S2 glass fibre reinforced epoxy composites are widely used in aeronautical applications owing to their excellent strength to weight ratio. Drilling glass fibres can be cumbersome due to their abrasive nature and poor thermal conductivity. Moreover, the use of conventional coolants is not desirable due to contamination and additional costs for cleaning the machine part. An alternative is to use environmentally friendly coolants such as liquid nitrogen (LN₂) which have been previously employed in machining metals and composites. The current study investigates the effect of drilling S2 glass fibre composite in a bath of LN₂. The study aims to evaluate the effect of spindle speed, feed rate and the presence of cryogenic cooling on the form and dimensional tolerances of the hole (hole size, circularity, cylindricity and perpendicularity). Design of experiments and analysis of variance (ANOVA) were used to determine the contribution of the input parameters on the analysed hole quality metrics. Results indicated that drilling S2 glass fibre in a cryogenic bath increased hole size significantly beyond the nominal hole diameter. The hole circularity and cylindricity were reduced compared to holes drilled under dry condition under all cutting parameters due to enhanced thermal stability during the drilling process. The current study aims to provide the scientific and industrial communities with the necessary knowledge on whether cryogenic bath cooling strategy provides better hole quality output compared to dry drilling and other cryogenic cooling strategies which were previously reported in the open literature.

Unconventional reservoirs, including gas shales and tight gas sands, have gained prominence in the energy sector due to technological advancements and escalating energy demands. The oil industry is eagerly refining techniques to decipher these reservoirs, aiming to reduce data collection costs and uncertainties in reserve estimations. Characteristically, tight reservoirs exhibit low matrix porosity and ultra-low permeability, necessitating artificial stimulation for enhanced production. The efficacy of the stimulation hinges on the organic material distribution, the rock’s mechanical attributes, and the prevailing stress field. Comprehensive petrophysical analysis, integrating standard and specialized logs, core analyses, and dynamic data, is pivotal for a nuanced understanding of these reservoirs. This ensures a reduction in prediction uncertainties, with parameters like shale volume, porosity, and permeability being vital. This article delves into an intricate petrophysical evaluation of the Nene field, a West African unconventional reservoir. It underscores the geological intricacies of the field, the pivotal role of data acquisition, and introduces avant-garde methodologies for depth matching, rock typing, and the estimation of permeability. This research highlights the significance of unconventional reservoir exploration in today’s energy milieu, offering a granular understanding of the Nene field’s geological challenges and proffering a blueprint for analogous future endeavours in unconventional reservoirs.

This paper describes a novel Deep Learning architecture to assist with steering a powered wheelchair. A rule-based approach is utilized to train and test a Long Short Term Memory (LSTM) Neural Network. It is the first time a LSTM has been used for steering a powered wheelchair. A disabled driver uses a joystick to provide desired speed and direction, and the Neural Network provides a safe direction for the wheelchair. Results from the Neural Network are mixed with desired speed and direction to avoid obstacles. Inputs originate from a joystick and from three ultrasonic transducers attached to the chair. The resultant course is a blend of desired directions and directions that steer the chair to avoid collision. A rule-based approach is used to create a training and test set for the Neural Network system and applies deep learning to predict a safe route for a wheelchair. The user can over-ride the new system if necessary.