Nonconventional wells (NCWs) are applied to increase the well deliverability and access the difficult formations. The nonconventional wells have been used to refer for the advanced wells such as highly deviated, horizontal, fishbones or multilateral wells. These wells offer a great potential to maximize the hydrocarbon recovery, however, it is difficult to predict their performance. In the literature, numerous mathematical models were developed to predict the well-productivity. However, the available models have been developed by employing one or more simplifying assumption(s), which may lead to over or under estimate the hydrocarbon production. This paper presents an effective technique to estimate the productivity for nonconventional wells.
In this work, artificial intelligence (AI) technique was utilized to determine the well-productivity for wide range of conditions. The developed models can determine the well performance without introducing the complexity associated with the numerical approaches. Artificial neural network was utilized to estimate the hydrocarbon production for two types of nonconventional wells; fishbone multilateral and hydraulically fractured horizontal wells. Reliable models are presented to quantify the performance of nonconventional wells producing from heterogeneous and anisotropic formations. The developed models evaluate the importance of reservoir properties and well configuration on the well deliverability. Total of 850 data sets were utilized to construct the intelligence models and to validate the prediction performance. Moreover, mathematical equations were extracted utilizing the optimized ANN models. The extracted correlations showed acceptable prediction errors, the absolute error around 7.4% in average.
The novelty of this work is that effective models are proposed to quantify the productivity of nonconventional wells. The proposed models can be utilized to refine commercial software to narrow down the deviations between the actual measurements and simulation outputs. Also, this work can contribute to enhance our understanding of the oil-field management by improving the prediction of well deliverability. Consequently, this study can help in optimizing the well planning for complex wells such as hydraulically fractured horizontal and fishbone multilateral wells.
