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"Kondisi fasilitas stasiun anjungan lepas pantai "E" di PT. X dengan dengan panjang pipeline offshore ± 1.955 kilometer dan pipeline onshore ± 10.057 kilometer serta jumlah SCE (safety critical equipment) sebanyak 25.601 unit, juga PCE (process critical equipment) sebanyak 60.164 unit dan adapun 17 insiden yang sangat berharga sepanjang tahun 2011-2013 sangat berguna untuk dilakukan analisa lebih jauh oleh peneliti. Tesis ini mengulas insiden hydrocarbon release dengan menggunakan analisa Bow-Tie untuk menentukan faktor dominan dari penyebab insiden yang ada di stasiun lepas pantai "E" di PT. X pada tanggal 26 Februari 2013. Dengan ditetapkannya faktor dominan maka manajemen dapat membuat program kerja untuk membantu keberjalan proses fasilitas menjadi lebih aman dan memberi dampak yang baik bagi PT. X dari segi bisnis.Teknik analisa bahaya tradisional seperti Failure Modes and Effect Analysis (FMEA) dan Fault Tree Analysis (FTA) sudah terlalu sering digunakan untuk menganalisis suatu bahaya. Teknik analisa FMEA dan FTA ini bersifat sebab-akibat linier dan kurang baik dalam analisa bahaya (Song, 2012). Metode Bow-tie menyediakan visualisasi yang mudah dipahami dari hubungan antara penyebab gangguan bisnis, eskalasi peristiwa kecelakaan, pencegahan peristiwa dan langkah-langkah kesiapsiagaan untuk membatasi dampak bisnis (Lewis, 2010). Diagram Bow-tie telah berkembang sebagai metode yang sangat berguna berguna untuk menggambarkan dan memelihara sistem manajemen risiko yang melekat dalam pekerjaan operasi sehari-hari dan juga telah terbukti dalam industri lepas pantai di seluruh dunia (Saud, Israni, & Goddard, 2013).

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The condition of Offshore Platform Station "E" in PT. X with its length of offshore pipeline reach to ± 1.955 kilometers and length of onshore pipeline ± 10.057 kilometers, as well as its total number of SCE (Safety Critical Equipment) 25.061 units and total number of of PCE (Process Critical Equipment) of 60.164 units, also it has 17 valuable incidents occured during 2011 - 2013, which all are very useful for further analysis by researcher. This thesis will analyze hyrdocarbon release incidents by using Bow-Tie analysys to determine dominan factors of causes of incidents happened at Offshore Station E at PT. X on 26th February 2013. By the establishment of dominant factors, the management can develop work programs to ensure the process of the facility to be safer and cause good impact for PT. X in term of business.Traditional hazard analysis techniques such as Failure Modes and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) are too frequently used to analyze a hazard. These FMEA and FTA analysis techniques are tend to be causal linear and poor in analyzing hazard (Song, 2012). Bow-tie method provides easily understandable visualization of relationship among business disruption causes, incident escalations, events prevention, and preparedness measures to limit business impact (Lewis, 2010). Bow-tie Diagram has developed to be a very useful method to describe and maintain risk management system that embedded in the daily operational works and also become a proven method in offshore industry worldwide (Saud, Israni, & Goddard, 2013)."

"Dalam tahapan awal eksplorasi hydorkarbon, diperlukan suatu metode awal yang dapat mendeteksi adanya perangkap hydrokarbon dimana perangkap ini disebabkan oleh adanya struktur patahan. Metode Magnetik adalah salah satu metode Geofisika yang dapat digunakan untuk mengetahui struktur permukaan bawah tanah, sehingga metode magnetik ini digunakan untuk menduga lokasi struktur patahan yang berlaku sebagai perangkap hydrokarbon. Dari hasil data pengukuran, kita mengaplikasikan KOREKSI DIURNAL dan KOREKSI IGRF, kemudian dilakukan UP WARD CONTINUATION untuk menghilangkan efek?efek lokal, dan ketika pemodelan digunakan metode FORWARD MODELLING untuk memperoleh gambaran awal mengenai struktur perlapisan bawah tanah. Daerah penelitian didominasi oleh lapisan sedimen dan juga banyak fosil?fosil yang tersingkap ke permukaan, hal ini dapat menjadi indikasi awal keberadaan hydokarbon pada daerah tersebut. Lokasi penelitian didominasi oleh nilai anomali negatif yang dikarenakan tebalnya lapisan sedimen yang memiliki nilai suseptibilitas rendah dan kenaikan anomali cenderung dikarenakan oleh adanya kenaikan lapisan dan juga kehadiran lapisan yang lebih magnetik. Dari hasil pemodelan diketahui terdapat 2 patahan yang diperkirakan menjadi perangkap hydrokarbon dimana pada daerah tersebut ditunjukkan adanya perubahan nilai anomali magnetik yang drastis.

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In the first step on hydrocarbon exploration, we need some preface method that can detect the hydrocarbon trap where it was because fault structure. Magnetic method was one of geophysics methods that can use to know the structure under the surface. After the aqusition data, we apply the DIURNAL CORRECTION and IGRF CORRECTION, after that we apply UP WARD CONTINATION to separate from local effect, and at modelling we use the FORWARD MODELLING to estimate the structure under surface. The exploration territory was dominated by sediment rock and many fossil show up into the surface, and this is can be first indication that in that place there are hydrocarbon. Location of exploration was dominated by negative anomaly and this is because the thick of sediment rock and the rise of anomaly because there was some layer go up into the surface and also because there are more magnetic layer. From the modelling result we know they are 2 fault that can be indicated the hydrocarbon trap where in that place occur drasctic anomaly changing."

"[Lapangan “X” merupakan lapangan gas terbesar di delta mahakam dengan luas area permukaan yang mancapai 1350km2 dan total akumulasi gas terproduksi mencapai 8 tcf sejak tahun 1990 hingga saat ini. Penurunan produksi yang cukup tajam melatarbelakangipengembangan gas di zona dangkal (shallow gas). Sedimen pada zona dangkal ini tersusun oleh endapan deltaik berumur Miosen Atas – Pliosen dengan batupasir sebagai batuan reservoar utama. Keberadaan fluida gas pada batupasir akan berdampak pada penurunan kecepatan gelombangP dan densitas batuan sehingga memberikan kontras impendansi akustik yang kuat terhadaplapisan shale. Kontras impedansi akustik ini terlihat sebagai anomali amplitudo (brightspot)pada seismik. Adanya kenaikan nilai amplitudo seiring dengan bertambah besarnya sudutdatang menjadi hal yang menarik dalam interpretasi shallow gas ini.Tujuan dari penelitian ini adalah untuk mendeteksi keberadaan shallow gas di lapangan “X”menggunakan atribut AVO Sismofacies dengan 2 sumur yang dijadikan referensi untukpemodelan synthetic AVO. Penulis menggunakan 2 sumur lainnya sebagai kalibrasi terhadapanomali AVO dari Sismofacies cube yang dihasilkan.Metode AVO sismofacies ini tidak menggunakan parameter intercept (A) dan gradient (B)untuk kalkulasi AVO melainkan menggunakan dua data substack yaitu Near dan Far stack.Crossplot antara Near dan Far pada zona water bearing sand dan shale diambil untukmendapatkan background trend sehingga anomali yang berada diluar trend tersebut dapatdiinterpretasikan sebagai gas sand.Hasil dari analisis AVO Sismofacies ini cukup baik dan menunjukkan kesesuaian denganinterpretasi gas di beberapa sumur dan efek Coal berkurang jika dibandingkan Far stack.Meskipun demikian interpretasi AVO ini sebaiknya diintergrasikan dengan analisis dariatribut seismik lainnya untuk memperkuat interpretasi;Field “X” is a giant gas field in mahakam delta which cover 1350km2 of the area with totalcummulative gas production has reached 8 tcf since 1990 to recently. A significantdecreasing of gas production has led to produce gas accumulation in shallow zone as aneffort to fight againts this decline. Shallow zone is a deltaic sediments which depositedduring Upper Miocen to Pliocene with dominant reservoir is sandstone.The presence of gas in sandstone has an impact on decreasing of velocity P as well as densitywhich giving a contrast of acoustic impedance to the overlaying shale. Contrast ofimpedance can be observes in seismic as an amplitude anomaly or so called a brightspot. Anincrease of amplitude along the offset become more interesting in shallow gas interpretation.The aim of this study is to detect shallow gas accumulation di field “X” by using AVOSismofacies attribute with 2 wells as references to model respons of AVO. The result ofAVO sismofacies will be a cube and the interpreation will be calibrated with 2 existing wellscontaining proven gas bearing sands.AVO Sismofacies method will introduce Near and Far substack to be used in the calculationinstead of using common AVO paramter intecepth (A) and gradient (B). A crossplot betweensubstacks will create a background trend from water bearing zone and shale hence anyoutliers can, then,be interpreted as gas anomaly.AVO Sismofacies result is encouraging and some of AVO anomaly has been well calibratedwith existing wells. Coal effect which led to misintepretaion in shallow gas sand isdiminished compared to Far stack. Despite of this result, this anomaly interpretation need tobe intergrated with anothers seismic attribute to gain the level of confidence for shallow gasinterpretation., Field “X” is a giant gas field in mahakam delta which cover 1350km2 of the area with totalcummulative gas production has reached 8 tcf since 1990 to recently. A significantdecreasing of gas production has led to produce gas accumulation in shallow zone as aneffort to fight againts this decline. Shallow zone is a deltaic sediments which depositedduring Upper Miocen to Pliocene with dominant reservoir is sandstone.The presence of gas in sandstone has an impact on decreasing of velocity P as well as densitywhich giving a contrast of acoustic impedance to the overlaying shale. Contrast ofimpedance can be observes in seismic as an amplitude anomaly or so called a brightspot. Anincrease of amplitude along the offset become more interesting in shallow gas interpretation.The aim of this study is to detect shallow gas accumulation di field “X” by using AVOSismofacies attribute with 2 wells as references to model respons of AVO. The result ofAVO sismofacies will be a cube and the interpreation will be calibrated with 2 existing wellscontaining proven gas bearing sands.AVO Sismofacies method will introduce Near and Far substack to be used in the calculationinstead of using common AVO paramter intecepth (A) and gradient (B). A crossplot betweensubstacks will create a background trend from water bearing zone and shale hence anyoutliers can, then,be interpreted as gas anomaly.AVO Sismofacies result is encouraging and some of AVO anomaly has been well calibratedwith existing wells. Coal effect which led to misintepretaion in shallow gas sand isdiminished compared to Far stack. Despite of this result, this anomaly interpretation need tobe intergrated with anothers seismic attribute to gain the level of confidence for shallow gasinterpretation.]"

"Penelitian ini didasarkan pada kebutuhan akan peningkatan akurasi dalam memprediksi properti reservoir, yang sangat penting untuk eksplorasi dan produksi hidrokarbon yang efektif. Metode tradisional sering kali kurang akurat dalam memberikan estimasi yang tepat, sehingga adopsi MARS bertujuan untuk mengatasi kekurangan ini. Penerapan Metode MARS bertujuan untuk mengevaluasi sensitivitas dan dampak transformasi Multi Attribute Rotation Scheme (MARS) dalam meningkatkan karakterisasi reservoir di Lapangan X, Cekungan Sunda. Metode ini bekerja dengan memperkirakan atribut baru dalam arah perubahan maksimum properti target dalam ruang Euclidean berdimensi n yang dibentuk oleh beberapa atribut, kemudian menskalakan atribut ini ke properti unit target. Hasil MARS diterapkan untuk memprediksi distribusi porositas, volume shale, dan saturasi air menggunakan atribut elastis yang diturunkan dari data seismik. Hasil penelitian menunjukkan bahwa metode MARS memiliki sensitivitas yang cukup terhadap perubahan atribut elastis, menghasilkan transformasi optimal untuk memprediksi sifat petrofisika reservoir. Transformasi MARS meningkatkan akurasi karakterisasi reservoir dibandingkan dengan metode konvensional. Pola distribusi porositas, volume shale, dan saturasi air yang diperoleh dari metode MARS konsisten dengan data log dan karakteristik geologi Cekungan Sunda. Temuan ini menunjukkan bahwa MARS dapat menjadi alat yang berharga untuk meningkatkan prediksi properti reservoir, yang mengarah pada pengambilan keputusan yang lebih baik dalam eksplorasi dan produksi hidrokarbon.

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This research is based on the need to improve the accuracy of predicting reservoir properties, which is crucial for effective hydrocarbon exploration and production. Traditional methods often lack precision in providing accurate estimates, thus the adoption of MARS aims to address these shortcomings. The application of the Multi Attribute Rotation Scheme (MARS) method aims to evaluate the sensitivity and impact of the MARS transformation in enhancing reservoir characterization in Field X, Sunda Basin. This method works by estimating new attributes in the direction of maximum change of the target property in an n-dimensional Euclidean space formed by multiple attributes, and then scaling these attributes to the target unit properties. The MARS results are applied to predict the distribution of porosity, shale volume, and water saturation using elastic attributes derived from seismic data. The research findings show that the MARS method has sufficient sensitivity to changes in elastic attributes, producing optimal transformations for predicting reservoir petrophysical properties. The MARS transformation improves the accuracy of reservoir characterization compared to conventional methods. The distribution patterns of porosity, shale volume, and water saturation obtained from the MARS method are consistent with log data and the geological characteristics of the Sunda Basin. These findings suggest that MARS can be a valuable tool for enhancing reservoir property predictions, leading to better decision-making in hydrocarbon exploration and production."

"Azotobacter chroococcum has a great potential as biosurfactant producing bacteria and was used as co-inoculant to promote the rate hydrocarbon biodegration...."

"The limited of ozon depleting substance (ODS) such as CFC and HCFC refrigrants has encourage researchers to find a new alternative refrigrant

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"[Lapangan X merupakan lapangan mature yang berada di Cekungan Sumatera Tengah. Lapangan ini memiliki struktur antiklin produk dari reverse oblique-slip fault yang membentuk zona patahan di sisi Barat Lapangan X. Zona ini terbukti menghasilkan hidrokarbon ditunjukan oleh sumur produksi X-027, X-153 dan X 154. Sehingga zona patahan ini memiliki potensi untuk di eksplorasi lebih lanjut. Namun, kondisi seismik di zona ini chaotic sehingga sulit untuk menginterpretasikan zona patahan. Penelitian ini akan menggunakan metode geoelectric IVEL dan continuous wavelet transform (CWT) untuk mendapatkan informasi keberadaan hidrokarbon dizona patahan Lapangan X. Geoelectric IVEL (Inversion Vertical Electrical Logging) menggunakan metode vertical sounding schlumberger yang diolah untuk menghasilkan penampang resistivitas medium. Hasil penampang resistivitas medium pada penelitian ini menunjukkan adanya kemiripan nilai resitivitas dengan nilai log resistivitas sumur untuk zona reservoar 350sd dan 550sd (10-20 ohmm). Nilai resistivitas ini terlihat juga di zona patahan yang dijadikan indikator hidrokarbon. Hasil dalam domain kedalaman membantu dalam interpretasi kedalaman reservoar di zona patahan. Analisis continuous wavelet transform (CWT) pada penelitian ini menunjukan amplitudo tinggi pada frekuensi rendah 5-20 Hz dan merupakan indikasi adanya hidrokarbon. Amplitudo tinggi pada frekuensi rendah telihat juga di zona patahan, pada posisi dimana IVEL menunjukan nilai resistivitas sebagai indikator.

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Hidrocarbon X field is a mature field in Central Sumatera Basin. It has anticline structure as a result of reverse oblique-slip fault that produces fault zone in the North side of X Field. It is proved hydrocarbon with the production well X-027, X-153 and X-154. However, it is very difficult to interpret the fault zone with the available seismic data because of the chaotic seismic condition in fault zone. This study uses Ivel Geoelectric method and Continuous Wavelet Transform (CWT) to get hydrocarbon indicator in fault zone. Geoelectric IVEL (Inversion Vertical Electrical Logging) using vertical sounding schlumberger is processed to get medium resistivity section. Medium resistivity section from geoelectrical IVEL at reservoir zone showes similar resistivity value with resistivity log (10-20ohm) for reservoar 350sd and 550sd. This value is showed in fault zone as hydrocarbon indicator. Medium resistivity geoelectrical IVEL is depth domain. It is helpful for interpretation of reservoir depth at fault zone, that is not able to be done by seismic. Continuous wavelet transform (CWT) showes high amplitude at low frequency (5-20Hz) as hydrocarbon indicator. High amplitude at low frequency is showed in fault zone where IVEL showes the hydrocarbon indicator;X field is a mature field in Central Sumatera Basin. It has anticline structure as a

result of reverse oblique-slip fault that produces fault zone in the North side of X Field. It is proved hydrocarbon with the production well X-027, X-153 and X-154. However, it is very difficult to interpret the fault zone with the available seismic data because of the chaotic seismic condition in fault zone. This study uses Ivel Geoelectric method and Continuous Wavelet Transform (CWT) to get hydrocarbon indicator in fault zone. Geoelectric IVEL (Inversion Vertical Electrical Logging) using vertical sounding schlumberger is processed to get medium resistivity section. Medium resistivity section from geoelectrical IVEL at reservoir zone showes similar resistivity value with resistivity log (10-20ohm) for reservoar 350sd and 550sd. This value is showed in fault zone as hydrocarbon indicator. Medium resistivity geoelectrical IVEL is depth domain. It is helpful for interpretation of reservoir depth at fault zone, that is not able to be done by seismic. Continuous wavelet transform (CWT) showes high amplitude at low frequency (5-20Hz) as hydrocarbon indicator. High amplitude at low frequency is showed in fault zone where IVEL showes the hydrocarbon indicator;X field is a mature field in Central Sumatera Basin. It has anticline structure as a result of reverse oblique-slip fault that produces fault zone in the North side of X Field. It is proved hydrocarbon with the production well X-027, X-153 and X-154. However, it is very difficult to interpret the fault zone with the available seismic data because of the chaotic seismic condition in fault zone. This study uses Ivel Geoelectric method and Continuous Wavelet Transform (CWT) to get hydrocarbon indicator in fault zone. Geoelectric IVEL (Inversion Vertical Electrical Logging) using vertical sounding schlumberger is processed to get medium resistivity section. Medium resistivity section from geoelectrical IVEL at reservoir zone showes similar resistivity value with resistivity log (10-20ohm) for reservoar 350sd and 550sd. This value is showed in fault zone as hydrocarbon indicator. Medium resistivity geoelectrical IVEL is depth domain. It is helpful for interpretation of reservoir depth at fault zone, that is not able to be done by seismic. Continuous wavelet transform (CWT) showes high amplitude at low frequency (5-20Hz) as hydrocarbon indicator. High amplitude at low frequency is showed in fault zone where IVEL showes the hydrocarbon indicator.;X field is a mature field in Central Sumatera Basin. It has anticline structure as a

result of reverse oblique-slip fault that produces fault zone in the North side of X Field. It is proved hydrocarbon with the production well X-027, X-153 and X-154. However, it is very difficult to interpret the fault zone with the available seismic because of the chaotic seismic condition in fault zone. This study uses IVEL Geoelectric method and Continuous Wavelet Transform (CWT) to get hydrocarbon indicator in fault zone. Geoelectric IVEL (Inversion Vertical Electrical Logging) using vertical sounding schlumberger is processed to get medium resistivity section. Medium resistivity section from geoelectrical IVEL at reservoir zone showes similar resistivity value with resistivity log (10-20ohm) for reservoar 350sd and 550sd. This value is showed in fault zone as hydrocarbon indicator. Medium resistivity geoelectrical IVEL is depth domain. It is helpful for interpretation of reservoir depth at fault zone, that is not able to be done by seismic. Continuous wavelet transform (CWT) showes high amplitude at low frequency (5-20Hz) as hydrocarbon indicator. High amplitude at low frequency is showed in fault zone where IVEL showes the hydrocarbon indicator, X field is a mature field in Central Sumatera Basin. It has anticline structure as a

result of reverse oblique-slip fault that produces fault zone in the North side of X

Field. It is proved hydrocarbon with the production well X-027, X-153 and X-154.

However, it is very difficult to interpret the fault zone with the available seismic data

because of the chaotic seismic condition in fault zone. This study uses Ivel

Geoelectric method and Continuous Wavelet Transform (CWT) to get hydrocarbon

indicator in fault zone.

Geoelectric IVEL (Inversion Vertical Electrical Logging) using vertical sounding

schlumberger is processed to get medium resistivity section. Medium resistivity

section from geoelectrical IVEL at reservoir zone showes similar resistivity value

with resistivity log (10-20ohm) for reservoar 350sd and 550sd. This value is showed

in fault zone as hydrocarbon indicator. Medium resistivity geoelectrical IVEL is

depth domain. It is helpful for interpretation of reservoir depth at fault zone, that is

not able to be done by seismic. Continuous wavelet transform (CWT) showes high

amplitude at low frequency (5-20Hz) as hydrocarbon indicator. High amplitude at

low frequency is showed in fault zone where IVEL showes the hydrocarbon indicator]"

"Manajemen Insiden merupakan bagian yang diperlukan dalam pengelolaan layanan IT. Penerapan manajemen insiden dapat meningkatkan layanan dan membantu mencapai tujuan organisasi. Insiden IT dapat menjadi referensi dan dapat membentuk pola yang dapat dipelajari sehingga dapat dijadikan prediksi sebuah insiden di masa datang. Riset ini membandingkan beberapa teknik awal machine learning (Random Forest, SVM, Multi Layer Perceptron) dengan beberapa teknik terbaru machine learning (RNN, LSTM, GRU) untuk memprediksi insiden IT. Kami menggunakan dataset ITSM data insiden. Grid search akan mencari kombinasi parameter terbaik hingga didapatkan optimal model. Cross-validation sebagai evaluasi teknik digunakan untuk menguji kinerja optimal model. Kami menggunakan 5-fold dan 10-fold cross-validation digunakan untuk mengevaluasi kinerja model dengan membagi dataset menjadi data pelatihan dan data uji. Hasil perbandingan menunjukkan bahwa akurasi tertinggi sebesar 98,866% yang dihasilkan oleh teknik machine learning LSTM pada 5-fold dan 10-fold cross-validation, SVM mempunyai akurasi terendah sebesar 97,837% yang dihasilkan oleh teknik machine learning SVM pada 5-fold dan 10-fold cross-validation.

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Incident Management is a part of managing IT services. The application of Incident Management can improve services and help achieve organizational goals. IT incidents could be a reference and can be learned so that they can be used as predictions of future incidents. This research compares the factors that cause incidents by using some initial machine learning techniques (Random forest, SVM, Multilayer perceptron) and compared with the latest machine learning techniques (RNN, LSTM, GRU) to predict IT incidents. We use the ITSM incident dataset. Grid search will find the best parameter combination until the optimal model is obtained, subsequently cross-validation as an evaluation technique is used to test the optimal performance of the model. We use 5-fold and 10-fold cross-validation to evaluate the performance of the model by dividing the dataset into training data and test data. The results of the comparison show that the highest accuracy of 98,866% produced by LSTM machine learning techniques at 5-fold and 10-fold cross-validation, SVM has the lowest accuracy of 97.837% produced by machine learning SVM at 5-fold and 10-fold cross-validation."