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Abstrak :
[ABSTRAK
Reservoar gas pada lapisan tipis merupakan sesuatu yang menarik untuk dianalisis karena reflektor yang mengalami destruktif apabila berada dibawah tuning thickness atau 1/4λ. Penelitian ini adalah penelitian dengan mencoba preconditioning kembali data PSTM gather yang nantinya akan menjadi input analisis AVO. Peningkatan resolusi dan nilai signal to noise ratio menjadi tujuan dari penelitian ini. Usaha prosesing yang dilakukan adalah proses F-K Filter, Trimming Statics dan Super Gather. Setelah melalui ketiga proses diatas, analisis AVO pun dilakukan dengan metode cross plotting. Hasil preconditioning pada penelitian ini menunjukkan bahwa ada anomali AVO yang merupakan jenis AVO klas IV. Hasil ini diperkuat dengan metode analisis Cross Plotting pada setiap atribut dari AVO itu sendiri.

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ABSTRACT
Gas reservoir on thin layer is something interested to analyze due to destructive reflector which is below 1/4λ or tuning thickness condition. This research is trying to do re-preconditioning on PSTM gather which will be input of AVO analysis. Increased in resolution and signal to noise ratio become goal of this research. Processing efforts had been done, such as: F-K Filter, Trimming Statics and Super Gather. After all process above, AVO analysis was conducted by using Cross Plotting method. Result of re-preconditioning in this research tells class IV of AVO anomaly. The result is strengthen by analysis method of Cross Plotting which are came from AVO attributes of Cross Plot.;Gas reservoir on thin layer is something interested to analyze due to destructive reflector which is below 1/4λ or tuning thickness condition. This research is trying to do re-preconditioning on PSTM gather which will be input of AVO analysis. Increased in resolution and signal to noise ratio become goal of this research. Processing efforts had been done, such as: F-K Filter, Trimming Statics and Super Gather. After all process above, AVO analysis was conducted by using Cross Plotting method. Result of re-preconditioning in this research tells class IV of AVO anomaly. The result is strengthen by analysis method of Cross Plotting which are came from AVO attributes of Cross Plot., Gas reservoir on thin layer is something interested to analyze due to destructive reflector which is below 1/4λ or tuning thickness condition. This research is trying to do re-preconditioning on PSTM gather which will be input of AVO analysis. Increased in resolution and signal to noise ratio become goal of this research. Processing efforts had been done, such as: F-K Filter, Trimming Statics and Super Gather. After all process above, AVO analysis was conducted by using Cross Plotting method. Result of re-preconditioning in this research tells class IV of AVO anomaly. The result is strengthen by analysis method of Cross Plotting which are came from AVO attributes of Cross Plot.]

Abstrak :
[ABSTRAK
Identifikasi keberadaan hidrokarbon di bawah permukaan bumi merupakan salah satu tujuan utama dalam eksplorasi lapangan minyak bumi dalam usaha mengidentifikasi keberadaan hidrokarbon. Impedansi-poisson yang merupakan salah satu metoda yang digunakan untuk mendiskriminasi sifat fisis batuan terhadap fluida dengan cara mengamati sensitivitas dari rasio poisson telah diterapkan lebih lanjut untuk menghasilkan suatu metoda turunan yang lebih baik. Pendekatan sifat fisika batuan antara impedansi-poisson dengan log sumuran yang merepresentasikan properti batuan menghasilkan suatu metoda turunan yang dinamakan impedansi-litologi. Sedangkan pendekatan sifat fisis fluida yang terkandung didalam batuan terhadap impedansi-poisson menghasilkan diskriminasi kandungan fluida didalam batuan yang kemudian dinamakan impedansi-fluida. Metoda TCCA – Target Coeffisien Corellation Analysis – yang digunakan untuk mencari koefisien korelasi tertinggi dari sifat fisis batuan terhadap impedansi-poisson telah digunakan dalam penelitian ini untuk menghasilkan log sumuran impedansi-litologi dan impedansi-fluida yang kemudian di propagasi dengan neural network. Hasil propagasi impedansi-litologi digunakan sebagai input untuk kalkulasi atribut koherensi yang diperkuat dengan hasil propagasi impedansi-fluida untuk menghasilkan prediksi sebaran batuan reservoar. Dari hasil penelitian pada horison FS33 terlihat pola channel yang terbentuk dan tervalidasi dengan data sumur. Demikian juga pada sayatan horison FS37, pola channel batuan reservoar terlihat dengan jelas dan tervalidasi terhadap dua sumur yang dilalui. Sedangkan pada sayatan horison FS42 selain teridentifikasi pola channel reservoar yang terbentuk, teridentifikasi juga batuan karbonat yang divalidasi dengan data sumur dan data batuan inti

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ABSTRACT
Hydrocarbon identification in subsurface is one of main goals in petroleum exploration so that the litho-fluid content discriminations are a part of hydrocarbon identifications which have been widely applied today. Poisson-impedance which is one of the new methods that are used to discriminate rocks by examining the sensitivity of physical rock properties of poisson-ratio has been further developed to produce derivatives method. Physical properties approaches between poissonratio and a well-log which represents rock properties can be used to get highest correlation to produce a new derivative well-log named lithology-impedance. As a fluid-rock properties approach between poisson-ratio and a well-log represents litho-fluid content properties produces a new derivative well-log named fluidimpedance. TCCA method –Target Coeffisien Corellation Analyst– is used to find the highest correlation coefficient of the physical properties of rock fluid on the poisson ratio has been used in this study to generate two new derivatives well-log which would be propagated by means of neural-networks. The result of lithologyimpedance propagation is further proceed with seismic coherence attribute as a reflection of geology and stratigraphy forms which are then combined with fluidimpedance propagation result to emphasize reservoir prediction distribution laterally. The study results of FS33 slicing discovers sand channels pattern and validated by well-log. Similarly with horizon slicing of FS37, patterns of sand channels reservoir are clearly visible and validated against two well-logs that passed. While on horizon slicing of FS42 besides discovering sand channels, carbonate rocks is also identified which is validated by well-log and core sample analyst.;Hydrocarbon identification in subsurface is one of main goals in petroleum exploration so that the litho-fluid content discriminations are a part of hydrocarbon identifications which have been widely applied today. Poisson-impedance which is one of the new methods that are used to discriminate rocks by examining the sensitivity of physical rock properties of poisson-ratio has been further developed to produce derivatives method. Physical properties approaches between poissonratio and a well-log which represents rock properties can be used to get highest correlation to produce a new derivative well-log named lithology-impedance. As a fluid-rock properties approach between poisson-ratio and a well-log represents litho-fluid content properties produces a new derivative well-log named fluidimpedance. TCCA method –Target Coeffisien Corellation Analyst– is used to find the highest correlation coefficient of the physical properties of rock fluid on the poisson ratio has been used in this study to generate two new derivatives well-log which would be propagated by means of neural-networks. The result of lithologyimpedance propagation is further proceed with seismic coherence attribute as a reflection of geology and stratigraphy forms which are then combined with fluidimpedance propagation result to emphasize reservoir prediction distribution laterally. The study results of FS33 slicing discovers sand channels pattern and validated by well-log. Similarly with horizon slicing of FS37, patterns of sand channels reservoir are clearly visible and validated against two well-logs that passed. While on horizon slicing of FS42 besides discovering sand channels, carbonate rocks is also identified which is validated by well-log and core sample analyst.;Hydrocarbon identification in subsurface is one of main goals in petroleum exploration so that the litho-fluid content discriminations are a part of hydrocarbon identifications which have been widely applied today. Poisson-impedance which is one of the new methods that are used to discriminate rocks by examining the sensitivity of physical rock properties of poisson-ratio has been further developed to produce derivatives method. Physical properties approaches between poissonratio and a well-log which represents rock properties can be used to get highest correlation to produce a new derivative well-log named lithology-impedance. As a fluid-rock properties approach between poisson-ratio and a well-log represents litho-fluid content properties produces a new derivative well-log named fluidimpedance. TCCA method –Target Coeffisien Corellation Analyst– is used to find the highest correlation coefficient of the physical properties of rock fluid on the poisson ratio has been used in this study to generate two new derivatives well-log which would be propagated by means of neural-networks. The result of lithologyimpedance propagation is further proceed with seismic coherence attribute as a reflection of geology and stratigraphy forms which are then combined with fluidimpedance propagation result to emphasize reservoir prediction distribution laterally. The study results of FS33 slicing discovers sand channels pattern and validated by well-log. Similarly with horizon slicing of FS37, patterns of sand channels reservoir are clearly visible and validated against two well-logs that passed. While on horizon slicing of FS42 besides discovering sand channels, carbonate rocks is also identified which is validated by well-log and core sample analyst., Hydrocarbon identification in subsurface is one of main goals in petroleum exploration so that the litho-fluid content discriminations are a part of hydrocarbon identifications which have been widely applied today. Poisson-impedance which is one of the new methods that are used to discriminate rocks by examining the sensitivity of physical rock properties of poisson-ratio has been further developed to produce derivatives method. Physical properties approaches between poissonratio and a well-log which represents rock properties can be used to get highest correlation to produce a new derivative well-log named lithology-impedance. As a fluid-rock properties approach between poisson-ratio and a well-log represents litho-fluid content properties produces a new derivative well-log named fluidimpedance. TCCA method –Target Coeffisien Corellation Analyst– is used to find the highest correlation coefficient of the physical properties of rock fluid on the poisson ratio has been used in this study to generate two new derivatives well-log which would be propagated by means of neural-networks. The result of lithologyimpedance propagation is further proceed with seismic coherence attribute as a reflection of geology and stratigraphy forms which are then combined with fluidimpedance propagation result to emphasize reservoir prediction distribution laterally. The study results of FS33 slicing discovers sand channels pattern and validated by well-log. Similarly with horizon slicing of FS37, patterns of sand channels reservoir are clearly visible and validated against two well-logs that passed. While on horizon slicing of FS42 besides discovering sand channels, carbonate rocks is also identified which is validated by well-log and core sample analyst.]

Abstrak :
ABSTRAK
Lapangan Penobscot berada pada Cekungan Scotian, Canada. Cekungan tersebut merupakan salah satu mega area penghasil hidrokarbon di dunia yang telah dieksplorasi dan diproduksi selama lebih dari 50 tahun. Salah satu tahapan penting setelah eksplorasi lapangan adalah melakukan karakterisasi reservoar untuk pengembangan lapangan. Pengidentifikasian reservoar berdasarkan kandungan fluida, porositas, dan ketebalan menggunakan data seismik sangat krusial dalam bidang geologi dan geofisika. Penelitian ini menggunakan metoda analisa AVO dan inversi simultan pada data seismik pre-stack CDP gather lapangan Penobscot. Inversi dan analisa AVO digunakan untuk membedakan batuan reservoar yang megandung hidrokarbon dari batuan lain disekitarnya. Goodway mengajukan suatu terobosan baru terhadap metoda AVO inversion yang didasarkan atas Lamé parameters λ dan μ, dan density ρ, atau Lambda-Mu-Rho (LMR). Penampang reflektivitas yang menunjukkan kontras parameter Lambda dan Mu dapat membedakan secara optimal antara fluida dan litologi suatu reservoir. Pada kasus ini penampang reflektifitas dari kontras parameter Mu-Rho dapat digunakan sebagai litologi indikator. Sedangkan interpretasi penampang reflektivitas Lambda-Rho dapat menunjukkan identifikasi fluida, dalam hal ini gas. Hasil analisa pada lapangan Penobscot menunjukkan bahwa pada kedalaman antara 2478?3190 m (formasi Missisauga) didominasi oleh Sandstone terdapat indikasi adanya hidrokarbon berupa gas. Hasil analisa AVO, terlihat adanya anomali AVO kelas III pada TWT 2000 ms dan kehadiran gas pada zona tersebut. Lambda-Rho pada zona tersebut bernilai 33,5 - 35 Gpa*g/cc, nilai Mu-Rho pada zona tersebut bernilai 32 - 35 Gpa*g/cc. Analisa crossplot well menunjukkan bahwa pada area target mempunyai harga Lamda-Rho 35 ? 40 GPa*g/cc dan harga Mhu-Rho 49 ? 71 GPa*g/cc.

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ABSTRACT
Penobscot field located at Scotian Basin, Canada. Scotian basin is one of the mega-producing areas of hydrocarbon in the world that have been explored and produced for over 50 years. One of important steps after exploration of the field is to conduct a Reservoir Characterization for field development. The identification of reservoirs rocks using seismic reflection data is a very important topic in geology as well as geophysics area. In this study, AVO analysis and simultaneous inversion methods gained to pre-stack CDP gather seismic data of Penobscot field. Inversion and AVO analysis gained to distinguish the reservoir rocks that contained hydrocarbon with the surrounding rocks. Goodway proposed a new approach to AVO inversion based on the Lamé parameters λ and μ, and density ρ, or Lambda-Mu-Rho (LMR). The reflectivity section showing Lambda parameter and Mu contrast will be able to differentiate between litology and fluid reservoir optimally. In this case, reflectivity section of parameter contrast of Mu-Rho can be used as litology indicator. Reflectivity interpretation of Lambda-Rho section can predict fluid indicator, in this case gas. Analysis result for Penobscot field indicate that the depth of 2478?3190 m (Missisauga Formation) dominated by Sandstone and have gas indication. Based on AVO analysis, there is Class III AVO anomaly on TWT 2000 ms and the existence of gas on that zone. Lambda-Rho value on that zone is between 33,5 - 35 Gpa*g/cc. Mu-Rho value on that zone is between 32 - 35Gpa*g/cc. Based on well crossplot analysis in target area, Lamda-Rho value is between 35 ? 40 GPa*g/cc and Mhu-Rho value is between 49 ? 71 GPa*g/cc.

Abstrak :
Penelitian ini terletak di lapangan X, tepatnya di Jambi. Reservoar gas pada lapangan ini merupakan bagian dari sub cekungan Jambi, dimana litologinya berupa sandstone pada Formasi Air Benakat. Metoda Atribut Dekomposisi Spektral sangat baik untuk mengidentifikasi lapisan tipis berdasarkan parameter frekuensi. Pada penelitian ini menggunakan CWT (Continuous Wavelet Transform) dengan menggunakan wavelet Mexican Hat sebagai wavelet input. Frekuensi dominan dari reservoar gas ditunjukan pada 30 Hz. Metode lain yang digunakan adalah Spectral Ratio yang berfungsi untuk menghitung besar Q Factor. Berdasarkan hasil perhitungan, analisis nilai Q Factor menunjukan nilai yang kecil yaitu 140,75 , pada zona M, 184,89 pada zona N, dan 89,10 pada zona O relatif terhadap zona referensi. Nilai Q Factor yang kecil pada zona reservoar menunjukan koefisien atenuasi yang besar. ...... This research is located in Field X, the South side of Sumatra. Gas Reservoirs in the field were formed at Air Benakat Formation. The spectral decomposition method is very good tool to identify the thin layers based on frequency parameters. In this research, the author using CWT (Continuous Wavelet Transform) with respect to Mexican Hat wavelet type as wavelet. From gas reservoir, it was found the frequency dominant around 30 Hz. Spectral Ratio method is used to estimate Q Factor value. Based on calculation, Q Factor values is 140,75 for M zone, 184,89 for N zone, and 89,10 for O zone, relative to reference zone. Q factor that is small in reservoir, represent a large attenuation.

Abstrak :
Reservoar gas batu pasir pada formasi Arang telah berhasil di karakterisasi dengan mengintegrasikan ketiga metode dari inversi simultan, analisis LMR dan analisis AVO. Karakterisasi difokuskan dalam dua hal yaitu identifikasi litologi dan kandungan fluidanya. Pada studi ini masing-masing metode akan menghasilkan parameter fisis yang sensitif terhadap karakter dari reservoar.Inversi simultan menghasilkan tiga parameter fisis berupa impedansi P (Zp), impednasi S (Zs), dan rasio Vp/Vs. Sementara transformasi LMR akan menghasilkan dua parameter fisis yaitu Mu-rho dan Lamda-rho. Identifikasi litologi dilakukan melalui analisis parameter fisis Mu-rho dan impedansi S sedangkan identifikasi kandungan fluida melalui analisis parameter Lamda-rho, impedansi P, dan rasio Vp/Vs. Analisis AVO dilakukan untuk mengetahui tipe kelas anomali dari gas yang mengisi reservoar melalui analisis gradien. Hasil studi menunjukan parameter Mu-rho dan lamda rho berhasil menggambarkan persebaran reservoar gas batu pasir secara 3D. Hasil impedansi S, impedansi P, dan Vp/Vs juga menujukan indikasi dari reservoar batu pasir di daerah yang sama. Terakhir berdasarkan analisis AVO tipe gas dalam reservoar adalah kelas IIp. ...... Gas sand resrvoir at Arang formation has been characterized by integrating three method from simultaneous inversion, LMR analysis, and AVO analysis.Characterization is focused on two things, litologi identification and fluid content. Each method in this study will produce parameter which sensitive to reservoar character. Simultaneous inversion results three physical parameters P-impedance, S-impedance, and ratio Vp/Vs. Whereas LMR transformation results two parameters, Lamda-rho and Mu-rho. Litology identification is done with Mu-rho and S-impedance analysis while fluid content identification is done with Lamda-rho, P-impedance, and ratio Vp/Vs. AVO analysis has purpose to know anomaly type from gas in reservoar through gradient analysis. This study shows that Mu-rho and Lamda-rho analysis can deliniate Gas Sand Reservoar in 3D form. While S-impdance, P-impedance, and Vp/Vs also indicate gas sand reservoar in the same spot. Finally based on AVO analysis, gas type in reservoar is class IIp.

Abstrak :
Penelitian ini menggunakan inversi simultan untuk karakterisasi reservoir batupasir dengan target upper dan lower Sihapas di Cekungan Sumatera Tengah. Inversi Simultan dilakukan pada data angle gather dari 3D seismik pre-stack time migration dan data sumur sebagai kontrol data. Data seismik terdiri atas 280 inline dan 760 crossline. Agar kualitas data meningkat, data seismik diubah menjadi domain sudut, dilakukan proses conditioning data untuk mereduksi noise dan meningkatkan signal to noise ratio S/N. Dari angle gather kemudian dibagi menjadi tiga domain yang berbeda yaitu near angle 5-15 , mid angle 14-24 , dan far angle 23-34. Analisa pra-inversi dilakukan untuk melihat korelasi antara hasil inversi dengan kontrol data sumur untuk mendapatkan error yang kecil. Hasil inversi simultan adalah impedansi-p, impedansi-s, densitas, dan rasio Vp/Vs untuk melihat sebaran litologi batupasir di zona target. Pada model impedansi-p didapatkan nilai pasir sebesar 23.000-34.000 ft/s g/cc, impedansi-s sebesar 13.000-21.000 ft/s g/cc, rasio Vp/Vs sebesar 1.5-1.8, dan densitas kurang baik dalam menggambarkan sebaran pasir karena tidak mampu memisahkan antara shale dan batupasir. Sebaran batupasir banyak ditemukan di daerah target Lower Sihapas. ...... In this research we used simultaneous inversion for characterization sandstones reservoir with target upper and lower Sihapas in Sumatera Tengah basin. Simultaneous inversion is performed by angle gather from 3D seismic data pre stack time migration and one well data as a control. Seismic data has 280 inline and 760 crossline. For improving data quality, seismic data is changed to angle domain, doing the conditioning data process to decrease noise and improves signal to noise ratio S N. From angle gather divided into difference three domain there are near angle 5 15 , mid angle 14 24 , and far angle 23 34. Pre Inversion analysis is done to get the small error. Simultaneous inversion's result are p impedance, s impedance, density, and Vp Vs ratio to see the distribution sandstone lithology in the target zone. In p impedance's model is gotten the value of sandstone is 23000 34000 ft s g cc, s impedance is 13000 21000 ft s g cc, Vp Vs ratio is 1.5 1.8 and density is not good for distributing of sandstone because can not separates between sandstone and shale. A lot of distribution of sandstone is found in targer area Lower Sihapas.

Abstrak :
Metode inversi EEI dapat mengkarakterisasi reservoar batuan, baik litologi maupun kandungan fluida pengisi pori. Metode EEI diharapkan dapat mengkarakterisasi reservoar di lokasi penelitian yang memiliki perselingan batuan pasir dan lempung dengan ketebalan kurang dari 60 ft. Parameter-parameter yang digunakan untuk melakukan inversi EEI pada penelitian ini adalah parameter yang memiliki koefisien korelasi yang tinggi antara log target dan log parameter pada sudut tertentu best chi angle . Parameter yang digunakan untuk melakukan inversi EEI pada penelitian ini adalah impedansi P AI , Vp/Vs, porositas total PHIT , dan volum lempung VCL. Hasil dari penelitian menunjukkan bahwa Formasi Lower Sihapas memiliki batuan pasir yang lebih dominan dibandingkan dengan Formasi Upper Sihapas. Pada batuan pasir di Formasi Lower Sihapas terdapat konten minyak yang ditandai dengan nilai volume lempung rendah, Vp/Vs rendah, dan saturasi air bernilai 25 - 60. ......EEI inversion method can characterize reservoir rock, either lithology and fluid content. EEI method hopefully can characterize reservoir in research area that have lithology of sand and shale which have less than 60 ft thickness. Parameters that are used in EEI inversion are parameters that have high value of correlation coefficient Parameters that are used in this research are P impedance, Vp Vs, total porosity PHIT , and volume of clay VCL. The results show that lithology in Lower SIhapas Formation have more dominant sandstone than Upper Sihapas Formation. Sandstone in Lower Sihapas Formation have oil content which is identified by low value of VCL, low value of Vp Vs, and water saturation value range from 25 60.