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"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."

"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%."

"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%."

"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."

"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"

"[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.]"

"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."

"Potensi hidrokarbon di Lapangan 'OZ', Cekungan Bonaparte belum dimanfaatkan karena risiko pengeboran yang tinggi yang disebabkan oleh heterogenitas reservoir. Karena sifat reservoir yang heterogen, maka dilakukan identifikasi dan karakterisasi untuk melihat sebaran litologi dan fluida reservoirnya. Metode Probabilistic Neural Network (PNN) adalah metode utama dalam analisis multi-atribut untuk menemukan hubungan nonlinier antara data seismik dan data sumur di Lapangan 'OZ' dan kemudian menghasilkan model untuk distribusi data sinar gamma, porositas, dan saturasi air dengan nilai koefisien korelasi masing-masing pelatihan sebesar 0,8871, 0,9778, 0,9719 dan koefisien korelasi validasi sebesar 0,7836, 0,8554, 0,8187. Integrasi antara model distribusi data sinar gamma, porositas, saturasi air, ditambah dengan hasil inversi impedansi akustik (AI), dapat menjadi sarana untuk mengklasifikasikan dan mengidentifikasi distribusi reservoir hidrokarbon. Lapangan 'OZ' memiliki karakteristik reservoir yang mengandung gas hidrokarbon dan memiliki litologi batupasir bersih dengan sesar normal sebagai traps serta batupasir rapat dan batuan serpih sebagai seal yang tersebar di bagian Selatan dan Tengah lapangan OZ.The hydrocarbon potential in the 'OZ' Field, Bonaparte Basin has not been exploited due to the high drilling risk caused by reservoir heterogeneity. Due to the heterogeneous nature of the reservoir, identification and characterization were carried out to see the distribution of lithology and reservoir fluids. The Probabilistic Neural Network (PNN) method is the main method in multi-attribute analysis to find a nonlinear relationship between seismic data and well data in the 'OZ' Field and then generate a model for the distribution of gamma ray, porosity, and water saturation data with the respective correlation coefficient values. -each training is 0.8871, 0.9778, 0.9719 and the validation correlation coefficient is 0.7836, 0.8554, 0.8187. The integration between the distribution model of gamma ray data, porosity, water saturation, coupled with the results of acoustic impedance inversion (AI), can be a means to classify and identify the distribution of hydrocarbon reservoirs. The 'OZ' field has reservoir characteristics containing hydrocarbon gas and has a clean sandstone lithology with normal faults as traps as well as dense sandstone and shale rock as seals which are scattered in the Southern and Central parts of the OZ field."

"ABSTRAKDi daerah penelitian dan sekitarnya, reservoir karbonat pada Formasi Kujung adalah salah satu target eksplorasi. Salah satu syarat batuan karbonat sebagai reservoir yaitu harus mempunyai porositas dan permeabilitas yang baik agar mampu menyimpan dan mengalirkan hidrokarbon. Semakin besar angka porositas berarti pori-pori di dalam batuan tersebut semakin banyak, selain itu pori-pori yang saling terkoneksi akan meningkatkan permeabilitas batuan. Pengetahuan dan pemahaman mengenai porositas pada batuan karbonat dan penyebarannya sangat penting dalam eksplorasi. Pada penelitian ini dilakukan indentifikasi sebaran porositas Formasi Kujung I dengan metode penerapan aplikasi atribut pada data seismik 3D yang meliputi inversi, ant-track serta spectral decomposition. Berdasarkan penerapan atribut seismik tersebut, disimpulkan bahwa pada zona target porositas berkembang baik, selain itu ditemukan adanya fenomena low frequency shadow zone yang dapat digunakan sebagai indikator hidrokarbon pada zona target tersebut. Dengan adanya dua parameter yaitu porositas serta indikator hidrokarbon, pada penelitian ini dapat disimpulkan bahwa zona target sangat potensial sebagai reservoar yang mengandung hidrokarbon dan dapat dikembangkan sebagai target eksplorasi.

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ABSTRACTStudy area and its surroundings, carbonate reservoir of Kujung Formation is one of the exploration target. The requirements of carbonate rock as a reservoir is must have a good porosity and permeability. Higher number of porosity it is mean there are lot of the pores in the rock, and also interconnected lot of pores will be increased permeability. So the carbonate rock with good porosity and permeability will be able to store and flow hydrocarbons. Knowledge and understanding of porosity in carbonate rocks and the distribution is very important in exploration. In this research, the identification of the distribution of porosity Kujung I Formation is using seismic attribute method. The seismic attribute application on the 3D seismic data covering the inversion, ant track as well as the spectral decomposition. The final result of applied seismic attributes, concluded that the porosity of the target zone is well developed. The study also found the phenomenon of low frequency shadow in the target zone that could be as an indicator of hydrocarbons. With two parameters, porosity and hydrocarbon indicators, the study summarized that the target zone is a potential reservoir with hydrocarbons possibility and could be developed as an exploration target."

"ABSTRAKStudi yang dilakukan dalam penelitian ini untuk menganalisis multiatribut dari data seismik dan data log dalam mengidentifikasi litologi bawah permukaan, terutama dalam mengidentifikasi penyebaran sourcerockpada suatu daerah target tertentu. Metode multiatribut dalam penelitian ini dilakukan untuk memprediksi log vshaleyang diturunkan dari log resisitivity. Untuk mendapatkan gambaran distribusi sifat fisik batuan di bawah permukaan, maka hubungan antara data log dengan data seismik dapat membantu menghasilkan volume sebaran sifat fisik batuan yang tadinya hanya didapat dari tempat yang memiliki data log hingga menjadi sebaran pada seluruh area yang terdapat data seismik. Kemampuan neural network untuk mengenali pola dan menemukan hubungan non linear antara data seismik, data multiatribut dan sifat fisik batuan dari data log dimanfaatkan untuk mendapatkan sebaran sifat fisik batuan pada volume seismik dengan mengacu pada data log. Aplikasi geostatistik digunakan untuk mendapatkan model distribusi spasial di mana hasil dari prediksi volume shale dipakai sebagai data sekunder sedangkan data dari lubang sumur digunakan sebagai data primer, sehingga diharapkan mendapatkan kemenerusan secara lateral.

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ABSTRACTThe Study conducted in this research to analyze the multi attributes of seismic data and log data in identifying subsurface lithology, especially in identifying sourcerock deployment in a given target area. Multi attributes method in this research is done to predict log vshale derived from log resistivity. To obtain a description of the distribution of the physical properties of rocks below the surface, the relationship between log data and seismic data can help to generate volume distribution of physical properties of rocks that were only obtained from places that have log data to be distributed in all areas of seismic data. The ability of neural networks to recognize patterns and find nonlinear relationships between multi attribute data seismic data and the physical properties of rocks from log data are utilized to obtain the distribution of physical properties of rocks at seismic volumes with reference to log data. The geostatistical application is used to obtain spatial distribution model where the result of the shale volume prediction is used as secondary data while the data from the wellbore is used as the primary data, so it is expected to obtain lateral generation."