The accurate determination of key parameters, including the CO₂-hydrocarbon solubility ratio (Rs), interfacial tension (IFT), and minimum miscibility pressure (MMP), is vital for the success of CO₂-enhanced oil recovery (CO₂-EOR) projects. This study presents a robust machine learning framework that leverages deep neural networks (MLP-Adam), support vector regression (SVR-RBF) and extreme gradient boosting (XGBoost) algorithms to obtained accurate predictions of these critical parameters. The models are developed and validated using a comprehensive database compiled from previously published studies. Additionally, an in-depth analysis of various factors influencing the Rs, IFT, and MMP is conducted to enhance our understanding of their impacts. Compared to existing correlations and alternative machine learning models, our proposed framework not only exhibits lower calculation errors but also provides enhanced insights into the relationships among the influencing factors. The performance evaluation of the models using statistical indicators revealed impressive coefficients of determination of unseen data (0.9807 for dead oil solubility, 0.9835 for live oil solubility, 0.9931 for CO₂-n-Alkane interfacial tension, and 0.9648 for minimum miscibility pressure). One notable advantage of our models is their ability to predict values while accommodating a wide range of inputs swiftly and accurately beyond the limitations of common correlations. The dataset employed in our study encompasses diverse data, spanning from heptane (C₇) to eicosane (C₂₀) in the IFT dataset, and MMP values ranging from 870 psi to 5500 psi, covering the entire application range of CO₂-EOR. This innovative and robust approach presents a powerful tool for predicting crucial parameters in CO₂-EOR projects, delivering superior accuracy, speed, and data diversity compared to those of the existing methods.

Multilateral well drilling technology has recently assisted the drilling industry in improving borehole contact area and reducing operation time, while maintaining a competitive cost. The most advanced multilateral well drilling method is Fishbone drilling (FbD). This method has been utilized in several hydrocarbon fields worldwide, resulting in high recovery enhancement and reduced carbon emissions from drilling. FbD involves drilling several branches from laterals and can be considered as an alternative method to hydraulic fracturing to increase the stimulated reservoir volume. However, the expected productivity of applying a Fishbone well from one field to another can vary due to various challenges such as Fishbone well design, reservoir lithology, and accessibility. Another challenge is the lack of existing analytical models and the effect of each Fishbone parameter on the cumulative production, as well as the interaction between them. In this paper, analytical and empirical productivity models were modified for FbD in a dry gas reservoir. The modified analytical model showed a higher accuracy with respect to the existing model. It was also compared with the modified empirical model, which proved its higher accuracy. Finally, machine learning algorithms were developed to predict FbD productivity, which showed close results with both analytical and empirical models.

The oil and gas industry faces significant challenges due to wear on drilling motor components, such as thrust pins and inserts. These components are critical to the efficiency and reliability of drilling operations, yet are susceptible to wear, leading to significant economic losses, operational downtime, and safety risks. Despite previous research on wear-resistant materials and surface treatments, gaps exist in understanding the unique properties of thrust pins and inserts. The aim of this study is to enhance mechanical system performance by characterizing the wear resistance of these components. Through chemical analysis, hardness assessments, and metallographic examinations, the study seeks to identify specific alloys and microstructures conducive to wear resistance. Key findings reveal that AISI 9314 thrust pins exhibit superior wear resistance with a tempered martensite microstructure and a hardness of 41 HRc, whereas AISI 9310 inserts are less resistant, with a hardness of 35 HRc. The research employs advanced techniques, including a pin-on-disc tribometer, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and profilometry, to evaluate wear behavior, visualize wear patterns, analyze elemental composition, and quantify material loss and surface roughness. Our findings demonstrate that optimizing the material selection can significantly enhance the durability and efficiency of drilling motors. This has profound implications for the oil and gas industry, offering pathways to reduce maintenance costs, improve operational efficiency, and contribute to environmental sustainability by optimizing energy consumption and minimizing the carbon footprint of drilling operations.

The development of unconventional resources continues to be propelled by innovations that enhance economic efficiency and maximize reservoir contact within operational constraints. Among the most recent of these is the U-shaped, or “horseshoe,” well design, which connects two parallel horizontal laterals with a 180° turn, effectively doubling the reservoir exposure from a single wellhead. This paper provides a comprehensive literature review of the current state of knowledge on U-shaped well technology. It surveys the operational drivers for their adoption, the critical drilling, and completion technologies that have enabled its successful implementation and discusses key design considerations for a successful operation. Field data from major North American shale basins, including the Permian, Eagle Ford, Bakken, and Haynesville, demonstrate substantial economic benefits, such as capital cost savings of approximately 20–25% compared to traditional methods. Lifecycle assessments indicate notable environmental advantages, including a 29.3% reduction in carbon emissions, a 15.8% reduction in water use, and a 50% decrease in land disturbance. Despite these clear benefits, gaps remain regarding long-term performance validation, stimulation of curved sections, and fracture modeling accuracy. Addressing these gaps is essential to fully realize the potential of U-shaped wells as a sustainable and economically attractive approach in the evolving landscape of unconventional energy development.

Poor wellbore cleaning is a significant challenge in oil drilling, primarily due to the accumulation of cuttings at the bottom of the well, particularly in deviated and horizontal wells. This study addresses this issue by employing Computational Fluid Dynamics (CFD) with the commercial software ANSYS FLUENT (2023-R1) to simulate a solid–liquid multiphase flow in an annulus. The primary objective is to analyze the cuttings concentration, pressure loss, and solid velocity profiles across various drilling parameters, including drill pipe rotation, the flow rate, rate of penetration, inclination angle, and fluid rheology. Our results underscore the critical role of these parameters in enhancing cuttings transport efficiency. Specifically, the drill pipe rotation, flow rate, and rate of penetration emerge as the most influential factors affecting the wellbore cleaning performance. With a validated model exhibiting an average error of 4.24%, this study provides insights into optimizing drilling operations to improve wellbore cleaning and increase hydrocarbon recovery.