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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 186 Documents
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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
Analyze of Water Injection Performance Surveillance in “ATHENA” Field Andi Dewi; Firdaus; Deny Fatryanto
Journal of Earth Energy Engineering Vol. 11 No. 2 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.6356

Abstract

To measure the success of waterflood activities, we need evaluation and analysis. To support evaluation and analysis need to be done assessment of well connectivity to the response of injection wells, performance wells with Hall-plot and Voidage Replacement Ratio, and calculate water breakthrough time with method Buckley-Leverret whether according to the actual field. To examine these required supporting data such as field history, production and injection history, fluid level measurement data. The results of the study showed the well ATH-43 less response (poor response) and the well ATH-37 and ATH-33 gave good response (good response) and gain oil obtained by 8,196 barrels. The hall-plot evaluation showed that the well ATH-04 had no formation/normal damage, and the results of the VRR showed the VRR < 1. The results of the calculation of water breakthrough time calculations with actual show the well experiencing breakthrough earlier than the calculation. (Premature breakthrough).
Investigation of Horizontal Well for Cyclic Steam-Solvent Stimulation to Escalate Heavy Oil Production Ahmad Muraji Suranto; Allen Haryanto Lukmana; Ristiyan Ragil Putradianto; M Rizqi Asy'ari
Journal of Earth Energy Engineering Vol. 11 No. 2 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.8130

Abstract

Indonesia's oil reserves that have been exploited on a large scale are light oil because the recovery technique is much easier than the heavy oil. Generally, heavy oil has a high viscosity compared to light oil. In most cases, to reduce high oil viscosity (greater than 50 cp) using steam injection. Cyclic steam stimulation (CSS) is one of processes that commercially developed by numerous oil company to producing of heavy oil reservoir. The CSS can apply in vertical well or horizontal well. The research of CSS in horizontal well is still limited reported in the literature. On the other hand, the horizontal well has drainage area more wide compared with vertical well. In this study, solvent addition in cyclic steam stimulation will be tested with reservoir simulation. The steam was injected on the well, after that soaking time and the last was producing of liquid fluid reservoir. As the result, the oil production increases 3 times higher compared to without solvent. Furthermore, cumulative steam oil ratio (CSOR) and cumulative energy oil ratio (CEOS) decrease about 50% and 16%, respectively. In here, effect of solvent added in the steam, the steam distribution and drainage area wider.
Predicting Rate of Penetration and optimization Weight on bit using Artificial Neural Networks Tien Hung Nguyen
Journal of Earth Energy Engineering Vol. 11 No. 2 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.8170

Abstract

Obtaining the maximum Rate of Penetration (ROP) by optimization drilling parameters is the aim of every drilling engineer. This is because it could save time, reduce cost and minimize drilling problems. However, ROP depends on a lot of parameters which lead to difficulties in its prediction. Therefore, it is necessary and important to investigate a solution predicting ROP with high accuracy to determine the suitable drilling parameters. In this study, a new approach using Artificial Neural Network (ANN) has been proposed to predict ROP from real – time drilling data of several wells in Nam Rong - Doi Moi field with more than 900 datasets included important parameters such as the weight on bit (WOB), weight of mud (MW), rotary speed (RPM), standpipe pressure (SPP), flow rate (FR), torque (TQ). In the process of training the network, algorithms and the number of neurons in the hidden layer were varied to find the optimal model. The ANN model shows high accuracy when compared to actual ROP, therefore it can be recommended as an effective and suitable method to predict the ROP of other wells in the research area. Besides, base on the proposed ANN model, authors carried out experiments and determind the optimal weight on bit value for the drilling interval from 1800 to 2300 m of wells in Nam Rong Doi Moi field
Eco-Friendly Bridging Material: Experimental Characterization of Eggshells as an Affordable Natural Waste Non-Damaging Lost Circulation Material to Reduce Drilling Fluid Cost in Reservoir Drill-In-Fluid System Muhammad Rizqi Al Asy'ari; M Naufal Afifabyan; Riska Karimah
Journal of Earth Energy Engineering Vol. 11 No. 2 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.9158

Abstract

Lost circulation nowadays became one of the major problems in many drilling operations worldwide. This problem is complicated for it can significantly cause non-productive time during drilling operations. This research aims to study an eggshells as a LCM and to provide further insight about the reliability and cost analysis of eggshells as a potential drilling fluid additive. Moreover, the research successfully identified the usage of an abundant natural waste, i.e. eggshells, as an environmentally friendly fluid additive. This research also investigated the technical feasibility of the eggshells and also its economics impact on drilling operations. In addition, it is found that the eggshells can also act as a non-damaging LCM for production zone that is more affordable as compared to other natural waste loss circulation material and current commercially chemical. Series of laboratory tests were conducted such as mud balance for the density test, rheological test using viscometer Fann Vg, filtration loss test with filter press, and also alkalinity (pH) test. An excellent result from filtration loss test i.e. decreased fluid losses and showed great improvement almost same as commercially CaCO3 result in the filter cake thickness. The research proves great potential of the utilization of eggshells as a multi-purpose additive in a drilling fluid. Economic analysis also suggests that it can possibly be implemented and to be further developed for a large-scale field operations. Finally, it is found that using eggshells as LCM can reduce the cost up to 72.2 % cheaper than commercial CaCO3. It is also safe for drilling in the production zone (pay-zone) because of its solubility on acid that it can disappear during the acidizing job. If this paper can be implemented on a wide-range scale it will be very beneficial to reduce other commercial additives usage without losing its reliability.
Stuck Pipe Detection in Geothermal Operation with Support Vector Machine Sarwono Sarwono; Lukas; Maria Angela Kartawidjaja; Raka Sudira Wardana
Journal of Earth Energy Engineering Vol. 11 No. 2 (2022)
Publisher : Universitas Islam Riau (UIR) Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jeee.2022.9258

Abstract

One of the biggest problems during drilling operation is a stuck pipe in which the drill string would stick or freeze in the well. This challenge leads to a significant amount of remedial costs and time. Many researchers have investigated different factors regarding the stuck pipe. These factors include poor hole cleaning, improper mud design, key seating, balling up of bit, accumulation of cutting and caving, poor bottom hole assembly configuration, and differential pressure. Since geothermal drilling targets lost circulation zones at reservoir depth, the chance of getting stuck pipe events becomes higher. Many publications reported that lost circulation events that lead to stuck pipe events have become the top non-productive time (NPT) contributor to costs in many geothermal drilling projects. The consequences of a stuck pipe are very costly, that include lost time when releasing the pipe, time, and cost of fishing out the parted Bottom Hole Assembly (BHA), and efforts to abandon the tool(s) in the hole. Despite many observations that have been done to develop a system in avoiding stuck pipe incidents in oil and gas drilling operations using artificial intelligence (AI), few works have been developed for geothermal drilling operations. In this research, we propose a method to build an early warning system model for stuck pipe conditions based on a Support Vector Machine. Based on the experiment result Support Vector Machine Algorithm showed good performance with 89% accuracy and 81% recall for limited training dataset.
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.

Page 16 of 19 | Total Record : 186

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