Spatial Distribution Of Porosity Research Articles

Approach to Perforation Interval Selection Uses AI-Assisted Image Interpretation

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216856, “ML-Driven Integrated Approach for Perforation Interval Selection Based on Advanced Borehole-Image AI-Assisted Interpretation,” by Alexander Petrov, SPE, Mounir Belouahchia, SPE, and Abdelwahab Noufal, SPE, ADNOC. The paper has not been peer reviewed. _ In the complete paper, the authors propose an artificial-intelligence (AI)-assisted work flow that uses machine-learning (ML) techniques to identify sweet spots in carbonate reservoirs. This process involves annotation of geologic features using a well database, with supervision from subject-matter experts (SMEs). The resulting ML model is tested on new wells and can identify pay zones, perforation intervals, and stress analysis. The models successfully detect fractures, breakouts, bedding planes, vugs, and slippage passages with pixel-level precision, reducing borehole-image (BHI) analysis time. BHI Interpretation and Preprocessing The use of BHIs requires manual interpretation and data identification, heavily relying on the expertise and time of SMEs. A widely adopted approach to address this challenge is the use of supervised computer-vision algorithms, a subfield of AI. These algorithms optimize the task function or model based on examples they have learned from data during training. However, when applied to BHIs, certain ML challenges must be addressed, including the following: - Detecting features in wells from different reservoirs using a model trained on wells from one reservoir can be highly challenging because reservoirs may exhibit distinct geological characteristics. - The handling of parts of BHIs with missing data, depicted by vertically slanted white strips, poses considerable difficulty. Therefore, the authors created a deep-learning approach based on a generative adversarial network architecture to fill the gaps automatically (Fig. 1). - The labels provided by geologists often do not have pixelwise precision, causing the machine to become confused while trying to learn inconsistent patterns. The authors use a convolutional neural network (CNN) to compute a probability map for pixels belonging to specific classes. In this application, a class is defined as any of the heterogeneities in the BHI; however, this method is applicable to any type of heterogeneities in an image. After training, the CNN module provides the optimal probability for each pixel in the image. To classify regions in the BHI based on heterogeneities, a class-specific threshold is established. Pixels with values above the thresholds are assigned to the corresponding class, while those below the thresholds are assigned to the background. BHI-Derived Porosity A new approach for borehole-derived porosity was developed in-house to overcome the limitations of existing techniques widely used in the industry. This approach capitalizes on BHIs for multiple analyses, including structural dip assessment, fault and fracture identification, and determination of minimum and maximum horizontal stress orientation. However, its primary strength lies in quantifying the fraction of secondary porosity in heterogeneous or dual-porosity carbonate formations. The authors have devised a novel method that uses borehole electrical images to conduct porosity and image connectivity analysis. By implementing this technique, essential information can be extracted regarding the spatial distribution of porosity and the extent of secondary porosity fraction.

Rock typing and uncertainty assessment in geological and petrophysical properties by integrating electrofacies, hydraulic flow units, and geostatistical techniques in the Kangan gas field, Zagros basin

The structural complexities of hydrocarbon reservoirs make it difficult to correlate geological and petrophysical properties. A successful field development depends on accurately mapping the spatial distributions of reservoir key parameters. In this study, we present results on rock type analysis, estimation, and uncertainty evaluation of geological and petrophysical data of 33 wells in one of the south Iranian gas fields. This paper is divided into two parts. In the first part, we used a reservoir rock classification theme based on identifying electrofacies (EFs) and hydraulic flow units by analyzing both log and core data. In the second part of the paper, we performed estimation, uncertainty evaluation, and assessment of the porosity–thickness relationship of the high-quality EFs using geostatistical techniques. We used sequential simulation schemes to map the spatial distributions of porosity and thickness of the high-quality EFs across the field. Using probabilistic approaches, the generated multiple realizations were used to quantify the local and spatial uncertainties. Variogram analysis showed that property distributions had a higher continuity and minimum variance in the NW–SE direction. Based on spatial uncertainty analysis, we found that the indicator-based sequential simulated maps exhibited less spatial uncertainty. Furthermore, the obtained probability maps indicated that the SE part of the study area is more suitable for drilling and production scenarios.

An advanced ensemble modeling approach for predicting carbonate reservoir porosity from seismic attributes

This study uses a machine learning (ML) ensemble modeling approach to predict porosity from multiple seismic attributes in one of the most promising Main Dolomite hydrocarbon reservoirs in NW Poland. The presented workflow tests five different model types of varying complexity: K-nearest neighbors (KNN), random forests (RF), extreme gradient boosting (XGB), support vector machine (SVM), single layer neural network with multilayer perceptron (MLP). The selected models are additionally run with different configurations originating from the pre-processing stage, including Yeo–Johnson transformation (YJ) and principal component analysis (PCA). The race ANOVA method across resample data is used to tune the best hyperparameters for each model. The model candidates and the role of different pre-processors are evaluated based on standard ML metrics – coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). The model stacking is performed on five model candidates: two KNN, two XGB, and one SVM PCA with a marginal role. The results of the ensemble model showed superior accuracy over single learners, with all metrics (R2 0.890, RMSE 0.0252, MAE 0.168). It also turned out to be almost three times better than the neural net (NN) results obtained from commercial software on the same testing set (R2 0.318, RMSE 0.0628, MAE 0.0487). The spatial distribution of porosity from the ensemble model indicated areas of good reservoir properties that overlap with hydrocarbon production fields. This observation completes the evaluation of the ensemble technique results from model metrics. Overall, the proposed solution is a promising tool for better porosity prediction and understanding of heterogeneous carbonate reservoirs from multiple seismic attributes.

Estimation of Reservoir Porosity Using Seismic Post-Stack Inversion in Lower Indus Basin, Pakistan

Seismic post-stack inversion is one of the best techniques for effective reservoir characterization. This study intends to articulate the application of Model-Based Inversion (MBI) and Probabilistic Neural Networks (PNN) for the identification of reservoir properties i.e. porosity estimation. MBI technique is applied to observe the low impedance zone at the porous reservoir formation. PNN is a geostatistical technique that transforms the impedance volume into porosity volume. Inverted porosity is estimated to observe the spatial distribution of porosity in the Lower Goru sand reservoir beyond the well data control. The result of inverted porosity is compared with that of well-computed porosity. The estimated inverted porosity ranges from 13-13.5% which shows a correlation of 99.63% with the computed porosity of the Rehmat-02 well. The observed low impedance and high porosity cube at the targeted horizon suggest that it could be a probable potential sand channel. Furthermore, the results of seismic post-stack inversion and geostatistical analysis indicate a very good agreement with each other. Hence, the seismic post-stack inversion technique can effectively be applied to estimate the reservoir properties for further prospective zones identification, volumetric estimation and future exploration.

Investigation on instability modes of sand under biaxial shearing accounting various specimen generation techniques in DEM

Instabilities in granular material usually initiate at the microscale due to the presence of internal heterogeneity arising from variation in the particle packing arrangement. The present work elucidates the effect of various specimen generation techniques adopted in DEM on the initial sample heterogeneity and its subsequent influence on the instability response of sand under biaxial shearing. Dense and loose specimens are prepared using five different specimen generation techniques and homogeneity within the specimens has been analyzed in terms of spatial variation of porosity, anisotropy in the contact force and fabric structure. The initiation and subsequent evolution of different instability modes during shearing have been examined based on the spatial porosity distribution, relative particle displacement and particle rotation. Localized instability mode has been observed to emerge in dense specimens in form of cross-type shear bands with varying inclination and thickness for these different specimen generation techniques. Porosity and particle rotation based instability prediction indicate a delayed onset of shear band in comparison to identifications from relative particle displacement and bifurcation of local strains. The loose specimen exhibits bulging type diffused instability accompanied by large relative particle displacement and rotation scattered across the specimen.

Porosity prediction using 3D seismic genetic inversion at F3 Block, offshore Netherlands

Seismic and well information have been incorporated with a genetic inversion workflow using a supervised neural network in the F3 block, offshore Netherlands with a view to properly predicting porosity distribution within the selected reservoirs. This would provide spatial distribution of porosity within the field. Petrophysical parameters were estimated for the identified reservoirs (FS4, FS8, XYZ and ABC) across the four wells (F02-01, F03-02, F03-04 and F03-06) ranging from 780 to 1500 ms. Acoustic impedance (AI) and porosity cubes weregenerated using a genetic neural network. Thereafter, acoustic and porosity maps were extracted for the selected reservoirs. The results of the inversion reflect that the acoustic impedance in the region varies from 2000 to 6000 kPa.s/m, indicating unconsolidated and less compacted formation within the study area. Porosity within the selected reservoirs varies from 0.25 to 0.40, which reflects very good to excellent porosity values within the study area. The plot of porosity from well (F02-01) and the predicted porosity from genetic inversion reveals a relatively good correlation coefficient of 68%. The hydrocarbon regions within the F3 block are in the Upper Jurassic – Lower Cretaceous strata which are found below the interval available for this study. The study underscores that the genetic inversion algorithm has proven effective in predicting porosity within the study area.

Importance of appropriate segmentation in pore structure analysis of coral reef limestone from CT images

This paper conducts a critical evaluation of the applicability of traditional methods and neural network-based methods in separating pore space from the matrix on computed tomography (CT) scanning images of two types of coral reef limestone specimens with coarse and fine pores. By comparing with the manually labelled “ground truth,” it is shown that the Otsu method and other traditional methods have good segmentation accuracy and high efficiency when subjected to specific image preprocessing schemes, but their robustness is poor. UNet neural networks with a mixed contrast training dataset have high accuracy and strong robustness, but they are complex and computationally expensive. The three-dimensional reconstruction and analysis of the spatial distribution of porosity shows that the coral reef limestone specimen with coarse pores has a clear pore structure, while the rock matrix and pores are interlaced in the specimen with fine pores. Last, the statistical analysis of porosity and the pore distributions prove that the segmentation accuracy has a significant influence on the subsequent pore analysis. The current study highlights the importance of using appropriate image segmentation schemes and provides useful guidance for future interpretation of pore structure features of coral reef limestone based on CT scanned images.

Post-processing of additively manufactured IN625 thin-walled structures using laser remelting in directed energy deposition

Blown-powder directed energy deposition (DED) is especially useful for depositing on an existing part for the purpose of repair or adding features. Thin substrate deposition, wherein the width of the substrate is smaller than the laser diameter, is an important configuration for building and repairing complex components in various geometries. The present study seeks to understand the efficacy of in-line post-processing methods based on laser remelting (LR) for reducing internal (e.g., porosity) and external (e.g., surface roughness) for such thin-walled repair configurations. In this work, different levels of LR energy density and substrate width were evaluated using the LR process. Post processing outcomes in terms of deposit geometry as well as spatial distribution of porosity, pore density, pore volume fraction, pore diameter, and surface roughness were evaluated using X-ray microcomputed tomography and optical profilometry. The results showed that LR reduced pore density up to 88 % and reduced areal surface roughness to 4.159 μm. At some threshold, increasing LR energy density increased surface waviness and modified deposition geometry, this indicative of limits to the post processing method. Strategies for using LR to reduce total porosity, near-surface porosity, and improving surface roughness are briefly discussed.

A TRUNCATED GAUSSIAN RANDOM FIELD METHOD FOR MODELING THE POROSITY DEFECT IN COMPOSITE STRUCTURES

Reducing the cost of manufacturing of primary structures can be achieved by better control of the manufacturing tolerances and acceptable levels of variability. Predicting the effect of defects can also help clear quality concerns that affect the amount of parts that require repairs or need to be scrapped. In this paper, a numerical method is presented for the assessment of the effect of a variable distribution of porosity defects in composite structures at the macroscale. A numerical method is first implemented to simulate random porous zones over the 3-D surface of the composite part. Two or more material configurations can be considered: the sane one and the porous ones. In the numerical model, each element of the part mesh is assigned one of the materials. The random choice between the materials for each element is based on the simulation of a truncated Gaussian random field over the 3-D surface. A Monte Carlo simulation method is used to study the effect of the random spatial distribution of porosity on the mechanical performance of the structure. To illustrate this approach, the case of a curved stiffened panel with a compression load case is considered, and the mechanical performance is evaluated. By taking all the samples of random porosity distributions that have high values for displacement and stress, the critical zones of the structure are identified. Based on these predictive methods, a methodology could further be defined to select more critical areas for inspection and potentially reduce time required for quality controls.

Entropy generation analysis during adjoint variable-based topology optimization of porous reaction-diffusion systems under various design dimensionalities

Several studies attempted to enhance the performance of various reaction-diffusion systems, such as electrodes of electrochemical devices, by modifying the spatial distribution of porosity throughout the porous reactor. However, research is proceeding without knowing the theoretical limitation of the improvement. To connect optimization techniques with a well-established theoretical approach, this paper presents topology optimization for the design of diffusion fields in reaction-diffusion systems together with entropy generation analysis using nonequilibrium thermodynamics. Topology optimization of porosity distribution in reaction-diffusion systems was carried out using the adjoint variable methods. The optimization problem was formulated to maximize the reaction in the design domain. The results obtained from the investigation under various dimensionalities were compared and discussed. A formula for the local entropy generation of reaction-diffusion systems was derived and used to assess the topology optimization results. While the findings showed an insignificant difference between the overall reaction rate of 0D and 1D cases, optimization of higher dimensionalities (2D and 3D) considerably enhanced the overall reaction rate. The results revealed that the optimum porosity distribution corresponds to the most uniform and minimum entropy generation.

Modeling dynamic formability of porous ductile sheets subjected to biaxial stretching: Actual porosity versus homogenized porosity

This paper investigates the effect of porous microstructure on the necking formability of ductile sheets subjected to dynamic in-plane stretching. We have developed an original approach in which finite element calculations which include actual void distributions obtained from additively manufactured materials are compared with simulations in which the specimen is modeled with the Gurson–Tvergaard continuum plasticity theory (Gurson, 1977; Tvergaard, 1982) which considers porosity as an internal state variable. A key point of this work is that in the calculations performed with the continuum model, the initial void volume fraction is spatially varied in the specimen according to the void distributions included in the simulations with the actual porous microstructure. The finite element computations have been carried out for different loading conditions, with biaxial strain ratios ranging from 0 (plane strain) to 0.75 (biaxial tension) and loading rates varying between 10000s−1 and 60000s−1. We have shown that for the specific porous microstructures considered, the necking forming limits obtained with the Gurson–Tvergaard continuum model are in qualitative agreement with the results obtained with the calculations which include the actual void distributions, the quantitative differences for the necking strains being generally less than ≈25% (the calculations with actual voids systematically predict greater necking strains). In addition, the spatial distribution of necks formed in the sheets at large strains is very similar for the actual porosity and the homogenized porosity models. The obtained results demonstrate that the voids promote plastic localization, acting as preferential sites for the nucleation of fast growing necks. Moreover, the simulations have provided individualized correlations between void volume fraction, maximum void size and necking formability, and highlighted the influence of the heterogeneity of the spatial distribution of porosity on plastic localization.

Investigation of boundary layer impact on pervious concrete

ABSTRACT Industrial application of pervious concrete (PC) is limited by uncertainty in assessing performance characteristics in design phase. Compaction increases uniformity of characteristics in mass production. Spatial distribution of porosity indicates existence of boundary layers in laboratory samples, which affect mechanical properties of PC specimens and applications. This study analyses the impact of design parameters on boundary layers and porosity distribution of PC. The effect of boundary layers observed in laboratory scale samples is vital in scaling them up for industrial applications. Approximately 2400 laboratory specimens of 150 × 150 × 150 mm3 were cast with varying aggregate-to-cement (A/C) ratio (2.5–7.0), aggregate-size-distribution (12–25 mm), compaction energy (15–75 blows from standard proctor rammer) and compaction energy distribution (2 distributions in 3 layers) with 6 replications. Porosity and porosity distributions were analysed using image analysis techniques. The A/C ratio and compaction were observed to impact characteristics of the bottom boundary layer, which varies in thickness up to 25 mm. Optimising design parameters not only enhances the performance of pervious concrete but also optimally reduces the boundary layer effect. The boundary layer of the top face is not significantly affected by design parameters and was observed to be rather constant at 10 mm.

Correction: Geostatistical Rock Physics Inversion for Predicting the Spatial Distribution of Porosity and Saturation in the Critical Zone

Correction: Geostatistical Rock Physics Inversion for Predicting the Spatial Distribution of Porosity and Saturation in the Critical Zone

Geostatistical Rock Physics Inversion for Predicting the Spatial Distribution of Porosity and Saturation in the Critical Zone

Geostatistical Rock Physics Inversion for Predicting the Spatial Distribution of Porosity and Saturation in the Critical Zone

Zastosowanie metod uczenia maszynowego do charakterystyki porowatości i typu nasycenia przy użyciu atrybutów sejsmicznych

The application of machine learning (ML) tools and data-driven modeling became a standard approach for solving many problems in exploration geology and contributed to the discovery of new reservoirs. This study explores an application of machine learning ensemble methods – random forest (RF) and extreme gradient boosting (XGBoost) to derive porosity and saturation type (gas/water) in multi-horizon sandstone formations from Miocene deposits of the Carpathian Foredeep. The training of ML algorithms was divided into two stages. First, the RF algorithm was used to compute porosity based on seismic attributes and well location coordinates. The obtained results were used as an extra feature to saturation type modeling using the XGBoost algorithm. The XGBoost was run with and without well location coordinates to evaluate the influence of the spatial information for the modeling performance. The hyperparameters for each model were tuned using the Bayesian optimization algorithm. To check the training models' robustness, 10-fold cross-validation was performed. The results were evaluated using standard metrics, for regression and classification, on training and testing sets. The residual mean standard error (RMSE) for porosity prediction with RF for training and testing was close to 0.053, providing no evidence of overfitting. Feature importance analysis revealed that the most influential variables for porosity prediction were spatial coordinates and seismic attributes sweetness. The results of XGBoost modeling (variant 1) demonstrated that the algorithm could accurately predict saturation type despite the class imbalance issue. The sensitivity for XGBoost on training and testing data was high and equaled 0.862 and 0.920, respectively. The XGBoost model relied on computed porosity and spatial coordinates. The obtained sensitivity results for both training and testing sets dropped significantly by about 10% when well location coordinates were removed (variant 2). In this case, the three most influential features were computed porosity, seismic amplitude contrast, and iso-frequency component (15 Hz) attribute. The obtained results were imported to Petrel software to present the spatial distribution of porosity and saturation type. The latter parameter was given with probability distribution, which allows for identifying potential target zones enriched in gas.

Mono- and Multi-Objective CFD Optimization of Graded Foam-Filled Channels.

Graded foam-filled channels are a very promising solution for improving the thermal performance of heat sinks because of their customized structures that leave large amounts of room for heat transfer enhancement. Accordingly, this paper proposes a comprehensive optimization framework to address the design of such components, which are subjected to a uniform heat flux boundary condition. The graded foam is achieved by parameterizing the spatial distributions of porosity and/or Pores Per Inch (PPI). Mono- and multi-objective optimizations are implemented to find the best combination of the foam’s fluid-dynamic, geometrical and morphological design variables. The mono-objective approach addresses the Performance Evaluation Criterion (PEC) as an objective function to maximize the thermal efficiency of graded foams. The multi-objective approach addresses different objective functions by means of Pareto optimization to identify the optimal tradeoff solutions between heat transfer enhancement and pressure drop reduction. Optimizations are performed by assuming a local thermal non-equilibrium in the foam. They allowed us to achieve a 1.51 PEC value with H* = 0.50, ReH = 15000, iε = iPPI = 0.50, ε(0) = 0.85, ε(1) = 0.97, PPI(0) = 5, PPI(1) = 40, and ks→f = 104 as the design variables. For the three multi-objective functions investigated, one can extrapolate the optimum from the Pareto front via the utopia criterion, obtaining = 502 W/m2 K and Δp = 80 Pa, = 2790 and f = 42, = 0.011, and Δp* = 91. The optimal solutions provide original insights and guidelines for the thermal design of graded foam-filled channels.

Gaussian mixture model deep neural network and its application in porosity prediction of deep carbonate reservoir

To estimate the spatial distribution of porosity, model-driven or data-driven methods are usually used to establish the relationship between porosity and seismic elastic parameters. However, due to the strong heterogeneity and complex pore structures of carbonate reservoirs, porosity estimation of carbonates still represents a great challenge. The existing conventional model-driven and data-driven-based porosity estimation methods have high uncertainty. To characterize the complex statistical distribution of porosity, the nonlinear relationship between porosity and seismic elastic parameters, and the uncertainty of porosity estimation, we have used a Gaussian mixture model deep neural network (GMM-DNN) to invert porosity from seismic elastic parameters. We use a Gaussian mixture model to describe the complex distribution of porosity, and we apply a deep neural network to establish the nonlinear relationship among seismic compressional-wave (P-wave) velocity, density, and porosity. The outputs of the GMM-DNN provide an estimated probability distribution of porosity conditioned on the input seismic elastic parameters. The synthetic data example verifies the feasibility of this method. We further apply the GMM-DNN-based porosity inversion method to a deep complex carbonate reservoir in the Tarim Basin, Northwest China. The well-logging data are used to train the GMM-DNN; then, the P-wave velocity and density obtained by prestack amplitude-variation-with-offset inversion are fed into the trained network to reasonably estimate the porosity distribution of the whole target reservoir and evaluate its uncertainties.

Chemical vapor infiltration of additively manufactured preforms: Pore‐resolved simulations and experimental validation

AbstractThe densification of additively manufactured porous preforms by chemical vapor infiltration (CVI) is studied using pore‐resolved simulations and experiments. Experimentally, 3D printed silicon carbide (SiC) preforms are subject to CVI synthesis using methyltrichlorosilane (MTS) precursor to obtain high purity SiC/SiC composites. Optical images of the cross sections of the processed preforms are analyzed to obtain the spatial porosity distribution. The numerical method is based on a level set formulation to capture the spatial distribution and time evolution of the pore scale microstructural characteristics. The coupled transport and kinetic effects are represented using a dimensionless Thiele modulus. Simulations are initialized using representative synthetic preform geometries comprising of packed particles based on the size distribution of the powder used for 3D printing. The simulation results are validated against the experimental observations in terms of total density and the distribution of residual porosity. The densification characteristics, porosity classification, concentration profiles, and structure functions are analyzed as functions of processing temperature and Thiele modulus.

Estimation of Reservoir Porosity Using Seismic Post-Stack Inversion in Lower Indus Basin, Pakistan

Seismic post-stack inversion is one of the best techniques for effective reservoir characterization. This studyintends to articulate the application of Model-Based Inversion (MBI) and Probabilistic Neural Networks (PNN) for theidentification of reservoir properties i.e. porosity estimation. MBI technique is applied to observe the low impedancezone at the porous reservoir formation. PNN is a geostatistical technique that transforms the impedance volume intoporosity volume. Inverted porosity is estimated to observe the spatial distribution of porosity in the Lower Goru sandreservoir beyond the well data control. The result of inverted porosity is compared with that of well-computed porosity.The estimated inverted porosity ranges from 13-13.5% which shows a correlation of 99.63% with the computed porosityof the Rehmat-02 well. The observed low impedance and high porosity cube at the targeted horizon suggest that it couldbe a probable potential sand channel. Furthermore, the results of seismic post-stack inversion and geostatistical analysisindicate a very good agreement with each other. Hence, the seismic post-stack inversion technique can effectively beapplied to estimate the reservoir properties for further prospective zones identification, volumetric estimation and futureexploration.

High-throughput investigation of laser powder bed fabricated Inconel 718 alloy: Fabrication, microstructure and performance

Additive manufacturing provides a possibility to realize high throughput investigation of material preparation and processing. In order to thoroughly investigate the effect of the key parameters on the soundness, microstructure and mechanical properties of the fabricated parts, sixty three combinations of laser power (twelve levels) and scanning speed (sixteen levels) with fifty eight levels of laser energy densities were realized in a single Inconel 718 alloy bar shaped specimen using the laser powder-bed fusion (L-PBF) technique. The combination of parameters varied from bottom to top of the bar, with each combination repeating every eight layers. Its spatial porosity distribution was measured by the Computed tomography (CT) test; its microstructure was observed by optical microscope, scanning electronic microscope and EBSD; the microhardness was measured as well. A comprehensive view of the relationship of these parameters and the porosity fraction, melt pool stacking, primary grain arm spacing and microhardness was obtained. Although high laser power and slow scanning speed are helpful to achieve low porosity, but the laser power has more significant effect on the quality of the specimen than the scanning speed. Only if at high levels the volumetric laser energy density could be used as an index to correlate to the quality of the specimen. The processing chart for the guidance of L-PBF was obtained. As the laser power is higher than 140 W and the scanning speed lower than 1000 mm/s, sound specimens would be realized. This method demonstrated the great potential for high throughput investigations of the L-PBF process.