Large-scale Volume Research Articles

A Systematic Literature review: Role of Deep Learning in Big Data Applications

Deep learning (DL) has changed how we handle huge amount of data like big data. The ability of DL to handle large scale (Volume), speed (Velocity), diversity (variance), and structured data (accuracy) is the focus of this paper, and DL helps perform important aspects of solving complex problems. Pattern recognition, real-time speech processing, and hybrid data processing (e.g., text, images, and video) are all feasible for DL models such as convolutional neural networks (e.g., CNNs) and convolutional neural networks (RNNs). Research shows that DL can improve decision-making and efficiency in areas such as cybersecurity, healthcare and finance with the help of big data. It also focuses on finding the right balance between controlling energy costs and achieving the right results. The study identified solutions to this problem, including energy conservation, simple design and recycling methods. It also explains how DL helps clean and sanitize big data, identify important patterns, and manage irrelevant data. The future of DL is improving data management, enabling real-time analytics and working with emerging technologies such as fog computing and intelligent connectivity (IoT). This study shows that DL is a powerful tool that can shape the future of society.

Constructing Dynamic Macropores in Thermo-Responsive Hydrogel Actuator for Large-Deformable Gripper.

Poly(N-isopropyl acrylamide) (PNIPAm)-based smart hydrogels are widely employed in emerging applications such as drug delivery and tissue engineering, because their lower critical solution temperature (LCST) is close to physiological conditions. However, the dense chain collapse during the thermo-responsive phase transition restricts water diffusion, resulting in limited volumetric change. Here, a pure PNIPAm hydrogel that achieves a large-scale volume transition by incorporating PNIPAm microgels, is presented. During its thermo-responsive shrinkage, the microgels contract to 10% of their original volume, generating open macropores that serve as efficient water channels, thereby facilitating volume change of hydrogel bulk. In contrast to conventional PNIPAm hydrogels with static porous structures, these dynamic macropores disappear when the microgels return to their initial state at lower temperatures, preserving the mechanical integrity of the entire hydrogel. This enhanced deformability enables the bilayer hydrogel actuator to achieve bending angles exceeding 1150°, a sixfold increase over traditional PNIPAm-based actuators, allowing it to function as an intelligent gripper capable of capturing small, mobile organisms. This approach, which addresses the inherent challenge of achieving large-scale deformability in conventional bulk PNIPAm hydrogels, is distinct from existing strategies.

Technology and Research on Improving Oil Recovery by Volume Fracturing with Phase Change Permeability in Ultra-low Permeability Reservoirs

Abstract The conventional water injection development production wells in ultra-low permeability reservoirs have low efficiency, low production and low liquid content, and poor economic benefits. Traditional water drive development techniques are difficult to adapt to the requirements of economically effective development in ultra-low permeability reservoirs. Therefore, it is urgent to adjust development ideas and transform water injection development methods. A major development experiment was carried out in the Y284 area of Huaqing Oilfield to transform the water injection development method. Based on the mechanism of reverse osmosis flooding in oil recovery wells, this article uses dynamic monitoring technologies such as downhole microseismic to achieve three-dimensional reservoir transformation, and establishes a large-scale fracturing technology of “collaborative permeability flooding, volume fracturing, and multi-stage temporary plugging” for reverse osmosis wells in ultra-low permeability reservoirs. Research has shown that large-scale fracturing technology for water injection wells can improve single well oil recovery speed by increasing the complexity of fracture networks. The important factors affecting the effectiveness of water injection wells include reservoir properties, fracturing transformation scale, cumulative water injection volume, remaining oil distribution, and heterogeneity of aquifers. At the same time, the construction parameters of large-scale volume fracturing for water injection wells, namely 140-170m, are quantitatively determined based on the actual situation of the mining field ³ The amount of sand added, a sand ratio of 17-22%, and a large displacement of 8-10m3/min, 1400-2000m ³ The amount of liquid entering the ground can most effectively achieve the conditions for reservoir transformation. The conclusion is that large-scale fracturing technology for conversion wells provides technical support for volume fracturing and optimization design of fracturing parameters for low production wells in ultra-low permeability reservoirs, and can provide guidance and reference for the efficient development of other similar ultra-low permeability reservoirs.

Application of Simul-frac technology in Longdong shale oil field

Abstract This paper for the transformation of unconventional reservoir large-scale volume fracturing of long operation cycle, equipment occupancy, through the well optimization, fracturing design optimization, supporting equipment, construction organization mode upgrade, in the changqing oilfield longdong shale oil HX platform realized a single set of fracturing vehicle for two well synchronous shunt fracturing, created a single unit 17 hours fracturing 10 shale oil reservoir transformation new record. Compared with the production mode of two sets of fracturing units on the same platform, the efficiency of synchronous shunt fracturing operation of single unit increased by 32.1% and the number of equipment used decreased by 17.3%, which initially reflected the advantages of synchronous shunt fracturing technology in improving quality and efficiency. Through the output tracking analysis of the horizontal Wells of synchronous shunt fracturing transformation for half a year, the four Wells showed good transformation effect. Synchronous shunt fracturing technology in changqing oilfield shale oil platform successful application for the future unconventional oil and gas resources cluster well fracturing transformation speed to provide new ideas, the technology can improve the efficiency of oil service enterprises, reduce the completion cycle and comprehensive operation cost, for oilfield company, can quickly build production effect.

Distributed Neural Representation for Reactive In Situ Visualization.

Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. This opens up new possibilities for in situ visualization. However, the efficient application of INRs to distributed data remains an underexplored area. In this work, we develop a distributed volumetric neural representation and optimize it for in situ visualization. Our technique eliminates data exchanges between processes, achieving state-of-the-art compression speed, quality and ratios. Our technique also enables the implementation of an efficient strategy for caching large-scale simulation data in high temporal frequencies, further facilitating the use of reactive in situ visualization in a wider range of scientific problems. We integrate this system with the Ascent infrastructure and evaluate its performance and usability using real-world simulations.

Interactive Volume Visualization via Multi-Resolution Hash Encoding Based Neural Representation.

Implicit neural networks have demonstrated immense potential in compressing volume data for visualization. However, despite their advantages, the high costs of training and inference have thus far limited their application to offline data processing and non-interactive rendering. In this article, we present a novel solution that leverages modern GPU tensor cores, a well-implemented CUDA machine learning framework, an optimized global-illumination-capable volume rendering algorithm, and a suitable acceleration data structure to enable real-time direct ray tracing of volumetric neural representations. Our approach produces high-fidelity neural representations with a peak signal-to-noise ratio (PSNR) exceeding 30 dB, while reducing their size by up to three orders of magnitude. Remarkably, we show that the entire training step can fit within a rendering loop, bypassing the need for pre-training. Additionally, we introduce an efficient out-of-core training strategy to support extreme-scale volume data, making it possible for our volumetric neural representation training to scale up to terascale on a workstation with an NVIDIA RTX 3090 GPU. Our method significantly outperforms state-of-the-art techniques in terms of training time, reconstruction quality, and rendering performance, making it an ideal choice for applications where fast and accurate visualization of large-scale volume data is paramount.

Volume density maps of the 862 nm DIB carrier and interstellar dust. Hints on the role of carbon-rich ejecta from AGB stars

The carbonaceous macromolecules imprinting the numerous absorptions called diffuse interstellar bands (DIBs) in astronomical spectra are omnipresent in the Galaxy and beyond. They represent a considerable reservoir of organic matter. However, their chemical formulae, formation, and destruction sites remain unknown. Their spatial distribution and the local relation to other interstellar species is key to tracing their role in the lifecycle of organic matter. Volume density maps bring local instead of line-of-sight distributed information and allow for new diagnostics to be captured. We present the first large-scale volume (3D) density map of a DIB carrier and compare it with an equivalent map of interstellar dust. The DIB carrier map was obtained through hierarchical inversion of sim 202,000 measurements of the 8621 nm DIB obtained with the Gaia -RVS instrument. It covers about 4000 pc around the Sun in the Galactic plane. We built a dedicated interstellar dust map based on the extinction towards the same target stars. At the simeq 50 pc resolution of the maps, the shape of the 3D DIB distribution is found to be remarkably similar to the 3D distribution of dust. On the other hand, the DIB-to-dust local density ratio increases in low-dust areas. It is also increasing away from the disk, however, the minimum ratio is found to be shifted above the Galactic plane to Z=simeq +50pc. Finally, the average ratio is also surprisingly found to increase away from the Galactic Center. We suggest that the three latter trends may be indications of a dominant contribution of material from the carbon-rich category of dying giant stars to the formation of the carriers. Our suggestion is based on recent catalogs of asymptotic giant branch (AGB) stars and estimates of the mass fluxes of their C-rich and O-rich ejecta.

In situ coal permeability and favorable development methods for coalbed methane (CBM) extraction in China: From real data

Permeability plays a significant role in the economic viability of coalbed methane (CBM) development. In China, in situ permeability obtained from injection fall-off tests range from 0.0001 to 41 mD at depths of 130–1728 m, with the majority (78%) falling between 0.01 and 1 mD. Coal reservoirs with high rank, common cleat mineralization, and depth-dependent high stress may result in extremely low permeability. Low-rank coal and cataclastic coal exhibit more favorable permeability conditions at shallow depths. Heterogeneity in coal rank, coal texture, and stress magnitude results in a discrete distribution of permeability both regionally and vertically. Most deep seams are highly compressed with extremely low permeability (<0.1 mD) as high stress levels. Multilateral horizontal wells work best with high permeability seams, while vertical wells with conventional hydraulic fracturing demonstrate limited adaptability to deep and low-permeability seams due to insufficient proppant-supported fractures. With increasing depth, both vertical and horizontal wells should employ larger fracturing scales to create effectively supported zones with higher fracture conductivity and reduced stress sensitivity to enhance CBM productivity. Horizontal wells with large-scale volume fracturing demonstrate superior performance compared to vertical wells in extracting CBM from deep, low-permeability, and high gas saturation reservoirs.

Large-Scale Production of Specialized Metabolites In Vitro Cultures.

For centuries plants have been intensively utilized as reliable sources of food, flavoring, and pharmaceutical ingredients. However, plant natural habitats are being rapidly lost due to the climate change and agriculture. Plant biotechnology offers a sustainable approach for the bioproduction of specialized plant metabolites. The unique structural features of plant-derived specialized metabolites, such as their safety profile and multi-target spectrum, have led to the establishment of many plant-derived drugs. However, there are still many challenges to overcome regarding the production of these metabolites from plant in vitro systems and establish a sustainable large-scale biotechnological process. These challenges are due to the peculiarities of plant cell metabolism, the complexity of plant specialized metabolite pathways, and the correct selection of bioreactor systems and bioprocess optimization. In this book chapter, we attempted to focus on the advantages of plant in vitro systems and in particular plant cell suspensions for their cultivation as a source of plant-derived specialized metabolites. A state-of-the-art technological platform for plant cell suspension cultivation from callus induction to lab-scale cultivation, extraction, and purification is presented. Possibilities for bioreactor cultivation of plant cell suspensions in benchtop and large-scale volumes are highlighted, including several examples and patents for industrial production of specialized metabolites.

RmdnCache: Dual-Space Prefetching Neural Network for Large-Scale Volume Visualization.

Volume visualization plays a significant role in revealing important intrinsic patterns of 3D scientific datasets. However, these datasets are often large, making it challenging for interactive visualization systems to deliver a seamless user experience because of high input latency that arises from I/O bottlenecks and limited fast memory resources with high miss rates. To address this issue, we have proposed a deep learning-based prefetching method called RmdnCache, which optimizes the data flow across the memory hierarchy to reduce the input latency of large-scale volume visualization. Our approach accurately prefetches the content of the next view to fast memory using learning-based prediction while rendering the current view. The proposed deep learning architecture consists of two networks, RNN and MDN in respective spaces, which work together to predict both the location and likelihood distribution of the next view for defining an optimal prefetching range. Our method outperforms existing state-of-the-art prefetching algorithms in reducing overall input latency for visualizing real-world large-scale volumetric datasets.

Exploration of fs-laser ablation parameter space for 2D/3D imaging of soft and hard materials by tri-beam microscopy

Tri-beam microscopes comprising a fs-laser beam, a Xe+ plasma focused ion beam (PFIB) and an electron beam all in one chamber open up exciting opportunities for site-specific correlative microscopy. They offer the possibility of rapid ablation and material removal by fs-laser, subsequent polishing by Xe-PFIB milling and electron imaging of the same area. While tri-beam systems are capable of probing large (mm) volumes providing high resolution microscopical characterisation of 2D and 3D images across exceptionally wide range of materials and biomaterials applications, presenting high quality/low damage surfaces to the electron beam can present a significant challenge, especially given the large parameter space for optimisation. Here the optimal conditions and artefacts associated with large scale volume milling, mini test piece manufacture, serial sectioning and surface polishing are investigated, both in terms of surface roughness and surface quality for metallic, ceramic, mixed complex phase, carbonaceous, and biological materials. This provides a good starting place for those wishing to examine large areas or volumes by tri-beam microscopy across a range of materials.

A coupled matrix-fracture productivity calculation model considering low-velocity non-Darcy flow in shale reservoirs

The flow behavior of shale reservoirs is extremely complex. The mainly used development method of large-scale volume fracturing of horizontal wells is associated with a series of problems such as low productivity in general, fast decrease in productivity, and difficulty to increase productivity. Therefore, it is necessary to evaluate the productivity of shale oil wells and determine the key control factors. Considering low-velocity non-Darcy flow, this paper establishes an analytical solution model for productivity calculation of multi-fracture shale oil horizontal wells with coupled matrix-fracture flow. Calculating the oil production rate by considering low-velocity non-Darcy flow in shale oil reservoirs obtains values closer to the actual oil production rate on site. The average relative error of the oil production rate of the H8 shale oil well is 4.27%. The average relative error of the oil production rate of the TMS shale oil wells is 34.97%, which exceeds the rate of the semi-analytical model by 47.84%. Sensitivity analysis shows that the oil production rate of multi-fracture shale oil horizontal wells is most sensitive to fracture cluster spacing and flowing bottom hole pressure. With decreasing fracture cluster spacing and flowing bottom hole pressure, both the matrix pressure gradient and the apparent permeability of the matrix increase. These increases not only increase the oil production rate but also reduce the influence of the non-Darcy effect on the oil production rate caused by boundary effect. In addition, the limits of the impact of non-Darcy effects caused by boundary effect on oil production rate have been clarified. The results enable not only the prediction of the oil production rate, cumulative oil production, and recovery factor of multi-fracture horizontal shale oil wells, but also the finding of the shale oil sweet spot according to the influence of matrix permeability and crude oil viscosity on the oil production rate.

OAG: Linking Entities Across Large-Scale Heterogeneous Knowledge Graphs

Different knowledge graphs for the same domain are often uniquely housed on the Web. Effectively linking entities from different graphs is critical for building an open and comprehensive knowledge graph. However, linking entities across different sources has thus far faced various challenges, including the increasingly large-scale volume of the data, the heterogeneity of the graphs, and the ambiguity of real-world entities. To address them, we propose a unified framework LinKG. Specifically, we decouple the problem into different linking tasks based on the unique properties of each type of entity. To link word sequence based entities, we propose an LSTM-based method to capture word dependencies. To link entities of large scale, we utilize the hashing technique and convolutional neural networks for scalable and accurate linking. To link ambiguous entities, we propose heterogeneous graph attention networks to leverage heterogeneous structural information. Finally, to validate the design choices of different LinKG modules, we characterize the relationships between different tasks based on the single-domain and multi-domain transfer models. Extensive experiments demonstrate the effectiveness of LinKG with an overall F1-score of 95.15%, based on which we deploy and release the Open Academic Graph (OAG)—the largest publicly available heterogeneous academic graph to date.

Endothelial structure contributes to heterogeneity in brain capillary diameter.

The high metabolic demand of brain tissue is supported by a constant supply of blood flow through dense microvascular networks. Capillaries are the smallest class of vessels in the brain and their lumens vary in diameter between ~2 and 5 μm. This diameter range plays a significant role in optimizing blood flow resistance, blood cell distribution, and oxygen extraction. The control of capillary diameter has largely been ascribed to pericyte contractility, but it remains unclear if the architecture of the endothelial wall also contributes to capillary diameter. Here, we use public, large-scale volume electron microscopy data from mouse cortex (MICrONS Explorer, Cortical mm3) to examine how endothelial cell number, endothelial cell thickness, and pericyte coverage relates to microvascular lumen size. We find that transitional vessels near the penetrating arteriole and ascending venule are composed of two to six interlocked endothelial cells, while the capillaries intervening these zones are composed of either one or two endothelial cells, with roughly equal proportions. The luminal area and diameter are on average slightly larger with capillary segments composed of two interlocked endothelial cells vs one endothelial cell. However, this difference is insufficient to explain the full range of capillary diameters seen in vivo. This suggests that both endothelial structure and other influences, including pericyte tone, contribute to the basal diameter and optimized perfusion of brain capillaries.

Ukrainian National Biographical Archive as a Tool and Material in Ethno-Psychological Research

The purpose of the article is to demonstrate the possibilities of using the electronic "Ukrainian National Biographical Archive" (UNBA) in the field of humanitarian knowledge based on a specific applied historical and psychological study. The research methodology is a combination of general scientific methods of analysis of theoretical material and statistical and comparative methods of analysis of specific empirical data, their systematisation and generalisation. The scientific novelty of the research is characterised by the use of information technologies, large volumes of data, and methods of statistical processing in the traditionally humanitarian field of research, which is characterised by a predominantly descriptive approach. Conclusions. On the example of the proposed study, an approach is presented that will allow to significantly expand the possibilities of both specific biographical investigations and studies of socio-historical, ethnological, and historical-psychological directions. Large-scale volumes and detailed structuring of the biographical archive (UNBA) according to its thematic, chronological, and gender distribution allows presenting the system itself as the core of the scientific activity of the virtual historical and biographical laboratory. Analogues of this approach do not exist in Ukraine today due to the uniqueness of the biographical archive (UNBA) itself, which combines the widest possible coverage of national biographical material with its detailed structuring. As a perspective of further research in the presented direction, we see the study of the dynamics of changing the archetypal features of the generalised image of Ukrainians in the temporal perspective. This includes answers to the question: how were the spheres of social activity of men and women correlated in different periods, the scaling of which is possible from centuries to one generation, i.e. 20-30 years? How statistically significant are these changes? We consider another interesting direction to be the identification of archetypal characteristics of Ukrainians depending on the places of social activity, birth, etc. – the so-called geographical rubrics. Keywords: Ukrainian National Biographical Archive, personalities, archetypes, thematic headings, domains, statistics.

ESRGAN-based visualization for large-scale volume data

ESRGAN-based visualization for large-scale volume data

Realtime Calibration of an Industrial Robot

In large scale, complex and low volume manufacturing systems, robotics are now considered unavoidable for automating the factory operations. The aerospace industry focuses on a high variety and quality but extremely low volume. The precision it requires for numerous tasks is unique and distinct from any other manufacturing industry. This can comprise accurate position, module assembly, inspection, fastening, etc. The scale of the robot invites different types of errors during operation, which can be either because of the kinematics of the robot or because of the environment (noise, temperature, load, etc.). There are packages available from robot manufacturers for the correction and compensation of errors on the robot to achieve accuracy. There are two associated problems: 1. cost and 2. static nature. They are very costly and they do not provide correction in realtime fashion (dynamic); the robot stops, waits for the correction, and then moves to the next position. The external tool to monitor the accuracy also requires attaching with the robot permanently. These are dedicated resources. These tools for accurate measurement are expensive and attached permanently to a robot, which means wastage of resources. These measuring tools are called metrology devices and attaching these devices and the robot to the network means that other robots/machines can also use these expensive tools for measurement. Our aim was to address two problems in this project: 1. calibration (error correction and compensation of robot) and 2. dynamic and realtime processing. It helped to perform the dynamic error correction and the compensation of an industrial robot. The results showed the error correction was achieved in the region of 0.02 mm.

Percolation Characteristics and Injection Limit of Surfactant Huff-n-Puff in a Tight Reservoir.

For the development of tight reservoirs, large-scale volume fracturing is frequently utilized as an effective production enhancement strategy. However, there is a significant decrease in productivity after fracturing. Improvement of production through secondary surfactant huff-n-puff has become one of the methods. In this paper, the characteristics of surfactant percolation during huff-n-puff were analyzed from macroscopic and microscopic perspectives. The production variation characteristics of the huff-n-puff were calculated by experiments and numerical methods. From Stokes' equations and phase field equations, solutions were found to analyze the effect of interfacial properties on surfactant percolation from the microscopic perspective. The findings demonstrated that a surfactant with a high displacement efficiency could not considerably increase huff-n-puff production, whereas the percolation rate had a wider influence. The surfactant with ultralow interfacial tension (<1 × 10-2 mN/m) and a higher wetting angle (>12.6°) has a faster percolation rate. Significant huff-n-puff production can be obtained in the percolation rate range of 1.38 to 1.63 m/PV. Simultaneously, the concepts of limit and optimal injection volume were established and utilized to characterize the influence of injection parameters on production under nonextension fracture situations. Based on the data, in order to obtain high production in a short time, the injection strength should be near to the value at fracture extension, and the optimum injection volume is 1000-1200 m3/m. The findings of this study have the potential to guide the selection of the surfactant and injection parameters in the field.

Effect of In-Situ Stress on Hydraulic Fracturing of Tight Sandstone Based on Discrete Element Method

The tight sandstone reservoir in the Qianfoya formation of well PL-3 of the Puguang gas field in Sichuan, China, obtained a high-yield gas flow after a volume fracturing treatment. However, the stimulated reservoir volume (SRV), fracture morphology, scale and formation law still remain unclear. Based on particle flow discrete-element theory in this paper, we carried out a few trials of the Brazilian splitting test, uniaxial compression and triaxial compression of rock mechanics. Meanwhile, the research also testified to the conversion relationship between macroparameters and microparameters, established the numerical simulation on hydraulic fracturing through PFC2D discrete element software, and finally analyzed the influence of difference coefficients on the fracturing effect, in terms of different in-situ stresses. The conclusions are as follows: firstly, the influence of in-situ stress is essential for the direction, shape and quantity of fracture propagation, and the fractures generated by hydraulic fracturing are mainly tension fractures, accounting for over 90% of the total longitudinal fractures. Secondly, it is indicated that when the difference coefficient is small in the in-situ stress, the fractures formed by hydraulic fracturing expand randomly around the wellbore. When the difference coefficient Kh of in-situ stress is above 0.6, the development of hydraulic fractures is mainly controlled by in-situ stress; as a result, the fractures tend to expand in the vertical direction of the minimum horizontal principal stress and the fracture shape is relatively singular. When the difference coefficient of in-situ stress was 0.3, in total, 3121 fractures were generated by fracturing, and the fractal dimension D value of the fracture network complexity was 1.60. In this case, this fracturing effect was the best and it is the easiest to achieve for the purpose of economical and effective development on large-scale volume fracturing.

Characteristics and mechanism of imbibition oil recovery in the ultra-low-permeability volcanic oil reservoir in the Santanghu Basin

Imbibition oil recovery (IOR) is an important method to improve the recovery factor (RF) of low-permeability reservoirs. To explore the characteristics and mechanism of IOR in ultra-low-permeability volcanic oil reservoirs (ULPVORs) in the Santanghu Basin, this study conducted various imbibition experiments under NMR monitoring using actual cores from the reservoir, including spontaneous imbibition (SI) and forced imbibition (FI) experiments involving different rock sample sizes. Further, we obtained the IOR characteristics of ultra-low-permeability volcanic rocks (ULPVRs). Meanwhile, based on the experimental test results of the pore structure and casting thin section, combined with imbibition theory, the mechanism of the IOR in ULPVRs was obtained. The results demonstrated that the IOR characteristics of the ULPVRs are significantly different from those of conventional tight sandstone and shale. Conventional tight sandstone and shale evenly produce oil on the rock surface during IOR, whereas ULPVRs exhibits extremely few and scattered oil production points on the rock surface during IOR. Unlike conventional tight sandstone and shale plunger cores where the RF of SI is generally approximately 30%, the IOR of ULPVRs is particularly poor, with RFs of SI being 4.59% and 2.70% for plunger and full-diameter cores, respectively, and the RF of FI is only 10.19% for the plunger core. In addition, ULPVRs are extremely heterogeneous, and the pore structure and production characteristics of IOR are extremely complicated. The oil produced by SI in the plunger and full-diameter cores comes from middle pores (0.01 µm<r < 0.1 µm) and large pores(r > 0.1 µm), respectively, while the oil produced by FI in the plunger core comes from large pores. Conventional tight sandstone and shale are not conducive to seepage owing to their small pore sizes, however, their compatibility between the pore and throat sizes makes them favorable for IOR. The extremely poor matching between the pore and throat sizes of the ULPVRs is unfavorable for seepage, and is primarily responsible for its poor IOR effect. For the ULPVORs in the Santanghu Basin, the key to EOR is to improve the seepage capacity of the reservoir throat, such as further large-scale volume fracturing, rather than implementing too many measures for IOR. The results of this study can provide a reference and basis for the efficient development of ULPVORs.