A new refracturing candidate selection method for multi-fractured horizontal wells in tight oil reservoirs

Refracturing technology has been widely applied to increase the production rate of multi-fractured horizontal wells (MFHWs) and enhance ultimate recovery. However, due to the complicated relationships between various parameters, it is often difficult to select candidate wells for refracturing. A novel method combining analytic hierarchy process (AHP), grey relation analysis (GRA) and fuzzy logic was proposed in this study to evaluate refracturing potential of wells from reservoir quality and initial completion efficiency. First, based on the parameters of the constructed wells, several elements were chosen as appraisal factors. AHP was used to establish a multi-parameter evaluation system for refracturing treatment. Then, GRA was used to calculate the weight coefficient and sort out the main factors that influence fracturing result. Finally, GRA was coupled with fuzzy logic to determine the classification threshold of reservoir quality. A novel comprehensive evaluation score (CES) was proposed to determine the priority of the refracturing wells. The method was successfully applied to a tight oilfield in Northeast China and the optimum refracturing potential area was selected based on the reservoirs quality and completion efficiency by using quadrant analysis. Average daily oil production increased from 2.3 to 17.6 tons per day after refracturing treatment. The method can be used to identify refracturing candidates quickly and economically.