--> ABSTRACT: Facies And Lithology-based Geostatistical Modeling Of The Eunice Monument South Unit (EMSU) Reservoir, New Mexico, by Raymond A. Garber and W. Scott Meddaugh; #90906(2001)

Datapages, Inc.Print this page

Raymond A. Garber1, W. Scott Meddaugh1

(1) Chevron Petroleum Technology Company, Houston, TX

ABSTRACT: Facies And Lithology-based Geostatistical Modeling Of The Eunice Monument South Unit (EMSU) Reservoir, New Mexico

Reservoir models have evolved from conceptual hand-drawn schematic figures into detailed, stochastically-based digital models based on well log-derived values. Often the geological basis for the digital models has been lost. Recent trends are to incorporate rock-based data into the models. In most cases, model accuracy is improved with the addition of the rock data.

An example of lithology-based reservoir modeling is the EMSU full field project. This reservoir modeling effort differed from previous efforts in that first a deterministic facies distribution was used to constrain a stochastic lithology distribution and second, a stochastic distribution of porosity was constrained by lithology. Previous models based on stochastic porosity and permeability distributions without facies or lithology constraints could not be effectively history-matched during flow simulation.

The EMSU Field produces from the Permian (Guadalupian) Grayburg formation and consists largely of dolomitized grainstones and mud-poor packstones deposited in high-energy shelf-crest shoals on a carbonate ramp. More shoreward are low porosity, low permeability lagoonal mud-rich packstones and wackestones. The reservoir is layered by thin, fine-grained aeolian sandstones deposited during lowstands. Core descriptions were used to map three facies - deep water, shoal, lagoon - and to determine the relative amounts of seven lithologies (sandstone, mudstone, wackestone, mud-rich packstone, mud-poor packstone, grainstone, and rudstone).

The modeling workflow involved: (1) reservoir characterization; (2) facies mapping; (3) geostatistical analysis; (4) stochastic distribution of lithology constrained by facies using multi-binary SIS; (5) lithology-constrained porosity distribution by SGS; and, (6) permeability distribution by lithology-dependent transforms.

AAPG Search and Discovery Article #90906©2001 AAPG Annual Convention, Denver, Colorado