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All Journal Scientific Contribution Oil and Gas LEMBARAN PUBLIKASI MINYAK DAN GAS BUMI
Egie Wijaksono
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Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Energy Engineering Environmental Science

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Reducing Residual Moveout Seismic Anisotropy Model Using Three-Ray GMA (General Moveout Approximation) Egie Wijaksono; Humbang Purba
Scientific Contributions Oil and Gas Vol. 43 No. 3 (2020): SCOG
Publisher : Testing Center for Oil and Gas LEMIGAS

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Abstract

A “ hockey stick” phenomenon is one of anisotropic effects that should be eliminated in marineseismic data. It can increase residual moveout at the far offsets and impact to the distortion of reflection eventamplitude, eventually, reduce the seismic imaging quality. Conventional hyperbolic moveout approximation, analgorithm isotropic model commonly used for seismic processing, has a drawback in supressing such phenomenon.It is also not reliable for medium anisotropy model and long offset data. Many researchers formulated non-hyperbolic moveout approimations but it has limitation analysis for inteval offset-depth ratio (ODR) more thanfour. We present three-ray generalized moveout approximation (three-ray GMA) for transversely isotropicmedium with vertical axis of symmetry (VTI), which is a modified non-hyperbolic moveout approximation fromoriginal GMA, to cover up of the weakness of the hyperbolic approximation. The objective of this study is toeliminate “ hockey stick” effect and minimize the residual moveout much smaller at once at the far offsets (offset-depth ratio > 4). In this study, we used synthetic data for single layer model in VTI medium to calculate relativetraveltime error for each recent method over a range of offsets (0 ≤ ODR ≤ 6) and anisotropic parameters (0 ≤ ≤ 0.5). We also make comparative method for multi layer and implement it in a velocity analysis and residualmoveout calculation. The three-ray GMA shows a better capability than comparative method to reduce residualmoveout for larger offset. This result is important for enhancing seismic imaging
PREDIKSI KECEPATAN GELOMBANG S DENGAN MACHINE LEARNING PADA SUMUR “S-1”, CEKUNGAN SUMATERA TENGAH, INDONESIA Sthevanie Dhita Sudrazat; Humbang Purba; Egie Wijaksono; Waskito Pranowo; Muhammad Irsyad Hibatullah
Lembaran Publikasi Minyak dan Gas Bumi Vol. 54 No. 1 (2020): LPMGB
Publisher : BBPMGB LEMIGAS

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Data kecepatan gelombang S (shear) sangat diperlukan untuk karakterisasi reservoar dalam menentukan zona reservoar. Namun data kecepatan gelombang S sangat terbatas dan tersedia pada sumur tertentu saja. Penelitian ini dilakukan untuk memprediksi nilai kecepatan gelombang S dengan menggunakan metode supervised machine learning pada sumur S-1 lapangan migas di cekungan Sumatra Tengah. Simulasi algoritma machine learning dilakukan melalui tahapan sebelum dan setelah tuning pada algoritma library Scikit learn dan algoritma artificial neural network (ANN). Selain itu, parameter dan jumlah data yang digunakan dalam memprediksi nilai kecepatan gelombang akan menentukan nilai error dan akurasi. Hasil analisis menunjukkan bahwa algoritma yang digunakan untuk memperoleh akurasi terbaik pertama dalam memprediksi kecepatan gelombang S, yaitu random forest dengan nilai parameter n_estimator terbaik 10 dan algoritma kedua yang terbaik yaitu k-nearest neighbor dengan nilai parameter n_neighbor terbaik 5.
Co-Authors Humbang Purba Humbang Purba Muhammad Irsyad Hibatullah Sthevanie Dhita Sudrazat Waskito Pranowo