Lapangan ‘B’ merupakan lapangan prospek hidrokarbon yang berlokasi di offshorecekungan Kutai, Kalimantan Timur. Untuk mengetahui karakterisasi reservoir lapangan‘B’, dilakukan pemodelan porositas dan saturasi air menggunakan inversi AI, multiatributseismik dan probabilistic neural network. Penelitian ini menggunakan data seismik 3DPSTM dan data sumur (AND-1, AND-2, AND-3 dan AND-4). Pada data seismik dan datasumur dilakukan inversi AI untuk mengetahui sifat litologi area penelitian. Kemudian,hasil AI ditransformasikan untuk mendapatkan model porositas. Metode multiatributseismik menggunakan beberapa atribut untuk memprediksi model porositas dan saturasiair. Setelah itu, diaplikasikan sifat non-linear dari probabilistic neural network sehinggamenghasilkan model porositas dan saturasi air hasil probabilistic neural network (PNN).Model porositas dan saturasi air transformasi AI, multiatribut seismik dan PNN divalidasidengan nilai porositas dan saturasi air data sumur untuk mengetahui apakah modelporositas dan saturasi air tersebut merepresentatifkan nilai data sumur. Validasi dilakukanpada 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 adalah11 – 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 airAND-1 hasil multiatribut seismik sekitar 4 – 63% dan PNN sekitar 18 – 63%. Nilaiporositas 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 seismiksekitar 63 – 85% dan PNN sekitar 63 – 85%. Kemudian, metode multiatribut seismik danPNN didapatkan nilai korelasi antara parameter target dengan parameter prediksi. Modelporositas multiatribut seismik memiliki korelasi 0.840836 dan PNN memiliki korelasi0.936868. Model saturasi air multiatribut seismik memiliki korelasi 0.915254 dan PNNmemiliki korelasi 0.994566. Model porositas transformasi AI memiliki rentang yanglebih tinggi dibandingkan dengan data sumur. Model porositas dan saturasi air metodePNN memiliki rentang nilai yang cukup dekat dengan data sumur dan memiliki korelasiyang 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 impedansiakustik 25384 – 26133 ((ft/s)*(g/cc)), porositas sekitar 15 – 27% dan nilai saturasi airsekitar 11 – 63%.
The 'B' field is a hydrocarbon prospect field located in the offshore Kutai Basin, EastKalimantan. To determine the characterization of the ‘B’ field reservoir, porosity andwater saturation modeling was carried out using AI inversion, seismic multiattribute andprobabilistic 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 wascarried out to determine the lithological characteristics of the research area. Then, the AIresults were transformed to obtain a porosity model. The seismic multiattribute methoduses several attributes to predict the porosity and water saturation model. After that, thenon-linear properties of the probabilistic neural network were applied to produce theporosity and water saturation model of the probabilistic neural network (PNN). Theporosity and water saturation model of AI transformation, seismic multiattribute and PNNwere validated with the porosity and water saturation values of the wells data to determinewhether 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 watersaturation value of the well data for AND-1 around 25.3 - 35.9% and 45 - 60%, and theporosity and water saturation value of AND-2 around 11 - 35% and 15 - 82%. Theporosity value of AND-1 as a result of AI transformation is around 16 - 67%, the seismicmultiattribute about 11.5 - 27% and the PNN about 11.5 - 27%. The water saturation valueof 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 seismicmultiattribute around 11 - 21.5% and the PNN around 11 - 21.5%. The water saturationvalue of AND-2 result from the seismic multiattribute around 63 - 85% and PNN around63 - 85%. Then, the multiattribute seismic and PNN methods obtained the correlationvalue between the target parameter and the predicted parameter. The seismicmultiattribute porosity model has a correlation of 0.840836 and PNN has a correlation of0.936868. The multiattribute seismic water saturation model has a correlation of 0.915254and PNN has a correlation of 0.994566. The AI transformation porosity model has ahigher range than the wells data. The PNN method of porosity and water saturation modelhas a fairly close range of values to wells data and has a higher correlation than themultiattribute seismic method. Therefore, the porosity and water saturation model of thePNN method is the best prediction model. Based on the PNN model, the field target zonereservoir 'B' has an acoustic impedance value about 25384 – 26133 ((ft/s) * (g/cc)), aporosity of 15 - 27% and a water saturation of 11 - 63%. |
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