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"Fractal analyses methods (rescale-range analyses, RIS and Power Spectrum analyses) were applied to verify wireline logs correlation through the fluvial to marine Sihapas Group (Lower-Middle Miocene) in the Central Sumantra Basin. This study was a test the applicability of a fractal analysis of well log data (gamma-ray, density, porosity and sonic logs) specially for well log correlation purposes. These analyses reveal that the fractal dimension of gamma-ray logs is the best log for well log correlation purposes with correlation coefficient 0.713, and the porosity logs shows 0.702. However, the density and sonic logs have correlation coefficient 0.533 and 0.542 respectively. The fractal dimensions for each stratigraphic unit suggests being different. In these cases, the fractal analyses will be more use as a tool for environmental determination than for correlation.Analyses of the procedures adopted suggest, however, that the fractal geometry concepts in wireline log analyses should be treated with caution. Changes in the calculation procedures can cause larger variation in the estimate of the fractal dimension. The study was also suggested that the Rescale range analyses is more stable than the Power Spectrum method. These difficulties should be considered for the correlation purposes. The validity and suitability of fractal geometry in the well log analyses need to be considered carefully. The application of fractal geometry in the well log correlation should be considered as an experiment of fractal geometry in the geological sciences. Much work should be done before the fractal geometry can be applied safely to particular geological analysis."

"Data well log disajikan dalam bentuk kurva-kurva log yang dapat menggambarkan sifat dan karakteristik batuan yang berada dibawah permukaan. Sifat-sifat fisik batuan yang penting untuk analisa log adalah; porositas, tingkat kejenuhan air dan permeabilitas. Dengan dua parameter pertama banyaknya hidrokarbon di lapisan formasi akan dapat dihitung, sedangkan dengan parameter terakhir, akan dapat ditunjukkan pada tingkat mana hidrokarbon dapat diproduksi. Daerah penelitian adalah sumur T-5 pada kedalaman 1800 m hingga 2225 m dan terletak pada lapangan ?X?, dengan geologi cekungan Jawa Barat Utara. Data yang digunakan merupakan data log elektrik, log radioaktif serta log akustik yang didapat dari proses logging pada sumur pemboran. Secara kualitatif, pada interval kedalaman 1890-1977 m dengan ketebalan lapisan 2-5 m, dilihat dari rendahnya harga kurva log Gamma Ray (30-50 APIU), defleksi kurva SP yang menurun atau relatif rendah, log densitas rendah (2,20-2,45 gr/cm3), log neutron tinggi (0,1-0,3 p.u), dan log caliper membaca kurang dari 8,5 inci yang memberikan indikasi terbentuknya kerak lumpur sehingga mengindikasikan lapisan tersebut permeabel, Litologi yang dianggap sebagai batuan reservoir adalah formasi batugamping bersih, dengan fluida yang didominasi oleh gas, yaitu pada kedalaman 1890-1905 m dengan ketebalan 15.5 m serta pada kedalaman 1952-1977 m dengan ketebalan lapisan 25 m. Kemungkinan zona minyak ditunjukkan pada interval kedalaman 1910.5-1945 m."

"ABSTRACTTo define reservoir potential or to have a better understanding of reservoircharacterization become the most important part to get many subsurface information.It will be very useful to analyze and prospect new candidates. Reservoircharacterization combined with the formation evaluation data between vertical andhorizontal dimensions will produce a geologic model, which is used as an input forreservoir simulation.The objectives of this research is to develop a reservoir model within the producinginterval of interest defined as horizons ?E? where it plays as a main oil target. It is apart of the Salemba Field, Kutai Basin, East Kalimantan.A geostatistical method used for the study was stochastic since the data setavailability is good. But to have better self confidence, a glance of deterministicmethod was applied to see how the differences. There are three kind of stochasticmethod will try for facies modeling, there are: Object-base Modeling, FaciesTransition and Sequential Indicator Simulation. Each method was varied usingexponential types of variogram, which is considered as the best match use in MutiaraField.By using the existing software, it resulted more than 10 good scenarios andrealizations of geological model generated for this study. Also the criterion of themain ranking will use the OOIP and OGIP. The result also was calibrated withcurrent condition, cumulative production and recovery factor to see the remainingreserves."

"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.

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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"

"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.

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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 "