Semakin menipisnya cadangan minyak dan gas bumi di Indonesia mendorong kita untuk terus melakukan kegiatan eksplorasi minyak dan gas bumi. Tujuan penelitian ini ialah untuk mengidentifikasi hidrokarbon di Perairan Utara Bali sehingga bisa menambah data sumberdaya minyak dan gas bumi yang dimiliki Indonesia. Hubungan antara frekuensi dengan batuan yang tersaturasi fluida dapat mengindikasikan keberadaan hidrokarbon. Metode sweetness dan spectral decomposition merupakan metode yang memanfaatkan analisis pada domain frekuensi dan tidak bergantung kepada panjang offset lintasan sesimik yang merupakan permasalahan utama saat melakukan akuisisi data seismik di laut. Hasil penelitian ini menunjukkan metode atribut sweetness dapat mengidentifikasi keberadaan hidrokarbon memiliki nilai sebesar 1600-2200 dimana nilai ini sangat bergantung dengan nilai amplitudo dan frekuensi pada daerah penelitian. Sementara itu keberadaan potensi hidrokarbon pada metode spectral decomposition ditunjukkan oleh nilai frekuensi 30 Hz. Baik metode atribut sweetness ataupun spectral decomposition dapat mengidentifikasi keberadaan hidrokarbon di Perairan Utara Bali.

Lithofacies classification is one of the key modelling components in reservoir characterization. Log-facies classification methods aim to estimate a profile of facies at the well location based on the values of rock properties measured or computed in well log analysis (such as density, porosity, P-Wave, shale content and mineralogy). In this study, the classification of lithofacies was carried out in X field. The first step of classification lithofacies is cross-plot of each petrophysical data, the result of this step is used as a priori data to statistical facies classification (k-means algorithm). Lithofacies in this study were successfully separated into two facies namely sand and shale. The results obtained show that X Field is a gas saturated with sandstone as the main reservoir, especially in the Plover formation.

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.

A “ hockey stick” phenomenon is one of anisotropic effects that should be eliminated in marine seismic data. It can increase residual moveout at the far offsets and impact to the distortion of refl ection event amplitude, eventually, reduce the seismic imaging quality. Conventional hyperbolic moveout approximation, an algorithm 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 nonhyperbolic moveout approimations but it has limitation analysis for inteval offset-depth ratio (ODR) more than four. We present three-ray generalized moveout approximation (three-ray GMA) for transversely isotropic medium with vertical axis of symmetry (VTI), which is a modifi ed non-hyperbolic moveout approximation from original GMA, to cover up of the weakness of the hyperbolic approximation. The objective of this study is to eliminate “ hockey stick” effect and minimize the residual moveout much smaller at once at the far offsets (offsetdepth ratio 4). In this study, we used synthetic data for single layer model in VTI medium to calculate relative traveltime 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 residual moveout calculation. The three-ray GMA shows a better capability than comparative method to reduce residual moveout for larger offset. This result is important for enhancing seismic imaging.

Research on the application of the acoustic impedance (AI) seismic inversion and multi-attribute method was conducted with the aim to characterize the reservoir in the Bonaparte Basin. The modeling which used in the acoustic impedance inversion seismic method is model-based. Meanwhile, the multi-attribute seismic method used log porosity that appliying the linear regression method and using the stepwise regression technique. Based on the result of the sensitivity analysis and analysis using the seismic inversion acoustic impedance method, the sandstone reservoir zone that has the prospect of hydrocarbons containing gas is located in the Northeast-Southwest part of the study area which in WCB-1, WCB-3 and WCB-4 well with the acoustic impedance values are in the range of 4,800 - 13,000 (m / s) * (g / cc), and the porosity values generated from the analysis using the multi-attribute seismic method are in the range of 5 - 16% in WCB-1 and WCB-4, 2 - 10% on WCB-3.