Pore Pressure Prediction using Well-Logging Data in the West Baram Delta, Offshore Sarawak Basin, Malaysia
Dejen Teklu Asfha1, Haylay Tsegab Gebretsadik2, Wan Ismail Wan Yusoff3
1Dejen Teklu Asfha*, Department Of Geoscience, Universiti Teknologi Petronas, Seri Iskandar, Malaysia.
2Haylay Tsegab Gebretsadik, Department Of Geoscience, Southeast Asia Carbonate Research Laboratory, Universiti Teknologi Petronas, Seri Iskandar, Malaysia.
3Wan Ismail Wan Yusoff, Department Of Geoscience, Center For Subsurface Seismic Imaging, Universiti Teknologi Petronas, Seri Iskandar, Malaysia.

Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9172-9178 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9050118419/2019©BEIESP | DOI: 10.35940/ijrte.D9050.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Well-predicted pore pressure is vital throughout the lifetime of an oil and gas field starting from exploration to the production stage. Here, we studied a mature field where enhanced oil recovery is of high interest and pore pressure data is crucial. Moreover, the top of the overpressure zone in west Baram Delta starts at different depths. Hence, valid pore pressure prediction prior to drilling is a prerequisite for reducing drilling risks, increasing efficient reservoir modeling and optimizing costs. Petrophysical logs such as gamma-ray, density logs, and sonic transit time were used for pore pressure prediction in the studied field. Density logs were used to predict the overburden pressure, whereas sonic transit time, and gamma-ray logs were utilized to develop observed shale compaction trend line (OSCTL) and to establish a normal compaction trend line (NCTL). Pore pressure was predicted from a locally observed shale compaction trend line of 6 wells using Eaton’s and Miller’s methods. The predicted pore pressure using Eaton’s DT method with Eaton’s exponent 3 showed a better matching with the measured pressure acquired from the repeat formation test (RFT). Hence, Eaton’s DT method with Eaton exponent 3 could be applied to predict pore pressure for drilling sites in the study area and vicinity fields with similar geological settings.
Keywords: Eaton Method, Miller Method, Pore Pressure Prediction.
Scope of the Article: Regression and Prediction.