Hasil Pencarian  ::  Simpan CSV :: Kembali

"Sebagian besar kasus kecelakaan besar yang terjadi di sektor minyak dan gas disebabkan oleh kurangnya/ketidaktahuan akan pengelolaan asset integrity. Pada tahun 2021-2022 terdapat lima kasus kecelakaan terkait aset integrity di PT X. Untuk menjawab hal ini, PT X membuat Asset Integrity Management System (AIMS). Tujuan penelitian ini yaitu menganalisis AIMS di PT X berdasarkan konsep safety resilience. Manfaat penelitian yaitu memberikan perspektif implementasi safety resilience untuk menghadapi kejadian yang dapat diperkirakan atau tidak terduga seperti kegagalan pada aset di PT X. Penelitian ini merupakan penelitian semi kuantitatif dengan menggunakan desain studi analisis deskriptif, dan panduan analisis berdasarkan Resilience Analysis Grid. Unit analisis dalam penelitian ini mengambil dokumen terkait AIMS di PT X dan wawancara dengan stakeholder terkait AIMS di PT X. Hasil dari analisis empat faktor resilience pada AIMS di PT X adalah kemampuan respon (73,75%), kemampuan monitor (81,23%), kemampuan belajar (77,22%), dan kemampuan antisipasi (75,62%). Dari hasil tersebut, tingkat safety resilience pada AIMS sudah menuju level proactive dengan rata-rata sebesar 77%. Keterlibatan beberapa pihak, pembagian tanggung jawab yang jelas, dan penambahan indikator efektifitas AIMS, menjadi hal yang diperlukan untuk meningkatkan kemampuan resilience pada AIMS di PT X.

---

Most of the major accident cases that occur in the oil and gas sector are caused by the lack of/ignorance of asset integrity management. In 2021-2022 there were five cases of accidents related to asset integrity at PT X. To answer this, PT X created an Asset Integrity Management System (AIMS). The purpose of this research is to analyze AIMS at PT X based on the concept of safety resilience. The benefit of the research is to provide a perspective on the implementation of safety resilience to deal with predictable or unexpected events such as failures in assets at PT X. This research is semi-quantitative research using a descriptive analysis study design, and an analysis guide based on the Resilience Analysis Grid. The unit of analysis in this study took documents related to AIMS at PT X and interviews with stakeholders related to AIMS at PT X. The results of the analysis of the four resilience factors in AIMS at PT X are response capability (73.75%), monitoring capability (81.23%), learning capability (77.22%), and anticipation capability (75.62%). From these results, the level of safety resilience at AIMS has reached the proactive level with an average of 77%. The involvement of several parties, a clear division of responsibilities, and the addition of AIMS effectiveness indicators, are things that are needed to improve the resilience capabilities of AIMS at PT X."

"Offshore safety management, second edition provides an experienced engineer's perspective on the new Safety and environmental system (SEMS) regulations for offshore oil and gas drilling, how they compare to prior regulations, and how to implement the new standards seamlessly and efficiently. The second edition is greatly expanded, with increased coverage of technical areas such as engineering standards and drilling, and procedural areas such as safety cases and formal safety assessments. The new material both complements the SEMS coverage and increases the book's relevance to a global audience.Following the explosion, fire, and sinking of the Deepwater Horizon floating drilling rig in April 2010, the Bureau of Ocean Energy Management, Regulations, and Enforcement (BOEMRE) issued many new regulations. One of them was the Safety and Environmental System rule, which is based on the American Petroleum Institute's SEMP recommended practice, finalized in April 2013.Author Ian Sutton explains the SEMS rule, and describes what must be done to achieve compliance. Each of the twelve elements of the SEMS rule (such as Management of change and safe work practices) is described in the book, and guidance is provided on how to meet BOEMRE requirements."

"Peningkatan keselamatan dan efisiensi dalam industri minyak dan gas bumi di Indonesia masih memerlukan pendekatan yang canggih untuk memelihara sistem perpipaan yang ada. Disertasi ini membahas penerapan metode Risk Based Inspection (RBI) dengan dukungan teknologi machine learning (ML) dan deep learning (DL) untuk mengembangkan model yang mampu mengidentifikasi akar permasalahan dan solusi untuk menanggulangi kegagalan tersebut. Penelitian dilakukan pada sampel ex-spool berdiameter 16’’ melalui pengujian metalografi dan penggunaan algoritma AdaBoost, Random Forests, dan Gradient Boosting. Metode klasifikasi masalah dilakukan berdasarkan prinsip K-Means Clustering dan Gaussian Mixture Model dan penelitian divalidasi menggunakan metode k-fold cross-validation. Model yang dihasilkan mampu mengidentifikasi dan mengklasifikasikan jenis kegagalan ke dalam 3 kelompok sesuai jenis risikonya masing-masing serta memberikan beragam metode pemeliharaan material yang lebih ekonomis. Program artificial intelligence ini diharapkan mampu meningkatkan keselamatan dan keandalan operasi perpipaan minyak dan gas di Indonesia melalui penerapan berbagai metode pemeliharaan pipa di masa depan.

---

Improving safety and efficiency in the oil and gas industry in Indonesia still requires a sophisticated approach to maintain the existing piping systems. This dissertation discusses the application of the risk-based inspection (RBI) method with the support of machine learning (ML) and deep learning (DL) technology to develop a model that is able to identify the potential root-cause and its solutions to overcome these failures. The research was carried out on a 16'' diameter ex-spool sample through metallographic testing and the use of AdaBoost, Random Forests, and Gradient Boosting algorithms. The problem classification method was carried out based on the principles of K-means clustering and the Gaussian Mixture Model, while the research was validated using the k-fold cross-validation method. The resulting model is able to identify and classify types of failure into three groups according to each type of risk and provides a variety of more economical material maintenance solutions. It is hoped that this artificial intelligence program can support efforts to increase the safety and reliability of oil and gas pipeline operations in Indonesia through the application of various pipeline maintenance methods in the future."