A Toolbox for Automatic Mechanical Screening to Assist Candidate-well Selection for Hydraulic Fracturing Practices

A Toolbox for Automatic Mechanical Screening to Assist Candidate-well Selection for Hydraulic Fracturing Practices

Hydraulic Fracturing (HF) operation is a normal routine in most parts of the world, but some oil companies still struggle to perform one successful practice. The rout of this unsuccessful event is possibly poor candidate selection. This paper presents the development of a toolbox to automatically delve into unlimited amount of zone and well data and select specific zones in exact wells for special purposes like HF. MATLAB programming language is applied in such a way to integrate large amount of data from different disciplines. In addition, the missing logging data are predicted with Neural Network and Fuzzy Logic techniques to mainly obtain stress profile in each well. After data integration, they are mechanically screened based on the user selected parameters, cut-offs and weight factors. Moreover, it is possible to find zones with similar characteristics. This tool is applied to select the best zones for HF in M oil field located in south of Iran. The field has 585 zones, and each zone has more than 30 parameters form different disciplines. The result of this programming is printed schematically in stacked bar charts, which makes it easier to see the quality of each parameter