Oil & Natural Gas Projects
Exploration and Production Technologies
Mapping of Reservoir Properties and Facies Through Integration of Static and Dynamic
This project was selected in response to DOE's Oil Exploration and Production
solicitation DE-PS26-01NT41048 (focus area: Critical Upstream Advanced Diagnostics
and Imaging Technology). The goal of the solicitation is to continue critical
cross-cutting, interdisciplinary research to develop innovative technologies
for imaging and quantifying reservoir rock and fluid properties for improved
The principal goal was to develop algorithms for estimating reservoir properties
(permeability and porosity fields and boundaries between geologic facies) by
automatic history-matching of production data.
University of Tulsa -
University of Oklahoma -
Researchers developed an adjoint method for computing the sensitivity of production
data and time-lapse seismic data and coupled it with a limited-memory Broyden-Fletcher-Goldfarb-Shanno
optimization algorithm. The combination of these two procedures resulted in
an efficient, automatic history-matching algorithm for constructing reservoir
models consistent with both static and dynamic data.
The technology developed makes large-scale automatic history-matching feasible
and provides the opportunity to obtain a more accurate reservoir map of reservoir
properties by tracking fluid movement and quantifying fluid distributions. Knowledge
of the distribution of reservoir properties and fluids is critical for developing
a reservoir management plan to optimize oil recovery. Five Ph.D. students completed
the research requirements for their degrees by working on the project. All five
are now employed with oil industry firms, where they will be able to employ
novel technology to improve reservoir management.
Despite long-standing interest in the oil and gas industry, automatic history-matching
seldom has been used in practice due to a lack of computational efficiency.
Manual history-matching has been the norm. Consequently, reservoir engineers
often have made changes to reservoir simulation models that resulted in a history-matched
reservoir model inconsistent with static data and geologic information and interpretation.
As a result, the model obtained by history-matching often gave unreliable predictions
of future reservoir performance. When implemented in commercial software, the
algorithms and technology developed in this research project will provide a
platform for data integration with the history-matching process in order to
obtain a history-matched reservoir model that is consistent with all data and
information. This will result in better predictions of future production and
quantification of uncertainty in predicted reservoir performance and thus lead
to improvements in reservoir management.
- Developed automatic history-matching algorithms for large-scale problems.
- Demonstrated the feasibility of integrating seismic data into the history-matching
process to obtain improved reservoir models.
- Developed an adjoint method for computing sensitivity coefficients for
three-phase flow problems for use in automatic history-matching of production
data and time-lapse seismic data.
- Implemented a quasi-Newton's method for automatic history-matching and
demonstrated its applicability for large-scale history-matching problems.
- Developed a procedure for estimating relative permeabilities simultaneously
with absolute permeability and porosity fields by the automatic history-matching
of production data.
- Developed a new pluri-Gaussian model for generating facies maps and demonstrated
that it may be possible to adjust the location of boundaries between facies
by history-matching production data.
Geologic models containing millions of cells and reservoir simulation models
containing over several tens of thousands of gridblocks are common in today's
environment. Yet prior to this project, no technology existed for adjusting
all grid block permeabilites and porosities to obtain a model history-matched
to production and seismic data while honoring static data. This project developed
robust automatic history-matching algorithms for such large-scale problems and
demonstrated their applicability on field and pseudo-field examples containing
tens of thousands of grid blocks. The algorithms can be used to history-match
both production and seismic data. The other milestones pertain to specific problems
that needed to be overcome in order to attain automatic history-matching for
To integrate seismic data into reservoir description, researchers implemented
rock physics models in code to compute acoustic impedance from pressure and
saturation distributions and rock and fluid properties. Then the adjoint method
was implemented to compute the sensitivities of time-lapse seismic data (change
in acoustic impedance) to rock-property fields so that reservoir models can
be adjusted by history-matching time-lapse seismic data. The project demonstrated
that history-matching both production and time-lapse seismic data resulted in
a more accurate reservoir description than can be obtained by history-matching
only production data. Moreover, a more accurate description of the distribution
of reservoir fluids is obtained, which provides a means to monitor water and
gas injection projects.
One of two key steps for developing computationally feasible automatic history-matching
algorithms was the development and implementation of an adjoint method for computing
the sensitivity of production and time-lapse seismic data to rock property fields
specifically for multiphase flow problems. This method enables the gradient
of the objective function minimized to secure a history-matched model to be
obtained in less work than is required for one forward reservoir simulation
Lab relative-permeability curves provide an initial estimate of relative permeability
curves but may not be applicable for reservoir simulation models because of
the large grid blocks. Using power law models, researchers demonstrated that
it is possible to estimate relative permeability curves as well as the gridblock
porosities and absolute permeabilities by automatic history-matching
The variation of permeability and porosity within a specific facies is often
much less than the variability between facies. Thus it is important to estimate
facies boundaries during the history-matching process. The project developed
a novel pluri-Gaussian model that allows one to model the distribution of facies
and to implement a procedure for generating sensitivity coefficients so that
the location of boundaries between facies can be estimated by the automatic
history-matching of production data.