Evaluation of energy performance of the most prevalent housing archetypes in Jordan

The residential sector is responsible for the consumption of 46% of the building’s total primary energy consumption in Jordan. Despite the Jordanian government’s commitment to significantly reduce national emissions by 2050, building Operational Carbon (OC) has been under-researched in the Jordanian context. This study aims to present the development of an archetypes-based housing stock model. The model is then used to evaluate the impact of a series of suggested refurbishment scenarios, to reduce the stock’s operational carbon impact. First, the most prevalent dwellings are identified and categorized into ‘archetypes’ based on the analysis of a housing survey database on Jordanian dwellings. Subsequently, the performance of these archetypes is evaluated in terms of OC. Finally, the improvement scenarios are investigated, and their impact on OC is evaluated

An Application of Water Harvesting Façade Technology for Commercial Buildings/ The Case of Amman

Jordan is considered one of the countries with the scarcest of water resources as the third most water scarce country in the world. Placed at only 20% of the water poverty level the research investigates the application of rain water harvesting technology on commercial buildings in Amman/Jordan and achieving a balance between what buildings consume and what they produce. Emphasizing procedural mechanism of implementation, and verification of the economic feasibility of system through studying the effectiveness in terms of cost, quantity of building consumption and harvested water. This research seeks to implement a system which utilizes façade cladding and roof collection by using curtain wall transoms to direct rainwater into vertical mullions. Then commercial buildings in the case of Amman can significantly benefit from rainwater harvesting technique. Research addresses the quantity of water consumed through calculations from water authority and comparing it with other similar building, then calculate harvested water through annual rainwater captured potential’s formula. It concludes to understand the importance of system’s implementation as new approach to be used in Amman with a good aesthetic appeal of greenery, and the efficiency of using it through feasibility study

Unconventional reservoir characterization and formation evaluation: a case study of a tight sandstone reservoir in West Africa

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

Unsupervised machine learning technique for classifying production zones in unconventional reservoirs

Significant amounts of information are rapidly increasing in bulk as a consequence of the rapid development of unconventional tight reservoirs. The geomechanical and petrophysical characteristics of the wellbore rocks influence the sweet and non-sweet areas of tight unconventional reservoirs. Using standard approaches, such as data from cores and commercial software, it is difficult and costly to locate productive zones. Furthermore, it is difficult to apply these techniques to wells that do not have cores. This study presents a less costly way for the systematic and objective detection of productive and non-productive zones via well-log data using clustering unsupervised and supervised machine learning algorithms. The method of cluster analysis has been used in order to classify the productive and non-productive reservoir rock groups in the tight reservoir. This was accomplished by assessing the variability of the reservoir characteristics data that are forecasted by looking at the dimensions of the well logs. The Support vector machine as a supervised machine learning algorithm is then used to evaluate the classification accuracy of the unsupervised algorithms based on the clustering labels. The application made use of approximately ten different variables of rock characteristics including zonal depth, effective porosity, permeability, shale volume, water saturation, total organic carbon, young's modulus, Poisson's ratio, brittleness index, and pore size. The findings show that both clustering techniques identified the sweet areas with high accuracy and were less time-consuming