Fault cubes, salt bodies, sand channel volumes, gas chimney data and hydrocarbon probability cubes, extracted from 3-D seismic are rapidly becoming valuable tools for exploration and field development. Such seismic anomalies can be highlighted using a new technique that combines seismic attributes in a special way. Computer algorithms are developed to search through data volumes looking for specific types of anomalous seismic data using criteria referred to as "meta-attributes." Meta-attributes are an aggregation of a number of seismic attributes combined with the interpreter's insight through a neural network to detect a particular feature. One of the main features of the meta-attribute concept is combining "artificial intelligence" of neural networks with the "natural intelligence" of an interpreter. This leads to a more comprehensive integration of geological, petrophysical and seismic data. Non-linear interrelationships between data as well as knowledge versus geologic features and reservoir properties are defined implicitly at the natural scale. Meta attributes extracted from multiple input seismic volumes and derived attributes are used to predict facies, lithology and hydrocarbon probability, as well as for detecting faults, fractures, channels, gas chimneys or salt bodies. The meta-attribute approach is placed in the historical context, the technology is explained and examples for various seismic object detections problems are given.

Evolution of Attribute Technology

Seismic attribute technology has been gaining popularity ever since it was introduced by Taner et al (1979). These originally introduced "instantaneous attributes" and the subsequent multi-trace and pre-stack attributes have found many applications in different exploration and field development problems. Figure 1 shows the evolution of attribute technology during the last four decades. The text to the right of the road shows the actual applications and those to the left, the corresponding technologies. In recent years the advent of 3D and 4D technology and increasing computing power led to an explosion in useful attributes. Nowadays there is an increasing need to identify subsets or combinations of attributes that can highlight a given geological or reservoir property most effectively.

Figure 2: Evolution of- Attribute Technology (Available in full paper)

The first publication in which several attributes were combined for E&P applications was by Aminzadeh and Chatterjee (1985). This was accomplished by first, performing a "principal component analysis" (PCA) to transform the attributes to the "factor space", ensuring they are not correlated. This was followed by "clustering" to highlight gas related bright spots using several attributes in the factor space. Clustering or other conventional statistical tools (regression, cross plots, etc) allow a linear transformation to combine different attributes and compare their respective contribution and role in the classification process. Subsequently, it was suggested by, for example, Wong et al (2003), that in many circumstances, unconventional statistical methods may be required to fully capture and account for the non-linear relationship between the seismic data and reservoir properties. Neural network-based methods, offer one such possibility, allowing non-linear transformation of attributes to establish stronger and more accurate relationship between the seismic data (seismic attributes) and the geological features or reservoir properties (Schuelke et al, 1997 and Aminzadeh and de Groot, 2004).

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