Summary
Compared to clastic reservoirs, volcanic reservoirs exhibit higher
heterogeneity. Lithological facies type is one of the most important indicators
of favorable volcanic reservoirs. Traditionally, facies are identified by core
observation or log classification. However, spatial-distribution
characteristics and geological conceptual models, which are important in the
early stages of exploration, are seldom incorporated quantitatively in facies
prediction. Based on previous work, a new method has been developed to
incorporate volcanic spatial information with limited well data (three wells)
to improve facies prediction. This method was applied to a volcanic clastic
reservoir of the Cretaceous Yingchen member of the Xinshan fault depression,
northeastern China. For better well control, an artificial neural network
(ANN), a beta-Bayesian method (BBM), and a discriminant analysis (DA)
algorithm, were used to predict log-based facies. Confidence analysis was
applied to evaluate the log facies prediction. Analysis of variance (ANOVA)
verifies that the overall prediction accuracy is above 82%.
Indicator kriging was used to estimate the conditional probabilities of
facies occurrence given residual thickness. This is based on the assumption
that the residual thickness of the volcanic formation is controlled by distance
from the eruption center, a major factor defining the geological facies. The
geological conceptual models (areal sedimentary facies maps and diagenetic
facies maps) were converted into the conditional probability of facies
occurrence in given geological settings using multinomial logistic regression.
These conditional probabilities were combined with well-log facies data within
a Bayesian framework. Three favorable reservoirs were predicted based on the
method above, and the predictions were proved by the subsequent drilling.
Introduction
Volcanic reservoir quality is controlled by both lithofacies and diagenetic
effects. Traditionally, these effects are qualified by core observation and
well-log (especially image-log) interpretation. The spatial distribution of
reservoirs is characterized by high-resolution seismic interpretation. Even
though the importance of these features is well known among the geological
community, it is difficult to quantify and integrate these data into reservoir
modeling and flow simulation.
The area of study is in the early Cretaceous (Yingchen) volcanic formation,
Xinshan fault depression, Songliao basin, northeastern China. Several factors
have made the traditional approach less practical. First, the available well
data are limited (three wells were drilled in this area). Second, the volcanic
formation is deeply buried (3000 to 6000 m) in the Xinshan area. It has a high
seismic amplitude contrast with bounding sedimentary formations but low
intralayer reflection. Seismic properties appear homogeneous for most volcanic
formations (Zhao 1999); furthermore, compared to a clastic reservoir, the
spatial distribution of volcanic reservoirs is less continuous. Reservoir
quality is highly heterogeneous because of complex lithology, facies and
diagenetic overprints. Detailed characterization is, thus, more difficult to
conduct.
There are two major challenges: (1) to quantify conceptual geological models
and integrate them with other types of data (e.g., seismic and log data) and
(2) to accurately identify volcanic lithofacies with limited well-log data.
To incorporate diverse data into lithofacies prediction, reservoir-quality
assessment, and uncertainty reduction, two types of methods are frequently
used: statistical (geostatistics) methods and ANN methods. Geostatistics
methods such as cokriging or indicator cokriging (Goovaerts 1998; Deutsch and
Journel 1998; Yarus and Chambers 1994) are versatile and widely used. However,
these methods require cross-variogram models for all indicators, which are
tedious and time-consuming to construct. In addition, this approach does not
guarantee better results (Deutsch and Journel 1998). Alternatively,
object-based simulation, such as Boolean simulation (Yarus and Chambers 1994),
can be used to integrate geological models into reservoir modeling and
reproduce the exact geometry of the facies model. It requires good geometric
parameters, which can be estimated from outcrop analogs or detailed seismic
interpretation. However, those parameters are seldom available and are less
representative in this fault-complicated area. ANN methods are powerful for
integrating high-dimensional data and expressing complex, nonlinear
relationships between input and output. Several successful applications using
neural networks for data incorporation and facies prediction have been reported
(Wong et al. 1995; Siripitayananon et al. 2001; Bhatt and Helle 2002). However,
the computation cost of ANNs is high, and they do not provide any estimate of
uncertainties.
© 2006. Society of Petroleum Engineers
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History
- Original manuscript received:
4 June 2004
- Revised manuscript received:
4 April 2006
- Manuscript approved:
26 July 2006
- Version of record:
20 October 2006
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