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"ABSTRAKImpedansi akustik dan seismik inversi seismik multi-atribut adalah sejumlah metode seismik yang dapat digunakan untuk memetakan distribusi reservoar batu pasir. Dengan menggunakan metode ini, kita dapat memisahkan batupasir dan sumur serpih di Formasi Talang Akar yang ditemukan di Lapangan "Essen", Sub-Basin Ciputat. Kedua metode ini akan dibandingkan satu sama lain untuk mendapatkan hasil yang lebih valid dalam pemetaan reservoar batu pasir. Metode seismik inversi impedansi akustik yang digunakan dalam penelitian ini adalah metode berbasis model. Sedangkan metode multi-atribut yang digunakan adalah jaringan saraf dalam memetakan volume sinar gamma, volume serpih, dan porositas. Hasil inversi tidak dapat menggambarkan distribusi batupasir cukup baik karena rentangnya terlalu besar dan ada tumpang tindih pada nilai impedansi akustik batupasir dengan rentang (8000-12000) (m / s) * (g / cc). Hasil multi-atribut gamma ray, volume serpih dan porositas, telah terbukti secara konsisten menunjukkan distribusi batupasir yang memiliki kecenderungan dalam distribusi zona reservoar NW-SE (North West-South East). Dari hasil analisis yang dilakukan ada beberapa area potensial yang berpotensi menjadi area pengembangan selanjutnya, yaitu distribusi batupasir di bagian utara dengan porositas efektif tinggi, dan seal yang baik. Di selatan dengan volume besar batupasir, serta distribusi terbentuk pada saluran yang mengelilingi patahan.

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ABSTRACTAcoustic impedance and seismic multi-attribute seismic inversion are a number of seismic methods that can be used to map the distribution of sandstone reservoirs. Using this method, we can separate sandstones and shale wells in the Talang Akar Formation found in the "Essen" Field, Ciputat Sub-Basin. These two methods will be compared with each other to get more valid results in sandstone reservoir mapping. The acoustic impedance inversion seismic method used in this study is a model based method. Whereas the multi-attribute method used is a neural network in mapping the volume of gamma rays, shale volume, and porosity. The inversion results cannot describe the sandstone distribution well enough because the range is too large and there is an overlap in the acoustic impedance value of the sandstone with a range (8000-12000) (m / s) * (g / cc). The results of the multi-attribute gamma ray, shale volume and porosity, have been shown to consistently show the distribution of sandstones that have a tendency in the distribution of the NW-SE (North West-South East) reservoir zone. From the results of the analysis conducted there are several potential areas that have the potential to become further development areas, namely sandstone distribution in the north with high effective porosity, and good seals. In the south with a large volume of sandstones, and distribution is formed in the channel that surrounds the fault."

"Tujuan penelitian ini adalah untuk memahami bagaimana konsumen menggunakan atribut-atribut yang berbeda ketika mengevaluasi alternatif film yang akan ditonton di bioskop. Analisis konjoin digunakan untuk mengetahui tingkat kepentingan relatif atribut film dan utilitas level-level atribut film. Individu-individu yang memiliki struktur preferensi yang mirip kemudian dikelompokkan ke dalam beberapa segmen dengan menggunakan analisis kluster. Atribut-atribut yang digunakan dalam penelitian ini adalah genre, symbolism, country of origin, actor, director, information sources dan pricing strategy. Hasil analisis konjoin secara agregat menunjukkan bahwa genre merupakan atribut terpenting. Atribut terpenting berikutnya setelah genre adalah country of origin dan pricing strategy. Hasil analisis kluster menunjukkan bahwa individu-individu dapat dikelompokkan ke dalam tiga segmen yang berbeda berdasarkan tingkat kepentingan relatif atribut. Setiap segmen paling tidak memiliki satu hingga dua atribut yang membedakannya dengan segmen lain. Interpretasi, implikasi manajerial, dan keterbatasan akan dijelaskan lebih jauh.

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The purpose of this study is to understand how consumers use different attributes when evaluating movie alternatives at the cinema. The study uses conjoint analysis to identify the relative importance of movie attributes and the utilities of the levels of movie attributes. Individuals with similar preference structures are then grouped into some segments using cluster analysis. The attributes selected in this study are genre, symbolism, country of origin, actor, director, information sources, and pricing strategy.

The results of aggregate conjoint analysis show that genre is the most important attribute. Second most important is country of origin, followed closely by pricing strategy. The results of cluster analysis show that individuals can be grouped into three different segments based on their relative importances. Each segment has at least one or two attributes that distinguish it from other segments. Interpretations, managerial implications, and limitations are discussed further."

"Aplikasi metode Multiatribut pada data poststack seismik dan hasil inversinya telah dilakukan untuk mengestimasi kecepatan interval melalui pendekatan Neural Network. Estimasi kecepatan interval yang dihasilkan tersebut digunakan untuk memprediksi tekanan formasi di Lapangan Texaco 3D, Louisiana. Tujuan dari studi ini adalah untuk mengaplikasikan pendekatan geostatistik dan analisis Multiatribut dengan keterbatasan data yang dimiliki untuk memprediksi tekanan formasi. Hasil estimasi kecepatan interval menggunakan Multiatribut (10 atribut) menunjukkan korelasi yang sangat baik yaitu rata-rata korelasi prediksi log hasil atribut dan log validasi mencapai 79%, dengan tingkat kesalahan yang kecil berkisar rata-rata 175 - 292 m/s dari kecepatan validasi. Pendekatan Neural Network menghasilkan atribut polaritas semu (apparent polarity) sebagai atribut terbaik dalam estimasi kecepatan dengan error berkisar 108 m/s (berdasarkan hasil PNN) hingga 166 m/s (berdasarkan hasil MLFN). Anomali kecepatan rendah terdeteksi pada kedalaman 2800 - 2900 m dan sekitar kedalaman 3000 m, dengan gradient tekanan rata-rata mencapai 18 ? 22 ppg.

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Application of Multiattribute to poststack seismic data and the the seismic inversion result has been carried out to estimate the interval velocity, by using Neural Network approach. The result of estimated interval velocity is used to predict formation pressure in Texaco 3D Field, Louisiana. The purpose of this study is to apply the geostatistical approach and Multiattribute analysis to predict the formation pressure.

The results of estimated interval velocity using Multiattribute (10 attributes) show excellent correlation of the average correlation between predicted log and the real log reached 79%, with an error training and validation of a fairly small range from an average of 175-292 m/s validation of the velocity. The Neural Network approachment generating apparent polarity attribute as the best attribute of velocity estimation with errors ranging from 108 m/s (based on PNN) up to 166 m/s (based on the results of MLFN). Low velocity anomaly was detected at a depth of 2800 - 2900 m and approximately 3000 m depth, with the pressure gradient averaged 18-22 ppg."

"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%."

"[ABSTRAK Lapangan-X merupakan lapangan gas di Cekungan Kutai yang dikembangkan sejak tahun 1986. Reservoar lapangan-X merupakan endapan delta Miosen akhir yang berlapis, dimana dikarakterisasikan oleh formasi yang didominasi oleh lempung. Perselingan antara batupasir dan batuserpih menghasilkan heterogenitas porositas yang cukup tinggi. Salah satu metode yang efektif dalam mengatasi tingkat heterogenitas yang tinggi adalah dengan metode Artificial Neural Network (ANN). ANN menggunakan algoritma Probabilistic Neural Network (PNN) mampu mendiskriminasikan daerah yang memiliki sebaran porositas yang tinggi dan rendah dengan baik pada zona Fresh Water Sand (FWS) lapangan-X dibanding dengan metode Multiatribut linier yang cenderung merupakan nilai sebaran porositas rata-rata. Nilai korelasi hasil prediksi terhadap target menggunakan metode PNN mencapai 0.8610 dengan rata-rata kesalahan (average error) sebesar 0.0283, sementara nilai korelasi hasil metode Multiatribut linier hanya sebesar 0.7098 dengan rata-rata kesalahan (average error) sebesar 0.0398. Hasil PNN pada sayatan waktu +10 ms dari horizon FS33 berhasil mengkarakterisasikan sebaran porositas batupasir yang bersih dari lempung di bagian selatan daerah penelitian, dimana fasies pengendapan batupasir tersebut diinterpretasikan berasal dari dataran delta. Sementara sayatan waktu -10 ms dari horizon FS42, menunjukan sebaran porositas batugamping dengan fasies pengendapannya diinterpretasikan berasal dari lingkungan neritik (shelf). Dari penelitian ini, dapat disimpulkan bahwa metode PNN berhasil menggambarkan sebaran porositas batuan di zona Fresh Water Sand (FWS) lapangan-X dengan baik sehingga hasil prediksi penyebaran yang dilakukan mampu mendekati data- data sumuran.

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ABSTRACT X-field is a gas field in Kutai Basin and it has been developed since 1986. Reservoir of X-field is a multi layered upper Miocene deltaic deposits and characterized by a shaly formation. A highly intercalation between sand & shale unit in X-field has been contributed to the heterogeneity of porosity in the area. One of the effective methods to spatially quantify such heterogeneity of porosity is by using Artificial Neural Networks (ANN). ANN with Probability Neural Network (PNN) algorithm has been successfully retained more dynamic range, high and low frequency porosity content, compare to the Multiattributes linear which is tend to show a smoothed, or more averaged prediction. The correlation value from PNN methods can be up to 0.8610 with average error is 0.0283, while correlation value from Multiattribute linear only up to 0.7098 with average error is 0.0398. The time slice of PNN result at +10ms from horizon FS33 has been clearly figured out an accumulation of high porosity in the southern area of the interval target which is indicated as a clean sand lithology based on sensitivity analysis. And such accumulation has formed a distributaries channel trend which is interpreted as delta plain deposits. Meanwhile, the time slice of PNN result at - 10 ms from horizon FS42 has indicated a carbonate lithology which is interpreted as shelf deposits. From this study, it?s concluded that PNN algorithm as a nonlinear function has been successfully showed a better porosity distribution in the Fresh Water Sand (FWS) zone of X-field.;X-field is a gas field in Kutai Basin and it has been developed since 1986. Reservoir of X-field is a multi layered upper Miocene deltaic deposits and characterized by a shaly formation. A highly intercalation between sand & shale unit in X-field has been contributed to the heterogeneity of porosity in the area. One of the effective methods to spatially quantify such heterogeneity of porosity is by using Artificial Neural Networks (ANN). ANN with Probability Neural Network (PNN) algorithm has been successfully retained more dynamic range, high and low frequency porosity content, compare to the Multiattributes linear which is tend to show a smoothed, or more averaged prediction. The correlation value from PNN methods can be up to 0.8610 with average error is 0.0283, while correlation value from Multiattribute linear only up to 0.7098 with average error is 0.0398. The time slice of PNN result at +10ms from horizon FS33 has been clearly figured out an accumulation of high porosity in the southern area of the interval target which is indicated as a clean sand lithology based on sensitivity analysis. And such accumulation has formed a distributaries channel trend which is interpreted as delta plain deposits. Meanwhile, the time slice of PNN result at - 10 ms from horizon FS42 has indicated a carbonate lithology which is interpreted as shelf deposits. From this study, it?s concluded that PNN algorithm as a nonlinear function has been successfully showed a better porosity distribution in the Fresh Water Sand (FWS) zone of X-field.;X-field is a gas field in Kutai Basin and it has been developed since 1986. Reservoir of X-field is a multi layered upper Miocene deltaic deposits and characterized by a shaly formation. A highly intercalation between sand & shale unit in X-field has been contributed to the heterogeneity of porosity in the area. One of the effective methods to spatially quantify such heterogeneity of porosity is by using Artificial Neural Networks (ANN). ANN with Probability Neural Network (PNN) algorithm has been successfully retained more dynamic range, high and low frequency porosity content, compare to the Multiattributes linear which is tend to show a smoothed, or more averaged prediction. The correlation value from PNN methods can be up to 0.8610 with average error is 0.0283, while correlation value from Multiattribute linear only up to 0.7098 with average error is 0.0398. The time slice of PNN result at +10ms from horizon FS33 has been clearly figured out an accumulation of high porosity in the southern area of the interval target which is indicated as a clean sand lithology based on sensitivity analysis. And such accumulation has formed a distributaries channel trend which is interpreted as delta plain deposits. Meanwhile, the time slice of PNN result at - 10 ms from horizon FS42 has indicated a carbonate lithology which is interpreted as shelf deposits. From this study, it’s concluded that PNN algorithm as a nonlinear function has been successfully showed a better porosity distribution in the Fresh Water Sand (FWS) zone of X-field., X-field is a gas field in Kutai Basin and it has been developed since 1986. Reservoir of X-field is a multi layered upper Miocene deltaic deposits and characterized by a shaly formation. A highly intercalation between sand & shale unit in X-field has been contributed to the heterogeneity of porosity in the area. One of the effective methods to spatially quantify such heterogeneity of porosity is by using Artificial Neural Networks (ANN). ANN with Probability Neural Network (PNN) algorithm has been successfully retained more dynamic range, high and low frequency porosity content, compare to the Multiattributes linear which is tend to show a smoothed, or more averaged prediction. The correlation value from PNN methods can be up to 0.8610 with average error is 0.0283, while correlation value from Multiattribute linear only up to 0.7098 with average error is 0.0398. The time slice of PNN result at +10ms from horizon FS33 has been clearly figured out an accumulation of high porosity in the southern area of the interval target which is indicated as a clean sand lithology based on sensitivity analysis. And such accumulation has formed a distributaries channel trend which is interpreted as delta plain deposits. Meanwhile, the time slice of PNN result at - 10 ms from horizon FS42 has indicated a carbonate lithology which is interpreted as shelf deposits. From this study, it’s concluded that PNN algorithm as a nonlinear function has been successfully showed a better porosity distribution in the Fresh Water Sand (FWS) zone of X-field.]"

"ABSTRAKInterpretasi seismik yang dilakukan pada Lapangan D telah dilakukan dengan memprediksi beberapa volum properti log dengan menggunakan analisis multiatribut. Analisis ini dilakukan untuk memprediksi adanya sebaran reservoar batupasir pada zona D-28A. Selain untuk memetakan sebaran reservoarnya, analisis ini juga dilakukan untuk memprediksi adanya persebaran fluida hidrokarbon terutama hidrokabron minyak yang menjadi target pada penelitian ini. Daerah penelitian ini terletak di Lapangan D yang berada di Utara Jawa Barat. Lokasi penelitian ini dekat dengan sub-cekungan Ardjuna. Teknik analisis multiatribut ini membutuhkan input atribut tambahan yaitu model impedansi akustik yang didapatkan dengan metode inversi. Metode inversi yang digunakan pada penelitian ini adalah metode inversi model based. Hasil analisa terintegrasi dengan gabungan irisan ? irisan yang terbentuk dari volume gamma ray, resistivity, dan water saturation. Dari irisan ketiga volume ini terlihat adanya beberapa tren yang sama yang mengindikasikan adanya persebaran reservoar batupasir sekaligus adanya kandungan fluida yang diindikasikan sebagai fluida hidrokarbon minyak. Tren ini diindikasikan dengan nilai cut off gamma ray 70 API, resistivitas 1.7 ohm-m, dan saturasi air 0.9 0.SW.

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ABSTRACTSeismic interpretation performed on D Field has been carried out with some predicting volume by using the log property multiatribut analysis. This analysis was conducted to predict the distribution of reservoir sandstones in zone D-28A. In addition to map the distribution of reservoarnya this analysis is also performed to predict the distribution of hydrocarbon fluid, especially oil hidrokabron that being targeted in this study. The research area is located on the D Field located in the North West Java. The research location is close to the sub-basin Ardjuna. Multiatribute analysis techniques requires an additional attribute input, that input is acoustic impedance model that obtained by the inversion method. The inversion method used in this study is a model-based inversion methods. Results of the combined analysis is integrated with slices that are formed from the volume of gamma ray, resistivity and water saturation. The three volume of the slices have seen a couple of the same trends that indicate the distribution of reservoir sandstones at the same time their fluid content which is indicated as fluid hydrocarbon oil. The trends shown with cut off value of gamma ray 70 API, resistivity 1.7 ohm-m, and water saturation 0.9 0.SW.;"

"Cadangan migas semakin sulit ditemukan, hal ini dikarenakan kondisi geologi yang lebih kompleks. Kondisi yang kompleks ini menyebabkan kesulitan dalam menentukan persebaran reservoir. Maka dari itu diperlukan metode yang lebih bagus untuk mengatasi kondisi geologi yang kompleks tersebut. Penelitian ini menggunakan metode multiatribut dan Probabilistic Neural Network (PNN) yang dapat mencari hubungan antara atribut seismik dengan data yang dicari, untuk prediksi nilai properti dari batuan sekitarnya. Dari metode ini dihasilkan persebaran pada data porositas dengan nilai korelasi 0,52, saturasi air dengan nilai korelasi 0,73, dan shale content dengan nilai korelasi 0,58. Dimana gabungan dari data porositas, saturasi air, shale content, dan data impedansi akustik (AI) hasil inversi dapat menjadi petunjuk untuk identifikasi persebaran reservoir. Dari nilai porositas dan saturasi dapat dibuat persebaran hidrokarbon, dimana pada penelitian ini didapatkan nilai antara 0,01 – 0,03. Lapangan FA ini memiliki reservoir yang berada di antara sumur F-06, FA-05, FA-15, dan FA-18 dan menyebar ke arah barat dari sumur FA-05, FA-15 & FA-18.

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Oil and gas reserves are increasingly difficult to find due to more complex geological conditions. This complex condition causes difficulties in determining reservoir distribution. Therefore a better method is needed to overcome these complex geological conditions. This study uses a multi-attribute method and Probabilistic Neural Network (PNN) that can search for correlation between seismic attributes and the data sought, for the prediction of property values ​​from surrounding rocks. From this method the distribution of porosity data with a correlation value of 0.52 was generated, water saturation with a correlation value of 0.73, and shale content with a correlation value of 0.58. Where the combination of porosity data, water saturation, shale content, and acoustic impedance (AI) data of inversion results can be a clue to identify reservoir distribution. From the porosity and saturation values, hydrocarbon dispersion can be made, where in this study values ​​were obtained between 0.01 - 0.03. This FA field has a reservoir between wells F-06, FA-05, FA-15, and FA-18 and spreads westward from wells FA-05, FA-15 & FA-18."

"Perhitungan cadangan hidrokarbon merupakan suatu kajian untuk mengetahui jumlah minyak dan gas dari suatu lapangan yang diindikasikan memiliki cadangan hidrokarbon. Untuk mendapatkan perkiraan jumlah cadangan dilakukan beberapa proses yang terutama adalah pemodelan reservoar yang dapat dibagi menjadi dua tahap utama yaitu pemodelan struktur dan pemodelan properti. Analisis petrofisika bertujuan untuk mendapatkan parameter petrofisika yang berguna untuk karakterisasi batuan reservoar. Pada penelitian kali ini didapatkan bahwa batuan reservoar memiliki nilai porositas rata – rata sebesar 0.2, nilai kandungan lempung rata – rata sebesar 0.6 dan nilai saturasi air rata – rata sebesar 0.5. Analisis multiatribut seismik digunakan untuk melakukan persebaran parameter petrofisika pada volum seismik. Atribut yang digunakan adalah inversi seismik sebagai atribut eksternal, Instantaneous Frequency, Amplitude Envelope, Cosine Instantaneous Phase dan Instantaneous Phase. Berdasarkan hasil analisis petrofisika dan pemodelan reservoar didapatkan potensi gas pada area sumur SMR-01 dengan arah persebaran reservoar pada azimuth 45˚ dengan nilai major direction 3700 dan minor direction 3200. Lapangan “MSS” didapatkan perkiraan cadangan jumlah GIIP sebesar 776553 103 sm3.

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Calculation of hydrocarbon reserves is a study to determine the amount of oil and gas from a field which is indicated to have hydrocarbon reserves. To get an estimate of the amount of reserves, several processes are carried out, mainly reservoir modelling can be divided into two main stages, structural modelling and property modelling. Petrophysical analysis aims to obtain petrophysical parameters that are useful for characterizing reservoir rocks. In this study, it was found that the reservoir rock has an average porosity value is 0.2, an average clay content value is 0.6 and an average water saturation value is 0.5. Seismic multi-attribute analysis was used to perform the distribution of petrophysical parameters on seismic volume. The attributes used are seismic inversion as an external attribute, Instantaneous Frequency, Amplitude Envelope, Cosine Instantaneous Phase and Instantaneous Phase. Based on the results of petrophysical analysis and reservoir modelling, The gas reserves found in the SMR-01 well area with the reservoir distribution direction is 45˚ azimuth with a major direction value of 3700 and a minor direction of 3200. "MSS" field estimated reserves of GIIP are 776553 103 sm3."

"Lapangan “DEWI”, yang berlokasi di Cekungan Bonaparte Maluku Tenggara merupakan lapangan gas dengan reservoir utama yang terletak pada Formasi Plover, yang didominasi oleh batu pasir. Berdasarkan analisa struktur didapatkan bahwa lapangan ini memiliki satu sesar utama yang membagi blok utara dan blok selatan. Berdasarkan analisis petrofisika didapatkan bahwa zona prospek hidrokarbon dari lapangan ini terletak di formasi Plover dan Zona A. Penelitian ini bertujuan untuk menganalisis distribusi parameter petrofisika seperti porositas, volume shale, dan saturasi air yang penting dalam karakterisasi reservoir. Penelitian ini menggunakan analisis seismik multiatribut dan probabilistic neural network untuk memprediksi parameter petrofisika berdasarkan atribut dari data seismik. Hasil menunjukkan bahwa pada penelitian ini probabilistic neural network memiliki keunggulan dalam memprediksi parameter petrofisika untuk karakterisasi reservoir dibanding multiatribut konvensional. Berdasarkan hasil dari pemetaannya ditemukan variasi yang menarik dalam persebaran parameter petrofisika pada formasi Plover dan Zona A. Hasil dari penelitian ini dapat digunakan untuk menyediakan pemahaman baru dalam karakterisasi daerah berpotensi hidrokarbon di Lapangan “DEWI”.

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The “DEWI” field, which is located in the Bonaparte Basin, Southeast Maluku, is a gas field with the main reservoir located in the Plover Formation, which is dominated by sandstone. Based on structural analysis, it was found that this field has one main fault that divides the northern block and the southern block. Based on petrophysical analysis, it was found that the hydrocarbon prospect zone of this field is located in The Plover Formation and Zone A. This research aims to analyze the distribution of petrophysical parameters such as porosity, shale volume, and water saturation which are important in reservoir characterization. This research uses multi-attribute seismic analysis and probabilistic neural networks to predict petrophysical parameters based on attributes from seismic data. The results show that in this study the probabilistic neural network has advantages in predicting petrophysical parameters for reservoir characterization compared to conventional multi-attributes. Based on the results of the mapping, enticing variations were found in the distribution of petrophysical parameters in The Plover Formation and Zone A. The results of this research can be used to provide new insights into the characterization of potential hydrocarbon areas in the "DEWI" Field."

"Integrasi dari data sumur dan data seismik sangat berguna untuk mendapatkan interpretasi yang baik dalam proses eksplorasi hidrokarbon. Beberapa metode yang mengintegrasikan kedua data tersebut antara lain, metode inversi impedansi akustik dan metode seismik multiatribut. Metode inversi impedansi akustik dilakukan untuk memprediksi informasi sifat fisis bumi berdasarkan informasi rekaman seismik yang diperoleh. Pada metode ini, sifat fisis bumi yang dimodelkan adalah impedansi akustik. Sedangkan metode seismik multiatribut metode yang menggunakan lebih dari satu atribut untuk memprediksi beberapa properti fisik dari bumi. Metode ini digunakan untuk memprediksi persebaran porositas dari volum seismik. Kedua metode ini digunakan untuk mengkarakterisasi reservoar pada lapangan F3 di Belanda yang diduga terdapat akumulasi hidrokarbon. Hal ini terlihat dari adanya fenomena bright spots dan gas chimneys pada bawah permukaan yang berasosiasi dengan adanya akumulasi gas pada lapangan tersebut.

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Integration of well and seismic data are very useful to get good interpretation in the process of hydrocarbon exploration. Several methods that integrate both data are seismic inversion and multi-attribute seismic. Acoustic impedance inversion method is used to predict the physical properties of the earth based on information obtained by the seismic record. Multi-attribute seismic method is seismic method that uses more than one attribute to predict physical properties of the earth. This method is used to predict the distribution of porosity from seismic volume, which are applied to characterize the reservoir in the field F3 in the Netherland. The field has been indicated to have an accumulation of hydrocarbons. This indication can be seen from the phenomena of bright spots and gas chimneys on the sub-surface expressions which is associated with the accumulation of gas in the field."