Oil recovery in Block Shen-95 is estimated at 11.96% in 2006 based on 1998 volumetric estimate of oil originally in-place. The block is marked by low water injectivity and the inability to properly history match field performance. These are likely due to the high pour point nature of the crude oil in the block. The occurrence of paraffin precipitation at the injection well vicinity is due to cold waterflooding and regions of pressure reduction below bubble-point pressure may cause permeability reduction for this highly compartmentalized block by post-depositional faults.
As a first step, permeability has to be properly characterized towards obtaining
a reasonable and acceptable history match against performance forecast and simulation
studies of various enhanced oil recovery schemes. Permeability can be estimated
to an appreciable degree from the concept of hydraulic (flow) unit at cored
and uncored wells with the advantage of over 200 logged wells. Traditional regression
based approach results in average permeability with low data scatter compared
to core data (Wendt et al., 1986). Permeability
prediction from logs tend not to wholly honour permeability trend but is better
off with the application of discriminant analysis based on petrological variables
such as grain size (Jennings, 1999; Adams,
Hearn et al. (1984) introduced the concept of
flow unit to determine the distribution of rock types that most strongly control
fluid flow and defined a flow unit as a reservoir zone that is continuous both
laterally and vertically and has similar permeability, porosity and bedding
characteristics. In their study, stratigraphic sequence provided the initial
framework for flow unit delineation with petrophysical properties and petrographical
analysis. Flow unit has evolved from the initial conception by Hearn
et al. (1984) with the use of different parameters for delineation
which include reservoir process/delivery speed and k/Φ plotted as constant
lines on Pickett plot (Aguilera and Aguilera, 2002);
stratigraphic modified Lorenz plot (Gunter et al.,
1997); R35 (pore throat radius measured at 35% mercury saturation)
from Winlands equation with 4 basic flow units classified by Megaports
(R35>10 μm), Macroport (2-10 μm), Mesoport (0.5-2 μm)
and Microport (R35<0.5 μm) (Martin et
al., 1997) and combined delineation parameters kh/μ (transmissibility),
Φcth (storativity) and net-to-gross (Ti
et al., 1995). Other flow unit delineation parameters and models
are flow zone indicator (FZI) discussed below; a generalized approach by Lawal
and Onyekonwu (2005), integrating FZI, R35 and rb
(grain size) and statistical zonation based on permeability heterogeneity (Testerman,
1962). All these delineation methods are static based. Flow-based technique
exist but static-based are widely used. The short coming of a recent flow-based
technique (Schatz and Heinemann, 2007) is the use of
upscaled reservoir properties in a geological model. This is unlike the use
of core and well log data in static based techniques.
The flow unit discriminator parameter presented by Amaefule et al. (1993) has theoretical footing from the concept of bundle of capillary tubes considered by Kozeny and Carmen. Kozeny and Carmen both cited in Amaefule et al. (1993). This incorporated the Darcys and Poiseuilles laws for flow in a porous media and tubes and the concept of mean hydraulic unit. FZI is applied for flow unit delineation and no distinction shall be made between flow unit and Hydraulic Unit (HU), or hydraulic flow unit. Both flow unit and hydraulic unit shall be used interchangeably.
This study covers the FZI delineation concept from theory to practical application of HU delineation and prediction. Bayesian statistical method is used to predict HU at uncored wells having well-logs by considering different relationships between the well-logs. Furthermore, sensitivity analysis was carried out on different number of HU delineated categories with different combinations of well-log response discretization. The choice of optimal HU delineation and prediction scheme is verified. Verification of predicted HU in uncored wells is with respect to reservoir performance, plan view status map of HU distribution, interwell HU cross-section and perforation thickness linked to HU categories. As well as other traditional verification methods specifically for cored wells.
MATERIALS AND METHODS
HU theory: Amafule et al. (1993) presented
the method for the use of hydraulic flow units to divide rock facies as a result
of the considerable variation of permeability even in well defined rock type.
This method is based on mineralogy and texture which defines similar fluid flow
characteristics that is independent of lithofacies. Amafule
et al. (1993) used the concept of bundle of capillary tubes and gave
an equation which was re-arranged to isolate the variable that is constant within
a HU. This presented the optimal use of the equation. The re-arranged Eq.
where, k is permeability (md); Φe is effective porosity; Fs is shape factor; τ is tortuosity; and Sgv is surface area per unit grain volume (μm).
where, RQI is reservoir quality index (μm); Φz is pore volume-to-grain volume ratio and FZI is flow zone indicator (μm).
Equation 2-4 are used to compute the functions
for plotting a log-log plot based on (Eq. 5) for RQI versus
Φz. All data that have similar FZI will be on a straight line
slope equal to one and the value of FZI is determined at the intercept of the
slope at Φz=1. Thus, all data on the same straight line can
be taken to have similar pore throat attributes (the same hydraulic unit) governing
flow. Moreso, from Eq. 6 below, permeability can be computed
for all those points on the same straight line with same FZI.
FZI incorporates mineralogy and texture to efficiently discriminate rock facies having similar fluid flow attributes as demonstrated by statistical techniques. Particularly, pore geometry attributes that influence zoning was verified with petrographic data, in situ condition on RQI and capillary pressure to characterize pore throat structure.
HU classification, prediction and verification: Classification follows
the detailed methodology presented by Abbaszadeh et al.
(1996) using graphical analysis of probability and histogram plots of log
FZI and Wards hierarchical clustering method. This is verified with R35.
The traditional approach at predicting HU at uncored wells that have well-logs
relies on the Bayesian method (Abbaszadeh et al.,
1996). Other methods are Artificial Neural Network (ANN) (Aminian
et al., 2002, 2003) and the recent classification
tree concept (Perez et al., 2005). Of all the
applications of the classification tree concept to electrofacies, lithofacies
and HU prediction from well-logs, HU result was worst off. The Bayesian method
is applied to predict HU category from well-log based on established HU from
cored wells. It involves inferring the probable HU of a well at a particular
depth using well-log responses based on the HU probability database of discretized
well-log responses. The database is from the link of established HU at cored
wells to their respective well-log responses collectively. A problem of accuracy
arises as stated by Abbaszadeh et al. (1996)
on the number of bins required from the well-log discretisation exercise. It
is an issue for concern since accuracy increases with increase in number of
bins but increases computation time. Equation 7 shows the posterior
conditional probability p(HUj|x) for each HU category with respect
to a well-log response. And Eq. 8 is the corresponding HU probability
for independent multiple well-logs given a simplified expression for posterior
conditional probability. Equation 9 stands for intersection
of multiple well-logs. The application of the Bayesian technique to facies identification
similar to HU prediction in uncored wells by the study of Kapur
(1998) states a limit for increase of bins as regards to that study. Optimal
number of bins is related to the ratio of data size to number of bins. Furthermore,
the particular HU chosen is that of the highest probability. The application
of Eq. 7 and 8 are clearly stated by Abbaszadeh
et al. (1996) and Li and Anderson-Sprecher (2006).
While Eq. 9 is shown by Kapur (1998):
where, p(x|HUj) is the conditional probability of well-log response
x, for a particular HU and p(HUj) is prior probability of occurrence
of HU from core data classification. The use of Eq. 7 is by
the addition of the posterior probability computed for each well-log suggesting
mutually exclusive multiple well-logs:
where, xi represents well-log responses x1, x2,
p(HUj| x1, x2,
xn) is posterior
HU probability with respect to independent multiple well-log responses; the
other variables are similar to Eq. 7:
where, P(xi) is p(x1∩x2
.∩xn) the probability of intersection of multiple well-log responses; p(xi|HUj), p(x1∩x2
.∩xi|HUj) prior and conditional probability of intersection of multiple well-log responses for jth HUj; p(HUj|xi), p(HUj| x1∩x2
.∩xn) posterior jth HUj probability considering intersection of multiple well-log responses.
Verification is largely a qualitative process. Optimal HU definition is first
of all selected based on statistical analysis. The optimal HU involves the comparison
of core permeability and that derived from various HU definitions with different
combinations of well-log responses discretization. Secondly, the chosen definition
and discretization is applied for prediction in all uncored wells. The accuracy
of predicted HU is examined qualitatively by tying reservoir performance to
HU distribution from interwell cross-section obtained from interwell HU correlation.
This approach was initially applied by Martin et al.
(1997) as a process of linking reservoir performance to identify flow unit
quality and distribution in the reservoir. It was basically tying performance
to the individual delineated flow unit thickness at well locations (Gunter
et al., 1997). In this study, the link is tied to performance curves
with interwell HU cross-section, connectivity, perforation face HU thickness
and nearby injection well influence. Thirdly, the tie of HU to lithology.
Block Shen-95 is in Liaohe basin of the Jinganbao oil-bearing structure belt
of Damintun depression in Xinmin city, Liaoning Province, North-East China (Fig. 1). The block is faulted, anticline in nature, oil-bearing strata dipping NW,
major axis 6.8 km and minor axis 3.3 km, area of 15.8 km2 and structurally
above a naturally fractured carbonate reservoir. The block is divided into two
main sub-blocks North and South with respect to one of the major sealing faults
in the NE direction (Fig. 1). The block has a complex system of faults with
sealing, partial sealing and non sealing faults (faults are normal and growth
faults). Oil-bearing strata is of the Shahejie formation of the Paleogene period
(Eocene) within the subsurface depth of 1800-2230 m and consisting of an average
porosity of 19% and low permeability of 3 mD. Reservoir depositional environment
is fluviodeltaic (having 12 depositional sedimentary environments) with relatively
small sand body and poor connectivity due to its complex depositional structure.
Sedimentation took place when the basin was stable prior to depression. From
geological model, the reservoir has 3 zones and 17 layers which are heterogeneous
with high variation in thickness and poor horizontal connectivity. The top zone
is marked by downward fining and the 2 other zones by downward coarsening grain
size. Vertical reservoir heterogeneity from Lorenz plot is about 0.8082 from
5 cored wells within the block under study. There is no gas cap but alternating
cycles of oil-water interval and edge water with negligible pressure support.
Formation water is composed mainly of fresh water.
Oil Originally In-Place (OOIP) estimate stands at 2084x104 t by 1984 and 1440x104 t by 1998 based on volumetric estimates. Exploration of the block commenced in late 1988 and by late 2006 the block had 78 production (47 active producing wells) and 31 injection wells (8 active injection wells) with an average daily injection rate of 33 m3 day-1 and monthly instantaneous Voidage Replacement Ratio (VRR) of 1.04 compared to the peak injection rate of 125 m3 day-1 and VRR of 4.1 with 9 injection wells (7 active injection wells) by early 1991. By late 2006, watercut was 56.98% and cumulative oil production was 172.29x104 t.
Six cored well data are available for analysis (J17, J37-69, J43-67, J49-75,
J53 and J74, Fig. 1). J53 is out of the boundary under study
and cored zone is not known and has no SP well-log. The other wells have SP,
Acoustic-AC (S-Wave) and true formation resistivity (RT) well-logs except J17
with deep resistivity electrical log (RL3D). J37-69, J43-67, J49-75 and J74
are chosen for HU classification. All log data have already been depth shifted
and cored data was not filtered to honour vertical resolution with well-log.
||Cored well location with major faults indicated and block
This is to preserve reservoir heterogeneity or it may have a negative impact
on the inherent pore geometrical attributes related to FZI. Table
1 shows the distribution of core sample points in the 3 zones and available
HU classification: Probability plot of FZI for each well shows common
division into 4 HUs apart from that of J17 and J37-69 with 2-3 HUs
by visual observation with a straight line drawn through it (not shown due to
space restriction). To encompass the reservoir heterogeneity from the cored
wells, J37-69, J43-67, J49-75 and J74 were combined for classification to enable
reasonable HU prediction from well-logs. Probability plot from this indicated
4 visually distinguishable HU categories (Fig. 2) and histogram
plot of FZI and logarithm of FZI showed superposition of more than 4 log-normal
and normal distributions. However, with Wards hierarchical cluster analysis
on SPSS® optimal number of clusters were selected (5, 6 and 9
HU categories identified as HU5, HU6 and HU9, Fig. 2). The
selection process took into account the distribution of sample points to reasonably
prevent the domineering or overshadowing of larger data set of a particular
HU over another. RQI vs Φz were plotted for the 3 HU definitions
(HU5, HU6 and HU9) and for each HU category in the definition straight lines
with slope 450 were drawn to Φz = 1 at the mean FZI
for that category (Fig. 3). The earlier plot coupled with
permeability (k) vs porosity (Φ) (Fig. 4) for each HU
definition illustrates the narrowing of data scatter in each HU category with
the increase of HU categories from 5 to 9 and the improvement of coefficient
of correlation for kHU vs. kcore (HU5 = 0.9590, HU6 =
0.9679 and HU9 = 0.9856) especially for high permeability values. Table
2-4 are summary of core sample point distribution in each
HU category for each HU definition. Also, the superposition of pore throat radius
(R35) with Winlands equation on the classical division into
4 flow units shows encouraging results of the clustering process (Fig.
HU prediction: Table 3 shows the Spearmans rank
correlation of 5 cored wells within the boundary of interest with J17 least
appreciable compared to others. Both AC and SP logs are quite low.
||Well core data and logs
|*RL3D deep resistivity electrical log
||HU classification for HU5, HU6 and HU9
|| HU delineation for HU5, HU6 and HU9
||k-Φ delineation and relationship plot with R35
SP and RT logs are used. Firstly, SP was linearly normalized to give shale volume
(Vsh) as well-log response range, minimum and maximum values vary considerably.
RT and Vsh are used for HU inference.
A database consisting of probability of occurrence for each HU category corresponding
to well-log response discretized data range is assigned to all HU definitions
of HU5, HU6 and HU9.
||HU5 classification data distribution
|| HU6 classification data distribution
|| HU9 classification data distribution
This depends on the use of either intersection of multiple
well-logs, independent or mutually exclusive multiple well-logs. Different combinations
of Vsh and RT number of bins were used. The numbers of bins are RT-A, 6; RT-B,
8; Vsh-A, 3 and Vsh-B, 5. For RT, bin size was based on multiple of 2 and then
on logarithm of RT since it is not normally distributed and by observing the
distribution of the logarithm of RT for all wells collectively.
Predicting HU for each HU definition depends on the combination of well-log
data discretization based on 1 (Vsh-A and RT-A), 2 (Vsh-A and RT-B) and 3 (Vsh-B
and RT-B) for constructing a probability database for HU inference. In other
words, HU prediction was carried out on HU5-1 (Vsh-A and RT-A), HU5-2 (Vsh-A
and RT-B), HU5-3 (Vsh-B and RT-B); HU6-1 (Vsh-A and RT-A), HU6-2 (Vsh-A and
RT-B), HU6-3 (Vsh-B and RT-B) and HU9-1 (Vsh-A and RT-A), HU9-2 (Vsh-A and RT-B)
HU9-3 (Vsh-B and RT-B). The outcome of HU prediction leading to k prediction
for some wells and application to predict HU and k for well J17 (not included
in combined cluster analysis and probability database build-up) and the corresponding
k prediction from k-Φ relationship based on each sequence stratigraphy
zone is presented in Fig. 5 and 6 for correlation of predicted
permeability to core permeability. Figure 5 represents the
mutually exclusive concept (from each well-log by Eq. 7) and
Fig. 6 that of intersection of multiple well-logs by Eq. 9.
Comparing Fig. 5 and 6 give a glaring preference for the mutually
exclusive multiple well-logs over that of the intersection of multiple well-logs
consideration. Optimal k prediction is obtained with HU5-1 and HU6-3 having
the latter as the best combined k prediction. HU histogram for both is shown
in Fig. 7. Selection of the best HU definition and combination of well-log responses
discretization scheme with the aid of Fig. 5 is highly subjective. The choice
of HU definition can be narrowed down to a few but not essentially the best.
A good HU prediction fit from HU histogram plot should be a spread of data frequency
narrowed to the main diagonal which for the plots shown may be reasonable and
The major distribution of the frequency plot is within the vicinity of HU category
with high frequency as shown in Table 1-3.
HU histogram plot is rather a qualitative means approach.
Correlation between predicted and core permeability for different
HU inference combination (mutually exclusive multiple well-logs case)
Correlation between predicted and core permeability for different
HU inference combination (intersection of multiple well-logs case)
Statistical HU Comparison for cored wells J37-69 and J43-67
including well J17 by HU5-1 and HU6-3. (a) HU5-1: J37-69, (b) HU5-1: J43-67,
(c) HU5-1: J17, (d) HU6-3: J43-67, (e) HU6-3: J37-69 and (f) HU6-3: J17
6 shows the HU histogram for individual wells with HU5-1 and HU6-3. And Fig. 8-10 shows HU prediction, k prediction
and correlation plots for some wells including J17 not included in the combined
HU classification and probability database construction. Prediction of k for
J17 is reasonable considering that it was not included in HU classification
and inference build-up, but exhibited less number of HU categories from FZI
probability plot discrimination analysis.
On observation of the statistical HU comparison plot for cored wells and Table
2-4 one can draw a conclusion. The HU distribution (Fig.
7) depends on the core sample point distribution in each HU category for
each HU definition as seen in Table 2-4.
Also, correlation between predicted k and core k which depicts predicted HU
accuracy is hinged on the degree of correlation between well-log responses and
either FZI or k. Wells with high correlation coefficient between well-log responses
and FZI showed better prediction power as shown in Table 5
and Fig. 5. HU5-1 may be a better predictor from the cluster
of histogram to the main diagonal shown in Fig. 7 compared
to HU6-3. So, HU5-1 is chosen. Further analysis, prediction and verification
are therefore based on this HU definition.
||HU and permeability prediction Well J43-67 by HU5-1 and HU6-3.
(a) HU5-1: J43-67, (b) HU5-1: J43-67, (c) HU6-3: J43-67 and (d) HU6-3: J43-67
HU and permeability prediction Well J37-69 by HU5-1 and HU6-3.
(a) HU5-1: J37-69, (b) HU5-1: J37-69, (c) HU6-3: J37-69 and (d) HU6-3: J37-69
|| HU and k prediction Well J17 by HU5-1. (a) HU5-1: J17 and
(b) HU5-1: J17
The choice of HU definition was based on the predictive power of permeability on cored wells. However, the verification of predicted HU at uncored wells having well-logs and further validation at cored wells relies on the link to dynamic and static data. The use of dynamic data of reservoir performance curves is linked to the perforation thickness in each HU category and interwell HU cross-section. This also takes into cognizance injection well influence based on connectivity to production well. It generally accounts for HU connectivity and distribution. Static based comparison is linked to lithology. This is applicable to only cored wells.
HU is computed for all logged wells numbering over 100 from the probability
database for HU inference. In 15 interwell cross-sections covering the entire
block, 100 wells were used for HU correlation using the depositional layer structure
as a framework which was not entirely honored. A structural model based on this
correlation was built consisting of 51 layers. This model serves as the HU model
Dynamic HU verification: For the purpose of dynamic HU verification
12 wells were used to correlate reservoir performance to HU. These wells are
grouped into H (high productive wells), M (medium productive wells) and L (low
productive wells) by qualitative analysis. Classification was basically on initial
production rate, cumulative oil production and watercut. This was tied to perforation
thickness in each HU category, HU distribution, thickness and connectivity from
interwell cross-sections (Fig. 12-14).
The classification into H (4 wells), M (4 wells) and L (4 wells) were clearly
discernable except that of 2 wells. All apart from 1 well classified into H
has relatively high perforation thickness in the highest quality HU that is
||Spearmans rank correlation well log responses with FZI
|*RL3D (deep resistivity)
||Fence diagram HU model
||(a-d) Link of reservoir performance to perforation thickness
for each HU category and HU distribution for high production wells
||(a-d) Link of reservoir performance to perforation thickness
for each HU category and HU distribution for medium production wells
||(a-d) Link of reservoir performance to perforation thickness
for each HU category and HU distribution for low production wells
This was due to high connectivity with respect to 3 nearby injection
wells. The well in question, J37-57 has no HU #1. This is a good account for
verifying prediction, in particular, the influence of interwell connectivity.
Classification of wells J45-67 and J46-68 into M does not justify the perforation
thickness in each HU category and also HU distribution. However, for well J45-67
with respect to HU distribution, cumulative production and perforation distribution
this well may be classified into H. The isolation of this well by the partially
sealing faults may account for the low but steady production trend giving rise
to a possibly acceptable HU prediction. For J46-68, both HU distribution from
cross section and perforation thickness at various HU categories can not explain
the inability to tie these to performance. In this case, predicted HU may be
Static HU verification: This seeks to ground truth HU with available lithology from core. The well considered show high correlation to clay, silty clay and silt, specifically with the lowest quality (HU #5) and silt to a lesser degree with HU #3. HU#1 and #2 are rather correlated to gravel and fine sand and to a lesser extent with very fine sand. Considering well J74 shown in Fig. 15, it has significant correlation to lithology. However, HU does not entirely correspond to lithology but the flow characteristic of lithology. In addition, shown in Fig. 15 is the integration of R20 with a good link to HU. R20 is obtained from data local to the block. R20, pore throat radius at 20% mercury saturation was the best fit to 35 sample points from two wells with a correlation of 0.948.
Figure 16 gives a summary of the workflow from HU classification
to verification as applied in this study.
There is a dearth on the discussion of the Bayesian approach to multiple well-logs
and its simplified applications on HU. Most references limit presentation to
a well-log without further explanation on application to multiple well-logs.
HU inference in this study was largely done by addition of HU probability from
individual well-log responses for similar HU category at the same depth. This
connotes that HU probability estimation is independent of each well-log as in
Tang and White (2008) application to facies, but mutually
||Link of HU to lithology for well J74; Track 1 -HU, Track 2
-Lithology, Track 3 -R20
Therefore, each well-log can determine HU at a particular depth irrespective
of the fact that HU probability from other well-log might be zero or negligible
towards contributing to the sum of probability for a particular HU category.
Using the additive rule is a simplified approach. However, in comparison to
the intersection of multiple well-logs at the same depth, the simple additive
rule was slightly better off.
||Summary of HU classification, prediction and verification
This is based on permeability prediction. Also,
independent multiple well-logs gave higher HU prediction accuracy but with the
inability to predict 2 HU categories out of the 5 HU categories from the chosen
HU5 definition. Both simplification schemes clearly depicts the success of independent
multiple well-logs as applied by Tang and White (2008) and Li and Anderson-Sprecher (2006) to facies prediction.
The increase in the number of bins of well log discretization yields better
overall correlation until a certain limit where further increase as in the case
of HU6-3 inference gives an overall poor result from predicted permeability
correlation with core derived permeability. This is in line with the study of
Kapur (1998) for facies prediction by the Bayesian method
and the statement by Abbaszadeh et al. (1996).
However, the increase in number of bins resulted in remarkable improvement of
accuracy for certain HU categories. In particular, that of the highest quality
HU category compared to reduction in accuracy for the low quality categories.
This may be related to the poor well-log responses correlation to FZI and/or
permeability. There is a drift towards the non-discretization of well-log responses
with the independent multiple well-logs concept. The application results in
successful facies classification by the Bayesian technique in fitting prior
probability with probability distribution function (Beta and Gaussian distribution
function and Kernel density estimation, Tang and White (2008)
and Li and Anderson-Sprecher (2006)).
There remains, the non explanation of the failure to delineate HU #4 with well-logs during HU prediction for the chosen HU definition, HU5-1. The choice of HU5-1 definition over that of same number of categories (HU5-2 and HU5-3) and definitions with 6 and 9 HU categories (HU6-1, HU6-2, HU6-3; HU9-1, HU9-2 and HU9-3) is based on coefficient of determination of core permeability to permeability computed from the respective definitions.
The reasonable link of reservoir performance with HU distribution depicts the
relationship of the integration of reservoir performance to quality of flow
unit and its distribution. It attests to tying the spatial distribution of data
patterns of flow based attributes to similar performance regions by Martins
et al. (1997). As such, this significantly serves as a reasonable
means of qualitative HU prediction accuracy in uncored wells before the tedious
reservoir simulation process. From this point, it can be said that HU prediction
is relatively acceptable. However, for lithology, it was not entirely successful.
HU #5, the lowest quality hydraulic unit showed the best match with lithology
(clay, silty clay and silt). Not surprising, Amaefule et al. (1993) had
concluded that multiple flow unit exists within a particular rock type (lithofacies)
as pore geometry attributes can efficiently characterize HU. Ebanks
(1987) likewise stated that flow unit does not always coincide with lithofacies,
while Biniwale and Benhrenbruch (2004) stated that depositional
trends and diagenesis are the major factors that control HU quality.
The R20 correlation was suitable for computing pore throat radius using core permeability and porosity for verifying predicted HU. But, its use for verifying the classification of HU based on Wards hierarchical cluster analysis was not successful. This was as a result of the capillary data set not encompassing wide variability of reservoir heterogeneity. The situation is due to the low correlation of porosity to pore throat radius. Hence, Winlands R35 equation was used. It is widely accepted as it contains large data set of varying sandstone particle size. The geological time of the sandstone is within the same range as that of Block Shen-95 making it suitable for use.
The basic verification of the strength of flow unit delineation in uncored
wells is by simulation study, examples of case studies are Ti et al.
(1995), Gunter et al. (1997) and Svirsky
et al. (2004). This is time consuming. Svirsky et al. geostatistically
populated HU, stochastically across a reservoir and selected 3 realizations
based on P10, P50 and P90% recovery from uncertainty
analysis using streamline simulation. It may not be a true representation of
HU distribution across the reservoir and likewise it is time consuming. Interwell
HU correlation, though subjective as applied in this study seems a better approach
to populating the reservoir with further assessment of established HU by well-logs
and facies or depositional environment structural model.
Hydraulic Unit (HU) was delineated, predicted from well logs and validated
based on Kozeny-Carmen equation, Bayesian statistical method, reservoir performance,
petrophysical properties and lithology. The outcomes are:
||The mutually exclusive probability approach proved better
than the intersection of multiple well-log responses approach for prediction
of HU by the Bayesian technique. Whereas, independent multiple well-log
responses had better overall HU category prediction though failed to predict
2 HU categories out of the 5 HU categories
||HU prediction in uncored wells with the aid of well logs is
to a larger extent an art based on finding the optimal well-log responses
discretization for HU inference and the number of HU categories with its
corresponding core sample point distribution
||Permeability estimation may be considered satisfactory by
HU with respect to high reservoir heterogeneity, number of cored wells available
and poor well-log responses correlation to permeability and flow zone indicator
||The integration of reservoir performance and HU distribution
at both well locations and interwell cross-sections showed reasonable and
acceptable qualitative match for the wells considered. This is a means of
verifying predicted HU at both cored wells and uncored wells having well-log
data. The HU distribution was made possible by interwell HU correlation
using depositional cycles as a framework
||The validation of HU with lithology was reasonable for high
quality HU and had high accuracy for the lowest quality HU which corresponded
to R20, pore throat radius at 20% cumulative mercury saturation
The researchers thank Liaohe Oilfield Company for the funding of this research.