Sustainable oil and gas development demands eco-friendly and cost-effective drilling fluids. Water-based drilling fluids (WBDFs) are preferred over oil-based alternatives for their lower environmental impact, but they often suffer from excessive fluid loss in permeable formations, leading to thick filter cakes, reduced mud weight, and operational delays. Conventional chemical additives mitigate this issue but pose environmental and health risks due to their toxicity and non-biodegradability. This study explores the use of biodegradable additives extracted from avocado seed (AS), rambutan shell (RS), tamarind shell (TS) and banana trunk (BT) biomass in four particle sizes of 300, 150, 75 and 32 mu m to improve filtration control in WBDFs. All four materials were crushed by ball milling and characterized by Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM) and Energy-dispersive X-ray (EDX). In accordance with API Spec 13A recommendations, several water-based drilling fluids (WBDFs), including reference fluid and modified fluids formulated with biodegradable additives at a fixed percentage of 3 wt% and varied particle sizes, were prepared. The rheological and filtration properties of the formulated drilling fluids were investigated by conducting industry-standard rheology and filtration tests under LPLT conditions (100 psi, 25 degrees C) and HPHT conditions (1500 psi, 75 degrees C). The results show that 32 mu m tamarind shell powder delivered the strongest performance, reducing fluid loss by 82.4% under HPHT conditions and producing the thinnest mud cake (0.33 mm); it also reduced fluid loss by 72.8% under LPLT conditions, outperforming the other biodegradable materials.

Dry gas reservoirs play a pivotal transitional role in meeting the net-zero target worldwide. Accurate modelling and simulation of this energy source require fast and reliable prediction of the gas compressibility factor (Z-factor). The experimental measurements of Z-factor are the most reliable source; however, they are expensive and time-consuming. This makes developing accurate predictive models essential. Traditional methods, such as empirical correlations and Equations of States (EoSs), often lack accuracy and computational efficiency. This study aims to address these limitations by leveraging the predictive power of machine learning (ML) techniques. Hence in this study three ML models of Artificial Neural Network (ANN), Group Method of Data Handling (GMDH), and Genetic Programming (GP) were developed. These models were trained on a comprehensive dataset comprising 1079 samples where pseudo-reduced pressure (Ppr) and pseudo-reduced temperature (Tpr) served as input and experimentally measured Z-factors as output. The performance of the developed ML models was benchmarked against two cubic EoSs of Peng-Robinson (PR) and van der Waals (vdW), and two semi-empirical correlations of Dranchuk-Abou-Kassem (DAK) and Hall and Yarborough (HY), and recent developed ML based models, using statistical metrics of Mean Squared Error (MSE), coefficient of determination (R2), and Average Absolute Relative Deviation Percentage (AARD%). The proposed ANN model reduces average prediction error by approximately 70% relative to the PR equation of state and by over 35% compared with the DAK correlation, while maintaining robust performance across the full Ppr and Tpr of dry gas systems. Additionally paired t-tests and Wilcoxon signed-rank tests performed on the ML results confirmed that the ANN model achieved statistically significant improvements over the other models. Moreover, two physical equations using the white-box models of GMDH and GP were proposed as a function of Ppr and Tpr for prediction of the dry gas Z-factor. The sensitivity analysis of the data shows that the Ppr has the highest positive effect of 88% on Z-factor while Tpr has a moderate effect of 12%. This study presents the first unified, statistically validated comparison of ANN, GMDH, and GP models for accurate and interpretable Z-factor prediction. The developed models can be used as an alternative tool to bridge the limitation of cubic EoSs and limited accuracy and applicability of empirical models.

Accurate determination of the viscosity of carbon dioxide (CO 2 ) mixed with nitrogen (N 2 ) is vital for enhanced oil recovery (EOR) and carbon capture, utilisation and storage (CCUS/CCS). The determination of this important thermophysical property is usually through costly and time‐consuming experiments, which is not ideal for field recovery planning and rapid decision‐making. On the other hand, the conventional modelling relies largely on equations of state (EoS) and empirical correlations, which can be inaccurate for CO 2 –N 2 viscosity, particularly near supercritical conditions due to simplifying assumptions and limited transferability. Consequently, machine‐learning (ML) methods have gained popularity for fast and accurate prediction. Hence, in this study, ~3036 literature data points spanning pressures of 0.00127–160.99 MPa and temperature of 66.55–575.15 K were collected, cleaned and pre‐processed. Then, using pre‐processed data, several ML models, including gradient boosting (GB), extreme gradient boosting (XGBoost), LightGBM, CatBoost, random forest, three multilayer perceptron artificial neural networks (MLP‐ANNs), a stacking ensemble and a group method of data handling (GMDH) were developed. The developed models were benchmarked to predict CO 2 –N 2 viscosity as a function of temperature, pressure and the mole fractions of CO 2 –N 2 in the mixture. The analysis of the results indicate that the GB achieved the best performance with a correlation coefficient ( R 2 ) of 0.9933 ± 0.0011, root mean square error (RMSE) of 4.83 ± 0.39 μPa·s and mean absolute error (MAE) of 2.34 ± 0.10 μPa·s (mean ± 95% CI) for the test dataset, outperforming all other ML models and the utilised literature correlations. In addition, based on GMDH, two practical explicit equations within temperature ranges of T < 300 K and T > 300 K that predict the experimental viscosity with high accuracy were proposed. The sensitivity analysis also shows that the pressure has the highest positive impact, while temperature exhibited a comparably strong negative effect on viscosity.

Accurate determination of the viscosity of carbon dioxide (CO2) mixed with nitrogen (N-2) is vital for enhanced oil recovery (EOR) and carbon capture, utilisation and storage (CCUS/CCS). The determination of this important thermophysical property is usually through costly and time-consuming experiments, which is not ideal for field recovery planning and rapid decision-making. On the other hand, the conventional modelling relies largely on equations of state (EoS) and empirical correlations, which can be inaccurate for CO2-N-2 viscosity, particularly near supercritical conditions due to simplifying assumptions and limited transferability. Consequently, machine-learning (ML) methods have gained popularity for fast and accurate prediction. Hence, in this study, similar to 3036 literature data points spanning pressures of 0.00127-160.99 MPa and temperature of 66.55-575.15 K were collected, cleaned and pre-processed. Then, using pre-processed data, several ML models, including gradient boosting (GB), extreme gradient boosting (XGBoost), LightGBM, CatBoost, random forest, three multilayer perceptron artificial neural networks (MLP-ANNs), a stacking ensemble and a group method of data handling (GMDH) were developed. The developed models were benchmarked to predict CO2-N-2 viscosity as a function of temperature, pressure and the mole fractions of CO2-N-2 in the mixture. The analysis of the results indicate that the GB achieved the best performance with a correlation coefficient (R-2) of 0.9933 +/- 0.0011, root mean square error (RMSE) of 4.83 +/- 0.39 mu Pa.s and mean absolute error (MAE) of 2.34 +/- 0.10 mu Pa.s (mean +/- 95% CI) for the test dataset, outperforming all other ML models and the utilised literature correlations. In addition, based on GMDH, two practical explicit equations within temperature ranges of T < 300 K and T > 300 K that predict the experimental viscosity with high accuracy were proposed. The sensitivity analysis also shows that the pressure has the highest positive impact, while temperature exhibited a comparably strong negative effect on viscosity.

Meeting the global energy demand and sustainable development of conventional petroleum reserves necessitates the development of high-performance and environmentally friendly water-based drilling fluids (WBDF). Nevertheless, one of the major concerns of using WBDF is the fluid loss due to its penetration into the formation during the drilling operation. Various additives (fluid-loss agents) in the industry have been introduced to tackle the issue but at the cost of non-biodegradable hazardous chemicals. Due to the recent interest in environmentally friendly WBDF additives, this study looked at the suitability of the composite part of peanut shell powder (CSP). The CSP was selected because of affordability, accessibility and fibrous content. Following the American Petroleum Institute (API) guidelines for preparing the drilling mud additives, six different laboratory experiments were carried out for biodegradable drilling fluids prepared from CSP at different concentrations of 1, 2 and 3 wt

While Battery Electric Vehicles (BEVs) offer environmental benefits by reducing carbon emissions during use, their range remains limited compared to conventionally fuelled vehicles. This paper focuses on identifying factors that directly influence BEV range and explores strategies to mitigate range anxiety among potential users. Specifically, it reviews the impact of battery cell characteristics and vehicle lightweighting. Using the WLTP Class 3B drive cycle, energy consumption and Depth of Discharge (DoD) were evaluated across various battery capacities. Multiple Lithium-Ion battery models were simulated to analyse discharge behaviour, while vehicle mass composition was examined to assess the effectiveness of lightweighting in extending driving range. A lower initial State of Charge (SoC) and a standard discharge rate were used to estimate the remaining range, highlighting an approximate gain of up to 6 km at lower DoD levels. This work aims to accurately demonstrate how battery technology and structural weight impact energy consumption and usable range in BEVs. Current modelling approaches often overlook the relationship between driver discomfort and battery performance metrics. The main contribution is to address the gap by integrating Li-ion discharge modelling with vehicle dynamics to estimate range and compare cell characteristics. The ultimate goal is to support cost-effective strategies for increasing BEV usability, aligning them more closely with conventional vehicle expectations and enhancing journey flexibility.

Drilling fluids are vital in oil and gas well operations, ensuring borehole stability, cutting removal, and pressure control. However, fluid loss into formations during drilling can compromise formation integrity, alter permeability, and risk groundwater contamination. Water-based drilling fluids (WBDFs) are favored for their environmental and cost-effective benefits but often require additives to address filtration and rheological limitations. This study explored the feasibility of using vegetable waste, including pumpkin peel (PP), courgette peel (CP), and butternut squash peel (BSP) in fine (75 μm) and very fine (10 μm) particle sizes as biodegradable WBDF additives. Waste vegetable peels were processed using ball milling and characterized via FTIR, TGA, and EDX. WBDFs, prepared per API SPEC 13A with 3 wt% of added additives, were tested for rheological and filtration properties. Results highlighted that very fine pumpkin peel powder (PP_10) was the most effective additive, reducing fluid loss and filter cake thickness by 43.5% and 50%, respectively. PP_10 WBDF maintained mud density, achieved a pH of 10.52 (preventing corrosion), and enhanced rheological properties, including a 50% rise in plastic viscosity and a 44.2% increase in gel strength. These findings demonstrate the remarkable potential of biodegradable vegetable peels as sustainable WBDF additives.

This research aims to evaluate the effect of porous filter configuration on flow characteristics within filter using CFD simulation. This simulation model was chosen for comprehensive analysis that considers different variables affecting the filter performances. Dynamics of the flow and pressure drop under different flow conditions and filter geometry were studied. The Euler-Lagrangian approach was used to model multiphase flow, and the standard K-ε model was used for turbulence characterisation. Particle size distribution was characterized using Rosin-Rammler distribution. The initial status of these properties was obtained by previous experimental references and their evolution over time was simulated at the cell level of the model using User-Defined Functions (UDF). The main conclusions of the study are: i) The pressure drop increased with flow rate and thickness of the filter but decreased with increasing filter length and diameter. ii) There is no significant change in velocity ratio with the distance from the filter inlet at the filter centre line except the first 5% from the inlet and last 5% close to the end. iii) it was identified that higher radial velocity ratios imply a less particle deposition within the filter media. The results also shows that the particle loading on 290 mm filter is 50% - 60% lower than the other filters but evenly distributed across the filter. However, the pressure drop decreased with the filter length. 55mm filter that has the highest radial velocity ratio performs poorly in particle trapping.

Addressing environmental concerns in extracting and producing hydrocarbon resources requires environmentally friendly water-based drilling fluid (WBDF) additives. These additives not only protect the environment but also offer operational benefits. One of the major challenges of WBDF is fluid loss into the formation and its degradability in high-temperature conditions, which induce cost, formation alteration and environmental pollution. This study explored the feasibility of wasted watermelon rind powder (WRP) and coffee ground waste powder (CGWP) as potential WBDF additives to regulate fluid loss and modify other rheological properties. WRP and CGWP were characterized using Energy Dispersive X-Ray (EDX) and Fourier Transform Infrared Spectroscopy (FTIR) tests. The thermal stability of the proposed additives was confirmed using a thermogravimetric analysis (TGA) test. Several experiments were conducted to investigate the impact of WRP and CGWP with different weight concentrations (wt.%) on the rheological and drilling fluid properties of conventional WBDF. The results show that at 3wt.%, the WRP improved the plastic viscosity by 75% and the CGWP increased the plastic viscosity by 100%. At the same time, the CGWP reduced the gel strength substantially, while WRP increased the gel strength. At an optimum 3wt.%, the CGWP reduced fluid loss by 53%, resulting in the thinnest filter cake thickness. These results demonstrate the potential use of the studied materials as multifunctional additives in drilling operations with WBDF.

Brake rotor discs are essential components in cars, requiring materials that have excellent durability and efficient frictional characteristics in different conditions. Gray Cast Iron (GCI) is frequently used because of its high melting point, excellent thermal conductivity, and cost-efficiency. Although there have been efforts to replace it with lighter alloys, GCI remains to be suggested because of its exceptional strength and machinability. The present research used Siemens NX software to perform a transient thermo-mechanical analysis in order to assess the thermal and structural effectiveness of four brake disc designs: solid, circular drilled, square drilled, and triangle drilled discs. The Finite Element Analysis (FEA) results show that triangular drilled discs showed better heat dissipation, which led to a temperature decrease to 206.85 °C at 3.5 s, compared to 238.97 °C in solid discs. Furthermore, the von-Mises stress exhibited its minimum value in triangular drilled discs, at 320 MPa. Conversely, solid discs had the greatest displacement values across all time intervals. The findings underscore the importance of carefully choosing appropriate materials and optimizing the design of the brake disc to enhance its performance and guarantee safety.

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The mechanical properties of tissue scaffolds are essential in providing stability for tissue repair and growth. Thus, the ability of scaffolds to withstand specific loads is crucial for scaffold design. Most research on scaffold pores focuses on grids with pore size and gradient structure, and many research models are based on scaffolding with vertically arranged holes. However, little attention is paid to the influence of the distribution of holes on the mechanical properties of the scaffold. To address this gap, this research investigates the effect of pore distribution on the mechanical properties of tissue scaffolds. The study involves four types of scaffold designs with regular and staggered pore arrangements and porosity ranging from 30% to 80%. Finite element analysis (FEA) was used to compare the mechanical properties of different scaffold designs, with von-Mises stress distribution maps generated for each scaffold. The results show that scaffolds with regular vertical holes exhibit a more uniform stress distribution and better mechanical performance than those with irregular holes. In contrast, the scaffold with a staggered arrangement of holes had a higher probability of stress concentration. The study emphasized the importance of balancing porosity and strength in scaffold design.

AbstractDental implants have received a lot of attention and have been used to treat symptoms such as missing teeth and bad teeth. Due to the wide range of occupations and ages of patients, the functions and aims of implants are different. There are many kinds of dental implant shapes. However, with the popularity of dental implants, the problems caused by the some of the dental implant shapes have received much attention. In fact, some implants were used incorrectly. This makes the stress distribution around the implant unreasonable; it not only affects the surrounding bone resorption but also causes mechanical fracture of the implant. This work aims to evaluate the mechanical features of five different kinds of dental implant systems. By applying engineering systems of investigations such as FEM, five types of dental implants and surrounding bone tissue were modeled and simulated under vertical loads of 90 N. Distributions of stresses and deformations in the bone were obtained and ranked according to statistical scores, which were used to judge the optimum geometry of implants. The analytical results showed that the cylindrical implant is the most optimum shape among the other types of implants.

Accurate estimation of two-phase compressibility factor (Z factor) in gas condensate reservoirs is essential for understanding phase behaviour and reliable simulation studies. Experimental measurements of Z factor in wide operational conditions are cumbersome and costly. Hence, engineers rely on existing models such as cubic equation of states (EoSs) and empirical models for estimating such important property. In this study initially the accuracy of prevalent available Z factor models for prediction of two-phase gas condensate Z factor were examined. Then, several smart models including two multilayer perceptron neural networks known as feedforward neural network (FFNN) and cascade forward neural network (CFNN) optimized with Levenberg-Marquardt (LM) and Bayesian-Regularization (BR) algorithms and one Adaptive Neuro Fuzzy Inference System (ANFIS) optimized with Particle Swarm Optimization (PSO) were developed based on 19518 data points for the same task. The databank covers gas condensate two-phase Z factor, compositional variations, molecular weight of heptane plus (MWC7+) and gas specific gravity in wide range of pressure and temperature. The results indicate that the FFNN-BR predicts all experimental data with high accuracy with an average absolute relative deviation of 0.321%. Furthermore, the accuracy of the developed model over two cubic EoSs and three empirical models from literature was confirmed. Finally, based on the sensitivity analysis, it was found that the pseudoreduced pressure (Ppr), MWC7+, and molar content of C7+ in the mixture have the highest impact on prediction of two-phase Z factor. The proposed tools can be utilized for accurate prediction of gas condensate two-phase Z factor to ensure accurate simulation studies and better phase behaviour treatments.

The two-phase frictional pressure drop has a dominant effect in many industrial applications associated with the multiphase flow. This study investigated the accuracy of several available methods for predicting two-phase frictional pressure drop of different pipe diameters using 4124 experimental data points. It is observed that the performance of the existing methods is poor in a wide range of operating conditions. Then, several Artificial Neural Network models were proposed, including six multilayer perceptron (MLP) and one Radial Basis Function (RBF) using the same data sets. The weights and biases of the ANNs were optimized using Levenberg-Marquardt (LM), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG), Resilient Backpropagation (RB), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Statistical error analysis indicates that neural network incorporated with the Genetic Algorithm (MLP-GA) predicts the entire data set with a Root Mean Square Error of 0.525 and an Average Absolute Relative Error percentage of 6.722. Finally, the sensitivity analysis was carried out, indicating that the mass flux (G) has the highest direct impact on the two-phase frictional pressure drop.

Accurate estimation of gas condensate fluid properties is a challenging task due to the evolving condensate liquid from the gas phase below the saturation pressure. Among the fluid properties, viscosity of condensate liquid has the largest prediction uncertainty. The existing literature methods cannot cope with the nonlinearity and physics of gas condensate mixture (transition from a single phase to two phases) below the saturation pressure. Hence, in this study based on the experimental condensate viscosity data, a simple linear equation as a function of pressure, temperature and solution gas to oil ratio was developed. For this purpose, a comprehensive data source of 1368 experimental data points acquired from open literature has been used. For developing the new condensate viscosity correlation, an artificial intelligence (AI) method known as the Takagi–Sugeno–Kang (TSK) fuzzy algorithm was utilized. The accuracy of the developed correlation was compared with five previously published literature models. The superiority of the new correlation over the existing literature models is confirmed by statistical parameters of a least root mean square error of 0.0194, a mean average error of 0.0163 and an average absolute relative deviation percentage of 7.123. The proposed condensate viscosity correlation is valid in a pressure range of 0.25–75.84 MPa), a temperature range of 303–443.15°K and Rs of 41.96–3496 scf/STB.The proposed correlation can be used as an alternative approach to the existing models for accurately estimating the gas condensate viscosity and for conducting reliable reservoir simulation studies.

This study investigates the pressure drop in horizontal pipes packed with large particles that result in small pipe-to-particle diameter ratio both experimentally and numerically. Two horizontal pipes of 0.1905 and 0.0254 m ID filled with cylindrical or spherical particles are used to collect the experimental data for single and two-phase flows. The porosity has same value for both pipes when they packed with cylindrical particles which is 0.75, however has different values when packed with spherical particles, 0.7 for the large pipe and 0.57 for the small pipe. The Roe-type Riemann solver proposed by Santim and Rosa Int J Numer Methods Fluids 80 (9), 536–568, [ 36 ] which uses the Drift-Flux model is modified aiming to predict the pressure drop in porous media through the implementation of a new source term in the system of equations. Empirical models available in the literature are used to calculate the single and two-phase flows pressure drop. The motivation is to verify the solver capability to reproduce the two-phase flow pressure drop in porous media and to compare some empirical models existing in the literature against the experimental data provided modifying some empirical coefficients when necessary.

Liquid dropout occurs in gas condensate reservoirs below the dew point pressure around near wellbore region as a result of depletion from production of such reservoirs. Forecasting production as well as optimizing future recoveries of gas condensate reservoirs are highly desirable. This is not possible to achieve without accurate determination of liquid dropout viscosity (mu(c)) below the dew point. The focus of research in past decades has been on the development of accurate viscosity prediction models below the dew point pressure to ensure accurate condensate production forecast. Gas condensate production forecast and optimization around wellbore region and condition are complicated due to unique gas condensate behaviour that violates thermodynamic laws. Current methods are based on correlation estimation, however the accuracy of these correlations are less than satisfactory, and root cause is due to the miscapturing of complex behaviour of gas condensate reservoir near the wellbore region. These motivated the consideration of modern numerical approaches such as the Least Square Support Vector Machine (LSSVM) and Artificial Neural Network (ANN) used in this paper. These methods are considered as more data behaviour oriented, with the capability of capturing the fluid complexity of gas condensate in such conditions. In this study viscosity of condensate phase near the wellbore region was modelled using machine learning techniques including ANN and LSSVM. For this purpose, over 300 viscosity data sets were collected from published literature and experimental studies worldwide. This databank includes API gravity, reservoir temperature, solution gas to oil ratio (Rs), specific gas gravity, fluid compositions and reservoir pressure. Six well known previously published viscosity correlations refined using least-square approach to match the experimental data. Qualitative and quantitative error analysis of developed LSSVM and ANN showed their performance superiority over refined literature correlations. The new proposed models can be embedded as an extra feature of commercial reservoir simulation packages for optimization and future recoveries of gas condensate reservoirs.

Inflow Performance Relationships (IPRs) are important element for reservoir engineers in the design of new wells and also for monitoring and optimizing existing wells. IPRs are used to determine optimum production of gas rate and condensate rate in a well for any specified value of average reservoir pressure and predict the performance. Jokhio and Tiab proposed a simple method of establishing IPR for gas condensate wells. The method uses transient pressure test data to estimate effective permeability as a function of pressure. Effective permeability data used to convert production bottomhole flow pressure into pseudopressure to establish well performance. Despite the effectiveness of the method, single phase correlations were used in PVT calculations of each phase, which over simplified the fluid flow in gas condensate wells. Single phase dry gas equations do not reflect the multiphase flow behaviour of gas condensate wells below the dew point. Due to this limitation Jokhio and Tiab method modified by this study and new analytical IPRs for gas condensate well proposed. The major improvement of the above method is incorporating new viscosity correlation developed by this study and using two-phase compressibility factor as key parameters for predicting gas condensate inflow performance. Therefore, the main contribution of this study is development of viscosity correlation which is a critical issue in predicting gas condensate inflow performance both above and below the dew point. Optimization techniques and nonlinear regression used to develop a new viscosity correlation for high temperature heavy gas condensate reservoirs under depletion. The application of the new model is illustrated with field example for current IPR curves. Compositional simulation study of the well performed in PIPSIM simulator. The proposal approach provides reasonable estimates of simulator input reservoir properties (e.g. IPRs). Accuracy of the new method compared with compositional simulation study. The proposed method presents average absolute relative deviation (AARD) of 5.8% for gas IPR and 7.5% for condensate IPR compare to compositional simulation results. New method provides a tool for quick estimation of gas condensate wells without need of relative permeability curves and expensive and time consuming compositional simulation.