About This Item
- Full text of this item is not available.
- Abstract PDFAbstract PDF(no subscription required)
Share This Item
The AAPG/Datapages Combined Publications Database
Houston Geological Society Bulletin
Abstract
Abstract: The Benefits of Integrating
Seismic
and Petrophysical
Data![Next Hit](/data/images/arrow_right.gif)
![Previous Hit](/data/images/arrow_left.gif)
![Next Hit](/data/images/arrow_right.gif)
![Previous Hit](/data/images/arrow_left.gif)
![Next Hit](/data/images/arrow_right.gif)
By
1GeoSystems
2e-Seis Inc., Houston, Texas
Introduction
One of the major problems that exists in current exploration and
production projects is the successful prediction of reservoir quality
(specifically permeability) in inter-well areas. This
presentation focuses on the integration of pore geometry data
(from direct image analysis of rock samples) with wireline logs in
key wells and extrapolation to
seismic
data
in order to improve
the accuracy of field-wide and regional permeability prediction.
This is of particular importance both offshore (deep Gulf of
Mexico) and onshore (Wilcox, Vicksburg, etc.), geological
provinces in which porosity and permeability are often not fundamentally
related.
Integration for Reservoir Characterization
It is important for exploration & production companies to take
full advantage of all available data
when undertaking reservoir
description, evaluation and characterization (Figure 1). One of
the problems that still exists is how to integrate
seismic
data
and
petrophysics. The past several decades have witnessed important
advances in this regard, such as
seismic
-based lithology determination
and the gross identification of porous versus non-porous
zones. The problem with such porosity-based
seismic
solutions is
that producibility is often more a function of permeability than
of porosity.
Whether we are interpreting wireline logs, thin sections, seismic
data
or production rates and pressures, we have one thing in
common: the rocks. Even though all of our measurements are at
vastly different scales and utilize many different forms of measurement
techniques, they are all measurements that include
some aspect of the rock. It therefore follows that if we are going
to integrate disparate
data
sets correctly, the integration must be
done in a manner that is consistent with petrophysical theory.
Pore Geometry
The methodology presented here is based or1 the unifying
concept of pore geometry as a fundamental control on both
petrophysics and geophysics. The petrophysical properties
controlled by pore geometry influence compressional and shear
wave velocity data
that form the basis of seismology. Pore geometry
influences the characteristics of both the compressional and
shear waves (Davies and Young, 2001). For example, resistance to
shear is a function of grain packing and the type and degree of
grain cementation. Loosely packed, uncemented rocks obviously
have different shear characteristics than tightly packed
uncemented rocks. Clay cemented sandstones will respond differently to shear than silica cemented sandstones. Packing and
cementation are aspects of rock diagenesis that obviously affect the
size, shape and arrangement of the pores in the rock (pore geometry) and are fundamental controls on permeability and porosity.
Rock-based integration requires viewing the goal of each discipline (including geology, geophysics, and petrophysics) as the accurate description and characterization of the rocks. Thus thorough rock characterization is an essential precursor to the
Figure 1. Accurate reservoir description is aided by integration of data
from all disciplines. How can geophysical
data
be quantitatively
integrated in reservoir studies?
End_Page 18---------------
data
integration in our methodology. For example, minor
changes in depositional environment can result in significant
changes in mineralogy, lithology and texture which affect the
pore geometry. Diagenesis commonly operates within a framework
established at the time of deposition: thus changes in
packing and cementation (pore geometry) can relate to changes
in depositional environment. Thus knowledge of depositional
environment and diagenesis is important to reservoir characterization.
The depositional/diagenetic environment
interpretation
is then related to sequence stratigraphic concepts. An understanding
of sequence stratigraphy provides macro-scale controls
on rock distribution that can often be seen at the
seismic
scale.
Petrophysical Integration
In many reservoirs and geological provinces, it is not possible to predict, with any degree of accuracy, permeability from a knowledge of porosity alone (Figure 2). Thus prediction of sufficient hydrocarbon-charged porosity does not necessarily imply that the rock will produce. Although porosity and permeability are independent in a global sense, there exists a close relationship between porosity and permeability within rocks with specified pore geometry (Rock Type). Thus permeability varies as a function of both porosity and Rock Type in both sandstone and carbonate reservoirs (Calhoun, 1960: Davies et al., 1999). Because porosity and permeability are closely related within each Rock Type, permeability can be predicted from a knowledge of both porosity and Rock Type (Figure 2).
In our methodology, pore geometry is measured directly in small
rock samples using a scanning electron microscope that is
specially equipped for image analysis (Davies et al., 1999). This
allows for the identification of Rock Types in intervals with
conventional or sidewall cores (Figure 2). Neural Network
Models are used to identify Rock Types and to predict porosity
and permeability using only combinations of wireline log
responses. This allows for field-wide extrapolation of Rock Type-derived
data
(specifically permeability) to all non-cored intervals
and wells. Equations are developed that relate Rock Types to the
sonic and shear
data
. These relationships are used as the basis
fur integration of the petrophysical and
seismic
data
allowing for
inter-well predictions of the distribution of reservoir quality for
exploration and development projects.
The S and P impedance data
is determined using the entire
pre-stack
seismic
data
set after careful pre-processing and migration
to preserve AVO effects. This results in lithology and fluid
volumes for the entire
seismic
section. The lithology and porosity
interpreted from the
seismic
data
are then presented as a series of
cross sections or block diagrams.
Because a relationship has been developed between permeability,
porosity, Rock Type, Vp and Vs, the seismic
data
can now he
used to create permeability and Rock Type volume for the
seismic
sections (Figure 3). Because the
seismic
data
yields both porosity
Figure 2. Deep water, turbidite sandstone reservoirs, Offshore Gulf of of Mexico, differentiated on the basis of image-derived Rock Types (based on analysis of pore geometry). Note that each Rock Type has similar values of porosity but widely different values of permeability, and that the highest porosity is not associated with the highest permeability. In this and similar reservoirs, the successful predictor of permeability requires a knowledge of the pore geometry.
End_Page 19---------------
and Rock Type, permeability can be predicted in the interval(s)
of interest using the algorithms developed earlier in the workflow.
Values of permeability can be assigned to individual grid
cells on the basis of inter-well seismic
data
.
Conclusions
Inter-well and regional predictions of reservoir quality can be
based on quantitative integration of petrophysical and seismic
data
("
seismic
petrophysics"). The methodology presented here
allows
seismic
data
to be used to predict reservoir permeability
based on integration of
seismic
data
and
data
regarding the
detailed pore geometry of the reservoir intervals. This approach
is extremely useful in difficult environments (offshore, deep
water) and in field development projects.
References Cited
Davies, D. K. and R. A. Young, 2001, The benefits of integrating seismic
and petrophysical
data
, Proceedings, SW Section, AAPG, Dallas, 10-13 March, p. 1-12.
Calhoun, J.C., 1960, Fundamentals of reservoir engineering: Norman, Oklahoma, University of Oklahoma Press, 426p.
Davies, D. K., R. K. Vessell and J. B. Auman, 1999, Improved prediction of reservoir behavior through integration of quantitative geological and petrophysical data
: SPE Reservoir Evaluation and Engineering, v. 2, p.149-160.
Figure 3. Seismic
cross section showing the distribution of Rock Types. Rock Type 1, sand with high permeability (>2000md) and high porosity (>>25%). Rock Type 2, sand with moderate permeability (500-2000md) and high porosity (>25%). Rock Type 3, sand with low permeability
(<500md) and high porosity (>25%).
End_of_Record - Last_Page 21---------------