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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%."

"Produksi 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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Production 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."

"Analisa multi atribut adalah salah satu metode statistik menggunakan lebih dari satu atribut untuk memprediksi properti fisik dari batuan. Tujuan analisa ini adalah adalah mencari hubungan antara log dengan data seismik. Hubungan ini digunakan untuk memprediksi Volome dari properti log pada semua volume seismik Pada penelitian ini analisa multiatribut diaplikasikan pada lapangan X daerah cekungan sumatera selatan dengan menggunakan 5 data sumur. Target dari penelitian ini adalah memprediksi penyebaran porositas di lapangan X. Sumursumur yang dipilih adalah sumur yang tersebar merata dan mewakili area yang akan diprediksi penyebaran porositasnya. Jumlah atribut yang digunakan di tentukan oleh proses step wise regression. Metode multiatribut yang linier transformasinya terdiri dari deret bobot yang diperoleh dari minimalisasi least square. Pada metoda non linier, neural network di gunakan dalam proses training dengan menggunakan atribut yang sudah ditentukan sebelumnya.Tipe neural network yang digunakan adalah PNN ( Probabilistic Neural Network ),tipe ini dipilih karena mempunyai hasil korelasi yang paling baik dibandingkan dengan tipe neural network yang lain. Untuk mengetahui tingkat kepercayaan dari transformasi multiatribut dilakukan proses crossvalidasi. Hasilnya multiatribut menunjukan korelasi sebesar 0.65 dan neural network 0.69.

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Multi-attribute analysis is a statistic method using more than one attribute to predict physical properties of rocks. The aim of this analysis is to find a relationship between log and seismic data. The relationship is used for predicting volume of log property at all seismic volumes. In this study the multi-attribute analysis is applied to area X, which is a cavity region in South Sumatera, using five well data. The aim of the study is to predict porosity distribution at area X. The wells that were chosen were those that were spread evenly and represented areas where the distribution of porosity will be predicted. The quantity of attributes used is determined by a step wise regression process. A linear multiattribute method comprises of a series that is achieved by a minimised least square. In a non-linear method, neural network is used in the training process with predetermined attributes. The neural network type used was PNN (Probabilistic Neural Network ), this type was chosen because of the best correlation result. To verify the validity of the multi-attribute transformation, a crossvalidation was conducted. The result shows a 0.65 correlation and a 0.69 neural network."

"Reservoir karbonat diperkirakan mengandung hampir 60% dari total cadangan hidrokarbon dunia dan diperkirakan memiliki 50% dari total produksi hidrokarbon. Hidrokarbon umumnya terdapat pada batuan berpori. Porositas batuan karbonat umumnya memiliki heterogenitas yang tinggi, kompleksitas, dan random. Salah satu metode yang efektif untuk mengatasi heterogenitas adalah metode neural network. Sehingga penelitian ini bertujuan untuk menetukan distribusi porositas dengan neural network pada batuan karbonat dengan menggunakan 2 data sumur dan data seismik 2D post stack time migration (PSTM) pada lapangan T. Seismik atribut yang digunakan sebagai input proses probabilistic neural network berupa data seismik dan hasil inversi serta log yang akan diprediksi penyebarannya. Digunakan step wise regression dan validation error untuk menentukan atribut terbaik yang akan digunakan. Hasil prediksi nilai porositas menggunkan probabilistic neural network dengan input atribut terbaik yang telah terpilih menghasilkan korelasi yang lebih baik 0.81 dengan error 0.03 dibanding dengan metode multiatribut yang menggunakan persamaan linier yaitu 0.66 dengan error 0.04 dan hasil model log prediksi mendekati log aktual. Hasil distribusi porositas dapat dianilisis bahwa nilai porositas pada sumur C1 memiliki nilai porositas efektif yang rendah dibandingkan dengan sumur C4.

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Reservoir carbonate mostly contains 60% of total hydrocarbon preserves in the world, and it is predicted about 50% which is produced hydrocarbon. Commonly, hydrocarbon is found in the rock pores. The porosity of carbonate, generally, has high heterogeneity, complexity, and random. One of effective methods to solve the problem is neural network. The aim of this study is to determine the distribution of porosity using neural network for carbonate in T field. Seismic attribute is used as input in neural network process which is seismic data, inversion result, and well log. Step wise regression and validation error are used to determine the best attributes that will be used to.

The prediction result of porosity using probabilistic neural network with the best attribute has better correlation than using multi attributes for linier method. The correlation and error value using neural network are 0.08% and 0.03%, while the value of correlation and error using multi attribute for linier method are 0.06% and 0.04%, respectively. The predicted log model is approaching the actual log. The result of porosity distribution shows that the porosity value of well C1 has lower effective porosity than well C4."

"Prediksi tekanan pori sebelum melakukan proses pengeboran menjadi hal yang sangat penting karena dapat merepresentasikan pemetaan migrasi hidrokarbon, serta analisa konfigurasi tutupan dan geometri cekungan. Disisi lain penentuan tekanan pori dapat membantu dalam pembuatan desain program casing dan lumpur. Penelitian ini dilakukan pada lapangan X, Cekungan Kutai Kalimantan Timur dimana secara regional cekungan ini tersusun atas endapan- endapan sedimen yang berumur tersier yang memperlihatkan endapan-endapan fase trangresi dan regresi laut. Prediksi tekanan pori pada penelitian ini menggunakan metode yang dikembangkan oleh Eaton, metode ini membutuhkan data pengukuran geofisika seperti data kecepatan seismik dan data log sumur. Prediksi tekanan pori diturunkan dari kecepatan seismik 3D yang diperoleh dari hasil pemodelan kecepatan dengan menggunakan metode Impedansi akustik Inversion, dimana metode tersebut mampu untuk memprediksi kecepatan lebih akurat untuk menetukan karakteristik litologi dan daerah yang berstruktur komplek. Proses yang dilakukan pada penelitian ini dimulai dengan menentukan parameter-parameter perhitungan dengan Metode Eaton pada 5 sumur dengan data kecepatan sonic dan seismic, selanjutnya melakukan perhitungan nilai overburden, Tekanan Hidrostatik, Normal Compaction trend NCT dan Model distribusi prediksi tekanan pori. Dari hasil prediksi tekanan pori dapat memperlihatkan penyebaran/ distribusi zona overpressure pada lapangan X yang dilalui oleh 5 sumur, penyebaran ini menjadi penting untuk membantu dalam program untuk menentukan pengeboran sumur di area tersebut.

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Pore Pressure prediction prior to drilling is paramount importance as it can represent of mapping hydrocarbon migration, as well as to analyse of trap and basin geometric configurations. Side of is other pore pressure determination can be assist in design of casing and mud program. This research was conducted in X field , Kutai basin, East Kalimantan, where is by regional this basin is composed of tertiary deposits which to show sedimentary deposits of marine tracres and regressions. The pore pressure prediction in this study using developed methods by Eaton, this method requires geophysical measurement data such as seismic velocity data and well log data.

The pore pressure prediction is derived from the 3D seismic velocity obtained from the velocity modeling results using the Inversion acoustic impedance method, where the method is able to predict more accurate velocities to determine lithologic characteristics and complex structured regions. The process performed in this study begins by determining the calculation parameters with the Eaton Method on 5 wells with sonic and seismic velocity data, then performing overburden value calculation, Hydrostatic Pressure, Normal Compaction Trend NCT and Pore pressure prediction distribution model. From the predicted pore pressures can show the distribution of overpressure zones in the X field through which 5 wells, this distribution is important to assist in the program to determine drilling wells in the area."

"ABSTRAKTesis ini membahas bagaimana proses mengaplikasikan metoda neural network pada data seismik tiga dimensi untuk memprediksi porositas dan saturasi air pada suatu reservoar dengan membuat volum pseudo log. Studi kasus penelitian ini adalah reservoar karbonat build-up pada formasi Parigi di lapangan X, merupakan lapangan gas yang baru ditemukan, di cekungan Jawa Barat Utara, dimana studi reservoar perlu dilakukan untuk program pengembangan produksi, salah satunya dari aspek geofisika. Studi ini menggunakan analisis pendekatan statistik dari atribut seismik terhadap data sumur yakni log porositas dan saturasi air untuk mendapatkan multi atribut dengan korelasi terbaik yang digunakan sebagai input dalam proses prediksi dengan menggunakan metoda Neural Network. Dengan menerapkan Neural Network, hasil prediksi memiliki korelasi dan resolusi yang lebih tinggi mendekati data akutal log porositas dan saturasi air. Hasil dari penelitian ini adalah distribusi porositas dan saturasi air secara kuantitatif berupa pseudo log volum tiga dimensi dimana data ini dapat menjadi data pendukung dalam studi reservoar lebih lanjut seperti pemodelan geologi, simulasi reservoar dan perhitungan cadangan. Hasil pseudo log ini kemudian diinterpretasi dan dipetakan untuk karakterisasi reservoar dan penentuan lokasi sumur. Dari pseudo log porositas, reservoar target secara stratigrafi dapat dibagi menjadi empat lapisan berdasarkan perbedaan nilai porositasnya. Untuk pseudo log saturasi air, dapat terlihat tiga kontak dan zona fluida reservoar, dimana terdapat zona gas, zona transisi, dan zona air. Dari hasil interpretasi distribusi reservoar tersebut direkomendasikan untuk pemboran dua sumur di lapangan X untuk memproduksi cadangan gas dan 1 sumur eksplorasi untuk membuktikan kandungan gas pada struktur build-up lain didekatnya.

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ABSTRACTThe focus of this study is the process how to apply neural network method in 3D seismic data to generate pseudo log of both porosity and water saturation in a reservoir. Case of this study is carbonate build-up at Parigi formation in X field, a new gas discovery field, in North West Java basin where resevoir study for production development including geophysical aspect is very necessary. This study used statistical analysis approach based on corelation between seismic atribut and well log data which are log porosity and water saturation to get multi attribute as input for prediction process. Applying Neural Network can improve correlation and resolution between pseudo log and actual log both porosity and water saturation. The result of this study is quantitative ditribution of both porosity and water saturation in 3D psudo log volume which can be used for data supporting in geological modeling, reservoir simulation and reserves estimation. Then, those pseudo log are interpreted and mapped for characterization and well location delineation. Based on pseudo porosity log , the reservoir can be divided in four layers with different porosity value. For pseudo water saturation log, we can see contact and fluid zones of the reservoir which consist of gas zone, transition zone and water zone. According to interpretation pseudo log of both the porosity and the water saturation, can be recommended to drill two wells in X field to drain gas reserves and one exploration well to prove gas accumulation in other build-up stucture where is located near of X field"

"Lapangan ALIA yang berada di Delta Mahakam, Cekungan Kutai, Kalimantan Timur terdiri lebih dari 550 akumulasi reservoar yang secara struktur saling menumpuk dan terkompartemenkan. Lapangan ini sudah menghasilkan minyak kurang lebih selama 40 tahun. Terdapat lebih dari 400 sumur yang sudah di bor pada lapangan ini. Selama ini eksplorasi mengandalkan data sumur dan baru pada tahun 2011 dilakukan survey seismik 3D. Berdasarkan hasil survey seismik 3D tersebut, tesis ini memanfaatkan data seismik untuk karaktrerisasi reservoar lebih detail dengan menggunakan metode AVO dan Inversi Simultan. Metode AVO dan Inversi Simultan digunakan untuk mengetahui pola dan anomali hidrokarbon dari penampang seismik. Metode ini akan menghasilkan beberapa sifat fisika properti reservoar seperti Impedansi gelombang P, Impedansi Gelombang S, densitas Dn, dan Lamda-rho. Hasil analisa pada lapangan ALIA menunjukan bahwa pada zona reservoar target yaitu Top R0-35 memiliki anomali pada Attribute AVO Intercept/A (-), Gradient/B (-), Product/A*B (+). Selain zona reservoar target, Zona reservoar lain juga memiliki pola anomali yang sama yaitu pada Top Horizon R0-1. Hasil analisa Inversi simultan juga menunjukan bahwa zona reservoar tersebut memiliki anomali hidrokarbon dengan nilai Impedansi P (Zp) antara 2000 - 4000 ms-1gcc-1, nilai Impedansi S (Zs) berkisar antara 900 - 2050 ms-1gcc-1, dan Densitas (Dn) berkisar antara 1.7-2.11 gcc. Lambda Rho juga memiliki anomali yang sama dengan nilai berkisar antara 8.8-14.6 Gpa*g/cc.

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ALIA field is located in Kutai basin, Mahakam Delta East Kalimantan, comprises of over 550 unconnected accumulations/reservoirs in structurally stacked and compartementalized deltaic sands. It has produced oil and gas for 40 years and more than 400 wells have been drilled in the field. Exploration and Development of this field was rely on well data and 3D seismic survey just conducted on 2011. Based on 3D seismic result, this thesis utilize seismic data for reservoar characterization by using AVO analyses and Simultaneous Inversion to get more detail of results.

Simultaneous inversion and AVO analyses is novel method in reservoir characterization. The method will produce several physical properties of reservoir such as P-wave Impedance, S-wave Impedance, density, Vp/Vs ratio, Lamda-rho and mu-rho. These physical properties could be used to estimate the type and content of reservoir lithology. Simultaneous AVO inversion needs P-wave sonic log, S-wave sonic log and density as input.

Analyses result from ALIA field at reservoar target zone, at Top R0-35 having the anomaly AVO Intercep/ A (-), Gradient B (-) and Product A*B (+). Another reservoar zone also having the same anomaly at Top Horizon R0-1. Simultaneous Inversion result also showed hydrocarbon anomaly with Impedance P (Zp) value around 2000 - 4000 ms-1gcc-1, Impedance S (Zs) around 900 - 2050 ms-1gcc-1, and Density (Dn) around 1.7-2.11 gcc. Lambda Rho showed hydrocarbon anomaly with value around 8.8-14.6 Gpa*g/cc"

"[Lapangan “X” merupakan lapangan gas terbesar di delta mahakam dengan luas area permukaan yang mancapai 1350km2 dan total akumulasi gas terproduksi mencapai 8 tcf sejak tahun 1990 hingga saat ini. Penurunan produksi yang cukup tajam melatarbelakangipengembangan gas di zona dangkal (shallow gas). Sedimen pada zona dangkal ini tersusun oleh endapan deltaik berumur Miosen Atas – Pliosen dengan batupasir sebagai batuan reservoar utama. Keberadaan fluida gas pada batupasir akan berdampak pada penurunan kecepatan gelombangP dan densitas batuan sehingga memberikan kontras impendansi akustik yang kuat terhadaplapisan shale. Kontras impedansi akustik ini terlihat sebagai anomali amplitudo (brightspot)pada seismik. Adanya kenaikan nilai amplitudo seiring dengan bertambah besarnya sudutdatang menjadi hal yang menarik dalam interpretasi shallow gas ini.Tujuan dari penelitian ini adalah untuk mendeteksi keberadaan shallow gas di lapangan “X”menggunakan atribut AVO Sismofacies dengan 2 sumur yang dijadikan referensi untukpemodelan synthetic AVO. Penulis menggunakan 2 sumur lainnya sebagai kalibrasi terhadapanomali AVO dari Sismofacies cube yang dihasilkan.Metode AVO sismofacies ini tidak menggunakan parameter intercept (A) dan gradient (B)untuk kalkulasi AVO melainkan menggunakan dua data substack yaitu Near dan Far stack.Crossplot antara Near dan Far pada zona water bearing sand dan shale diambil untukmendapatkan background trend sehingga anomali yang berada diluar trend tersebut dapatdiinterpretasikan sebagai gas sand.Hasil dari analisis AVO Sismofacies ini cukup baik dan menunjukkan kesesuaian denganinterpretasi gas di beberapa sumur dan efek Coal berkurang jika dibandingkan Far stack.Meskipun demikian interpretasi AVO ini sebaiknya diintergrasikan dengan analisis dariatribut seismik lainnya untuk memperkuat interpretasi;Field “X” is a giant gas field in mahakam delta which cover 1350km2 of the area with totalcummulative gas production has reached 8 tcf since 1990 to recently. A significantdecreasing of gas production has led to produce gas accumulation in shallow zone as aneffort to fight againts this decline. Shallow zone is a deltaic sediments which depositedduring Upper Miocen to Pliocene with dominant reservoir is sandstone.The presence of gas in sandstone has an impact on decreasing of velocity P as well as densitywhich giving a contrast of acoustic impedance to the overlaying shale. Contrast ofimpedance can be observes in seismic as an amplitude anomaly or so called a brightspot. Anincrease of amplitude along the offset become more interesting in shallow gas interpretation.The aim of this study is to detect shallow gas accumulation di field “X” by using AVOSismofacies attribute with 2 wells as references to model respons of AVO. The result ofAVO sismofacies will be a cube and the interpreation will be calibrated with 2 existing wellscontaining proven gas bearing sands.AVO Sismofacies method will introduce Near and Far substack to be used in the calculationinstead of using common AVO paramter intecepth (A) and gradient (B). A crossplot betweensubstacks will create a background trend from water bearing zone and shale hence anyoutliers can, then,be interpreted as gas anomaly.AVO Sismofacies result is encouraging and some of AVO anomaly has been well calibratedwith existing wells. Coal effect which led to misintepretaion in shallow gas sand isdiminished compared to Far stack. Despite of this result, this anomaly interpretation need tobe intergrated with anothers seismic attribute to gain the level of confidence for shallow gasinterpretation., Field “X” is a giant gas field in mahakam delta which cover 1350km2 of the area with totalcummulative gas production has reached 8 tcf since 1990 to recently. A significantdecreasing of gas production has led to produce gas accumulation in shallow zone as aneffort to fight againts this decline. Shallow zone is a deltaic sediments which depositedduring Upper Miocen to Pliocene with dominant reservoir is sandstone.The presence of gas in sandstone has an impact on decreasing of velocity P as well as densitywhich giving a contrast of acoustic impedance to the overlaying shale. Contrast ofimpedance can be observes in seismic as an amplitude anomaly or so called a brightspot. Anincrease of amplitude along the offset become more interesting in shallow gas interpretation.The aim of this study is to detect shallow gas accumulation di field “X” by using AVOSismofacies attribute with 2 wells as references to model respons of AVO. The result ofAVO sismofacies will be a cube and the interpreation will be calibrated with 2 existing wellscontaining proven gas bearing sands.AVO Sismofacies method will introduce Near and Far substack to be used in the calculationinstead of using common AVO paramter intecepth (A) and gradient (B). A crossplot betweensubstacks will create a background trend from water bearing zone and shale hence anyoutliers can, then,be interpreted as gas anomaly.AVO Sismofacies result is encouraging and some of AVO anomaly has been well calibratedwith existing wells. Coal effect which led to misintepretaion in shallow gas sand isdiminished compared to Far stack. Despite of this result, this anomaly interpretation need tobe intergrated with anothers seismic attribute to gain the level of confidence for shallow gasinterpretation.]"