Pore pressure prediction and modeling using well-logging data in one of the gas fields in south of Iran

Abstract

Knowledge of pore pressure is essential for cost-effective, safe well planning and efficient reservoir modeling. Pore pressure prediction has an important application in proper selection of the casing points and a reliable mud weight. In addition, using cost-effective methods of pore pressure prediction, which give extensive and continuous range of data, is much affordable than direct measuring of pore pressure. The main objective of this project is to determine the pore pressure using well log data in one of the Iranian gas fields. To obtain this goal, the formation pore pressure is predicted from well logging data by applying three different methods including the Eaton, the Bowers and the compressibility methods. Our results show that the best correlation with the measured pressure data is achieved by the modified Eaton method with Eaton׳s exponent of about 0.5. Finally, in order to generate the 3D pore pressure model, well-log-based estimated pore pressures from the Eaton method is upscaled and distributed throughout the 3D structural grid using a geostatistical approach. The 3D pore pressure model shows good agreement with the well-log-based estimated pore pressure and also the measured pressure obtained from Modular formation Dynamics Tester.