Characterizing Carbonate Reservoir to Capture the Details of the Vertical and Spatial Heterogeneity Using an Integrated Workflow: A Case Study from Onshore Abu Dhabi
Authors: Ahmed J. Z. , Fatah A. B. Ab , Malla L. , Blooshi H. Al , Maskari S. Al 
Published: November 2024

Reservoir characterization involves the analysis of reservoir rocks and fluids to comprehend their properties, behaviors, volume distribution, and flow patterns. These models help forecast and enhance oil and gas production. Understanding reservoirs, located hundreds or thousands of meters underground, is a significant challenge. While well logs provide detailed, high-resolution data, they are limited to a few locations. To extrapolate these findings across the entire reservoir, geostatistics becomes crucial, acting as an advanced form of interpolation that considers spatial relationships between data points. The study aims to characterize a carbonate reservoir using seismic inversion methodology to capture the details of the vertical and spatial heterogeneity. The effectiveness of these workflows relies on the seismic inversion algorithm, geological constraints and transferring the results to the depth grid. The integrated approach used in this study tries to address some of these issues by integrating different data from all domains and capturing details beyond the seismic bandwidth.

In geostatistics, spatial correlations of measured values are established and used for unbiased estimations at unknown locations. Estimation methods offer a deterministic ‘best guess’ based on available data; kriging is a well-known technique in this category. Simulation methods generate various plausible scenarios, termed ‘realizations,’ without asserting the accuracy of any single outcome over others. These approaches, like Markov chain Monte Carlo (MCMC), help represent realistic spatial variability and quantify uncertainty. Beyond mere interpolation, Geostatistical Seismic Reservoir Characterization leverages all existing data and expert insights. With Bayesian inference, diverse information types are combined to yield more accurate predictions representing a composite posterior probability. Seismic data plays a key role due to its broad lateral coverage compared to the point-specific data from wells. However, industry practices often underutilize available information.

Geomodelers and geophysicists both aim to model the reservoir but with different focuses and tools. The former relies on geological understanding and geostatistical framework, which may not be accurate far from wells. Seismic data, if utilized, serves mainly as a contouring aid. Conversely, geophysicists apply seismic data and rock physics deterministically, leading to models that suit seismic interpretations but may lack geological details. Geostatistical seismic reservoir modeling merges these approaches, using both types of data to make several plausible models and assess uncertainty. It involves stochastic processes, which are random by nature and lead to various possible outcomes. These methods rely on statistical analysis of spatial continuity and use randomness to produce different results. However, they are different from stochastic inversion methods, so the two are not completely interchangeable.