Understanding Reservoirs Through Quantitative Interpretation (QI)Wed, 01/22/2014 - 10:16
Seismic data has recently established a precedent to build on through the use of other techniques for improved reservoir characterization. Companies are interested in the composition of the subsurface geology, but more specifically, in the fluids a given formation contains, such as oil, gas, or water. Their behavior is paramount to any exploration prospect, and is determined by the fluid type, lithology, porosity, and pressure of the rock unit. The challenge for seismic data firms is to understand this behavior in order to quantitatively interpret the reservoir. This has given rise to Quantitative Interpretation (QI), which involves data processing, modeling, and inversion methods to interpret a geological target’s composition and behavior.
Amplitude Versus Offset (AVO) is an essential technique that contributes to the QI process by extrapolating the fluid-related properties of rocks from seismic amplitude and phase response. This technique is able to compile and integrate information either via its attributes or inversions. If no regional wells have been drilled, attribute-based AVO analysis takes advantage of stack rotations of seismic data, whereas if wells exist, statistical rock physics are included in the equation. By approaching an even more quantitative route, inversion-based AVO analysis takes into consideration rock physics constraints in the simultaneous inversion of all available seismic data.
Stochastic Forward Modeling is yet another tool that determines the success of QI. By executing seismic amplitude and rock property forward modeling in a nondeterministic probability manner, the range of results of all candidate lithologies and fluid combinations in the depth range of interest can then be captured. In addition, deterministic modeling provides an understanding of the variation in expected seismic response at the well locations for different fluid scenarios and can be compared to behavior of the population described by the stochastic modeling results. In parallel to the previously mentioned method, the use of a Bayesian fluid classification framework allows prior geological knowledge to be incorporated into a probability prediction. By capturing uncertainty and quantifying risk, Bayesian updating is used to make quantitative predictions based on inverted seismic data and stochastic rock physics models, generating lithology and fluid probability volumes. Multivariate rock property probability density functions are created using stochastic forward modeling of depth dependent elastic rock physics models. These are quantitatively compared to equivalent inverted data to make the appropriate predictions.
A company that has recognized the value of QI is CGG Veritas. CGG’s Global 4D Inversion enables more accurate QI of production effects using 4D seismic data. This is executed by incorporating rock physics constraints in the simultaneous inversion of all available 4D wave studies to reduce uncertainty and produce results which are more consistent with the observed production history of the field. Global 4D Inversion can then be cascaded with 4D Bayesian fluid classification to provide interpretation of the evolution of fluid distribution and assess uncertainty in predicting fluid movements. Besides CGG’s efforts, EMGS has also developed a QI workflow integrating seismic inversion, 3D EM resistivities, and exploration well data to estimate hydrocarbon volume and map a 3D spatial distribution of hydrocarbon pore volume. By the reliable interpretation of reservoir properties, appraisal costs are significantly reduced resulting in a more efficient development program. Results of an EMGS QI project were compared to a rigorous reservoir simulation model developed for Statoil’s Troll field in the North Sea using all well logs, production data, and time-lapse seismic data. EMGS predictions were within 10% of corresponding hydrocarbon volume quantities, and volume predictions were significantly more accurate than those obtained by the extrapolation of saturation profiles interpreted from exploration well logs alone. The final product resulted in a cost effective, quick, and reliable reservoir model, which proved the potential for EM to play an important role in efficient field development planning.