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"[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.]"

"Penelitian ini menggunakan data seismik 3 dimensi dan 5 data sumur dari lapangan w. Target penelitian yaitu batuan karbonat pada formasi Tuban di cekungan Jawa Timur Utara. Penelitian bertujuan menentukan distribusi porositas karbonat, dengan menggunakan neural network berdasarkan inversi dan atribut seismik. Inversi seismik model based dan linier programming sparse spike, menghasilkan impedansi akustik pada lapisan di bawah horizon Top Carbonate hingga horizon Base Carbonate, mengalami peningkatan signifikan pada rentang 38076 - 46857 ((ft/s)*(g/cc)). Atribut seismik sweetness, rms amplitude, dan reflection intensity, digunakan sebagai atribut eksternal, untuk tahap multiatribut linier regresi dan neural network. Multiatribut linier regresi dan neural network dilakukan untuk memprediksi porositas bedasarkan atribut-atribut internal maupun eksternal. Hasil analisis multiatribut yang diaplikasikan pada data raw seismik dan 5 volum atribut eksternal, yaitu log porositas prediksi, memiliki nilai korelasi sebesar 0.712 terhadap log porositas. Dan, nilai validasinya sebesar 0.573. Sedangkan, Probabilistic Neural Network menghasilkan porositas prediksi dengan nilai korelasi sebesar 0.661 dan nilai validasinya sebesar 0.485. Berdasarkan multiatribut linier regresi maupun probabilistic neural network, porositas rata-rata pada lapisan reservoar karbonat sebesar 10-15% di bagian utara. Sedangkan, di bagian selatan, porositas rata-rata hanya di bawah 6%.

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This study uses three-dimensional seismic data and 5 well data from w field. The research target is carbonate rocks of the Tuban formation in North East Java basin. The study aims to determine the distribution of porosity carbonate, by using neural network algorithm, based on acoustic impedance inversion and seismic attributes. Models based inversion and linear programming sparse spike inversion result in acoustic impedance, in the layers below the horizon Top Carbonate to horizon Base Carbonate, experienced a significant increase impedance in the range 38076-46857 ((ft/s)*(g/cc)). Some seismic attribute; sweetness, rms amplitude, and reflection intensity, are used as external attributes for multi attribute linear regression and neural network. Multi attribute linear regression and neural network is done to predict porosity based on attributes of both internal and external.

The results of the analysis that is applied to the data multi attribute raw seismic and 5 volumes of external attributes, is called log porosity prediction, have a correlation value of 0.712 to log porosity original. And the value of its validation is 0.573. Meanwhile, Probabilistic Neural Network is producing log porosity prediction with correlation value of 0.661 and the value of its validation by 0485. Multi attribute based linear regression and probabilistic neural network, average porosity of the reservoir layer of carbonate of 10-15% in the north. Meanwhile, in the southern part, average porosity of just under 6%."

"ABSTRAKProduksi sumur minyak sangat ditentukan oleh parameter porositas dan permeabilitas. Kedua parameter ini dapat menggambarkan karakter reservoar, salah satunya pada reservoar karbonat. Permeabilitas reservoar karbonat sulit diestimasi karena heterogenitasnya cukup tinggi. Untuk melakukan penentuan zona permeabilitas, diperlukan metoda yang dapat memberikan estimasi yang akurat. Estimasi permeabilitas yang umum digunakan adalah menggunakan parameter gelombang Stoneley pada log Dipole Shear Sonic Imager DSI dan metoda rocktyping. Pendekatan pertama menggunakan log DSI untuk mengestimasi zona permeabilitas melalui parameter gelombang Stoneley. Sedangkan pendekatan kedua, metoda rocktyping digunakan untuk memperoleh hasil estimasi zona permeabilitas bawah permukaan dengan berdasarkan analisis Flow Zone Indicator FZI . Kedua pendekatan ini metoda DSI dan rocktyping , sama-sama dapat mengestimasi zona permeabilitas pada suatu reservoar. Dalam penelitian ini, kami melakukan perbandingan antara kedua metoda ini untuk mengetahui kesamaan dan perbedaan antara keduanya, seberapa efektif dan akurat dalam penentuan zona permeabilitas pada masing-masing tipe pori di reservoar karbonat. Dari hasil penelitian tersebut dapat disimpulkan bahwa hasil estimasi nilai permeabilitas berdasarkan tipe pori lebih efektif dan efisien menggunakan metoda rocktyping. Yang mana dari hasil tersebut didapat bahwa nilai kedalaman yang didominasi oleh tipe pori crack memiliki nilai permeabiitas yang paling tinggi. Yakni, 3617,689 mD pada Upper reservoir dan 1814,108 mD pada Reef reservoir.

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ABSTRACTProduction of oil well is determined by parameter of porosity and permeability. Both of these parameters can describe the reservoir character, one of them in the carbonate reservoir. The permeability of the carbonate reservoir is difficult to estimate because of the high heterogeneity. To determine the permeability zone, a method that can provide accurate estimates is needed. The commonly used permeability estimation is to use the Stoneley wave parameters in the Dipole Shear Sonic Imager log DSI and the rocktyping method. The first approach uses the dsi log to estimate the permeability zone via the Stoneley wave parameter. While the second approach, rocktyping method is used to obtain the estimate of sub surface permeability zone based on Flow Zone Indicator FZI analysis. Both of these approaches DSI method and rocktyping , can equally estimate the permeability zone in a reservoir. In this study, we compared the two methods to determine the similarities and differences between the two, how effective and accurate the determination of permeability zones in each pore type in the carbonate reservoir. From the results of this study can be concluded that the results of permeability value estimation based on pore type more effective and efficient using method rocktyping. Which of the results obtained that the value of depth is dominated by pore crack type has the highest permeabiitas value. Namely, 3617.689 mD on the top reservoir and 1814.108 mD at the Reef reservoir."

"Komposit keramik berpori adalah suatu campuran dari dua atau lebih material yang dibuat agar terbentuk pori-pori yang cukup tinggi, dimana ditandai dengan tingginya nilai porositas. Salah satu bahan baku dalam pembuatan keramik berpori tersebut adalah alumina, namun porositas dari alumina memiliki nilai yang sangat rendah. Untuk itulah diperlukan bahan aditif yang berfungsi untuk meningkatkan karakterisasi dari porositas keramik tersebut. Bahan aditif yang digunakan untuk penelitian ini adalah abu terbang. Adapun tujuan dari penelitian ini adalah untuk mengetahui karakterisasi dari campuran antara alumina dan abu terbang terhadap susut bakar, porositas, kekerasan, konduktivitas elektrik dan luas permukaan spesifik. Penelitian yang dilakukan akan mengetahui pengaruh dari ukuran partikel D50 dari alumina, komposisi antara alumina dan abu terbang, dan termperatur sinter. Berdasarkan dari pengujian yang dilakukan bahwa dengan penambahan abu terbang dapat meningkatkan porositas menjadi 41,13% pada ukuran partikel D50 alumina 10, 85 μm, komposisi abu terbang 20% dan temperatur sinter 8000C. Untuk persentase susut bakar terendah adalah 3,92% pada ukuran partikel D50 alumina 10, 85 μm, komposisi abu terbang 0% dan temperatur sinter 8000C. Namun dengan adanya penambahan abu terbang, sifat kekerasan dari alumina akan berkurang dimana yang terendah adalah 109 HV pada pada ukuran partikel D50 alumina 10,85 μm, komposisi abu terbang 20% dan temperatur sinter 8000C. Untuk nilai konduktivitas elektrik yang tertinggi adalah 739 μS/cm pada ukuran partikel D50 alumina 0,619 μm, komposisi abu terbang 20% dan temperatur sinter 8000C. Sedangkan hasil pengukuran luas permukaan spesifik menunjukkan bahwa semakin besar ukuran partikel D50 alumina dan meningkatnya kompoisi abu terbang maka luas permukaan spesifik akan semakin besar. Namun, dengan semakin tinggi temperatur sinter maka nilai luas permukaan spesifik akan menurun

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Porous ceramic composite is a mixture of two or more materials that are made in order to form quite high pores, which is characterized by high porosities. One of the raw material in the manufacture of porous ceramic is alumina, but the porosity of alumina has very low value. It needs additive that serves to improve the characteristics of the ceramic porosity. The additive used for this test is fly ash. The purposes of this research are to determine the characteristics of a mixture of alumina and fly ash on the shrinkage, porosity, hardness, electrical conductivity and specific surface area. Tests conducted will determine the effects of particle size D50 of alumina, compositions between alumina and fly ash, and sinter termperatures. Based on testing performed by addition of fly ash, the porosity will be increase to 41.13% at particle size D50 alumina 10, 85 μm, 20% fly ash composition and sinter temperatur of 8000C. Lowest shrinkage percentage is 3.92% at particle size D50 alumina 10, 85 μm, 0% fly ash composition and sinter temperatur 8000C. But with the addition of fly ash, hardness properties of alumina will be reduced, where the lowest was 109 HV on the D50 particle size of 10.85 μm alumina, 20% fly ash composition and sinter temperatur 8000C. The highest electrical conductivity is 739 μS / cm at a particle size of 0.619 μm D50 alumina, 20% fly ash composition, and sinter temperatur 8000C. While the specific surface area measurement shows that larger particle size D50 alumina composition and increasing of fly ash composition will cause increasing of the specific surface area. But, the higher sinter temperatur will makes decreasing of the specific surface area."