Identifying faults and gas chimneys using multiattributes and neural networks

Abstract

Modern visualization and image processing techniques are revolutionizing the art of seismic interpretation. Emerging technologies allow us to interpret more data with higher accuracy in less time. The trend is shifting from horizon-based toward volume-based. New insights are gained by studying objects of various geologic origins and their spatial interrelationships. The standard way of highlighting objects is through seismic attribute analysis. Various attributes are tested in a trial-and-error mode, and one is selected as the optimal representation of the desired object. The selected attribute, which may be a mathematical composite of several attributes, is not sensitive to a particular geologic object but highlights any seismic position with similar attribute response.