Hasil Pencarian  ::  Simpan CSV :: Kembali

"[Identifikasi hidrokarbon merupakan salah satu tujuan utama dalameksplorasi lapangan minyak bumi, dan perkiraan nilai saturasi merupakan salah satu bagian penting dari identifikasi hidrokarbon. Dalam penentuan perkiraan saturasi tersebut, atribut seismik dapat digunakan baik secara numerik maupun analitik. Meskipun hubungan antara atribut seismik dengan karakteristik batuan reservoar tidak dapat didefinisikan secara explisit, namun penggunaan atribut seismik ini dapat membantu dalam proses karakterisasi reservoar. Proses prediksi nilai saturasi air pada Lapangan Shinta dilakukan denganmenggunakan transformasi log resistivitas berdasarkan persamaan Archi, dimana faktor sementasi diasumsikan sebesar 0.62, dengan eksponen sementasi yaitu - 2.15 serta faktor resistivitas air yaitu 0.04. Selanjutnya nilai saturasi air transform tersebut dijadikan sebagai target untuk penyebaran nilai saturasi fluida dengan menggunakan metode PNN (Probabilistic Neural Network). Metode ini dipilih karena memberikan korelasi yang lebih baik yaitu sebesar 68.8% dengan rata-rata error = 0.114, dibandingkan dengan Metode Multi-Layer Feed Forward (MLFN) yang memberikan nilai korelasi sebesar 50.9% serta multi-atribut sebesar 34.4%. Hasil dari penyebaran saturasi air tersebut selanjutnya diintegrasikan dengan data produksi dan dapat disimpulkan bahwa hasil penyebaran Sw telah mendekati kondisi aktual pada daerah disekitar sumur SNT-10 dan SNT-12. Hal ini ditunjukkan dengan nilai saturasi air yang dihasilkan yaitu 45-85% memiliki kesesuaian dengan profil produksi dimana kurva Water Cut dengan GOR masing-masing sumur naik secara cepat yang mengindikasikan air dan gas semakin banyak terproduksi, dibandingkan dengan minyak;Hydrocarbon identification is one of the main objectives of the Oil and Gas exploration and the estimation of saturation level is one of the important parameter to identify the possibility of hydrocarbon. The estimation of saturation value either using the numeric or the analytical method, seismic attribute coud be used. Even the relationship between seismic attribute and reservoir characterization could not be defined explicitely, but the used of this seismic atribut could give more assistance during the characterization process. Water saturation prediction in Shinta Field started with the transformation of the resistivity log using the Archi Equation, with the cementation factor (α) is 0.6, cementation exponent (m) is -2.15 and resistivity formation water (Rw) is 0.04. Furhter, this transformation result is used as the target of the fluid saturation prediction using the Probabilistic Neural Network (PNN). This method has been done through Artificial Neural Network, either using PNN method gives the better correlation i.e. 68.8% with the RMS value 0.114 compare to MLFN method which gave the correlation of 50.9% and Multi atribut with correlation level is34.4%. Lateron, the result of Sw distribution has been integrated with production data which it can be concluded that the result has approach to the real condition of SNT-10 and SNT-12 well. It can be seen from the saturation value i.e. 45-85% which in line with the production figure, where the Water Cut and the GOR of each well has increased significantly, than oil. It can be assumed that the water and gas production are more produced compared to oil.;Hydrocarbon identification is one of the main objectives of the Oil andGas exploration and the estimation of saturation level is one of the importantparameter to identify the possibility of hydrocarbon. The estimation of saturationvalue either using the numeric or the analytical method, seismic attribute coud beused. Even the relationship between seismic attribute and reservoircharacterization could not be defined explicitely, but the used of this seismicatribut could give more assistance during the characterization process.Water saturation prediction in Shinta Field started with the transformationof the resistivity log using the Archi Equation, with the cementation factor (α) is0.6, cementation exponent (m) is -2.15 and resistivity formation water (Rw) is0.04. Furhter, this transformation result is used as the target of the fluid saturationprediction using the Probabilistic Neural Network (PNN). This method has beendone through Artificial Neural Network, either using PNN method gives the bettercorrelation i.e. 68.8% with the RMS value 0.114 compare to MLFN methodwhich gave the correlation of 50.9% and Multi atribut with correlation level is34.4%.Lateron, the result of Sw distribution has been integrated with productiondata which it can be concluded that the result has approach to the real condition ofSNT-10 and SNT-12 well. It can be seen from the saturation value i.e. 45-85%which in line with the production figure, where the Water Cut and the GOR ofeach well has increased significantly, than oil. It can be assumed that the waterand gas production are more produced compared to oil.;Hydrocarbon identification is one of the main objectives of the Oil andGas exploration and the estimation of saturation level is one of the importantparameter to identify the possibility of hydrocarbon. The estimation of saturationvalue either using the numeric or the analytical method, seismic attribute coud beused. Even the relationship between seismic attribute and reservoircharacterization could not be defined explicitely, but the used of this seismicatribut could give more assistance during the characterization process.Water saturation prediction in Shinta Field started with the transformationof the resistivity log using the Archi Equation, with the cementation factor (α) is0.6, cementation exponent (m) is -2.15 and resistivity formation water (Rw) is0.04. Furhter, this transformation result is used as the target of the fluid saturationprediction using the Probabilistic Neural Network (PNN). This method has beendone through Artificial Neural Network, either using PNN method gives the bettercorrelation i.e. 68.8% with the RMS value 0.114 compare to MLFN methodwhich gave the correlation of 50.9% and Multi atribut with correlation level is34.4%.Lateron, the result of Sw distribution has been integrated with productiondata which it can be concluded that the result has approach to the real condition ofSNT-10 and SNT-12 well. It can be seen from the saturation value i.e. 45-85%which in line with the production figure, where the Water Cut and the GOR ofeach well has increased significantly, than oil. It can be assumed that the waterand gas production are more produced compared to oil., Hydrocarbon identification is one of the main objectives of the Oil andGas exploration and the estimation of saturation level is one of the importantparameter to identify the possibility of hydrocarbon. The estimation of saturationvalue either using the numeric or the analytical method, seismic attribute coud beused. Even the relationship between seismic attribute and reservoircharacterization could not be defined explicitely, but the used of this seismicatribut could give more assistance during the characterization process.Water saturation prediction in Shinta Field started with the transformationof the resistivity log using the Archi Equation, with the cementation factor (α) is0.6, cementation exponent (m) is -2.15 and resistivity formation water (Rw) is0.04. Furhter, this transformation result is used as the target of the fluid saturationprediction using the Probabilistic Neural Network (PNN). This method has beendone through Artificial Neural Network, either using PNN method gives the bettercorrelation i.e. 68.8% with the RMS value 0.114 compare to MLFN methodwhich gave the correlation of 50.9% and Multi atribut with correlation level is34.4%.Lateron, the result of Sw distribution has been integrated with productiondata which it can be concluded that the result has approach to the real condition ofSNT-10 and SNT-12 well. It can be seen from the saturation value i.e. 45-85%which in line with the production figure, where the Water Cut and the GOR ofeach well has increased significantly, than oil. It can be assumed that the waterand gas production are more produced compared to oil.]"

"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.

---

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."

"Penelitian ini membahas karakterisasi zona reservoar hidrokarbon berdasarkan analisis petrofisika. Penelitian dilakukan dengan melakukan evaluasi formasi dan analisis petrofisika. Dalam evaluasi formasi dibutuhkan parameter-parameter fisika untuk mengevaluasi dan memprediksi kandungan minyak dan gas bumi dalam batuan reservoar. Parameter-parameter fisika tersebut adalah kandungan lempung, porositas, kejenuhan air dan permeabilitas yang didapatkan dari analisis petrofisika. Dalam penelitian ini dilakukan analisis petrofisika dari 7 data sumur. Berdasarkan hasil akhir analisis petrofisika, reservoar zona target pada lapangan penelitian adalah reservoar pada sumur Lisburne 1 dengan kandungan lempung sebesar 9%, porositas efektif 24% dan saturasi air 10%. Litologi pada reservoar ini merupakan batupasir dengan ketebalan reservoar sebesar 53,64 meter. Reservoar ini terletak pada kedalaman 1978 - 2154 ft.

---

This study discusses the caracterization of hydrocarbon reservoir zones based on petrophysical analysis. The study was conducted by formation evaluation and petrophysical analysis. In formation evaluation physics parameters needed to evaluate and predict the content of oil and gas in the reservoir rocks. The physical parameters are the clay content, porosity, water saturation and permeability obtained from petrophysical analysis.

In this study carried petrophysical analysis of 7 well data. Based on the final results of petrophysical analysis, reservoir target zone on the research field is reservoir at Lisburne 1 well with the clay content is 9%, effective porosity is 24% and water saturation is 10%. Lithology in this reservoir is sanstone with a reservoir thickness is 53,64 meters. The reservoir lies at a depth 1978 - 2154 ft."

"ABSTRACTReservoir basement yang retak adalah reservoir yang terletak di lapisan basement, terdiri batu kristal baik batuan metamorf atau batuan beku. Waduk ini biasanya ada fraktur sebagai porositas sekundernya mengandung hidrokarbon. Indonesia memiliki potensisumber daya reservoir basement fraktur, termasuk Subbasin Jambi di bagian utara dari Cekungan Sumatra Selatan. Dalam penelitian ini, atribut pelacakan semut dijalankan menggunakan semut triple runmelacak dan menghasilkan gambar fraktur pada data seismik 3D yang didominasi oleh NE-SW orientasi dan orientasi NW-SE kecil. Orientasi fraktur dikonfirmasi dengan FMI gambar "POME-1" berarah baik NE-SW. Resistivitas tinggi pada log resistivitas menunjukkan kandungan hidrokarbon dalam fraktur yang diidentifikasi.

---

ABSTRACTA cracked basement reservoir is a reservoir located in the basement layer, composed Crystal stones are either metamorphic or igneous rocks. This reservoir usually exists fracture as a secondary porosity containing hydrocarbons. Indonesia has potential fracture basement reservoir resources, including the Jambi Subbasin in the northfrom the South Sumatra Basin. In this study, the ant tracking attributes were run using triple run ants track and produce fracture images on 3D seismic data dominated by NE-SW orientation and orientation of the NW-SE is small. Fracture orientation is confirmed by FMI the image "POME-1" is trending both NE-SW. High resistivity in the resistivity log shows the hydrocarbon content in the identified fracture."

"Basement merupakan batuan yang kompak dan memiliki porositas yang sangat kecil, sehingga dibutuhkan porositas sekunder agar dapat menyimpan hidrokarbon. Rekahan merupakan salah satu porositas sekunder, namun tidak semua rekahan dapat menjadi porositas sekunder yang baik. Rekahan terbuka dapat menyimpan hidrokarbon dan menukung produktivitas dan kualitas dari basement reservoir. Penelitian ini bertujuan untuk mengidentifikasi rekahan terbuka dengan menggunakan log FMI dan atribut seismik pada lapangan "I", Cekungan Barito. Hasil dari interpretasi log FMI dari kedua sumur menyatakan bahwa arah azimuth dari rekahan terbuka adalah Timur-Timur Laut mdash;Barat-Barat Daya, Utara-Barat Laut Selatan-Tenggara dan Barat-Barat Laut Timur-Tenggara serta dapat diketahui arah stress horizontal. Orientasi stress dari log FMI didukung oleh data leak-off test LOT dan log densitas dapat mengidentifikasi stress regime yang berlaku pada cekungan Barito. Orientasi azimuth dan dip dari open fracture hasil dari analisa open fracture digunakan sebagai input di stereonet, sebagai filter orientasi untuk melakukan ant-tracking. Atribut ant-track berdasarkan dari algoritma ant-colony optimization dimana atribut ini hanya memetakan objek yang kontin dan digunakan sebagai metode untuk meningkatkan resolusi dari atribut geometri seismik lainnya. Atribut yang sensitif terhadap rekahan yaitu varian digunakan sebagai input untuk atribut ant-track. Hasil menunjukkan bahwa ant-track dan image log berkorelasi dengan baik. Dengan digunakannya atribut ant-track, patahan dan rekahan dapat diidentifikasi dengan resolusi dan visualisasi lebih baik dibandingkan hanya menggunakan atribut varian saja, sehingga memudahkan interpretasi.

---

basement has very small matrix porosity, secondary porosity is needed to storing hydrocarbon within the basement. Fracture is a secondary porosity, yet not all fracture can be a good secondary porosity. Open fracture have capability to storing hydrocabon, also have important roles in productivity and quality of the pre tertiary basement reservoir. This study is aimed at identifying open fractures using FMI log and seismic attributes in the I Field, Barito Basin. Interpreted image log FMI on two wells gives the azimuth and dip orientation of open fractures ENE ndash WSW, NNW SSE and WNW ESE and also the horizontal stress orientation. The stress orientation from image log combined with leak off test LOT data and density log used to prevailing stress fields.

Azimuth and dip orientation of the open fracture from FMI log is the input of the stereonet, as an orientation filter for ant tracking. Ant track attribute is based on ant colony optimization algorithm that captures only continuous features and used as edge enhancement methods for fracture sensitive attributes. The fracture sensitive attribute variance applied as an input data for ant track attribute. Results showed that ant track map and image log analysis correlated very well. Also, by applying ant track attribute to the fracture sensitive attributes succesfully able to identify faults and fractures with better resolution and visualization than only using the fracture sensitive attribute itself, so it makes easier to interpret and get the information."

"Prediksi nilai Pore Pressure ini dilakukan dengan menggunakan metode Eaton dengan input data berupa data sonikdan data densitas. Dengan adanya data pendukung seperti leak-off test LOT dan repeat formation test RFT maka nilai prediksi ini dapat mendekati nilai tekanan aktualnya. Penelitian ini dilakukan dengan menggunakan sumur sebagai kalibrasi data, serta menggunakan neural network sebagai metode prediksinya. Nilai Pore Pressure ini mengestimasi dua jenis batuan yaitu shale dan karbonat. Karena perbedaan litologi, maka digunakan nilai konstanta empiris yang berbeda untuk setiap litologi. Nilai estimasi ini kemudian dikalibrasi dengan data RFT dan data berat jenis lumpur. Penentuan fracture pressure tekanan rekahan dilakukan dengan menggunakan data LOT dimana datanya diperoleh berdasarkan jumlah tekanan saat terjadi kebocoran pada suatu batuan. Setelah semua nilai tersebut diperoleh, maka dihasilkan nilai estimasi yang kemudian diprediksi dengan titik lain menggunakan parameter kecepatan seismik, frekuensi seismik, acoustic impedance, dan simultaneous impedance. Prediksi tersebut dilakukan dengan menggunakan data sumur sebagai data sampel. Hasil yang diperoleh menunjukan nilai error dengan menggunakan sumur relatif lebih mendekati data aktualnya. Menggunakan nilai korelasi tersebut, maka diperoleh permodelan yang kemudian dapat dimanfaatkan sebagai penentuan area pengeboran.

---

Determination of drilling area is very important because it related to safety in oil and gas industry. Determination of pore pressure value can minimize the drilling hazard. Eaton method used in pore pressure prediction with sonic and density as a parameter. With leak off test LOT and repeat formation test RFT as a support data, pore pressure prediction can be more accurate. This research using well log as a parameter input and calibrator, using a neural network as a prediction method. The reservoir of the field is carbonate reef with shale above the reservoir. Because of the difference of the lithology, then we use two different empirical value in every lithology. The pore pressure prediction calibrate with RFT and mud weight data and the fracture gradient that calibrate with LOT data. Value of the pore pressure prediction then correlates with the other seismic, frequency, acoustic impedance, and simultaneous impedance attribute. The correlation is using a neural network, and the result of the prediction show good correlation with pore pressure prediction on well log data. As it shows a good correlation, so it can use as a determining factor of drilling location on field ldquo X rdquo "

"Reservoir sandstone merupakan target utama atau terpenting dalam eksplorasi hidrokarbon di Formasi Mungaroo AA daerah Offshore Cekungan Carnarvorn Utara. Salah satu lapangan dengan reservoir sandstone pada Formasi Mungaroo AA berada di Lapangan Wheatstone. Identifikasi zona reservoir potensial hidrokarbon merupakan suatu hal yang fundamental dalam industri Migas. Salah satu instrumen yang saat ini menjadi sangat powerfull karena begitu luas pemanfaataannya dan nilai investasi yang besar karena tidak semahal analisa core dan well testing adalah well log. Data well log hingga saat ini masih dilakukan intepretasi secara manual atau analisa kurva. Interpretasi secara manual memakan waktu lebih lama dan melelahkan sehingga dapat mengurangi keakuratan dalam interpretasi. Seiring perkembangan waktu dibutuhkan pemanfaatan yang lebih pada data well log dibutuhkan sebuah metode yang dapat meningkatkan kualitas interpretasi atau analisis sumur, yaitu data mining.Dalam penelitian ini, metode data mining Support Vector Machine (SVM) diterapkan untuk identifikasi zona reservoir potensial hidrokarbon dari data well log di Formasi Mungaroo AA, Lapangan Wheatstone, Cekungan Carnarvorn Utara. Data well log yang digunakan berasal dari lima (5) sumur yang dibor di Lapangan yang sama. Penerapan SVM membutuhkan proses pelatihan, satu sumur digunakan sebagai data latih dan fungsi yang diperoleh darinya diterapkan pada 4 sumur yang tersisa. Fase klasifikasi akan meliputi 2 fase, yaitu fase penentuan litologi (sandstone dan non-sand) dan penentuan potensi hidrokarbon (produktif dan non-produktif). Kedua fase ini diterapkan secara bertahap menggunakan metode SVM.Hasil penelitian didapatkan nilai rata-rata akurasi pada fase penentuan litologi (sandstone dan non-sand) menunjukkan nilai sebesar 0.98 sedangkan pada fase penentuan potensi hidrokarbon (produktif dan non-produktif) menunjukkan nilai sebesar 0.93. Hasil akhir pengujian hipotesis t dengan membandingkan distribusi nilai Net To Gross (NTG) hasil prediksi dengan NTG field report menunjukkan menunjukkan bahwa distribusi antara keduanya mendekati. Meskipun hasil pengujian hipotesa yang didapatkan mengatakan distribusi nilai NTG mendekati, peneliti merekomendasikan bahwa metode data mining dapat digunakan sebagai alat verifikasi dalam mengidentifikasi zona reservoir potensial hidrokarbon. Hal ini dapat mengurangi ketidakpastian dan meningkatkan kualitas analisis sumur.

---

Sandstone reservoir occupies the first position or dominates as a hydrocarbon resource. The sandstone reservoir is the main or most important target for hydrocarbon exploration in the Mungaroo AA Formation in the Offshore area of ​​the North Carnarvon Basin. One of the fields with a sandstone reservoir in the Mungaroo AA Formation is the Wheatstone Field. Identification of potential hydrocarbon reservoir zones is a fundamental matter in the oil and gas industry. One of the instruments that are currently very powerful because of its wide use and large investment value because it is not as expensive as core analysis and well testing is the well log. Well log data is still being interpreted manually or curve analysis. Manual interpretation takes longer and is tiring so it can reduce the accuracy of interpretation. Along with the development of time, more use of well log data is needed, and a method that can improve the quality of interpretation or well analysis is needed, namely data mining.

In this study, the Support Vector Machine (SVM) data mining method was applied to identify potential hydrocarbon reservoir zones from well log data in the Mungaroo AA Formation, Wheatstone Field, North Carnarvon Basin. The well log data used is from five (5) wells drilled in the same field. The application of SVM requires a training process, one well is used as training data, and the functions derived from it are applied to the remaining 4 wells. The classification phase will include 2 phases, namely the lithology determination phase (sandstone and non-sand) and the determination of the hydrocarbon potential (productive and non-productive. These two phases are implemented in stages using the SVM method.

The results showed that the average accuracy value in the lithology determination phase showed a value of 0.98 while the hydrocarbon potential determination phase showed a value of 0.93. The result of testing the t hypothesis by comparing the distribution of the predicted NTG value with the NTG field report shows that the distribution between the two is identical. Although the results of the hypothesis testing obtained say the distribution of NTG values ​​is identical, the researcher recommends that the data mining method can be used as a verification tool in identifying potential hydrocarbon reservoir zones. This can reduce uncertainty and improve the quality of well analysis"

"Skripsi ini membahas tentang metode vertical seismic profiling (VSP) dan aplikasinya dalam karakterisasi reservoar. VSP merupakan pengukuran seismik yang dilakukan dengan menempatkan receiver di lubang bor. Karakter reservoar yang akan diteliti yaitu batu pasir pada lapangan Sejati, yang merupakan salah satu lapangan minyak dan gas VICO Indonesia yang terletak di Cekungan Kutai, Kalimantan Timur. Data yang tersedia dalam penelitian ini yaitu data VSP (near & far offset) dan well log dari satu sumur eksplorasi. Karakterisasi reservoar berdasarkan metode inversi data VSP. Langkah-langkah pengerjaan yaitu crossplot, correlation, picking horizon, initial model, inversion analysis (QC), dan final inversion. Dari hasil inversi memperlihatkan adanya sebaran impedansi rendah pada zona target. Karena besarnya tuning thickness maka perlu dilakukan studi lebih lanjut untuk memastikan apakah impedansi rendah tersebut mengindikasikan lapisan sand atau bukan.

---

This study are about vertical seismic profiling (VSP) and it?s application for reservoir characterization. VSP is an seismic measurement which is put some receivers in borehole. The character of reservoir will be studied is sandstone in Sejati field, which is one of VICO?s oil and gas fields at Kutai Basin, East Kalimantan. The data avalaible of this study are VSP (near & far offset) and well log from one exploration well. Reservoir characterization based on VSP inversion method. The sequence step of this study are crossplot, correlation, picking horizon, initial model, inversion analysis (QC), and final inversion. The inversion result show low impedance at target zone. Because tuning thickness is very high, so it is needed detailed study for identifying sand distribution at target zone."

"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 %.

---

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 %."