Decarbonizing aviation aims to reduce greenhouse gas emissions from aircraft operations, paving the way for sustainable air travel. This endeavor requires adopting advanced technologies, alternative fuels, high-performance coatings and efficient engineering solutions that minimize environmental impact while maintaining performance and safety standards. Two of the most prevalent and cost-effective methods for applying protective and functional coatings to aerospace components are thermal spray and physical vapor deposition. These techniques enhance the durability and efficiency of several components, resulting in fuel savings and an extended service life across the aviation industry. This supports the broader aim of transitioning the aviation industry to net-zero emissions and sustainable growth. This roadmap explores how these two coating techniques can promote sustainable aviation and identifies the challenges and opportunities in the aerospace sector for researchers and manufacturers of thermal spray and physical vapor deposition (PVD) coatings. It also proposes research directions to address these challenges and discusses the role of AI, which is crucial for breakthrough technologies in process optimization and integration, new coating development and coating design optimization. The roadmap is organized into 20 concise subsections, each focusing on a specific topic. Renowned specialists in each area were invited to summarize the current status of their field, discuss the challenges it faces, and offer recommendations for necessary research and development to overcome these issues. Together, these contributions vividly highlight the essential elements of the field and the challenges that lie ahead. The innovative ideas and concepts outlined in the roadmap reveal that the future path is both expansive and far-reaching. A decade after the JTST released its roadmap on thermal spray, which emphasized the processes, coatings, and applications of thermal spray, the current roadmap shifts its focus to spray processes and vapor deposition methods aimed at decarbonizing aviation. Academic and industry experts are collaborating to share insights on how thermal spray and vapor deposition techniques and coatings can advance sustainable aviation and the necessary research to overcome associated challenges. This roadmap can serve as a valuable reference point for researchers aiming to understand the field’s trajectory and identify critical gaps to address.

Ice accretion on aircraft, wind-turbine blades, power networks, civil infrastructure, and exposed sensors poses severe safety risks and economic costs. Passive icephobic surfaces mitigate icing by delaying heterogeneous nucleation, altering droplet impact/solidification and wetting transitions, and/or weakening the ice–substrate bond so that accreted ice sheds under modest aerodynamic, gravitational, or vibrational loads. This review synthesizes recent progress using a unified mechanism framework linking (i) nucleation and early freezing, (ii) droplet dynamics during impact or condensation/frosting, and (iii) ice accretion and removal governed by interfacial fracture. Smooth low-surface-energy coatings, textured (superhydrophobic) surfaces, slippery liquid-infused porous surfaces (SLIPS), and low-interfacial-toughness strategies are critically compared in terms of achievable performance ranges, failure modes, durability limits, fabrication scalability, and test-method dependence. Ice-adhesion measurement approaches (push-off, pull-off/tensile, centrifugal) are assessed and a minimum reporting checklist is provided to improve comparability. Case studies across aviation, wind energy, power infrastructure, sensors, and emerging civil-engineering coatings highlight that durability and scale-dependent failure modes remain the dominant barriers to durable, energy-free icing mitigation. The review concludes with priorities for eco-friendly chemistries, self-healing or renewable layers, standardized testing/reporting, and data-driven (machine learning-assisted) optimization to accelerate translation into durable passive ice-mitigation technologies.

Water droplet erosion (WDE) is a critical degradation phenomenon that significantly affects component lifespan and performance in power generation, aerospace, and wind energy industries. The incubation period—the initial phase before visible material loss occurs—is particularly crucial for maintenance planning and material selection yet remains challenging to predict accurately due to the complex interplay of material properties and impact conditions. Traditional empirical models have shown limited predictive capability due to their reliance on numerous adjustable parameters with insufficient physical interpretation. This study aimed to develop and validate a machine learning (ML) approach for accurately predicting the WDE incubation period across different metallic materials and impact conditions. The performance of various ML algorithms is evaluated while investigating the effect of data transformation techniques on prediction accuracy. A range of ML models—linear regression (LR), decision tree regressor (DT), random forest regressor (RF), gradient boosting regressor (GBR), and artificial neural networks (ANN)—were trained and validated using experimental data from five different alloys under various impact conditions. Data transformation methods significantly enhanced model performance, with the LR model using Box-Cox transformation achieving the highest accuracy (R2 > 90

The critical role of high-speed water droplet impacts spans a broad range of natural and industrial applications, particularly in water droplet erosion management in steam and wind turbine blades, pipes, and aircraft wings. Understanding erosion dynamics is vital for ensuring structural integrity and operational efficiency. This paper presents a numerical investigation into high-speed droplet impacts under realistic conditions, considering factors such as air velocity and the presence of gas cavities within the droplet. The study employs a compressible volume of fluid method to accurately model droplet deformation and the resulting pressure forces. The impact modeling of compressible liquid droplets, impinged at speeds up to 150 m/s, is performed. Our simulations reveal distinct behaviors between impact with and without co-flow (stagnation flow). In co-flow conditions, additional pressure peaks emerge, reaching approximately half the magnitude of the primary peak. Furthermore, internal cavities within the droplet induce secondary pressure peaks that surpass the initial impact pressure—an effect not observed in dense droplet impacts. This newly uncovered pressure peak is expected to play a crucial role in understanding water erosion mechanisms. Additionally, the paper investigates the effects of the cavity's position, size, and number on impact pressure variations. Numerical results show that the presence of a secondary gaseous bubble increased the maximum pressure by nearly one-third under the same impingement conditions. The insight gained from this research could contribute to a deeper understanding and more effective mitigation strategies for water droplet erosion under realistic impact scenarios.

Ice accretion (icing) on aircraft surfaces is a significant safety risk through airfoil shape modification and reduction in aerodynamic efficiency. This process occurs when an aircraft flies through clouds of supercooled water droplets that freeze upon impact on exposed surfaces. To counter this hazard, electro-thermal de-icing systems integrate heaters in critical regions to melt ice and reduce performance losses. In this study, a multiphysics computational model is used to simulate ice accretion and electro-thermal de-icing on a NACA-0012 airfoil, accounting for factors such as airflow, droplet impingement, phase changes, and heat conduction. The model’s predictions are validated against experimental data, confirming its accuracy. A cyclic electro-thermal ice protection system (ETIPS) is then tested under both standard and severe supercooled large droplet (SLD) conditions, examining how droplet size and angle of attack affect de-icing performance. Simulations without an active de-icing system show severe aerodynamic degradation, including an 11.1% loss of lift and a 48.2% increase in drag at a 12∘ angle of attack. For large droplets (median 200 μm), the drag coefficient increases by 36.5%. Under harsh icing conditions, the effectiveness of the de-icing system is found to depend on droplet size, angle of attack, and heater placement. Even with continuous heater operation, ice continues to accumulate on the leading edge at higher angles of attack. While the ETIPS performs effectively against large droplets in heated zones, unheated regions experience significant ice buildup (especially with 200 μm droplets). This indicates that additional or extended heaters may be necessary to ensure complete protection in extreme conditions.

Ice accretion on aircraft surfaces poses a serious safety concern, as it can alter airfoil geometry, degrade aerodynamic performance, and potentially lead to accidents. This phenomenon occurs when supercooled water droplets freeze upon impact with the aircraft, particularly during flight through droplet-laden clouds. The severity of ice accumulation is influenced by several factors, including airspeed, ambient temperature, droplet size, liquid water content, angle of attack, and exposure duration. Electro-thermal de-icing systems, which use embedded heaters beneath the wing surface to melt ice and prevent its formation, are widely utilized to mitigate these risks. In recent years, numerical simulations have gained increasing attention as an efficient and cost-effective alternative to wind tunnel and flight tests. In this study, a computational framework is employed to model ice accretion and de-icing on a NACA-0012 airfoil. The simulation captures the coupled effects of airflow, droplet impingement, freezing, and heat transfer through the airfoil surface using a conjugate heat transfer approach. The predicted heater temperature variations over time exhibit good agreement with experimental data, validating the accuracy of the de-icing model. In addition, the study analyzes the ice accretion profile and aerodynamic performance degradation in the absence of electro-thermal de-icing, as well as surface temperature, water film thickness, and ice layer evolution during de-icing. Finally, the effect of icing under harsh conditions has been tested by incorporating large supercooled droplets impact and freezing. This led to increased ice accumulation in regions between the leading edge and unprotected areas, which can be mitigated by increasing heater power or extending the heating duration. The present work contributes to improving the accuracy of in-flight icing simulations and optimizing de-icing strategies.

This study sheds light on the complex dynamics of hollow droplet impacts and highlights the unique behaviors that differentiate them from their dense counterparts. The impact dynamics of hollow droplets on surfaces at varying angles were investigated through a combination of experimental and numerical methods. Two-view imaging technique is used to capture the droplet flattening during the experimental study. A three-dimensional compressible solver is developed to model the droplet impact using the volume of fluid method to capture the liquid and gas interface. The study revealed two distinct behaviors when comparing the flattening of hollow droplets to that of dense droplets. First, a unique counter-jet formation was observed following the collision of a hollow droplet perpendicular to the surface, indicating an inherent characteristic of hollow droplet flattening. The length of this counter-jet was primarily influenced by the droplet velocity and liquid viscosity, with the perpendicular velocity component playing a key role in its size. Second, unlike dense droplets that recoil and form a dome shape upon impact on hydrophobic surfaces, hollow droplets form a donut shape due to disturbances caused by bubble rupture during spreading. These disturbances fragmented the liquid sheet, preventing the droplet from recoiling and resulting in a distinctive donut shape. On surfaces with different orientations, the hollow droplet exhibited two velocity components, where the normal component controls the counter-jet size while the tangential component induces tangential motion. The donut shape splat was also observed on surfaces with different orientations.

In this paper is proposed an analytical technique which can be used to obtain the forced response of a cantilevered tube conveying fluid. Considering the pipe subjected to an arbitrary harmonic force either distributed or concentrated, an analytical solution is found using Green’s function method. The solution obtained of the closed form satisfies the differential equations in a classical sense. The presented method giving the exact solutions is more precise than the classical eigenfunction expansion or Galerkin’s method with no need to the eigenfunctions or eigenvalues.

In this paper, an analytical technique is proposed to obtain the forced response of a cantilevered tube conveying fluid. By considering the pipe subjected to an arbitrary harmonic force, either distributed or concentrated, an analytical solution is found using Green’s function method. The closed-form solution obtained satisfies the differential equations governing the vibrating tube conveying fluid. The proposed method, which provides exact solutions, is more accurate than the classical eigenfunction expansion or Galerkin’s method and eliminates the need for eigenfunctions, eigenvalues, or infinite series.

Summary A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, different machine learning (ML) models were developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. These ML algorithms include gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN) with a main focus on and comparison of the two latter algorithms (XGB and DNN). Based on existing mathematical models, a leading-edge framework was developed where a large database of Kr and capillary pressure (Pc) curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were used to estimate analytical Kr curves based on Darcy’s law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the ML model. The entire data set was split into 80% for training and 20% for testing. The k-fold cross-validation technique was applied to increase the model’s accuracy by splitting 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, nine folds were used for training and the remaining fold was used for model validation. Once the model was trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The ML model learns to capture fluid flow behavior inside the core from the training data set. In terms of applicability of these ML models, two sets of experimental data were needed as input; the first was the analytically estimated Kr curves from the steady-state drainage coreflooding experiments, while the other was the Pc curves estimated from centrifuge or mercury injection capillary pressure (MICP) measurements. The trained/tested model was then able to estimate Kr curves based on the experimental results fed as input. Furthermore, to test the performance of the ML model when only one set of experimental data is available to an end user, a recurrent neural network (RNN) algorithm was trained/tested to predict Kr curves in the absence of Pc curves as an input. The performance of the three developed models (XGB, DNN, and RNN) was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective crossplots along with comparisons of ground truth vs. artificial intelligence (AI)-predicted curves indicated that the model is capable of making accurate predictions with an error percentage between 0.2% and 0.6% on history-matching experimental data for all three tested ML techniques. This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The developed ML models by no means replace the need to conduct drainage coreflooding or centrifuge experiments but act as an alternative to existing commercial platforms that are used to interpret experimental data to predict Kr curves. The two main advantages of the developed ML models are their capability of predicting Kr curves within a matter of a few minutes as well as with limited intervention from the end user. The results also include a comparison between classical ML approaches, shallow neural networks, and DNNs in terms of accuracy in predicting the final Kr curves. The research presented here is an extension of the state-of-the-art framework proposed by Mathew et al. (2021). However, the two main aspects of the current study are the application of deep learning for the prediction of Kr curves and the application of feature engineering. The latter not only reduces the training/testing time for the ML models but also enables the end user to obtain the final predictions with the least set of experimental data. The various models discussed in this research work currently focus on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

The Solution Precursors Plasma Spray (SPPS) is an emerging thermal spray process that utilizes a solution as a liquid feedstock suitable for the deposition of sub-micron-sized particles for applications in thermal barrier coatings and super-icephobic coatings.During the SPPS process, the droplet undergoes several thermo-physical stages, including an aerodynamic breakup, solvent vaporization, and precipitation of the dissolved solute to form a particle.Several parameters such as droplet size, solute concentration, thermophysical characteristics of the precursor, velocity, and temperature field of plasma affect the final morphology of the particle forming the coatings.In this study, droplets are composed of zirconium acetate as the solute dissolved in a mixture of water and ethanol.To address the challenging problem of particle morphologies by SPPS, the present work develops a numerical approach to model solvent evaporation and shell formation based on coupled heat and mass transfer equations within a single droplet in a plasma field.Subsequently, the calculated shell thickness is validated against a carefully designed experiment in a radio frequency plasma reactor using a droplet generator.Additionally, the effects of different heating rates, droplet size, and residence time on particle morphology are investigated, paving the way for a better understanding of SPPS.

Characterizing thermally sprayed coatings remains challenging due to the interplay between different operating and process parameters. Currently, no general framework exists for accurately predicting the coating characteristics under specific operating conditions. In this paper, artificial intelligence models were employed to investigate a case study of generating superhydrophobic coatings by suspension plasma spray (SPS), an emerging thermal spray process that can produce coatings with micro and nano-scale features. The approach aimed to relate key thermal spray process parameters such as plasma torch nozzle diameter, plasma power, standoff distance, grit-blast effect, and suspension solvent type to different coating characteristics such as water contact angle, sliding angle, and surface roughness. Machine learning (ML) algorithms of both tree-based (ranging from linear regression and random forest to improved gradient boost) and deep-neural network models were investigated using a recent dataset of SPS experiments. Following the training of the ML models, selected algorithms were tested on unseen SPS data points at different operating conditions. The ML models were able to predict the sliding angles with good accuracy of over 80% based on a limited dataset. Finally, a state-of-the-art generative adversarial network (GAN) was employed to generate realistic scanning electron microscope (SEM) images of SPS coatings with specific sliding angles. These GAN-generated SEM images were qualitatively and visually satisfactory, paving the way for a machine-learning approach to controlling thermally sprayed coating microstructures.

Evaluation of petrophysical properties such as porosity, permeability, and irreducible water saturation is crucial for reservoir characterization to determine the hydrocarbon initially in place and further optimize hydrocarbon production. However, estimation of these parameters is challenging for carbonate rocks due to their heterogeneity. One of the ways to determine petrophysical properties is the use of nuclear magnetic resonance (NMR), which involves applying a magnetic field to the formation and detecting signals emitted from pore spaces. The main objective of this study is to develop an empirical correlation for porosity, permeability, and irreducible water saturation by comparing NMR and laboratory measurements for carbonate rocks in the Middle East. Furthermore, machine learning (ML) approach was applied to predict these petrophysical parameters utilizing NMR data. Different ML algorithms such as tree-based and neural networks were trained to estimate these petrophysical properties of carbonate rocks. The obtained results from ML algorithms were further compared with core measurements to ensure their accuracy. The results showed that the use of T2 spectrum as an input provided more accurate results than NMR features. It can be proven by observing the performance of deep neural networks algorithm, where the models showed R2 values of 0.87 and 0.74 for porosity prediction using T2 and features extraction approaches, respectively. The same behavior was followed for the permeability estimations as deep neural networks model scored R2 = 0.81 (T2 approach) and R2 = 0.74 (features extraction approach). Similarly, determination of irreducible water saturation was more accurate using T2 approach (R2 = 0.87), whereas features extraction technique also exhibited a decent performance (R2 = 0.71). Also, T2 approach is more convenient since it is more straightforward to generate T2 spectrum from NMR measurements and use it for the ML models. Furthermore, based on the machine learning approach, gradient boosting and deep neural networks models performed with higher accuracy than other algorithms. This can be attributed to their strong configuration, which is able to find patterns between input and output parameters. This study provides more insight into petrophysical properties determined from NMR measurements in carbonates using ML techniques. This is useful in better characterizing carbonate reservoirs in the Middle East through accurate estimations of hydrocarbon resources and related reserves.

Drop impact on a dry substrate is ubiquitous in nature and industrial processes, including aircraft de-icing, ink-jet printing, microfluidics, and additive manufacturing. While the maximum spreading factor is crucial for controlling the efficiency of the majority of these processes, there is currently no comprehensive approach for predicting its value. In contrast to the traditional approach based on scaling laws and/or analytical models, this paper proposes a data-driven approach for estimating the maximum spreading factor using supervised machine learning (ML) algorithms such as linear regression, decision tree, random forest, and gradient boosting. For this purpose, a dataset of hundreds of experimental results from the literature and our own—spanning the last thirty years—is collected and analyzed. The dataset was divided into training and testing sets, each representing 70% and 30% of the input data, respectively. Subsequently, machine learning techniques were applied to relate the maximum spreading factor to relevant features such as flow controlling dimensionless numbers and substrate wettability. In the current study, the gradient boosting regression model, capable of handling structured high-dimensional data, is found to be the best-performing model, with an R2-score of more than 95%. Finally, the ML predictions agree well with the experimental data and are valid across a wide range of impact conditions. This work could pave the way for the development of a universal model for controlling droplet impact, enabling the optimization of a wide variety of industrial applications.

Increasing global oil demand, combined with limited new discoveries, compels oil companies to maximize the value of existing resources by employing enhanced oil recovery (EOR) techniques aimed at the remaining oil. Estimating residual oil saturation (Sor) in the reservoir after conventional recovery techniques, such as waterflooding is critical in screening the suitable EOR technique and in further field development and production prediction. The objective of this work is to provide an artificial intelligence (AI) workflow to assess Sor of carbonate rocks, which will aid in the development of a long-term strategy for efficient production in this fourth industrial age. In the present work, two-phase lattice Boltzmann method (LBM) simulation was used with the benefit of high parallelization schemes. After applying the CPU-based solver using LBM on thousands of carbonate rock digital images, an AI-based workflow was developed to estimate Sor. Different advanced tree-based regression models were tested. Relevant input features were extracted from complex carbonate micro-CT images including porosity, absolute permeability, pore size and pore-throat size distributions, as well as rock surface roughness distribution. These features were fed into the learning models as inputs; while the output used to train and test the models is based on the direct simulation results of Sor from the image dataset. The results showed that extracting the engineered features from images aided in building a physics-informed machine learning model (ML) capable of accurately predicting Sor of carbonate rocks from their dry images. Three ML models were trained and tested on more than 1000 data points, namely gradient boosting, random forest, and xgradient boosting. Even with such small number of data points, the three models yielded promising results. Gradient boosting algorithm showed the highest predictive capability among the three techniques, with an R2 of 0.71. Increasing the number of data points is expected to help the models capture wider ranges of rock properties, and consequently, result in an increase in the prediction capability of the models. To the best of our knowledge, this is the first study that leverages machine learning to estimating residual oil saturation in complex carbonate. This work will contribute to the development of a novel framework for estimating accurately and reliably residual oil saturation of heterogeneous rocks. As a result, this research will aid in providing decision-makers with a simple tool for screening the most suitable EOR technique for optimal asset use.

Estimation of petrophysical properties is essential for accurate reservoir predictions. In recent years, extensive work has been dedicated into training different machine-learning (ML) models to predict petrophysical properties of digital rock using dry rock images along with data from single-phase direct simulations, such as lattice Boltzmann method (LBM) and finite volume method (FVM). The objective of this paper is to present a comprehensive literature review on petrophysical properties estimation from dry rock images using different ML workflows and direct simulation methods. The review provides detailed comparison between different ML algorithms that have been used in the literature to estimate porosity, permeability, tortuosity, and effective diffusivity. In this paper, various ML workflows from the literature are screened and compared in terms of the training data set, the testing data set, the extracted features, the algorithms employed as well as their accuracy. A thorough description of the most commonly used algorithms is also provided to better understand the functionality of these algorithms to encode the relationship between the rock images and their respective petrophysical properties. The review of various ML workflows for estimating rock petrophysical properties from dry images shows that models trained using features extracted from the image (physics-informed models) outperformed models trained on the dry images directly. In addition, certain tree-based ML algorithms, such as random forest, gradient boosting, and extreme gradient boosting can produce accurate predictions that are comparable to deep learning algorithms such as deep neural networks (DNNs) and convolutional neural networks (CNNs). To the best of our knowledge, this is the first work dedicated to exploring and comparing between different ML frameworks that have recently been used to accurately and efficiently estimate rock petrophysical properties from images. This work will enable other researchers to have a broad understanding about the topic and help in developing new ML workflows or further modifying exiting ones in order to improve the characterization of rock properties. Also, this comparison represents a guide to understand the performance and applicability of different ML algorithms. Moreover, the review helps the researchers in this area to cope with digital innovations in porous media characterization in this fourth industrial age – oil and gas 4.0.

Characterizing heterogeneity is crucial to assess the variability of rock properties in carbonate reservoir samples. This work introduces an original multiscale approach to simulate permeability and porosity in heterogeneous carbonate samples using 3D X-ray computed tomography images. The main novelty of our approach is to introduce a quantitative heterogeneity description in terms of texture classification using machine learning. The rock texture classification result is then used to upscale rock properties simulations from fine to coarse scale. The fine scale properties are investigated based lattice Boltzmann method, while a Darcy-scale flow simulator is adopted for estimating coarse scale properties. In addition, due to the critical role played by petrophysical properties at fine scale, a 3D printing technique is employed to validate experimentally the numerical simulations at this scale. Finally, we present an application of our proposed approach on a real carbonate sample from the Middle East carbonate oilfield reservoir.

Accurate estimation of permeability is critical for oil and gas reservoir development and management, as it controls production rate. After assessing numerical techniques ranging from pore network modeling (PNM) to the lattice Boltzmann method (LBM), an AI-based workflow is developed for a quick and accurate estimation of the permeability of a complex carbonate rock from its X-ray micro-computed tomography (micro-CT) image. Following features engineering using both image processing and PNM, we trained and tested the workflow on thousands of segmented 3D micro-CT images using both shallow and deep learning algorithms to assess the permeability. A broad variety of supervised learning algorithms are implemented and tested, including linear regression, support vector regression, improved gradient boosting, and convolutional neural networks. Additionally, we explored a hybrid physics-driven neural network that takes into account both the X-ray micro-CT images and petrophysical properties. Finally, we found that the predicted permeability of a complex carbonate by machine learning (ML) agrees very well with that of a more computationally-intensive voxel-based direct simulation. In addition, the ML model developed here provides a substantial reduction in computation time by roughly three orders of magnitude compared to that of the LBM. This paper highlights the crucial role played by features engineering in predicting petrophysical properties by machine and deep learning. The proposed framework, integrating diverse learning algorithms, rock imaging, and modeling, has the potential to quickly and accurately estimate petrophysical properties to aid in reservoir simulation and characterization.

Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.