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"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"

"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."

"[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%."

"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."

"ABSTRAKCekungan Sumatera Selatan merupakan salah satu Cekungan busur belakang (back-arc basin) yang terbentuk akibat proses subduksi antara lempeng Indo Australia dengan lempeng Eurasia pada Jaman Pra Tersier hingga Tersier awal (Sarjono dan Sardjito,1989). Formasi AirBenakat yang terdapat pada Cekungan Sumatera Selatan telah terbukti menghasilkan hidrokarbon dan telah dieksplorasi sejak tahun 1979. Target pada Top M, Top N dan Top O merupakan zona prediksi dengan ketebalan 0.69 meter hingga 22.3 meter. Berdasarkan data produksi zona-zona tersebut terbukti terdapat hidrokarbon. Analisa petrofisika dan evaluasi formasi merupakan tahap awal untuk mengkarakterisasi reservoar dengan menganalisa sifat batuan seperti porositas, kandungan lempung, permeabilitas, dan saturasi air. Metode multiatribut seismik merupakan salah satu metode statistika menggunakan lebih dari satu atribut untuk memprediksi beberapa properti fisik dari bumi. Pada analisis ini dicari hubungan antara log dengan data seismik pada lokasi sumur dan menggunakan hubungan tersebut untuk mempredikasi atau mengestimasi volume dari properti log pada semua lokasi pada volum seismik. Inversi sparse-spike digunakan sebagai external attribute, metode neural network digunakan untuk meningkatkan korelasi antara aktual dan log prediksi. Berdasarkan hasil petrofisika porositas pada zona target berkisar 9.6-26% dan reservoar yang berpotensi terdapat gas memiliki saturasi air sebesar 8.9%-39.7, dan reservoar yang berpotensi terdapat oil memiliki saturasi sebesar 40-43%,dengan kandungan lempung sekitar 10.7%-26.9%.

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ABSTRACTSouth Sumatera Basin is a back-arc basin that was formed by subduction process between Indo-Autralia Plate and Eurasia Plate in Pre-Tersier mass until Early Tersier mass (Sarjono and Sarjito,1989). AirBenakat formation where is located in South Sumatera Basin has been proven that it has produced and it has been producted since 1979. Target zones at Top M, Top N, Top O are prediction zone that have been proven as reservoir zone by production data. Target zones have thickness between 0.69 meter until 22.3 meter. Petrophysical analysis and evaluation formation is first step to characterize reservoir with analyzing rock properties such as porosity, clay volume, permeability, and saturation of water. Multi-attributes seismik is one statistical method that uses more than one attribute to predic earth?s physical properties. In this analysis, we find the the correlation between log data with seismic data in well location, and use this corelation to predict or estimate volume of log property in all seismic volume. Sparse-spike inversion is used as external attribute, neural network method is used to increase corellation between actual and predict log. Based on petrophyscal analysis result, target zones have porosity values 9.6-26% and reservoir that is potential for being gas reservoir have saturation of water values 8.9-39.7% , reservoir that is potential for being oil resevoir have saturation of water values 40-43%, with volume clay values 10.7-26.9% "

"Atribut seismik merupakan informasi yang diperoleh dari data seismik yang dapat digunakan untuk memprediksi suatu target petrofisika baik secara numerik maupun analitik. Walaupun hubungan antara atribut seismik dengan karakteristik suatu batuan dan reservoar tidak dapat didefinisikan secara spesifik, banyak sumber yang menunjukkan bahwa atribut seismik merupakan salah satu parameter untuk mengklasifikasikan karakter dari suatu batuan. Skripsi ini menunjukkan hasil proses yang dilakukan oleh ANN yang dapat membuat suatu hubungan antara atribut seismik dengan saturasi air (Sw). Tujuan utama penelitian ini adalah memprediksi penyebaran lateral saturasi air (Sw) yang diperoleh dari atribut seismik. Pada tahap awal, hubungan antara log saturasi air (Sw) dengan satu tras seismik yang berhimpit ditentukan dengan menggunakan metode ANN. Setelah jaringan terbentuk, metode tersebut diterapkan untuk seluruh tras seismik yang ada pada suatu volume seismik. Atribut seismik yang dijadikan masukan adalah amplitudo, impedansi akustik, frekuensi sesaat, dan kuat refleksi (amplitudo sesaat). Jaringan yang digunakan adalah Backpropagation dengan 5 lapisan yang masing-masing memiliki 40, 30, 20, 10 dan 1 neuron. Metode pelatihannya menggunakan metode resilent backpropagation. Hasil proses jaringan ini memiliki nilai korelasi 96 % dengan nilai validasi sebesar 60 % dan nilai rata-rata error kuadrat (rmse) 3.01 %.

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A seismic attribute information, which is obtained from seismic data, can be used to predict petrophysical properties analytically as well as numerically. Although the relationship between seismic attributes with rock properties can not be specifically defined, many papers indicated that seismic attributes can be used to characterize the rock.

This work shows the application of ANN algorithm to generate the relationship between seismic attributes and water saturation (Sw). The main objective of this study is to predict the lateral distribution of water saturation (Sw), which is derived from seismic data. The first step, the relationship between water saturation (Sw) and a trace seismic, which coincide with well log data, is determined using the ANN. After the network is defined, the method can then be applied to all existing seismic traces in a seismic volume. The input of seismic attributes is amplitude, acoustic impedance, instantaneous frequency, and reflection strength (amplitude envelope).

We use bacpropagation network with 5 layers each having 40, 30, 20, 10 and 1 neuron. The training method is resilent backpropagation. This network produces good agreement between predicted water saturation (Sw) and targeted water saturation (Sw), which is indicated by correlation coefficient of 96 %, validation coefficient of 60 % and root mean square error (rmse) of 3.01 %."

"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."

"ABSTRAKDalam studi ini, dilakukan identifikasi interkoneksi antar sumur berdasarkankonsep evaluasi flow unit dan bekerja berdasarkan fungsi bobot dari masing –masing parameter petrofisika. Terdapat limadata sumur yang mana digunakansebagai data utama dalam studi ini. Selain itu juga tersedia data core yangdigunakan sebagai pengontrol dari nilai properti petrofisika yang dihasilkan.Inversi AI juga tersedia pada penelitian ini sebagai data utama yang digunakanuntuk interpolasi permeabilitas antar sumur. Interpretasi dilakukan padapenampang seismik composite line dan objek dalam studi ini berupa reservoarkarbonat yang berada pada Formasi Batu Raja, Lapangan X, Jawa Barat Utara.Studi yang dilakukan meliputi perhitungan nilai properti petrofisika serta analisispermeabilitas formasi berdasarkan evaluasi flow unit. Hasil pemodelan akanmenunjukkan bahwa sumur yang memiliki koneksi satu sama lain akan beradapada klaster flow unit 1 dengan rentang permeabilitas 39.24 - 84.06 milidarsidengan indikasi aliran fluida yang tinggi.

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ABSTRACTIn this study , identification the interconnections between wells based on flow unitevaluation concept were performed and work on each petrophysical parameterfunction. There are five wells data which used as the main data in this study. Coredata is also available as controller of calculated petrophysical property. AcousticImpedance Inversion is also available as main data to do the permeabilityinterpolation between wells. Interpretation performed on a cross section ofseismic composite line and the object in this study is carbonate reservoir locatedin North West Java, Baturaja Formation, X Field. Study was conducted on thepetrophysical property and formation permeability analysis based on flow unitevaluation by FZI calculation from permeability and porosity core which isderived from conventional core analysis. Modeling result will show that the wellconnectedto each other will be on flow unit 1 cluster with 39.24 – 84.06millidarcy permeability interval with high fluid flow indicated."

"ABSTRACTMetode rock typing adalah suatu metode yang dapat digunakan untuk menentukan nilai permeabilitas batuan dan mengklasifikasikan tipe batuan menjadi beberapa kelompok berdasarkan kondisi batuan sebenarnya. Penelitian ini menggunakan beberapa metode rock typing, yaitu metode Lucia, Flow Zone Indicator FZI, Winland R35, dan Pore Geometry Structure PGS. Penelitian ini menggunakan tiga sumur yang memiliki data core sebagai sumur referensi untuk digunakan metode-metode tersebut. Tujuan utama adalah melakukan komparasi dan memilih metode terbaik dari keempat metode tersebut. Kemudian menggunakan hasil metode rock typing untuk membuat model klasifikasi dan diaplikasikan kedalam sumur target yang tidak memiliki data core. Untuk klasifikasi, penelitian ini menggunakan dan melakukan komparasi metode Na ve Bayes dan Random Forest. Hasil yang diperoleh menunjukkan bahwa metode Lucia dan Na ve Bayes adalah metode rock typing dan classifier terbaik untuk penelitian ini. Kedua metode tersebut memiliki crossplot hubungan AI dan SI yang distribusinya terseparasi dengan baik berdasarkan kelas tipe batuannya. Sehingga untuk penelitian selanjutnya, hasil tersebut dapat digunakan dan diaplikasikan kedalam model seismik.

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ABSTRACTRock typing is a method that can be used to determine permeability value of rocks and classify rock type in reservoir rocks into different units based on actual rocks conditions. This study uses several rock typing methods, that are Lucia, Flow Zone Indicator FZI, Winland R35, and Pore Geometry Structure PGS. This study uses three wells that have core data as reference wells for those methods. First objective is comparing those four methods and choose the best method for our study. Then, using the result of rock typing method to make a classification model and is applied into target wells that don rsquo t have core data. For classification, this study uses and compares Na ve Bayes and Random Forest method. The result shows Lucia and Na ve Bayes is the best rock typing and classifier method. Those methods able to have AI and SI crossplot which distributed separately well based on its rock type. So for future works, that results can be used and applied into seismic model."