Using Quantitative Interpretation Techniques to Improve Reservoir Understanding

Quantitative Interpretation (QI) has long been considered a domain of advanced geophysics. However, there exist several easily applied QI techniques you can apply today to maximize the return from your seismic data.

QI has become an essential toolset providing critical technology and techniques for improved hydrocarbon exploration and production, and adding considerable value in subsurface evaluation processes.

In simple terms, QI is the process of understanding and estimating geology using geophysical data and applications. Alternatively, to understanding the architecture of the reservoir, QI focuses on understanding the rock itself down to grain and pore fluid scale. Where conventional seismic inversion delineates reservoir geometry, QI helps the interpreter characterize the rock type, fluid composition, and flow characteristics of the reservoir.

Unfortunately, even today, perhaps too few geoscientists harvest the opportunities and advantages of QI due to its perceived complexity. However, the following techniques are easy to apply, provide valuable insights into your seismic data, and will improve your reservoir understanding.

Colored Inversion

Traditionally, seismic inversion has always been a time-consuming and expensive process requiring specialized expertise and access to considerable computing power. Rewind 10-15 years and seismic inversion was certainly not as widely used and a slow process carried out by specialists within the industry, often by processing companies. Colored inversion was introduced to overcome these obstacles and unlock the value of seismic inversion, allowing the everyday geoscientist to apply it to their data.

Originally developed by BP, colored inversion was a cost-efficient and quick alternative to unconstrained sparse spike inversion. It shapes the spectrum of the seismic data to that of the earth, and in doing so improves the resolution and converts that data to a relative band-limited impedance, which makes visual comparison with geology much easier.

Figure 1 (left): Non-color inverted seismic. Figure 2 (right): Color inverted seismic. 

Colored inversion provides users with a range of advantages:

• Improved resolution of the seismic by extending the usable bandwidth, especially on the low-frequency end of the spectrum.

• Band-limited impedance is often a better interpretation domain than band-limited reflectivity.

• Easier to relate impedances to reservoir properties. An impedance log simply “looks more like geology” than a reflectivity series or log.

• The data becomes represented in layers rather than boundary reflections. 

It is usually best to apply colored inversion early in the QI process, to the basic AVO stacks, or the intercept and gradient – for data conditioning purposes to ensure they have the same spectrum before combining them for later operations, such as Extended Elastic Impedance.

Figure 3: Colored inversion effect on the seismic spectrum.

Amplitude Versus Offset (AVO)

Amplitude versus Offset (AVO) is central to most QI studies as it enables geoscientists to determine the elastic properties from which different lithofacies can be identified. AVO analysis has become commonplace in the oil industry, and geophysicists regularly use it as a tool to assess fluid content, porosity, density, or velocity of rocks from seismic data. 

A Dutch oil and gas company successfully used AVO analysis to assist de-risking their prospects. Based in Amsterdam, the company operates oil and gas fields offshore Netherlands and had several prospects and targets to explore for oil. Cegal initiated an AVO and rock physics study to add value to the extensive high-quality 3D seismic data set, as well as de-risking the Gillian prospect. Based on these and other observations and studies, effective fault seal along the Kiwi fault was considered a medium risk possibility, and the Gillian Unit joint venture decided to drill in mid-2015 as a result.

Extended Elastic Impedance (EEI)

Extended Elastic Impedance (EEI) has become a valuable tool for exploration, prospect evaluation, and reservoir characterization. As it can be used to relate to petrophysical properties and lithofacies, it is appreciated as a quick and objective method for connecting seismic inversion results with porosity, fluid content, lithology, and other reservoir properties.

EEI is a way of combining seismic volumes to enhance the visual representation of various trends or properties. EEI volumes can be obtained directly from your seismic data via a linear projection of the coordinate system related to the partial angle stack. EEI logs, obtained from elastic logs (P-velocity, S-velocity, and density) can be directly related to petrophysical properties of interest. 

EEI is a method of combining seismic volumes to be able to highlight various trends and properties using your seismic data. It is an impedance which ranges between acoustic impedance (AI) and gradient impedance (GI). The method projects a line through the cloud of data points on a scatter plot, where AI and GI are displayed in the x- and y-axis, respectively. Such AI/GI cross-plots is an effective way to discriminate different lithologies and fluid effects. By changing the original AVO angle range to a modified, so-called Chi-angle to extend the angle range for AVO projections, EEI can be optimized as a function of the Chi-angle to highlight the differences between lithofacies, such as hydrocarbon and brine sands, or between sands and shales.

Figure 4: The chi-angle controls the projection of the data to discriminated different clouds of points within the dataset.

Figure 5: EEI projection showing a chi-angle that separates sand and shale lithofacies.

Using this approach, estimations of porosity, fluid content, and lithology become easier to be interpreted and understood. The EEI method allows a user to directly invert for an EEI volume that describes a petrophysical parameter of interest or lithofacies discriminator. Hence, QI is no longer just for specialized experts. These three techniques promise to make QI accessible to a broader set of users through its easy application and its ability to provide value-adding insights into seismic data.

Why not use colored inversion, AVO, and EEI to improve your reservoir understanding?