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"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%.

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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%."

"Inversi seismik merupakan suatu metode yang telah banyak dilakukan dalam eksplorasi hidrokarbon. Salah satu metode inversi yang sering digunakan adalah inversi deterministik yang mampu menghasilkan model bawah permukaan bumi. Akan tetapi, data seismik memiliki keterbatasan frekuensi yang menyebabkan inversi deterinistik tidak dapat menampilkan model dengan frekuensi tinggi. Sehingga, lapisan tipis pada reservoir tidak dapat diidentifikasi dengan baik. Permasalahan tersebut dapat diatasi dengan inversi stokastik. Inversi stokastik menggunakan variogram dan konsep Bayesian untuk menghasilkan model permukaan bawah dengan resolusi yang lebih detail. Penerapan inversi stokastik dilakukan pada Lapangan “Purwo” Cekungan Sumatra Selatan yang telah terbukti sebagai penghasil migas di Indonesia. Lapangan “Purwo” memiliki reservoir batu pasir pada formasi Talang Akar yang tersaturasi gas. Formasi tersebut memiliki struktural trap berupa tilted fault block dengan lingkungan pengendapan berupa fluvial dan deltaic. Hal tersebut memungkinkan adanya heterogenitas reservoir yang menyebabkan reservoir memiliki ketebalan yang tipis. Identifikasi reservoir tipis pada penelitian ini menggunkan integrasi antara inversi stokastik, analisis petrofisika, dan multiatribut seismik. Hasil analisis petrofisika menunjukkan adanya reservoir tipis yang potensial dengan ketebalan 13 ft. Sedangkan tunning thicknes data seismik adalah sebesar 74 ft. Hasil inversi stokastik mampu mendeliniasi reservoir tersebut dengan baik dibandingkan inversi determnistik. Hasil inversi stokastik digunakan sebagai atribut eksternal dan mampu menghasilkan model porositas yang lebih detail dengan menggunakan metode multiatribut seismik. Hasil persebaran porositas menunjukkan formasi Talang Akar bagian bawah memiliki reservoir yang potensial dengan rentang nilai porositas sebesar 0.10 – 0.26 v/v.

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Seismic inversion is a widely used method in hydrocarbon exploration. One commonly employed inversion method is deterministic inversion, which is capable of producing subsurface models. However, seismic data has frequency limitations that prevent deterministic inversion from depicting high-frequency models. Consequently, thin layers within reservoirs cannot be well-identified. This issue can be addressed using stochastic inversion. Stochastic inversion employs variograms and Bayesian concepts to produce more detailed subsurface models. The application of stochastic inversion was conducted in the “Purwo” Field of the South Sumatra Basin, which has been proven to be a hydrocarbon-producing area in Indonesia. The “Purwo” Field has a sandstone reservoir in the Talang Akar Formation saturated with gas. This formation features a structural trap in the form of a tilted fault block with fluvial and deltaic depositional environments. Such characteristics suggest reservoir heterogeneity, resulting in thin reservoir layers. Thin reservoir identification in this study integrates stochastic inversion, petrophysical analysis, and seismic multi-attribute analysis. Petrophysical analysis results indicate the presence of a potential thin reservoir with a thickness of 13 feet, while the seismic tuning thickness is 74 feet. Stochastic inversion successfully delineates the reservoir more effectively than deterministic inversion. The stochastic inversion results are used as external attributes and can generate more detailed porosity models using seismic multiattribute methods. The porosity distribution results show that the lower part of the Talang Akar Formation contains a potential reservoir with porosity values ranging from 0.10 to 0.26 v/v. "