Capillary pressure is a significant parameter in characterizing and modeling petroleum reservoirs. However, costly laboratory measurements may not be sufficiently available in some cases. The problem amplifies for carbonate reservoirs because relatively enormous capillary pressure curves are required for reservoir study due to heterogeneity. In this work, the laboratory measurements of capillary pressure and formation resistivity index were correlated as both parameters are functions of saturation. Forty-one core samples from an Iraqi carbonate reservoir were used to develop the correlation according to the hydraulic flow units concept. Flow zone indicator (FZI) and Pore Geometry and Structure (PGS) approaches were used to identify

Capillary pressure is a significant parameter in characterizing and modeling petroleum reservoirs. However, costly laboratory measurements may not be sufficiently available in some cases. The problem amplifies for carbonate reservoirs because relatively enormous capillary pressure curves are required for reservoir study due to heterogeneity. In this work, the laboratory measurements of capillary pressure and formation resistivity index were correlated as both parameters are functions of saturation. Forty-one core samples from an Iraqi carbonate reservoir were used to develop the correlation according to the hydraulic flow units concept. Flow zone indicator (FZI) and Pore Geometry and Structure (PGS) approaches were used to identify

The permeability is the most important parameter that indicates how efficient the reservoir fluids flow through the rock pores to the wellbore. Well-log evaluation and core measurements techniques are typically used to estimate it. In this paper, the permeability has been predicted by using classical and Flow zone indicator methods. A comparison between the two methods shows the superiority of the FZI method correlations, these correlations can be used to estimate permeability in un-cored wells with a good approximation.

Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy

      Many geophysical methods have been applied to locate groundwater in Nigeria’s rural and urban villages. Locating groundwater in low permeability formations like shales and siltstones is even more challenging due to the difficulty of mapping fracture zones within these formations. The fracture zones serve as potential aquifers in low permeability formations and have been the object of groundwater search in shales, siltstones and other low permeability formations. The electrical resistivity method has proven helpful in fracture mapping within low permeability formations due to the existing resistivity contrast usually observed between the fractured and non-fractured sections in the Shales and Siltstones. Three vertical electri

Flow unit and reservoir rock type identification in carbonates are difficult due to the intricacy of pore networks caused by facies changes and diagenetic processes. On the other hand, these classifications of rock type are necessary for understanding a reservoir and predicting its production performance in the face of any activity. The current study focuses on rock type and flow unit classification for the Mishrif reservoir in Iraq's southeast and the study is based on data from five wells that penetrate it. Integration of several methods was used to determine the flow unit based on well log interpretation and petrophysical properties. The flow units were identified using the Quality Index of Rock and the Indicator of Flow Zone. Th

A 3D Geological model was generated using an advanced geostatistical method for the Cretaceous reservoir in the Bai Hassan oil field. In this study, a 3D geological model was built based on data from four wells for the petrophysical property distribution of permeability, porosity, water saturation, and NTG by using Petrel 2021 software. The geological model was divided into a structural model and a property model. The geological structures of the cretaceous reservoir in the Bai Hassan oil field represent elongated anticline folds with two faults, which had been clarified in the 3D Structural model. Thirteen formations represent the Cretaceous reservoir which includes (Shiranish, Mashurah, U.kometan, Kometan Shale, L. Kometan, Gulnen

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F