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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.
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Articles 5 Documents
 1 
Search results for , issue "Vol. 11 No. 1 (2022)" : 5 Documents clear
Evaluation of Remaining Gas Reserves Using the Material Balance Method for Planning Gas Field Development Dyah Rini Ratnaningsih; Ahmad Muraji Suranto; Cahyadi Julianto
Journal of Earth Energy Engineering Vol. 11 No. 1 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.6632

Abstract

The demand of energy in the world will increase due to the increasing population and industrial activity. Currently, the fossil energy is relatively cheaper compared to other energy sources, especially natural gas. The “CJ” field is a gas field located in the South Sumatra Basin, Indonesia with a reservoir located in the Basalt Telisa Limestone (BTL) formation. This gas field consists of 3 wells namely Well GTA-1, GTA-2, and GTA-3 which produced from 1951 to 1991. In 1991 the three wells were suspended and will be reopened in 2021 due to request from buyers for 10 years. The research method is collecting and consisting of data on reservoir, production, and physical properties of the gas. The next step is to calculate the value of the gas formation volume factor and Z-factor (gas compressibility factor/gas deviation factor) with various pressures. After it, determine the type of drive mechanism using the Cole Plot method. After knowing the type of drive mechanism, determine the current OGIP value using the material balance method. If the OGIP value is known, the next calculation is the Recovery Factor (percentage of the amount of gas that can be produced to the surface), Ultimate Recovery (UR) and finally the value of Remaining Reserve (RR). Based on the calculation, the OGIP value obtained by the material balance method with P/Z vs GP plots is 83.46 BSCF, Recovery Factor of 80.22%, Ultimate Recovery of 66.96 BSCF, and remaining gas reserve 15.45 BSCF. The maximum flow rate could be obtained by remaining reserve divided contract period. From these results, the maximum reserve value that can be produced to the surface for 10 years is 4.23 MMSCFD. Therefore “CJ" Field meet the needs of buyer to fulfil the requirement number which is only 4 MMSCFD.
Experimental Study of Polymer Injection on Oil Recovery Factor Enhancement Using Homogenous and Heterogenous Micromodel Porous Media Boni Swadesi; Roiduz Zumar; Sinosa Husenido; Dedy Kristanto; Indah Widiyaningsih; Sri Murni
Journal of Earth Energy Engineering Vol. 11 No. 1 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.6791

Abstract

Polymer injection is one method of chemical enhanced oil recovery, which increase oil recovery by improving mobility when viscous fingering occurred in waterflooding operation. The result of polymer injection is better sweep efficiency, which is presented by more even distribution of the injected fluid. However, in common laboratory evaluation for polymer injection testing, it was no visual observation that presents directly for the fluiddistribution. This experimental study was carried out to visually observe the polymer injection mechanism to displace oil by micromodel as porous media. The micromodel used in this study is transparent acrylic material which was etched by laser engraving technology to create grains that resemble reservoir rocks. The micromodel was saturated by brine water and light oil respectively as initial reservoir fluids. Then, the water was injected as waterflooding operation to displace oil in a micromodel. Hydrolyzed Polyacrylamide (HPAM) polymer with various concentrations were injected into the micromodel as the last scenario. Through this experiment, the movement and distribution of fluids in chemical enhanced oil recovery especiallypolymer injection was able to be recorded for further analysis. Observation for each scenario was done by Digital Image Analysis (DIA). The micromodel flooding results showed that the higher concentration of polymer would give higher oil recovery. The front stability and good distribution of polymer will result in better sweep efficiency, then higher oil recovery will be achieved. This experiment gives result visually how polymer enhance oil recovery. This experiment is expected to be leading innovation for Enhanced Oil Recovery (EOR) laboratory studies in Indonesia
Integrated Completion Study for Hpht Sour Gas Well Development in Carbonate Reservoir X Steven Chandra; Wijoyo Niti Daton; Ellen Setiawan
Journal of Earth Energy Engineering Vol. 11 No. 1 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.7133

Abstract

The increasing need for energy sources and the decreasing available reserves have promoted oil and gas companies to explore and manage marginal reservoirs, such as the sour gaseous environment. This is to maintain the balance of energy supply and demand. Due to the supply of Natuna Gas Field, the gap in gas supply-demand is likely to decrease by 20%, as regards the example of a potential sour gaseous environment (Batubara, 2015). Therefore, the immediate development of this potential source is very relevant. The sour field approximately shares 40% of Indonesia’s total gas reserve with 75% recovery, at an estimated OGIP of 222 TSCF. However, this environment is economically unproductive due to having high carbon dioxide (CO2) and hydrogen sulfide (H2S) contents, which are toxic and corrosive. Based on previous studies, the X-reserves reportedly contained 32% CO2 and 7072 ppm H2S, with fluid gravity of 42 API. This discretionary source of CO2 was recently brought into production from a well with a depth of 8400 ft, perforated at a limited interval of 7100 to 7700 ft. The harsh environment presented many challenges to the completion of the design, as well as the need to incorporate corrosion effects with unique equipment and material selection for the tubular structure. Therefore, this study aims to determine reservoir fluids and production performance, as well as also predict the corrosivity of dissolved CO2 in the natural gas. With the simulation and prediction, the proper material and equipment selection was obtained, based on the required sour service. The results showed that the wet gas reservoir of the X-field produced an optimum rate of 19.1063 MMSCFD. For the completion of the design, Nickel Alloy SM2535 or SM2242 was needed, due to damages in form of corrosion and pitting
Evaluation of the use of Water Alternated Gas Injection for Enhanced Oil Recovery Bright Kinate; Adaobi Nwosi-Anele; Ifeanyi Nwankwo
Journal of Earth Energy Engineering Vol. 11 No. 1 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.7410

Abstract

Hydrocarbon can be naturally produced from underneath fractured sandstone when pressure can no longer force fluids to the surface facilities. A satisfactory recovery factor for this production was conducted through the cost-effective enhanced oil recovery (EOR) method. Water alternated gas (WAG) injection is a promising EOR technique that combines the advantages of waterflooding and gas injection to achieve better mobility control, improved sweep efficiency, and overall recovery from the given reservoir. Therefore, this study aims to investigate the relationship of a miscible WAG to a core flood model using numerical simulation techniques (Eclipse Reservoir Simulator – Black Oil Model Option). In this case, reservoir X consisting of three wells drilled 15 years after the initial forecast showed that production cannot be sustained by natural depletion. Furthermore, the optimal WAG ratio was selected with different simulation scenarios using oil recovery factors to perform 12 simulation runs and study the influence of the WAG cycle period. The most effective WAG cycle scenario was 90W-30G with an oil recovery factor of 0.54684 (54.68 %) and cumulative production of 14.987MMSTB. The 30W-90G produced the lowest oil recovery factor and cumulative production of 0.47468 (47.47%) and 12.996 MMSTB, respectively. Therefore, a higher water cycling period is required for better oil recovery. The recovery is also enhanced by lowering the rate of water to gas injection. The results showed that despite the predicted higher recovery factor, a lower WAG ratio indicated a potential of relatively low-pressure maintenance which can affect future recovery from the reservoir.
ROP Prediction with Supervised Machine Learning; a Case Study : Supervised Machine Learning Ganesha R Darmawan; Dedi Irawan
Journal of Earth Energy Engineering Vol. 11 No. 1 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.7772

Abstract

Optimum drilling penetration rate, known as the rate of penetration (ROP) has played a big role in drilling operations. Planning the well ROP always becomes a challenge for drilling engineers to calculate the drilling time needed for the section. Optimum ROP is achieved when the time to drill the section is as planned. Many empirical approaches were develop to model the ROP based on the drilling parameters, and might not always match the actual ROP. In some cases, the actual ROP was slower than planned, which may increase the drilling cost, which needs to be avoided. Hence, some approaches using artificial intelligence (AI), and supervised machine learning have been develop to overcome it. Supervised machine learning is used to developed a ROP model and ROP prediction for one of the development fields, based only on two wells drilling parameters data. The model was trained using Gradient Boosting, Random Forest, and Support Vector Machine. Drilling parameter test data then is used to validate the model. The model of Random Forest shows a good or promising result with R2 of 0.90, Gradient Boosting shows R2 of 0.86, and Support Vector Machine with R2 0.72. Based on the models generated, the Random Forest has shown a good trend which could be used for modeling ROP in the future development wells

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