Geology Reference
In-Depth Information
mite and anhydrite, and reveal porosity (Longman
1981). The predominance of specific pore-types can
be derived from the velocity-deviation log calculated
by combining the sonic log with neutron-porosity or
density logs (Anselmetti and Eberli 1999). Facies dif-
ferentiation requires the integration of different logs.
Possible relationships between log response and car-
bonate rock type are evaluated by
cross-plots
(Pickett
1977). This method can only be used if cores or cut-
tings from selected wells are available and log responses
from control wells can be checked resulting in signifi-
cant rock type clusters. A log which is not yet run in
many wells is the Nuclear Magnetic Resonance (NMR)
log. It is particularly suitable for carbonate reservoirs
and allows analysis of porosity, permeability and pore
type distribution at the same time.
Cuttings.
Thin-section cuttings provide a low-cost
source of subsurface data that can be used to generate
high-resolution sequence stratigraphic frameworks for
wells that lack cores or high-quality wireline logs
(Coffey and Read 2002). Reservoir predictions in wells
with limited core coverage can be undertaken by inte-
grating semi-quantitative microfacies data from cut-
tings with core data and log analysis data. This allows
us to understand the log response in terms of porosity
and microfacies and to reconstruct the rock succession
from uncored parts of the wells. The method is rel-
evant if you have to interpret old or incomplete well
data or if log responses need to be understood in terms
of pores types and complex lithologies. This method
can also be applied to identify exploration targets in
areas based on cuttings and log data alone.
Cuttings should be sufficiently large (>5 mm) in
order to show compositional variability. An important
requirement for interpreting cuttings is information on
the cutting sampling interval and the rate-penetration.
Microfacies techniques are applied to well cuttings
after they have been calibrated against core data
(Rodriguez Schelotto and Carozzi 1981) and after hav-
ing considered vertical variations in qualitative and
quantitative changes in lithotypes (Koch 1991). Chan-
ges are expressed by shifts in percentages of microfacies
types. Mixed microfacies types and lithologies can re-
sult either from (1) actual lithologic variations or inter-
bedding within the cutting sample interval, or (2) down-
hole mixing. Assessing these possibilities requires com-
paring the lithologic variations in cuttings with the
variations known from cores or outcrops.
17.1.4.3 Cores and Cuttings
Lithofacies identification from logs is frequently diffi-
cult in shallow-water carbonate reservoirs and requires
the investigation of cores and cuttings. Unlike log data
which are plotted on a scale with meter thick marks,
core data are plotted on centimeter-scales.
Core analysis
aims at differentiating lithofacies
types and recognizing reservoir compartmentalization.
These types are defined on the basis of lithological and
textural criteria. Some authors group the lithofacies
types according to their position within depositional
models, differentiating e.g. a phylloid core facies, a
mound-cap facies and a mound-flank facies. Cores are
studied by visual inspection that may reveal fracture
and faults as well as bedding structures, or in thin sec-
tions. The descriptions usually follow the Shell system
(Swanson 1981) and should consider lithology, rock
color, depositional and diagenetic fabrics, and compo-
sition and textures. Many of these criteria are exhib-
ited by microfacies data (see Sect. 17.1.5.2). Rock clas-
sifications used in reservoir studies are those of Dun-
ham (1962; Fig. 8.5), Embry and Klovan (1972; Sect.
8.3.2), and Choquette and Pray (1970; Fig. 7.5).
17.1.4.4 ReservoirRelated Outcrop Analog
Studies
Outcrop studies provide compositional, geometrical and
dimensional data for carbonate bodies and basic data
for 3D modeling (Eggendorf et al. 1999). Common
methods used in reservoir-related outcrop studies in-
clude measuring stratigraphic sections, determining se-
quence stratigraphic boundaries, sampling and study-
ing of thin sections with regard to microfacies, and
petrophysical measurements conducted on core plugs
drilled in the field or from hand specimens to deter-
mine porosity and permeability (Doherty et al. 2002).
The use of outcrop data in comparing with subsur-
face data and modeling requires digitalization of out-
crop data. Questions that are touched upon by outcrop
analogs are:
•
Large-scale geometry
. In which way does the seis-
mic pattern reflect the actual geometries, e.g. of cyclic
carbonates, reefs, drowned platforms or slopes (Schlager
Note that the classification of mud-supported rocks
(mudstone, wackestone) and grain-supported rocks
(packstone, grainstone) is not necessarily congruent
with low and high porosities! Packstones should be
differentiated into samples whose intergranular areas
are completely filled with mud (exhibiting petro-
physical data similar to wackestones or mudstones),
and samples whose intergranular spaces are incom-
pletely filled (similar in petrophysical data to grain-
stone), see Kerans et al. (1994).
