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All Journal Jurnal Geofisika Scientific Contributions Oil and Gas
Ekkal Dinanto
Institut Teknologi Bandung
Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Energy Physics

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Ekstrapolasi Frekuensi Rendah pada Full Waveform Inversion (FWI) dengan menggunakan Deep Learning. Part 1 : Validasi data Sintetik Asido Saputra Sigalingging; Ignatius Sonny Winardhie; Ekkal Dinanto
Jurnal Geofisika Vol 19 No 2 (2021): Special Issues | Seismic Quantitative Interpretation |Jurnal Geofisika
Publisher : Himpunan Ahli Geofisika Indonesia (HAGI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36435/jgf.v20i2.518

Abstract

Kandungan seismik frekuensi rendah sangat berperan penting terhadap hasil inversi pada pemodelan Full Waveform Inversion(FWI). Kehilangan frekuensi rendah dari data seismik akan membuat model akhir FWI sulit untuk konvergen. Penelitian ini melakukan ekstrapolasi frekuensi rendah dengan menggunakan deep learning. Dataset diperoleh dengan melakukan pemodelan ke depan seismik akustik menggunakan finnite difference dari beberapa model P-Wave sintetis. Data seismik hasil pemodelan kemudian dipisah menggunakan filter Butterworth, sehingga diperoleh data seismik dengan frekuensi rendah < 5 Hz, dan frekuensi tinggi > 5 Hz. Data frekuensi tinggi dan frekuensi rendah masing-masing berurutan sebagai input dan output data yang akan digunakan sebagai data latih model deep learning. Model deep learning Multi-Ouput Reggresion yang digunakan dalam penelitian ini yakni Multi-Layer Perceptron(MLP) dan Convolutional Neural Networks(CNN). Evaluasi dan testing yang dilakukan terhadap model deep learning memberikan hasil akurasi yang baik. Berdasarkan hasil validasi akurasi model deep learning yang dilakukan, kedua model deep learning potensial untuk diterapakan pada ekstrapolasi frekuensi rendah data real sebelum dilakukan pemodelan Full Waveform Inversion(FWI).
Real Data-Driven Seismic Low Frequency Extrapolation: A Case Study from The Asri Basin, Java Sea, Indonesia Ignatius Sonny Winardhi; Asido Saputra Sigalingging; Ekkal Dinanto
Scientific Contributions Oil and Gas Vol 48 No 3 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i3.1748

Abstract

The Asri Basin, located in the Java Sea, Indonesia, is a significant hydrocarbon province with regions that remain underexplored. The available legacy seismic data, however, are limited in quality, particularly due to their narrow frequency bandwidth and the absence of low-frequency components. This limitation poses a significant challenge for advanced seismic imaging techniques such as Full Waveform Inversion (FWI), which rely low-frequency data to generate accurate and reliable subsurface models. This study aims to reconstruct the missing low-frequency (<10 Hz) components from the band-limited seismic data to enhance the applicability of FWI. A real-data-driven, self-supervised learning approach for low-frequency extrapolation is implemented to address this challenge. Using a modified U-Net architecture, the framework is trained directly on the available band-limited seismic data, eliminating the need for synthetic or labeled datasets. The self-supervised workflow employs a frequency-specific masking strategy that enables the model to learn and predict the missing low-frequency content from higher-frequency inputs. The results demonstrate that the proposed method effectively recovers low-frequency signals, achieving accurate reconstruction down to <5 Hz, reducing residual amplitudes compared to conventional methods, and preserving the mid-to-high frequency spectrum. This approach provides a promising solution for overcoming data limitations and mitigating cycle-skipping issues in FWI applications within the Asri Basin and comparable geological settings.
Machine Learning-Based Prediction of Shear Wave Velocity: Performance Evaluation of Bi-GRU, ANN, and The Greenberg-Castagna Empirical Method Muhammad Raihan Ulil; Sonny Winardhi; Ekkal Dinanto
Scientific Contributions Oil and Gas Vol 48 No 3 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i3.1797

Abstract

Shear wave velocity (Vs) is recognized as an important elastic parameter for lithology and fluid identification in oil and gas exploration. However, Vs data is not always recorded in well logs. Various empirical approaches are often used to estimate Vs, but these methods show limitations in terms of accuracy and time efficiency. With technological advances, machine learning has become an effective and efficient alternative for predicting Vs from well log data. This study is utilizing the Bi-GRU model, a sophisticated artificial neural network specifically designed to process sequential data. This capability makes Bi-GRU particularly suitable for predicting log Vs data. Four Bi-GRU modeling scenarios are being developed with different hyperparameter configurations and are being compared with ANN models using two input variations: with and without Vp data. The results show that scenario 2 (Bi-GRU with five hidden layers, batch size 64, learning rate 0.005) is achieve the best performance, with R² values of 0.9787 (without Vp) and 0.9868 (with Vp). The MAE values obtained are being recorded as 9.36 (without Vp) and 11.22 (with Vp). Compared to shows ANN, MLR, and empirical Castagna methods, the Bi-GRU model show a more significant improvement in prediction accuracy. These findings are indicating that Bi-GRU have strong potential for accurately and efficiently predicting Vs from well log data.
Prediction of S-Wave Using Conventional Method and Machine Learning Dayyan Dhaifullah; Winardhi, Sonny; Dinanto, Ekkal
Scientific Contributions Oil and Gas Vol 49 No 1 (2026)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v49i1.1988

Abstract

Shear-wave velocity (Vs) is an essential metric for subsurface characterization and CO₂ storage evaluation. However, Vs measurements are frequently unavailable in mature fields due to limited data acquisition. This research employs a machine-learning approach utilizing a Fully Connected Neural Network (FCNN) to predict Vs and Vp/Vs logs at a potential CO₂ injection site within a heterogeneous carbonate reservoir. Seismic elastic properties, particularly Acoustic Impedance (AI) and Vp/Vs, play a crucial role in assessing reservoir capacity by linking elastic responses to petrophysical properties such as porosity and water saturation. Conventional approaches, including the Castagna empirical relationship and Multiple Linear Regression (MLR), are commonly used for Vs estimation. Nevertheless, these methods often inadequately account for fluid-related effects. To address this limitation, this study examines two predictive approaches: (1) indirect Vp/Vs derived from predicted Vs, and (2) direct prediction of Vp/Vs prediction using a FCNN model. The findings indicate that direct Vp/Vs prediction demonstrate stronger correlation with observed data (R = 0.8023) and improved sensitivity to lithological and fluid variations compared to traditional methods. These findings underscore the advantage of directly predicting fluid-sensitive elastic properties through machine learning, providing a more reliable framework for reservoir characterization and CO₂ storage assessment in data-constrained carbonate formations.
Co-Authors Asido Saputra Sigalingging Asido Saputra Sigalingging Dayyan Dhaifullah Ignatius Sonny Winardhi Ignatius Sonny Winardhie Muhammad Raihan Ulil Sonny Winardhi Winardhi, Sonny