Search Results (3)

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. Full article

Fishbone drilling (FbD) involves drilling multiple micro-holes branching out in various directions from the primary vertical or deviated wellbore. FbD is similar to multilateral micro-hole drilling and can be employed to boost hydrocarbon production in naturally fractured formations or during refracturing operations by connecting existing natural fractures. Key design elements in fishbones include determining the number, length, and spacing between the branches, and the angle at which the branches deviate from the main borehole. Fishbone wells have emerged as a promising technology for improving well performance and reducing environmental impact. In this paper, we present a comprehensive review of the different applications of fishbone wells in conventional and renewable energy systems. We discuss the potential of fishbone wells for enhanced oil and gas recovery, as well as their application in unconventional resources such as coal bed methane. Moreover, we examine the feasibility of fishbone wells in renewable energy systems, such as geothermal energy and carbon capture, utilization, and storage (CCUS). We highlight the various benefits of fishbone wells, including reduced carbon footprint, enhanced efficiency, and increased sustainability. Finally, we discuss the challenges and limitations associated with fishbone wells in different energy systems. This review provides a comprehensive overview of the potential and challenges of fishbone wells in reducing carbon footprint and improving well performance in a wide range of energy systems. Full article

The accurate prediction of underground formation lithology class and tops is a critical challenge in the oil industry. This paper presents a machine-learning (ML) approach to predict lithology from drilling data, offering real-time litho-facies identification. The ML model, applied via the web app “GeoVision”, achieves remarkable performance during its training phase with a mean accuracy of 95% and a precision of 98%. The model successfully predicts claystone, marl, and sandstone classes with high precision scores. Testing on new data yields an overall accuracy of 95%, providing valuable insights and setting a benchmark for future efforts. To address the limitations of current methodologies, such as time lags and lack of real-time data, we utilize drilling data as a unique endeavor to predict lithology. Our approach integrates nine drilling parameters, going beyond the narrow focus on the rate of penetration (ROP) often seen in previous research. The model was trained and evaluated using the open Volve field dataset, and careful data preprocessing was performed to reduce features, balance the sample distribution, and ensure an unbiased dataset. The innovative methodology demonstrates exceptional performance and offers substantial advantages for real-time geosteering. The accessibility of our models is enhanced through the user-friendly web app “GeoVision”, enabling effective utilization by drilling engineers and marking a significant advancement in the field. Full article