Next Generation Geological Modeling for Hydrocarbon Reservoir Characterization - Advances in Data, Methods, Models and Their Applications in Geoscience - page 215

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Next Generation Geological Modeling for
Hydrocarbon Reservoir Characterization
M. Maučec, J.M. Yarus and R.L. Chambers
Halliburton Energy Services, Landmark Graphics Corporation,
USA
1. Introduction
Hydrocarbon reservoir characterization is a process for quantitatively assigning reservoir
properties, recognizing geological and geophysical information and quantifying
uncertainties in spatial variability (Fowler et al. , 1999). It represents an indispensable tool for
optimizing costly reservoir management decisions for hydrocarbon field development. In
fact reservoir characterization is the first step in the reservoir development program taking
into account structural and depositional architecture, pore systems, mineralogy of the
reservoir, post deposition diagenesis and the distribution and nature of reservoir fluids. The
technologies and tools for reservoir characterization continue to develop and expand,
particularly with the aggressive proliferation of three-dimensional (3D) and lately 3D time-
lapse (4D) data. However, one of the most critical areas of inquiry remains development of
workflows that best capture and represent geological uncertainty and apply that in the
integrated environment to optimize reservoir development and production planning (Goins,
2000; Kramers, 1994).
Although the challenges to find, develop and produce ever new hydrocarbon resources are
numerous, the ability of petroleum industry to increase the recovery from existing resources
has become a global endeavor. It has been generally accepted that the conventional oil
production practices produce, on average, approximately one third of the original oil in
place (Kramers, 1994) where estimated remaining unrecovered mobile oil varies with
different depositional environments. For example, depositional systems with more
complicated stratigraphy and facies architecture, such as fluvial systems or deep-sea fans,
may demonstrate even larger amounts of unrecovered mobile oil, ranging from 40-80%
(Tyler and Finley, 1991; Larue and Yue, 2003). This common industrial knowledge
represents a great incentive to increase the overall production, geared by large capital
investments in Smart Reservoir Management workflows and Enhanced Oil Recovery (EOR)
operations (Alvarado and Manrique, 2010). Still, the use of oversimplified and uncertain
geological models based on a sparse data from limited number of widely-spaced wells can
render hydrocarbon recovery forecasting as a daunting task. Moreover, inaccurate
description of reservoir heterogeneities is probably one of the outstanding reasons for
erroneous description of reservoir connectivity leading to the failure in predicting field
performance (Damsleth et al. , 1992). Overestimating performance could lead to investment
disasters, whereas its underestimation could lead to under-designed production facilities
that restrict hydrocarbon recovery.