American Oil and Gas Reporter - October 2020 - 46

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FracFocus: Monitoring Insights
Data mining the high-resolution, highfidelity geochemical fingerprint data from
a rock baseline allows a group of reservoir
characterization indices (RCI) to be developed, including a reservoir quality
index (RQI) and an oil-in-place index
(OIPI). Different from other rock-based
core analysis, RCI provides an independent dataset directly from the oil extracted from rock samples that is correlated with independent petrophysical
data. Integrating the dynamic reservoir
monitoring data with static RCI data
helps identify well spacing, lateral landing
zones and other key factors controlling
unconventional well performance, and
significantly improves the operator's ability to predict performance of future development strategies.
In a Midland Basin study, the technology was used to analyze produced oil
samples collected from both legacy and
infill horizontal wells. Fingerprints consisting of more than 2,000 compounds
resolved from each produced oil sample
provided new insights into well communication through time, as well as quantitative vertical drainage variations against
a vertical profile established using core/cutting samples. Specifically, RCI generated
from the workflow identified similar zonal
contributions in wells landed in four different intervals, revealed distinct differences in drainage geometries and overlapping drainage geometries in the four
landing zones, and discerned changes in
parent well drainage geometry profiles
over time.
Distinctive Patterns
Recognizing the distinctive patterns
of different oils is central to geochemical
fingerprinting technology. Technically,
all oils contain largely the same hydrocarbon compounds. The difference is in
the relative abundance (pattern) of those
compounds. Particularly in unconventional
reservoirs, geochemical fingerprints tend
to differ only in fine detail, requiring analytical data of extremely high precision
and accuracy to recognize the subtle distinguishing patterns.
46 THE AMERICAN OIL & GAS REPORTER

One of the main applications of geochemical fingerprinting in reservoir monitoring is allocating production in hydraulically fractured wells with multiple
stratigraphic zones each contributing to
the total oil volume. The process requires
establishing a baseline (end member) for
each potentially contributing formation,
identifying unique geochemical fingerprints for each formation/layer, and building a numerical model based on those
distinguishing patterns to calculate each
source formation's allocation.
In a conventional reservoir, the baseline
is established by collecting oil samples
from single-zone intervals. However, because horizontal wells drilled at relatively
tight vertical and lateral spacing in stacked
pays must be hydraulically stimulated,
the produced oil contributions from each
zone and layer penetrated by the created
fractures at each stage along the lateral
are commingled.
Two specific technical advancements
enable geochemical fingerprinting as an
unconventional reservoir monitoring tool.
First is a patented physical extraction
laboratory workflow that addresses fractionation between oil extracted using solvents from core and produced oil. This is
particularly important in unconventional
reservoirs, where the typical recovery
factor is less than 10%. Solvents can
extract 100% of the mobile and immobile
organic matter from the rock, consequently
leading to a misrepresented baseline for
production allocation.
Second is improved vertical resolution
to a stratigraphic interval of 10-20 feet
or less using multidimensional gas chromatography (GCXGC) to provide the
fine-scale pattern recognition and analysis
to distinguish between the fine layers of
rock in stacked unconventional reservoirs.
GCXGC routinely resolves up to 3,000
compounds from whole oil samples, compared with about 100 compounds for traditional gas chromatography.
GCXGC data from cuttings and oil
samples is processed using a specialized
software package and three-step workflow:
· Data preprocessing, including base-

line removal, noise attenuation, 3-D "blob"
detection, and aligning chromatograms
of all cutting and oil samples to identify
common blobs;
· Data screening using duplicate samples and pattern recognition to identify
unique geochemical patterns of each fine
stratigraphic interval; and
· Model development and production
allocation calculations, with the preprocessed geochemical data from step one
used to develop the reservoir characterization indices, and results from steps two
and three used for production monitoring.
Reservoir Characterization
A group of RCIs are extracted from
the geochemical information data mined
from the cuttings samples. As noted, the
indices are designed to be a fast and independent reservoir characterization tool
to delineate key properties for lateral
well landing decisions and serve as the
baseline for production allocation using
only geochemical data of oils extracted
from cuttings samples.
Despite its usefulness, the RCI method
has limitations and is not intended to replace traditional core analysis. Because
of how they are calculated, the indices
are unitless and relative (i.e., only comparable within the same set of samples).
RCI calculations also are impacted by
sample size, preservation methods, age
and other conditions. RCI does not provide
absolute values of reservoir properties.
For instance, RQI indicates the presence
of higher-permeability rock, but does not
provide an absolute permeability value
for use in reservoir models.
There are several RCI types, including
fluid mobility and water saturation, but
the oil-in-place and reservoir quality indices proved particularly robust and repeatable in the Midland Basin project.
OIPI is an indicator of oil saturation and
represents the producible oil. RQI is an
indicator of reservoir permeability. Data
screening and pattern recognition generated a family of clean geochemical fingerprints collected from cutting samples
to generate the baseline profile with

American Oil and Gas Reporter - October 2020

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