Evaluation Of Porosity Research Articles

Manufacturing of Thermoset Polyimide Composites by Laser Sintering

Selective Laser Sintering (SLS) is an additive manufacturing technique that builds 3D models layer by layer using a laser to selectively melt cross sections in powdered polymeric materials, following sequential slices of the computer-aided design (CAD) model. SLS generally uses thermoplastic polymeric powders such as polyamides. The resultant 3D-printed objects are often weaker in their strength compared to traditionally processed materials, due to their higher porosity. This paper described the process development of using melt-processable imide oligomers terminated with reactive 4-phenylethynylphthalic anhydride (4-PEPA) to conduct laser sintering (LS). The first successful 3D-printing of high temperature RTM370 thermoset polyimide carbon fiber composite was further post-cured to promote additional crosslinking for achieving higher temperature (Tg = 370°C) capability. Another novel imide oligomer, RTM385-SLS, formulated with a complex melt viscosity [h*] of ~104-105 poise is also suitable for LS. RTM385-SLS resin powder was mixed with 20-25% of hexagonal boron nitride (h-BN) and subjected to LS to print out “Green” specimens which could be further post-cured to afford a thermally conductive but electrically insulating composites with high Tg of 385°C. The cured composite specimens were then subjected to mechanical testing, thermal conductivity and porosity evaluation as well as SEM characterization.

Mitigating autogenic shrinkage in high‐performance concrete using wollastonite: A structural enhancement approach

AbstractThis study investigates the influence of wollastonite fiber as a reinforcement in high‐performance concrete (HPC), focusing on its potential to mitigate autogenic shrinkage and enhance overall structural properties. HPC known for its superior strength and low permeability, often suffers from intensified autogenic shrinkage, leading to micro‐cracking and compromising durability. To address this challenge, wollastonite was incorporated at different substitutions (0%, 5%, 10%, and 15%) for both cement and aggregate. The experimental program involved a systematic evaluation of compressive strength, flexural strength, porosity, water absorption, and corrosion resistance with tests conducted at different curing periods. We found that the addition of 10% wollastonite significantly enhances compressive strength (up to 20% after 90 days) and flexural strength (maximum improvement of 21%). The water permeability was decreased by 29% and improved resistance to steel reinforcement corrosion, demonstrating enhanced durability compared to conventional HPC. However, diminishing returns were observed at higher wollastonite replacement levels (15%), indicating a threshold beyond which mechanical and durability benefits were reduced due to potential issues with fiber dispersion and workability. Our findings suggest that wollastonite is a promising, cost‐effective additive for improving HPC. The study contributes new understandings into optimizing HPC mixtures for greater durability and sustainability, while also reducing the environmental footprint through partial cement replacement. Future research is needed to explore the long‐term performance of wollastonite‐reinforced HPC under different environmental conditions and its combination with other supplementary cementitious materials for further enhancement.

Low-grade fly ash in portland cement blends: A decoupling approach to evaluate reactivity and hydration effects

Fly ash with low glass content is often prohibited from use in concrete due to the low reactivity and/or the inclusion of contaminants. However, the scarcity of high-quality fly ash promotes the evaluation of the feasibility of using fly ash with low glass content (e.g., low-grade fly ash) in concrete. This study proposes a decoupling method to quantitatively estimate the degree of reaction of fly ash with extremely low glass content, which partially replaces cement, and the degree of hydration of the hosting cement, simultaneously. The estimation is derived from the contents of calcium hydroxide and chemically bonded water in hydrated binary cement pastes, which can be determined by thermogravimetric analysis-based experiments and theoretically validated stoichiometric parameters. The results exhibit that the fly ash tends to retard the early-age hydration of cement but promotes its later-age hydration, resulting in a higher ultimate degree of reaction of cement than the reference paste. The microstructural and porosity evaluation shows that the fly ash, though has relatively low degrees of reaction due to its low glass content, can result in a more tortuous pore network of the hydrated pastes, which could be potentially more resistant to the penetration of water and aggressive chemicals.

Applicability of ensemble learning in total organic carbon and porosity evaluation of shales

Accurate evaluation of total organic carbon (TOC) content and porosity is of paramount significance for assessment and target interval selection for shale reservoirs. This study takes shales from the western Chongqing area as an exemplary case to delve into the applicability and reliability of ensemble learning in evaluating TOC content and porosity. The results indicate that although both Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) algorithms are suitable for evaluating TOC content and porosity in shales, LightGBM algorithm is preferred due to its comprehensive advantages, including higher accuracy, stronger generalization capability, and faster operating speed. For TOC content evaluation, the four most important logging parameters identified by LightGBM and RF are consistent, but exhibit different orders: DEN (compensated density) > GR (gamma ray) > U (uranium) > CNL (compensated neutron) and DEN > U > GR > CNL, respectively. For porosity evaluation, LightGBM and RF identify the same type and order of the three most important logging parameters: AC (acoustic transit time) > DEN > U. This similarity may be attributed to the fact that both algorithms utilize Classification and Regression Tree (CART) as base learners. The dependence plots between SHAP (SHapley Additive exPlanations) values and logging parameters reveal that the role of each logging parameter in the evaluation model is segmented, rather than exhibiting a continuous linear contribution. In conclusion, given the exceptional performance of ensemble learning algorithms, they, especially LightGBM algorithm, are highly recommended for shale evaluation.

Evaluation of Reservoir Porosity and Permeability from Well Log Data Based on an Ensemble Approach: A Comprehensive Study Incorporating Experimental, Simulation, and Fieldwork Data

Evaluation of Reservoir Porosity and Permeability from Well Log Data Based on an Ensemble Approach: A Comprehensive Study Incorporating Experimental, Simulation, and Fieldwork Data

A New Porosity Evaluation Method Based on a Statistical Methodology for Granular Material: A Case Study in Construction Sand

Sand porosity is an important compactness parameter that influences the mechanical properties of sand. In order to evaluate the temporal variation in sand porosity, a new method of sand porosity evaluation based on the statistics of target sand particles (which refers to particles within a specific particle size range) is presented. The relationship between sand porosity and the number of target sand particles at the soil surface considering observation depth is derived theoretically, and it is concluded that there is an inverse relationship between the two. Digital image processing and the k-means clustering method were used to distinguish particles in digital images where particles may mask each other, and a criterion for determining the number of particles was proposed, that is, the criterion of min(Dao). The execution process was implemented by self-written codes using Python (2021.3). An experiment on a simple case of Go pieces and sand samples of different porosities was conducted. The results show that the sum of the squared error (SSE) in the k-means method can converge with a small number of iterations. Furthermore, there is a minimum value between the parameter Dao and the set value of a single-particle pixel, and the pixel corresponding to this value is a reasonable value of a single-particle pixel, that is, the min(Dao) criterion is proposed. The k-means method combined with the min(Dao) criterion can analyze the number of particles in different particle size ranges with occlusion between particles. The test results of sand samples with different densities show that the method is reasonable.

Evaluation of Ammonium Nitrate(V) Morphology and Porosity Obtained by SEM and Tomography Imaging.

This paper presents an evaluation of the morphology of fertilizer-grade and prill-grade ammonium nitrate(V). All samples were analyzed using X-ray powder diffraction (XRD), scanning electron microscopy (SEM) and tomography techniques. The XRD results revealed that despite various provenances, all samples exhibited similar Pmmm symmetry and diffraction patterns. SEM images indicated that prill ammonium nitrate(V) showed a more complex external and internal crystal structure than fertilizer-grade counterparts. Furthermore, tomography analysis revealed that each prill ammonium nitrate(V) sample demonstrated distinct porosity characteristics, including varying pore sizes and distribution patterns. Both methods confirmed that fertilizer-grade ammonium nitrate(V) in the cross-section had a pumice structure, and porous prill ammonium nitrate(V) had a rather complex structure, with a central cavity observed only in the case of Sample 4. The appearance of a central cavity can be explained by the different conditions or manufacturing processes of porous prill ammonium nitrate(V). Moreover, the fertilizer-type ammonium nitrate(V) exhibited the lowest surface-to-volume ratio of ca. 21% compared to the porous-type ammonium nitrate(V). This, together with the lowest surface area of ca. 116 mm2, confirmed the lowest absorption capacity of the fertilizer-grade ammonium nitrate(V) disclosed by the ammonium nitrate(V) producer.

Evaluation of Porosity in AISI 316L Samples Processed by Laser Powder Directed Energy Deposition

Directed energy deposition-laser beam/powder (DED-LB/Powder) is an additive manufacturing process that is gaining popularity in the manufacturing industry due to its numerous advantages, particularly in repairing operations. However, its application is often limited to case studies due to some critical issues that need to be addressed, such as the degree of internal porosity. This paper investigates the effect of the most relevant process parameters of the DED-LB/Powder process on the level and distribution of porosity. Results indicate that, among the process parameters examined, porosity is less affected by travel speed and more influenced by powder mass flow rate and laser power. Additionally, a three-dimensional finite element transient model was introduced, which was able to predict the development and location of lack-of-fusion pores along the building direction.

Generalised and automated method for surface analysis of roughness and subsurface porosity using micro-computed tomography

As additive manufacturing enables the production of intricate, high-value parts with functional integration, inspection is gaining importance to ensure safety for use. Since the surface quality of laser beam powder bed fusion parts has proven to be inherently inhomogeneous, the measured values are dependent on the measurement spot, making surface quality difficult to characterise using conventional methods. Combined with the fact that the complex shape of the parts potentially complicates measurements further, a new surface characterisation method is required to adequately capture the quality of additively manufactured parts on the entire surface. In this work, a novel method is proposed that is both capable of meeting the above requirements and additionally allows the correlation of the results with the process data and the evaluation of the near-surface porosity. At the same time, the local quality deviations can be visualised and roughness hotspots found and correlated with the process.

Use of confocal microscopy to measure the porosity of cement containing sulfocalcic binder: A comparative study

This work proposes an effective methodology for measuring the porosity of mixed hydraulic binders applying image analysis (IA) with confocal microscopy (CM). CM offers a certain simplification of sample preparation compared to the standard procedure EN 480-11, as the steps of sample staining and pore masking are eliminated. A set of cement and a hydraulic clinker-free sulfocalcic binder pastes was prepared with various aeration. Total air content, micro air content and the air pore spacing factor according to EN 480-11 standard were determined for hardened pastes using measuring chords. Subsequently, a new simple Surface porosity evaluation for IA methodology using CM was proposed and results were compared with the standard evaluation. The results of both IA evaluations achieved high agreement and the new porosity Surface evaluation was found to be promising for the determination of total air content. Porosity values measured by both IA methods were further compared with MIP.

Optimization and Application of XGBoost Logging Prediction Model for Porosity and Permeability Based on K-means Method

The prediction and distribution of reservoir porosity and permeability are of paramount importance for the exploration and development of regional oil and gas resources. In order to optimize the prediction methods of porosity and permeability and better guide gas field development, it is necessary to identify the most effective approaches. Therefore, based on the extreme gradient boosting (XGBoost) algorithm, laboratory test data of the porosity and permeability of cores from the southern margin of the Ordos Basin were selected as the target labels, conventional logging curves were used as the input feature variables, and the mean absolute error (MAE) and the coefficient of determination (R2) were used as the evaluation indicators. Following the selection of the optimal feature variables and optimization of the hyper-parameters, an XGBoost porosity and permeability prediction model was established. Subsequently, the innovative application of homogeneous clustering (K-means) data preprocessing was applied to enhance the XGBoost model’s performance. The results show that logarithmically preprocessed (LOG(PERM)) target labels enhanced the performance of the XGBoost permeability prediction model, with an increase of 0.26 in its test set R2. Furthermore, the application of K-means improved the performance of the XGBoost prediction model, with an increase of 0.15 in the R2 of the model and a decrease of 0.017 in the MAE. Finally, the POR_0/POR_1 grouped porosity model was selected as the final predictive model for porosity in the study area, and the Arctan(PERM)_0/Arctan(PER0M)_1 grouped model was selected as the final predictive model for permeability, which has better prediction accuracy than logging curves. The combination of K-means and the XGBoost modeling method provides a new approach and reference for the efficient and relatively accurate evaluation of porosity and permeability in the study area.

Porosity Evaluation of Coarse Fillings in Single Joint for Artificial Rock Using Electrical Resistivity

Porosity Evaluation of Coarse Fillings in Single Joint for Artificial Rock Using Electrical Resistivity

Unsupervised contrastive learning: Shale porosity prediction based on conventional well logging

Porosity is a pivotal factor affecting the capacity for storage and extraction in shale reservoirs. The paucity of labeled data in conventional well logs interpretation and supervised learning models leads to inadequate generalization and diminished prediction accuracy, thus limiting their effectiveness in precise porosity evaluation. This study introduces a contrastive learning – convolutional neural network (CL-CNN) framework that utilizes CL for pretraining on a vast array of unlabeled data, followed by fine-tuning using a traditional CNN on a curated set of labeled data. Applied to the Subei Basin in Eastern China, the framework was tested on 130 labeled data and 2576 unlabeled data points from well H1. The results indicate that the CL-CNN framework outperforms traditional CNN-based supervised learning and other machine learning models in terms of prediction accuracy for the dataset under consideration. Furthermore, it demonstrates the potential for extensive porosity assessment across different logged depths. Due to its efficacy and ease of use, the proposed framework is versatile enough for application in reservoir evaluation, engineering development, and related fields. The innovative contribution of this research is encapsulated in its unique methodology and procedural steps for the accurate prediction of shale reservoir porosity, thus significantly enriching the existing body of knowledge in this domain.

Integrating Rock Typing and Petrophysical Evaluations to Enhance Reservoir Characterization for Mishrif Formation in Garraf Oil Field

Accurate reservoir characterization is essential for successful hydrocarbon extraction, especially in complex fields such as the Garraf Oil Field. This study aims to enhance reservoir characterization by integrating different petrophysical assessments and rock typing methodologies. Density, neutron, and sonic porosity evaluations were used to assess porosity, while gamma-ray logs and resistivity measurements were used to determine shale volume. The Archie equation was employed to estimate water saturation and sensitivity analysis was used to determine the cutoff values. The study also utilized rock typing techniques, including hydraulic flow unit assessment and Rock fabric number cross-plots, to categorize reservoir rocks into flow units and identify unique rock types. The combination of these approaches led to the precise identification of reservoir heterogeneities and optimal oil production zones. The results showed that the Gamma-ray log is the best method for determining shale volume, and the closest method for porosity determination is the density log. The water resistivity value was estimated at 0.016, while the Archie parameters (a,m,n) were 1.1, 2.1, and 3.7, respectively, with cutoff values of 0.22 for shale volume, 0.11 for porosity, and 0.56 for water saturation. The study identified five rock types ranging from packstone, pack to wackstone, wackstone, wack to mudstone, and mudstone. Overall, the integration of petrophysical evaluations and rock typing techniques facilitates the accurate delineation of oil-rich zones with enhanced reservoir connectivity.

Study on Main Controlling Factors and Fluid Identification Methods of Low-resistivity Reservoirs based on BP Nerve Network Verification in X Area of Ordos Basin

The low-resistivity oil layers refer to a formation whose resistivity response is opposite to the typical oil layers and similar to water layer. Distinguishing it from water layer in logging interpretation is very difficult, which brings great difficulties to oil and gas exploration and development. The exploration and development research shows that there is obvious resistivity anomaly in X area of Ordos Basin, and typical low-resistivity oil layers are developed in formation A in this area, so it is difficult to accurately divide oil layers and water layers only from logging response. In order to identify the fluid properties in low-resistivity oil layer areas, based on conventional logging data of formation A in X area, through petrophysical experimental analysis, combined with formation water analysis data, logging, oil test and other data, the main control factors of low-resistivity oil layers in X area are deeply studied in many aspects, and a more effective fluid identification method is put forward by combining the secondary evaluation of porosity and permeability and the flow unit method. The BP nerve network simulation cross plot is used to verify the identification results. The research shows that the main control factors of low resistivity oil layers in the study area are related to the salinity of water and pore connectivity, and also to the lithologic properties of different formations. Based on the study of the main control factors of low-resistivity oil layers, a multi-factor fluid identification method is proposed. The coincidence rate between the evaluation results of this identification method and oil test is 85.7%, which can identify the oil-water reservoirs in X area more effectively and accurately, finally identify the location of low-resistivity oil layers and boost the growth rate of oil and gas production.

Characterization and fractography of stir-cast ZA-27 composites reinforced with Zircon silicate and MoS2 solid lubricant particles

Zinc Aluminum alloy (ZA-27) is a primarily known alloy used in bearings. In this work the alloy is reinforced with Zircon silicate in varying amounts ranging from 1.5 wt% to 6 wt%, incrementally increasing by 1.5 wt%, along with 3 wt% Molybdenum di sulfide (MoS2) microparticles via a stir-casting process. Zircon silicate is prized for its increased hardness and thermal stability, and MoS2 provides remarkable wear resistance to the composite. The primary goal of this study is to investigate how Zircon silicate particles and MoS2 affect the strength and fracture behavior of the prepared composites. The current study shows a decrease in both load bearing and percentage elongation as the Zircon silicate concentration increases, owing to weaker MoS2 secondary particles inside the ZA-27 matrix. To provide a suitable explanation for crack development, the study also includes microstructure analysis, evaluation of porosity, microhardness, CTE evaluation, and the influence on the fracture mechanism of the ZA-27 composites.

Factors Influencing the Sand Production in the Lakwa Oilfield of Upper Assam Basin, India

Abstract Sand production is a crucial concern of the Lakwa oilfield of the Upper Assam (India) basin as it develops numerous issues related to oil and gas production. Sand production from oil wells has a negative impact on the economy of oil production since it can harm both surface and subsurface equipment as well as impair the well productivity. The main objective of this paper is to know the various reasons causing the sand production in the Lakwa oilfield of the Upper Assam Basin. Porosity evaluation, grain size analysis, rock-thin section analysis, scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis have been employed to evaluate the mineralogical constituents and their textural variations to find out the facts behind the sand production of Lakwa oilfield. The analyses show that the studied reservoir sandstones are matrixdominated, quartz-wacke type and have poorly sorted texture. The porosity value ranging from 21.4 % to 24.55 % indicates a sand production problem within the rock. The grain size analysis shows that the sandstones are fine-dominated and represent 52.24 to 61.32 % of the total volume. The recorded clay minerals are kaolinite, illite, smectite and chlorite. Feldspars are reported to alter into clay minerals under SEM analysis. The dominance of fine-grained minerals with clay within the rock is the main reason for the sand production in the wells of the Lakwa oilfield. Moreover, the migratory nature of clays in presence of water makes them more susceptible to get loosened and disintegrate, thereby making sand and clay mobile and hence affecting the cementation of the sandstone.

Porosity Evaluation and Analysis for 316L Stainless Steel by Selective Laser Melting Using Laser Ultrasonic Technology

Porosity Evaluation and Analysis for 316L Stainless Steel by Selective Laser Melting Using Laser Ultrasonic Technology

Effect of Gating System Design on the Quality of Aluminum Alloy Castings

In this paper, a naturally pressurized gating system has been designed to reduce the turbulence of the melt during casting. The influence of gate dimensions, foam filters, a trident gate and a vortex element were evaluated. Their effect on melt velocity, flow characteristics, number of oxides, casting properties and mechanical properties were observed. ProCAST Simulation software v.2023 and a water flow test were also evaluated to assist in the experimental evaluation of the castings. Melts showed a relationship between melt velocity and porosity of castings. Quantitative evaluation of the surface porosity showed a trend of decreasing porosity with decreasing melt velocity. The greatest reduction in the melt velocity was achieved by a M4 design, which was associated with the highest reduction in the oxides. The pores analyzed proved the presence of oxide layers on their inner surface and a possible theory of pore formation when the initiator of porosity is entrained double oxide layers. The best metal yield was achieved with M1, but the difference between M2 and M4 was negligible (2–5% yield difference), so it can be stated that the beneficial effect of the M4 design in providing the best quality castings is not negated by the increase in metal yield.

Quantitative characterization of organic and inorganic pores in shale based on deep learning

Organic matter (OM) maturity is closely related to organic pores in shales. Quantitative characterization of organic and inorganic pores in shale is crucial for rock-physics modeling and reservoir porosity and permeability evaluation. Focused ion beam-scanning electron microscopy (FIB-SEM) can capture high-precision three-dimensional (3D) images and directly describe the types, shapes, and spatial distribution of pores in shale gas reservoirs. However, due to the high scanning cost, wide 3D view field, and complex microstructure of FIB-SEM, more efficient segmentation for the FIB-SEM images is required. For this purpose, a multiphase segmentation workflow in conjunction with a U-net is developed to segment pores from the matrix and distinguish organic pores from inorganic pores simultaneously in the entire 3D image stack. The workflow is repeated for FIB-SEM data sets of 17 organic-rich shales with various characteristics. The analysis focuses on improving the efficiency and relevance of the workflow, that is, quantifying the minimum number of training slices while ensuring accuracy and further combining the fractal dimension (FD) and lacunarity to study a simple and objective method of selection. Meanwhile, the computational efficiency, accuracy, and robustness to noise of the 2D U-net model are discussed. The intersection over the union of automatic segmentation can amount to 80%–95% in all data sets with manual labels as ground truth. In addition, calculated by the FIB-SEM multiphase segmentation, the organic porosity is used to quantitatively evaluate the OM decomposition level. Deep-learning-based segmentation shows great potential for characterizing shale pore structures and quantifying OM maturity.