Actual Fluid Research Articles

The Tribological Reduction Mechanism of the Rubber Hexagonal Surface Texture of the Screw Pump Stator

This paper develops a composite weaving structure, combining hexagonal micro-bumps and hexagonal grooves, in the design of the rubber surface of the screw pump. This allows us to solve the problem of high torque and fast wear of the rubber stator during the operation of screw pump lifting oil recovery, based on the bionic hexagonal surface structure, traditional surface damping principle, and fluid dynamic pressure lubrication theory. Finite element analysis is first conducted to quantitatively analyze the impacts of the parallel side distance, groove width, and groove depth on the surface flow field and wall pressure field of the composite hexagonal structure. Based on the simulation law, the rubber surface laser structure is then designed and prepared by nanosecond laser processing. Afterward, tribological experiments are conducted under the condition of long-term immersion in the actual extraction fluid of shale oil wells. This aims at simulating the actual downhole oil production conditions and quantitatively studying the impact of the size of the composite hexagonal structure on the lubrication characteristics of the friction part of the stationary rotor, as well as the effect of abrasion reduction. The results show that, within the simulation range, the smaller the parallel side distance, the higher the load-carrying capacity. In addition, the hexagonal weave with a parallel side distance of 3 mm has a higher wall load carrying capacity than that with distances of 4 mm and 5 mm. When the groove width is equal to 0.4 mm, the oil film load carrying capacity is higher than that in the case of 0.2 mm. When the groove depth increases, the oil film pressure first increases and then stabilizes or decreases after reaching 0.3 mm. In the hexagonal weave, the friction ratio of the rotor is equal to 0.4 mm. In the tribological experiment of hexagonal weave, the smaller the parallel side distance, the smaller the friction coefficient, and the 0.5 mm weave has the highest performance.

Machine Learning-Guided Fluid Resuscitation for Acute Pancreatitis Improves Outcomes.

Ariel Dynamic Acute Pancreatitis Tracker (ADAPT) is an artificial intelligence tool using mathematical algorithms to predict severity and manage fluid resuscitation needs based on the physiologic parameters of individual patients. Our aim was to assess whether adherence to ADAPT fluid recommendations versus standard management impacted clinical outcomes in a large prospective cohort. We analyzed patients consecutively admitted to the Los Angeles General Medical Center between June 2015 to November 2022 whose course was richly characterized by capturing more than 100 clinical variables. We inputted this data into the ADAPT system to generate resuscitation fluid recommendations. and compared to the actual fluid resuscitation within the first 24 hours from presentation. The primary outcome was the difference in organ failure in those who were over (>500cc)- versus adequately (within 500cc) resuscitated with respect to the ADAPT fluid recommendation. Additional outcomes included ICU admission, SIRS at 48 hours, local complications, and pancreatitis severity. Among the 1083 patients evaluated using ADAPT, 700 were over-resuscitated,196 were adequately resuscitated, and 187 were under-resuscitated. Adjusting for pancreatitis etiology, gender, and SIRS at admission, over-resuscitation was associated with increased respiratory failure (Odd Ratio (OR) 2.73 [95%CI 1.06, 7.03]) as well as ICU admission (OR 2.40 [1.41, 4.11]), more than 48 hours of hospital length of stay (OR 1.87 [1.19, 2.94]), SIRS at 48 hours (OR 1.73 [1.08, 2.77]) and local pancreatitis complications (OR 2.93 [1.23, 6.96]). Adherence to ADAPT fluid recommendations reduces respiratory failure and other adverse outcomes compared to conventional fluid resuscitation strategies for acute pancreatitis. This validation study demonstrates the potential role of dynamic machine learning tools in acute pancreatitis management.

Enhanced Anti-Interference Photoelectrochemical DNA Bioassay: Grafting a Peptide-Conjugated Hairpin DNA Probe on a COF-Based Photocathode.

Precise and sensitive analysis of specific DNA in actual human bodily fluids is crucial for the early diagnosis of major diseases and for a deeper understanding of DNA functions. Herein, by grafting a peptide-conjugated hairpin DNA probe to a covalent organic framework (COF)-based photocathode, a robust anti-interference photoelectrochemical (PEC) DNA bioassay was explored, which could specifically resist potential interference from nonspecific proteins and reducing species. Human immunodeficiency virus (HIV) DNA was used as the target DNA (tDNA) for the PEC DNA bioassay. The vinyl-functionalized COF (COF-V) was modified with meso-tetra(4-carboxyphenyl)-porphine (TCPP) and polydopamine (PDA) to fabricate a PDA/TCPP/COF-V photocathode, which served as the photocurrent signal transducer. Toward the unconventional recognition element, a hairpin DNA probe (hDNA) was efficiently linked with a linear zwitterionic peptide (LZP) to form the LZP-hDNA bioconjugate, which was then grafted onto the COF-based photocathode. The grafting of the LZP generated a sturdy anti-interference interface on the signal transducer. For tDNA probing, AgInS2 (AIS) quantum dots acted as signal quenchers, marked on signaling DNA (sDNA) to obtain AIS-sDNA labeling, and a striking drop in the photocurrent signal was achieved through λ-exonuclease (λ-Exo)-aided target recycling. This novel peptide-conjugated hairpin DNA probe endowed the PEC DNA bioassay with an impressive anti-interference property without requiring tedious steps. By combining the excellent photoelectric properties of the COF-based photocathode with an effective signaling strategy, accurate and sensitive results for tDNA probing were achieved.

A Multimode Optical Sensor for Highly Selective and Sensitive Detection of Hypochlorous Acid in Water and Body Fluid.

Hypochlorous acid (HClO), as an important reactive oxygen species (ROS), plays a crucial role in our daily life and in biological systems, and its convenient and accurate detection is significant and imperative. In this work, a self-calibrated multimode optical sensor for convenient and accurate HClO detection was elaborately fabricated based on a multifunctional metal-organic framework platform with catalytic active metal nodes, fluorescent responsive bridging ligands, and intrinsic pores for functional molecule accommodation. The sensor shows not only turn-on and ratiometric fluorescence response but also color change in response to HClO. The detection limits are as low as 16.9, 17.3, 66.5, and 63.2 nM for ratiometric fluorometry, absorbance-based colorimetry, and smartphone-based fluorescenceand color analysis, respectively. The accuracy and practicability of this sensor were also demonstrated by the detection of hypochlorous acid in actual water and body fluid samples, and the recovery rates ranged from 97.8 to 103.8%.

Modelling of Fluid Permeability at the Interface of the Metal-to-Metal Sealing Surface.

This paper presents a method for modelling the permeability of fluid at the interface formed between flat parallel plates and the sharp-edged ridges of a metal gasket. This work was divided into three stages. In the first stage, numerical calculations simulating the deformation (compression of the gasket) were performed. The calculations were carried out using thermomechanical static analysis with commercial software. The purpose of these calculations was to determine the contact area of the gasket ridges with the plates, the deformation of the gasket ridges, and the reaction force resulting from the degree of compression of the gasket. In the second part of this work, analytical calculations were performed to estimate the tightness level. The analytical model proposed in this paper was based on Darcy's equation, simulating fluid flow through a ring-shaped porous layer. The analytical model also took into account the shape of the roughness profile of the sealed surfaces. A mathematical Ausloos-Berman function based on fractal theory was used to represent it. In the last part of this work, experimental tests were carried out to determine the actual fluid permeability and thus verify the numerical and analytical calculations.

The use of End Expiratory Occlusion Test vs. Inferior Vena Cava Respiratory Variation for the prediction of volume responsiveness in mechanically ventilated patients with sepsis

Abstract Sepsis being a chief prominent fatal condition in critically ill patients. In septic shocked patients, the first-line therapeutic intervention is fluid resuscitation in an attempt to improve their cardiac output (COP). However, fluids must be given only if the chance of improving COP exists. So, assessing fluid responsiveness is crucial. To our knowledge, yet no studies comparing the End- expiratory-occlusion (EEO) test and the variation of the inferior-vena-cava diameter with respiration (ΔIVC) in hypovolemic cases. So, our aim is to use both tests as indices for responsiveness to fluid in septic mechanically ventilated (MV) patients. The Patients and Method: Thirty four MV septic patients were enrolled and baseline COP assessment was performed followed by an EEO test applied to each patient, after which, COP was measured to detect the probable responders (defined as an increased COP by ≥ 15%) and non-responders. Then, ΔIVC was assessed for the same patients to predict the probable responders (with ΔIVC >12%) and non-responders. Finally, fluid therapy was initiated as per the guidelines of surviving sepsis campaign (2021) followed by COP re-assessment to determine actual fluid responders\non-responders. Results Sixty-seven% of the cases were responding to fluid. Receiver operating characteristic showed areas under curve for EEO and ΔIVC in predicting responsiveness to fluid were 0.597 and 0.925, respectively. EEO (32.4%) was predictive with 47.8% sensitivity and 100% specificity. The ΔIVC (64.7%) was predictive with 91.3% sensitivity and 100% specificity. Conclusion IVC-respiratory-variation showed better values in prediction of response to fluid in MV patients with sepsis than EEO test.

Numerical Study on the Enhanced Oil Recovery by CO2 Huff-n-Puff in Shale Volatile Oil Formations

The Sichuan Basin’s Liangshan Formation shale is rich in oil and gas resources, yet the recovery rate of shale oil reservoirs typically falls below 10%. Currently, gas injection huff-n-puff (H-n-P) is considered one of the most promising methods for improving shale oil recovery. This study numerically investigates the application of the CO2 huff-n-puff process in enhancing oil recovery in shale volatile oil reservoirs. Using an actual geological model and fluid properties of shale oil reservoirs in the Sichuan Basin, the CO2 huff-n-puff process was simulated. The model takes into account the molecular diffusion of CO2, adsorption, stress sensitivity effects, and nanopore confinement. After history matching, through sensitivity analysis, the optimal injection rate of 400 tons/day, soaking time of 30 days, and three cycles of huff-n-puff were determined to be the most effective. The simulation results show that, compared with other gases, CO2 has significant potential in improving the recovery rate and overall efficiency of shale oil reservoirs. This study is of great significance and can provide valuable references for the actual work of CO2 huff-n-puff processes in shale volatile oil reservoirs of the Sichuan Basin.

An electrochemical point-of-care testing device for specific diagnosis of the albinism biomarker based on paradigm shift designs

L-tyrosine is a recognized biomarker of albinism, whose endogenous level in human bodies is directly linked to melanin synthesis while no attention has been paid to its specific diagnosis. To this end, we have developed an electrochemical point-of-care testing device based on a molecularly imprinted gel prepared by a universal paradigm shift design to achieve the enhanced specific recognition of the L-tyrosine. Interestingly, this theoretically optimized molecularly imprinted gel validates the recognition pattern of L-tyrosine and optimizes the structure of the polymer itself with the aid of computational chemistry. Besides, modified extended-layer MXene and Au nanoclusters have significantly improved the sensing activity. As a result, the linear diagnostic range of this electrochemical point-of-care testing device for L-tyrosine is 0.1–100 μM in actual human fluids, which fully covers the L-tyrosine levels of healthy individuals and people with albinism. The diagnosis is completed in 90 s and then the results are transmitted by Bluetooth low energy to the smart mobile terminal. Therefore, we are convinced that this electrochemical point-of-care testing device is a promising tool in the future smart medical system.

An adaptive weighted integration method for multipath ultrasonic flowmeter

Abstract The error introduced by the integration method is an important factor affecting the measurement accuracy of the multipath ultrasonic flowmeter. An adaptive weighted integration method (adaptive weighted integration method for velocity profile of circular section) is proposed to reduce the integration error, taking a DN400 double-side eight-path ultrasonic flowmeter as an example. This method is based on the velocity distribution information in the full flow range and the integration weights are determined by the principle of minimum error. The applicability of this method is verified by numerical simulation and actual fluid flow experiments. The results show that the integration error of the proposed method is superior to the Gauss–Jacobi and optimized weighted integration method for circular section integration methods, and the maximum integration error is reduced from 0.0877% and −0.0355% to 0.0220% in the flow range of 125–2500 t h−1.

Research on the application of artificial intelligence tools to predict the operating temperature and pressure of the pipeline transportation system at the Hai Thach - Moc Tinh field

The observation of operating parameters for the single-phase or multi-phase flows of oil and gas pipelines always plays an important role and prerequisite in daily operation. The inlet and outlet parameters of the pipeline such as pressure and temperature will greatly affect the efficiency of the production and transportation process of fields. These parameters are very important. It must be required to observe carefully and strictly. In fact, pressure and temperature signal gauges will always be set up at the inlet and outlet of the pipeline for continuously variable transmission of the signal to the central control room, then the operator can observe regularly. Alarms will be triggered when the signal goes out of the setting operation area. However, the operational parameters transmitted from this gauge are only available after the actual fluid flow passes through the pipeline. This will limit when the operator needs to apply calculation methods to pre-predict the flow regime, and operating parameters of the fluid in the pipeline when fluid flows has not passed through the pipeline. From that approach, the paper presents the result of research on the application of machine learning algorithms (ML) to build models to predict operating conditions of three-phase flows in the pipeline at Hai Thach- Moc Tinh field based on input parameters such as the open of wellhead flow control valve of wells located in wellhead platform WHP-MT1, WHP-HT1, and the commercial gas volume. The results of the research show that the ML calculation method gives the result which is only +/-2 barg/20C difference compared to the field data obtained from pressure (HT1-PT-0911) and temperature (HT1-TI-0911) signals set up at the outlet of the transportation pipeline at the Hai Thach-Moc Tinh field.

Understanding the phase behavior during CO2 flooding by dissipative particle dynamics

Understanding the phase behavior of a CO2-oil–water surfactant system is critical for carbon dioxide flooding engineering. Using dissipative particle dynamics, we investigate how the alkanes in crude oil are extracted after the injection of supercritical carbon dioxide into the formation, and what effect this extraction has on the miscible and immiscible displacement of carbon dioxide and crude oil. The images of the extraction are exhibited at the molecular level, and the effects of temperature and pressure on the extraction are investigated. The phase distribution of carbon dioxide, oil, and water under immiscible phase is simulated using actual reservoir fluid data based on a comprehensive understanding of extraction. Oil tends to be spread at the interface of carbon dioxide and water when it is immiscible. The miscible pictures were generated under conditions of reduced CO2-oil–water miscible pressure by adding surfactants to the CO2-oil–water system, and the fluid distribution characteristics under immiscible and miscible phases were examined. dissipative particle dynamics (DPD) simulation demonstrates that when the molar proportion of carbon dioxide is less than 0.4, the solid phase surface can always create a water film of varied thickness, regardless of whether the surface charge of the solid phase is positive or negative. As the molar fraction of carbon dioxide increases, the water film’s thickness decreases.

Growth mechanism of high‐voltage electric pulse rock breaking 3D plasma channel in drilling fluid environment

AbstractHigh‐voltage electric pulse rock breaking has excellent potential for exploiting deep geothermal resources. Numerous researchers have conducted experimental studies on this topic, particularly in rock mechanics, where the breakdown occurs. However, there has been limited scholarly research on drilling fluid. Therefore, the study focuses on the drilling fluid suitable for electric pulse drilling, considering the characteristics of electric pulse rock breaking, which differ from traditional rock breaking. The study focused on the impact of various drilling fluid parameters on the effectiveness of electric impulse rock breaking using red sandstone as the experimental material. This was investigated using the finite element method, and indoor electric rock‐breaking tests were conducted in a drilling fluid environment. The results indicate that the plasma channel mainly grows in the permeable layer of the drilling fluid, resulting in shallow rock breaking depth in the drilling fluid environment. The pore permeated by drilling fluid guides the growth of the plasma channel. The higher the conductivity of the drilling fluid, the closer the ion channel of rock breaking by electric pulse is to the rock surface. This results in a smaller crushing volume and shallower damage depth, which is more detrimental to rock breaking by an electric pulse. The viscosity of drilling fluid can impede the breakdown to some extent. In this paper, the influence of drilling fluid parameters on electro‐pulse rock‐breaking technology is preliminarily studied, which has significant reference value for the selection of actual drilling fluid.

Prediction of Adolescents' Fluid Intelligence Scores based on Deep Learning with Reconstruction Regularization.

The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents. We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.0. A total of 148 ROIs were selected and 604 features were proposed by FreeSurfer segmentation. The training and testing sets were divided in a ratio of 7:3. To predict fluid intelligence scores, we used AE, MLP and classic machine learning models, and compared their performance on the test set. In addition, we explored their performance across gender subpopulations. Moreover, we evaluated the importance of features using the SHapley Additive Explain method. Results: The proposed model achieves optimal performance on the test set for predicting actual fluid intelligence scores (PCC = 0.209 ± 0.02, MSE = 105.212 ± 2.53). Results show that autoencoders with refactoring regularization are significantly more effective than MLPs and classical machine learning models. In addition, all models performed better on female adolescents than on male adolescents. Further analysis of relevant characteristics in different populations revealed that this may be related to gender differences in underlying fluid intelligence mechanisms. We construct a weak but stable correlation between brain structural features and raw fluid intelligence using autoencoders. Future research may need to explore ensemble regression strategies utilizing multiple machine learning algorithms on multimodal data in order to improve the predictive performance of fluid intelligence based on neuroimaging features.

Online optimization of petrochemical process via case-based reasoning and conditional mutual information

The strict mechanism model of petrochemical processes is often complex, resulting in long modeling times, low computational efficiency, and poor accuracy, which limits the application of mechanistic models in process optimization and advanced control. The advent of big data technology provides new solutions for this problem. Herein, a new method based on historical Case-Based Reasoning (CBR) is proposed to process online optimization and calculate variable attribute weights for case retrieval using conditional mutual information. Considering the process time-lag characteristics, a piecewise sequential CBR method for optimization is further proposed, which directly recognizes the pattern based on the industrial past data and amply utilized the process information. For case study and method validation, the approach is applied to an actual fluid catalytic cracking (FCC) process, converting low-quality heavy oils into valuable light transportation fuels and chemicals such as gasoline and propylene. As demonstrated by the case study results, the proposed method increases the average yield of gasoline and total liquid respectively by 2.5 % and 4.0 % while decreasing the average yield of coke by 1.3 %. The results further indicate that the overall optimization performance is comparable to many advanced intellectual optimization algorithms, allowing favorable operability and ensuring good robustness for different optimization targets. It could provide a solution with high accuracy and good adaptability for online process optimization in complex chemical processes.

Model-based interpretation of bottomhole pressure records during matrix treatments in layered formations

During injection treatments, bottomhole pressure measurements may significantly mismatch modeling results. We devise a computationally effective technique for interpretation of fluid injection in a wellbore interval with multiple geological layers based on the bottomhole pressure measurements. The permeability, porosity and compressibility in each layer are initially setup, while the skin factor and partitioning of injected fluids among the zones during the injection are found as a solution of the problem. The problem takes into account Darcy flow and chemical interactions between the injected acids, diverter fluids and reservoir rock typical in modern matrix acidizing treatments. Using the synchronously recorded injection rate and bottomhole pressure, we evaluate skin factor changes in each layer and actual fluid placement into the reservoir during different pumping jobs: matrix acidizing, water control, sand control, scale squeezes and water flooding. The model is validated by comparison with a simulator used in industry. It gives opportunity to estimate efficiency of a matrix treatment job, role of every injection stage, and control fluid delivery to each layer in real time. The presented interpretation technique significantly improves accuracy of matrix treatments analysis by coupling the hydrodynamic model with records of pressure and injection rate during the treatment.

Newtonian Heating on Casson Mixed Convection Transport by Ternary Hybrid Nanofluids over Vertical Stretching Sheet: Numerical Study

Numerous physical characteristics occurring from the structure and micro-motions of fluid nanoparticles can be examined using the governing models of nanofluids. Also, It can describe the demeanor of a vast field of actual fluids. This achieved a significant turn in the enhancement of heat transfer that improves manufacturing and engineering modernization. Depending on that, the assumptions that form our aims are numerically examining energy transport and heat transfer via Casson ternary hybrid nanofluids that contain the states of combined convection flow over a vertical stretching sheet. Also, Newtonian heating boundary conditions and magnetohydrodynamics (MHD) effects are investigated. Mathematical models (governing equations) that emulate and construct the conduct of these boosted ternary hybrid nanofluids are formed by developing the Tiwari and Das models. These governing equations are converted to partial differential equations (PDEs) employing appropriate substitutions. An accurate and efficient method called the Keller box numerical method is executed to get outcomes to the physical model. These numerical calculations are found and presented by employing MATLAB codes, to acquire tables of numerical outcomes, and graphic exhibits, which provide the impacts of important parameters on the physical quantities supporting heat transfer. Furthermore, the new results, in the Newtonian fluid case, were compared with prior literature, which were in excellent agreement. The studied problem parameters are examined at a Casson parameter domain of 1 to 5, a mixed convection parameter domain of -1 to 3, a conjugate parameter of 0.2 to 1, a magnetic parameter domain of 0.1 to 5, and a nanoparticle volume fraction parameter of 0.001 to 0.008. The numerical consequences deduced that the local skin friction rises when the conjugate and mixed convection parameters are increased. But the opposite case happens when the effects are obtained for the nanoparticle volume fraction, magnetic, and Casson parameters. Also, the Nusselt number rises with growing nanoparticle volume fraction and Casson parameters.

Strategies to deal with genetic analyzer-specific DNA methylation measurements.

Targeted bisulfite sequencing using single-base extension (SBE)can be used to measure DNA methylation via capillary electrophoresis on genetic analyzers in forensic labs. Several accurate age prediction models have been reported using this method. However, using different genetic analyzers with different software settings can generate different methylation values, leading to significant errors in age prediction. To address this issue, the study proposes and compares four methods as follows: (1) adjusting methylation values using numerous actual body fluid DNA samples, (2) adjusting methylation values using control DNAs with varying methylation ratios, (3) constructing new age prediction models for each genetic analyzer type, and (4) constructing new age prediction models that could be applied to all types of genetic analyzers. To test the methods for adjusting values using actual body fluid DNA samples, previously reported adjusting equations were used for blood/saliva DNA age prediction markers (ELOVL2, FHL2, KLF14, MIR29B2CHG/C1orf132, and TRIM59). New equations were generated for semen DNA age prediction markers (TTC7B, LOC401324/cg12837463, and LOC729960/NOX4) by drawing polynomial regression lines between the results of the three types of genetic analyzers (3130, 3500, and SeqStudio). The same method was applied to obtain adjustment equations using 11 control DNA samples. To develop new age prediction models for each genetic analyzer type, linear regression analysis was conducted using DNA methylation data from 150 blood, 150 saliva, and 62 semen samples. For the genetic analyzer-independent models, control DNAs were used to formulate equations for calibrating the bias of the data from each genetic analyzer, and linear regression analysis was performed using calibrated body fluid DNA data. In the comparison results, the genetic analyzer-specific models showed the highest accuracy. However, genetic analyzer-independent models through bias adjustment also provided accurate age prediction results, suggesting its use as an alternative in situations with multiple constraints.

Three-Stage Rapid Physical Design Algorithm for Continuous-Flow Microfluidic Biochips Considering Actual Fluid Manipulations

With the continuous development of microfluidic technology, continuous-flow microfluidic biochips (CFMBs) are being increasingly used in the Internet of Things. The automation design of CFMBs has also received widespread attention. The architecture design of CFMBs is divided into a high-level synthesis stage and a physical design stage. Among them, the problem of the physical design stage is very complex. At this stage, the chip architecture is generated based on the device library and a set of flow paths, taking into account the actual fluid manipulations, while minimizing the cost of the chip, such as the number of ports, total length of flow channels, number of flow channel intersections. As fabrication technology advances, the number of devices integrated into CFMBs is increasing. The existing physical design algorithms can no longer meet the design requirements of CFMBs in terms of time. Therefore, we propose a three-stage rapid physical design algorithm for CFMBs considering the actual fluid manipulations. The proposed algorithm includes a port-driven preprocessing stage, a force-directed quadratic placement stage, and a negotiation-based routing stage. In the port-driven preprocessing stage, a port-driven preprocessing algorithm is proposed to generate connection matrices between ports and devices to reduce the number of ports introduced. In the force-directed quadratic placement stage, we model the placement problem as an extremum problem of a quadratic cost function, which mathematically reduces the search space significantly and shortens the running time of the algorithm significantly. In the negotiation-based routing stage, a heuristic negotiation-based routing algorithm and a flow channel strategy that prioritizes the construction of parallel execution are proposed to reduce the running time of the algorithm while ensuring that the number of crossings in the routing solution is close to the optimal solution. Experimental results confirm that our proposed method is able to generate the high-quality solutions quickly. Under general scale problems, compared to the existing method based on ILP, our proposed method achieves a speedup ratio of 23,171 in terms of CPU time and optimizations in terms of number of ports and port reuse of 3.18% and 6.52%, respectively. These optimizations come at the cost of only a slight increase in the number of intersections, the flow length, and the number of flow valves. In addition, our proposed method can effectively solve large-scale problems that cannot be solved by existing method based on ILP.

The Use of Intraosseous Infusion in the Early Resuscitation of Patients With Extremely Severe Burns.

According to research, shock, the most common complication of extremely severe burns, is also the leading cause of mortality among patients with such burns. The case fatality rate reaches 83.45% when the total burn area exceeds 90%. The American Heart Association in 2020 recommended the intraosseous (IO) access after the peripheral access and prior to the central venous access when venous cannulation is either difficult or delayed. The use and experience with intraosseous infusion in extremely severe burns are still limited. We report efficacy and safety results from 19 burn patients treated with IO infusion between June 2020 and December 2022. In these patients, the mean injury time of burns was 1.55 ± 1.10 hours, the mean burn surface area was 86.24% ± 11.33%, the mean catheterization time was 49.68 ± 10.11 seconds, and the mean emergency retention time was 2.75 ± 1.74 hours, the mean actual fluid supplement amount was 5,533.68 ± 3,077.19 mL, the mean hourly urine volume of the patient was 93.31 ± 60.94 mL, the mean emergency detention time was 4.16 ± 2.97 hours, and the mean duration of hospitalization was 34.50 ± 25.38 days. The results demonstrated a clinically meaningful improvement and higher response rate vs peripheral venous cannulation and an acceptable safety profile in those patients.

Assessment of Depth of Mud-Filtrate Invasion and Water Saturation Using Formation-Tester Measurements: Application to Deeply Invaded Tight-Gas Sandstones

Formation pressure/fluid measurements are impacted by mud-filtrate invasion, which may require long fluid pumpout durations to acquire hydrocarbon samples with minimal mud-filtrate contamination. However, unlike other well-logging instruments, formation testers do not have a fixed depth of investigation that limits their ability to pump out mud filtrate until acquiring original formation fluids (i.e., sensing the uninvaded zone). We use an in-house petrophysical and fluid-flow simulator to perform numerical simulations of mud-filtrate invasion, well logs, and formation-tester measurements to estimate the radial distance of invasion and the corresponding radial profile of water saturation. Numerical simulations are initialized with the construction of a multilayer petrophysical model. Initial guesses of volumetric concentration of shale, porosity, water saturation, irreducible water saturation, and residual hydrocarbon saturation are obtained from conventional petrophysical interpretation. Fluid-flow-dependent petrophysical properties (permeability, capillary pressure, and relative permeability), mud properties, rock mineral composition, and in-situ fluid properties are obtained from laboratory measurements. The process of mud-filtrate invasion and the corresponding resistivity and nuclear logs are numerically simulated to iteratively match the available well logs and estimate layer-by-layer formation water saturation. Next, using our multiphase formation testing simulator, we numerically simulate actual fluid sampling operations performed with a dual-packer formation tester. Finally, we estimate irreducible water saturation by minimizing the difference between the hydrocarbon breakthrough time numerically simulated and measured with formation-tester measurements. The examined sandstone reservoir is characterized by low porosity (up to 0.14), low-to-medium permeability (up to 40 md), and high residual gas saturation (between 0.4 and 0.5). The deep mud-filtrate invasion resulted from extended overbalanced exposure to high-salinity water-based mud (17 days of invasion and 1,800 psi overbalance pressure) coupled with the low mud-filtrate storage capacity of tight sandstones. Therefore, the uninvaded formation is located far beyond the depth of investigation of resistivity tools, whereby deep-sensing resistivities are lower than those of uninvaded formation resistivity. Through the numerical simulation of mud-filtrate invasion, well logs, and formation-tester measurements, we estimated radial and vertical distributions of water saturation around the borehole. Likewise, we quantified the hydrocarbon breakthrough time, which matched field measurements of 6.5 hours. The estimated radius of invasion was approximately 2.5 m, while the difference between estimated water saturation in the uninvaded zone and water saturation estimated from the deep-sensing resistivity log was approximately 0.13, therefore improving the estimation of the original gas in place.