Journal of Geoscience, Engineering, Environment, and Technology
Not a member yet
306 research outputs found
Sort by
Application of Lineament Density Extraction Based on Digital Elevation Model for Geological Structures Control Analysis in Suwawa Geothermal Area
Full text linkThe tectonic condition of Gorontalo, which is located in the north of Sulawesi Island has implications for the spread of geothermal potential. The area in Gorontalo with the largest geothermal potential is the Suwawa area, Bone Bolango Regency. Therefore, this study aims to develop a model of lineament extraction from a digital elevation model and analyze the geological structure control based on the lineament distribution. This research is useful for the development of knowledge in the geothermal field, especially the study of permeability and structural control in geothermal areas. This research is beneficial for the community because it can detect the permeability zone in more detail which is the basis for the utilization of geothermal potential. The factors studied in this study are the geological lineament density and the geological structures. To achieve the research objectives, extraction methods and model analysis include analysis of permeable and control of geological structures. The lineament extraction model from the digital elevation model in the Suwawa geothermal area shows that there is a moderate to high agreement for lineament extraction from NATIONAL DEM data and low to moderate agreement for lineament extraction from SRTM data. The lineament distribution showing moderate to high density occupies the southern, eastern, and western parts of the Suwawa geothermal area. The presence of a lineament controls the circulation of geothermal fluids in the Suwawa geothermal area
The Role of Inertinite Characteristics and Coal Porosity of Seam A-1 of Muara Enim Formation in West Merapi, Lahat, South Sumatera, Indonesia
Full text linkCoal contains a complex network of nano-, meso-, and a macro-pore can store fluids and allow fluids to flow through it. Nanoporosity in coal is primarily a result of molecules that have aromatic molecular structures and have been preserved in coal. Most adsorbate compounds, including gases, are stored here. The study area is located in South Sumatera, West Merapi Area, Lahat Regency. Geologically, the area in South Sumatra Basin belongs to the Middle-Late Miocene Muara Enim Formation. Using the ply-by-ply method, coal samples were taken directly from Seam-A in the coal mine walls outcrop, based on macroscopically determinable lithotype information. During laboratory analyses, coal is microscopically analyzed to determine the amount of porosity, permeability, and vitrinite reflectance. The purpose of this study is to investigate the change in composition and characteristics of inertinite macerals when the porosity value is varied.. Vitrinite content is between 91.00-92.80 %; liptinite 0.90-3.40%; inertinite 3.70-4.80%; mineral matter 0.7%-1.8%. Withh a vitrinite reflectance average of 0.34-0.36%, the variation in composition is an indication of changes in plant communities or coal facies. It is generally classified as sub-bituminous coal (ASTM). Porosity value of seam A upper is 1.9% and seam A lower 1.51%, permeability value seam A upper is 70.1 mD and seam A lower 27.1%. Composition of mineral matter in seam A upper is 0.8% and seam A lower 1.7%. The increasing number of inertinite pore is followed by lower porosity value. The inertinite maceral is predominantly aromatic with a high level of cross-linking, and exhibits a high level of aromatization and condensation. They have the highest carbon and the lowest oxygen hydrogen content. A coal maceral's porosity is composed of void spaces, such as open cell lumens preserved in semifusinite and sclerotinite. The porosity of cleats is the percentage of volume in relation to volume of coal, and the porosity of permeability. In coal, semifusinite has extensive interconnected pores that can form significant conduits for fluid flow
Back matter JGEET Vol 07 No 02 2022
No full text  
The Role of Fractal Micro-Pore to Absorption of Methane Gas, Case Study: Coal of Tanjung Formation, Arang Alus Area, Banjar District, South Kalimantan, Indonesia
Full text linkThe Tanjung Formation is one of the coal bearing formations in the Barito Basin, South Kalimantan. The coal seams in the Tanjung Formation in the Arang Alus area have 4 (four) seams,there are seam A, B, C, and D. The age of these coal seams are Eocene - Oligocene with a thickness between 0.5 - 2 meters. This study aims to determine the characteristics of micropore fractal and methane gas absorption from coal samples taken by channel sampling on exposed coal in the open pit. The method used is SEM analysis, vitrinite reflectance (Ro,max), adsorption isotherm, and fractal calculation. The four coal seams based on vitrinite reflectance values (0.52 %- 0.62 belong to the sub-bituminous rank. Based on the methane gas absorption capacity for coal seam C of 450 SCF/ton while coal seams A, B and D of 308 SCF/ton, 336 SCF/ton and 407 SCF/ton, the fractal pore dimension value in seam coal C = 1.963 is higher than seam coal A = 1.933, B = 1.940 , and D = 1.943. The small size of the fractal pore dimension value caused by the degree of regularity of the micropore distribution in each coal seam methane differences
Characteristics of Kedondong Trass and Bobos Trass as Cement Raw Material, Cirebon, West Java, Indonesia
Full text linkThe use of cement materials in construction continues to increase every year, consumes lots of raw material and emits CO2 from clinker production. To eliminate this negative effect, alternative materials are needed. Trass is natural pozzolan which is formed from silica-alumina rich volcanic rocks. As supplementary cementitious material, trass is sufficiently durable and reduce clinker proportion in cement mixture, thus more environmentally friendly.
This research aims to determine characteristics and composition of Kedondong trass and Bobos trass, Cirebon, West Java as raw material for pozzolan cement. The study was conducted using petrography and XRD analysis to determine mineralogy of rocks. XRF analysis was carried out to determine chemical composition as well as other tests to determine trass quality.
Kedondong trass is originated from andesite intrusion and andesitic breccia, while Bobos trass is formed from hypersthene-andesite intrusion. Based on mineralogy analysis, trasses have similar mineral composition consist of plagioclase, quartz, pyroxene, hornblende, and sanidine. XRD analysis shows abundance of cristobalite and tridymite from each samples. This mineralogy is confirmed by geochemistry result, which is the samples contain more than 70% SiO2 + Al2O3 and less than 4% SO3. Other chemical characteristics that have been tested are moisture content, ignition loss, and clay content in which all of those parameters meet the industrial standard for cement material
Rock characteristics of post-caldera volcanoes in Dieng volcanic complex (DVC), Central Java, Indonesia
Full text linkThe Dieng volcanic complex (DVC) has one of the densest post-caldera volcanisms activity presents in Indonesia, yet its population density is considerably high. Therefore, it is important to identify the rock characteristics produced by the DVC post-caldera volcanoes to understand the risks and future hazards (i.e., eruption style). Based on lithology, we have classified DVC post-caldera volcanoes as (1) pyroclastic domain (PD; including Pagerkandang, Merdada, and Pangonan), and (2) lava domain (LD; including Prambanan, Kendil, Pakuwaja, Sikunir, Sikarim, and Seroja). PD is characterized by the domination of pyroclastic materials (mostly ash and lapilli) with oxidized scoria and volcanic lithics (fresh and/or altered) as the main components. The oxidized scoria clasts are moderately vesicular (27–41 % vesicularity; ) and phenocryst poor (<5 % phenocryst crystallinity, ), with plagioclase, pyroxene, and oxides as the main phenocryst phases. The LD is composed predominantly of lava. The observed lavas are typically dense (mostly <1 % , phenocryst rich (21–47 % ), and include plagioclase, pyroxene, biotite, amphibole, and oxides as the main phenocryst phases. Such differences in mineralogy and textures (i.e., vesicularity and crystallinity) suggest that PD and LD were likely sourced from different magmatic sources with different eruption styles (explosive and effusive styles, respectively). We have suggested that civilization settlements near PD are facing major threats from explosive magmatic, phreatomagmatic, and phreatic eruptions that could produce significant fallouts, ballistic materials, and highly destructive pyroclastic density currents. LDs pose a threat in the form of effusive magmatic eruptions such as lava flows and/or domes
Estimation of density log and sonic log using artificial intelligence: an example from the Perth Basin, Australia
Full text linkIt is well understood that with a large number of data, an excellent interpretation of the subsurface condition can be produced, and also our understandings of the subsurface conditions can be improved significantly. However, having abundant subsurface geological and petrophysical data sometimes may not be possible, mainly due to budget issues. This situation can generate issues during hydrocarbon exploration and/or development activities.
In this paper, the authors tried to apply artificial intelligence (AI) techniques to estimate outcomes values of particular wireline log data, using available petrophysic data. Two types of AI were selected and these are artificial neural network (ANN), and multiple linear regression (MLR). This research aims to advance our understanding of AI and its application in geology. There are three objectives of this study: (1) to estimate sonic log (DT) and density log (RhoB) using different types of AI (ANN and MLR); (2) to assess the best AI technique that can be used to estimate certain wireline log data; and (3) to compare the estimated wireline log values with the real, recorded values from the subsurface.
Findings from this study show that ANN consistently provided a better accuracy percentage compared to MLR when estimating density log (RhoB). While using different set of data and technique, estimation of sonic log (DT) produced different accuracy level. Moreover, crossplot validation of the results show that the results from ANN analysis produced higher trendline reliability (R2) and correlation coefficient (R) than the results from MLR analysis. Comparison of the estimated RhoB and DT log data with the original recorded data shows minor mismatch. This is evident that AI technique can be a reliable solution to estimate particular outcomes of wireline log data, due to limited availability of the original recorded subsurface petrophysic data. It is expected that these findings would provide new insights into the application of AI in geology, and encourage the readers to explore and expand the many possibilities of the application of AI in geology
