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All Journal Techno.Com: Jurnal Teknologi Informasi TEKNOLOGIA Jurnal Migasian Akamigas Balongan Indramayu Petro : Jurnal Ilmiah Teknik Perminyakan Jurnal Migasian
RAKA SUDIRA WARDANA
UNIVERSITAS PERTAMINA
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Mathematics Public Health

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Pore Pressure Prediction Using Artificial Neural Network Based On Logging Data RAKA SUDIRA WARDANA; Meredita Susanty; Hapsoro B.W
Jurnal Migasian Vol 4 No 1 (2020): Jurnal Migasian
Publisher : LPPM Akademi Minyak dan Gas Balongan Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36601/jurnal-migasian.v4i1.97

Abstract

Pore pressure is a critical parameter in designing drilling operations. Inaccurate pore pressure data can cause problems, even incidents in drilling operations. Pore pressure data can be obtained from direct measurement methods or estimated using indirect measurement methods such as empirical models. In the oil and gas industry, most of the time, direct measurement is only taken in certain depth due to relatively high costs. Hence, empirical models are commonly used to fill in the gap. However, most of the empirical models highly depend on specific basins or types of formation. Furthermore, to predict pore pressure using empirical models accurately requires a good understanding in determining Normal Compaction Trendline. This proposed approach aims to find a more straightforward yet accurate method to predict pore pressure. Using Artificial Neural Network Model as an alternative method for pore pressure prediction based on logging data such as gamma-ray, density, and sonic log, the result shows a promising accuracy.
Designing Liquid-Gas Rate Window of Aerated Drilling Using Guo-Ghalambor Method Fauzia Fadhila Anwar; Raka Sudira Wardana
Jurnal Migasian Vol 4 No 2 (2020): Jurnal Migasian
Publisher : LPPM Akademi Minyak dan Gas Balongan Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36601/jurnal-migasian.v4i2.135

Abstract

Loss circulation is a common problem in geothermal drilling due to naturally fractured formation and depleted reservoir pressure. This problem might lead to another problem such as a stuck pipe. In some cases, LCM is not effective in curing loss in a naturally fractured formation and cannot be used to cure loss circulation in the production zone. One of the methods that can be used to prevent loss circulation and also preventing reservoir damage in geothermal drilling is underbalanced drilling or aerated drilling. In an underbalanced or aerated drilling operation, the ratio of air injection rate ad liquid rate is critical to ensure the cutting carrying capacity while preventing hole problems. Usually, computer simulations are used to determine the safe gas-liquid rate limit due to the complexity of the multiphase flow in an underbalanced drilling system. Since the simulation software is not always available, a simpler and reliable method is needed to determine the gas-liquid rate limit in aerated drilling. the purpose of this paper is to design the operating window of the gas-liquid rate ratio in aerated drilling. the purpose of this paper is to design the operating window of gas-liquid rate ratio in aerated drilling using a simple yet reliable method such as the Guo-Ghalambor Liquid-Gas Rate Window method. The result of this research is a gas-liquid rate envelope that can be used to promote good cutting transport, preventing formation and borehole damaged while preventing loss circulation in geothermal well.
PREDIKSI LAJU PENETRASI PENGEBORAN MENGGUNAKAN JARINGAN SARAF TIRUAN Fadhil Rhisnanda; Raka Sudira Wardana; Bastian Andoni
JURNAL TEKNOLOGIA Vol 2 No 1 (2019): Jurnal Teknologia
Publisher : Aliansi Perguruan Tinggi Badan Usaha Milik Negara (APERTI BUMN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (5105.641 KB)

Abstract

Various efforts have been made to reduce drilling costs in the oil and gas upstream industry. One of it is by maximizing drilling Rate Of Penetration (ROP), the speed at which a drill bit breaks the formation underneath it to deepen the borehole. High ROP resulted in shorter drilling times can reduce drilling costs. This is the ideal condition that is expected in every drilling process. However, many factors such as environmental factors (rock formations, wellbore size, drilling mud), drilling parameters (weight on bits, rotational speed, flow rate, hydraulics, etc.) and the characteristics of the bits determine the ROP. Among all, drilling parameters is the only one that can be customized to generate the highest ROP during the drilling process. Choosing drilling parameters to generate the highest ROP in the various environmental condition is not a trivial thing. Moreover, the correlation among these parameters is not linear, and some other factors also affect ROP. Some empirical ROP models that can be used requires parameters that are not always available in the operation field. This study proposes an Artificial Neural Network (ANN) to predict ROP. Using formation type and drilling parameters data as the input, the model produces a great degree of accuracy (R-square at least 0.8). It shows that ANN can become a better alternative to find the optimum drilling parameter to achieve the highest ROP.
Stuck Pipe Detection For North Sumatera Geothermal Drilling Operation Using Artificial Neural Network Sarwono Sarwono; Lukas Lukas; Maria Angela Kartawidjaja; Raka Sudira Wardana
Jurnal Migasian Vol 6 No 1 (2022): Jurnal Migasian
Publisher : LPPM Akademi Minyak dan Gas Balongan Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36601/jurnal-migasian.v6i1.192

Abstract

One of the most common problems encountered during geothermal drilling operations is stuck pipe. The risk of stuck pipe is higher in geothermal drilling operations since geothermal drilling targets the lost circulation zone at reservoir depth. The stuck pipe problem can cause a significant increase in drilling time and costs. The cost of a stuck pipe includes the time and money spent on extracting the pipe, fishing the parted BHA, and the effort required to plug and abandon the hole. Therefore preventing stuck pipes is far more cost effective than the most effective freeing procedures. Many researchers are working to identify the symptoms to reduce the risk of a stuck pipe. Due to the complexion of stuck pipe’s symptoms and indicators, some researcher proposed artificial intelligence (AI) as the tool to predict stuck pipes. Although researches have been made to build systems employing artificial intelligence (AI) to avoid stuck pipe occurrences in oil and gas drilling operations, few works have been done for geothermal drilling operations. This paper describes a study that employed Artificial Neural Networks (ANN) approaches to predict stuck pipe incidents. Field data were collected from 6 geothermal wells drilled in North Sumatera fields. ANN was used to construct models to forecast stuck pipe incidents. The investigation found that ANN showed good performance with 84% accuracy and 74% recall for the limited training dataset. These ANN approaches provide good predictions that can help increase response time and accuracy in preventing stuck pipes.
BUILD RATE PREDICTION USING ARTIFICIAL NEURAL NETWORK FOR POSITIVE DISPLACEMENT MOTOR APPLICATION IN FIELD X Raka Sudira Wardana
PETRO:Jurnal Ilmiah Teknik Perminyakan Vol. 7 No. 1 (2018): April
Publisher : Jurusan Teknik Perminyakan Fakultas Teknologi Kebumian dan Energi Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (354.055 KB) | DOI: 10.25105/petro.v7i1.3225

Abstract

One of the critical issue in drilling activity is well steering. Well trajectory as the result of well steering can affect well placement in reservoir, completion issue and anti-collision issue, etc. Steering wellbore to the wrong trajectory can cause damage and increase drilling cost. Directional driller performs well steering by giving steering command and controlling drilling parameters. This command is adjusted based on steering behavior of drilling BHA. Steering behavior is the ability of the drilling BHA in deviating wellbore based on given steering command and drilling parameter. By understanding the steering behavior of drilling BHA, directional driller can predict build rate and turn rate produced so accurate well trajectory can be accomplished. Several factors that affect steering behavior are steering command, formation characteristic, drilling assembly mechanism and drilling parameters. Obstacle in understanding steering behavior is the absence of correlation that connects each factor. Artificial Neural Network (ANN) is a tool that can find the relation between input parameters and output parameter without generating correlation, and use new input data to predict the value of the output. This research shows that Artificial Neural Network can be used as a tool to analyze steering behavior and predict build rate based on steering behavior. Using formation characteristic, steering mode, weight on bit, rotary speeds, jet impact force, motor bent angle and stabilizer size from 10 wells in field X as input parameters, ANN generates a model which later validated in predicting build rate from new dataset. The good agreement between prediction data and the actual data is showed in the results.
PERFORMANCE COMPARISON ANALYSIS BETWEEN RSS AND CONVENTIONAL MUD MOTOR IN MAVVAR FIELD Raka Sudira Wardana; Bastian Andoni
PETRO:Jurnal Ilmiah Teknik Perminyakan Vol. 8 No. 3 (2019): SEPTEMBER
Publisher : Jurusan Teknik Perminyakan Fakultas Teknologi Kebumian dan Energi Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1837.352 KB) | DOI: 10.25105/petro.v8i3.5513

Abstract

Rotary Steerable System (RSS) has transformed the directional drilling industry by producing smoother borehole, reducing torque and drag and enhancing the Rate Of Penetration (ROP). Despite the advantages of using RSS, the conventional steerable motor is still widely used in deviated well due to its lower daily cost. Therefore, to optimize the performance and the cost of the drilling operation, it is critical to analyze when the RSS outweigh the conventional mud motor. This paper analyzes the performance between Rotary Steerable System and Conventional Steerable Motor, based on these following parameters: Rate of Penetration (ROP), overall drilling cost, borehole quality and lost in hole cost. This empirical study uses literature study and quantitative data analysis from several wells in Mavvar Field in compliment. The result shows that the Rotary Steerable System (RSS) provides better performance and more efficient in cost.
ANALISA PREDIKSI TEKANAN PORI FORMASI MENGGUNAKAN PERSAMAAN EATON Weny Astuti; Raka Sudira Wardana; Jan Friadi Sinaga
PETRO:Jurnal Ilmiah Teknik Perminyakan Vol. 8 No. 3 (2019): SEPTEMBER
Publisher : Jurusan Teknik Perminyakan Fakultas Teknologi Kebumian dan Energi Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1600.432 KB) | DOI: 10.25105/petro.v8i3.5515

Abstract

Prediksi tekanan abnormal formasi merupakan hal yang  penting pada operasi pengeboran. Prediksi tekanan pori formasi yang tepat bisa mencegah terjadinya permasalahan pada pengeboran seperti pipe sticking, lost circulation, kick hingga blowout. Tekanan pori formasi bisa diukur secara langsung melalui Repeat Formation Tester (RFT) atau Modular Dynamic Tester (MDT), namun hal ini proses ini tidak dilakukan di setiap kedalaman dan hanya bisa dilakukan setelah proses pengeboran selesai dilakukan. Untuk itu perlu dilakukannya prediksi tekanan pori formasi dengan menggunakan data – data logging menggunakan persamaan empiris. Salah satu persamaan yang umum digunakan yaitu persamaan Eaton (1975). Pada paper ini dibahas analisa prediksi tekanan formasi menggunaan persamaan Eaton untuk sumur X di lapangan Y. Hasil prediksi menunjukkan adanya zona tekanan pori abnormal pada sumur X.
Evaluation of Aerated Drilling in K-01 Geothermal Well using Guo Ghalambor’s Gas-Liquid Rate Window Raka Sudira Wardana; Khansa Rasyidah
PETRO:Jurnal Ilmiah Teknik Perminyakan Vol. 9 No. 4 (2020): DESEMBER
Publisher : Jurusan Teknik Perminyakan Fakultas Teknologi Kebumian dan Energi Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (711.286 KB) | DOI: 10.25105/petro.v9i4.8158

Abstract

Common problem in geothermal drilling is loss circulation problem. One of the common methods to cure loss circulation problem is using Lost Circulation Materials (LCM), but in the production zone, using LCM can damage the production zone. Therefore, underbalanced drilling is method that can be used to prevent loss circulation problems in production zone in geothermal well. One of the most common underbalanced drilling methods is aerated drilling or foam drilling. Aerated drilling was used to overcome the loss of circulation problem in production zone in K-01 Geothermal Well. Even though, the aerated drilling was already used, but the loss circulation problem was still occurred. The purpose of this research is to evaluate aerated drilling operation in K-01 Well using Guo Ghalambor’s Gas-Liquid Rate Window and make recommended gas-liquid rate for the next drilling operation. Gas-Liquid Rate Window is constructed using characteristic of the formation, drilling parameters, daily drilling report and also fluid injection characteristics that was used for aerated drilling operation in K-01 geothermal well. Using the constructed Gas-Liquid Rate Window, an evaluation is carried out for the drilling operation in K-01 geothermal well.  The gas-liquid rate parameters used in aerated drilling operations is evaluated while checking the loss circulation event from the mud logging data. After the evaluation of the aerated drilling is carried out in then a suggestion is made for the next drilling operation. Based on the evaluation, the combination of gas-liquid rates that was used on the 9.875"hole section in K-01 Well was in the outside of the constructed GLRW therefore loss circulation problem occurred. The recommended gas-liquid rate combination from this research can be used to determine the gas-liquid rate combination to prevent loss circulation problems, wellbore damage and cutting transport problems.
REDUCING THE RISK OF WELL INTEGRITY INCIDENT BY INTEGRATING TOPSIS AND AHP MULTICRITERIA DECISION-MAKING ANALYSIS Ragil Sudira Wardana; Manahan Siallagan; Raka Sudira Wardana
PETRO: Jurnal Ilmiah Teknik Perminyakan Vol. 10 No. 1 (2021): MARET
Publisher : Jurusan Teknik Perminyakan Fakultas Teknologi Kebumian dan Energi Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (371.554 KB) | DOI: 10.25105/petro.v10i1.8302

Abstract

With current oil price downturn many oil wells become uneconomic. These uneconomic wells are left in an inactive state and become idle wells. Idle well is an environmental liability due to its risk of well integrity problems. Impacted by the downturn, the number of idle wells in the industry has been increasing in the industry. One of the solutions to mitigate these liabilities is by conducting plug & abandonment (P&A) on high-risk idle wellss. This research develops a combined framework of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the Analytic Hierarchy Process (AHP) as a risk assessment framework to prioritize high-risk idle wells for the P&A activity. In the assessment framework, surface condition, subsurface condition, and public exposure factors are taken as evaluation criteria to determine the risk level. The result of this research is 247 idle wells considered as high-risk wells and submitted as P&A candidates. The empirical result from this research can serve as a reference for oil companies in conducting a risk assessment on idle wells to design the proper activities to reduce environmental liabilities.
Pore Pressure Prediction Using Artificial Neural Network Based On Logging Data RAKA SUDIRA WARDANA; Meredita Susanty; Hapsoro B.W
Jurnal Migasian Vol 4 No 1 (2020): Jurnal Migasian
Publisher : LPPM Institut Teknologi Petroleum Balongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36601/jurnal-migasian.v4i1.97

Abstract

Pore pressure is a critical parameter in designing drilling operations. Inaccurate pore pressure data can cause problems, even incidents in drilling operations. Pore pressure data can be obtained from direct measurement methods or estimated using indirect measurement methods such as empirical models. In the oil and gas industry, most of the time, direct measurement is only taken in certain depth due to relatively high costs. Hence, empirical models are commonly used to fill in the gap. However, most of the empirical models highly depend on specific basins or types of formation. Furthermore, to predict pore pressure using empirical models accurately requires a good understanding in determining Normal Compaction Trendline. This proposed approach aims to find a more straightforward yet accurate method to predict pore pressure. Using Artificial Neural Network Model as an alternative method for pore pressure prediction based on logging data such as gamma-ray, density, and sonic log, the result shows a promising accuracy.
Co-Authors Bastian Andoni Bastian Andoni Fadhil Rhisnanda Fauzia Fadhila Anwar Hapsoro B.W Jan Friadi Sinaga Khansa Rasyidah Lukas Lukas Manahan Siallagan Maria Angela Kartawidjaja Meredita Susanty Ragil Sudira Wardana Sarwono Sarwono Weny Astuti