American Oil and Gas Reporter - September 2016 - 44

SpecialReport: Reservoir Optimization & Modeling
The results of the seismically-constrained geomodeling work
include lithological and reservoir property maps, as well as associated uncertainty measurements for selected facies and properties. This approach is enabling optimal identification of sweet
spots for reservoir development and well placement in the unconventional formation, and demonstrates the value of incorporating stratigraphic, geological, petrophysical, engineering and geophysical data into an integrated subsurface reservoir model.
At the current stage of reservoir science, abundant information is available from multiple disciplines. Well information, including wireline logs, core and production data, have demonstrated their value and have contributed greatly to understanding conditions at the borehole in terms of geological, reservoir and engineering properties.
While analyzing these data provides a global context to understanding the big picture of the reservoir, dense 3-D seismic
datasets provide a wealth of both quantitative and qualitative subsurface information away from wells, in the form of "elastic properties" that relate directly to observations at the wellbore.
These elastic properties consist of critical calibration information extracted from 3-D seismic data, namely compressional (P) velocity, shear (S) velocity, and density. These seismic data
link directly to the same elastic properties obtained from wireline data measured in the well bore. These two independent measurements of elastic properties, derived from different primary
sources, calibrate with each other to establish relationships between lithofacies and reservoir rock properties.
Essentially, elastic properties bridge the well-centric world with
the seismic-centric world to significantly increase the understanding of the subsurface so that more intelligent decisions can be
made. As the Powder River Basin project demonstrates, 3-D seismic data can be transformed into a far more valuable and superior calibrated petrophysical subsurface volume, which then can
be blind-tested against known well data.
FIGURE 1
Log Facies and Seismic Facies (Well A)

B

A

C

44 THE AMERICAN OIL & GAS REPORTER

Technical Background
Observations from various geoscience disciplines provide value in their own right because of their unique perspectives. However, unless all data are integrated to provide a valuable common
solution, each type of data, when analyzed and interpreted independently, may lead to different conclusions. As a result of each
discipline potentially being "blind" to the contributions of the others, an accurate mathematical tool is required to combine various sources of prior geoscience information in an unbiased and
consistent manner to achieve an improved subsurface understanding.
Bayes' theorem is a statistical tool used to manipulate conditional probabilities. Mathematically, Bayes' theorem defines the
relative weight given to prior information from different disciplines. Accordingly, data from geology, well logs, seismic and
reservoir engineering can be honored quantitatively without bias
and in a way that converges into one solution space. Using Bayes'
theorem, geostatistical inversion provides a robust method to effectively characterize a reservoir into discrete facies/properties
that exhibit a range of production capacities to allow more effective well placement.
The geostatistical inversion workflow starts with prior information on the reservoir, which typically includes some knowledge of local geology, rock physics information describing the
various rock types, and geophysical and engineering properties.
Geostatistical information is provided from well data analyses and interpretations of the geological environment, including
a stratigraphic model that defines horizons and faults interpreted from 3-D seismic data and deterministic inversion results. Probability distribution functions (PDFs) are a key element in the workflow. PDFs describe the range of values expected for lithofacies,
elastic properties, seismic noise levels, and relative lithofacies proportions.
Integrated Petrophysics Model
A three-phased approach was employed in the Powder River Basin study. The first phase required petrophysics and rock
physics modeling of wireline logs calibrated to core data. The core
facies were upscaled and tied to the wireline log facies, and the
log facies were upscaled and tied to the seismic facies. Modeling the tight sandstone from well log data yielded five unique lithology types discriminated by the seismic elastic response.
Figure 1A illustrates the log facies relative to the seismic facies
for well A. Figure 1B shows the log-derived elastic properties (the
same that may be derived from 3-D seismic data) colored by eight
log facies on the Z-axis. The eight log facies are identifiable based
on all available wireline and core data (gamma ray, neutron porosity, resistivity, etc.).
Figure 1C shows P-impedance versus the P and S velocity ratio
(Vp/Vs) colored by five seismic facies on the Z-axis. Because only
five lithofacies were separated adequately in elastic space (Pimpedance versus Vp/Vs), the original eight lithofacies were
reduced to five by merging certain lithofacies that had a great deal
of overlap in elastic space. This yielded a more meaningful result,
and facilitated a more accurate discrimination of realistically
identifiable lithofacies within the 3-D seismic volume that also
were observable in core and log data.



Table of Contents for the Digital Edition of American Oil and Gas Reporter - September 2016

Contents
American Oil and Gas Reporter - September 2016 - Cover1
American Oil and Gas Reporter - September 2016 - Cover2
American Oil and Gas Reporter - September 2016 - Contents
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American Oil and Gas Reporter - September 2016 - Cover3
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