A. Abdollahipour; M. Fatehi Marji; H. Soltanian; E. A. Kazemzadeh
Abstract
The permeability and coupled behavior of pore pressure and deformations play an important role in hydraulic fracturing (HF) modeling. In this work, a poroelastic displacement discontinuity method is used to study the permeability effect on the HF development in various formation permeabilities. The numerical ...
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The permeability and coupled behavior of pore pressure and deformations play an important role in hydraulic fracturing (HF) modeling. In this work, a poroelastic displacement discontinuity method is used to study the permeability effect on the HF development in various formation permeabilities. The numerical method is verified by the existing analytical and experimental data. Then the propagation of a hydraulic fracture in a formation with a range of permeabilities is studied. The time required for propagation of an HF to 10 times its initial length is used to compare the propagation velocity in the formations with different permeabilities. The results obtained show that the HF propagation can be significantly delayed by a permeability less than almost 10-9 D. Also the effect of HF spacing on the propagation path is studied. It was shown that the stress shadowing effect of HFs remained for a longer spacing than in the elastic model due to the required time for fluid leak-off in the formation. Also the propagation angles are higher in the poroelastic model predictions than the elastic model. Therefore, it is proposed to use the poroelastic model when studying multi-HF propagation in order to avoid errors caused by neglecting the pore fluid effects on the HF propagation paths.
R. Gholami; A. Moradzadeh
Abstract
Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are ...
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Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are very expensive and time-consuming. Well log data is an alternative approach for prediction of permeability because they are usually available for all of the wells. Hence, attempts have been made to utilize well log data to predict permeability. However, because of complicate and non-linear relationship of well log and core permeability data, usual statistical and artificial methods are not completely able to provide meaningful results. In this regard, recent works on artificial intelligence have led to the introduction of a robust method generally called support vector machine (SVM). The term “SVM” is divided into two subcategories: support vector classifier (SVC) and support vector regression (SVR). The aim of this paper is to use SVR for predicting the permeability of three gas wells in South Pars filed, Iran. The results show that the overall correlation coefficient (R) between predicted and measured permeability of SVR is 0.97 compared to 0.71 of a developed general regression neural network. In addition, the strength and efficiency of SVR was proved by less time-consuming and better root mean square error in training and testing dataset.
N. I. Aziz; I. Porter; F. Sereshki
Abstract
The volumetric changes in the coal matrix (Coal Shrinkage), permeability under various gas environment conditions as well as perographical properties were studied in the laboratory. The shrinkage and permeability of coal were examined with respect to changing gas type and confining pressures. The shrinkage ...
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The volumetric changes in the coal matrix (Coal Shrinkage), permeability under various gas environment conditions as well as perographical properties were studied in the laboratory. The shrinkage and permeability of coal were examined with respect to changing gas type and confining pressures. The shrinkage tests were carried out in high-pressure bombs while the permeability study was conducted in a specially constructed high-pressure chamber. Methane (CH4), carbon dioxide (CO2), nitrogen, (N2) and a 50% -50% volume mixture of CO2/CH4 gas were used in the study. The tests showed that under different pressure levels gas type affected permeability and shrinkage characteristics of coal. This paper presents a case study of Tahmoor Colliery, NSW, Australia and an overall discussion on coal shrinkage, permeability and coal petrography data of Tahmoor that permits a better understanding of the gas regime in this mine. The results are important to the further understanding of the inter-relationship between gas flow, the coal matrix and permeability in ‘normal’ and ‘tight’ coal conditions (locally referred to as disturbed coal).
