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Abstrak :
Lapangan Z yang berlokasi di Sub-cekungan Cipunegara merupakan salah satu lapangan penghasil gas dengan reservoir berlitologi karbonat platform dan karbonat reef. Berdasarkan 7 data sumur dan data seismik 3D PSTM, sumur A1, A3, G1, W1, C18, C19, dan C26 menunjukan bahwa zona interest dalam penelitian ini yaitu layer F menunjukan kedalaman dan karakteristik yang berbeda. Selanjutnya, berdasarkan data sumur yang tersedia akan dilaksanakan analisa petrofisika yang bertujuan untuk mengkarakterisasi reservoir berdasarkan properti batuan, seperti porositas, densitas, saturasi air, kecepatan batuan (Vp dan Vs) dan sebagainya. Dalam ketiadaan data Vs, nilai Vs tersebut akan didapat dengan dilakukannya proses deep learning. Setelah data Vs didapatkan, dilaksanakan analisa sensitivitas melalui crossplot yang bertujuan untuk mencari parameter yang sensitive terhadap perubahan litologi. Hasil didapat parameter AI cukup sensitive sehingga dipakai untuk proses inversi. Inversi dalam penelitian ini adalah jenis model based. Berdasarkan peta persebaran AI hasil inversi, lapisan F dengan litologi karbonat ditandai dengan warna hijau sampai kuning-jingga dengan nilai cut-off 6800 ((m*s)/(g/cc)). Selanjutnya akan dilaksanakan proses validasi hasil inversi AI menggunakan deep learning sebagai pendekatan yang berbeda. Hasil deep learning menunjukan nilai validasi yang cukup baik. Hal ini dapat disimpulkan bahwa inversi AI dan deep learning dapat dipakai sebagai inovasi yang baik untuk proses karakterisasi reservoir.

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Field Z, located in the Cipunegara Sub-basin, is one of the gas-producing fields with carbonate platform and carbonate reef reservoirs. Based on 7 wells data and 3D PSTM seismic data, A1, A3, G1, W1, C18, C19, and C26 wells show that the zone of interest in this study named the F layer shows different depths and characteristics. Furthermore, based on available well data, will be carried out a petrophysical analysis that aims to characterize the reservoir based on rock properties, such as porosity, density, water saturation, rock velocity (Vp and Vs), and so on. In the absence of data Vs, the value of Vs will be obtained by doing a deep learning process. After the Vs data is obtained, a sensitivity analysis is carried out through a cross plot that aims to find parameters that are sensitive to changes in lithology. The result shows that the AI parameter is quite sensitive, so that Acoustic Impedance or AI is used for the inversion process. Inversion in this research is a model-based type. Based on the AI distribution map of inversion results, the F layer with carbonate lithology is marked in green to yellow-orange with a cut-off value of 6800 ((m*s)/(g/cc)). Furthermore, the AI inversion result will be validated using deep learning as a different approach than usual. The deep learning results shows a good validation score. It can be concluded that AI inversion and deep learning can be used as good innovations for reservoir characterization processes.

Abstrak :
Lapangan ‘B’ merupakan lapangan prospek hidrokarbon yang berlokasi di offshore cekungan Kutai, Kalimantan Timur. Untuk mengetahui karakterisasi reservoir lapangan ‘B’, dilakukan pemodelan porositas dan saturasi air menggunakan inversi AI, multiatribut seismik dan probabilistic neural network. Penelitian ini menggunakan data seismik 3D PSTM dan data sumur (AND-1, AND-2, AND-3 dan AND-4). Pada data seismik dan data sumur dilakukan inversi AI untuk mengetahui sifat litologi area penelitian. Kemudian, hasil AI ditransformasikan untuk mendapatkan model porositas. Metode multiatribut seismik menggunakan beberapa atribut untuk memprediksi model porositas dan saturasi air. Setelah itu, diaplikasikan sifat non-linear dari probabilistic neural network sehingga menghasilkan model porositas dan saturasi air hasil probabilistic neural network (PNN). Model porositas dan saturasi air transformasi AI, multiatribut seismik dan PNN divalidasi dengan nilai porositas dan saturasi air data sumur untuk mengetahui apakah model porositas dan saturasi air tersebut merepresentatifkan nilai data sumur. Validasi dilakukan pada sumur AND-1 dan AND-2. Nilai porositas dan saturasi air data sumur untuk AND- 1 adalah 25.3 – 35.9% dan 45 – 60%, dan nilai porositas dan saturasi air AND-2 adalah 11 – 35% dan 15 – 82%. Nilai porositas AND-1 hasil transformasi AI sekitar 16 – 67%, multiatribut seismik sekitar 11.5 – 27% dan PNN sekitar 11.5 – 27%. Nilai saturasi air AND-1 hasil multiatribut seismik sekitar 4 – 63% dan PNN sekitar 18 – 63%. Nilai porositas AND-2 hasil transformasi AI sekitar 52 – 72%, multiatribut seismik sekitar 11 – 21.5% dan PNN sekitar 11 – 21.5%. Nilai saturasi air AND-2 hasil multiatribut seismik sekitar 63 – 85% dan PNN sekitar 63 – 85%. Kemudian, metode multiatribut seismik dan PNN didapatkan nilai korelasi antara parameter target dengan parameter prediksi. Model porositas multiatribut seismik memiliki korelasi 0.840836 dan PNN memiliki korelasi 0.936868. Model saturasi air multiatribut seismik memiliki korelasi 0.915254 dan PNN memiliki korelasi 0.994566. Model porositas transformasi AI memiliki rentang yang lebih tinggi dibandingkan dengan data sumur. Model porositas dan saturasi air metode PNN memiliki rentang nilai yang cukup dekat dengan data sumur dan memiliki korelasi yang lebih tinggi dibandingkan dengan metode multiatribut seismik. Oleh sebab itu, model porositas dan saturasi air metode PNN merupakan model prediksi terbaik. Berdasarkan model PNN, reservoir zona target lapangan ‘B’ memiliki nilai impedansi akustik 25384 – 26133 ((ft/s)*(g/cc)), porositas sekitar 15 – 27% dan nilai saturasi air sekitar 11 – 63%. ......The 'B' field is a hydrocarbon prospect field located in the offshore Kutai Basin, East Kalimantan. To determine the characterization of the ‘B’ field reservoir, porosity and water saturation modeling was carried out using AI inversion, seismic multiattribute and probabilistic neural network. This study uses 3D PSTM seismic data and wells data (AND-1, AND-2, AND-3 and AND-4). In seismic data and wells data, AI inversion was carried out to determine the lithological characteristics of the research area. Then, the AI results were transformed to obtain a porosity model. The seismic multiattribute method uses several attributes to predict the porosity and water saturation model. After that, the non-linear properties of the probabilistic neural network were applied to produce the porosity and water saturation model of the probabilistic neural network (PNN). The porosity and water saturation model of AI transformation, seismic multiattribute and PNN were validated with the porosity and water saturation values of the wells data to determine whether the porosity and water saturation models represent the wells data values. Validation was carried out on AND-1 and AND-2 wells. The porosity and water saturation value of the well data for AND-1 around 25.3 - 35.9% and 45 - 60%, and the porosity and water saturation value of AND-2 around 11 - 35% and 15 - 82%. The porosity value of AND-1 as a result of AI transformation is around 16 - 67%, the seismic multiattribute about 11.5 - 27% and the PNN about 11.5 - 27%. The water saturation value of AND-1 resulted from seismic multiattribute around 4 - 63% and PNN around 18 - 63%. The porosity value of AND-2 transformed by AI around 52 - 72%, the seismic multiattribute around 11 - 21.5% and the PNN around 11 - 21.5%. The water saturation value of AND-2 result from the seismic multiattribute around 63 - 85% and PNN around 63 - 85%. Then, the multiattribute seismic and PNN methods obtained the correlation value between the target parameter and the predicted parameter. The seismic multiattribute porosity model has a correlation of 0.840836 and PNN has a correlation of 0.936868. The multiattribute seismic water saturation model has a correlation of 0.915254 and PNN has a correlation of 0.994566. The AI transformation porosity model has a higher range than the wells data. The PNN method of porosity and water saturation model has a fairly close range of values to wells data and has a higher correlation than the multiattribute seismic method. Therefore, the porosity and water saturation model of the PNN method is the best prediction model. Based on the PNN model, the field target zone reservoir 'B' has an acoustic impedance value about 25384 – 26133 ((ft/s) * (g/cc)), a porosity of 15 - 27% and a water saturation of 11 - 63%.

Abstrak :
Potensi hidrokarbon di Lapangan 'OZ', Cekungan Bonaparte belum dimanfaatkan karena risiko pengeboran yang tinggi yang disebabkan oleh heterogenitas reservoir. Karena sifat reservoir yang heterogen, maka dilakukan identifikasi dan karakterisasi untuk melihat sebaran litologi dan fluida reservoirnya. Metode Probabilistic Neural Network (PNN) adalah metode utama dalam analisis multi-atribut untuk menemukan hubungan nonlinier antara data seismik dan data sumur di Lapangan 'OZ' dan kemudian menghasilkan model untuk distribusi data sinar gamma, porositas, dan saturasi air dengan nilai koefisien korelasi masing-masing pelatihan sebesar 0,8871, 0,9778, 0,9719 dan koefisien korelasi validasi sebesar 0,7836, 0,8554, 0,8187. Integrasi antara model distribusi data sinar gamma, porositas, saturasi air, ditambah dengan hasil inversi impedansi akustik (AI), dapat menjadi sarana untuk mengklasifikasikan dan mengidentifikasi distribusi reservoir hidrokarbon. Lapangan 'OZ' memiliki karakteristik reservoir yang mengandung gas hidrokarbon dan memiliki litologi batupasir bersih dengan sesar normal sebagai traps serta batupasir rapat dan batuan serpih sebagai seal yang tersebar di bagian Selatan dan Tengah lapangan OZ.
The hydrocarbon potential in the 'OZ' Field, Bonaparte Basin has not been exploited due to the high drilling risk caused by reservoir heterogeneity. Due to the heterogeneous nature of the reservoir, identification and characterization were carried out to see the distribution of lithology and reservoir fluids. The Probabilistic Neural Network (PNN) method is the main method in multi-attribute analysis to find a nonlinear relationship between seismic data and well data in the 'OZ' Field and then generate a model for the distribution of gamma ray, porosity, and water saturation data with the respective correlation coefficient values. -each training is 0.8871, 0.9778, 0.9719 and the validation correlation coefficient is 0.7836, 0.8554, 0.8187. The integration between the distribution model of gamma ray data, porosity, water saturation, coupled with the results of acoustic impedance inversion (AI), can be a means to classify and identify the distribution of hydrocarbon reservoirs. The 'OZ' field has reservoir characteristics containing hydrocarbon gas and has a clean sandstone lithology with normal faults as traps as well as dense sandstone and shale rock as seals which are scattered in the Southern and Central parts of the OZ field.