Reservoir and production engineers rely heavily on well production rates to optimize well activities such as ensuring optimum reservoir monitoring. Individual gas well rates are not readily available, rather, they can be estimated thru multi-phase flow meter (MPFM) and well test analysis. These methods are associated with certain limitations such as high cost, high uncertainty, and technically elaborate calculations. Consequently, empirical and numerical calculations are employed with well test data to calculate daily rates. These practices lead to inaccurate gas rate estimations.

A model with an ability to provide accurate estimates of gas rates for a gas reservoir can serve as a handy tool for the subsurface engineers in addressing well and reservoir optimization strategies. This work presents artificial intelligence models to estimate gas rates in a gas field containing ten wells. The aim is to develop a correlation that is simple and easy to incorporate yet providing robust answers on a global scale. Multiple machine learning tools are employed. These include; Artificial Neural Network (ANN), Functional Network (FN), and Adaptive Neuro Fuzzy Inference System (ANFIS).

Production data from a dry gas field X was used for the model development. Data cleaning and data reduction steps were carried out to ensure the input parameters for the proposed model are physically relevant and accurate. Missing these steps would result in the development of an erroneous correlation, i.e., garbage -in garbage-out (GIGO). This led to finalization of certain basic well-head parameters which are available at any typical well and had direct impact on the output production rate. The target parameter for model training is the gas rate. A rigorous comparison between the investigated artificial intelligence models was conducted by calculating average absolute percentage error (AAPE) and coefficient of determination. The comparative analysis shows that the intelligent model is able to predict the gas rate in condensate wells with accuracy in excess of 90%. Examples of such large accuracy has not been reported previously.

ANN performs a step ahead as compared to the various intelligent algorithms used in this study. This paper sheds light on the potential of the Industrial Revolution 4.0 for the Pakistani Oil and Gas Sector. Data-driven artificial intelligent models are capable of validating the well test and multiphase flow meter results. In addition, it can prove to be a vital tool in an engineer's tool-kit to reduce uncertainties in gas rate measurements.

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