A Abbaszadeh Shahri; F Rezaei; S Mehdizadeh Farsad; K Mehdizadeh Farsad; N Panaei
Abstract
The hydraulic fracture process is commonly used in the production of oil and natural gas reservoirs as a means of increasing well productivity and extending the production lifetime of the reservoir. The productivity of a hydraulically fractured oil or gas well is directly related to how well the well ...
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The hydraulic fracture process is commonly used in the production of oil and natural gas reservoirs as a means of increasing well productivity and extending the production lifetime of the reservoir. The productivity of a hydraulically fractured oil or gas well is directly related to how well the well bore is connected to the fracture. Some rock formations contain natural fracture systems that can further increase a well’s productivity, provided that the generated hydraulic fracture can grow such that these natural fractures. In this paper, at the first, a conventional area with productive formation and its surrounding layers were defined and then by use of finite element method the simulation was executed. The results indicated that a correct fracture generation will increase the production and efficiency. It is clear that this study is applicable for real data.
A Abbaszadeh shahri; R Hosseini; F Rezaei; K Mehdizadeh; N Panaei
Abstract
Artificial Neural Network methods (ANN) are computational methods, which capable to predict a specific log or classify different data. Unlike the digital computers, which require the completely definite and distinguished rules, the ANN methods do not need a pure mathematical model; rather like the human ...
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Artificial Neural Network methods (ANN) are computational methods, which capable to predict a specific log or classify different data. Unlike the digital computers, which require the completely definite and distinguished rules, the ANN methods do not need a pure mathematical model; rather like the human brain has the ability to learn by recognized and determined examples. The target of the present paper is to establish and prove the Petrophysical Analysis as powerful approach in prediction and diagnosis of rock reservoir porosity by use of petrophysical logs, in which by a high accuracy suggested Petrophysical Analysis based solution the porosity can be estimated using conventional logging data. On the basis of the available petrophysical data, the proposed method was examined in one of the southwest oil field of Iran. The obtained results of network analysis conditioning to reliability to data with different tests such as regression, root mean square and SPLine showed that the amount of network error in terms of available data in engineering range with a high acceptable safety factor could be used to predict and estimate porosity. This method with ability of cost reduction and viability can help and provide a large variety in this field for further extended research.
