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Scientific Contributions Oil and Gas
Published by LEMIGAS
ISSN : 20893361     EISSN : 25410520     DOI : -
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Energy
The Scientific Contributions for Oil and Gas is the official journal of the Testing Center for Oil and Gas LEMIGAS for the dissemination of information on research activities, technology engineering development and laboratory testing in the oil and gas field. Manuscripts in English are accepted from all in any institutions, college and industry oil and gas throughout the country and overseas.
Arjuna Subject : Energi (semua kategori) - Teknologi Bahan Bakar
Articles 6 Documents
 1 
Search results for , issue "Vol 45 No 1 (2022)" : 6 Documents clear
The Feasibility Study of Reservoir Geomechanics from Brittleness Evaluation Benyamin Elilaski Nababan; Harnanti Yogaputri Hutami; Fatkhan Fatkhan; Sonny Winardhi
Scientific Contributions Oil and Gas Vol 45 No 1 (2022)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.45.1.920

Abstract

A detailed understanding regarding the rocks Brittleness Index is helpful in oil and gas exploration as upfront information to determine the rock fracture gradient. Researchers have proposed several methods to estimate the rock Brittleness Index. However, different ways may yield different results and lead to varying interpretations regarding the Brittleness Index classifi cation. This paper evaluates the Brittleness Index of an Indonesian gas well using three approaches based on the elastic properties log data, elastic properties rock physics modeling, and mineralogical rock physics modeling to assess the consistency of the methods. The results obtained in this study suggest that elastic properties-based and mineralogical methods produced a consistent Brittleness Index. However, the vertical resolution is different. It indicates that the Brittleness Index estimated from the actual log data showed higher resolution than the Brittleness Index calculated from the rock physics modeling. Combining TOC data with the Brittleness Index is recommended to optimize hydraulic fracturing design and planning. For further investigation, the authors will be suggesting direct sampling from cores and laboratory measurements to obtain the in-situ mechanical properties of shale rocks.
Strategy Formulation of Natural Gas Continuity Supply (Case Study PT ABC) Dhanu Saptowulan; Idqan Fahmi; Bagus Sartono
Scientific Contributions Oil and Gas Vol 45 No 1 (2022)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.45.1.921

Abstract

This study aims to formulate a strategy for PT ABC in maintaining the continuity of natural gas supply. Feasibility analysis and decision tree method are used to determine the chosen strategy in maintaining the continuity of natural gas supply. Internal and external analysis are used to identify the key success factors of the company in implementing the chosen strategy and then summarized and evaluated using IFE and EFE matrix. To formulate implementation strategies by aligning key internal and external factors, IE and SWOT matrix are used. QSPM matrix is used to determine the priority of the implementation strategy. The results show IFE and EFE score are 2.55 and 2.76 respectively, so that PT ABC has suffi cient internal resources to maintain the continuity of natural gas supply and able to respond well to opportunities and threats. This condition can be managed best with hold and maintain strategies which are market penetration and product development. QSPM Matrix analysis show that product development group strategy has the highest Total Attractiveness Score (TAS) thus become priority to be executed and then market penetration strategy.
Build of Machine Learning Proxy Model for Prediction of Wax Deposition Rate in Two Phase Flow Water-Oil Jalest Septiano; Amega Yasutra; Silvya Dewi Rahmawati
Scientific Contributions Oil and Gas Vol 45 No 1 (2022)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.45.1.922

Abstract

Wax deposit is one of the major fl ow assurance experienced in the process of oil production and transportation from sub- surface to surface. Large amounts of data are required to perform modeling using existing thermodynamic models such as carbon number data from HGTC. In this paper, a machine learning algorithm using unifi ed model approach from Huang (2008). Two types of input are implemented in order to simulate infl uence of feature selection used in training and testing machine learning which are input A consists of water volume fraction (fw), shear stress (τw), effective viscosity (μe), wax concentration gradient (dC/dT), and temperature gradient (dT/dR) and input B consists of water volume fraction (fw), shear stress (τw), effective viscosity (μe), wax concentration gradient (dC/dT), temperature gradient (dT/dR), shear stripping variable (SV) dan diffusion variable (DV). The random forest with Ntree = 500 known to be the best machine learning method compared to others. Based on accuracy parameter it achieves error parameter R-squared (R2) for training, testing and total data for input A and B are 0.999, 0.992, 0.9975 and 0.999, 0.993, 0.9977, respectively.
A Case Study of Primary and Secondary Porosity Effect for Permeability Value in Carbonate Reservoir using Differential Effective Medium and Adaptive Neuro-Fuzzy Inference System Method Reza Wardhana; Amega Yasutra; Dedy Irawan; Mochammad Wahdanadi Haidar
Scientific Contributions Oil and Gas Vol 45 No 1 (2022)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.45.1.923

Abstract

Pore system in a carbonate reservoir is very complex compared to the pore system in clastic rocks. According to measurements of the velocity propagation of sonic waves in rocks, there are three types of carbonate pore classifi cations: Interpartikel, Vugs and Crack. Due to the complexity of various pore types, errors in reservoir calculation or interpretation might occur. It was making the characterization of the carbonate reservoir more challenging. Differential Effective Medium (DEM) is an elastic modulus modeling method that considers the heterogeneity of pores in the carbonate reservoir. This method adds pore-type inclusions gradually into the host material to the desired proportion of the material. In this research, elastic modulus modeling will be carried out by taking into account the pore complexity of the carbonate reservoir. ANFIS algorithm will also be used in this study to predict the permeability value of the reservoir. Data from well logging measurements will be used as the input, and core data from laboratory will be used as train data to validate prediction results of permeability values in the well depths domain. So, permeability value and pore type variations in the well depth domain will be obtained.
Source Rock Potential of Nampol Formation Sumbermanjing Area, Malang, East Java, Indonesia Based on Geochemistry Analysis of the Selected Sample Carolus Prasetyadi; Achmad Subandrio; M. Gazali Rachman; Antu Ridha Falkhan Barizi; Muhammad Muslim
Scientific Contributions Oil and Gas Vol 45 No 1 (2022)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.45.1.924

Abstract

Nampol Formation of the Southern Mountains of eastern Java (Indonesia) has a distribution from its type location in Pacitan to the South Malang area. In the research area, this formation consists of clastic limestone with black shale inserts, claystone, siltstone, carbonate sandstone and claystone which are interpreted to be deposited in a restricted platform interior environment with closed water circulation. A total of three samples were analyzed to evaluate the organic matter content, kerogen type, thermal maturity, and hydrocarbon generating potential. Samples were taken from clastic carbonate deposits of the Nampol Formation. Based on the results of geochemical analysis, the three samples from the Nampol Formation have a TOC content of 3.48 - 26.18 wt% and possess good to excellent hydrocarbon generating potential. Hydrogen Index (HI) values for the studied samples ranged from 43 to 86 mg HC/g TOC and S1+S2 results ranged from 1.52 to 19.55 mg HC/g rock, indicating that the sample has the potential to produce gas. All three samples were dominated by Type III kerogen and were thus considered gas prone based on the HI vs. Tmax diagrams. The three samples were categorized as thermally immature based on Tmax pyrolysis analysis and Vitrinite Refl ectance (VR) values in the range of 0.44 to 0.46 % Ro. Based on the results obtained, the black shale and coal in the Nampol Formation has the capability to generate hydrocarbon but are considered as an immature source rock that can be predicted to produce gas at its peak maturity.
Long Short-term Memory (LSTM) Networks for Forecasting Reservoir Performances in Carbon Capture, Utilisation, and Storage (CCUS) Operations Utomo Pratama Iskandar; Masanori Kurihara
Scientific Contributions Oil and Gas Vol 45 No 1 (2022)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.45.1.943

Abstract

Forecasting reservoir performances during the carbon capture, utilization, and storage (CCUS) operations is essential to monitor the amount of incremental oil recovered and CO2 trapped. This paper proposes predictive data-driven models for forecasting oil, CO2, and water production on the existing wells and future infill well utilizing long short-term memory (LSTM) networks, a deep learning variant for time series modeling. Two models are developed based on the number of phases referred to: 3-phases (3P) and 1-phase (1P), one interest phase at a time. The models are trained on the dataset from multiple wells to account for the effect of interference of neighboring wells based on the inverse distance to the target well. The performance of the models is evaluated using walk-forward validation and compared based on quality metrics and length and consistency of the forecasting horizon. The results suggest that the 1P models demonstrate strong generalizability and robustness in capturing multivariate dependencies in the various datasets across eight wells with a long and consistent forecasting horizon. The 3P models have a shorter and comparable forecasting horizon. The 1P models show promising performances in forecasting the fluid production of future infill well when developed from the existing well with similar features to the infill well. The proposed approach offers an alternative to the physics-driven model in reservoir modeling and management and can be used in situations when conventional modeling is prohibitively expensive, slow, and labor-intensive.

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