The extraction of faults in a 3D seismic volume is done in different ways and this step is one of the most important steps in seismic interpretation. Each method has advantages and disadvantages. Some methods are standard approaches, and some are more recent technologies. To get a better understanding of the different methods and to verify those, was the goal of this master thesis. In this thesis four different fracture detection methods were tested: Manual fault interpretation, the Ant-Tracking Workflow, the Grey-Level-Co-Occurrence Matrix (GLCM) and the Fuzzy K-Means Clustering method. Analysis was performed on a sub-crop of the 3D seismic cube Kerry in the Kupe area (Taranaki Basin, offshore Western New Zealand). Testing of the four fault detection methods focussed on the comparison of the methodologies, the resulting faults and their characteristics, the influences on the results, improvements in fault detection and suggestions for the selection of the right method. Considering the difficulties in fault extraction in the Tangahoe Formation, which is characterized by weak and discontinuous seismic reflections, Fuzzy K-Means Clustering was able to mostly differentiate faults from other seismic facies, whereas Ant-Tracking and GLCM showed great difficulty to do so. Additionally, Ant-Tracking falsely extracted faults, when to close or crossing each other. Through cognition, manual fault interpretation was of no great difficulty in the Tangahoe Formation, but is proven ineffective regarding its processing time. Thus, Manual Fault Interpretation is effective, if the factor time is subordinate. Ant-Tracking and GLCM are excellent quick look methods. Precaution should be taken, before relying on the automatically extracted faults of the Ant-Tracking result, because often incorrect fault paths are tracked. Generally, Fuzzy K-Means Clustering has been proven to be the most effective and detailed method to detect fracture zones and could be of use in future to automate the fault extraction process.