A Workflow for Static Reservoir Modeling Guided by Seismic Data in a Fluvial System

Realistic and accurate static geologic models are an essential element needed to predict the behavior of subsurface reservoirs and play an important role in petroleum engineering. Data used in the development of a static geologic model are gathered from various sources, such as seismic, log, and cor...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical geosciences 2017-11, Vol.49 (8), p.995-1020
Main Authors: Koneshloo, Mohammad, Aryana, Saman A., Grana, Dario, Pierre, John W.
Format: Article
Language:English
Subjects:
Chemistry and Earth Sciences
Computer Science
Divergence
Earth and Environmental Science
Earth Sciences
Engineers
Frameworks
Geologists
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Impedance
Meandering
Mimicry
Modelling
Petroleum
Petroleum engineering
Petroleum geology
Physical properties
Physics
Representations
Reservoirs
Seismic activity
Seismic data
Seismological data
Statistics for Engineering
Workflow
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Realistic and accurate static geologic models are an essential element needed to predict the behavior of subsurface reservoirs and play an important role in petroleum engineering. Data used in the development of a static geologic model are gathered from various sources, such as seismic, log, and core data, each of them providing information on different physical properties of interest and with varying degrees of resolution. Compiling all data from various sources into a single representation of the subsurface formation of interest is a daily challenge for many petroleum geologists and engineers. This paper describes a framework to develop and select process-mimicking models that are consistent with available seismic attributes, namely impedance. Using a process-mimicking modeling package, 75 models of a fluvial meandering system are generated, one of which is chosen as the “true” model and masked thereafter. The implemented selection method relies on the degree of similarity in the histogram of representations of clusters of all possible patterns in the seismic impedance domain based on each process-mimicking model and that of the “true” model at several resolutions. The results demonstrate the effectiveness of the use of a weighted average divergence distance across multiple levels to select process-mimicking models that honor seismic data the best.
ISSN:1874-8961
1874-8953
DOI:10.1007/s11004-017-9696-8