Commercial Simulation Research Articles

Realistic 3D Simulators for Automotive: A Review of Main Applications and Features.

Recent advancements in vehicle technology have stimulated innovation across the automotive sector, from Advanced Driver Assistance Systems (ADAS) to autonomous driving and motorsport applications. Modern vehicles, equipped with sensors for perception, localization, navigation, and actuators for autonomous driving, generate vast amounts of data used for training and evaluating autonomous systems. Real-world testing is essential for validation but is complex, expensive, and time-intensive, requiring multiple vehicles and reference systems. To address these challenges, computer graphics-based simulators offer a compelling solution by providing high-fidelity 3D environments to simulate vehicles and road users. These simulators are crucial for developing, validating, and testing ADAS, autonomous driving systems, and cooperative driving systems, and enhancing vehicle performance and driver training in motorsport. This paper reviews computer graphics-based simulators tailored for automotive applications. It begins with an overview of their applications and analyzes their key features. Additionally, this paper compares five open-source (CARLA, AirSim, LGSVL, AWSIM, and DeepDrive) and ten commercial simulators. Our findings indicate that open-source simulators are best for the research community, offering realistic 3D environments, multiple sensor support, APIs, co-simulation, and community support. Conversely, commercial simulators, while less extensible, provide a broader set of features and solutions.

A Comparative Study of Data-Driven Prognostic Approaches under Training Data Deficiency

In industrial system health management, prognostics play a crucial role in ensuring safety and enhancing system availability. While the data-driven approach is the most common for this purpose, they often face challenges due to insufficient training data. This study delves into the prognostic capabilities of four methods under the conditions of limited training datasets. The methods evaluated include two neural network-based approaches, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks, and two similarity-based methods, Trajectory Similarity-Based Prediction (TSBP) and Data Augmentation Prognostics (DAPROG), with the last being a novel contribution from the authors. The performance of these algorithms is compared using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets, which are made by simulation of turbofan engine performance degradation. To simulate real-world scenarios of data deficiency, a small fraction of the training datasets from the original dataset is chosen at random for the training, and a comprehensive assessment is conducted for each method in terms of remaining useful life prediction. The results of our study indicate that, while the Convolutional Neural Network (CNN) model generally outperforms others in terms of overall accuracy, Data Augmentation Prognostics (DAPROG) shows comparable performance in the small training dataset, being particularly effective within the range of 10% to 30%. Data Augmentation Prognostics (DAPROG) also exhibits lower variance in its predictions, suggesting a more consistent performance. This is worth highlighting, given the typical challenges associated with artificial neural network methods, such as inherent randomness, non-intuitive decision-making processes, and the complexities involved in developing optimal models.

Ray-Tracing-Assisted SAR Image Simulation under Range Doppler Imaging Geometry

In order to achieve an effective balance between SAR image simulation fidelity and efficiency, we proposed a ray-tracing-assisted SAR image simulation method under range doppler (RD) imaging geometry. This method utilizes the spatial traversal mode of RD imaging geometry to transmit discrete electromagnetic (EM) waves into the SAR radiation area and follows the Nyquist sampling law to set the density of transmitted EM waves to effectively identify the beam radiation area. The ray-tracing algorithm is used to obtain the backscatter amplitude and real-time slant range of the transmitted EM wave, which can effectively record the multiple backscattering among the components of the distributed target so that the backscattering subfields of each component can be correlated. According to the RD condition equation, the backscattering amplitude is assigned to the corresponding range gate, and the three-dimensional (3D) target is mapped into the two-dimensional (2D) SAR slant-range coordinate system, and the SAR target simulated image is directly obtained. Finally, the simulation images of the proposed method are compared qualitatively and quantitatively with those obtained by commercial simulation software, and the effectiveness of the proposed method is verified.

Inverse Problem of Permeability Field under Multi-Well Conditions Using TgCNN-Based Surrogate Model

Under the condition of multiple wells, the inverse problem of two-phase flow typically requires hundreds of forward runs of the simulator to achieve meaningful coverage, leading to a substantial computational workload in reservoir numerical simulations. To tackle this challenge, we propose an innovative approach leveraging a surrogate model named TgCNN (Theory-guided Convolutional Neural Network). This method integrates deep learning with computational fluid dynamics simulations to predict the behavior of two-phase flow. The model is not solely data-driven but also incorporates scientific theory. It comprises a coupled permeability module, a pressure module, and a water saturation module. The accuracy of the surrogate model was comprehensively tested from multiple perspectives in this study. Subsequently, efforts were made to address the permeability-field inverse problem under multi-well conditions by combining the surrogate model with the Ensemble Random Maximum Likelihood (EnRML) algorithm. The research findings indicate that modifying the network structure allows for improved integration of the outputs, resulting in prediction accuracy and computational efficiency. The TgCNN surrogate model demonstrated outstanding predictive performance and computational efficiency in two-phase flow. By combining the surrogate model with the EnRML algorithm, the inversion results closely aligned with those from the commercial simulation software, significantly improving the computational efficiency.

Prediction of Remaining Useful Life of Aero-engines Based on CNN-LSTM-Attention

Accurately predicting the remaining useful life (RUL) of aircraft engines is crucial for maintaining financial stability and aviation safety. To further enhance the prediction accuracy of aircraft engine RUL, a deep learning-based RUL prediction method is proposed. This method possesses the potential to strengthen the recognition of data features, thereby improving the prediction accuracy of the model. First, the input features are normalized and the CMAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset is utilized to calculate the RUL for aircraft engines. After extracting attributes from the input data using a convolutional neural network (CNN), the extracted data are input into a long short-term memory (LSTM) network model, with the addition of attention mechanisms to predict the RUL of aircraft engines. Finally, the proposed aircraft engine model is evaluated and compared through ablation studies and comparative model experiments. The results indicate that the CNN-LSTM-Attention model exhibits superior prediction performance for datasets FD001, FD002, FD003, and FD004, with RMSEs of 15.977, 14.452, 13.907, and 16.637, respectively. Compared with CNN, LSTM, and CNN-LSTM models, the CNN-LSTM model demonstrates better prediction performance across datasets. In comparison with other models, this model achieves the highest prediction accuracy on the CMAPSS dataset, showcasing strong reliability and accuracy.

DIGITAL SOLUTION FOR OPTIMIZATION OF FORMATION TESTING DESIGN

Evaluation of pumping time to receive a clean sample during fluid sampling job with wireline formation tester as well as evaluation of pumping scenarios to minimize sampling duration under existing constrains (reservoir and sandface constrains, safety etc.) are actual problems especially for fluid sampling in low-permeability formation. The article describes the digital solution allowing to simulate mud filtrate invasion in near-wellbore zone during well drilling and mud filtrate displacement by reservoir fluid during fluid sampling job with taking into account a formation damage dynamics. In this way, the software is able to evaluate depth of mud filtrate invasion and simulate both the multiphase inflow dynamics and the skin-factor dynamics during sampling job and predict sampling duration. To take into account a complex, essentially non-1D geometry of multiphase fluid flow in near-wellbore zone during fluid sampling, the special upscaling technique was developed. This upscaling technique is based on modification of 1D model by introduction of «effective» mud filtrate invasion profile and «geometry factor» which depend on ratio of vertical to horizontal permeabilities. The correlations between empirical model parameters and ratio of vertical to horizontal permeabilities were obtained by matching results of «pseudo 2D» simulations of pumping dynamics with results of 2D / 3D simulations conducted using commercial simulators ECLIPSE® and COMSOL Multiphysics®. The developed «pseudo 2D» model is computationally fast ones that allows to effectively conduct sensitivity study as well as adjust the model parameters and sampling scenario in real time in accordance with the real-time wireline formation tester measurements.

A fast and reliable semi-analytical method for assessing energy replenishment from fracturing-flooding in low-permeability and tight oil reservoirs

The development of low-permeability and tight oil reservoirs is challenged by insufficient natural energy and rapid production decline. Fracturing-flooding is a technique that relies on high-pressure and large-volume fluid injection to replenish reservoir energy, making it a significant method for rapidly boosting formation energy. To evaluate the energy replenishment effect of fracturing-flooding technology in low-permeability and tight reservoirs, this study proposes a semi-analytical method for quick calculation. This approach employs dimensionless simplification, Pedrosa's substitution, Laplace transformation, and Stehfest inversion methods to derive pressure solutions for both the stimulation region and the external matrix region, each with varying flow capacities. The average formation pressure (AFP) of the reservoir is determined using the area-weighted average method, and numerical verification is performed using a commercial simulator. A case study from the Binnan area, along with a sensitivity analysis, demonstrates that after 30 days of fracturing-flooding, the AFP of the reservoir increases to 46.97 MPa, the corresponding reservoir pressure coefficient rises from 1.2 to 1.68, and reservoir energy increases by 40%. The factors influencing energy replenishment are ranked as follows: reservoir thickness, injection rate, stress sensitivity coefficient, matrix permeability, stimulation region radius, and mobility ratio. This study provides theoretical guidance for optimizing fracturing-flooding development schemes in low-permeability and tight oil reservoirs and offers valuable reference for the industry.

Development of coupled fluid-flow/geomechanics model considering storage and transport mechanism in shale gas reservoirs with complex fracture morphology

Field observations frequently demonstrate stress fluctuations resulting from the reservoir depletion. The development of reservoirs, particularly the completion of infill wells and refracturing, can be significantly impacted by stress changes in and around drainage areas. Previous studies mainly focus on plane fractures and few studies consider the influence of complex transport and storage mechanism and irregular fracture geometry on stress evolution in shale gas reservoirs. Based on the embedded discrete fracture model (EDFM) and finite-volume method (FVM), a coupled geomechanics/fluid model has been successfully developed considering the adsorption, desorption, diffusion and slippage of shale gas. This model achieves coupling simulation of natural fractures, hydraulic fractures with complex geometry, storage and transport mechanism, reservoir stress, and pore-elastic effect. The open-source software OpenFOAM is used as the main solver for this model. The stress calculation and productivity simulation of the model are verified by the classical poroelasticity problem and the simulation results of published research and commercial simulator with EDFM respectively. The simulation results indicate that σxx, σyy, σxy and Δσ changes with time and space due to the time effect and anisotropy of formation pressure depletion; Due to the influence of different mechanisms on shale gas storage and transport, the reservoir pressure and stress distribution under different mechanisms are different; Among them, the stress with full mechanisms differs the most compared to the stress without any mechanism. The reservoir with stronger stress sensitivity (smaller Biot coefficient) is less sensitive to formation pressure depletion, and the stress variation range is smaller. For reservoirs with weak stress sensitivity, formation pressure depletion is more likely to lead to stress reversal. Under the influence of fracture geometry, the pressure depletion regions caused by the three types of fracture geometry are approximately rectangular, parallelogram and square, respectively. The corresponding σxx, σyy and Δσ also have great differences in spatial distribution and values. Therefore, the time effect, shale gas storage and transport mechanism and the influence of complex fracture geometry should be considered when predicting pressure depletion induced stress under the condition of simultaneous production. This study is of great significance for understanding the evolution law of stress induced by pressure consumption, as well as the design of infill wells and repeated fracturing.

Iterative Placement of Decoupling Capacitors using Optimization Algorithms and Machine Learning

Abstract. In this work, an approach for optimum placement of on-board decoupling capacitors (decaps) is presented, which aims at reducing transient noise in power delivery networks (PDNs). This approach is based on a genetic algorithm (GA) and accelerated by the use of an artificial neural network (ANN) as surrogate model to efficiently determine the fitness of a decap design, i.e., of a particular choice for the position and the type of the decaps to integrate in the printed circuit board (PCB). The ANN is trained by a suitable set of reference designs labeled by the impedance at the power pin of the integrated circuit (IC), which is computed by commercial simulation software. Several iterative runs of the GA are performed with an increasing number of decaps to identify a design with the least number of decaps necessary to reduce the distance of the frequency domain input impedance of the considered point-to-point connection from a desired target impedance as far as possible. This approach is successfully applied to generate an optimum decap design for a PDN with 52 possible decap positions and decaps chosen from three types.

Enhancing Highway Driving: High Automated Vehicle Decision Making in a Complex Multi-Body Simulation Environment

Automated driving is a promising development in reducing driving accidents and improving the efficiency of driving. This study focuses on developing a decision-making strategy for autonomous vehicles, specifically addressing maneuvers such as lane change, double lane change, and lane keeping on highways, using deep reinforcement learning (DRL). To achieve this, a highway driving environment in the commercial multi-body simulation software IPG Carmaker 11 version is established, wherein the ego vehicle navigates through surrounding vehicles safely and efficiently. A hierarchical control framework is introduced to manage these vehicles, with upper-level control handling driving decisions. The DDPG (deep deterministic policy gradient) algorithm, a specific DRL method, is employed to formulate the highway decision-making strategy, simulated in MATLAB software. Also, the computational procedures of both DDPG and deep Q-network algorithms are outlined and compared. A set of simulation tests is carried out to evaluate the effectiveness of the suggested decision-making policy. The research underscores the advantages of the proposed framework concerning its convergence rate and control performance. The results demonstrate that the DDPG-based overtaking strategy enables efficient and safe completion of highway driving tasks.

A multi-channel fusion variational autoencoder-based RUL prediction approach for multi-sensor systems

Abstract Deep learning (DL)-based approaches have demonstrated remarkable performance in predicting the remaining useful life (RUL) of complex systems, which is beneficial for making timely maintenance decisions. However, the majority of these DL methods suffer from a lack of interpretability, and it is difficult to mine the degradation features in the presence of significant measurement noises. To remedy the deficiency, a multi-channel fusion variational autoencoder (MCFVAE)-based approach is proposed. A feature fusion module is designed to capture and fuse the multi-channel features, which facilitates the disclosure of the degradation information from the multi-sensor data. A variational inference module is further introduced to generate the compressive representations and project them into a latent space as an interpretable component, which can display the degradation degree of the multi-sensor systems. A regressor module is finally utilized to establish the relationship between the compressive representations and the RUL. The superior feature fusion and distribution characteristics learning abilities of the MCFVAE contribute to achieving robust and interpretable RUL prediction. The effectiveness and superiority of the proposed method are experimentally validated through a publicly available Commercial modular aero propulsion system simulation dataset and compared with the existing methods.

Dual-band dual-truncated R-slot loaded monopole patch antenna using a single-layer bandstop FSS reflector for gain enhancement

New reflector structure (RS) based on ultra-wide stopband single-layer frequency selective structure (FSS) for mid-band sub-6 GHz 5G applications is designed. The proposed RS is arranged into array of 7 × 7-unit cell of FSS elements for antenna gain and front-to-back ratio enhancement. The single antenna element of volume 30 × 20 × 1.6 (in mm3) consists of a dual-quarter-circular truncated edge-fed rectangular slot (R-slot) loaded rectangular patch (R-patch) antenna with degraded ground structure (DGS) to operate at 3.6 GHz mid-band sub-6 GHz 5G and 5.8 GHz WBAN band. The proposed unit cell FSS element, the RS of volume 55.5 × 55.5 × 13.2 (in mm3) and the associated antenna are designed on lossy thin FR-4 substrate material and investigated numerically using commercial computer simulation technology microwave studio (CST-MS) software. The studied dual-structure based RS made with array of single-layer FSS and monopole R-patch antenna is fabricated and measured. The measured impedance bandwidth (IBW) of 48.28% (3.06–4.85 GHz) and 86.20% (5–10 GHz), peak gain of 5.36 and 7.45 dBi, radiation efficiency of 67.11% and 80.16%, and front-to-back ratio of 12.23 dB and 22.84 dB are achieved at the resonant frequencies 3.5 and 5.8 GHz respectively. The FSS reflector associated with the dual-band monopole R-patch antenna performances are in good agreement with the portable wireless applications requirements.

Rapid well-test analysis based on Gaussian pressure-transients

Reservoir performance forecasting technology commonly involves flow testing and fluid sampling in appraisal wells. This study introduces a new Gaussian Pressure Transient (GPT) method for rapidly evaluating well-test data. The GPT method provides an alternative for the current rate transient analysis (RTA) methodology, which relies on specialized knowledge and involves subjective steps of tangent line-fitting based on flow regime assumptions. The newly proposed method was validated with field well-test data and is different from the traditional PTA method in that it directly fits Gaussian well performance curves to well-test data. The GPT method can be used (1) to determine the uncertain reservoir parameters, and (2) predict future well productivity under production conditions. Well-test data from an offshore condensate discovery with low well deliverability are analyzed. The test data encompass five individual tests, all revealing low gas and condensate flow rates based on GPT matches. The method involves estimations of the hydraulic diffusivity and can generate both short-term and long-term well-performance forecasts. A sensitivity analysis explores the impact of the various parameters on the recoverable resource volume, identifying which data acquisition would be needed to improve the production forecast's accuracy. Validation with field tests and commercial simulator outputs demonstrates the model's robustness in formation evaluation and predicting well performance. The study recommends hydraulic fracturing for future field development, and includes simulation results valuable for reservoir management and decision-making.

Explor-A-Thora: A Novel Three-Dimensionally Printed Pleural Simulator.

For procedural education, the shift from the traditional apprenticeship model to simulation-based mastery has become increasingly accepted as the gold standard and has underscored the importance of high-fidelity, cost-effective training options. However, cost-effective pleural procedure simulators providing both realistic haptic feedback and ultrasound compatibility are lacking. We aimed to create a pleural procedure simulator with characteristics of human tissue, at low cost and with ultrasound compatibility. This work used design-based research principles and a collaborative rapid iteration approach in collaboration with the University of California, San Francisco, Makers Lab and design-based researchers at the University of California, Berkeley, which led to the creation of a three-dimensionally printed pleural procedure simulator. The needs assessment indicated significant discomfort with pleural procedures and a request for more accessible simulation opportunities. Iterative prototyping resulted in a three-dimensionally printed rib cage and a series of innovations in the fluid pocket and skin layers to provide realistic tactile feedback and ultrasound imaging compatibility. The final model costs significantly less than commercial simulators, with durable components and replaceable parts that can be reused multiple times. The development of a low-cost, high-fidelity pleural procedure simulator addresses the current limitations of commercially available pleural simulators. By integrating three-dimensional printing technology and easily accessible materials, we were able to produce a simulator that closely replicates the feel of human tissue, allows ultrasound use, and is adaptable for different patient anatomies and clinical scenarios. This novel simulator is a scalable solution to elevate the standard of procedural education and ultimately positively affect patient care.

Estimation of CO2 storage capacities in saline aquifers using material balance

Estimating CO2 storage capacities is crucial in developing carbon capture and storage projects. Material balance equation (MBE) methods, widely employed for oil and gas reserve estimation, offer a direct approach to estimating CO2 storage capacities. However, previous MBE methods rely on an original fluid in-place volume calculated using volumetric methods to estimate CO2 storage capacities, lacking validation for accuracy. It is essential to accurately estimate the original fluid in-place volume, representing the pore volume, as it substantially influences CO2 storage capacity. This study presents a refined MBE method that ensures accurate estimates of CO2 storage capacities by validating the original fluid in-place volumes in saline aquifers. The accuracy of this method was evaluated by comparing it with a commercial reservoir simulator for a synthetic aquifer example and the Sleipner L9 model. In the synthetic aquifer example, the relative error in CO2 storage capacity estimation with the proposed MBE method was only 2.09%, even when short-term (1-year) injection data were utilized. The proposed MBE method demonstrates consistent accuracy in estimating CO2 storage capacities under different aquifer properties, operating conditions, and MBE-related conditions. The proposed MBE method also accurately estimated the CO2 storage capacity in the Sleipner L9 model, achieving a relative error of 3.47%.

Computational modelling and prediction of nonlinear deflection responses of bonded joint component with multiple damage (crack and delamination) – An experimental validation

The present research evaluates the presence of damage, including delamination and cracks, affects the deflection response of an adhesively bonded single lap joint (SLJ). In this study, the numerical analysis is performed for intact and influence of different damages in the adherend of SLJ in the commercial simulation software (ABAQUS). The circular delamination of radius ‘R’ 10 mm and crack having length ‘l’ 20 mm, the radius of curvature ‘Rc’ 0.2 mm are imposed into the laminate adherend using node-to-surface interaction technique. The stability of the simulation model is verified by comparing the deflection values from published results and experimental data. The confirmed simulation model is broadened to examine the impact of design factors (fiber orientation, loading position, delamination shape, crack position and orientation, and delamination position with and without crack) on the deflection response for various overlapping lengths (25, 30, 35, and 40 mm) for final design recommendation. Additionally, the sensitivity analysis was performed using a surrogate model (i.e., variance-based method) by constructing the second-order polynomial function to quantify the influence of damages under the input parameters. The detailed features and multivariate model functions have been analysed for the various damage cases of SLJ.

Simulation of Gas Sweetening Process using Extended NRTL and Stages Efficiency as Modeling Approach

Gas sweetening is a process to remove CO2 and H2S from natural gas. The current established technology is by using Amine contactor where the solvent used is in form of Amine solution. To simulate the effect of different solvent, electrolyte-NRTL is used to model the equilibrium, and mass transfer-kinetic is used to model the rate-based processes. This modeling approach is rather complex and available only in commercial and proprietary process simulation software. Therefore we propose an alternative modeling approach where we use extended NRTL and stage efficiency to model the acid gas absorption processes. We find that this approach is quite good to describe CO2 absorption, yet unsuccessful to calculate the H2S absorption. Inadequate vapor liquid equilibrium parameter regression for H2S, specifically at low partial pressure might cause the problem. However the stage efficiency approach shows good result where it is comparable to rate-based model and corresponds to current understanding of physico-chemical phenomenon of acid gas absorption.

Techno‐economic and carbon footprint analyses of steam Rankine cycle

AbstractSteam Rankine cycle (SRC), which is mainly utilised in power generation sector, faces external irreversibility in its daily operation causing inefficiency in the system. To address this issue, reheat Rankine cycle (RHRC) and regenerative Rankine cycle (RRC) have been widely studied and implemented in power plants to improve thermal efficiency and reduce external irreversibility of Rankine cycle. This study investigates the implementation of different RRC configurations in a combined heat and power plant, including RRC with modified thermal deaerator, RRC with open feed water heater (OFWH) and closed feed water heater (CFWH). A base case simulation model was first constructed using commercial simulation software Aspen HYSYS for the basic SRC system based on actual plant data. Various scenarios were then evaluated for their profitability and sustainability through techno‐economic analysis (TEA) and carbon footprint analysis (CFA). From both analyses, the scenario of RRC with CFWH showed the greatest long‐term potential, generating the highest annual profit of $ 771 691 and carbon footprint reduction of 14.63%, while RRC with modified thermal deaerator showed the greatest potential in the short run with the highest return of investment (ROI) of 201.51% and shortest payback period (PBP) of 0.50 year.

Multiphase flow simulation of fractured karst oil reservoirs applying three-dimensional network models

The characteristics of karst reservoirs are extremely varied and anisotropic, exhibiting notable differences in porosity, permeability, and corresponding fluid flow pathways. Fractured karst petroleum reservoirs, such as distinct caverns and fractures, are an example of a typical discrete media type. The traditional reservoir modeling approach and discrete fracture-like local refinement models are unsuitable for field application of fractured karst oil reservoirs due to the needs of high fidelity geological description and huge computing efforts. Based directly on the spatial characteristics of seismic surveys, a numerical simulation model in three dimensions, akin to a node-like network, is presented here for cracked karst oil reserves. First, the watershed image processing technique and the automatic connection identification procedure are used to extract the three-dimensional node-network model. After that, automatic differentiation is used to build the numerical finite volume scheme, and the proper gradient-based adjoint approach is used to conduct the related historical matching rapidly. After validation by a synthetic model in a commercial simulator, this proposed three-dimensional network numerical model is used for a field reservoir block of deep formation in the Tarim basin to demonstrate its computational efficiency and viability for enormously comparable karst oil reservoirs.

The Analysis of Permeability Influence on CO2 Plume based on Reservoir Simulation

Abstract Permeability is critical in site characterization and geoscience studies for carbon capture and storage projects. There is significant uncertainty of CO2 movement in the subsurface caused by permeability. This study aims to analyze the influence of permeability on the movement of CO2 plumes using a synthetic single-porosity model based on reservoir simulation. By simplifying geomechanics, employing tornado experimental design, as well as utilizing Pearson and Spearman correlations from 30,000 neural network proxy models based on Latin Hypercube and Monte Carlo experimental design, this research successfully explains the impact of permeability on CO2 movement. The reservoir can withstand pressures up to 6500 psi without rock failure. The total CO2 injection with a 90% probability is ∼1.3 TSCF equivalent, with an injection rate of 30 MMSCFD, optimized for a duration of 120 years. Initially, the CO2 movement tends to be upward during injection due to gravitational effects, but over time, it tends to move laterally. Vertical permeability exhibits a negative correlation with total CO2 injection, lateral distribution of CO2 plumes, and CO2 solubility in water. On the other hand, horizontal permeability shows a positive correlation with total CO2 injection. Factors such as perforation location and layer thickness also influence the extent of permeability’s role. However, the lateral and vertical movement of CO2 in this study has not been fully identified, and more complex quantification formulas are needed in commercial simulators. Despite the limitations in data and computational resources, this research successfully explains the movement of CO2 plumes and highlights the importance of other factors such as porosity, perforation location, layer thickness, among others. The recommendations of this study include the use of more complex quantification formulas to model the movement of CO2 plumes, along with increasing the complexity of the model.