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SocietycdPetroleumEngineers
SPE 36604
Completion Ranking Using Production Heterogeneity Indexing
R.D. Reese,
Copyright 1M,
Schlumberger
Soaey
GeoQuest
of Petrolaum Engineers, Inc
heferogeneify indexing may be used for this purpose. It
involves the computer analysis of historical production data
which is then integrated with available completion and rock
data to develop performance ranking criteria and assessment
tools.
Thm paper was prepared for presmlallon at the 1SS6 SPE Annual Techn!cal COnferOnCe Ond
Exhlbmon held m Denver, Colorado, U S A, G9 OctcbW 1996
Th!s paper was selected for presenla!!on by an SPE Program Comm!t!ee following rewaw of
tnlormabon conlamed m an abstract submmed by the author(s) Contenls of the paper, as
presented, have not been revmwed by the Soc,ely of Petroleum Engfneers and are subpct to
corractmn by the author(s). The Malerlal, as presented, does not necassanly reflect any
position of the SJXIety of Pelroleum Enginaers, !1sofkars, or members Pap+rs presented al
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Permission to copy IS resbclad to an nbstract 01 not mofe than 300
words. Illuslratm% may not b+ coped.
The abstract should contain conspicuous
aclcnolvmdgment of tiere and by Mom the paper was presented Write Llbranan, SPE, PO.
SOX 833s36 Richardson. TX 750S3.383E. US A., fax 01.214-952-9435
Production Heterogeneity
Indexes
Anomalies are observable in oil and gas well production data
that are attributable to one or more of the following factors:
Abstract
This paper describes an analysis method for production data
referred to as heferogeneiry indexing which quantifies well
of assessing
performance
anomalies
for the purpose
completion efficiency.
It has been proven useful for
determining the most successful completion practices in a
given area and formation as well as a surveillance tool for
primary and secondary recovery operations.
Time well is on production
Reservoir pressure
Completion method and efficiency
Reservoir quality and tank size
With proper analysis methods, the effect of the first two
factors can be normalized out so that assessment of completion
efficiency and/or reservoir quality becomes possible. This
assessment is accomplished with heterogeneity indexing which
may be generally defined as foJ1ows:
aided
normalization
methods,
computer
Utilizing
heterogeneity signatures and indexes may be extracted from
time-based production data. The paper illustrates how to
calculate these indexes, integrate this information with
petrophysical and completion data, and then interpret it for the
purpose of assessing the effectiveness of various types of
completion methods. In developing
this method, fluid
types of reservoir
modeling
of various
simulation
heterogeneities were used to develop a series of type curves.
The signatures in these type curves exhibit characteristics
which have been shown to relate to reservoir properties and
completion efficiency.
Equation I
where HI ~,,ud is the heterogeneity index for any type of fluid
production ratio. Ffuid may be oil, gas, barrels of oil
equivalent, total liquid, gas-oil ratio or water cut and may
consist of either “rate” or “cumulative” numbers.
Introduction
When subjected to appropriate analysis methods, oil and gas
production da[a exhibit well performance anomalies useful to
the assessment of completion efficiency. This information can
be used to rank the effectiveness of different completion
methods being used with a particular producing formation in a
given geographical region, Completion efficiencies are also
useful for identifying specific wells with poor or superior
performing completions. An analysis method referred to as
A well with no heterogeneity (an average well for the field or
formation) has an HI equal to 1.0. When HI is examined over
n periods of time, a heterogeneity signature is obtained. An
example is shown in Figure 1. The shape of this signature,
relative to the benchmark of HI = 1.0, can be indicative of
completion or reservoir anomalies.
Different variations and combinations of HI can be used in
analysis work depending
on whether well completion
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2
R.D. REESE
36604
An application of Equation 1 includes the evaluation of well
completions for behind pipe water channeling and/or the long
term effects of a water drive in the reservoir, This can be
analyzed using a delta HI relationship such as that shown in
Equation 2.
efficiency or reservoir characterization is being studied. For
example, the early time portion of an HI signature is useful for
evaluating completion efficiency, and the late time portion for
reservoir characterization. The heterogeneity signature shown
in Figure I is an example. This well is indicated to have a
good completion efficiency (from the early time portion of the
signature which is greater than 1.0) and limited reservoir tank
size (from the late time portion which is declining), From 3-D
seismic, the well was later shown to be located in a small fault
block which was consistent with its heterogeneity signature,
which indicated a small tank size or drainage area.
Equation 2
The influx of abnormal water production will be seen as a
positive value in Equation 2 with an increasing slope on the
signature plot.
Equation 1 is generally based on a ratio of cumulative
production data to normalize out discontinuities
from
temporary well shut-ins. The factors which influence it in a
typical well under primary recovery are flow rate (q) and the
decline rate (d,). Thus, HI becomes a function of the well’s
initial flow rates (q, ) and decline rate (d,) compared with the
same factors for the average well in the field or formation. The
first variable, q,, is influenced by a) how efficiently fluids are
able to move from the reservoir into the wellbore (referred to
in this paper as completion efficiency) and b) the near
wellbore rock quality and reservoir conditions. The second
variable, d, , is influenced by a) the reservoir geometry and
rock quality in the extended drainage area around the well and
b) the existing reservoir drive mechanism. These factors cause
the decline rate, d,, to be sensitive to various types of reservoir
heterogeneities. The shape of HI signatures is influenced by
these heterogeneities and shows information not normally
apparent from the standard production plots typically used by
industry.
Completion Efficiency
Equation 1 can be modified to incorporate rock quality near
the wellbore and thereby define the well’s cmnplerion
eficiency, as shown in Equation 3.
Equation 3
Completion Eficiency,
CE = H~F[uid,=l,n – HI Rock
CE is used to compare fluid performance with reservoir
quality. Near wellbore petrophysical or core data can be used
in Equation 3 to define the term HfR,,Ckand thereby relate
production data to reservoir quality. One example is shown in
Equation 4.
Equation 4
Type curves for various families of heterogeneity signature
phenomena observable in producing wells were developed
from simulation studies. Various reservoir heterogeneities and
completion efficiencies were incorporated in a standard 3-D
fluid simulation
model to develop the basic type curves
shown in Figures 2 and 3. They include the following:
Type 1 Good completion,
size
Type 2 Poor completion,
size
Type 3 Good completion,
size
Type 4 Poor completion,
size
Completion E@ciency, CE = HIFlui~l=l,,n –
(0x h)weii
(@x ‘)Avg. Well
Equation 4 defines a heterogeneity signature which is time
dependent due to the first term, H[~/Mi~.For the analysis of CE,
only the early life portion of the heterogeneity is used for
determining the first term in the Equation 4. A well with
average production performance in its early life and average
rock data from logs or cores has a completion efficiency of
zero. Wells with normalized production data better than the
normalized rock data have a positive completion efficiency,
whereas wells with normalized rock data better than the
normalized production data
have a negative completion
efficiency.
larger than average reservoir tank
larger than average reservoir tank
smaller than average reservoir tank
smaller than average reservoir tank
An example completion efficiency signature is shown in
Figure 4. This is the same well whose production
heterogeneity signature is shown in Figure 1. The completion
efficiency signature has been comected for a H]ROCL= 0.95
value (based on Phi* H).
These type curves approximate actual heterogeneity signatures
observed in thousands of wells from a number of fields and
formations. They are designed to be used as a comparator with
actual well data to assess performance. For best results, they
should be calibrated to specific formations and geographical
regions using completion and rock data from that area.
In practice,
304
the analysis of heterogeneity
signatures
is an
COMPLETION
36604
RANKING USING PRODUCTION HETEROGENEITY
INDEXING
3
empirical exercise dependent on computer processing methods
and are most useful when calibrated to specific reservoirs and
formations for each individual application.
production data to compute CE is 1 year, wi[h 4 being better.
For reservoir spacing or drive response studies, use longer life
data (10 years +/-). Do not include wells drilled later in the life
of the field if considerable pressure depletion has occurred.
Completion Ranking System
The above analysis methods can be used to examine large
groups of wells with different types of completions. The
objective is to obtain information from the well’s production
and petrophysical data which will assist in determining the best
completion method for a particular formation in a given
geographical area or field. The steps to achieve this are briefly
outlined below.
- If the response to EOR operations is
EOR Criteria
important, extend the time interval to include production
response during injection operations. Otherwise, simplify the
analysis by excluding this type of production data. Special
considerations, which are beyond the scope of this paper, must
be used in analyzing production responses from EOR
operations.
Extended time intervals may also show response to water drive
or behind pipe water channeling in wells with a bad cement
job,
Sten One - Database Desis?n
Wells must be grouped by formation and put in a database
containing a) well data (location, depth, TD, operator, field,
reservoir, etc.), b) completion data (date of completion,
perforated interval, completion method, etc.), c) monthly
production data (must include the full history of the well from
the date of first production), and d) any available reservoir or
rock data from core, well logs, pressure build-up tests, or fluid
samples (net pay, kh, @h,original BHP pressure, API gravity,
etc.). [t is important to note that the effectiveness of this
analysis method is directly dependent on the number of wells
being studied, For good confidence
in results, it is
recommended to use a minimum of approximately 100 wells
in the data set with no less than 10 wells for each completion
method being analyzed. Results improve with the number of
wells available for analysis.
Recompletion Criferia - Decide what criteria will be required
to classify a workover as a new completion. This will require
some knowledge of the field’s history, completion practices,
and geology,
Considerable new CE data may be obtained by looking at
qualified recompletion and adding them to the study database
as additional cases.
Step Two - Analysis
A heterogeneity analysis on the entire group of wells,
including all the different completion methods, is performed in
one analysis. The heterogeneity signatures must then be
classified according to heterogeneity index type (see Figures 2
and 3). For small data sets (under 100-200 wells), this can be
done manually, but for larger numbers of wells a pattern
recognition program (neural net) is necessary to classify wells.
This becomes mandatory when using data sets in excess of
1000 wells.
Irr choosing geological data against which to normalize the
production data, consider the following:
Phi*H
Kh
OOIP
Porosity*Net Pay - especially useful for
long term heterogeneity studies, but also
effective for CE
Permeability *Nef Pay - [he best parameter
for CE studies if it is available
Original Oil-in-Place - can be useful for
long term studies. Use as an empirical
comparator with Phi*H
The production
constructed
to
Hydrocarbons, or
or EOR analysis,
GOR signatures.
determining how
The following additional data and criteria are useful for
inclusion in the database in order to achieve a complete or
correct analysis.
heterogeneity
signature, HfFJjU,, can be
reflect Total
Fluid, Total Equivalent
Total Water. In certain types of waterflood
it may also be useful to use Water Cut or
Use the following suggested guidelines for
to setup the signature analysis:
Tofaf Fluid - use for analysis of short term overall completion
efficiency (especially when normalizing to Phi*H or Kh) as
well as for long term drive response or water channeling
analysis.
Time Duration Criteria - How many years of continuous
production data are required for the analysis? Set this number
after visually reviewing well plots and considering well
history.
Totai Equivalent Hydrocarbons - use when normalizing to
OOIP data. Also use for gas well analysis where water IS not
present. Use for overlaying with Total Fluid to spot impaired
wells or areas of field with OOIP anomalies.
Completion studies should focus on the initial flow period of
the well. Minimum recommended
time for continuous
305
36604
R.D. REESE
4
I“om[ Wafer - use for overlaying with Total Fluid to spot water
anomalies indicative
of water drive breakthrough
or
channeling.
Water Cut - use for assessing
waterflood or C02 patterns.
vertical
heterogeneity
●
in
●
Gas/Oil Ratio - use for assessing vertical heterogeneity in gas,
C02 or solvent injection operations.
Identification of potential superior and poor conrpletions
within a specific completion
method well group
Development of a pe@ormance yardstick for early ife
completion eflciency assessment for new wells
Examples
Construct the second component of Equation 3, the rock
property index, using one of the following formulas:
Example One - Analysis of Stimulation Methods
An example of scatter plot analysis is shown in Figure 5. The
axes of Figure 5 are defined as follows:
Equation 5
khwe,l
‘l R(),k= ~h
avg.We/[
or
4hwei\
@ov8,we/1
or
X-Axis
Cumulative Total Fluid per well at 10 years
of producing life
Y-Axis
Completion efficiency based on cumulative
total fluid and Phi *H. Plotted number is a
sum of CE for the first four years from the
date of first production for each well
oolpwt+i
001PUV8,Weil
or any other appropriate and available rock data.
For wells which have had their completion interval modified
over a period of time, it will be necessary to define a time
dependent fli~,,,~ as shown in the following:
In this case the objective was to determine which type of
stimulation method was most effective in a particular field.
Five different groups of wells, representing
different
stimulation methods were analyzed. These include:
Equation 6
CE = HICTlj=l,,n - ‘]k’OL’k(,i~e= 1+ “)
Acid
Acid
Acid
Sand
Sand
where HICTF is the heterogeneity index for Cumulative Tolal
Fluid. Other variables than HICTF may be used.
CE anomalies can be most readily identified using scatter
plots with a summed value of CE at some standard time
in[erval, such as four years from the date of first production for
each well, being placed on the Y-axis and either cumulative
oil or cumulative total fluid on the X-axis. Use the longest
possible time interval to calculate cumulative numbers for the
X-axis that the integrity of the data will support (ten years is a
good interval).
Different symbols were used in Figure 5 for each of the five
stimulation methods. A summary of the data in this graph is
shown in Figure 6. Note that the sand frac with low sand
concentrations was the best performer and the acid frac with
sand was the worst for this particular field.
Example Two - Identification
Impairment
Useful surveillance information can be obtained by mapping
CE to identify regional trends in completion efficiency or
reservoir heterogeneity. Map a summed value of CE at some
standard time marker to identify completi cm anomalies or as an
alternative, classify each well’s heterogeneity signature by one
of the four type curves shown in Figures 2 and 3 and bubble
map these heterogeneity types to determine trends in reservoir
quali[y.
lndica~ion
formation
of bes~ overall completion
method for
of
Wells with Potential
Figure 7 shows a number of wells from a carbonate reservoir
which have CE data, based on total produced fluid at four
years, computed two ways using a) a normalized Kh value and
b) a normalized OOIP value. The X-Axis is total cumulative
oil at ten years of producing life.
Note that three wells clearly stand out as potentially impaired.
They have very low completion efficiencies as well as low
cumulative oil recoveries. Further analysis and modeling of
these wells using conventional well performance analysis tools
would be justified.
Final results that can be obtained from this completion ranking
system include the following:
●
Only
Frac
Frac with Sand
Frac - high sand concentration
Frac - low sand concentration
a
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36604
COMPLETION
RANKING USING PRODUCTION HETEROGENEITY
Assumptions
The following assumptions are made in order to assure the
validity of this analysis method:
All wells being analyzed are in (he same producing
formation (in some cases it is possible to obtain
meaningful empirical correlations
from commingled
formations)
The complete monthly well production history is available
back to the beginning of the life of each well.
or allowable
are placed on
No artificial rate restrictions
the wells being analyzed
All wells are operated with an equivalent type artificial lift
system
All wells are producing under similar reservoir pressure
conditions. (It may be possible to make corrections for
large variations in reservoir pressure if pressure data is
available for the wells in question).
Sufficient numbers of wells are available to perform a
meaningful normalization of the data. One hundred or
more wells usually yield excellent results, but fewer wells
can be used. Do not use this technique for less than fifteen
wells.
Conclusions
Heterogeneity indexing and completion efficiency anaIysis are
useful empirical tools for ranking the effectiveness of different
completion methods as well as in identifying specific wells
with poor or superior performing completions. The method
can also be used in working with very large sets of wells as a
quick screening tool to identify operators using superior well
completion methods,
The methods presented are useful to production
reservoir engineers as a low cost assessment tool
determining the best completion practices in a given area
formation. The results may also be used as part of
surveillance programs
for benchmarking performance
different groups of wells.
and
for
and
well
of
307
INDEXING
5
RD. REESE
6
36604
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2
18
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+POa CmQkUOm Smll T*
1.6
Type 2
E
~ ;;
T
0,6 ~mm
0.6 +
0.4
‘
02
1)
HI=I,O
.m.
Index Type
(OilCum)
Heterogeneity
*+*,6+
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.m.
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Curves
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0.5
0.4
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0.1
0
-0.1
-0.2
-0.3
-0.4
●
0,6
o
.,.
Type 4
● ***66*6*6**
Figure 3
Generalized Heterogeneity Index Type Curve
Type 2 and 4
2.2
2
1.8
-
●
“oloa304050
Figure 1
Typical Heterogeneity Signature (based on cumulative oil)
mmmm’’”m
■ ,.mm~a
Time - Montha
1CO11OI2U
Flgura 4
Completion Efficiency Signature for well with good completion
but limited drainage area (adapted from Figure 1)
bbms
Figure 2
Generalized Heterogeneity Index Type Curve
Type 1 and 3
308
36604
COMPLETION
RANKING USING PRODUCTION
HETEROGENEITY
Completion Efficiency Study
CE = 0.0174*CTF
-2.48 (Best Fit)
20
*
15
2
~
10
,Q
&
U15
c
o
s=
o
g
0
-5
-lo
-15
Cum. Tot. Fluid (O,W,G) - MBOE
~
+ Acid Only
■ Acid Frac
❑ AF w/Sand
ASF - High Sd
Figure 5
Typical Completion Efficiency Plot for various types of
completions
Summaw
of Comdetion
Completion
Method
SF - LOW Sd
Acid Only
SF - High Sd
Acid Frac
AF w/Saint
Efficiency
No.
Wells
13
5
17
3
1
Avg. Compl,
Efficiency
3.65
0.48
-0.19
-1.60
-2.32
Figure 6
Summary of Completion Efflclency Data from Figure 5
Avg.
CTF
261.36
340.05
139.78
49,99
210.31
● SF-
LW
Sd
INDEXING
7
8
R.D, REESE
36604
Completion Efficiency
60
40
20
0
-40
-60
-80
1
●
I
..
●
I
1
I
I
I
Cum m ulative Oil
Figure 7
Completion Efficiency Plot Showing Three Wells with
Potential Impairment
310