Process identifies anomalies from original data without bias
The primary task facing a seismic interpreter is to recognize and attribute a geologic significance to observable patterns in the seismic response. The most apparent patterns are found in seismic reflections. In recent years, the industry is using more subtle patterns and connecting them to such attributes as porosity, lithology, and fluid content, as well as underground structure.
The separation of such patterns and their use as potential identifiers of subsurface characteristics comprises attribute analysis, a customary instrument in the geoscientist’s toolkit. Over the years, seismic data volumes have increased in terms of geographic area covered, depth of interest, and the number of attributes. New and potentially disruptive technologies have developed to take advantage of all the attributes available in the seismic data.
One new technology, based on unsupervised neural networks (UNN), reveals deeper insights into the seismic response and thereby reduces exploration risk. Unsupervised neural network technology can help interpreters recognize seismic anomalies that may indicate the presence of hydrocarbons, often when conventional techniques fall short. This new technology may also find application in the prediction of lithologies and fluid properties, as well as in estimating the size of reservoirs.
The self-organizing map (SOM), a form of UNN and a powerful pattern recognition method, was initially developed by Prof. Teuvo Kohonen of Finland during the 1970s-80s. Based on this approach, UNNs assist the interpreter in prospect evaluation by:
- Enabling the rapid comparison of large sets of seismic attributes
- Identifying combinations of attributes that reveal seismic anomalies
- Distilling the interpretation process to identify hydrocarbons with greater speed and accuracy.
Supervised vs. unsupervised UNNs
For several years there have been a few commercial tools in the upstream industry that are based on “supervised” neural networks. A supervised neural network (SNN) operates on data that has been classified, i.e., “ground truth” is known in specific locations, providing reference points for calibration of the network. In terms of seismic data, for example, a segment of a seismic survey at each logged well is employed to calibrate the SNN. Supervised neural networks unite the seismic data at the well to the known conditions from the well, while inferring geophysical properties away from wells.
But what happens if there is no well log or other forms of ground truth available, such as in a greenfield exploration?
This is the advantage of unsupervised neural networks, which do not need an “answer” beforehand and cannot, therefore, be biased.
UNNs can be applied to “unclassified” data, which operates on the seismic data response alone. Hence, the technology can be used to identify where even exploratory wells should be drilled by identifying geobodies that are different than their surroundings.
The edge of salt has been interpreted in this example along with several channel systems working through the data. Operating on this same data using a UNN produces results where the same edge of the salt outline can be seen, but “anomalous” areas (in white) stand out.
A few geobodies are associated with the previously interpreted channels in the original amplitude data. The red horizontal line across marks the time slice which displays a horizontal view of the same salt dome. In this time slice, the edge of the salt body has been interpreted along with a few of the major faults created by the salt uplift.
The white regions have been identified by the UNN as “anomalies” that have distinctly different properties than their surroundings, potentially indicating the presence of hydrocarbons. These would be regions that deserve further analysis by the interpreter.
The UNN analysis also yields a classification volume in which the interpreter can see in greater details stratigraphic and structural elements that might not have been interpreted in the original amplitude volume.
The UNN Process
The following four tasks comprise a thorough analysis using an unsupervised neural network:
- Carry out an assessment that reveals the right choice of seismic attributes
- Perform an appropriate interpretation of attributes for the geologic trends of interest
- Select the well information, where available, and calibrate the data
- Generate new attribute volumes – The UNN classification and classification reliability.
One key to an effective analysis and interpretation is the selection of the best seismic attributes, exposed by a systematic assessment of the data. Using Eigenvalue and Principal Component Analysis (PCA), it is practical to ascertain the relative contribution for each of the attributes to help guide the selection process. Running multiple UNN analyses using different sets of attributes also may help understand their impact and if the results change with different sets of attributes.
A major transformation is required to take full advantage of the explosion of data in the oil field. Unsupervised neural network technology facilitates greater insights into all forms of data, but perhaps the greatest value to the oil and gas industry is derived from its application to seismic interpretation. The technology is proving to reveal new insights into interpretation and increasing the reliability of the results. Unsupervised neural networks can also be used to correlate well information with well log data while improving the value of reservoir simulation. This new technology has the potential to be “disruptive” to the industry by providing a tool that comes close to the direct detection of hydrocarbons.