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"Ground moving radartarget classification is one of the recent research issues that has arisen inan airborne ground moving target indicator (GMTI) scenario. This work presentsa novel technique for classifying individual targets depending on their radarcross section (RCS) values. The RCS feature is evaluated using the Chebyshevpolynomial. The radar captured target usually provides an imbalanced solutionfor classes that have lower numbers of pixels and that have similarcharacteristics. In this classification technique, the Chebyshev polynomial?sfeatures have overcome the problem of confusion between target classes withsimilar characteristics. The Chebyshev polynomial highlights the RCS featureand is able to suppress the jammer signal. Classification has been performed byusing the probability neural network (PNN) model. Finally, the classifier withthe Chebyshev polynomial feature has been tested with an unknown RCS value. Theproposed classification method can be used for classifying targets in a GMTIsystem under the warfield condition."

"Ground moving radar target classification is one of the recent research issues that has arisen in an airborne ground moving target indicator (GMTI) scenario. This work presents a novel technique for classifying individual targets depending on their radar cross section (RCS) values. The RCS feature is evaluated using the Chebyshev polynomial. The radar captured target usually provides an imbalanced solution for classes that have lower numbers of pixels and that have similar characteristics. In this classification technique, the Chebyshev polynomial’s features have overcome the problem of confusion between target classes with similar characteristics. The Chebyshev polynomial highlights the RCS feature and is able to suppress the jammer signal. Classification has been performed by using the probability neural network (PNN) model. Finally, the classifier with the Chebyshev polynomial feature has been tested with an unknown RCS value. The proposed classification method can be used for classifying targets in a GMTI system under the warfield condition."

"Lapangan “X” merupakan salah satu lapangan yang terletak di Cekungan Sunda. Lapangan ini merupakan salah satu zona potensi hidrokarbon di Indonesia, dengan salah satu zona potensi hidrokarbon terletak pada batuan reservoir karbonat build-up yang berada di Formasi Upper Baturaja. Studi ini akan menghasilkan volume petrofisika semu untuk properti petrofisika volume shale, porositas, dan saturasi air menggunakan lima kombinasi atribut seismik yang ditentukan melalui analisis multi-atribut, yang kemudian nilai korelasi dan errornya akan ditingkatkan probabilistic neural network (PNN). Integrasi dari ketiga metode ini bertujuan untuk memberikan gambaran dan pemahaman baru terhadap karakterisasi daerah yang berpotensi hidrokarbon di Lapangan ”X”.

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”X” Field is one of the fields located in Sunda Basin. “X” Field is one of the hydrocarbon potential zones in Indonesia, with one of its hydrocarbons potential zones located in the carbonate build up reservoir in the Upper Baturaja Formation. This study will produce pseudo petrophysical volumes for petrophysical properties such as shale volume, porosity, and water saturation using five seismic attributes combination from the seismic multi-attributes method. Probabilistic neural network (PNN) is used to improve the correlation and error value from the log. The integration of these three methods aims to provide new insights and understanding of the characterization of hydrocarbon potential areas in “X” Field."

"Cekungan Banggai memiliki potensi hidrokarbon yang terakumulasi pada reservoir karbonat dengan porositas ganda – porositas primer dan sekunder akibat diagenesis. Hal tersebut menyebabkan variasi nilai porositas secara vertikal maupun lateral sehingga menyulitkan identifikasi zona hidrokarbon yang potensial. Untuk mengidentifikasi zona reservoir karbonat dengan porositas ganda, penelitian ini mengintegrasikan metode inversi seismik, multiatribut seismik, dan Probabilistic Neural Network (PNN), yang bertujuan untuk meningkatkan akurasi dalam pemodelan karakteristik reservoir. Hasil penelitian menunjukkan bahwa karbonat build-up Formasi Mantawa memiliki impedansi akustik rendah (6200–10000 m/s*g/cc), porositas tinggi (21%–28%), dan saturasi air rendah (30%–47%), yang mengindikasikan adanya batuan berpori yang mengandung hidrokarbon. Diharapkan dengan penerapan integrasi metode tersebut, distribusi reservoir, porositas, dan saturasi air dapat dipetakan dengan lebih akurat berdasarkan hasil dari prediksi Probabilistic Neural Network (PNN) dan membuka peluang eksplorasi lebih lanjut di zona prospektif Lapangan ZENA.

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The Banggai Basin has hydrocarbon potential accumulated in carbonate reservoirs with dual porosity – primary and secondary porosity due to diagenesis. This causes variations in porosity values both vertically and laterally, making it difficult to identify potential hydrocarbon zones. To identify carbonate reservoir zones with dual porosity, this study integrates seismic inversion methods, multi-attribute seismic analysis, and Probabilistic Neural Network (PNN), aimed at improving the accuracy of reservoir characteristic modeling. The results show that the carbonate build-up of the Mantawa Formation has low acoustic impedance (6200–10000 m/s*g/cc), high porosity (21%–28%), and low water saturation (30%–47%), indicating the presence of porous rocks containing hydrocarbons. It is expected that with the application of the integration of these methods, the distribution of the reservoir, porosity, and water saturation can be mapped more accurately based on the results from the predictions of the Probabilistic Neural Network (PNN), thus opening up further exploration opportunities in the prospective zones of the ZENA Field."

"Cekungan Banggai memiliki potensi hidrokarbon yang terakumulasi pada reservoir karbonat dengan porositas ganda – porositas primer dan sekunder akibat diagenesis. Hal tersebut menyebabkan variasi nilai porositas secara vertikal maupun lateral sehingga menyulitkan identifikasi zona hidrokarbon yang potensial. Untuk mengidentifikasi zona reservoir karbonat dengan porositas ganda, penelitian ini mengintegrasikan metode inversi seismik, multiatribut seismik, dan Probabilistic Neural Network (PNN), yang bertujuan untuk meningkatkan akurasi dalam pemodelan karakteristik reservoir. Hasil penelitian menunjukkan bahwa karbonat build-up Formasi Mantawa memiliki impedansi akustik rendah (6200–10000 m/s*g/cc), porositas tinggi (21%–28%), dan saturasi air rendah (30%–47%), yang mengindikasikan adanya batuan berpori yang mengandung hidrokarbon. Diharapkan dengan penerapan integrasi metode tersebut, distribusi reservoir, porositas, dan saturasi air dapat dipetakan dengan lebih akurat berdasarkan hasil dari prediksi Probabilistic Neural Network (PNN) dan membuka peluang eksplorasi lebih lanjut di zona prospektif Lapangan ZENA.

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The Banggai Basin has hydrocarbon potential accumulated in carbonate reservoirs with dual porosity – primary and secondary porosity due to diagenesis. This causes variations in porosity values both vertically and laterally, making it difficult to identify potential hydrocarbon zones. To identify carbonate reservoir zones with dual porosity, this study integrates seismic inversion methods, multi-attribute seismic analysis, and Probabilistic Neural Network (PNN), aimed at improving the accuracy of reservoir characteristic modeling. The results show that the carbonate build-up of the Mantawa Formation has low acoustic impedance (6200–10000 m/s*g/cc), high porosity (21%–28%), and low water saturation (30%–47%), indicating the presence of porous rocks containing hydrocarbons. It is expected that with the application of the integration of these methods, the distribution of the reservoir, porosity, and water saturation can be mapped more accurately based on the results from the predictions of the Probabilistic Neural Network (PNN), thus opening up further exploration opportunities in the prospective zones of the ZENA Field."

"Pada area Cekungan Tarakan terdapat beberapa titik lokasi yang dapat diindikasikan sebagai zona prospek, akan tetapi lingkungan batu klastik membuat zona prospek tersebut sulit untuk ditentukan batas-batasnya. Penelitian ini bertujuan untuk melakukan karakterisasi reservoir di Lapangan "X" Cekungan Tarakan Kalimantan Utara dengan menggunakan pendekatan Probabilistik Neural Network (PNN) dan analisis seismik multi-atribut. Metode seismic multi-atribut digunakan untuk mencari atribut apa yang paling sensitif, lalu metode probabilistic neural network ini digunakan untuk memprediksi distribusi properti petrofisika, termasuk porositas, volume shale, dan saturasi air pada reservoir. Data yang digunakan meliputi data seismik post-stack, data well log, serta parameter petrofisika dari sumur yang relevan. Hasil penelitian menunjukkan bahwa kombinasi atribut seismik yang paling sensitif untuk karakterisasi reservoir di Lapangan "X" melibatkan atribut seperti amplitudo seismik, frekuensi dominan, dan fase instan. Penerapan PNN berhasil meningkatkan akurasi prediksi distribusi porositas, volume shale, dan saturasi air dengan tingkat korelasi yang memadai, di mana porositas mencapai korelasi 0.646, volume shale 0.403, dan saturasi air 0.695. Berdasarkan analisis ini, dapat dikatakan bahwa metode PNN juga dapat menurunkan nilai error yang dimana memberikan gambaran yang lebih detail mengenai karakteristik reservoir berdasarkan parameter yang ada, yang berguna untuk perencanaan eksplorasi dan produksi hidrokarbon di masa depan di Cekungan Tarakan.

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In the Tarakan Basin area, there are several locations that can be indicated as prospect zones, but the clastic rock environment makes it difficult to determine the boundaries of these zones. This study aims to characterize the reservoir in the "X" Field, Tarakan Basin, North Kalimantan, using a Probabilistic Neural Network (PNN) approach and multi-attribute seismic analysis. The multi-attribute seismic method is used to identify the most sensitive attributes, while the probabilistic neural network method is employed to predict the distribution of petrophysical properties, including porosity, shale volume, and water saturation in the reservoir. The data used in this study includes post-stack seismic data, well log data, and petrophysical parameters from relevant wells. The results show that the combination of seismic attributes most sensitive for reservoir characterization in the "X" Field involves attributes such as seismic amplitude, dominant frequency, and instant phase. The application of PNN successfully improved the accuracy of predicting the distribution of porosity, shale volume, and water saturation with adequate correlation levels, where porosity achieved a correlation of 0.646, shale volume 0.403, and water saturation 0.695. Based on this analysis, it can be concluded that the PNN method also reduces error values, providing a more detailed picture of the reservoir characteristics based on available parameters, which is useful for future hydrocarbon exploration and production planning in the Tarakan Basin."