Shale barriers within the bituminous oil sand deposits of the McMurray Formation have a detrimental effect on the steam-assisted gravity-drainage chamber growth and oil recovery. Typically, the non-net shale barrier lateral extents are too small to be detected with a few widely spaced delineation wells. The information on net reservoir and shale interval thicknesses collected from wells, along with a vertical indicator variogram, provide limited information about the horizontal extent and connectivity of these intervals. In this paper, a novel quantitative approach for predicting the lateral extents of the barriers, using thickness information provided by well log data, is proposed. The proposed approach is based on moments of inertia (MOI) applied to the shale objects to determine their effective size. The MOI calculation is aimed to simplify the almost infinite complexity of shale bodies into summary size parameters that can be readily understood and calibrated to production parameters. A case study is presented for optimal well placement accounting for uncertainty in the shale barrier sizes.

Predictive systems use historical and other available data to predict an event. In this paper we tries to compare the power of Artificial Neural Network (ANN) and Decision Tree (DT) in prediction of aerology events with time series streams and events stream using combination of K-means clustering algorithm and Decision Tree C5 algorithm and ANN. We try to find the effective parameters on events occurrences. Firstly, we find the closest time series record for any events; therefore, we have gathered different parameters value when an event is occurring. Using Kmeans we add a field to dataset which determines the cluster of each record and after that we predict the events using C5 algorithm and ANN. This framework and time series model can predict future events efficiently. We gathered 1961 until 2005 data of aerology organization for Tehran Mehrabad Station. This data contains some fields such as wet bulb, relative humidity, amount of cloud, wind speed and etc. This dataset includes 17 types of events. Using this framework the closest event can be predicted. The C5 method is able to predict events with 79.55% accuracy and ANN with 72.87% accuracy. Applying K-means clustering algorithm the prediction increase to 94.59% for C5 and 92.66% accuracies for ANN. We use 10-fold cross validation to evaluate our prediction rate. This framework is the first estimation in the area of event prediction for a huge dataset of aerology and can be extended in many different datasets in any other environments.

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Predictive systems use historical and other available data to predict an event. In this paper we propose a general framework to predict the Aerology events with time series streams and events stream using combination of K-means clustering algorithm and Decision Tree C5 algorithm. Firstly, we find the closest time series record for any events; therefore, we have gathered different parameters value when an event is occurring. Using Kmeans we add a field to data set which determines the cluster of each record after that by using C5 algorithm we predict events. C5 Decision Tree Algorithm is one of the well-known Decision Tree Algorithms. This framework and time series model can predict future events efficiently.We gathered 1961 until 2005 data of aerology organization for Tehran Mehrabad Station. This data contains some fields such as wet bulb, relative humidity, amount of cloud, wind speed and etc. This data set includes 17 types of events. Time series models can predict next time series parameters value and by using this Framework the closest event can be predicted. The C5 method is able to predict Events with Correct 74.11 percent and Wrong 25.89 percent. But with the aims of K-means clustering algorithm the prediction increase to 85 percent and wrong to 15 percent.90 percent of data was used for training set and 10 percent for test set. We use 10-fold cross validation to evaluate our prediction rate. This framework is the first estimation in the area of event prediction for a huge data set of aerology and can be extended in many different data sets in any other environments.