SPE 63281
Maximizing Profitability in Reservoirs Using New Technologies For Continuous
Downhole Pressure Systems
James L. Buchwalter, SPE, Gemini Solutions, Inc., and Ray E. Calvert, Gemini Solutions, and Colin S. McKay, Wood
Group, and Stephen J. Thompson, Wood Group
Copyright 2000, Society of Petroleum Engineers Inc.
This paper was prepared for presentation at the 2000 SPE Annual Technical Conference and
Exhibition held in Dallas, Texas, 1–4 October 2000.
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Abstract
Continuous down hole data in conjunction with new reservoir
analysis tools made to work with this data have the potential to
revolutionize the accuracy of reservoir management. The
economic value of continuous down hole pressure data and the
array of available options justify the use of these systems in
almost all petroleum reservoir developments. The greatest
value of these gauges is that with new data analysis tools
reservoirs can be accurately managed early in the producing
life, thus optimizing both short and long term reservoir
management strategies.
Traditionally, the value of these systems has been for
completion optimization using a small subset of downhole
data. The full value of the complete data stream has been
ignored due to the large volumes of data, and the lack of
software systems for efficiently working with these data.
Consequently the reservoir has not been fully understood.
A system of software tools has been developed to capture the
full value of the data from permanent down hole gauges. This
new software system automates the filtering of these data in an
intelligent fashion. The resulting filtered pressure data can
then input into a variety of reservoir analysis tools, for
example reservoir simulation programs can now have a
continuous reservoir simulation. Reservoir and production
engineers can always have the optimal production strategy for
the reservoir based on the current data.
These tools are very easy to use, so models can be developed
quickly and continually maintained with minimal effort.
Typically it takes less than a week to build the initial model,
and only a few hours a month to update and maintain an
accurate history match.
The paper will include a brief review of the down hole
technologies and the software system which makes the data
accessible to reservoir simulation and other reservoir analysis
tools. Applications for the filtered data in both Gulf of
Mexico and North Sea reservoirs will be introduced.
Description of the Data Analysis System
The data analysis system used to perform these studies
consists of a data filtering system co-developed by Wood
Group and Gemini Solution Inc. This was used in conjunction
with Gemini’s commercial PC simulation and mapping
software- Merlin and Apprentice. Apprentice is a digitizing,
geostatistic characterization, mapping package that can be
used to generate maps, calculate volumetrics, and export grid
data to various simulators. Merlin is a full-featured black-oil
simulator with pre and post processors, and a transparent
interface to Apprentice. Both packages run on the PC, and can
effectively build models of 100000 cells or more on a high end
Pentium PC.
The filtering package combined with Merlin and Apprentice is
called Prophet. Prophet provides operators the means to
efficiently manage down hole data sets of any size, and then
quickly build and maintain accurate continuous reservoir
simulations in a “real time” environment for reservoir
management.
The filtering system is needed because pressures may be
measured on a second by second interval, but only a small
subset of these pressures are normally necessary to correctly
characterize the reservoir. On the other hand, at selected times
such as during a buildup or well choke change pressures are
needed on a finer interval for proper reservoir analysis. In
addition, most operators would embrace a system that can
automatically update to include new pressure data, or even
new wells that come on line. The Prophet filtering system
provides tools for all of the needs and much more.
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JAMES L. BUCHWALTER
The Prophet filtering system works as follows. At the onset of
production an operator creates a project that will contain a
series of user defined filter files for all well in the reservoir.
Examples of possible filters might include a pressure point per
day, and a pressure point every 30 minutes unless the pressure
changes by more than 5 psi during the 30 minutes. The initial
filter files are created to allow the user to start viewing the
data on different scales. After the filter files are created, the
remaining pressure points can be smoothed, deleted, as added
in more detail in selected intervals. The “add data” feature is
particularly useful after the data sets become large. For
example, after ten years a project will have read tens of
millions of pressure points to create the filtered files. Since the
project is typically updated weekly or monthly the time for
each incremental update was small. Suppose however that
after ten years it is realized that it would have been beneficial
to add detailed pressure data within a two day interval in year
5. Recreating the entire data stream from scratch using a new
filter might be an overnight process. Prophet allows the user to
selectively add data only within the interval of interest using
new filter constraints. As a result, instead of reading 10 years
of data to recreate a new filter file, only the selected days of
interest are read, and automatically inserted into the existing
filter file. This is of particular usefulness in reservoir
simulation where the entire data stream is of value.
Each time a user opens the project any new dates which have
pressure data – either earlier or later, are automatically
imported. The update can easily be self automated - for
example an operator may choose to run the project update at
midnight daily or weekly so that the filtered data files are
available for review at the commencement of the next business
day.
The other components of the Prophet system include an easy
to use PC simulation and mapping package which allows
models to be quickly built and maintained by typical reservoir
and production engineers. In the past, due to the high cost
associated with simulation, only the largest reservoirs were
simulated. The costs were high due to three main reasons.
First, Unix workstations and the geological and reservoir
simulation software that run in the Unix environment are
expensive to purchase. Second, Unix based hardware and
software typically require a significant amount of support to
keep the systems up and running. Third, since the interfaces
between the user and the calculation modules do not allow one
to rapidly build the initial model, run the cases and analyze the
results, companies incur the significant expense of training
and maintaining simulation experts.
The first two reasons can be mitigated by shifting to the PC
environment since high-end PCs now have the power to run
large models in a reasonable time period. Both the PC itself
and the geological and reservoir simulation software that run
on the PC are far less expensive to purchase and support than
the Unix workstation-based alternatives. The third reason for
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expensive simulation studies, the lack of first-rate interfaces
that will significantly lower the man-hours and experience
required to complete a study, is an issue that has only recently
been addressed satisfactorily by systems such as the one
described here. Today production and reservoir engineers can
use models built on PC’s to manage their assets in real time,
and the right answer for the data collected any date is always
available.
Data Filtering Examples
Let’s look briefly at some sample raw data sets that were
filtered with the Prophet system to see the power of the filter
tool. The filtered pressure files below were built from a raw
pressure stream of about 120,000 pressures collected over 8
days. The total time required to import this data for multiple
filters took less than a minute. About 5 minutes of additional
time was required to clean up the data stream using the tools
described below. The raw data source for all examples were
quartz gauges.
The first data set we will examine is from a well that
mistakenly had both good and bad pressures simultaneously
returned from the down hole quartz gauge. The filtered data
corresponds to a 30 minute 5 psi filter (Fig. 1). Keep in mind
that the original raw pressure file for this well contains about
120,000 entries.
After applying a box delete tool the original filter file was
reduced to a filtered file (Fig. 2). This data stream proved
correct and was acceptable for input into other reservoir
analysis systems. The data can be saved in a variety of
formats, either in whole or within a user defined range.
In a second example, data filtering, smoothing, and insertion
within an interval will be illustrated. The original filtered data
file was created using a 30 minute and 5 psi pressure
constraint (Fig. 3).
As a first step the erroneous pressures at the bottom of the plot
were removed (Fig 4).
Next the spike was examined in closer detail (Fig. 5).
It was unclear at first glance whether the pressure spike was a
result of a partial buildup or just some additional bad data. To
study the spike in more detail, additional data was added to the
spike interval corresponding to a 5 minute and 2 psi filter
using the original quartz pressure files (Fig 6).
The data lies almost at identical times and represents a 26 psi
spike – adding these additional data showed the operator that
this was also bad data and the spike was deleted (Fig. 7).
The last tool applied to the data was a time weighted pressure
smoothing algorithm to reduce the entire data stream to less
than 60 points (Fig. 8). In summary a 2500 fold decrease in
SPE 63281 MAXIMIZING PROFITABILITY IN RESERVOIRS USING TECHNOLOGIES FOR CONTINUOUS DOWNHOLE PRESSURE SYSTEMS
the number of data points was accomplished in this example
without an appreciable loss in the data character.
These tools allow large complex down hole pressure (as well
as other data streams) to be filtered quickly yield a continuous
data stream with variable sample rates suitable for capturing
all important events surrounding data changes.
Application - Determining Gas Reservoir Size With
Limited Down Hole Pressure Data
Introduction. Historically it can take several years to have a
reasonable description of the reservoir, in part because down
hole pressures are typically measured yearly at best. The
following application shows how a limited data stream of less
than 90 days was used to accurately determine the size of a
gas reservoir. Rapidly determining the reservoir size can allow
operators to use this knowledge to significantly impact early
life development decisions to maximize both production and
economics, and minimize the need to use sketchy exploration
data. In this case the operator was able to accurately and
quickly determine the reserves around the well to be able to
optimize the development strategy.
In the field case presented, a down hole bottom gauge was run
in the first well completion string right above the perforation
depth. The gauge is a SDG digital 16K gauges from Wood
Group. The reading of the gauges are accomplished through a
computer on the platform. The local PC stores the data on a
continuous basis and, through intranet, sends a daily file to the
company offices. Instantaneous readouts of the data are also
accessible from the office. Daily files are processed by the
filtering software to visualize trend and when needed,
transferred data to simulation software.
A team effort was used to study the reservoir, with both the
operator and Gemini Solutions Inc. building models of the
reservoir. The operator later reconfirmed results using a
commercial well test analysis package.
Methodology for Determining Reserves. The following
methodology was used for developing an accurate reservoir
description and development strategy.
1.
A simulation model was built based on static information
available including seismic interpretations, logs, and
cores.
2.
The initial simulation model was used to create a
production forecast.
3.
The well was produced for 60 days and the model forecast
was compared to the actual producing history.
4.
The possible parameters to be changed in the model in
order to match the recorded production were identified
3
5.
A history match was accomplished after modifying the
identified parameters and integrating the recorded
production and filtered bottom hole pressure data.
6.
The corrected simulation model was used to re-estimate
the original hydrocarbons in place and develop an optimal
production strategy before rig demobilization.
Model Description. The reservoir shown is a gas
condensate reservoir in the Gulf of Mexico lying deeper than
8000 feet (Fig. 9). The reservoir was originally delineated
using seismic data, and the exploration well number 2 tested
more than 23 MMCF/D.
Two other offset wells shown on the maps were drilled after
the discovery well, but the resolution of the seismic was
unable to positively determine the extent of the reservoir limits
around well number 2. With this uncertainty the original gas in
place in the reservoir and an accurate development plan could
not be accurately determined.
History Match Procedure. One of the principal difficulties
in analyzing continuous pressure data streams is the fact that
while flowing bottom hole pressures are available on a second
by second basis, well flow rates at best are probably known on
a daily average basis. Consequently, if in the model, rate
specified wells are used to control the wells then a technique is
needed to calculate average daily pressures corresponding to
these rates. This is very difficult. In addition, even if a
satisfactory average pressure technique were devised – much
of the important analysis ability for the continuous data stream
would be lost due to data averaging. Prophet has a variety of
averaging different filters – but only pressure and time interval
filters were applied to the original data for this project.
Averaging filters were not applied to the resulting data subset.
Step 1 – Radial Model for Estimating Permeability
and Skin. A simple radial model was constructed and the
longest buildup period was modeled to estimate the
approximate permeability and skin around the well. The
resulting history match is shown in Fig. 10.
The match above showed a permeability of 10 MD and a skin
of 2. These numbers were input as starting values for the full
field model. An exact match is not necessary at this stage
because further adjustments in these parameters were made in
the full field model.
Step 2 – Full Field Model and Rate Specified Wells. In
the next step a full field model was constructed using seismic
derived structure and net sand maps, and the permeability and
skin estimated in step 1. In order to obtain an acceptable
history match boundaries were placed on both sides of the
producing well approximately 900 feet from the well. The
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JAMES L. BUCHWALTER
resulting boundaries superimposed on a net sand map around
the well are shown in Fig 11.
The resulting gas in place within the area in communication
with the well is approximately 6.2 BCF. It is now believed that
additional prospective area around the well identified by
seismic is nonproductive.
The bottom hole pressure history match for the well is shown
in Fig. 12 and Fig. 13. The model results are shown as a line,
and the filtered historical data are shown as squares. The
flowing pressures can never be exactly matched with rate
specified wells because the measured well rates are daily
averages, and the true rate history variation is much greater as
evidenced by the variability in the pressure history.
Nevertheless this match was suitable for concluding that the
gas in place and reservoir permeability and skin were
approximately correct.
As this was a gas condensate reservoir the condensate yield
curve was slightly adjusted to match the recorded condensate
history. This curve is shown in Figure 14.
Step 3 – Full Field Model and Pressure Specified
Wells. As a confirmation for the model constructed in step 2 a
pressure specified well type was assigned to each startup, and
a very detailed rate history was predicted from the filtered
subset of the recorded bottom hole pressure data. The skin on
the well was slightly adjusted from 2 to 3 until the cumulative
gas production curve was exactly matched. The resulting
model is capable of forecasting the well rates on a continuous
basis using the recorded bottom hole pressures. The pressure
and production matches are shown in Figure 15. The adjusted
model was used to forecast an accurate production profile for
the reservoir, and a workover strategy for completions in the
future.
Accurately Characterizing an Oil Reservoir Using
Limited Down Hole Pressure Data
A depletion drive sandstone oil horizon with one producing
well and about 215 days of production and downhole gauge
pressure history is the next case study. The reservoir has
reasonable dip and the primary producing mechanism is
thought to be fluid expansion aided by rock compaction
particularly during the early producing life.
The goals of this study were to match the first 115 days of
production and pressure history to establish the hydrocarbons
in place and accurate reservoir description. Details for this
study could not be released at the time of this printing due to
confidentiality so only a general overview of the study will be
presented.
The reservoir in question is thought to have variable rock
compressibility, and an unknown contribution of rock
compressibility on permeability reduction. Reservoir size is
SPE 63281
not certain – seismic data interpretation does not have the
necessary resolution to precisely define the reservoir limits.
The unknown variables made the history match technique
uncertain, and two opposite sets of assumptions with respect
to compressibility were used to attempt the history match. The
goal for this study was not necessarily to obtain a unique
history match, but rather, understand the error bars based on
the current knowledge available, and develop a successful
development strategy that would work for either reservoir
description.
The production and pressure histories showed more than 7
partial buildups, and 2 complete buildups over the first 215
days of production. Both sets proved very important. The
partial buildups allowed an understanding of the general
pressure trend, and the complete buildups help characterize
permeability and its change as reservoir pressure declined and
pore volume changed due to rock compressibility.
The first history match assumed constant permeability and
compressibility, and the second history match assumed
permeability and compressibility versus pressure relationships.
Acceptable history matches using both assumptions were
obtained, and the resulting original oil in place and
recoverable hydrocarbons were remarkably close for the two
techniques in spite of the different history match techniques.
Most importantly, the limited down hole pressure data
completely changed the operator’s view of the reservoir and
the forecast for future performance. For example a several
fold change in the original oil in place was determined, and
confirmed by a new seismic reinterpretation. Without the
continuous pressure data it may have taken years to
accomplish the detailed analysis performed here.
Accurately Characterizing an Water Drive Gas
Reservoir Using Limited Down Hole Pressure Data
Another application that is of particular importance around the
world is correctly characterizing the influence of water influx
in gas reservoirs. Traditional techniques for identifying water
influx such as material balance require very accurate average
bottom hole pressure histories. Since in the typical reservoir
bottom hole pressures may only be measured once every 12 to
18 months, and 4 or more average pressures at different stages
of production are needed before the water influx can be
characterized, the time required for identifying water influx in
most gas reservoirs is 4 or more years. Unfortunately this
delay in identifying water influx can result in the irreversible
loss of reserves that lie behind in the water invaded gas zone.
Outrunning the water by accelerating gas production can
improve the water drive gas recovery factors by 5-30% in
these reservoirs. This is accomplished by accelerating
production from existing wells, and if economics justify,
drilling additional wells.
In this example the benefit of real time simulation was seen
upon examining a continuous pressure data stream that had
SPE 63281 MAXIMIZING PROFITABILITY IN RESERVOIRS USING TECHNOLOGIES FOR CONTINUOUS DOWNHOLE PRESSURE SYSTEMS
been collected over a 10-year period (Fig 16). The operator
had no efficient tool for examining the pressures as a
continuous data stream, and for the first 8 years of production
used short segments of the data for well optimization and well
test analysis. The reservoir was thought to be a depletion drive
gas reservoir. The reservoir was produced for about 9 months
of the year, and shut in for the remaining 3 months due to
insufficient gas demand. After 8 years the entire data stream
was evaluated and from the fact that the shut in bottom hole
pressures built up at a rate of 2-3 psi per week along with
further analysis it was determined that the reservoir was waterdrive. The operator immediately changed the operating
procedure accelerating gas production to outrun the water
influx, which significantly increased the recoverable reserves.
Interestingly, the water influx could have been detected almost
9 years earlier, where the benefit would have been much
greater. With the Prophet system, data streams like this are
now analyzed in less than 30 minutes of man effort.
Overview of Down Hole Pressure Gauge Technology
Permanent continuous downhole reservoir monitoring systems
have only been applied to a small number of the world’s oil
and gas wells.
Cost, reliability, risk, environmental
limitations, arduous data evaluation processes and the lack of
awareness have limited the widespread use of these
technologies. New technologies for deployment, data
management and interpretation have lowered the cost of the
entire well monitoring process. The various technologies
available will be summarized below.
Electronic Systems. The vast majority of downhole pressure
and temperature systems installed today are electronic
systems. The early electronic systems utilized strain gauge
technology for pressure measurement; this technology had
limited resolution and suffered from inherent drift problems
over time at pressure and temperature. A number of other
sensor technologies were tried and tested including quartz
capacitance sensors. Today resonating quartz, which utilizes
the inverse piezoelectric effect to induce the resonator to
vibrate at its mechanical resonant frequency, has been the
primary sensor technology utilized in downhole pressure
measurement. This technology provides the highest accuracy
and resolution of pressure measurement.
In recent years improvements in the manufacturing process
have improved both the reliability and ruggedness of the
sensors. In addition, market demands have resulted in reduced
diameter and higher pressure and temperature rated sensors. In
addition the running of dual pressure sensors has the benefit of
increased reliability over time. The redundant sensor can be
used to verify readings or provide the bottom hole pressure in
the event that one of the sensors becomes problematic
Non–Electronic Systems. High temperature operation and
long-term reliability of permanent downhole monitoring
systems have been improved by using sensors that do not
5
require any downhole electronics. The two non-electronic
technologies currently available are Fiber Optic and Electrical
Resonating Diaphragm.
Fiber Optic. The initial Fiber Optic Well Monitoring
(FOWM) system was developed from a Joint Industry Project
(JIP) specifically to address high temperature operation and
long-term reliability. FOWM sensors are based on the micromachined resonator concept with the structure being micromachined in silicon. The pressure or temperature sensor is a
resonator chip that is mounted in a silicon tube. Changes in
pressure and temperature induce forces in the sensor structure
that modify its natural resonant frequency. The sensor is so
small that it can be brought into vibration by shining it with
light of the correct wavelength and frequency. By exciting the
sensor with a modulating laser, the resonance frequency can
be measured by a second laser beam that is reflected back to
surface along the fiber. The second laser beam is then
measured on the surface to determine the resonant frequency
of the sensor. This frequency is then used to derive the
pressure or temperature surrounding the sensor.
Electrical Resonating Diaphragm. Electrical resonating
diaphragm sensors have been in commercial use over the last
six years2. They have reliably operated up to 390 degrees
Fahrenheit 2. The high reliability and temperature rating is
accomplished by having the sensing electronics at surface
rather than downhole.
This technology uses a downhole electrical resonating
diaphragm that is excited by an electrical pulse from the
surface. The diaphragm then returns a signal related to the
pressure or temperature of the downhole environment. A
surface acquisition system then measures the responding
signal, which is then converted to pressure or temperature.
This system generally consists of two pressures and one
temperature sensor. Each sensor requires a separate pair of
electrical conductors back to the surface acquisition unit.
Additional sensors would require additional conductor pairs.
In order to accomplish this a standard openhole 7-conductor
wireline is currently used to transmit the electrical signals.
Success using this wireline can be attributed to the fact that
constant voltage is not required to power downhole
electronics. This eliminates the potential across the electrical
insulation and therefore minimizes the problems associated
with electrical leakage.
Conclusions
Early characterization of the reservoir with continuous down
hole pressure data in conjunction with easy to use PC
simulation software offers the potential to revolutionize the
way reservoirs are developed and managed in the future. In
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JAMES L. BUCHWALTER
SPE 63281
fact, recent developments in these technologies make these
systems economical for the majority of the wells drilled in the
world today. Historically and even today this is not largely
recognized because filtering and data analysis systems that can
make real time reservoir analysis a reality are just entering the
market place, and not recognized by most operators.
The real benefits derived from these technologies lie not so
much is applying down hole pressure data in the conventional
work flow for reservoirs that have been developed over the
past 40 years, but changing the work flow process to take
advantage of the accurate reservoir descriptions that can be
obtained early in the life, and using this knowledge to
minimize costs and maximize production for reservoirs. With
this knowledge it becomes difficult to imagine where this
technology is not applicable. The case studies discussed here
are representative of typical reservoirs around the world, and
the potential value just in these reservoirs totals many millions
of dollars.
Acknowledgements
We thank Mike Flecker formerly of Wood Group for his
vision that helped make Prophet possible, and for his many
important contributions to the development of the Prophet
system.
References
1. W.G. Hazlett, SPE, and J.L. Buchwalter, SPE, and R.E.
Calvert, Gemini Solutions, Inc., and T.M. Campbell, SPE,
and R.A. Molohon, SPE, Mariner Energy, Inc.: “TimeCritical Decision Making Using PC-Based Reservoir
Simulation,” paper SPE 53980 presented at the 1999 SPE
Latin American and Caribbean Petroleum Engineering
Conference held in Caracas, Venezuela, 21–23 April
1999.
2. Michael J. Flecker/Wood Group, Stephen J.
Thompson/Wood Group, Colin S. McKay/Wood Group,
James L. Buchwalter/Gemini Solutions, Inc: “Maximizing
Reservoir Production Using New Technologies for
Permanent Continuous Downhole Sensors,” paper OTC
12153 presented at the 2000 Offshore Technology
Conference held in Houston, Texas, 1–4 May 2000.
Fig. 1–Well A8 raw filtered data stream using 30 minute and 5
psi filter.
.
Fig. 2–Well A8 corrected data stream for 30 minute and 5 psi
filter
SPE 63281 MAXIMIZING PROFITABILITY IN RESERVOIRS USING TECHNOLOGIES FOR CONTINUOUS DOWNHOLE PRESSURE SYSTEMS
7
Fig. 3–Well A11 raw data stream for 30 minute and 5 psi
filter.
Fig. 5–Well A11 showing the pressure spike
Fig. 4 – Well A11 corrected data stream for 30 minute and 5
psi filter.
Fig. 6–Well A11 with additional data added to the pressure
spike
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JAMES L. BUCHWALTER
Fig. 7–Well A11 after correctly filtering out the pressure spike
Fig. 8–Well A11 after smoothing the corrected data
SPE 63281
Fig. 9–Gas reservoir structure map producing from well 2
Fig. 10 – Gas reservoir pressure buildup match
SPE 63281 MAXIMIZING PROFITABILITY IN RESERVOIRS USING TECHNOLOGIES FOR CONTINUOUS DOWNHOLE PRESSURE SYSTEMS
Fig. 11–Gas reservoir boundaries needed to match historical
data
Fig.12–Gas reservoir flowing bottom hole pressure match
using rate specified wells
9
Fig. 13–Gas Reservoir pressure history match using rate
specified wells.
Fig.14–Gas Reservoir condensate yield curve needed for
history match.
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JAMES L. BUCHWALTER
Fig. 15–Gas reservoir history match with pressure specified
wells
P erm an en t D o w n h o le P ressu re D ata
2700
2450
B HP (psi)
2200
1950
1700
1450
1200
950
700
D ec-99
Ju n -99
D ec-98
Ju n -98
D ec-97
Ju n -97
D ec-96
Ju n -96
D ec-95
Ju n -95
D ec-94
Ju n -94
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Ju n -93
D ec-92
Ju n -92
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Ju n -91
D ec-90
Ju n -90
D ec-89
Ju n -89
D ec-88
D a te
Fig 16- Water driver gas reservoir continuous downhole
pressure data stream
SPE 63281