III microscopic pore structures are mainly in the Lower Member of Kalpintag Formation. The sedimentation controls the heterogeneity of microscopic pore structure, and the differences on composition and particle size of sandstone lead to differentiation of microscopic pore structures. The Lower Member of the Kalpintag Formation experiences stronger compaction and cementation but weaker dissolution than the Upper Member of the Kalpingtag Formation, and thus the microscopic pore structure of Upper Member of the Kalpintag Formation is significantly worse that of the Lower Member o the Kalpingtag Formation. The Upper Member of the Kalpintag Formation with high content of brittle mineral develops microscopic fractures due to tectonic rupture, thus the permeability is improved and the heterogeneity of microscopic pore structures is enhanced; but the Lower Member of Kalpintag Formation is characterized by attrition crushing of particles and strong compaction. Keywords: Pore throat, Fractal dimension, Heterogeneity of microscopic pore structure, Tight sandstone of Kalpintag Formation, Tarim Basin