Unlock the value of seismic data

Seismic data and reservoir properties derived from it are frequently under-used by oil and gas asset teams because they lack the tools to properly interpret and incorporate the results. Fugro-Jason’s David Timko discusses how technology can enable asset teams to fully realise the value of their seismic data.

“There are a number of factors influencing the validity and usefulness of traditional attribute and character recognition analysis.”
-David Timko

Geoscientists have struggled for years to extract information from their seismic data that can be provided by a simple structural interpretation. One goal has been identification of lithology and estimates of associated reservoir properties. Typically these have been estimated from seismic attributes and seismic character recognition.

There are a number of factors influencing the validity and usefulness of traditional attribute and character recognition analysis. Without knowledge of the underlying wavelet in the seismic data it is difficult to predict the seismic response from well-based synthetics. The relationships established between reservoir properties and seismic attributes are empirical and are difficult to extend laterally. Attributes are usually based on reflectivity data, relating to boundary properties rather than layer properties.

A set of tools is required which, when combined with rock property information resulting from seismic inversion, provides reliable 3D estimates of reservoir properties based on rigorous integration with well data and established geophysical elastic rock property relationships.

Well tie and wavelet estimation

The first step in this workflow is generating an appropriate time-to-depth relationship for each well and using this to reliably estimate the embedded seismic wavelet. This is essential even for traditional character and attribute analysis to ensure the correct seismic information is correlated to the well data. With the correct wavelet, wells can be used more effectively to predict the seismic response.

Lithology definition and estimation of elastic properties

A set of discrete lithologies should be defined, which differentiate good reservoir from bad based on both the physical rock characteristics (porosity, shale/clay content, etc) as well as fluid saturations. One must then understand the elastic properties associated with each lithology to determine whether seismic data can be used to identify them. This is done by cross-plotting the elastic log data associated with each lithology, firstly at well log scale to determine the most optimistic separation and then repeated at seismic resolution to determine the separation that can be expected when using seismic data as the source of information.

The crossplots can be used to create probability density functions (PDFs) for each lithology, determining the likelihood that a particular combination of elastic parameters represents a sample from that lithology.

Fluid and facies prediction

The PDFs defined by the well data can be applied to volumes of elastic parameters estimated from seismic inversion to determine the probability that a sample from the seismic dataset is associated with a particular lithology.

The outputs from this process are lithology probability volumes, which can be used to build a "most-likely" lithology volume. A-priori information regarding the expected proportion of each lithology is used to ensure the PDFs are weighted appropriately.

Body capture and geology modelling

The "most-likely" lithology volumes can be used as input to a body capture routine to identify connected bodies of a particular lithology. Lithology probability volumes can be used to identify bodies that exceed a probability threshold.

Viewed in 3D, these bodies highlight the distribution and connectivity of the reservoir.

The final step is to incorporate this lithology information into geological models. Using rigorous zonal sampling algorithms, the lithology and reservoir property volumes can be accurately upscaled and mapped into the models. The lithology probability volumes can be used either indirectly as trends to guide sequential indicator simulation, or directly to define lithologies within the model. Lithology based relationships between elastic and reservoir properties can be used to further define the reservoir properties within the bodies.

With all this technology available on the geoscientist's desktop, the true value of seismic data can now be explored and used to build the most accurate models of the sub-surface possible.

About

David Timko is Product Champion for Interpretation and Analysis with Fugro-Jason, a leading provider of seismic inversion and reservoir characterisation products and services. He joined the company in 2004 as Regional Technical Manager in Dubai after 24 years experience in the exploration, production, seismic and reservoir characterization sectors of the industry primarily with Amoco and BP.