U.S. patent application number 11/678353 was filed with the patent office on 2008-08-28 for method to optimize production from a gas-lifted oil well.
This patent application is currently assigned to INTELLIGENT AGENT CORPORATION. Invention is credited to Chad Lafferty, Lawrence Lafferty, Donald K. Steinman.
Application Number | 20080202763 11/678353 |
Document ID | / |
Family ID | 39714582 |
Filed Date | 2008-08-28 |
United States Patent
Application |
20080202763 |
Kind Code |
A1 |
Lafferty; Chad ; et
al. |
August 28, 2008 |
Method to Optimize Production from a Gas-lifted Oil Well
Abstract
A method for determining and reporting a general production
state of a gas-lift system for an oil well, the oil well having
associated tubing, casing, and gas-lift valves, and wherein sensor
signals from the welt and its associated tubing and casing are
input into mathematical models. The method comprises the steps of:
a) extracting values from the mathematical models that indicate
instantaneous states of production; b) supplying the sensor signals
and the values to an associative memory agent; and c) using the
associative memory agent to associate the sensor signals and the
values to generate the general production state.
Inventors: |
Lafferty; Chad; (Atlanta,
GA) ; Lafferty; Lawrence; (Atlanta, GA) ;
Steinman; Donald K.; (Missouri City, TX) |
Correspondence
Address: |
Law Offices of Tim Headley
7941 Katy Fwy, Suite 506
Houston
TX
77024-1924
US
|
Assignee: |
INTELLIGENT AGENT
CORPORATION
Houston
TX
|
Family ID: |
39714582 |
Appl. No.: |
11/678353 |
Filed: |
February 23, 2007 |
Current U.S.
Class: |
166/372 |
Current CPC
Class: |
E21B 43/122
20130101 |
Class at
Publication: |
166/372 |
International
Class: |
E21B 43/18 20060101
E21B043/18 |
Claims
1. A method for determining and reporting a general production
state of a gas-lift system for an oil well, the oil well having
associated tubing, casing, and gas-lift valves, and wherein sensor
signals from the well and its associated tubing and casing are
input into mathematical models, the method comprising the steps of:
a. extracting values from the mathematical models that indicate
instantaneous states of production; b. supplying the sensor signals
and the values to an associative memory agent; and c. using the
associative memory agent to associate the sensor signals and the
values to generate the general production state.
2. The method according to claim 1, wherein the operation of the
associative memory agent comprises pattern recognition and use of
knowledge of past well behaviors.
3. The method according to claim 1, wherein the step of extracting
values includes a step of deducing instantaneous states of the
gas-lift valves by using the mathematical models.
4. The method according to claim 2, wherein the step of associating
uses probabilistic classification to generate the general
production state.
5. The method according to claim 3, wherein the step of deducing
includes determining if abnormal conditions exist based on the
received sensor signals, and wherein an associative memory agent is
used to make the determination.
6. The method according to claim 3, further comprising, after the
step of generating the general production state, the step of
reporting the general production state.
7. The method according to claim 5, wherein the step of determining
if abnormal conditions exist uses a Finite Fourier Transform
combined with ordered statistics.
8. A system for diagnosing problems in, and reporting the general
state of, the production mode of the gas-lift operations on a well,
the well having associated gas-lift valves and sensors, the system
comprising: a. a personal computer for receiving reports of signals
from the sensors; b. means stored on the personal computer for
generating mathematical models to deduce the states of the gas-lift
valves and the states of the production mode by using as inputs
both the sensor signals and a knowledge base, to generate multiple
states over time of the production mode; c. an associative memory
agent stored on the personal computer, and responsive to the
multiple states, for detecting and aggregating anomalies, and for
reporting a general state of the production mode.
9. The system of claim 8, wherein the associative memory agent
learns well behaviors for the specific conditions of a particular
well, and diagnoses gas injection and production problems in the
well based on pattern recognition and past well behaviors.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] None.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] None.
REFERENCE TO A "SEQUENCE LISTING," A TABLE, OR A COMPUTER PROGRAM
LISTING APPENDIX SUBMITTED ON A COMPACT DISC AND AN INCORPORATION
BY REFERENCE OF THE MATERIAL ON THE COMPACT DISC
[0003] None.
BACKGROUND OF THE INVENTION
[0004] (1) Field of the Invention
[0005] The invention relates to methods to optimize liquid
production from an oil well that is under gas lift.
[0006] (2) Description of the Related Art
[0007] As is well known in the art, gas-lift techniques are
employed in oil wells which have difficulty in producing
satisfactory levels of liquids based on natural formation pressure.
Typically, such wells have formation pressures which are
insufficient to impel liquids to the surface at economically
acceptable volumes.
[0008] The gas-lift technique involves injecting gas into the
casing of an oil well through one or more valves, typically located
at varying heights along the well. Depending upon the technique
being used, the gas may be injected substantially continuously into
the column of fluid in the well, thereby lightening this column of
liquid so as to enhance the natural formation pressure.
Alternatively, gas can be injected intermittently in a repeated or
cyclical process so as to produce successive slugs of liquid at the
well head.
[0009] Although gas-lift techniques provide excellent results for
certain types of oil wells, each well is different in terms of
downhole or formation pressure, downhole or formation temperature,
depth to the producing formation, geothermal gradient experienced
along the vertical height of the well, and numerous other factors.
In addition, gas-lift injection systems differ in terms of their
valve characteristics, injection pressure, injection volumes and
other factors. Thus, determining the optimal operating parameters
for a gas-lift technique is often a time consuming trial and error
process which may require extensive supervision and nevertheless
provide less than ideal production.
[0010] Typically entire oil fields are produced using the gas-lift
technique because the formation pressure everywhere in the field is
insufficient to produce satisfactory quantities of oil. The
gas-lift technique then must be applied to each well in the field.
A complex system of gas supply and piping must connect each well to
a central collection and distribution facility. Operating the
gas-lift system then becomes a situation of complex interaction
between the needs of each well and the overall need to enhance
production of each well in the field. While several software
programs and procedures exist that help control and optimize
production from the field while utilizing the available gas supply
efficiently, these programs assume that each well within the field
is operating efficiently. This assumption is often violated because
individual wells may not perform at their most efficient potential.
Thus the field management software is not as effective as it needs
to be to optimize field production.
[0011] There are many situations in which less than optimal
production from the well may occur. These situations involve the
pressure, temperature, production flow, gas injection rates, and
the states of the several valves in the well. In order to diagnose
a problem, it is necessary to consider many configurations of these
parameters and the implications of their current values. Further,
it is necessary to classify possible states of the oil well in
order that the diagnostic can relate to the existing production
state of the well.
[0012] Several attempts have been made to optimize oil well liquid
production under gas-lift that are based on so-called expert
systems that use a rules-based decision making process to identify
problems with the way in which a gas-lift technique is performing
on a given well. Such expert systems may not perform as well as
needed because the full set of data values required for making an
incontrovertible diagnosis may not be available. Accordingly the
system must be able to diagnose problems using whatever data is
available. Also, such expert systems may not diagnose lifting
problems correctly because the parameters of the operation change
during the life of the well. In order to account for the aging of
the well, the expert system would require continuous or
intermittent retuning to ensure effective diagnostic abilities. in
addition, many factors that influence the ability to diagnose
problems in a well under gas-lift are often overlooked by the
expert system because the developers of the systems cannot know all
possible conditions that may influence the operation at the time
that they develop the software program.
[0013] An early expert system that used a rules-based decision
making process which attempted to improve the rules based on the
results obtained is disclosed in the following patent, which is
incorporated herein by this reference: U.S. Pat. No. 4,918,620,
which states in the abstract, "A computer software architecture and
operating method for an expert system that performs rule-based
reasoning as well as a quantitative analysis, based on information
provided by the user during a user session, and provides an expert
system recommendation embodying the results of the quantitative
analysis are disclosed. In the preferred embodiment of the
invention, the expert system includes the important optional
feature of modifying its reasoning process upon finding the
quantitative analysis results unacceptable in comparison to
predetermined acceptance criteria." However, the method disclosed
in this patent does not allow for generating attributes from real
time data to compare to known symptoms of poor well behaviors.
Rather, it requires that an expert think of all the rules possible
in the system in order to account for novel behavior, and it cannot
adapt to data-drop-out when sensors fail in service.
[0014] Another expert system that uses a rules-based decision
making process that attempts to improve the rules based on the
results obtained is disclosed in the following patent, which is
incorporated herein by this reference: U.S. Pat. No. 6,529,893,
which states in the abstract, "The system uses an author interface,
an inference generator, and a user interface to draw authoring and
diagnostic inferences based on expert and user input. The inference
generator includes a knowledge base containing general failure
attribute information. The inference generator allows the expert
system to provide experts and users with suggestions relating to
the particular task at hand." However, the method disclosed in this
patent does not show how to deploy an expert system to diagnose
problems with gas-lift wells, and it is furthermore subject to the
limitations of rule-based-systems as described in the previous
paragraph.
[0015] Another expert system that uses a rules-based decision
making process which attempts to improve the rules based on the
results obtained is disclosed in the following patent, which is
incorporated herein by this reference: U.S. Pat. No. 6,535,863,
which states in the abstract, "The method improves the performance
of the system by evaluating how well the system's body of knowledge
solves/performs a problem/task and verifying and/or altering the
body of knowledge based upon the evaluation". However, the method
disclosed in this patent does not address monitoring and diagnosis.
Also, it requires a human to evaluate the results of the analysis,
and provide feedback to the software program regarding which rules
to accept and which to keep based on performance.
[0016] Another expert system that uses a knowledge-based decision
making process that attempts to improve the base of knowledge based
on the results obtained is disclosed in the following published
patent application, which is incorporated herein by this reference,
U.S. Patent Application No. 20060025975, which states in the
detailed description, "The weights of each network or expert are
determined at the end of a learning stage; during this stage, the
networks are supplied with a set of data forming their learning
base, and the configuration and the weights of the network are
optimized by minimizing errors observed for all the samples of the
base, between the output data resulting from network calculation
and the data expected at the output, given by the base." However,
the method disclosed in this patent requires an accurate model of
flow in the system in order to train it, and it will not diagnose
the origin of flow impairments.
[0017] Another expert system that uses a knowledge-based decision
making process that attempts to improve the base of knowledge based
on the results obtained is disclosed in the following patent, which
is incorporated herein by this reference: U.S. Pat. No. 6,236,894,
which states in the abstract, "A genetic algorithm is used to
generate, and iteratively evaluate solution vectors, which are
combinations of field operating parameters such as incremental
gas-oil ratio cutoff and formation gas-oil ratio cutoff values. The
evaluation includes the operation of an adaptive network to
determine production header pressures, followed by modification of
well output estimates to account for changes in the production
header pressure." However, the method disclosed in this patent does
not address individual well productivity, and it requires iterative
applications rather than recognizing and diagnosing problems from
the data presented.
[0018] Another expert system that uses a knowledge-based decision
making process that attempts to improve the base of knowledge based
on the results obtained is disclosed in the following patent, which
is incorporated herein by this reference: U.S. Pat. No. 6,434,435,
which states in the abstract, "The systems and the methods utilize
intelligent software objects which exhibit automatic adaptive
optimization behavior. The systems and the methods can be used to
automatically manage hydrocarbon production in accordance with one
or more production management goals using one or more adaptable
software models of the production processes." However, the method
disclosed in this patent requires production models of the
production process, which is itself subject to errors. Therefore,
the system disclosed in the '435 patent will not be fault tolerant
of failed or missing sensor data. Furthermore, the system disclosed
in the '435 patent does not produce a specific diagnosis of
unsatisfactory behavior.
[0019] In light of the foregoing, a need remains for a more
efficient method for diagnosing production problems from a gas-lift
well.
BRIEF SUMMARY OF THE INVENTION
[0020] A method for determining and reporting a general production
state of a gas-lift system for an oil well, the oil well having
associated tubing, casing, and gas-lift valves, and wherein sensor
signals from the well and its associated tubing and casing are
input into mathematical models. The method comprises the steps of:
a) extracting values from the mathematical models that indicate
instantaneous states of production; b) supplying the sensor signals
and the values to an associative memory agent; and c) using the
associative memory agent to associate the sensor signals and the
values to generate the general production state.
[0021] A system for diagnosing problems in, and reporting the
general state of, the production mode of the gas-lift operations on
a well, the well having associated gas-lift valves and sensors, the
system comprising: a) a personal computer for receiving reports of
signals from the sensors; b) means stored on the personal computer
for generating mathematical models to deduce the states of the
gas-lift valves and the states of the production mode by using as
inputs both the sensor signals and a knowledge base, to generate
multiple states over time of the production mode; and c) an
associative memory agent stored on the personal computer, and
responsive to the multiple states, for detecting and aggregating
anomalies, and for reporting a general state of the production
mode.
[0022] The method and system are readily adaptive to the widely
varying conditions experienced at different gas-lifted oil wells.
In another feature of the invention, the system learns well
behaviors for the specific conditions of a particular well, and
diagnoses gas injection and production problems in the well based
on pattern recognition and past well behaviors so as to reduce the
need for human operator involvement in the diagnosis of
problems.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0023] FIG. 1 is a schematic diagram of an oil well on which
gas-lift is being exercised.
[0024] FIG. 2 is a schematic diagram showing the inputs to the
associative memory agent of the present invention.
[0025] FIG. 3 is a flow chart illustrating how raw data from the
sensors is processed to yield a report.
DETAILED DESCRIPTION OF THE INVENTION
[0026] In FIG. 1, an oil well 12 has casing 14 and production
tubing 16, through which production from perforations 18 is
conveyed to the surface 19. When a formation 20 has a geological
pressure that is insufficient to cause the liquids in the formation
20 to reach the surface 19 in sufficient economic quantities, an
operator can assist the formation 20 by injecting gas from the
casing 14 from a supply line 22, passing through an inlet valve 24,
and from the casing 14 into the tubing 16, through gas-lift valves,
26, 28, 30, and 32. The injected gas mixes with the liquid in the
well and reduces the density of the liquid so that the column of
liquid is now light enough for the formation pressure to provide
sufficient lifting force to cause liquids to be produced in
economic quantities through a production valve 34 into a flow-line
36. Also disposed around the well 12 are pressure sensors 38 and 40
whose readings are conveyed to a control room where engineers and
other field personnel can monitor the well's production. The flow
of gas into the casing 14 can also be monitored by observing
pressure differentials between the pressure sensor 40 and other
pressure sensors disposed in other locations around the field.
Differences in pressure between the pressure sensor 40 and other
sensors indicate that gas is flowing in the lines. In addition,
outflow of oil and other liquids can be inferred from the
differential pressure between pressure sensor 38 and other pressure
sensors in other locations (not shown) on the flow line 36.
[0027] Often the design of the gas-lift technique does not account
for changing conditions during the productive life of the well, and
the gas-lift operation begins to behave in a manner that reduces
its lifting capabilities. This may occur for several reasons. The
gas-lift technique is designed so that gas is injected through the
lowest gas-lift valve 32. At that depth, the gas is most effective
in reducing the density of the liquid column and hence in lifting
the liquid to the surface. Less beneficial operation of the
technique may occur when any of the following conditions occurs: a)
insufficient gas is supplied to the valves 26-32 to lift the liquid
to the surface 19 in economic quantities; b) gas is being injected
through more than one valve; c) no gas is being injected because of
insufficient pressure in the gas delivery system; d) no gas is
being injected because the pressure of liquid in the tubing 16 is
greater than the gas pressure in the casing 14; e) too much gas is
being injected; f) one or more of the valves is faulty and doesn't
respond to casing or tubing pressure correctly; and g) other
reasons having to do with the mechanics of operating the gas-lift
system.
[0028] Surveillance engineers over the years have identified most
of the causes of poor gas-lift performance in individual wells.
Each of these causes can be labeled with a name and associated with
a set of circumstances the realization of which cause the
performance. Most often, the causes for particular behaviors are
not clearly demarcated so that a rules based diagnosis is
inadequate to identify the cause of the problem. The set of names
of the causes of poor performance are called "states" of the
gas-lift operation. Hence, each state represents a particular set
of conditions causing the known poor performance.
[0029] The present invention operates by using an associative
memory technology such as that sold by Saffron Technologies, Inc.
However, the present invention can use any associative memory
technology, and is not restricted to the associative memory
technology sold by Saffron Technologies. The associative memory
technology uses mathematical and symbolic evidence from sensors and
known event types to associate that set of sensor readings,
mathematical model results, and symbolic evidence with the states
of the gas-lift operation. In addition to the associative memory
technology, other important elements of the invention are a
knowledge base, a database, and simulation software.
[0030] The knowledge base includes the states of the system and
their symptoms. Such knowledge base exists in the form of
spreadsheets and written documents in which the states of the
system and the associated symptoms are tabulated. This knowledge
base is used for training the associative memory to recognize each
of approximately 70 different states for a gas-lift system.
[0031] The database includes examples of the readings of sensor
values at such times during which the system is exhibiting such
behaviors. The database can also be a database kept by an operating
oil company of the sensor readings for use by engineers and other
company personnel to diagnose operations of gas-lift fields.
[0032] The output of simulation software represents the behavior of
gas-lift systems when undergoing such behaviors. Such simulators
have been developed by companies in the oil field services
industry. In the preferred embodiment, the simulation software is
the program "DynaLift" produced by Weatherford. As an illustration
of the output of the simulation software, the simulation software
provides information about the state (open/closed) of each
injection valve in the system based on known pressures in the
system and the valve's performance attributes that are provided in
specification sheets from the valve manufacturer.
[0033] An engineer developing an intelligent agent for diagnosing
gas-lift problems then combines the knowledge base and databases
(real, simulated, or a combination of both) with the associative
memory technology to produce an intelligent agent that is trained
to recognize various states of the system. The combining activity
is described below.
[0034] Referring now to FIG. 2, in the process of combining the
various sources of information, an associative memory agent 50
receives input from four sources. Data 52 from sensors on the well
and flow-lines includes pressures, temperatures, flow rates, valve
states, injection rates, production rates, etc. The data 52 is
conditioned by a signal conditioning process 54 to convert analog
sensor readings into information about the well. In addition to the
absolute values of the sensor readings, the signal conditioning
process 54 computes the time averages of the sensor values, the
standard deviations of the averages, Finite Fourier Transforms
(FFT) of the data, long term and short term time derivatives of the
sensor readings, and other mathematical functions of the data, to
produce attributes of the data stream that are useful to describe
the states of the system.
[0035] Mathematical models 56 of the dynamics of the fluids (gas
and liquids) flowing in the well are also used to estimate
quantities that are not measured directly, such as whether gas-lift
valves in the well are open and flowing. Additional mathematical
models include models for calculating the deepest point of
injection, the inflow/outflow curves, and operating rates vs. lift
gas injection rates. The results obtained from the mathematical
models are then transmitted to the associative memory agent 50.
[0036] A library 58 of associative memory agents trained on both
data from wells and on data generated by a simulator computer
program is also an input to the associative memory agent 50. An
analysis knowledge base 60 is used to configure the memory of the
associative memory agent 50 to receive the data 52 and conditioned
attributes from the other data sources, e.g., from simulators.
Approximately seventy distinct anomalous well states exist that are
covered by the analysis knowledge base 60.
[0037] An associative memory agent 50 is used for monitoring and
diagnosis of gas lift systems by employing the following steps.
[0038] 1. Prior to operation of the diagnosis system, a knowledge
base is constructed which identifies the set of conditions (normal
or abnormal) which are to be recognized. Each condition is a system
state which is defined by a vector of attribute values. [0039] 2.
Prior to operation of the diagnosis system, the knowledge base is
used to train an associative memory. Training the memory means that
we store in a memory each condition and the vector of attribute
values that define the condition. [0040] 3. During the operation of
the diagnostic system, a vector of attribute values describing a
well's current state (the input context) is compared to the set of
conditions stored in the memory. The input context may or may not
include all of the attribute values needed for fully characterizing
the well's state. A scoring algorithm is used to determine the
degree of similarity between each stored condition and the well's
existing condition. [0041] 4. Since the input context may or may
not contain all of the attributes needed for fully characterizing
the well's state, the diagnosis system produces a rank-ordered list
of the most likely diagnosis.
[0042] A variety of implementation methods exist for storing
information in an associative memory and for scoring input
contexts. A sparse matrix implementation of an associative memory
is described in "Comparative Analysis of Sparse Matrix Algorithms
for information Retrieval", Nazli Goharian, Ankit Jain, Qian Sun.
Information Retrieval Laboratory, Chicago, Ill. This particular
paper also describes an inverse document frequency algorithm for
scoring. Note that although this paper addresses the use of
associative memories for text processing applications, similar
techniques can be applied to implement a gas lift diagnosis
system.
[0043] Referring now to FIG. 3, in step 70 the method of the
present invention begins. The method may be performed in real-time
(i.e., as time-series data become available) or in a batch mode.
Each data "snapshot" is processed one-at-a-time, though many of the
system's algorithms look back at previous data values to calculate
current system state. For each data sampling interval (i.e., for
each data time stamp), the following steps are repeated.
[0044] In step 72 time series pressure data 52 from well sensors 38
and 40 are received. In step 74 the data 52 is processed using an
algorithm that employs a Finite Fourier Transform ("an FFT") and
ordered statistics to generate a set of values that describe the
periodicity of the pressure signals. The associative memory agent
50 classifies each input pressure channel, including tubing
pressure and casing pressure, as either normal or abnormal. The
associative memory agent 50 has been trained to distinguish between
normal and abnormal states. Abnormal states include behaviors such
as flow instability and heading. Thus, in step 74 the associative
memory agent 50 assesses casing and tubing pressures to determine
if heading is present. Heading is a cyclical variation in casing or
tubing pressure that indicates potential problems in the gas-lift
system.
[0045] In steps 76, 78, and 80 values in addition to pressure
heading states are obtained or calculated in order to identify
anomalous well conditions. In step 76 time series values such as
well fluid level and gas flow rates are received. Step 76 includes
analysis of data to identify conditions such as fluctuating input
gas rates. Step 78 includes receipt of well configuration data such
as the well depth, valve characteristics, and the location of
injection valves in the system. Step 80 includes receipt of
mathematical model outputs such as the calculated state of each
valve (open/closed) and deepest point of injection. The values
generated by these mathematical models depend on parameters such as
gas injection rates and pressures.
[0046] For each data sampling interval, in step 82 the associative
memory agent 50 combines heading information and the values from
steps 76, 78, and 80 into a record that represents the well's
current operating state. The associative memory agent 50 compares
these operating conditions with patterns in the anomaly detection
memory to determine whether an anomalous condition is present. In
some cases, some input data values may be missing; and, in that
case, the associative memory agent 50 computes a probabilistic
value for the well condition. The anomaly detector provides the
likelihood of several possible anomalies in a format where the most
likely condition, A is assigned to the highest probability, the 2nd
most likely condition, B, is assigned to the second most probable
state, and the 3rd most likely condition, C, is assigned the third
highest probability, etc. In order to obtain these events in rank
order, the memory is queried by using software commands.
[0047] The representation of knowledge in the associative memory
agent 50 enables queries using only partial data. Hence, if a data
stream becomes unavailable, the query can still be executed, and
the agent will return a set of probabilities the values of which
take into account the missing data stream. Thus, in step 80 there
may be more than a single probabilistic result, which is a clear
advantage of the invention because, in the absence of complete
data, it would otherwise be difficult to determine whether anomaly
A or B or C exists.
[0048] Because the method of the present invention processes data
one time-stamp at a time, in step 82 anomaly detection identifies
anomalies that exist at a particular point in time. In step 84
anomaly states over time are aggregated to determine the system
state. In both steps 82 and 84, the method of the present invention
compares the received data with the knowledge base 60. In a simple
case, a condition such as high separator pressure may exist over an
uninterrupted period of time. In a boundary case, the well may
cycle between an abnormal state (e.g., excessive injection) with
intermittent periods of normalcy. In more complex cases, a well may
cycle from one abnormal state such as multi-point injection to
another abnormal state such as surging repeatedly. In this case,
the well is best characterized as surging, because multi-point
injection is a symptom of extreme surging, and this cycling between
states can be indicative of the well's true problem. In step 84,
anomaly aggregation "smoothes" anomaly detections over time in
order to provide operators with a clear and understandable picture
of the well's condition over time, not just its instantaneous
state. Various implementations of the data aggregation function are
possible, including rule-based approaches and pattern-matching
methods such as associative memories or neural nets.
[0049] The system continuously processes time-series data, an
interval at a time, and in step 86 generates a continuous picture
of the gas-lift system's state over time. In a bounded period of
time such as twenty-four hours, the system may be nominal for some
periods of time, suffering from condition A during other periods of
time, and suffering from condition B or C during yet other periods.
Also in step 86, the method generates recommendations for steps to
take to correct problems. This method repeats as long as data are
available. In step 88, if there are no more data, then in step 90
the program ends.
* * * * *