摘要：Re-fracturing treatments of horizontal wells are increasingly gaining popularity to address the issue of rapid production decline and low recovery ratio for the conglomerate reservoir of the Mahu Oilfield. How to effectively select the horizontal wells with potentiality for re-fracturing and conduct the re-fracturing operation to achieve the purpose is the key problem that needs to be investigated urgently. However, the conventional methods for vertical wells are not in our consideration, and some methods for horizontal wells have their limits for the Mahu reservoir. To cope with problems mentioned above, fourteen factors from geology parameters, engineering parameters, and production performance parameters are considered to establish a multi-level evaluation model to quantify the potentiality of each horizontal well for re-fracturing in the Mahu Oilfield. First, the analytic hierarchy process (AHP) is used to obtain the weights of various factors affecting the productivity of horizontal wells, and on this basis, the subordination degree and evaluation matrix are then calculated, and finally, the fuzzy synthetic determination is obtained to determine the candidate wells for re-fracturing. The results have shown that the weights corresponding to engineering parameters obtained by the AHP method are the largest, followed by geology parameters, and the weights of production performance parameters are the minimal relatively; the number of fractures and the sand quantity of single cluster are the main controlling factors in engineering factors, and the initial formation pressure is the main controlling factor in geological factors; there is obvious correlation between the cumulative oil production after 90 days of primary fracturing with final cumulative production. Wells M15, M13, and M7 rank top three among the candidate wells. Through re-fracturing treatment by temporary plugging, the daily oil production of well M15 has increased significantly and is even higher than that of the primary hydraulic fracturing stimulation, confirming the reliability of the proposed selection method.
