Application of Pressure-Volume (P-V) Fractal Models in Modeling Formation Pressure and Drilling Fluid Determination in an Oilfield of SW Iran

Document Type : Research Paper


1 Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Petroleum, Materials and Mining Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

3 Geoscience Faculty, Research Institute of Petroleum Industry (RIPI), Tehran, Iran


Accurate knowledge of pore and fracture pressures is essential for drilling wells safely with the desired mud weight. By definition, overpressure occurs when the pore pressure is higher than the normal hydrostatic pressure and is associated with specific environmental conditions in a particular part of the earth. This study focuses on the formation pressure studies’ domain for an oilfield in SW Iran. It generally consists of carbonate rocks with no shale interbeds except for the Kazhdumi Formation. This study is based on information from 23 wells and the interpretation of seismic data. The effective, pore, and fracture pressure models are determined from combined geostatistical models and compared with fractal models. The highest correlation between the final effective pressure cube and the velocity cube is related to the lower Fahliyan Formation with 86% and Ilam with 71%, which indicates the accuracy of the modeled data with the original data. Based on the final formation pressure cubes, the maximum pore pressure is 10,000 psi in the Gadvan Formation up to the upper Fahliyan Formation, and the maximum fracture pressure is 13,000 psi in the lower Fahliyan up to the Gotnia Formation. Based on the Logratio matrix obtained from the pressure-volume (P-V) fractal model, the maximum overall accuracy (OA) in the dominant limestone intervals is 0.74 at depths of 2000-3000 meters, which is related to the Asmari to Sarvak Formations. Furthermore, it indicates a high correlation of the pore pressure cube model obtained from the combination of sequential Gaussian simulation (SGS) and co-kriging models with acoustic impedance inversion (AI) for minimizing the time and cost of drilling in new wells of the studied field.


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