Hendra Cordova

Optimization of Oil Production in CO 2 Enhanced Oil Recovery

Oil production have several stage i.e. primary, secondary and tertiary. In tertiary stage, the effort to increase oil production is called as enhanced oil recovery (EOR). EOR is performed by injecting material or energy from outside reservoir. There are several EOR methods that have been developed and implemented in the oil field, including thermal recovery, chemical flooding, and solvent flooding. One of solvent flooding is CO2 EOR by injecting CO2 to reservoir. CO2 EOR method has capability to increase 5–15% oil recovery. In addition, injecting CO2 to reservoir have good impact to reduce global warming effect. However, to obtain the optimum result of CO2 EOR needs several parameter to be optimized, such as mass flow rate, pressure and temperature injection. There are several equation that have been used to build a model of CO2 EOR pressure drop. There are Fanning equation for injection well, Darcy equation for reservoir formation and Beggs-Brill equation for production well. The model has been validated using PIPESIM software for injection well model and have mean error 2.204%. Meanwhile reservoir formation model has been validated using COMSOL Multiphysics software and have mean error 3.863%. The optimization of CO2 EOR using Duelist Algorithm provide increasing the net profit 42.47% from 26,548.62 USD/day to 37,826.39 USD/day.

Optimization of Mud Injection Pressure in Oil Drilling Using Duelist Algorithm

Drilling fluid or commonly called mud, is used to lift drilling cutting to the surface, cool and lubricate the bit and drill string, as supporting walls in the borehole using mud cake, and control the formation pressure. The injection pressure is very important because if the injection pressure of the mud is not right, it is going to cause various effects, such as causing fracture in the wellbore, causing heat in the bit and drill string, weak wall since weak mud cake formed in the well, and being able to cause kick or stuckpipe. Because of the impact of the mud injection pressure is significant to drilling performance, the pressure of mud injection in the oil drilling needs to be optimized using Duelist Algorithms to minimize the amount of pressure drop at each hole diameter, i.e. at 17 in. hole diameter in 1269.68 ft depth and at 12.25 in. hole diameter in 2132.55 ft depth. Before being optimized, the pressure of mud injection was modeled first using bingham-plastic method for calculating pressure drop in six different sections of the wellbore, i.e. surface equipment, drill pipe, drill collar, bit, annulus around drill collar, and annulus around drill pipe. Afterwards, it was summed to calculate the total pressure drop in each hole diameter. The total initial pressure drop for 17 in. hole diameter was 978 psi and for 12.25 in. hole diameter was 1875 psi. In order to minimize the pressure drop, it was necessary to do optimization using Duelist Algorithm. The optimized variables were mud density and flow rate. Sensitivity analysis was utilized to obtain behaviour of the system. Duelist Algorithm optimization was performed using 200 iterations, 200 duelists, 80% learning probability, 10% innovate probability, 95% luck coefficient, and 10% board of champion. After being optimized using Duelist Algorithm method, for 17 in. hole diameter, mud density became 9 ppg and flow rate became 505 gpm. Therefore, the pressure drop became 695 psi. Meanwhile, for 12.25 in. hole diameter, mud density became 9.18 ppg and flow rate became 603 gpm. Therefore the pressure drop became 1145 psi. The results show that the optimization of mud injection operating condition in drilling process provide reduction of pressure drop in mud injection that would be give good impacts in the drilling efficiency and safety. This result will be useful for drilling engineer to set up the drilling equipment and mud properties.

Analisa Performansi dan Monitoring Berbasis Web pada Pembangkit Listrik Tenaga Surya di Fakultas Teknologi Industri ITS

Kebutuhan masyarakat terhadap listrik sangat meningkat dikarenakan semakin maraknya perangkat elektronik. Maka dari itu pemilihan PLTS sebagai alternatif karena indonesia merupakan negara yang berada di garis katulistiwa. Untuk menunjang kinerja PLTS dibutuhkan monitoring berbasis web untuk monitoring kinerja PLTS saat terjadi kerusakan. Dengan menggunakan sistem pemantauan nirkabel raspberry pi sebagai pengganti zigbee yang dirancang untuk menggantikan penggunaan kabel secara konvensional. Dari hasil analisa monitoring nilai rata-rata throughtput yang didapat adalah sebesar 0,60822047 Kbps. rata-rata nilai delay yang terjadi hanya bernilai 0,469370341 ms dengan packet loss yang didapat kan sebesar 0%. Efisiensi rata-rata PV array didapatkan sebesar 10% dan efisiensi sistem PV sebesar 4%. Dalam satu hari mendapatkan 4,26297 kWh, sedangkan Rp 1467,28 persatu kWh nya. Maka dalam segi ekonomi menghemat uang sebesar Rp 6254,965731 perharinya, Dan perbulannya menghemat sebesar Rp 187.648,9719. peramalan effisiensi rata-rata PV selama beroperasi sebesar 12%, sedangkan nilai effisiensi aktualnya 10%, kesalahan peramalan effisiensi sebesar 2%.

Mapping system for the surface temperature in the Ijen Crater by Landsat-8 imagery with supervised image segmentation method based on fuzzy logic

Mapping temperature will affect the exploration of geothermal resources. On February 11 2013, Landsat-8 was launched to continue the mission of the satellite before. Because of this conditions it is necessary to research on the particular application of Landsat data for mapping the land surface temperature in the mountainous area of Ijen, Banyuwangi, East Java. The image used in this study has dimensions 242 × 237 with 336 KB of the data. The canals on Landsat-8 in used is channel 6, channel 5, channel 3, channel 10, channel 11. Application of these channel based on the capabilities of each sensor between Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). Supervised Image Segmentation methods used for classification with a total inputs 36 class and output 3 class. Based on the rules of membership function in fuzzy logic, 3 category states of cold category, medium category and hot category. The minimum temperature value obtained from the calculation of the surface temperature is 288 K and then the maximum temperature value obtained is 309 K. 13 point coordinates are used as reference for the validation data, so the result of measurement temperature is appropriate because error value only 7%.