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^

This paper was prepared for presentation at the 1999 SPE Middle East Oil Show held in Bahrain, 20-23 February 1999.

This paper presents a case history of defining the field development plan for a complex; heavily faulted layered; undersaturated oil reservoir, with significant degrees of structural and production uncertainties. In such case, good reservoir management practices and reservoir monitoring are the main keys to understanding the reservoir behavior.The reservoir has numerous challenges, which complicate reservoir management; like complex geology, pressure support for different layers, water injection optimization, scale depositions, and commingled production which introduces uncertainties regarding the production and injection contribution. This leads to difficulties to identify bypassed oil in the reservoir. Therefore frequent production logging, monitoring the producing water salinity, and key data from wells and RFT/MDT of the new infill wells were used for managing such uncertainties'. This served as primary keys to identify different vertical and lateral flow barriers, and was used as a basis for water injection optimization in such challenging conditions. The reservoirs were studied by means of analytical methods and integration approach of wells' and reservoir surveillance data for understanding the structural configuration, investigate various production problems, optimize water injection strategy, and identify bypassed oil and poorly swept areas. The methods defined an extensive portfolio of infill drilling and other cost saving rigless activities to restore production potential of the field. This approach added about 22 MMSTB of oil reserves which represent 8 % increase in the ultimate oil recovery, and flattened the oil production for more than 5 years.New infill wells were confidently identified to achieve all of the following objectives a) access bypassed reserves b) access attic oil reserves c) adding another drainage point to the existing producers. The presented reservoir management practices has proven its ability to timely support the operational decisions, pinpoint infill wells, and prolong the life of a mature asset. It is not moving away from detailed dynamic model, but these practices are required in similar uncertainties conditions to develop right sense of understanding of reservoir behavior, and provide invaluable input data which adds credibility to the dynamic model. 2 IPTC 17538

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