Identifying the type and distribution of reservoir fluids is one of the main things on well logging logs and well testing. Several methods have been proposed to identify the type of reservoir fluids that in general, it can be divided into two groups of methods; direct ( More
Identifying the type and distribution of reservoir fluids is one of the main things on well logging logs and well testing. Several methods have been proposed to identify the type of reservoir fluids that in general, it can be divided into two groups of methods; direct (e.g. well testing) and indirect methods (e.g. seismic and log interpretation). Petrophysical logs due to their high resolution and more conformity are more frequently used than seismic data. This study aims to identify reservoir fluid types in PLs, based on 3 classes of oil, oil-water and water, in carbon reservoir. Suggested method applies wavelet decomposition as well as classification and was applied to PLs in five wells of an oil field in southwestern Iran. Eventually, obtained results have been evaluated by well testing responses.
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Permeability is one of the important characteristics of oil and gas reservoirs that is difficult to predict. In the present solution, experimental and regression models are used to predict permeability, which includes time and high costs associated with laboratory measu More
Permeability is one of the important characteristics of oil and gas reservoirs that is difficult to predict. In the present solution, experimental and regression models are used to predict permeability, which includes time and high costs associated with laboratory measurements. Recently, machine learning algorithms have been used to predict permeability due to better predictability. In this study, a new ensemble machine learning model for permeability prediction in oil and gas reservoirs is introduced. In this method, the input data are labeled using the lithology information of the logs and divided into a number of categories and each category was modeled by machine learning algorithm. Unlike previous studies that worked independently on models, here we were able to predict the accuracy of such a square mean error by designing a group model using ETR, DTR, GBR algorithms and petrophysical data. Improve dramatically and predict permeability with 99.82% accuracy.
The results showed that group models have a great effect on improving the accuracy of permeability prediction compared to individual models and also the separation of samples based on lithology information was a reason to optimize the Trojan estimate compared to previous studies.
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