Several authors have introduced various mathematical equations to calculate the critical flow rate necessary to keep the gas wells unloaded. The most widely used equation is that of Turner et al. However, Turner's equation required empirical adjustment with different ranges of data which made the application rather questionable. In this paper we present a new approach for calculating the critical flow rate necessary to keep gas wells unloaded. This approach still adopts Turner's basic concepts, but with considering different flow conditions that result in different flow regimes. Hence it explains the previous discrepancies of the drop model with different data ranges, and presents a new set of equations that eliminates the need for empirical adjustment and better matches actual data records. Introduction: The gas well loading phenomenon is one of the most serious problems that reduces, and eventually cuts production in gas wells. This phenomenon occurs as a result of liquid accumulation; either water and/or condensate in the well bore. Over time, these liquids cause an additional hydrostatic back pressure on the reservoir which results in a continued reduction of the available transport energy. The well therefore starts slugging which even gives more chance for liquid accumulation that completely overcomes the reservoir pressure and cause the well to die. Figure (1) illustrates the development of the loading phenomenon in a gas well. Typical solutions were to unload the well artificially, either mechanically (using pumps) or with gas lift (kicking with Nitrogen through coiled tubing). However, in addition to the expenses, and loss of production, the artificial lift solutions remain temporary and the well is subject to reloading again. Therefore, thoughts have been directed to develop some solutions that enable the well of continuously unloading itself without the aid of external help (unloading operations). Numerous theories have offered methods for predicting, and controlling the onset of load up. Turner et al's method (1969) for predicting when gas well load up will occur is most widely used. They developed two physical models for transporting fluids up vertical conduits; these are the liquid droplet, and the liquid film models. A comparison of these two models with field data led to the conclusion that the onset of load up could be predicted adequately with the droplet model, but that a 20% upward adjustment of the equation was necessary. This upward adjustment improved the match and was empirically recognised by other research fellows working on the same subject. In April 1984, J.A. Lescarboura from Conoco Inc. adopting the same empirical adjustment published a paper in the Oil and Gas Journal of a computerised version of the droplet model to predict critical gas flow rates for continuous liquid removal from the well bore. More recently in March 1991, an Exxon research group working on the same field, published their paper in the (JPT) stating that they obtained a good match with their actual field records using the droplet model without any adjustment. They found practically that the critical flow rate required to keep low pressure gas wells unloaded can be predicted adequately with the liquid droplet model without the 20% upward adjustment. P. 189^

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TX 75083-3836, U.S.A., fax 01-972-952-9435. Abstract Artificial neural networks theory creates, with other theories and algorithms, a new science. This science deals with the human body as an excellent source, through which it can simulate some biological basics and systems, to be used in solving many scientific, and engineering problems. Neural networks are tested successfully in so many fields as pattern recognition or intelligent classifier, prediction, and correlation development. Recently, Neural network has gained popularity in petroleum applications. In this paper we applied this technique in PVT parameters determinations. The application interests in the estimation of the bubble point pressure through a designed neural network. As this value well estimated, it then used with other variables in a second network to determine oil FVF at this value of bubble point pressure. A comparison study between the performance of neural network and other published correlations has shown an excellent response with smallest absolute relative average error, and highest correlation coefficient for the designed networks among all correlations.

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