Hasil untuk "Oils, fats, and waxes"

YE HONGYING, CAO CHENG, ZHAO YULONG et al.

Carbon capture, utilization and storage (CCUS) is a key technology for achieving carbon neutrality, providing the dual benefits of enhanced energy production and reduced CO₂ emissions through CO₂-enhanced oil and gas recovery (EOR/EGR) and geological storage. However, the large-scale application of CCUS technology faces technical challenges such as engineering design and risk assessment. Traditional approaches, which rely on empirical formulas, experimental verification, and physical models, suffer from low computational efficiency, limited model accuracy, and difficulties in handling multi-dimensional coupling when addressing complex systems. Machine learning (ML), with its powerful data-driven analytical capabilities and adaptive optimization features, can establish high-precision prediction models, optimize operating parameters, predict reservoir fluid behavior, and assess leakage risks. This enables real-time monitoring and intelligent decision-making for complex systems, enhancing the safety and economic efficiency of CCUS technology. This study systematically reviews the applications of ML in CO₂-enhanced oil and gas recovery and geological storage. In terms of CO₂-enhanced oil and gas recovery, the applications cover percolation mechanism modeling, well pattern design optimization, production prediction and evaluation, multi-objective optimization, minimum miscibility pressure prediction, gas adsorption curve prediction, and CO₂-CH₄ diffusion assessment. For CO₂ geological storage, the applications include reservoir selection, research on CO₂ dissolution and diffusion mechanisms, geological storage performance prediction, and risk assessment. ML demonstrates significant advantages in improving prediction accuracy, optimizing operating parameters, and enhancing computational efficiency. It has made important progress in key fields such as reservoir selection, gas adsorption prediction, and storage performance prediction. However, challenges remain in terms of adaptability to complex geological scenarios, model universality, dynamic data processing capabilities, and physical interpretability.

Xueqiang Guo, Mingzhe Dong, Qian Sang et al.

Laminated continental shale oil reservoirs have the potential for commercial development. In this paper, a new simulation method for interlayer and intra-layer coupled flow in laminated shale reservoirs is established. This method simulates the structural characteristics of shale-sandstone longitudinal interlayer distribution by dual-porositysystem, and combines with chemical reaction model to characterize the desorption process of ad-/absorbed oil from kerogen in shale layers. Then, the intra-layer and interlayer interfacial flow mechanism in the depletion process is investigated, and the contribution of interfacial flow and desorption is analyzed. The results indicate that the sandstone layer is the main oil-producing layer, accounting for over 90% of the total oil production. However, the interlayer flow and kerogen desorption in the shale layers make significant contributions, resulting in an enhancement of 13.41% and 42.64% in the total oil production, respectively. Additionally, the desorption of ad-/absorbed oil from kerogen enhances the energy of both the shale and sandstone layers, significantly increasing their production. Moreover, higher pressure drawdown, total organic carbon (TOC) content, desorption rate, and horizontal permeability of sandstone layers are advantageous for the exploitation of shale oil.

SANG Shuxun, HE Junjie, HAN Sijie, KHADKA Kumar, ZHOU Xiaozhi, LIU Shiqi, UPENDRA Baral, SAUNAK Bhandari

Coal measure gas is an important type of unconventional natural gas, and its formation and accumulation are the result of the coupling configuration of tectonic sedimentation. The Lesser Himalayan orogenic belt in Nepal is a key area for studying the development and enrichment patterns of coal measure gas reservoirs in complex structural areas. In this study, the coal measure gas reservoirs of the Gondwana Group and Surkhet Group in the Tansen area of the Lesser Himalayan orogenic belt in Nepal were taken as the research objects. The types and combination characteristics of coal measure gas reservoirs in Gondwana and foreland basins were analyzed. The development of microscopic pore-fracture system morphology and pore structure characteristics of different coal measure gas reservoirs were analyzed. The evolution process of pore-fracture systems and the formation mechanisms of dominant pore-fracture systems in coal measure gas reservoirs under the action of thrust nappe were discussed. Finally, potential favorable reservoirs, favorable areas, and resource potential of coal measure gas were preliminarily predicted. The results showed that: (1) The combination types of coal measure gas reservoirs in the Lesser Himalayan orogenic belt of Nepal mainly included the “source-reservoir integration” type of coal-shale gas, the “lower source-upper reservoir” type of coal-tight sandstone gas and shale gas-tight sandstone gas, and the “source-reservoir adjacent” type of coal-shale gas-tight sandstone gas. (2) The mesopores and organic matter micropores related to shale minerals were well developed, accounting for 64.6% of total pore volume and 98.1% of total specific surface area. The coal seam mainly developed micropores, and the total specific surface area reached 8.22 m2/g. In tight sandstones, intergranular pores and microfractures were predominant, demonstrating the highest permeability among all types of reservoirs. (3) The shale pore-fracture system had the dual effects of destruction and regeneration. The evolution of pore-fracture system in coal measure gas reservoirs with different lithologies varied under the action of thrust nappe. The coal seam mainly experienced cataclastic deformation, resulting in the development of more micropores, while the tight sandstones were mainly characterized by the formation and propagation of structural fractures. (4) The coal-shale combination of the Bhainskati Formation of the Surkhet Group in the Tansen area was the dominant coal measure gas reservoir type. The Jhadewa mining area in the southeast of Tansen area was a potential favorable area for coal measure gas. It was preliminarily estimated that the coal measure gas resources in this area reached 5.04×108 m3. This study preliminarily identifies the potential favorable reservoirs and favorable areas of coal measure gas in the Lesser Himalayan orogenic belt of Nepal, providing direction for the evaluation and exploration of oil and gas resources in Nepal.

Mehdi Nickzamir, Seyed Mohammad Sheikh Ahamdi Gandab

A novel hybrid Random Forest and Convolutional Neural Network (CNN) framework is presented for oil-water classification in hyperspectral images (HSI). To address the challenge of preserving spatial context, the images were divided into smaller, non-overlapping tiles, which served as the basis for training, validation, and testing. Random Forest demonstrated strong performance in pixel-wise classification, outperforming models such as XGBoost, Attention-Based U-Net, and HybridSN. However, Random Forest loses spatial context, limiting its ability to fully exploit the spatial relationships in hyperspectral data. To improve performance, a CNN was trained on the probability maps generated by the Random Forest, leveraging the CNN's capacity to incorporate spatial context. The hybrid approach achieved 7.6% improvement in recall (to 0.85), 2.4% improvement in F1 score (to 0.84), and 0.54% improvement in AUC (to 0.99) compared to the baseline. These results highlight the effectiveness of combining probabilistic outputs with spatial feature learning for context-aware analysis of hyperspectral images.

Gaixia Cui, Shouyu Xu, Qinlian Wei et al.

Abstract The Longdong region is a recently discovered area for exploring natural gas in the Upper Paleozoic era within the Ordos Basin. The primary layer that produces gas in this area is the Shan1 member of the Permian Shanxi Formation. The research focused on analyzing the petrological characteristics, physical properties, pore structure, and diagenesis characteristics of the reservoir in the 13th member of the Permian Shanxi Formation in the Longdong area of the southwest Ordos Basin. The research was conducted by combining core observation, cast-thin members, physical properties, and other relevant data. Additionally, the study also discussed the primary factors that influence reservoir performance. The analysis indicates that the dominant sandstone types found in the 13th member of the mountain reservoir are primarily quartz sandstone and lithic quartz sandstone. These sandstone types originate from high-energy environments and are located far from the source. The predominant types of pores are intragranular pores and cutting-karst pores, with the pore size primarily falling within the microporous to mesoporous range. The sedimentary facies and diagenesis exert control over the physical qualities of the reservoir. Specifically, the sedimentary facies determines the fundamental physical conditions of the reservoir, while the structure of the sand body significantly influences its physical properties. Compaction is the primary cause of the increased density of the layer's physical properties. The presence of illite is the main factor contributing to the densification of the reservoir through cementation. The limited extent of reservoir dissolution has a minor effect on enhancing reservoir quality.

Alexander D. Kurilov, Anastasia V. Gubareva, Sergei A. Zubkov et al.

Magnetic fluids exhibit tunable structures and electrophysical properties, making them promising for adaptive optical systems, biomedical sensors, and microelectromechanical devices. However, the dynamic evolution of their microstructure under varying magnetic fields remains insufficiently explored. This study investigates the structural and dielectric properties of transformer oil-based magnetic fluids containing 0.2-10 vol% magnetite nanoparticles, across a frequency range of 20 Hz to 10 MHz. Particular attention is given to the dynamics of aggregate reorientation in response to alternating magnetic fields. Experimental results demonstrate that low nanoparticle concentrations lead to a linear increase in dielectric permittivity and conductivity, consistent with the Maxwell-Wagner model. In contrast, higher concentrations exhibit conductivity saturation and dispersion effects due to the formation of elongated aggregates. An analysis based on the Boyle polarization model describes the relaxation and structural changes associated with aggregation dynamics. Changes in the magnetic field orientation induce aggregate reconfiguration and significant structural transformations. At early stages, elongated chains form, subsequently thickening until an equilibrium state is reached. Elevated temperatures accelerate these processes by reducing medium viscosity and aggregate order. The findings highlight the critical role of reorientation dynamics in designing high-speed magnetic sensors, vibration isolation systems, and adaptive devices operating in dynamic magnetic environments.

尚阿晨，席俊，刘德果，李英英，吴枭 SHANG Achen，XI Jun，LIU Deguo，LI Yingying，WU Xiao

为建立花生球蛋白致敏原的免疫学快速检测方法,采用碱溶酸沉法提取花生球蛋白，经SDS-PAGE检测蛋白纯度后，免疫5只6~8周龄的Balb/c雌性小鼠制备花生球蛋白鼠源多克隆抗体（多抗）血清，并进行免疫学特性鉴定。结果表明：免疫结束后得到的5个多抗血清效价均在1∶ 51 200 以上，均具有一定的敏感性，其中2号小鼠的多抗血清敏感性较好，半数抑制浓度为320 ng/mL；制备的多抗血清特异性较强，2号小鼠的多抗血清与伴花生球蛋白、大豆球蛋白、β-伴大豆球蛋白、牛血清白蛋白、麦朊蛋白、乳清蛋白的交叉反应率均较低，在0.5%以下；Western blot鉴定结果表明成功制备出了花生球蛋白鼠源多克隆抗体。综上，获得了效价高、敏感性强、特异性优良的花生球蛋白鼠源多克隆抗体，为其单克隆抗体的制备及免疫学快速检测方法研究奠定了基础。 In order to establish a rapid immunological detection method for the arachin allergens,the arachin was extracted by alkali solution and acid precipitation method, and its purity was detemined by SDS-PAGE, and 5 Balb/c female mice aged 6 to 8 weeks were immunized to prepare the arachin mouse-derived polyclonal antiserum,the polyclonal antiserum immunological characteristics were identified. The results showed that the titers of the polyclonal antiserum obtained after the end of immunization were all above 1∶ 51 200, and all of them had certain sensitivities, among which the polyclonal antiserum from 2# mouse had the best sensitivities, with a half-inhibitory concentration of 320 ng/mL.The polyclonal antiserum from 2# mouse was not sensitive to conarachin, glycinin, β-conglycinin, bovine serum albumin, gluten protein, and skimmed milk powder,and the cross-reaction rate was low(all below 05%),indicating that the specificity of the polyclonal antiserum prepared was relatively strong. Western blot experiment results indicated the successful preparation of arachin mouse-derived polyclonal antiserum. In conclusion, a arachin mouse-derived polyclonal antibody with high titer, strong sensitivity and excellent specificity is obtained, which provide the material foundation for the preparation of monoclonal antibodies and the research of rapid immunological detection methods.

SONG Dekang,LIU Xiaoxue,SHAO Zeyu,JIANG Zhenxue,HOU Lili,WANG Yuchao,HE Shijie,LIU Jipeng

The study of formation conditions and accumulation mode of biogenetic mudstone gas reservoir in Sanhu Depression is essential for understanding the accumulation mechanisms and enrichment rules of such gas reservoirs. It holds significant theoretical and practical implications for guiding the exploration and development of Quaternary mudstone gas reservoirs. This research focuses on the Quaternary mudstone in the Sanhu Depression as the main subject. To determine the reservoir formation conditions and establish the accumulation mode, various experiments were conducted, including soluble organic carbon analysis, porosity determination, and chromatography-mass spectrometry analysis. The results reveal that the presence of high levels of soluble organic carbon and herbaceous humic organic matter, along with cold and dry conditions, create favorable conditions for the generation of biogenetic mudstone gas. The Quaternary formation in Sanhu Depression has the characteristics of high porosity and low permeability with numerous micro-nano pores that provide ample space for the occurrence of biogenetic gas. Gas flow primarily occurs through Fick diffusion and slip flow. The self-sealing effect of mudstone leads to the in-situ accumulation of biogenetic gas. However, during the late Himalayan tectonic movement, the gas containment of mudstone is disrupted. As a result of buoyancy, the gas migrates upward and accumulates in high parts of the mudstone, which are adjacent to the gas-generating center, and are superimposed longitudinally with sandstone biogenetic gas reservoirs.

Erum Mushtaq, Yavuz Faruk Bakman, Jie Ding et al.

Federated Learning (FL) aims to train a machine learning (ML) model in a distributed fashion to strengthen data privacy with limited data migration costs. It is a distributed learning framework naturally suitable for privacy-sensitive medical imaging datasets. However, most current FL-based medical imaging works assume silos have ground truth labels for training. In practice, label acquisition in the medical field is challenging as it often requires extensive labor and time costs. To address this challenge and leverage the unannotated data silos to improve modeling, we propose an alternate training-based framework, Federated Alternate Training (FAT), that alters training between annotated data silos and unannotated data silos. Annotated data silos exploit annotations to learn a reasonable global segmentation model. Meanwhile, unannotated data silos use the global segmentation model as a target model to generate pseudo labels for self-supervised learning. We evaluate the performance of the proposed framework on two naturally partitioned Federated datasets, KiTS19 and FeTS2021, and show its promising performance.

Amit Chakraborty, Srinandan Dasmahapatra, Henry Day-Hall et al.

We compare different jet-clustering algorithms in establishing fully hadronic final states stemming from the chain decay of a heavy Higgs state into a pair of the 125 GeV Higgs boson that decays into bottom-antibottom quark pairs. Such 4$b$ events typically give rise to boosted topologies, wherein bottom-antibottom quark pairs emerging from each 125 GeV Higgs boson tend to merge into a single, fat $b$-jet. Assuming Large Hadron Collider (LHC) settings, we illustrate how both the efficiency of selecting the multi-jet final state and the ability to reconstruct from it the masses of all Higgs bosons depend on the choice of jet-clustering algorithm and its parameter settings. We indicate the optimal choice of clustering method for the purpose of establishing such a ubiquitous Beyond the SM (BSM) signal, illustrated via a Type-II 2-Higgs Doublet Model (2HDM).

张洋1，严茂林2，葛玮玮1，陈畅1，张志丹2，田恬2，吴成亮1 ZHANG Yang1, YAN Maolin2, GE Weiwei1, CHEN Chang1, ZHANG Zhidan2, TIAN Tian2, WU Chengliang1

近年来，伴随着我国食用植物油自给率的持续走低，提高我国本土食用植物油料的供给、保障国家食用油安全上升为国家战略。通过梳理前人研究和官方信息，整理出2000—2019年我国传统八大食用植物油料的产量数据，并结合相关研究计算得出对应历年食用植物油本土供应量，同时建立Holt双参数线性指数平滑模型对我国本土食用植物油料和油脂的总供给进行预测，结合国家食用植物油发展规划，进一步分析当前制约我国食用植物油发展的因素，并提出针对性建议，以期为国家制定食用植物油料油脂发展规划，提高我国食用油自给率提供数据参考和技术支持。In recent years, with the continuous decline in the self-sufficiency rate of edible vegetable oil, it has become a national strategy to improve the supply of domestic edible vegetable oilseeds and ensure the safety of national edible oil. By combing previous studies and official information, the output data of China’s eight traditional edible vegetable oilseeds from 2000 to 2019 were sorted out, and the local supply of edible vegetable oils over the years in combination with relevant studies was calculated, while a Holt two parameters linear exponential smoothing model was established to predict the total supply of domestic edible vegetable oilseeds and oils. Combined with the national edible vegetable oil development plan, the factors restricting the development of edible vegetable oil in China were further analyzed, and targeted suggestions were put forward. It is expected to provide data reference and technical support for the state to formulate the development plan of edible vegetable oil and improve the self-sufficiency rate of edible oil in China.

John Gliksberg, Antoine Capra, Alexandre Louvet et al.

Coupling regular topologies with optimized routing algorithms is key in pushing the performance of interconnection networks of HPC systems. In this paper we present Dmodc, a fast deterministic routing algorithm for Parallel Generalized Fat-Trees (PGFTs) which minimizes congestion risk even under massive topology degradation caused by equipment failure. It applies a modulo-based computation of forwarding tables among switches closer to the destination, using only knowledge of subtrees for pre-modulo division. Dmodc allows complete rerouting of topologies with tens of thousands of nodes in less than a second, which greatly helps centralized fabric management react to faults with high-quality routing tables and no impact to running applications in current and future very large-scale HPC clusters. We compare Dmodc against routing algorithms available in the InfiniBand control software (OpenSM) first for routing execution time to show feasibility at scale, and then for congestion risk under degradation to demonstrate robustness. The latter comparison is done using static analysis of routing tables under random permutation (RP), shift permutation (SP) and all-to-all (A2A) traffic patterns. Results for Dmodc show A2A and RP congestion risks similar under heavy degradation as the most stable algorithms compared, and near-optimal SP congestion risk up to 1% of random degradation.

Mauricio FIALLOS TORRES, Adrián MORALES, Wei YU et al.

This study extends an integrated field characterization in Eagle Ford by optimizing the numerical reservoir simulation of highly representative complex fractured systems through embedded discrete fracture modeling (EDFM). The bottom-hole flowing pressure was history-matched and the field production was forecasted after screening complex fracture scenarios with more than 100 000 fracture planes based on their propped-type. This work provided a greater understanding of the impact of complex-fractures proppant efficiency on the production. After compaction tables were included for each propped-type fracture group, the estimated pressure depletion showed that the effective drainage area can be smaller than the complex fracture network if modeled and screened by the EDFM method rather than unstructured gridding technique. The essential novel value of this work is the capability to couple EDFM with third-party fracture propagation simulation automatically, considering proppant intensity variation along the complex fractured systems. Thus, this work is pioneer to model complex fracture propagation and well interference accurately from fracture diagnostics and pseudo 3D fracture propagation outcomes for multiple full wellbores to capture well completion effectiveness after myriads of sharper field simulation cases with EDFM.

Rusul M. Khazaal, Zainab Z. Ismail

Anaerobic biotreatment of real field petroleum refinery oily sludge (PROS) was investigated under using four different organic loads (OLs) in the order of OL4 > OL3 > OL2 > OL1, in bench-scale bioreactors. The bioremediation of raw PROS was carried out using mixed culture biocatalyst without chemicals addition or any type of pretreatment. The results revealed a potential performance of the used biocatalyst dominated by Pseudomonas aeruginosa (class: Gammaproteo) and Staphylococcus spp. (class: Bacilli) achieving significant removal of chemical oxygen demand (COD) and total petroleum hydrocarbons (TPH). The highest organic removal rate was recorded in OL4 followed by OL3, OL2, OL1, respectively indicating a positive relationship between the percentage removal of organics and their content. Maximum removal efficiencies of 96.7% and 90% were observed for COD and TPH, respectively in OL4 within 14 days only. Analysis of polycyclic aromatic hydrocarbons (PAHs) demonstrated that acenaphthylene and phenanthrene exhibited the maximum removal efficiency (almost complete) among the 8-priority PAHs tested in this study. However, the overall degradation of PAHs in the oily sludge was 83.3%.

Chen Haoyu, Wang Yuning, Lin Jianguo

In order to explore the sand erosion law of sand laden annular flow in oil and gas pipelines, by means of taking a normal bend as the research object, the STAR CCM+ software was used to conduct numerical simulation, the VOF model was used to calculate the flow field of oil and gas continuous phase, the Lagrange multiphase model was used to calculate the movement of discrete phase particles, and the DNV model was used to calculate the erosion of particles on the bent pipe. The research results show that secondary flow occurs in the elbow region, the oil film is no longer a regular annular distribution, the oil film thickness on the outer side is greater than that on the inner side, and the oil and gas phases are mixed; pipeline erosion is closely related to particle movement, and is affected by the factors such as mass flux, impact speed and impact angle of particles on the wall; the impact of particles concentrates on the outer wall of the elbow, the erosion area is also concentrated here, the erosion morphology is round, the maximum erosion rate occurs at the top of the outer wall of the elbow at 41.5°, where the particle mass flux is high, the impact speed is large, and the function value related to the impact angle approaches the maximum value; and the oil film on the pipe wall can slow down the particle movement speed and protect the pipeline from erosion. The research results have a certain guiding role for the safe operation of oil and gas field development projects.

Irina Deeva, Anna Bubnova, Petr Andriushchenko et al.

In this paper, a multipurpose Bayesian-based method for data analysis, causal inference and prediction in the sphere of oil and gas reservoir development is considered. This allows analysing parameters of a reservoir, discovery dependencies among parameters (including cause and effects relations), checking for anomalies, prediction of expected values of missing parameters, looking for the closest analogues, and much more. The method is based on extended algorithm MixLearn@BN for structural learning of Bayesian networks. Key ideas of MixLearn@BN are following: (1) learning the network structure on homogeneous data subsets, (2) assigning a part of the structure by an expert, and (3) learning the distribution parameters on mixed data (discrete and continuous). Homogeneous data subsets are identified as various groups of reservoirs with similar features (analogues), where similarity measure may be based on several types of distances. The aim of the described technique of Bayesian network learning is to improve the quality of predictions and causal inference on such networks. Experimental studies prove that the suggested method gives a significant advantage in missing values prediction and anomalies detection accuracy. Moreover, the method was applied to the database of more than a thousand petroleum reservoirs across the globe and allowed to discover novel insights in geological parameters relationships.

M. Elshendy, A. Fronzetti Colladon, E. Battistoni et al.

This study looks for signals of economic awareness on online social media and tests their significance in economic predictions. The study analyses, over a period of two years, the relationship between the West Texas Intermediate daily crude oil price and multiple predictors extracted from Twitter, Google Trends, Wikipedia, and the Global Data on Events, Language, and Tone database (GDELT). Semantic analysis is applied to study the sentiment, emotionality and complexity of the language used. Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) models are used to make predictions and to confirm the value of the study variables. Results show that the combined analysis of the four media platforms carries valuable information in making financial forecasting. Twitter language complexity, GDELT number of articles and Wikipedia page reads have the highest predictive power. This study also allows a comparison of the different fore-sighting abilities of each platform, in terms of how many days ahead a platform can predict a price movement before it happens. In comparison with previous work, more media sources and more dimensions of the interaction and of the language used are combined in a joint analysis.

Sabine Krist

Mohammadreza Khosravi-Nikou, Ahmad Shariati, Mohammad Mohammadian et al.

This study presents a robust and rigorous method based on intelligent models, namely radial basis function networks optimized by particle swarm optimization (PSO-RBF), multilayer perceptron neural networks (MLP-NNs), and adaptive neuro-fuzzy inference system optimized by particle swarm optimization methods (PSO-ANFIS), for predicting the equilibrium and kinetics of the adsorption of sulfur and nitrogen containing compounds from a liquid hydrocarbon model fuel on mesoporous materials. All the models were evaluated by the statistical and graphical methods. The predictions of the models were also compared with different kinetics and equilibrium models. The results showed that although all the models lead to accurate results, the PSO-ANFIS model represented the most reliable and dependable predictions with the correlation coefficient (R2) of 0.99992 and average absolute relative deviation (AARD) of 0.039%. The developed models are also able to predict the experimental data with better precision and reliability compared to literature models.

Dawei ZHU, Yongle HU, Mingyue CUI et al.

Using current Embedded Discrete Fracture Models (EDFM) to predict the productivity of fractured wells has some drawbacks, such as not supporting corner grid, low precision in the near wellbore zone, and disregarding the heterogeneity of conductivity brought by non-uniform sand concentration. An EDFM is developed based on the corner grid, which enables high efficient calculation of the transmissibility between the embedded fractures and matrix grids, and calculation of the permeability of each polygon in the embedded fractures by the lattice data of the artificial fracture aperture. On this basis, a coupling method of local grid refinement (LGR) and embedded discrete fracture model is designed, which is verified by comparing the calculation results with the Discrete Fracture Network (DFN) method and fitting the actual production data of the first hydraulically fractured well in Iraq. By using this method and orthogonal experimental design, the optimization of the parameters of the first multi-stage fractured horizontal well in the same block is completed. The results show the proposed method has theoretical and practical significance for improving the adaptability of EDFM and the accuracy of productivity prediction of fractured wells, and enables the coupling of fracture modeling and numerical productivity simulation at reservoir scale. Key words: hydraulic fracturing, grid refinement, embedded discrete fracture method, reservoir numerical simulation, productivity prediction, parameters optimization