http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/286 2026-04-20T21:55:02Z 2026-04-20T21:55:02Z TASNIA, FARIA AHSAN, TAUKIR SHAHRIAR, KHAN HASAN http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/1000 2025-09-10T14:39:19Z 2023-02-01T00:00:00Z

ANALYSIS OF PIPELINE HYDRAULICS OF LPG RETICULATION SYSTEM TASNIA, FARIA; AHSAN, TAUKIR; SHAHRIAR, KHAN HASAN While Bangladesh's natural gas output is steadily falling, demand for the fuel is rising in the residential, industrial, and power sectors. The Bangladeshi government has planned to stop supplying home customers with natural gas and replace it with liquefied petroleum gas (LPG), which some private companies will import. As part of the Jolshiri Abason project, 2243 acres of land will be developed for 8795 plots and 96745 flats, with LPG reticulation being installed as part of the housing project to distribute gas. In this study, a transmission and distribution network for liquefied petroleum gas has been planned for 19 sectors, where 96745 apartments and 8795 plots of land will be developed. Gas will be provided in each apartment of six people so that they can cook without any hassle. The total amount of energy needed to prepare a daily dinner in each apartment has been calculated to be 29406 KJ, or 0.6 kg of LPG, and the burner heat rating has been taken into account to be 6 KW so that the meal can be perfectly boiled. The 96745 flats need to be supplied with 580470 KJ/S (KW) of energy during the select time. A total of 41793.84 kg/hr of LPG must be vaporized and transmitted to the gas transmission and distribution network in order to supply this quantity of energy. The network for distributing and transmitting gas has been built to handle 20.00 MMSCFD of gas flow. 200DN, 150DN, 100DN, and 50DN are the pipe sizes that were intended. Pipeline hydraulics of the sector 17A is one of the prime interests of this thesis work. The pressure at the end of a 50DN pipeline in a certain sector, which must be in the range of 5 psi to 10 psi for end users of LPG for effective cooking, has been determined using the general flow equation and Weymouth equation. This specific sector contains 60 plots with a flow of 118632 SCF(V)/D overall where gas is delivered to end consumers via 50DN pipes that are linked to 100DN pipelines, which are linked to 150DN pipelines, which are ultimately linked to the main 200DN pipeline. Pressure is calculated as 5.548 psi and 5.198 psi at the 150 DN pipeline by using Weymouth equation and General flow equation respectively, which is linked to the main 200 DN pipeline from which LPG is delivered from the storage tank, and is 5 psi at the sector's end user. Again, the pressure is evaluated as 10.285 psi (for General flow equation) and 10.1 psi (for Weymouth equation) at the 150 DN pipeline, which is connected to the main 200 DN pipeline from which LPG is coming from the storage tank, for a pressure of 10 psi at the end user of a particular sector. ANALYSIS OF PIPELINE HYDRAULICS OF LPG RETICULATION SYSTEM

2023-02-01T00:00:00Z HOQUE, MOHAMMAD MAINUL RAFID-E-ELAHI http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/999 2025-09-10T14:40:04Z 2023-03-01T00:00:00Z

WELLBORE STABILITY IN SHALE HOQUE, MOHAMMAD MAINUL; RAFID-E-ELAHI With the use of the Kirsch equation, 2D Finite Element (RS2), and 3D Finite Element (RS3) calculations, this study examines the wellbore stability in shale formations. Shale formations are notorious for being complicated and diverse, which makes it difficult to drill them and keep them stable. In order to examine wellbore stability, the Kirsch equation, a mathematical model that determines the stress distribution around a circular hole in an elastic material, is utilized. The outcomes are contrasted with those of 2D and 3D finite element analyses, which are frequently employed in the field of rock engineering to assess the stability of subsurface structures. The comparison demonstrates that, while the Kirsch equation can offer a helpful approximation of the stress distribution in the shale formation, it is limited in its ability to handle complex geological situations. By taking into account the impacts of material nonlinearity, joint systems, and anisotropy, finite element analysis gives a more precise and thorough examination of wellbore stability. In addition to emphasizing the need for a thorough understanding of rock mass behavior, the study underscores the significance of employing finite element analysis to analyze the wellbore stability in shale formations. Wellbore Stability in Shale

2023-03-01T00:00:00Z ALOM, ASHRAFUL HASAN, NAZMUL http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/998 2025-09-10T14:40:28Z 2023-02-01T00:00:00Z

INTEGRATED RESERVOIR MODELING OF SALDA NADI GAS FIELD ALOM, ASHRAFUL; HASAN, NAZMUL The Integrated Reservoir Modeling of Shaldanadi Gas Field using Petrel software is a study aimed at improving the understanding of the reservoir characteristics and behavior of the Shaldanadi Gas Field. The modeling was carried out using Petrel, a powerful and widely used software in the oil and gas industry. The study utilized various techniques such as geophysical analysis, stratigraphic modeling, structural modeling, poperty modeling, well engineering of the reservoir. The working strategy includes geological works, engineering knowledge (well engineering, petrophysical analysis), fluid properties etc. By this integrated reservoir simulation static model, velocity model, porosity modeling, permeability modeling etc. The reservoir is devided into numbers of grid cell and each cell shows the gradual changing in porosity, permeability and other petrophysical properties. The gas-water contacts of each zone are also shown. After all, the findings of the study provide important insights into the reservoir behavior and could be used to support future decision making and operational activities in the Shaldanadi Gas Field. INTEGRATED RESERVOIR MODELING OF SALDA NADI GAS FIELD

2023-02-01T00:00:00Z TAJRIAN, MD.AUMIO BHUIYAN, MD ASHFAK HOSSAIN http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/997 2025-09-10T14:40:48Z 2023-03-01T00:00:00Z

Comparison of Machine Learning Models for Predicting Particle Size Parameters from Drilling Data TAJRIAN, MD.AUMIO; BHUIYAN, MD ASHFAK HOSSAIN Drilling efficiency is the capacity of a drilling operation to extract natural resources from subterranean reservoirs with maximum effectiveness. Drilling efficiency is crucial to the success of extraction operations, as it influences every aspect of the cost of the operation to the quantity and quality of the extracted natural resources. The effectiveness of a drilling operation is greatly affected by several factors, one of which is the particle size of the material being drilled. Drilling engineers must pay close attention to particle size characteristics, which characterize the size, shape, and density of the cuttings produced during drilling. Efficiency in drilling significantly impactsthe drilling process, which in turn affects the extraction technique. Since drilling is a complicated process used to recover precious natural resources, ineffective drilling will add additional expense and time to the project. The influence of particle size variables in drilling can be evaluated using machine learning by analyzing large datasets of drilling parameters and particle properties. In this study, machine learning algorithms are used to examine the relationship between particle size parameters and drilling performance, yielding insights into the optimal particle size parameters for a wide range of formations and drilling scenarios. To determine the characteristics of drilling and particle size most closely connected, three distinct machine learning techniques are used in this investigation. Among these methods, Random Forest shows the strongest correlation between these traits. This approach is used to anticipate particle size parameters for absent data points within the typical range for drilling parameters. The study can be used to evaluate the drilling performance and efficiency. A similar strategy can be implemented in formations with identical geological factors using the most applicable machine learning model. Comparison of Machine Learning Models for Predicting Particle Size Parameters from Drilling Data

2023-03-01T00:00:00Z