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INDONESIA
JOURNAL OF EARTH ENERGY ENGINEERING
Published by Universitas Islam Riau
ISSN : -     EISSN : 25409352     DOI : -
Core Subject : Science, Engineering,
Earth & Planetary Sciences Energy Engineering Materials Science & Nanotechnology
Journal of Earth Energy Engineering (eISSN 2540-9352) is a Bi-annual, open access, multi-disciplinary journal in earth science, energy, and engineering research issued by Department of Petroleum Engineering, Universitas Islam Riau. The journal is peer reviewed by experts in the scientific and engineering areas and also index in Directory of Research Journals Indexing (DRJI) and CrossRef Member.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol. 11 No. 3 (2022)" : 5 Documents clear
A Smart Solution for Fuel Smuggling Problem: The Reality and Challenges, Case Study of the Southern Region of Libya . Mohammed Alsharif Samba; Yiqian Li; Shamus
Journal of Earth Energy Engineering Vol. 11 No. 3 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.10350

Abstract

The phenomenon of smuggling is a crime that threatens countries in general. It is considered as a challenge for all countries to overcome this problem. The danger increases when the goods of smuggling are one of the most important natural resources in the country, which is the smuggling of oil or one of the oil derivatives, among which is fuel in a remarkable way. Where the smuggling groups smuggle the fuel across the land borders of south Libya. Given the presence of this crime, we are trying to shed light on it by asking many questions and knowing the position of the Libyan legislator regarding it. Were the solutions that decided useful or not? This paper was written as a result of the suffering suffered by the people in the south of Libya as a result of this crisis. However, the crise has described in general and provided the ideal solution that should be applied in all the countries. The solution was represented full system for the fuel distribution. The system is supported by monitoring sensors, indication sensors, and an artificial neural network system.
Capacitance Resistance Clustered Model for Mature Peripheral Waterflood Performance Prediction & Optimization Billal Aslam; Hasto Nugroho; Fahriza Mahendra; Rani Kurnia; Taufan Marhaendrajana; Septoratno Siregar
Journal of Earth Energy Engineering Vol. 11 No. 3 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.10633

Abstract

Optimizing water injection rate distribution in waterflooding operations is a vital reservoir management aspect since water injection capacities may be constrained due to geographic location and facility limitations. Traditionally, numerical grid-based reservoir simulation is used for waterflood performance evaluation and prediction. However, the reservoir simulation approach can be time-consuming and expensive with the vast amount of wells data in mature fields. Capacitance Resistance Model (CRM) has been widely used recently as a data-driven physics-based model for rapid evaluation in waterflood projects. Even though CRM has a smaller computation load than numerical reservoir simulation, large mature fields containing hundreds of wells still pose a challenge for model calibration and optimization. In this study, we propose an alternative solution to improve CRM application in large-scale waterfloods that is particularly suitable for peripheral injection configuration. Our approach attempts to reduce CRM problem size by employing a clustering algorithm to automatically group producer wells with an irregular peripheral pattern. The selection of well groups considers well position and high throughput well (key well). We validate our solution through an application in a mature peripheral waterflood field case in South Sumatra. Based on the case study, we obtained up to 18.2 times increase in computation speed due to parameter reduction, with excellent history match accuracy.
Determining Factors of Energy Intensity in the Manufacturing Industry of Provinces in Indonesia Peggy Hariwan; Feri Sunaryo; Muhammad Kholil
Journal of Earth Energy Engineering Vol. 11 No. 3 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.10649

Abstract

Energy is vital to Indonesia's economic activities in various sectors. Energy plays an important role in the sustainability of the economic structure, which includes is the manufacturing industry. However, limited natural resources are one of the challenges for policymakers. Although energy conservation policies have been implemented in Indonesia since 1982, their enforcement in the manufacturing industry sector has not been solutive in supporting the development of the manufacturing industry in all regions. This study aims to determine the relationship between the development of energy intensity and economic growth in 26 provinces of Indonesia, using the growth and share analysis method from the data the authors have obtained. The results showed that the paper and printed goods, cement, and non-metallic minerals industries are the sub-sectors with high energy consumption. Then, Riau, DKI Jakarta, and West Java provinces are in the dominant quadrant for economic growth, but their energy intensity is in the low/slow quadrant. This indicates that industries in these three provinces have efficient use of energy.
The Effect of Different Gas Water Ratio on Recovery Factor and CO2 Storage Capacity in Water Alternating Gas Injection. A Case Study: “V” Field Development, North Sea Sayen Girsang; Deny Fatryanto; Rohima Sera Afifah
Journal of Earth Energy Engineering Vol. 11 No. 3 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.6097

Abstract

CO2 injection is one of the Enhanced Oil Recovery (EOR) methods. In this study Water alternating gas (WAG) CO2 injection method was used to obain the maximum sweep efficiency. The purpose of this study was to analyze the effect of gas water ratio (GWR) value on recovery and CO2 storage capacity, and to analyze the best scenario in term of technical objective. This study was carried out using E300 reservoir simulator. The increase in recovery and CO2 storage were observed throught the parameters of recovery factor and CO2 storage capacity, while the determination of the best scenario in term of technical objective was observed using the parameters of objective function. This study was carried out in 3 different scenarios, which were the injection of 100% CO2, 60% CO2and 40% water, and 40% CO2 and 60% water Based on the observation, it was founded that third scenario with the GWR of 40:60 resulted the highest cumulative production and recovery factor with the value reaching 14.1 milliom m3 and 67.4%. Meanwhile the second scenario with the GWR of 60:40 has the highest CO2 storage capacity of 3 billion Sm3 CO2. The second scenario has the best performance in term of technical objective with the value of objective function reaching 0.45.
Oil Formation Volume Factor Prediction Using Artificial Neural Network: A Case Study of Niger Delta Crudes Chiebuka Okoro; Angela Nwachukwu
Journal of Earth Energy Engineering Vol. 11 No. 3 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.7121

Abstract

Artificial intelligence techniques provide an alternative to conventional empirical correlation methods when experimentally determined oil formation volume factors (OFVF) are lacking. A new mathematical model is proposed using an artificial neural network (ANN) for estimating the OFVF for the Niger Delta crude oils. The method consists of two stages: data decorrelation through principal component analysis (PCA) and OFVF estimation through ANN. Data decorrelation was used to reduce redundancy in the data which decreased the number of neurons in the hidden layer needed for an ANN to achieve high accuracy. In the development of the model, 316 data points were obtained from the Niger Delta region of Nigeria. Application of data cleaning, outliers’ elimination and PCA analysis reduced the data to 243 points. 213 data points were used to develop the model of which 75% was used for training, 15% for validation and 10% for testing. The remaining 30 data points were used to test the predictive capability of the proposed model. The results obtained were compared with widely accepted empirical correlations of Standing, Glaso, Vazquez, Ikiensikimama & Ajienka, and Al-Marhoun. The proposed new model performed better than all of them in terms of coefficient of correlation, AAPE and RMSE. Hence the ANN model will reduce cost, save time, and also predict the OFVF of Niger Delta crudes with higher precision.

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