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Accurate knowledge of pore and fracture pressures is essential for drilling wells safely with the desired mud weight (MW). Overpressure occurs when the pore pressure is higher than the normal hydrostatic pressure. There is a challenge regarding the pressure studies domain in an oilfield in SW Iran, where lack of geo-mechanical data limits exact mud window calculation. Also, the reservoir generally consists of carbonate rocks and contains no shale interbeds, so mechanical stratigraphy based on Gamma ray could not be applied. This study is to provide safe drilling considering MW to prevent the flow or loss in the vicinity of the new wells in the studied field. In this research, the formation pressures and mud window models are determined by combining geostatistical, intelligent, and conditional programming models and compared with real data. The conditional programming was also used to correct small out-of-range data. The highest correlation between the final effective pressure and velocity cube was observed in lower Fahliyan Formation with 0.86 and Ilam with 0.71.The modeled MW difference ranged between 2.5 and 30 PCF. Also, the maximum modeled MW is 150 PCF in the upper Fahliyan Formation. Heavy mud of more than 130 PCF is suggested for drilling the Khalij member and continues to the end of stratigraphy column. Best observed correlation comparing the drilled and modeled MW, especially achieved in the Fahliyan reservoir Formation with more than 100 PCF and the Ilam Formation with 80–100 PCF. Finally, 3D formation pressures are presented and recommended for further safe drillings.

In seismic methods, pore pressure is estimated by converting seismic velocity into pore pressure and calibrating it with pressure results during the well-testing program. This study has been carried out using post-stack seismic data and sonic and density log data of 6 wells in one of the fields in SW Iran. While an optimum number of attributes is selected, the General regression (GRNN) provides higher accuracy than Back Propagation (BPNN) at the initial prediction stages. However, Acoustic Impedance (AI) is the most applicable seismic attribute used as root and reverses AI for estimating P-wave and density. Using a set of attributes can train the system to estimate the property. The correlation coefficient of actual and predicted P-wave using an AI seismic attribute has been calculated as 0.74 and the multi-attribute technique as 0.79. Also, density and three attributes reach from 0.57 to 0.60, which shows a better relationship between seismic attributes and density. After determining optimum layers with the principal components analysis (PCA), formation pressure was modeled with the feed forward-backpropagation (FFBP-ANN) method.Five information layers, including gamma, Vp, AI, density, and overburden pressure, have the most linear convergence with the initial pressure model and are used to modify the ANN model of effective pressure.

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.