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

Abstract

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.

Determining the electrical facies utilizing multi-resolution graph-based and artificial neural network clustering methods in an Oilfield, SW Iran

“…This number is transferred to the output layer using the transfer function in the hidden layer. Some percent of the data are also used for comparison 1,4,51,55,63,[85][86][87] .…”

“…The function of artificial neurons, as the name implies, is to simulate biological neurons. The artificial neuron has a P input and an output 33,51,55,86,87 .…”

“…Recently, the electrifies predicted using the MRGC approach to generate rock mechanical properties such as Young's modulus, Poisson's ratio, unconfirmed compressive strength, and internal friction coefficient [52][53][54] . Kianoush 55 estimated an ANN based model of formation Pressure for Azadegan hydrocarbon Reservoir, SW Iran. Finally, Okhovvat, Riahi and Akbari Dehkharghani 56 used Kernel principal component analysis (KPCA) to improve the performance of electrical facies classification .…”

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

“…Finally, a novel approach has been done for estimating pore size distribution and capillary pressure in the hydrocarbon zone through a hydraulic flow unit framework using an NMR log. The workflow establishes a robust and cost-effective methodology for NMR T2 distribution correction in hydrocarbon zone for uncored and partially cored wells (Baykin et al, 2023;Fernandes et al, 2023a;Fernandes et al, 2023b;Jehanzaib et al, 2023;Kadkhodaie, 2021;Kianoush, 2023;Lai et al, 2023;Osinowo et al, 2023;Wang and Weijermars, 2023;Zhang et al, 2023b).…”

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units in the Asmari Formation, SW Iran

“…Hydraulic flow units (HFUs) is referred to as lateral continuity of reservoir units with consistent geological properties controlling the behavior of fluid flow in pores media (Amaefule et al, 1993;Gualda and Vlach, 2007; Finally, a novel approach has been done for estimating pore size distribution and capillary pressure in the hydrocarbon zone through a hydraulic flow unit framework using an NMR log. The workflow establishes a robust and cost-effective methodology for NMR T2 distribution correction in hydrocarbon zone for uncored and partially cored wells (Baykin et al, 2023;Fernandes et al, 2023a;Fernandes et al, 2023b;Jehanzaib et al, 2023;Kadkhodaie, 2021;Kianoush, 2023;Lai et al, 2023;Osinowo et al, 2023;Wang and Weijermars, 2023;Zhang et al, 2023b) (Abraham-A et al, 2023Duda et al, 2012;Yu et al, 2023;Zhang et al, 2023a;Zhang et al, 2023b;Zheng et al, 2023). The fuzzy c-means (FCM) method is an efficient tool for solving fuzzy clustering problems.…”