Open AccessIntegrated Pore Pressure Model Estimation – Case Study of Jambi Sub-basin, South Sumatera, Indonesia
Author(s) -
Ahmad Farhan Farabi,
Ignatius Sonny Winardhie,
Noor Cahyo Wibowo
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/873/1/012012
Subject(s) - overpressure , structural basin , pore water pressure , artificial neural network , geology , drilling , petroleum engineering , geotechnical engineering , computer science , engineering , geomorphology , artificial intelligence , physics , mechanical engineering , thermodynamics
Pore pressure estimation is crucial in drilling wells for safety purposes also a very effective method for dealing with drilling accidents. Determination of overpressure is the main foundation in the evaluation to minimize the non-productive time (NPT). Here we present several models to generate pore pressure analysis of well from Jambi Sub-basin, South Sumatera, Indonesia. The model for estimation pore pressure is carried out by 3 methods: Eaton, Yan & Han, and Kan & Swan. Those methods will be compared to gain a more accurate model estimation within the study area. Kan and Swan’s model show the best fit for estimation because this method is suitable for the formation of tester like MDT/DST on higher frequency with parameter of C1 = 0.001 and C2 = 0.0003 for Jambi Sub-basin. The velocity data to construct the 3D pore pressure model was also validated with well data using multi-attribute analysis. The multi-attribute analysis used 2 algorithms, namely step-wise regression and probabilistic neural network (PNN). The analysis show that PNN has a better correlation compared to step-wise regression. The analysis shows the overpressure zone depth is ranges from 1700 – 2000m on Gumai Formation with maximum pressure around 6500 psi. The peak of overpressure dominated by Gumai and Talang Akar formation is caused by the loading mechanism because the rate of sedimentation on thick shale sequence is higher than the rate of dewatering on those formations.