Core Slices Research Articles

Improved pore structure characterization and classification of strong diagenesis sandstones by data-mining analytics in Tazhong area, Tarim Basin.

The Silurian system in Tazhong area is characterized by extensive, low-abundance lithological reservoirs with strong diagenesis, resulting in significant heterogeneity. The complex pore structure in this area significantly impacts fluid control, making accurate characterization and classification of pore structures crucial for understanding reservoir properties and their influence on oil and gas distribution. Based on 314 Mercury Injection Capillary Pressure (MICP) samples in combination with core slices and thin casting slices observation, a pipeline of characterization and classification scheme by data-mining analytics of strong diagenesis sandstone pore structure types in the study zone is established, and the characteristics of different pore structures are clarified. According to the pore structure parameter abstracted by MICP data compression and variable analysis based on hierarchical clustering and principal component analysis (PCA) analysis, the variables are reasonably evaluated and screened, and the screened variables can be divided into three groups: mean pore throat radius-maximum pore throat radius-median pore throat radius-pore throat diameter mean variable group, microscopic mean coefficient variable group, and median pressure displacement pressure-relative sorting coefficient variable group. The combination of classification schemes analysed by decision tree model and linear discriminant analysis (LDA) model was determined. In the two-dimensional projection diagram of LDA model, a relatively obvious distribution of low displacement pressure, middle displacement pressure and high displacement pressure was obtained, and three distribution lines were nearly parallel. Based on the relevant information, 6 combined classification schemes suitable for final pore structure modelling were determined verified by microscopic observation. The correct characterization and classification of pore structure can be applied to the prediction of pore type, which can be used to improve the prediction of oil and gas distribution and oil and gas recovery in the future.

High resolution 3D images of sediment cores as powerful tool for exploring foraminiferal microhabitats

Benthic foraminifera are marine protists largely used as bioindicators and proxies of paleo- environments. Epifaunal species are supposed to live at or above the sediment surface and are therefore used as proxies for bottom water conditions, while infaunal inhabit the sediment column, thus tracing porewater chemistry. Traditional analytical methods based on core slicing, however, have a low resolution that does not allow to precisely characterise the preferential microhabitat(s) of indicator species.In this study we performed microtomographic analyses on an experimental sediment core, to observe the life-position of living foraminifera of two surface-dwelling species Ammonia confertitesta and Haynesina germanica, reported both as epifaunal or shallow infaunal. The images we obtained offered for the first time the possibility to observe each individual in 3D space with a numerical resolution of 13 μm/voxel.The results revealed that the two species are never located above or at the sediment surface and have their preferential microhabitats in a sub-superficial sediment layer constrained in the 0–500 μm interval below the surface. Rapid decrease of abundances below this layer suggests that their microhabitat could be even more specific than previously thought.μCT-scan of sediment cores is also a valuable tool to obtain high-resolution information about foraminiferal ecology. The described method is useful to assess the effective microhabitat of all foraminiferal species that are usually used as proxies for paleorecords, to ensure that the information we can obtain from them is attributable to bottom water or to porewater conditions at a specific sediment depth.

A sedimentary DNA record of the Atacama Trench reveals biodiversity changes in the most productive marine ecosystem.

The hadopelagic environment remains highly understudied due to the inherent difficulties in sampling at these depths. The use of sediment environmental DNA (eDNA) can overcome some of these restrictions as settled and preserved DNA represent an archive of the biological communities. We use sediment eDNA to assess changes in the community within one of the world's most productive open-ocean ecosystems: the Atacama Trench. The ecosystems around the Atacama Trench have been intensively fished and are affected by climate oscillations, but the understanding of potential impacts on the marine community is limited. We sampled five sites using sediment cores at water depths from 2400 to ~8000 m. The chronologies of the sedimentary record were determined using 210Pbex. Environmental DNA was extracted from core slices and metabarcoding was used to identify the eukaryote community using two separate primer pairs for different sections of the 18S rRNA gene (V9 and V7) effectively targeting pelagic taxa. The reconstructed communities were similar among markers and mainly composed of chordates and members of the Chromista kingdom. Alpha diversity was estimated for all sites in intervals of 15 years (from 1842 to 2018), showing a severe drop in biodiversity from 1970 to 1985 that aligns with one of the strongest known El Niño events and extensive fishing efforts during the time. We find a direct impact of sea surface temperature on the community composition over time. Fish and cnidarian read abundance was examined separately to determine whether fishing had a direct impact, but no direct relation was found. These results demonstrate that sediment eDNA can be a valuable emerging tool providing insight in historical perspectives on ecosystem developments. This study constitutes an important step toward an improved understanding of the importance of environmental and anthropogenic drivers in affecting open and deep ocean communities.

Securing Breakneck Pace of 5G Networks Air Interfaces Through Proactive AI Monitoring

The promise of 5G networks enabling emerging technologies comes with formidable new security challenges. This paper proposes an AI-driven real-time security monitoring and incident response framework tailored to 5G infrastructure. A multi-layered architecture is presented using specialized deep learning models for radio access, edge, core, and network slice threat detection. Models including CNNs, RNNs, and transformers perform traffic analysis, signal classification, and log anomaly detection. A centralized controller aggregates model outputs into an integrated threat intelligence engine that deduces attack context and recommends mitigations. Further, a conversational bot interacts with security analysts in natural language to explain threats, suggest responses, and answer queries. The intelligent assistant is designed using dialog trees and transformer networks trained on security datasets. Evaluation on real-world 5G trial networks demonstrates 95% accuracy in classifying radio signal spoofing attacks and 98% precision in identifying malware infections. Analyst surveys confirm improved productivity and faster incident response with the AI assistant. As 5G matures, robust analytics and AI collaboration will grow increasingly critical for secure network operations. This research aims to provide both a conceptual framework and proven techniques as key enablers.

X-ray chemical imaging for assessing redox microsites within soils and sediments

Redox reactions underlie several biogeochemical processes and are typically spatiotemporally heterogeneous in soils and sediments. However, redox heterogeneity has yet to be incorporated into mainstream conceptualizations and modeling of soil biogeochemistry. Anoxic microsites, a defining feature of soil redox heterogeneity, are non-majority oxygen depleted zones in otherwise oxic environments. Neglecting to account for anoxic microsites can generate major uncertainties in quantitative assessments of greenhouse gas emissions, C sequestration, as well as nutrient and contaminant cycling at the ecosystem to global scales. However, only a few studies have observed/characterized anoxic microsites in undisturbed soils, primarily, because soil is opaque and microsites require µm-cm scale resolution over cm-m scales. Consequently, our current understanding of microsite characteristics does not support model parameterization. To resolve this knowledge gap, we demonstrate through this proof-of-concept study that X-ray fluorescence (XRF) 2D mapping can reliably detect, quantify, and provide basic redox characterization of anoxic microsites using solid phase “forensic” evidence. First, we tested and developed a systematic data processing approach to eliminate false positive redox microsites, i.e., artefacts, detected from synchrotron-based multiple-energy XRF 2D mapping of Fe (as a proxy of redox-sensitive elements) in Fe-“rich” sediment cores with artificially injected microsites. Then, spatial distribution of FeII and FeIII species from full, natural soil core slices (over cm-m lengths/widths) were mapped at 1–100 µm resolution. These investigations revealed direct evidence of anoxic microsites in predominantly oxic soils such as from an oak savanna and toeslope soil of a mountainous watershed, where anaerobicity would typically not be expected. We also revealed preferential spatial distribution of redox microsites inside aggregates from oak savanna soils. We anticipate that this approach will advance our understanding of soil biogeochemistry and help resolve “anomalous” occurrences of reduced products in nominally oxic soils.

Characterization and performance of a new lipopeptide biosurfactant producing strain Bacillus Subtilis R1-2

Biosurfactants produced by native microorganisms have excellent surface activity and ideal environmental compatibility, and the mining of the biosurfactant-producing strains has become a key focus in the field of applied and environmental biotechnology. In this paper, we report a new lipopeptide-producing strain isolated from the production water of Daqing oilfield in China and identified as Bacillus Subtilis R1-2 based on 16S rRNA gene sequence analyses. A combination of ESI-MS and FT-IR analyses revealed that the strain R1-2 produced the surfactin family containing four members of the C12-surfactin, C13-surfactin, C14-surfactin and C15-surfactin, which is a representative family of the lipopeptide biosurfactants. The lipopeptide biosurfactant produced by the strain R1-2 exhibits excellent surface activity and good thermal stability over a temperature range between 20°C and 100°C and pH range between 3 and 14, and has a strong salt tolerance to NaCl concentration up to 140 g/L. In addition, the lipopeptide biosurfactant demonstrates significant properties in changing the contact angles of oil reservoir core slices from 86.2° to 39.0° and the wettability from strong oil-wet to strong water-wet, and therefore, resulted in a good oil removing ability with an efficiency of 64.84%, suggesting that the lipopeptide-producing strain R1-2 is promising in applications in environmental bioremediation and enhanced oil recovery.

A comprehensive study on the effect of rock corrosion and CO2 on oil-water interfacial tension during CO2 injection in heavy oil reservoirs

This study is focused on addressing issues related to the interaction mechanism between reservoir rocks and fluids during CO2 injection in heavy oil reservoirs. The results indicate that the carbonated water formed after high-pressure CO2 injection is causing corrosion in the core structure. Furthermore, the average corrosion rate increases from 0.358% as pressure increases from 8 MPa to 16 MPa to 0.742%. The core slices exhibit corrosion pits and cracks on their surface, as well as surface particles flaking and dissolving. The dynamic IFT between decyl trimethyl ammonium bromide solution and crude oil exhibited slower stabilization and less change without CO2 injection. However, with increasing pressure post CO2 injection, the dynamic IFT decreased at a more rapid rate. Furthermore, positive correlation between equilibrium IFT and temperature was observed with low-pressure CO2. In contrast, the surfactant played a more dominant role in reducing IFT with high-pressure CO2. The findings of this study can help for better understanding of the mechanism of microscopic action on reservoir rock and oil-water interface after CO2 injection, and offer theoretical basis and reference for CO2 injection in heavy oil reservoirs to enhance the recovery rate.

Preparation of amphiphilic Janus nanosheets based on thermally expandable microspheres and evaluation of their physical and chemical properties

Janus nanosheets can reduce the interfacial tension of oil and water and are of great significance for improving the recovery efficiency of remaining oil. A novel strategy for preparing amphiphilic Janus nanosheets is presented in this paper. Trialkoxysilane is adsorbed on the surface of thermally expandable microspheres (TEMS), and Janus spheres (TEMS@SiO2) are obtained by sol-gel treatment. Dodecyltrimethoxysilane (DTES) is used to modify TEMS@SiO2, which is combined with the thermal expansion of TEMS and the breaking of the outer shell of Janus spheres to prepare amino/alkyl composite silicon-based amphiphilic Janus nanosheets (ASAJN). Characterization of ASAJN is carried out via FTIR, TG, SEM, TEM, and contact angle tester, with the results showing that ASAJN boasts amphiphilicity and two-sidedness, and a sheet structure with a thickness of 210 nm. The stability, wettability evaluation, emulsification properties, and interface properties of ASAJN nanofluids have been evaluated, with the results showing that ASAJN nanofluids boast good stability without delamination after 30 days. ASAJN nanofluid can convert oil-wet core slices into water-wettable ones. ASAJN nanofluid manifests excellent emulsification, with the 240-hour emulsification index of ASAJN nanofluid and silica fluid respectively at0.40 and 0.021 under ultra-low concentration (1000 mg/L). ASAJN is obviously able to reduce oil–water interfacial tension. At 1000 mg/L (60 ℃) concentration, the oil-water interfacial tension is reduced to 2.6 mN/m, while that of nano-silica at the same concentration is 15.1 mN/m. ASAJN flooding can significantly improve the recovery efficiency of low-permeability reservoirs, increasing the recovery efficiency by 32.8 % at a permeability of 7.5 × 10-3 μm2.

Digital core reconstruction based on discrete element and Markov chain-Monte Carlo methods

The digital core is an essential platform for pore-scale flow simulation in geoenergy exploitation, CO2 storage, and hydrogen storage. However, the existing reconstruction methods, such as the Markov chain-Monte Carlo (MCMC) method, can not obtain the digital core reflecting the influence of complex stress. Moreover, the discrete element method (DEM)-based reconstruction, which can consider the effect of stress, has yet to be reported to consider the actual particle shape. To this end, a hybrid reconstruction method based on DEM and MCMC methods is proposed in this study. Firstly, watershed and level set algorithms are used to segment and extract the contours of rock particles, and clump in PFC2D is used to fill the contours to establish a clump template library. Then, the initial condition (ground condition) digital core slice is established with clump packing based on porosity and particle size distribution. Subsequently, the stress loading simulation is carried out to obtain 2D digital core slices with different stress combinations. Finally, the MCMC method obtains 3D digital cores. This proposed method is used to reconstruct the digital core of Belgian fieldstone with different stresses and effective moduli, and the evolution of pore structure characteristics is analyzed. Results show that the stress compresses the range of the pore radius distribution curve, resulting in the shortening of the throat length distribution; the conductivity of the model is poor. The maximum reduction of porosity and permeability is 6.56% and 24.81%, respectively. The model with a large effective modulus has a stronger resistance to deformation and can protect the pore structure. These results prove that the method proposed can obtain the digital core reflecting the stress effects and provide a reference for accurately describing the in-situ rocks' pore structure and the pore-scale flow simulation.

Multi-scale data joint inversion of minerals and porosity in altered igneous reservoirs—A case study in the South China Sea

There are abundant igneous gas reservoirs in the South China Sea with significant value of research, and lithology classification, mineral analysis and porosity inversion are important links in reservoir evaluation. However, affected by the diverse lithology, complicated mineral and widespread alteration, conventional logging lithology classification and mineral inversion become considerably difficult. At the same time, owing to the limitation of the wireline log response equation, the quantity and accuracy of minerals can hardly meet the exploration requirements of igneous formations. To overcome those issues, this study takes the South China Sea as an example, and combines multi-scale data such as micro rock slices, petrophysical experiments, wireline log and element cutting log to establish a set of joint inversion methods for minerals and porosity of altered igneous rocks. Specifically, we define the lithology and mineral characteristics through core slices and mineral data, and establish an igneous multi-mineral volumetric model. Then we determine element cutting log correction method based on core element data, and combine wireline log and corrected element cutting log to perform the lithology classification and joint inversion of minerals and porosity. However, it is always difficult to determine the elemental eigenvalues of different minerals in inversion. This paper uses multiple linear regression methods to solve this problem. Finally, an integrated inversion technique for altered igneous formations was developed. The results show that the corrected element cutting log are in good agreement with the core element data, and the mineral and porosity results obtained from the joint inversion based on the wireline log and corrected element cutting log are also in good agreement with the core data from X-ray diffraction. The results demonstrate that the inversion technique is applicable and this study provides a new direction for the mineral inversion research of altered igneous formations.

Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating

Optically Stimulated Luminescence (OSL) depth profiles can be used to extrapolate rock surface exposure ages by understanding a sample's light attenuation and OSL bleaching behaviors. However, the current measuring procedure for producing depth profiles, by measuring the luminescence of millimeter slices from surface core samples, offers limited resolution data, limiting parameter extrapolative precision. The use of OSL laser scanning measuring protocols on transverse core slices has potential for improving the data resolution of OSL depth profiles from core samples. An 11-year exposed rock surface was parameterized for light attenuation and luminescence bleaching rates using depth profile data from four surface cores collected via wafer and scan measuring techniques, to show the potential of OSL laser scanning to improve depth profile dataset resolutions and parameter extrapolative precision. Additionally, non-quartz OSL anomalies were filtered from scan datasets to determine if non-quartz mineral filtering can further improve the resolution and parameterization of depth profile datasets. OSL scanning measures improved the resolution of depth profiles over wafer derived depth profiles for the measured samples, and in comparing absolute uncertainties of parameters, broadly improved parameter precision using maximum likelihood estimate fitting protocols. Non-test dose corrected spatially resolved data error bounds were generally larger than from wafer derived data. When compared to unfiltered spatially resolved depth profile data, OSL anomaly filtered scanned depth profiles improved error bounds of anomaly affected data, and improved parameter extrapolative precision with roughly a similar effect as using non-filtered scan data. The absolute uncertainty in parameterization was impacted by scatter prevalent from scan datasets, which is caused by low OSL sensitivity from samples, the potential recording of remnant OSL, the lack of regenerative dose normalization applied to the scan dataset, as well as other scanning instrument measuring limitations. Still, these influences can be mitigated with optimized instrumentation and scanning protocols.

CO2 diffusive characteristics and influencing factors in the porous medium with saturated polyacrylamide solutions

Polyacrylamide has been widely utilized to store CO2 in reservoirs in order to alleviate the greenhouse impact. However, no studies have been conducted on the motion of CO2 in a polyacrylamide solution within porous media. This study illustrates the effect of polyacrylamide molecular weight, concentration, pressure, temperature, and pore size on the dynamic diffusion of CO2 in the core with saturated polyacrylamide solutions. The diffusion motion characteristics of CO2 were obtained by diffusion experiments. The microscopic analysis of core slices following testing showed the presence of polyacrylamide on rock particles. The pressure and CO2 mass fraction distribution inside the core were calculated by constructing a mathematical model. The results show that the increase in molecular weight and concentration of polyacrylamide decreased the diffusion ability of CO2, while temperature and pressure accelerated the diffusion of CO2. The polyacrylamide moved into the core to form an effective inhibiting effect, which was affected by CO2 and pressure. While the increase in pore size accelerates the flow rate of the polyacrylamide, it also enhances the sealing capacity of the polyacrylamide solution. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) showed that the core had a high content of polyacrylamide solution inside it, and these molecules were attached to the surface of the core particles in an irregular lamellar pattern. The calculation model was consistent with the diffusion experimental results, where the CO2 diffusion coefficient in the polyacrylamide solution was in the order of 10-12 m2/s under the conditions of 5–30 MPa, 323–343 K, and a core permeability of 60 × 10-3 μm2. The results of this paper provide a new calculation for evaluating the performance of polymer plugging.

Tensor train factorization under noisy and incomplete data with automatic rank estimation

As a powerful tool in analyzing multi-dimensional data, tensor train (TT) decomposition shows superior performance compared to other tensor decomposition formats. Existing TT decomposition methods, however, either easily overfit with noise, or require substantial fine-tuning to strike a balance between recovery accuracy and model complexity. To avoid the above shortcomings, this paper treats the TT decomposition in a fully Bayesian perspective, which includes automatic TT rank determination and noise power estimation. Theoretical justification on adopting the Gaussian-product-Gamma priors for inducing sparsity on the slices of the TT cores is provided, thus allowing the model complexity to be automatically determined even when the observed tensor data is noisy and contains many missing values. Furthermore, using the variational inference framework, an effective learning algorithm on the probabilistic model parameters is derived. Simulations on synthetic data demonstrate that the proposed algorithm accurately recovers the underlying TT structure from incomplete noisy observations. Further experiments on image and video data also show its superior performance to other existing TT decomposition algorithms.

Experimental Study on the Distribution of Retained Fracturing Fluids and Its Effect on the Permeability and Wettability in Tight Oil Reservoirs

It is not clear how the distribution of retained fracturing fluids and its effect on the permeability and wettability in tight oil reservoirs interact. Especially, there are more qualitative studies and less quantitative studies on this issue. Under laboratory experimental conditions, this paper clarifies the distribution of retained fracturing fluids in the core and reveals the influence rule of retained fracturing fluids on tight reservoir permeability and wettability. It is found that the main retention space of retained fracturing fluids in a tight reservoir is a microporous interval, and the residual oil after oil displacement by retained fracturing fluids mainly exists in the core in the form of dots or porphyries. The smaller permeability and porosity of the core will lead to more retained fracturing fluids. The permeability of different cores after fracturing fluid retention has decreased to varying degrees compared with that before fracturing fluid retention. The wettability of core slices before and after fracturing fluid retention was tested, and the effect of retained fracturing fluids on reservoir wettability was not significant. This study has important significance for improving the recovery of tight oil reservoirs and enhancing the understanding of postfracturing fluid retention.

Measuring Sarcopenia on MRI among Turkish Female Breast Cancer Patients

Background: Since the presence of sarcopenia in breast cancer patients is associated with increased postoperative complications, it is crucial to know its frequency in the prechemoteraphy period. It has been suggested that there are many factors associated with sarcopenia. This study focused on the frequency of sarcopenia over the pectoralis muscle area obtained in routine breast MRI among Turkish females who were newly diagnosed with breast cancer. Methods: In a prospective study, pectoralis muscle was manually contoured bilaterally, and the cross-sectional area on breast MRI was calculated in newly diagnosed breast cancer patients. The 'Core slices' program was used while calculating SMI. We accepted 7.4 kg/m2 as the threshold value for sarcopenia. Results: Overall, 17 patients (17 %) were sarcopenic. The analysis regarding the detection of sarcopenia in the patient groups < 65 and ≥ 65 years of age was not statistically significant (P<0.156). The relationship between the groups with BMI < 25 and ≥ 25 sarcopenia was statistically significant (P<0.001). Sarcopenia was found significantly more frequently in patients with metastasis (P<0,0001). Conclusion: This study recruited Turkish females who were newly diagnosed breast cancer. It is the first study in the literature to include the same race, gender, and malignancy in which sarcopenia was investigated. It was shown that breast MRI which is radiation-free can be used in the evaluation of sarcopenia in patients with breast cancer. This result is important for being prepared for possible complications before treatment.

Development model and identification of evaluation technology for Wufeng Formation–Longmaxi Formation quality shale gas reservoirs in the Sichuan Basin

In 2012, China's first national shale gas demonstrations areas were set up in the Sichuan Basin. After 10 years' construction and practice, the giant marine shale gas area of 10 × 1012 m3 level is built up in the Sichuan Basin, and shale gas steps into a new stage of large-scale benefit exploration and development. In order to systematically summarize the achievements in shale gas exploration and development and provide guidance and reference for the exploration and development of deep shale gas and shale oil & gas in other areas, this paper systematically summarizes the main characteristics, development models and key identification and evaluation technologies for quality reservoirs of Wufeng Formation–Longmaxi Formation shale gas by analyzing the electric property, lithofacies, reservoir parameters and microscopic porosity of key wells in the basin. And the following research results are obtained. First, biogenetic siliceous shale, calcareous shale and mixed shale are main lithofacies types in quality shale gas reservoirs, and they are formed in the environment of semi-deep and deep water continental shelf. Their lateral distribution is controlled by the paleogeomorphology and their vertical development is influenced by provenance, redox condition and paleo productivity. They are 25–90 m thick. Second, in the quality shale gas reservoirs develop organic pores, inorganic pore and microfractures (including lamina/bedding fractures), among which, organic pore is one of the main reservoir spaces and microfracture is not only indispensable reservoir space, but also production pathway. The reservoir space of shale gas is overall micro-nano pore, and macropores play an important role in shale gas enrichment. Third, three development models of quality reservoir are established, including sedimentary type, diagenesis type and reworking type. The sedimentary type is the foundation. Multiple quality reservoirs are developed in the high U/Th interval of graptolite belt at the bottom of Longmaxi Formation, and their thickness is mainly controlled by paleogeomorphology and especially greater in the depression area. The diagenesis type is divided into three forms, i.e., syngenetic-early diagenetic rigid support, middle-late diagenetic mineral-organic matter transformation, and overpressure relief compaction. The reworking type is dominated by quality reservoirs with microfractures. Fourth, the core technologies for identifying and evaluating quality shale gas reservoir include large-size core and rock slice observation, high-accuracy rock mineral identification, experimental gas content test and simulation, SEM microscopic characterization, 3D microscopic pore reconstruction, comprehensive geophysical interpretation and prediction and big data analysis. In conclusion, nearly 10 years' research and practice achievements in demonstration area construction can deepen the understanding on domestic quality shale gas reservoirs, promote the effective development of the theories and technologies related to shale gas reservoirs, improve the prediction accuracy of shale gas sweet spot zones/intervals, and expand the shale gas exploration and development achievements of demonstration areas.

Improving the efficiency of machine learning in simulating sedimentary heavy metal contamination by coupling preposing feature selection methods

Sediment cores were collected from Taihu Lake in China. The chronology was determined by radionuclide. Heavy metals and magnetic properties of each core slice were assessed, respectively. The concentrations of most heavy metals in sediments surged at 20 cm from the surface, accompanying the increase in the concentrations of single-domain magnetic particles. This may be resulted from the influence of anthropic activities on the lake's environment after the 1970s. Two feature selection methods, random forest (RF) and maximal information coefficient (MIC), were combined with support vector machine (SVM) model to simulate heavy metals, with the inclusion of selected magnetic and physicochemical parameters. Compared with the modeling results obtained with the full set of parameters, a reasonable simulation performance was obtained with RF and MIC. RF performed better than MIC by increasing the R2 of simulation models for Cd, Cr, Cu, Pb, and Sb. For heavy metals with high ecological risks (As, Cd, Cr, Hg, Pb, Sb), the correlation coefficients for observed and predicted data ranged from 0.73 to 0.97 with only 14–27% of the parameters selected by RF as input variables. The RF-RBF-SVM enabled heavy metal predictions based on the magnetic properties of the lake sediments.

Experimental investigation of nanofluid enhanced oil recovery by spontaneous imbibition.

Nanofluids have been recently proposed as new chemical agents for enhanced oil recovery. In this study, in order to reflect the effect of nanofluids on imbibition, the imbibition performance of manganese chloride (MnCl2) solution, sodium dodecylbenzene sulfonate (SDBS) solution, and silica (SiO2) nanofluids were studied by a spontaneous imbibition experiment at 25 °C and 0 MPa. The oil production from pores with different sizes and the imbibition efficiency were tested by nuclear magnetic resonance T2 spectroscopy and metering in spontaneous imbibition. In addition, the interfacial tensions between the imbibition liquids and oil were tested. The changes in the contact angle of the core slice before and after immersing in imbibition liquids were measured. The silica nanofluid is used as the imbibition liquid, and the shift of the T2 spectral peak to the left is not obvious and shifted by only 23.95-25.72 ms, the change in the contact angle is 6.63°-12°, the interfacial tension between the nanofluid and the simulated oil is 0.25-0.41 mN m-1, and the imbibition efficiency was slightly improved with increasing nanoparticle concentration, up to 57.40%, which improved by 16.14% and 32.95%, respectively, compared to the surfactant solution and the manganese chloride solution. This shows that the silica nanofluid can effectively improve oil production in small pores, reduce oil-water interfacial tension, and change rock wettability.

Design of pneumatic automatic core removal and slicing device and mechanical characteristics test for crab‐apple

AbstractThe current crab‐apple core removal and slicing equipment is mostly pure mechanical equipment, and there are problems such as high failure rate, low core removal rate and slice integrity rate, and so on. In order to solve these problems and improve the low mechanization level in the processing of the crab‐apple products, based on the study of mechanical characteristics of crab‐apple, an automatic crab‐apple core removal and slicing equipment was designed, which is driven by pneumatic system. Through drying, compression and shear test, the average moisture content of the crab‐apple was determined to be 84.57%. And the test results showed that, in the axial direction, the average compressive strength was 0.4137 MPa, and the shear strength was 0.05023 MPa. In the radial direction, the average compressive strength was 0.4462 MPa, and the shear strength was 0.05114 MPa, with anisotropy. The effects of different cutting wedge angle on the force on pulp were studied, and the relevant control system designed. Through the site test of authoritative institutions, the equipment has achieved 98.7% of complete core removal rate and 95.8% of complete ring‐shaped fruit slices rate, and the output has reached 750 kg/h, increasing productivity by more than 10% with low failure rate, and also provides an important reference for the research and development of core removal equipment of other multi‐core fruits. Crab‐apple fruit slices processing enterprises mostly use manual or semi‐automatic equipment for the crab‐apple removal, which restricts development of local crab‐apple processing industry chain. The study has designed an automatic crab‐apple removal and slicing equipment based on the study of physical characters of crab‐apple and analysis of different cutting wedge surface. The experimental results of crab‐apple physical characters show that the crab‐apple pulp is anisotropic. The site test results show that the equipment significantly improves the level of automation. And the core removal method also provide reference for other multi‐core fruits.

Learning From Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing

Network slicing is a critical technique for 5G communications that covers radio access network (RAN), edge, transport and core slicing. The evolving network architecture requires the orchestration of multiple network resources such as radio and cache resources. In recent years, machine learning (ML) techniques have been widely applied for network management. However, most existing works do not take advantage of the knowledge transfer capability in ML. In this paper, we propose a deep transfer reinforcement learning (DTRL) scheme for joint radio and cache resource allocation to serve 5G RAN slicing. We first define a hierarchical architecture for joint resource allocation. Then we propose two DTRL algorithms: Q-value-based deep transfer reinforcement learning (QDTRL) and action selection-based deep transfer reinforcement learning (ADTRL). In the proposed schemes, learner agents utilize expert agents’ knowledge to improve their performance on current tasks. The proposed algorithms are compared with both the model-free exploration bonus deep Q-learning (EB-DQN) and the model-based priority proportional fairness and time-to-live (PPF-TTL) algorithms. Compared with EB-DQN, our proposed DTRL-based method presents 21.4% lower delay for Ultra Reliable Low Latency Communications (URLLC) slice and 22.4% higher throughput for enhanced Mobile Broad Band (eMBB) slice, while achieving significantly faster convergence than EB-DQN. Moreover, 40.8% lower URLLC delay and 59.8% higher eMBB throughput are observed with respect to PPF-TTL.