Field-proven solutions already exist to reduce the loss of gas production whenliquid loading begins to occur. However, the choice of remedial technique, its feasibility,and its cost, vary considerably depending on a field's location, size export route, and theindividual operator's experience. The selection of the best remedial technique and thetimeframe within which the remedial action is undertaken are critical to a project'sprofitability. Although there are literature reviews available regarding solutions to liquidloading problems in gas wells, a tool capable of helping an operator select the bestremedial option for a specific field case still does not exist.This thesis proposes a newly developed decision matrix to screen the possibleremedial options available to the operator. The matrix can not only provide a criticalevaluation of potential solutions to the problem of liquid loading in gas wells vis-a?-visthe existing technical and economic constraints, but can also serve as a reference tooperators for investment decisions and as a quick screening tool for the selection ofproduction optimisation strategies. Under its current status of development, this new tool consists of a decisionalgorithm built around a decision tree. Unlike other data mining techniques, decisiontrees quickly allow for subdividing large initial datasets into successively smaller sets bya series of decision rules. The rules are based on information available in the publicdomain. The effectiveness of the matrix is now ready to be tested against real fielddatasets.
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