Applying horizon-based attributes to detect faults/fractures



Faults/fractures detection is an essential step of the full field study in hydrocarbon reservoir. Definition of exact location of these structural events is a critical step to optimize location of the development wells. Despite of the fast development of the geo science technology and oil industry in recent years, detection of the small geological events such as faults and fractures in some of the situations is still problematic.
There are some methods to identify the faults. One of these methods is direct interpretation of the faults on seismic sections. Considering most of the faults/fractures in carbonate reservoirs are too small to identify by conventional faults interpretation and are smaller than seismic resolution, this method could not specify all of the reservoir fractures. One of the new methods for distinguishing the faults and fractures which has been developed recently is used to seismic attributes which allows us to detect sub-seismic faults/fractures.
In this study we try to interpret faults/fractures in Kangan- Dalan reservoir formations with Upper Permian–Lower Triassic age in one of the hydrocarbon reservoirs in Persian Gulf. These layers were divided to five zones namely K1, K2, K3, K4 and K5. Total thickness of the reservoir layers varies from 400 meters to 600 meters and these formations including of sequences of Calcite, Dolomite and Anhydrite.
The geophysical data which have been used in the study consist of 3D seismic post- migrated data and interpreted time horizons in the study area. The seismic data have 25*6.25 m grid size with two second length of seismic traces and four milliseconds sampling rate. Petrel software has been also used foe seismic attribute generation and comparing them.
In the first step of the study, 3D seismic data as well as interpreted time horizons of the top and base of the reservoir were loaded in the mentioned software. Then, in order to detect the faults, some horizon- based Root Mean Square (RMS) amplitude, dip, azimuth and curvature attributes were generated using Top Kangan time map and 3D seismic data. In this regards, the suitable parameters were selected by performing some tests and quality control of the results. Then generated seismic attribute maps were evaluated and fault/ fractures patterns of the reservoir layer were determined and compared with each other using these maps. In this step, the resulted seimic attribute in fault/ fractures demonstration in reservoir level was investigated. Finally, it was found that in the study reservoir curvature attribute and root mean square (RMS) of amplitude are the best seismic attributes for faults/fractures detection.
The results of this study shows ability of the curvature attribute groups to detect sub-seismic faults/fractures. Moreover it shows using only one attribute could not be detect all of the fault/fractures in the reservoir; so some seismic attributes should be generated and results of the all attributes should be compared to find an accurate fault pattern.