Liquid loading in horizontal gas wells impairs gas production and if not diagnosed in a timely manner can kill the well. Liquid loading occurs when the gas production rate declines and gas velocity drops below the critical velocity required to carry liquid to surface. Different models used in conventional reservoirs such as droplet, film or transient multiphase flow models are also applied with modifications in unconventional gas reservoirs. However, none of these models show great success when applied to inclined and horizontal wells in shale gas reservoirs. This is due to the fact that these models are developed for vertical wells and cannot identify the right multiphase flow regime in inclined and horizontal sections of the well. It is also extremely hard defining the right liquid droplet size and shape or liquid film thickness as well trajectory changes. Furthermore, these models cannot accurately predict the transient between annular and slug flow regimes in horizontal wells. As more wells are produced in shale gas reservoirs, a great amount of information from production control and monitoring becomes available which can be used to build a data-based smart model for real time diagnostics of liquid loading in new wells. In this new approach, data (pressure, completion, and productions) from many wells which have experienced or not experienced liquid loading problems in the same area will be the basis for developing the smart model.

What is being proposed here is a unique approach that includes developing a data-based technology for the training of neural networks that can be used as a smart model in real time to identify the start of liquid loading in unconventional gas wells. This innovative technique incorporates a unique fuzzy pattern recognition algorithm and unsupervised analysis technique to identify the most influential parameters impacting liquid loading in unconventional gas wells. The main objective for this manuscript is to develop a smart model that can predict the dynamics of liquid-gas interface and identify the start of liquid loading. Finally, the minimum gas velocity/rate to avoid the liquid loading can be determined.

For this study, a Marcellus Shale reservoir is selected. Production and completions history of 160 wells are collected. First the study is performed on a single well where 70 percent of the information is used for neural network training purposes, 15% for calibration, and 15% for validation of the model. The results show that the smart model is able to precisely predict the start of the liquid loading in the well and raise a warning flag when the possibility of liquid loading is high. Next, series of wells in the region is picked and smart model is built based on the 70% training, 15% calibration, and 15% validation. This model is then used to predict the liquid loading in a different well in the same region as a completely blind well. The results show high accuracy and reliability in predicting the start of liquid loading. To overcome the implicit dependency of the model to Turner et al. critical velocity criteria during the training, unsupervised learning algorithm is used to predict the loading and unloading status of the wells. The technique showed great success in predicting the well status and confirmed with field observations.

The new smart model developed for Marcellus Shale shows great promise that this approach can be applied in other areas where limited history of production and liquid loading exists.

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