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"Proses prediksi litologi sekaligus kandungan fluidanya merupakan bagian terpenting dalam karakterisasi reservoar. Salah satu metode yang digunakan dalam proses ini adalah metode inversi seismik simultan. Pada Lapangan Poseidon, Cekungan Browse, Australia, parameter-parameter yang dihasilkan melalui inversi seismik simultan kurang dapat mengkarakterisasi reservoar dengan baik karena saling tumpang tindihnya nilai impedansi antara hydrocarbon sand, water sand, dan shale yang menyebabkan tingkat ambiguitas yang tinggi dalam interpretasi. Inversi Poisson Impedance memberikan solusi terhadap permasalahan tersebut dengan cara merotasi impedansi beberapa derajat yang didapatkan melalui koefisien c. Hasilnya menunjukkan bahwa PI memberikan hasil yang lebih baik dalam memisahkan zona reservoar tersaturasi hidrokarbon. Berdasarkan hasil crossplot LI-GR, crossplot ¼- effecitive porosity, dan crossplot FI-Sw dengan nilai c masing-masing 2.04, 2.28, dan 1.05 didapatkan nilai korelasi optimum masing-masing 0.74, 0.91, dan 0.82 menunjukkan bahwa litologi porous sand tersaturasi hidrokarbon berada berada pada nilai LI ≤2800(m/s)(g*cc), 𝜙𝐼 ≤-5500(m/s)(g*cc), dan FI ≤3750(m/s)(g*cc). Keberadaan nilai LI, ϕI, dan FI yang rendah ini berkorelasi baik dengan keberadaan hidrokarbon pada sumur. Masing-masing nilai c tersebut kemudian diaplikasikan pada data seismik. Hasilnya menunjukkan bahwa distribusi persebaran porous sand tersaturasi Hidrokarbon pada penampang inversi seismik terlihat pada arah timur laut-barat daya yang diperkirakan sebagai arah persebaran gas.

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The prediction process of lithology and fluid are the most important parts of reservoir characterization. One of the methods used in this process is the simultaneous seismic inversion method. In the Poseidon field, Browse Basin, Australia, the parameters generated through simultaneous seismic inversion are not able to characterize the reservoir accurately because of the overlapping impedance values between hydrocarbon sand and shale which causes a high level of ambiguity in the interpretation. The Poisson Impedance inversion provides a solution to this problem by rotating the impedance through the coefficient c. Based on the results of the LI-GR crossplot, the 𝜙I-effective porosity crossplot, and the FI-SW crossplot with c values of 2.04, 2.28, and 1.05 respectively, obtained the optimum correlations of 0.74, 0.91,and 0.82 respectively, indicating that hidrocarbon-saturated porous sand is at the value of LI ≤ 2800 (m/s)(g *cc), 𝜙I ≤ 5500 (m/s)(g*cc), and FI ≤ 4000 (m/s)(g*cc). The presence of low values of LI, 𝜙I, and FI correlates accurately with the presence of hydrocarbons in the well. The results show that the distribution of hydrocarbon saturated porous sand on the seismic inversion section is seen in the northeast-southwest direction which is estimated as the direction of gas distribution."

"[ABSTRAKIdentifikasi keberadaan hidrokarbon di bawah permukaan bumi merupakansalah satu tujuan utama dalam eksplorasi lapangan minyak bumi dalam usahamengidentifikasi keberadaan hidrokarbon. Impedansi-poisson yang merupakansalah satu metoda yang digunakan untuk mendiskriminasi sifat fisis batuanterhadap fluida dengan cara mengamati sensitivitas dari rasio poisson telahditerapkan lebih lanjut untuk menghasilkan suatu metoda turunan yang lebih baik.Pendekatan sifat fisika batuan antara impedansi-poisson dengan log sumuran yangmerepresentasikan properti batuan menghasilkan suatu metoda turunan yangdinamakan impedansi-litologi. Sedangkan pendekatan sifat fisis fluida yangterkandung didalam batuan terhadap impedansi-poisson menghasilkan diskriminasikandungan fluida didalam batuan yang kemudian dinamakan impedansi-fluida.Metoda TCCA – Target Coeffisien Corellation Analysis – yang digunakanuntuk mencari koefisien korelasi tertinggi dari sifat fisis batuan terhadapimpedansi-poisson telah digunakan dalam penelitian ini untuk menghasilkan logsumuran impedansi-litologi dan impedansi-fluida yang kemudian di propagasidengan neural network. Hasil propagasi impedansi-litologi digunakan sebagaiinput untuk kalkulasi atribut koherensi yang diperkuat dengan hasil propagasiimpedansi-fluida untuk menghasilkan prediksi sebaran batuan reservoar.Dari hasil penelitian pada horison FS33 terlihat pola channel yangterbentuk dan tervalidasi dengan data sumur. Demikian juga pada sayatan horisonFS37, pola channel batuan reservoar terlihat dengan jelas dan tervalidasi terhadapdua sumur yang dilalui. Sedangkan pada sayatan horison FS42 selainteridentifikasi pola channel reservoar yang terbentuk, teridentifikasi juga batuankarbonat yang divalidasi dengan data sumur dan data batuan inti

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ABSTRACTHydrocarbon identification in subsurface is one of main goals in petroleumexploration so that the litho-fluid content discriminations are a part of hydrocarbonidentifications which have been widely applied today. Poisson-impedance which isone of the new methods that are used to discriminate rocks by examining thesensitivity of physical rock properties of poisson-ratio has been further developedto produce derivatives method. Physical properties approaches between poissonratioand a well-log which represents rock properties can be used to get highestcorrelation to produce a new derivative well-log named lithology-impedance. As afluid-rock properties approach between poisson-ratio and a well-log representslitho-fluid content properties produces a new derivative well-log named fluidimpedance.TCCA method –Target Coeffisien Corellation Analyst– is used to find thehighest correlation coefficient of the physical properties of rock fluid on thepoisson ratio has been used in this study to generate two new derivatives well-logwhich would be propagated by means of neural-networks. The result of lithologyimpedancepropagation is further proceed with seismic coherence attribute as areflection of geology and stratigraphy forms which are then combined with fluidimpedancepropagation result to emphasize reservoir prediction distributionlaterally.The study results of FS33 slicing discovers sand channels pattern andvalidated by well-log. Similarly with horizon slicing of FS37, patterns of sandchannels reservoir are clearly visible and validated against two well-logs thatpassed. While on horizon slicing of FS42 besides discovering sand channels,carbonate rocks is also identified which is validated by well-log and core sampleanalyst.;Hydrocarbon identification in subsurface is one of main goals in petroleumexploration so that the litho-fluid content discriminations are a part of hydrocarbonidentifications which have been widely applied today. Poisson-impedance which isone of the new methods that are used to discriminate rocks by examining thesensitivity of physical rock properties of poisson-ratio has been further developedto produce derivatives method. Physical properties approaches between poissonratioand a well-log which represents rock properties can be used to get highestcorrelation to produce a new derivative well-log named lithology-impedance. As afluid-rock properties approach between poisson-ratio and a well-log representslitho-fluid content properties produces a new derivative well-log named fluidimpedance.TCCA method –Target Coeffisien Corellation Analyst– is used to find thehighest correlation coefficient of the physical properties of rock fluid on thepoisson ratio has been used in this study to generate two new derivatives well-logwhich would be propagated by means of neural-networks. The result of lithologyimpedancepropagation is further proceed with seismic coherence attribute as areflection of geology and stratigraphy forms which are then combined with fluidimpedancepropagation result to emphasize reservoir prediction distributionlaterally.The study results of FS33 slicing discovers sand channels pattern andvalidated by well-log. Similarly with horizon slicing of FS37, patterns of sandchannels reservoir are clearly visible and validated against two well-logs thatpassed. While on horizon slicing of FS42 besides discovering sand channels,carbonate rocks is also identified which is validated by well-log and core sampleanalyst.;Hydrocarbon identification in subsurface is one of main goals in petroleumexploration so that the litho-fluid content discriminations are a part of hydrocarbonidentifications which have been widely applied today. Poisson-impedance which isone of the new methods that are used to discriminate rocks by examining thesensitivity of physical rock properties of poisson-ratio has been further developedto produce derivatives method. Physical properties approaches between poissonratioand a well-log which represents rock properties can be used to get highestcorrelation to produce a new derivative well-log named lithology-impedance. As afluid-rock properties approach between poisson-ratio and a well-log representslitho-fluid content properties produces a new derivative well-log named fluidimpedance.TCCA method –Target Coeffisien Corellation Analyst– is used to find thehighest correlation coefficient of the physical properties of rock fluid on thepoisson ratio has been used in this study to generate two new derivatives well-logwhich would be propagated by means of neural-networks. The result of lithologyimpedancepropagation is further proceed with seismic coherence attribute as areflection of geology and stratigraphy forms which are then combined with fluidimpedancepropagation result to emphasize reservoir prediction distributionlaterally.The study results of FS33 slicing discovers sand channels pattern andvalidated by well-log. Similarly with horizon slicing of FS37, patterns of sandchannels reservoir are clearly visible and validated against two well-logs thatpassed. While on horizon slicing of FS42 besides discovering sand channels,carbonate rocks is also identified which is validated by well-log and core sampleanalyst., Hydrocarbon identification in subsurface is one of main goals in petroleumexploration so that the litho-fluid content discriminations are a part of hydrocarbonidentifications which have been widely applied today. Poisson-impedance which isone of the new methods that are used to discriminate rocks by examining thesensitivity of physical rock properties of poisson-ratio has been further developedto produce derivatives method. Physical properties approaches between poissonratioand a well-log which represents rock properties can be used to get highestcorrelation to produce a new derivative well-log named lithology-impedance. As afluid-rock properties approach between poisson-ratio and a well-log representslitho-fluid content properties produces a new derivative well-log named fluidimpedance.TCCA method –Target Coeffisien Corellation Analyst– is used to find thehighest correlation coefficient of the physical properties of rock fluid on thepoisson ratio has been used in this study to generate two new derivatives well-logwhich would be propagated by means of neural-networks. The result of lithologyimpedancepropagation is further proceed with seismic coherence attribute as areflection of geology and stratigraphy forms which are then combined with fluidimpedancepropagation result to emphasize reservoir prediction distributionlaterally.The study results of FS33 slicing discovers sand channels pattern andvalidated by well-log. Similarly with horizon slicing of FS37, patterns of sandchannels reservoir are clearly visible and validated against two well-logs thatpassed. While on horizon slicing of FS42 besides discovering sand channels,carbonate rocks is also identified which is validated by well-log and core sampleanalyst.]"

"ABSTRAKProses pemisahan litologi dan fluida reservoir merupakan bagian penting dalam mengkarakterisasi reservoir. Hal ini akan menjelaskan sifat fisis litologi batuan reservoir serta kandungan fluidanya dengan mengintegrasikan data geofisika dan data petrofisika. Proses ini sulit dilakukan di lapangan ldquo;B rdquo; apabila menggunakan parameter impedansi akustik dan LMR, karena masih memiliki tingkat ambiguitas yang cukup tinggi. Impedansi Poisson PI telah di implementasikan sebagai solusi untuk menjawab masalah tersebut. Pada crossplot antara Impedansi Akustik AI dan Impedansi Shear SI dilakukan rotasi kedua sumbunya dengan mengikuti tren litologi-fluida hingga memenuhi persamaan PI c = AI ndash; cSI. Untuk meningkatkan akurasi perhitungan PI, nilai c faktor optimalisasi rotasi dihitung melalui metode TCCA Target Correlation Coefficient Analysis . Mirip seperti EEI fungsi sudut, kemudian dilakukan korelasi dengan data sumur yang akan diprediksi. Analisis parameter sensitivitas dilakukan pada 2 sumur yang ada di lapangan ldquo;B rdquo;. Dari simultaneous inversion didapat parameter-parameter Zp, Zs dan densitas yang kemudian ditranformasi menjadi PI. Model PI kami menunjukan dengan jelas pemisahan litologi batuan reservoir hidrokarbon. Lithology Impedance LI hasil dari korelasi PI dengan GR mampu memisahkan sand dan shale dengan baik. Begitu pula dengan Fluid Impedance FI sebagai hasil korelasi PI dengan SW juga mampu memisahkan kandungan air di dalam reservoir dengan nilai Sw tinggi relatif terhadap gas dengan nilai Sw yang rendah. Zona Hidrokarbon diperkirakan berada pada kedalaman antara 2360-2400m. Hasil slicing pada volume Poisson Impedance inversion telah memberikan gambaran distribusi dan interpretasi litologi dan kandungan fluida yang jelas pada reservoir di lapangan ldquo;B rdquo;, Sumatera Selatan.

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ABSTRACTThe separation process of lithology and fluid reservoir is an important part in the characterization of reservoir. This would explain the physical properties of reservoir rock lithology and fluid content by integrating the geophysics and petrophysical data. This process is difficult to do in the field B when using parameters of acoustic impedance and LMR, because it still has a fairly high degree of ambiguity. Poisson impedance PI has been implemented as a solution to address the problem. In crossplot between Acoustic Impedance AI and Shear Impedance SI conducted a rotation of both axis according to the trend of lithology fluid to satisfy the equation of PI c AI ndash c SI. To improve the accuracy of PI calculation, the value of c optimization factor of rotation is calculated through the method of TCCA Target Correlation Coefficient Analysis . Much like EEI, then do the correlation with to be predicted wells data. Analysis of sensitivity parameter performed on two wells in the field B . Parameters Zp, Zs and density which obtained from the simultaneous inversion then transformed into PI. Our PI models clearly show the separation of rock lithology of hydrocarbon reservoir. Lithology impedance LI as a result of the PI GR correlation is able to separate sand and shale very well. Similarly, the impedance Fluid FI as a result of PI SW correlation is also able to separate the water content in the reservoir with high Sw value relative to gas with a low value of Sw. Hydrocarbon zone proven at 2360 2400 m. The slicing result of the volumes of Poisson impedance inversion has provided a clearly distribution and interpretation of lithology and fluid content reservoir at the field B of South Sumatera."

"Parameter Acoustic Impedance (AI) dan Shear Impedance (SI) umumnya digunakan untuk sensitivitas pemisahan litologi dan fluida, distribusi properti petrofisika, pemodelan fasies, dan estimasi reserve potensi migas. Namun parameter tersebut tidak sensitif untuk pemisahan litologi dan fluida pada Formasi Plover di lapangan-X cekungan Bonaparte. Analisis lanjut parameter AI dan SI dilakukan dengan merotasikan kedua sumbu parameter tersebut ke dalam sumbu parameter yang baru sehingga menghasilkan parameter Poisson Impedance (PI) yang memenuhi persamaan PI = AI - c*SI. Pemisahan litologi dan fl uida dapat ditentukan dengan pemilihan nilai c yang berbeda melalui analisis TCCA (Target Correlation Coefficient Analysis). Nilai c untuk indeks litologi (LI) diperoleh dengan melakukan korelasi antara PI dan rekaman Gamma Ray (GR) dan untuk indeks fluida (FI) melalui korelasi antara PI dan Saturasi Air (Sw). Pada penelitian ini, terdapat dua zona target, yaitu zona A (3760 - 3920 meter) dan zona B (3920 - 4010 meter). Batas pemisahan litologi antara batupasir dan batu serpih menggunakan koefisien c = 1.245 untuk zona-A dan c = 2.3433 untuk zona-B sedangkan indikasi fluida menggunakan koefisien c = 2.740 untuk zona-A dan c = 3.2607 untuk zona-B."