No abstract

No abstract

Superimposed tectonic movement and karst erosion resulted in a combination of fractures and irregular caves in deep/ultradeep carbonate rocks, typically along major fault swarms. Outlining these fault-karst reservoirs mainly depends on recognizing the strong reflection in seismic profiles; however, characterizing their internal structures is still difficult, which are represented as weak amplitude in seismic profiles. This study intended to propose a method to dissect the internal structure of fault-karst reservoirs, which contains four steps: (1) elimination of the signal interference by the covering bed with strong energy and recognition of internal reservoirs with low energy based on seismic data conversion, frequency division, and inversion; (2) gradient structure tensor analysis based on an anisotropic Gaussian filter for fault-karst reservoir outlining; (3) internal faults and cave recognition on the basis of wave-based inversion; and (4) reevaluation of the number and scale of these faults and caves based on seismic recognition and well test results and verification of their volumes and hydrocarbon reserves. The method was used in the evaluation of the fault-karst reservoir in the Halahatang (HLHT) oilfield, which is located in the north of Tarim Basin. The results show that the fault-karst reservoirs along major faults and their internal structures are effectively recognized, and the error of the predicted depth of the reservoirs decreases from more than 20 m before to less than 4 m now; the drilling success ratio increases from 70% to 90%. In addition, the method supports the recognition of untapped fault-karst reservoirs around shut-in wells, which provides guidance for sidetracking plans. Further, by comparing the geophysical volume of fault-karst reservoirs and the reserve predicted by production performance, the untapped reserve in a certain reservoir can be evaluated; on this basis, producing wells received high yields by targeted acid fracturing. In summary, the method effectively improves the prediction accuracy and the recovery efficiency of fault-karst reservoirs.

No abstract

No abstract