Field-proven solutions already exist to reduce the loss of gas production when liquid 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 the individual operator's experience. The selection of the best remedial technique and the timeframe within which the remedial action is undertaken are critical to a project's profitability. Although there are literature reviews available regarding solutions to liquid loading problems in gas wells, a tool capable of helping an operator select the best remedial option for a specific field case still does not exist. This thesis proposes a newly developed decision matrix to screen the possible remedial options available to the operator. The matrix can not only provide a critical evaluation of potential solutions to the problem of liquid loading in gas wells vis-à-vis the existing technical and economic constraints, but can also serve as a reference to operators for investment decisions and as a quick screening tool for the selection of production optimisation strategies. Under its current status of development, this new tool consists of a decision algorithm built around a decision tree. Unlike other data mining techniques, decision trees quickly allow for subdividing large initial datasets into successively smaller sets by a series of decision rules. The rules are based on information available in the public domain. The effectiveness of the matrix is now ready to be tested against real field datasets.