While hydraulic fracturing is perhaps the most widely used well completion technique for production or injection enhancement, often treatments are badly or inadequately designed and/or executed. Because fracture treatments are performed in fields which contain hundreds of wells, large databases are generated de facto. These databases contain considerable and valuable information, but they are rarely used by engineers for the purpose of improving or optimizing future treatments or to select the most promising refracturing candidates. There are two main reasons, which prevent such obvious use: lack of time and, especially, lack of appropriate tools.

There are, however, emerging methodologies, which can be applied for this exercise and they fall under the general category of Data Mining and Knowledge Discovery. Although these terms are already established, the specific tool used in the method and case study presented in this paper is new and innovative.

The method uses Self-Organizing Maps (SOMs) which are used to group (cluster) high-dimensional data. Clustering data can be done with multidimensional cross plots to a certain extent, but when a large amount of parameters (dimensions) is necessary, the cross plot loses its effectiveness and coherence.

The technique, as shown also in the case study of this paper, first identifies underperforming wells in relation to others in a given field. SOMs have been employed in this work to cluster different fracture input parameters (proppant volume, fluid volume, net pay thickness, etc.) of about 200 fracture treatments into different groups. To differentiate between these groups, the incremental post-fracture treatment production has been used as an output. The comparison of the different clusters with the corresponding output reveals a better practice for future treatments and possible refracture candidates. It is important to note that the output has not been included in the clustering process itself.

Once the wells are identified, a Neural Network is trained to rank the most promising wells for a refracture treatment and new optimum fracture designs are prepared which compare ideal performance with the one observed. These are then the criterion for deciding refracturing candidates as well as a significant aid in the design of treatments in new wells in the neighborhood.

This work and the methodology that it implies provide for a faster and more efficient way to analyze well performance data and, thus, to reach a verdict on the success or failure of past treatments. The technique leads to the definitive selection of refracturing candidates and to the improvement of future designs.

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