Integrated reservoir modeling has become an important part of day-to-day
decision analysis in oil and gas management practices. A very attractive and promising
technology is the use of time-lapse or 4D seismic as an essential component in subsurface
modeling. Today, 4D seismic is enabling oil companies to optimize production and
increase recovery through monitoring fluid movements throughout the reservoir. 4D
seismic advances are also being driven by an increased need by the petroleum
engineering community to become more quantitative and accurate in our ability to
monitor reservoir processes. Qualitative interpretations of time-lapse anomalies are being
replaced by quantitative inversions of 4D seismic data to produce accurate maps of fluid
saturations, pore pressure, temperature, among others.
Within all steps involved in this subsurface modeling process, the most
demanding one is integrating the geologic model with dynamic field data, including 4Dseismic
when available. The validation of the geologic model with observed dynamic
data is accomplished through a "history matching" (HM) process typically carried out
with well-based measurements. Due to low resolution of production data, the validation
process is severely limited in its reservoir areal coverage, compromising the quality of the
model and any subsequent predictive exercise. This research will aim to provide a novel
history matching approach that can use information from high-resolution seismic data to
supplement the areally sparse production data. The proposed approach will utilize
streamline-derived sensitivities as means of relating the forward model performance with
the prior geologic model. The essential ideas underlying this approach are similar to those
used for high-frequency approximations in seismic wave propagation. In both cases, this leads to solutions that are defined along "streamlines" (fluid flow), or "rays" (seismic
wave propagation). Synthetic and field data examples will be used extensively to
demonstrate the value and contribution of this work.
Our results show that the problem of non-uniqueness in this complex history
matching problem is greatly reduced when constraints in the form of saturation maps
from spatially closely sampled seismic data are included. Further on, our methodology
can be used to quickly identify discrepancies between static and dynamic modeling.
Reducing this gap will ensure robust and reliable models leading to accurate predictions
and ultimately an optimum hydrocarbon extraction.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/85950 |
| Date | 10 October 2008 |
| Creators | Jimenez, Eduardo Antonio |
| Contributors | Datta-Gupta, Akhill |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation, text |
| Format | electronic, born digital |
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