Flow Units Research Articles

An Effective Method for Reproducing the Flow of a Gas-Liquid Mixture Using a Flow Standard

The appearance on the market of measuring equipment of multiphase flowmeters used in the oil and gas industry to measure the amount of extracted hydrocarbons directly at wells without preliminary separation and performing the function of operational accounting raised the question of their metrological support in accordance with the requirements of regulatory documents of the sphere of state regulation in this field of measurements. His decision was to create a reference base, the basis of which was the State primary special standard for units of mass flow of gas-liquid mixtures GET 195-2011 and a verification scheme for measuring multiphase flow rates, regulated by GOST 8.637-2013. When transmitting units of volume and mass flow of a multiphase mixture with the required accuracy, the main problem is the difficulty of reproducing and maintaining stability of several flow parameters at once – mass flow and content of liquid components (water and oil simulator) and volume flow of the gas phase. It should also be borne in mind that test programs for the purpose of approving the type of multiphase flow measuring instruments, as well as verification and calibration methods, provide for changing the modes of reproduction of a gas-liquid mixture, covering the measurement ranges of multiphase flowmeters. Such standards used in practice represent a very complex measuring system consisting of storage tanks for components, supply pumping and compressor units, control equipment, mixers and separators, measuring instruments for the physical parameters of multiphase flow components and an automated control system. Given the high cost of multiphase standards, when creating them, effective methods and methods should be used to reproduce the flow of a multiphase mixture with specified parameters, as well as to make a fairly rapid transition to the next mode provided by the program or methodology, which will reduce the time and production costs for the procedure of transferring units of measurement from the standard to the working measuring instrument. This article describes a method for reproducing the flow rate of a gas-liquid mixture according to the original technological scheme introduced during the development of the 1st category standard. Constructive solutions are presented that increase the efficiency of the liquid component separation apparatus to increase the dosing accuracy, as well as expand the range of reproduction of the flow rate and parameters of the multiphase flow, ensuring the consistency of the composition of the liquid mixture at a given regime. An original rational method of compressing and maintaining the pressure of the gas phase is described, which allows you to quickly switch to the next mode, confirmed by the results of experimental studies of the standard.

Gravity and magnetic study of the Xitle volcanic field, Basin of Mexico – Inferences on the pre-eruptive topography

Results of a gravity and magnetic study over a lava field in the southern Basin of Mexico are used to investigate on lava emplacement and pre-eruptive topography. The characteristics of lava flows depend on multiple factors, including the lava composition, effusion rate, rheology, fluid content and underlying terrain. The ∼2000 years old volcanic field, formed by seven flow units emplaced by the Xitle eruption, covers an extensive area >80 km2 characterized by topographic relief on the Ajusco volcano flank and surrounding plain. The study is conducted on the northern end of the volcanic field. The gravity and magnetic study is based on regional-residual separation, vertical and horizontal derivatives, reduced to the pole, analytical signal, spectral analysis and forward modeling. The regional gravity field shows two anomaly lows in the central sector, with the residual field showing trends of gravity highs. The residual magnetic anomaly is characterized by trends of magnetic highs associated to the lava field. Depths estimated from spectral analysis of gravity and magnetic anomalies show shallow sources at ∼22 m and ∼46 m, and deep sources at ∼469 m and ∼268 m, respectively. The underground sequence is modeled as a volcanoclastic succession of varying thickness. The first vertical derivatives, gradients and analytical signal enhance the anomalies associated to lava thickness variations. Joint gravity and magnetic analysis constraints the deep/shallow anomaly sources in the volcanic field and relations to pre-eruptive structure.

Analysis of classification results of rock cores with different physical properties based on clustering algorithm

Abstract In response to the problem of high difficulty in development caused by different physical properties differences during water flooding in high water bearing oil fields, effective flow unit division and analysis were carried out in Zone 1 of Gangxi through flow unit analysis. The physical property classification of each rock core in the flow unit was determined using the GMM cluster algorithm. The results show that Class I flow units have high permeability and porosity, and good pore permeability performance; The porosity and permeability distribution range of Class II flow units is the widest, with some pore permeability performance comparable to or even higher than Class I, and overall pore permeability performance is good; The porosity and permeability of Class III flow units are lower than the first two types, and their pore permeability performance is relatively poor. The types of core porosity and permeability in the study area can be divided into four categories, with the majority being high permeability, ultra-high permeability, and high permeability. The new research results provide a reference basis for the study of physical property distribution and later well network deployment in the first block of Gangxi District.

All-optics technique for monitoring absolute cerebral blood flow: validation against magnetic resonance imaging perfusion.

The ability to monitor cerebral blood flow (CBF) at the bedside is essential to managing critical-care patients with neurological emergencies. Diffuse correlation spectroscopy (DCS) is ideal because it is non-invasive, portable, and inexpensive. We investigated a near-infrared spectroscopy (NIRS) approach for converting DCS measurements into physiological units of blood flow. Using magnetic resonance imaging perfusion as a reference, we investigated the accuracy of absolute CBF measurements from a bolus-tracking NIRS method that used transient hypoxia as a flow tracer and hypercapnia-induced increases in CBF measured by DCS. Twelve participants (7 female, years) completed a hypercapnia protocol with simultaneous CBF recordings from DCS and arterial spin labeling (ASL). Nine participants completed the transient hypoxia protocol while instrumented with time-resolved NIRS. The estimate of baseline CBF was subsequently used to calibrate hypercapnic DCS data. Moderately strong correlations at baseline ( and ) and during hypercapnia ( and ) were found between CBF values from calibrated DCS and ASL (range 34 to ). Results demonstrated the feasibility of an all-optics approach that can both quantify CBF and perform continuous perfusion monitoring.

Structural optimization and performance investigation of power flow adaptive flow devices based on multi-body coupling

Through the multi-body coupling between the components in the numerical calculation, the self-adaptive yaw motion of the power flow turbine is realized. Specifically, the changes of launching dynamic performance in different yaw structure combinations (different rod length, different tail chord length, different tail limiting Angle and rotation axis position) are investigated. The results show that under the same operating conditions, the turbine efficiency of the optimized structure combination is increased by 1.05 % and the yaw speed is increased by 16.7 %. Then, the influences of different tip immersion depth and wave period on thrust coefficient and efficiency coefficient of turbine at free liquid surface are compared. The conclusion is that the turbine efficiency is increased by 0.8 % when the tip depth is 0.21D compared with that without free liquid surface. When the wave period is 1.3, the turbine efficiency is increased by 1.4 % compared with that without free liquid surface. The results show that the selection of appropriate structure combination and installation depth is of great help to turbine efficiency and yaw speed during the yaw process of turbine power flow unit.

Reservoir and rock characterization for Mishrif Formation/ Zubair Field (Rafdiya and Safwan Domes) by nuclear magnetic resonance and cores analysis

The identification studies of rock and reservoir characteristics are essential for evaluating the efficiency of a reservoir by analyzing the types of rock and pores. In this study, Nuclear Magnetic Resonance (NMR) for six wells was utilized to identify the rock and reservoir characteristics of the Mishrif formation in the South of the Zubair Field, specifically the Rafdyia and Safwan domes, as well as the west flank. Specific correlations were utilized to describe the classification of rocks, estimate pore throat radius, and recognize possible flow intervals. The results revealed that the Mishrif Formation mainly consists of limestone, dolomite, and shale. The Safwan dome consists mainly of pack-wackestone, wackestone, and mudstone, while the Rafdyia dome is a mixture of packstone, grainstone, and wackestone with some shoal and rudist bioherm facies. The west flank is characterized by pack-wackestone, wackestone, and mudstone. These results were further supported by core analysis. Pore throat types in the Safwan dome range from Micropores to Nanopores, while the Rafdyia dome is dominated by Megapores, Macropores, and Mesopores. The back-shoal facies overwhelm the packstone microfacies. The west flank of the Mishrif Formation in the Zubair Field is mesoporous and micropores. Flow units were identified in the Safwan and Rafdyia domes, as well as the west flank. The Safwan dome has fifteen flow units in the north and seven in the south, while the Rafdyia dome has ten flow units in both the north and south regions, with five flow units on the west flank of the Zubair Field.

Enhancing permeability estimation methods in Brazilian pre-salt complex carbonate reservoirs

Carbonate pre-salt reservoirs pose formidable challenges for formation evaluation and estimation of petrophysical properties such as rock flow capacity. The high complexity of the pore system and its textural heterogeneity often lead to poor predictions when using conventional formation evaluation methods. This paper discusses and tests the applicability of two approaches to calculate the absolute permeability curves for the carbonate reservoirs of the Barra Velha Formation in the Santos Basin, proposing a fine-tuned workflow for these complex rocks. In developing our workflow, we used data from a case study well, comprising a complete well log suite and two formation tests, and routine core analysis results (RCAL) in core plugs and sidewall cores. Our workflow consists of: (1) reinterpreting the nuclear magnetic resonance (NMR) log in vugular intervals by decomposing the transversal relaxation time spectra; (2) calculating the absolute permeability by the Timur–Coates model and adjusting the coefficients with the RCAL results from representative samples; (3) applying the Flow Zone Indicator (FZI) method for rock typing to optimally define Hydraulic Flow Units (HFU); (4) checking the consistency of HFUs regarding pore geometry and lithological characteristics; (5) evaluating the performance of the models against core data and formation tests. The predicted permeability curves performed well for the NMR and FZI models when compared to the RCAL data from representative samples, with mean absolute errors for the logarithm of permeabilities equal to 0.78 and 0.87, respectively. This represents an improvement of 15% and 5% when compared to the simplistic approach that uses a power law to relate porosity to permeability from RCAL. These curves also reasonably reproduce the results from the formation tests for the top part of the reservoir, where matrix porosity dominates the flow. However, the NMR and FZI permeability curves underestimate the test results at the bottom part of the reservoir, where karstic and fracture features dominate, demonstrating limitations in these permeability estimation methods for this specific scenario. From our observations, we propose several improvements to enhance the current methods for estimating absolute permeability in pre-salt carbonate reservoirs. In the case of the NMR approach, it is essential to consider the effects of mud invasion in vugs for the correct estimation of the proportions of free and irreducible fluids. In the FZI approach, optimization in defining the quantity and limits of hydraulic flow units is important, which shows that four units are sufficient to represent the reservoir interval.

Unraveling the hidden relationships between seismic multiattributes, well-dynamic data, and Brazilian pre-salt carbonate reservoirs productivity: A shallow versus deep machine-learning approach

During the initial phases of an E&P project, the most reliable data on the reservoir’s deliverability are acquired via drill stem tests (DST), which provide the productivity per flow unit whenever production logging tool data are available. However, DSTs are restricted to a few kilometers, whereas seismic data cover large areas. The integration of these data has been challenging, particularly due to the difference in scale between them. So, a new workflow to determine the relationship between poststack seismic attributes and reservoir productivity using classic supervised (shallow) and deep-learning regression algorithms was developed. The DST parameters were predicted over the entire seismic cube, which can be extremely valuable for the decision-making process. The data set is from the Brazilian deep-water presalt carbonate reservoirs of the Mero Field, which is a well-explored area with a plethora of test and production data. It is adjacent to an underexplored Central Libra appraisal plan area, which is covered by the same seismic survey. Thus, any relationships between seismic attributes and well productivity data observed at the Mero Field are extrapolated to the adjacent underexplored area. Ten seismic attributes and DST data from 10 wells of the Mero Field were used to train shallow and deep-learning supervised regression algorithms for the prediction of flow capacity and productivity index seismic cubes. Twenty development wells (blind tests) were used for the assessment of our predictive models. The highest percentage of correct predictions at the blind test wells (85%) was obtained with random forest regression using six attributes derived from a spectrally balanced full-stack volume, and neither amplitude-variation-with-offset nor inversion data were needed. Deep learning provided lower performance (75%) at a higher computational cost. It demonstrated a new reservoir derisking tool that can be used for project optimization in areas covered by the same seismic survey.

A Survey on Models of Flows over Time with Flow Dependent Transit Times

Ford and Fulkerson introduce the maximum dynamic flow problems with fixed transit times on the arcs and developed the first well known algorithm that sends maximum flow from the source to the sink by augmenting along s-t paths and prove the maximum amount of flow is equal to the total capacity of the arcs in minimum cut. In flows over time with fixed transit time on the arc, the time it takes to traverse an arc does not depend on the current flow situation on the arc. But in real situation, the flow units travelling on the same arc at the same time do not necessarily experience the same pace, i.e., flow units are in general not entering and leaving an arc in the same order. In this paper, we discuss the time expanded graph for load-dependent and inflow[1]dependent transit times. We also discuss the discrete and continuous time flow, earliest arrival flow and quickest transhipment problem.

Controls of tectonics and paleoclimate on depositional–diagenetic evolution and pore types of Upper Cretaceous successions (Sarvak Formation) in the Abadan plain, Iran

This study examines the influence of tectonics and paleoclimate on the diagenetic evolution of Upper Cretaceous carbonate reservoirs (Sarvak Formation) in the Abadan Plain, Zagros Basin, Iran. Through petrographic analysis and scanning electron microscopy, the sedimentary facies, diagenetic processes, and pore system within the Sarvak Formation were analyzed. Facies analysis identified eight microfacies, which were deposited in shallow to deep settings of a carbonate ramp. Among these, the grain-dominated facies (packstone and grainstone) of shoal complexes and reef-talus settings were identified as the most important reservoir facies. The formation's complex diagenetic history includes marine, meteoric, and burial diagenetic processes, with meteoric diagenesis playing a significant role beneath paleoexposure surfaces. The Sarvak Formation was categorized into eight flow units, seven pore size classes, eleven electrofacies, eight velocity deviation zones, and thirteen Lorenz zones. The study's findings indicate that the most favorable reservoir units are located beneath the Cenomanian–Turonian exposure surface, characterized by significant dissolution of rudist-dominated facies due to meteoric processes and karstification. Tectonic uplift, along with weathering and erosion within a warm and humid (tropical) paleoclimate, significantly impacts the diagenetic evolution, pore types, and petrophysical properties of the Sarvak Formation. This research underscores the complex interplay between tectonics, paleoclimate, and petrophysical properties in the Abadan Plain, enhancing our understanding of carbonate systems in tectonically active and paleoclimatically dynamic regions.

Unique Energy‐Storage Behavior Driven by High Entropy in Metallic Glasses

AbstractThe rejuvenation or energy‐storage behavior in metallic glasses (MGs) has been extensively explored for its theoretical and practical significance. However, very limited research focuses on the rejuvenation of high entropy metallic glasses (HEMGs), leaving uncertainties about how configurational entropy influences this process. In this study, cryogenic temperature cycling (CTC) is utilized to unlock the rejuvenation potential of a series of La‐based MGs with different entropy values. Comparative analysis of these MGs reveals that increased entropy boosts the maximum rejuvenation degree from 37% to 65% and widens the range of energy states where rejuvenation occurs. The changed rejuvenation behavior induced by entropy increase, which has never been reported other studies, are related to glass's structures, fragility and coupling degree between β and α relaxations. Besides, a new rejuvenation mechanism is revealed in HEMGs, which differs significantly from unrestricted growth and interconnection of flow units observed in most MGs. The atomic motion in HEMGs is confined within the icosahedral backbone network during CTC. Surprisingly, this local motion can propagate through strong interactions between adjacent atoms, facilitating the activation of α relaxation. These discoveries offer novel approaches to tune energy‐storage behavior of MGs by modulating icosahedral network structures through entropy control.

Nanoindentation-induced serrated flow behavior and deformation mechanism in metallic glass: A combined experiments and molecular dynamics simulation

In this study, the deformation behaviors of bulk metallic glass (BMG) during the nanoindentation are presented via the experiments and molecular dynamics (MD). The relationship between shear transformation zone (STZ) formation and serrated flow dynamics are developed to explain the deformation characterization evolution of BMG. It is found that as the peak load increases, the hardness and elastic modulus significantly decrease. Creep behavior at room temperature was also revealed by analyzing the creep displacement curve and the stress index under different peak loads.The accumulation of free volume during the deformation process promotes more uniform creep deformation. Furthermore, the serrated flow behaviors were analyzed innovatively using the shear stress drops further statistically. At lower loads, the occurrence of serrated retention is due to energy exceeding the potential barrier required for flow unit activation, ultimately released in the form of kinetic energy. At higher loads, the transition from intermittent curve to smooth curve exhibits a typical self-organized critical (SOC) state dynamic characteristics, and ISE phenomenon is also observed. The results also suggested that the shear deformation of the spherical indenter is more pronounced, with the shear band forming at a 45° angle to the direction of the downward pressure. And based on the Cooperative shear model (CSM) theory, the STZ size was estimated and verify that the increment of STZ makes it easier to generate stronger plastic strain to dissipate STZ, ultimately leading to complete plastic deformation. Combining five fold symmetry and gradient atoms, the majority of atoms with five fold symmetry (LFFS>0.5) have stronger resistance to plastic deformation when the load rate is low.

A novel interferometry-based optical sensor to study the coagulation of human plasma

We propose and demonstrate a Twin-interferometry (TIM) based optical approach for studying and exploring anisotropy and directional dependent coagulation kinetics in human plasma under isothermal condition. Human plasma is obtained by centrifuging blood samples collected in Ethylenediamine tetra acetic acid (EDTA) coated vials at 6000 RPM in a laminar flow unit. 0.5 ml of plasma is collected in an optically flat disposable glass cuvette and subjected to induced coagulation by adding equal amounts of CaCl2 solution as coagulant at seven different concentrations. A Twin-interferometer is designed to record the rate of shift in Michelson’s fringes caused by rate of change of optical path difference of coagulating plasma in two orthogonal directions. A pair of interferograms, referred to as the Twin- interferogram (TIG), are recorded for all plasma samples with different CaCl2 concentration during its coagulation. A new approach for extracting various coagulation parameters from TIG, such as anisotropy, half coagulation time, constants governing the rate and dimensionality of coagulation, and their variation along distinct direction is demonstrated. TIG results are validated using polarizing optical microscopic (POM) and optical extinction techniques.

Multi-scale nonlinear reservoir flow simulation based on digital core reconstruction

During the exploitation of oil and gas reservoirs, changes in reservoir physical or fluid properties will lead to alterations in formation seepage capacity, subsequently impacting the productivity of oil and gas wells. Therefore, it is crucial to evaluate the variations in reservoir physical and fluid properties as well as their influence on oil and gas well productivity during reservoir exploitation.It employs a descriptive approach to investigate cross-scale (i.e., micro-scale → small-scale → large-scale) fluid seepage phenomena in water flooding. By leveraging digital core reconstruction technology and focusing on micro-pore network simulation, it establishes an analytical framework for studying cross-scale seepage fields, facilitating the exploration of the micro-seepage mechanism that governs non-homogeneous water flooding's impact on liquid production capacity within a reservoir. Integrating constraints from micro-pore network simulation and small-scale core displacement test data, while considering macro-heterogeneity near wellbore areas as flow unit divisions and superposition bases within large-scale reservoirs, it reveals the nonlinear flow characteristics of changes in liquid production capacity in water-flooding oil reservoirs based on microscopic digital core analysis. This approach aligns with findings from small-scale core testing and sweep coefficient correction while also addressing the non-uniform distribution characteristics of physical parameters within large-scale reservoirs.By integrating permeability field characteristics at three levels, this approach optimizes its technical advantages in describing dynamic permeability fields at each level, thereby facilitating precise prediction of water drive oil development effects.

The efficacy of integrated vermifiltration technology for treatment of food analysis laboratory wastewater

The macrophyte-assisted vermifiltration (MAVF) technology was applied for treatment of food analysis laboratory wastewater at Industrial Technology Institute (ITI), Sri Lanka for the first time. Three laboratory-scale sub-surface vertical flow units were established:1) without earthworms and plants (Control unit), 2) with earthworms (Eisenia fetida) (vermi-filtration unit (VF unit), 3) with both earthworms and macrophytes (Canna indica) (MAVF unit). Wastewater was fed batch-wise with a feeding rate of 10 mL/min and seven days hydraulic retention time (HRT) for eight consecutive batches. Physiochemical parameters, Chemical Oxygen Demand (COD), Total Phosphorus (TP), Total Nitrogen (TN), pH, of the influent and effluent of all three units were analyzed to investigate the treatment efficiencies. One-Way ANOVA was performed to evaluate the significant differences in treatment efficiencies. The mean COD removal efficiencies of the control unit, VF unit, and MAVF unit were 60.2 % ± 10.7, 71.6± 8.07 %, and 72.0 % ± 8.86, respectively. Average TN removal efficiencies of the control unit, VF unit, and MAVF unit were 8.05 % ± 5.75, 10.9±6.4 %, and 24.9 % ±12.84, respectively. Average TP removal efficiencies were 35.84 % ± 17.5, 35.84 % ± 17.5 %, and 40.0 % ±16.76, respectively. The COD removal displayed promising results, whereas the means for enhancing TP and TN removal should be further studied. The findings of this study sensitizes the testing laboratories to go green by adopting in-house cost effective laboratory wastewater treatment systems.

Permeability Upscaling Conversion Based on Reservoir Classification

Deep and ultra-deep reservoirs are characterized by low porosity and permeability, pronounced heterogeneity, and complex pore structures, complicating permeability evaluations. Permeability, directly influencing the fluid production capacity of reservoirs, is a key parameter in comprehensive reservoir assessments. In the X Depression, low-porosity and low-permeability formations present highly discrete and variable core data points for porosity and permeability, rendering single-variable regression models ineffective. Consequently, accurately representing permeability in heterogeneous reservoirs proves challenging. In the following study, lithological and physical property data are integrated with mercury injection data to analyze pore structure types. The formation flow zone index (FZI) is utilized to differentiate reservoir types, and permeability is calculated based on core porosity–permeability relationships from logging data for each flow unit. Subsequently, the average permeability for each flow unit is computed according to reservoir classification, followed by a weighted average according to effective thickness. This approach transforms logging permeability into drill stem test permeability. Unlike traditional point-by-point averaging methods, this approach incorporates reservoir thickness and heterogeneity, making it more suitable for complex reservoir environments and resulting in more reasonable conversion outcomes.

Applying machine learning for hydraulic flow unit classification and permeability prediction: case study from carbonate reservoir in the Southern part, Song Hong basin

Machine learning (ML) is an artificial intelligence (AI) that enables computer systems to classify, cluster, identify, and analyze vast and complex sets of data while eliminating the need for explicit instructions and programming. For decades, machine learning has become helpful for complex reservoir characterization such as carbonate reservoirs. Permeability prediction from well logs is a significant challenge, especially when the core data is rarely available due to its high cost. In this study, we aimed to bridge this gap by demonstrating the practical application of integrating Hydraulic Flow Units (HFU) and machine learning methods. Our goal was to provide a reliable estimation of permeability using core and wireline logging data in the complex Middle Miocene carbonate reservoir of the CX gas field in the southern part of the Song Hong basin. In the first step, due to the reservoir’s heterogeneity, the core plug dataset was classified into 5 HFUs based on the flow zone indicators (FZI) concept from the modified Kozeny-Carman equation using unsupervised machine learning - K-means method. The porosity - permeability for each HFU was defined after HFU clustering. In the second step, we designed three different workflows to predict permeability and HFU using supervised machine learning from a combination of core and log data. These workflows were rigorously test and compared with the core data. The most accurate result was chosen as the base, providing a high confidence level in our predictions’ reliability.

Correlating Capillary Pressure and Resistivity Index for Carbonate Reservoir

Capillary pressure is a significant parameter in characterizing and modeling petroleum reservoirs. However, costly laboratory measurements may not be sufficiently available in some cases. The problem amplifies for carbonate reservoirs because relatively enormous capillary pressure curves are required for reservoir study due to heterogeneity. In this work, the laboratory measurements of capillary pressure and formation resistivity index were correlated as both parameters are functions of saturation. Forty-one core samples from an Iraqi carbonate reservoir were used to develop the correlation according to the hydraulic flow units concept. Flow zone indicator (FZI) and Pore Geometry and Structure (PGS) approaches were used to identify the reservoir hydraulic flow units. The experimentally derived correlations can be used to predict capillary pressure from resistivity, which is widely available from well-logs. FZI and PGS rock typing methods were applied to characterize the reservoir rock types. For both methods, the log-log plot of Leverett J-function and capillary pressure versus resistivity index for each rock type represent a power-law model relationship between these parameters. Despite the good permeability-porosity prediction results, the FZI approach did not yield a good correlation between J and I. PGS resulted in a better performance in terms of both permeability-porosity prediction and Pc with I correlation because PGS honors the pore geometry and structure relationship with the mean hydraulic radius more than FZI. This work introduces a new correlating approach that aims to assist in reservoir characterization and simulation.

Flow unit classification and characterization with emphasis on the clustering methods: a case study in a highly heterogeneous carbonate reservoir, eastern margin of Dezful Embayment, SW Iran

Previous attempts to classify flow units in Iranian carbonate reservoirs, based on porosity and permeability, have faced challenges in correlating the rock's pore size distribution with the capillary pressure profile. The innovation of this study highlights the role of clustering techniques, such as Discrete Rock Type, Probability, Global Hydraulic Element, and Winland's Standard Chart in enhancing the reservoir's rock categorization. These techniques are integrated with established flow unit classification methods. They include Lucia, FZI, FZI*, Winland R35, and the improved stratigraphic modified Lorenz plot. The research accurately links diverse pore geometries to characteristic capillary pressure profiles, addressing heterogeneity in intricate reservoirs. The findings indicate that clustering methods can identify specific flow units, but do not significantly improve their classification. The effectiveness of these techniques varies depending on the flow unit classification method employed. For instance, probability-based methods yield surpassing results for low-porosity rocks when utilizing the FZI* approach. The discrete technique generates the highest number of flow unit classes but provides the worst result. Not all clustering techniques reveal discernible advantages when integrated with the FZI method. In the second part, the study creatively suggests that rock classification can be achieved by concurrently clustering irreducible water saturation (SWIR) and porosity in unsuccessful flow unit delineation cases. The SWIR log was estimated by establishing a smart correlation between porosity and SWIR in the pay zone, where water saturation and SWIR match. Then, the estimated saturation was dispersed throughout the reservoir. Subsequently, the neural network technique was employed to cluster and propagate the three finalized flow units. This methodology is an effective recommendation when conventional flow unit methods fail. The study also investigates influential factors causing the failure of flow unit classification methods, including pore geometry, oil wettability, and saturation in heterogeneous reservoirs.

On the promotion of serrated flows and shear band behaviors in a Zr-based metallic glass during ultrasonic vibration nanoindentation

Initiation and evolution of flow units and their correlation with mechanical behaviors of metallic glasses (MGs) are well known significant for understanding the rheological behavior of glassy substances. Here, the evolution of plastic deformation behavior of a typical Zr-based MG induced by ultrasonic vibration is investigated by means of nanoindentation, especially focusing on studying structural evolution through analysis of load-displacement curves. Furthermore, the micro-morphologies of residual indentations are characterized by using the scanning electron microscope (SEM) and atomic force microscope (AFM). Compared to quasi-static nanoindentation tests, a significant reduction in the hardness and elastic modulus of the MG by about 15 % and 30 %, respectively, is observed when subjected to ultrasonic vibration nanoindentation with an amplitude of 0.4 μm, accompanied by a pronounced indentation size effect. In addition, higher ultrasonic vibration amplitude induces larger indentation depth, more pronounced serrated flows in the load-displacement curves and more remarkable shear bands around the residual indentations, indicating ultrasonic vibration-promoted plastic flows in MG. This study provides an insight into the structural origin of plastic flows in MG systems under ultrasonic vibration, which will help to facilitate their application in the fields such as ultrasonic vibration assisted forming.