In the petroleum industry, accurately predicting production potential of undrilled unconventional horizontal wells requires the use of highly complex models and is a constant research in progress. Deterministic approaches such as decline curve analysis (DCA) are used in most cases to estimate production potential due to ease of implementation. The main disadvantage of using DCA by itself is that it requires an existing well to forecast the production, which is very expensive to drill. This paper shows the procedures for building a flexible machine learning based decline curve-spatial method that can be easily used to predict the estimated ultimate recovery (EUR) of newly proposed wells without the requirement of costly data or other time consuming methods. A type of artificial neural network (ANN) called feed forward neural network (FFNN) was used as the machine learning method during this process. In order to achieve this goal, production and well data were collected from public domain sources. Power law exponential (PLE) DCA method was implemented on a portion of the existing wells with sufficient production history. The data then was divided into training and test sets. The training set was fed into the ANN model and the results were compared with the results obtained from other inherently spatial methods such as universal kriging, geographically weighted regression (GWR), and generalized additive model (GAM). Finally, the EUR of the new wells were compared to the original training and test data to observe any discrepancies in the prediction and necessary adjustments to the model hyperparameters were made when there were discrepancies. Analyzing and observing all the results from the various combinations of methods indicates that using ANN without any spatial correlation is a less reliable method to estimate the production potential of new wells in the Marcellus shale gas reservoir when compared to other inherently spatial methods. This study shows that it is necessary to include spatial correlations between wells in the EUR prediction of new wells in the ANN models. The predictive strength of other spatial methods were within a reasonable range. One of the main input parameters that may increase the accuracy of the model are completion parameters. However, most of these completion parameters that will affect the production are very difficult and expensive to obtain. These parameters which could not be included as inputs can increase the accuracy of these models when available. This experimental study indicates that there are many other possible variations of this method other than the methods discussed in this paper. It also shows that the spatial correlation of wells is highly important when predicting the new well production in unconventional reservoirs. This is a flexible method, which can be easily modified to find better predictive methods for other areas of interest.

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