Diagenesis dan Properti Batuan Karbonat Miosen Tengah Cekungan Jawa Barat Utara

Daerah penelitian secara administratif terletak pada daerah Citeurep provinsi Jawa Barat dan secara geografis daerah penelitian terletak pada koordinat 106o 28' 26,4” – 107o 00” 00'BT dan 06o 28' 26,4” – 06o 30' 54” LS. Deskripsi megaskopis dan sayatan tipis menunjukan fasies yang terdiri dari Skeletal Packstone, Red Algae Packstone, Foram Packstone, dan Boundstone dengan lingkungan diagenesa mulai dari Mixing Zone, Fresh Water Phreatic, dan Meteoric Vadose. Porositas dan Permeabilitas didefinisikan dengan melakukan analisis routine dari core plug singkapan dan ditunjang oleh sayatan tipis untuk melihat jenis dari porositas yang ada. Grup dengan lingkungan diagenesa Meteoric Vadose memiliki porositas 10%-22% dan permeabilitas 0.03 mD – 1.3 mD, group dengan lingkungan diagenesa Fresh Water Phreatic memiliki porositas 3% - 24% dan permeabilitas 0.02 mD – 1.5 mD, sedangkan grup dengan lingkungan diagenesa Mixing Zone memiliki porositas 20% dan permeabilitas 0.03 mD

Utilization of Acoustic Wave Velocity for Permeability Estimation in Static Reservoir Modeling: A Field Case

Several researches have shown that P-wave velocity carries information on the complexity of the rock's pore geometry and pore structure. Their complexity can be characterized by critical porosity. Therefore, the P-wave velocity is used to estimate permeability. This research uses data taken from the Tomori formation from Banggai-Sula basin, Central Sulawesi, which is a carbonate rock reservoir. Also, this research aims to obtain a 3D permeability model by using acoustic wave velocity cube data. The results show that permeability can be modeled well using acoustic wave velocity data. Furthermore, compared to the raw data log of permeability, the modeling results using wave velocity based on critical porosity show good results. This method is another alternative to permeability modeling if acoustic wave velocity cube data is availabl

SOCIALIZATION OF CORAL REEF SUSTAINABILITY IN PARI CAY, SERIBU ISLANDS

Pari Cay was known as one of significant growth of coral reef area for marine ecosystem. Pari Cay belongs to the Seribu Islands which is located in the Jakarta Bay. The islands are formed from the assemblage of marine biota assemblage. Coral reefs are a comfortable environment for the life of various underwater biota which must be preserved and protected from damage and extinction. Fish catches of fishers in Jakarta Bay are strongly influenced by the preserved of the surrounding coral reefs. The reduced number of coral reefs will reduce the catch of fishermen's fish. Through this socialization is expected that citizens can take part and participate in preserving the marine and coastal environment, especially Pari Cay coral reefs. The increasing participation of the surrounding inhabitant in marine life sustainability is expected to improve the livelihoods of the communities both from fishing and tourism. The society enthusiasm of the Pari Island with this socialization at least can find out the desire of the Pari Island Citizens to preserve the coral reefs. These a continuation of the previous socialization and changes in the awareness of the Pari Island Citizens to preserving coral reefs have been look significant. 

The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data

Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns

HOW FAR THE ROCK TYPE AND LIMESTONE FACIES ARE INTERRELATED: A CASE STUDY IN OILFIELD, BANGGAI BASIN, CENTRAL SULAWESI

Located in the Central Sulawesi, this study about relation between rock type and limestonefacies can be known using qualitative and quantitative analysis. Qualitative analysis consist offacies analysis and quantitative analysis consist of quantify petrophysical property toperforming rock grouping based on Flow Zone Indicator. Qualitative analysis shows that thelithology dominantly consists of limestone. Then based on facies analysis shows there arethree reef system that are back reef lagoon, core reef and fore reef. Reef system thatarranged by lithofacies consists of wackstone with very good vuggy, packstone, grainstoneand mudstone. In the quantitative analysis based on petrophysical value there are consistfour rock type, wackstone with very good vuggy is dominated with rock type 1 which hasvalue of FZI > 1.32 micrometers, permeability 11.05 - 2233.98 md and porosity 16% - 42%.The packstone is dominated with rock type 2 which has value of FZI 1.31 - 0.66 micrometer,permeability 2.08 - 38.17 md, porosity 13% - 29%. The grainstone is dominated with rock type3 which has value of FZI 0.65 - 0.38 micrometers, permeability 0.1 - 8.89 md, porosity 8% -24% and the mudstone is dominated with rock type 4 has a value of FZI < 0.37 micrometer,permeability 0.01 - 0.16 md, porosity 6% - 12%. The relation between rock type and reef system facies is unrelated, but the rock type with lithofacies has a bit related which consistsof wackstone with very good vuggy has an excellent rock type, packstone has a good rocktype, grainstone has a fair rock type and mudstone has a poor rock type, there are due to thepresence of diagenetic control in carbonate rocks

The Implementation of Machine Learning in Lithofacies Classification Using Multi Well Logs Data

Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns