Predicting Reservoir Petrophysical Geobodies from Seismic Data Using Enhanced Extended Elastic Impedance Inversion

Abstract

The study aims to implement a high-resolution Extended Elastic Impedance (EEI) inversion to estimate the petrophysical properties (e.g., porosity, saturation and volume of shale) from seismic and well log data. The inversion resolves the pitfall of basic EEI inversion in inverting below-tuning seismic data. The resolution, dimensionality and absolute value of basic EEI inversion are improved by employing stochastic perturbation constrained by integrated energy spectra attribute in a Bayesian Markov Chain Monte Carlo framework. A general regression neural network (GRNN) is trained to learn and memorize the relationship between the stochastically perturbed EEI and the associated well petrophysical log data. The trained GRNN is then used to predict the petrophysical properties of any given stochastic processed EEI. The proposed inversion was successfully conducted to invert the volume of shale, porosity and water saturation of a 4.0 m thick gas sand reservoir in Sarawak Basin, Malaysia. The three petrophysical geobodies were successfully built using the discovery wells cut-off values, showing that the inverted petrophysical properties satisfactorily reconstruct the well petrophysical logs with sufficient resolution and an accurate absolute value at the well site and are laterally conformable with seismic data. Inversion provides reliable petrophysical properties prediction that potentially helps further reservoir development for the study field.

Geo-Body and Geostatistical Modelling of Carbonate Reservoir Facies Architecture and Characterization

“…Fig. (7) shows the extracted geo-bodies along the Upper, Middle, and Lower sequence. Holes are noticed within the geobodies near the D-8 well which signify the absence of these geo-bodies/reservoir facies near D-8.…”

“…It involves creating three-dimensional models of geological bodies within the subsurface using data from various sources like seismic surveys, well logs, and geological maps [6]. These models help in better understanding the spatial distribution and characteristics of reservoirs, such as porosity, fluid saturation, and other petrophysical properties which help in optimizing drilling and production strategies, reducing exploration risk, and improving reservoir management [7]. A common approach to body modeling is to use seismic data to create a structural framework of the subsurface, which is then refined by calibrating it with well-log and core data to build a more detailed model of the reservoir [8].…”

“…By analyzing the seismic attributes of the geobody, like amplitude, frequency, and phase, geoscientists can identify and map the location and properties of reservoir units and their associated features. Overall, geobody modeling is a powerful tool for reservoir characterization and helps in efficient and profitable hydrocarbon exploration and production [7].…”