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[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.] |
T44244-Lukman Denny Andika.pdf :: Unduh