[
ABSTRAKIdentifikasi 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
ABSTRACTHydrocarbon 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.]
T44383-Martin Krisnomurti.pdf :: Unduh