spe 124926 pa_diagnostico de gl

7/25/2019 SPE 124926 PA_Diagnostico de GL

1/13February 2010 SPE Production & Operations 111

Real-Time Diagnostics of Gas Lift SystemsUsing Intelligent Agents: A Case Study

G. Stephenson,SPE, Occidental Petroleum Corporation; R. Molotkov,SPE, Weatherford;N. De Guzman,SPE; and L. Lafferty,SPE, Intelligent Agents Corporation

Copyright 2010 Society of Petroleum Engineers

This paper (SPE 124926) was accepted for presentation at the SPE Annual TechnicalConference and Exhibition, New Orleans, 47 October 2009, and revised for publication.

Original manuscript received for review 29 July 2009. Revised manuscript received forreview 29 July 2009. Paper peer approved 08 October 2009.

Summary

This paper describes a new method to continuously monitor anddiagnose the condition of wells producing through continuous gaslift. The paper describes the application of this system in a matureonshore gas lift field in the western United States and the resultsobtained. A central problem related to the operation of gas liftwells is the ability to identify underperforming wells and to addressthe underlying issues appropriately and in a timely manner. Thisproblem is compounded by the trend toward leaner operations andrelative scarcity of application-specific domain knowledge. Thepurpose of this method is to address these issues by leveragingreal-time data, gas lift domain expertise, and proven steady-stateanalysis techniques in a desktop software application.

This system performs four key functions: Monitoring the wells

condition by collecting data, assessing the meaning of these data,recommending actions for correcting problems and responding tothreats, and explaining recommendations.

The performance of the system has met initial expectations andhas provided additional unforeseen benefits. This paper cites spe-cific cases that compare agent predictions to expert diagnoses andquantify the benefits of taking the recommended actions. What wasfound was that while the correct diagnoses of well performanceissues was beneficial, the real benefit was in allowing productionengineers to analyze a greater number of wells in far less time. Tothat end, the paper will discuss the role of this system as it relatesto the overall production management workflow.

The success of this project has demonstrated that intelligentagents can effectively perform functions historically performed bya handful of experts. The paper will discuss key system-design fea-

tures that enable this level of functionality as well as other potentialareas in which the technology can be extended in the future.

Introduction

One of the current challenges facing the upstream E&P industryis the growing scarcity of specialist domain expertise and trainedpersonnel needed to efficiently operate oil and gas assets. In casesin which these resources are limited or unavailable, automationtechnology has often been touted as a solution. While the introduc-tion of such technology has delivered numerous improvements inoperational efficiency, it also has introduced new challenges. Onesuch challenge involves the introduction of vast quantities of datathat result in minimal actionable information (Brul et al. 2008;Hite et al. 2007). Operators are faced not only with the information

technology task of managing these data, but also with the businesschallenge of leveraging the data to improve their profitability. Inresponse to this new challenge, a growing number of projects arebeing initiated to help close this gap between data and information.This paper discusses one such effort.

In this project, new technology has been developed to assistproduction engineers in the well-by-well optimization of gas liftsystems. Well-by-well optimization has long been recognized ashaving value (Mochizuki et al. 2006), but has often proven imprac-tical to carry out on a routine basis because of the labor-intensivenature of the work and the limited number of individuals with the

required level of expertise to perform it. This project sought tosolve this problem by developing a system of intelligent agentsthat leverage both real-time data and gas lift domain knowledge toassist engineers in these well-by-well optimization tasks.

An intelligent agent is defined as an autonomous entity thatobserves and acts upon an environment and directs its activitytoward achieving goals (Russell and Norvig 2003). In the contextof this project, intelligent agent systems are software applicationsdesigned for surveillance engineers and production managers todiagnose problems, provide advice on the proper course of action,explain how the agent arrived at its conclusions, and take actionon behalf of the operator, if desired.

Optimizing Gas Lift Wells

Value of Optimization. For many years, production engineershave recognized the value of gas lift optimization as a means togenerate incremental production and reduce operating expenses(Kanu et al. 1981). Although gas lift wells represent a relativelysmall portion of the well population, they generate a significantportion of the industrys oil and gas production. Industry experi-ence has shown that gas lift wells seldom operate under optimalconditions (Sclumberger Gas Lift Design and Technology 1999).As a result, significant production gains can be achieved throughthe optimization of gas lift systems.

Objectives.In general, gas lift optimization efforts have the com-mon objective of producing the greatest amount of oil with theleast amount of gas injection. For this to occur, a variety of relatedtasks must be accomplished. These include: (1) injecting as deeply

as possible; (2) lifting the well from a single point of injection;(3) injecting under stable, steady-state conditions; (4) injectingthe optimal amount of gas for the given production on the basisof individual well performance; (5) minimizing back pressure; and(6) allocating injection gas among a population of wells in the mostresource-efficient manner possible.

Optimization Project Classes. In order to achieve these objec-tives, operators often will initiate some form of optimizationproject. Such projects can be described using three main classesor tiers.

Tier 1. The first, most common tier of gas lift optimizationproject is well-by-well optimization. Well-by-well efforts includesuch tasks as troubleshooting wells, individual well modelingusing systems analysis, replacing gas lift valves, backpressurereduction, and other tasks that focus on improving the productionperformance of individual wells.

Tier 2. The second tier of optimization project can be definedas full-field optimization. Full-field optimization projects focuson managing the interactions of a population of wells with eachother, their production network, the gas distribution network, and,in certain cases, of wells with with the reservoir over time. Theaim of such projects is to maximize the net present value of theasset while honoring system constraints. Such projects generallyrequire sophisticated software models and specialists to run themand are dependent on prior steps carried out in Tier 1 (Nadar et al2008; Nadar et al. 2006).

Tier 3. The third tier of optimization project can be definedas real-time enabled full-field optimization. Tier 3 is actually

an extension of Tier 2, in which real-time data are used to con-tinuously update network and well models, enabling operators to


2/13112 February 2010 SPE Production & Operations

sustain the production enhancements achieved in Tier 2 over anextended period of time.

Research indicates that, of the three tiers, Tier 1 is by far themost commonly employed. This is because well-by-well optimiza-tion is often the least expensive of the three and generally producesthe greatest performance gains. In terms of the Pareto principle,well-by-well optimization is the 20% of effort that yields 80% of

the results. This is depicted in Fig. 1.While a number of gas lift automation projects have been carriedout to address Tier 3 (McKie et al. 2001; Litvak et al. 2002), this proj-ect is somewhat unusual in that it focuses on leveraging technology toallow engineers to more easily carry out tasks related to Tier 1.

Well Performance Issues. A variety of issues can impact theperformance of gas lift wells. These issues are frequently classi-fied as either inlet issues, outlet issues, or downhole issues (APIRP 11V5 1999).

Inlet Issues. Inlet issues include conditions that inhibit orobstruct the injection of gas into the well. Such conditions include(1) frozen or plugged injection-control valves, (2) inadequate sup-ply pressure to kick off or unload the well, and (3) unstable orirregular gas supply pressure.

Outlet Issues. Outlet issues include conditions downstream ofthe wellhead that impair a wells ability to flow. Such items includeexcessive backpressure because of production chokes, undersizedflowlines or manifolds, and high separator pressures.

Downhole Issues. Downhole issues include events occurringbelow the wellhead that impair a wells production performance.Such conditions include (1) multipoint injection, (2) valve cycling, (3)tubing-to-casing communication, (4) instability because of injectionin subcritical flow across an orifice, (5) plugged operating valves, (6)inadequate differential pressure at depth, (7) reopening upper valves,(8) flow-cutting gas lift valves, (9) temperature-locking injection-pres-sure-operated valves, and (10) circulating gas above the active fluidlevel in the tubing, as well as a variety of other known issues.

Tools and Techniques.A variety of tools and techniques have beendeveloped and used over time to aid in gas lift troubleshooting anddiagnostics. These include (1) flowing pressure and temperaturegradient surveys, (2) annular fluid levels, (3) CO2 tracer surveys(Peringod et al. 2009), (4) systems analysis (Mach et al. 1979),(5) calculation of valve state (Mayhill 1974), (6) evaluation of gaspassage, (7) dynamic simulation (Hall and Decker 1996; Mantecon2007), (8) surveillance of key performance parameters, and numer-ous other such techniques.

Historical Approach.Historically, well-by-well trouble-shootingand optimization has often been performed on an ad-hoc or as-needed basis. In most cases, an internal or external expert would becalled upon to assist in evaluating a well once it had been identifiedas having a problem. This expert then might use some or all of the

tools described above to assess the wells condition, identify the rootcause of performance problems, and recommend corrective actions.

This expert may even oversee the implementation of these correc-tive measures and evaluate the performance of the well after per-forming these services. In certain cases, a more proactive approachmight be used, in which an expert is placed in-house with the taskof systematically identifying underperforming wells and addressingwell performance issues in a prioritized, sequential manner.

Challenges With Historical ApproachThe approach described presents a variety of challenges to opera-tors. One of the most fundamental challenges is that this approachtends to be both reactive and episodic in nature, resulting in missedopportunites for production enhancement. In addition, much of thiswork requires individuals with specialist artificial-lift-domain exper-tise, which is increasingly scarce as the demographics of the industrychange over time. Even in those cases in which a resident expert ispresent in an asset, ones ability to detect and address the numerousopportunities in a field is limited by the labor-intensive nature of thework and the sheer volume of competing priorities. Finally, it is com-mon for problems in gas lifted wells to go undetected for months oreven years because gas lift is such a forgiving artificial lift method.Even those gas lifted wells that have a serious performance problemand are not producing optimally will often continue to produce fluids.With other forms of lift, failures tend to be catastrophic in nature andare identified and addressed much more quickly.

Addressing the Need

Role of Intelligent Agents.In order to address these challenges,an intelligent-agent system has been developed to provide real-time diagnostics of continuous gas lift wells. The role of theintelligent-agent system is to enable every surveillance engineer,regardless of experience or skill level, to make decisions that leadto optimization of wells with the knowledge of a world-class gaslift analyst.

Intelligent agents do this by providing engineers with the statusof all gas lifted wells under their control. Agents monitor the wellssituation by collecting and cleansing data, assessing the meaningof these data, recommending actions for correcting problems andresponding to threats, and explaining their assessment results andrecommendations. Agents can detect the initial symptoms of aproblem and prompt for corrective action before the wells perfor-mance seriously degrades. The performance of these key functionsenables surveillance engineers to optimize a much larger numberof wells on a continuous basis.

Once armed with these tools, engineers no longer have to assessthe state of each well manually. The agents integrate continuousdata such as pressure readings with well-test data and predictionsfrom commercially available systems-analysis tools, using diag-nostic principles stored in a knowledge base to determine eachwells condition and recommend corrective actions. The agentreviews all gas lifted wells in the field and prioritizes recommended

actions in accordance with pre-established criteria that includesincreased production potential and possible cost efficiencies.

Fig. 1Relative effort and value of optimization project classes.

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System Components.Knowledge Base. The central componentof the system is its knowledge base. The knowledge base is theportion of the system that captures the mental model of the engi-neer. In this project, it was important to have a system that wastolerant of missing or inaccurate data and could produce reliableanswers in less-than-ideal conditions. It was also desirable for thesystem to be able to learn from experience and generate increas-ingly reliable answers over time. In order to accommodate theseneeds, the knowledge base was assembled in a manner that wasnot hierarchical, combined both deterministic and probabilisticmethods, and was overdefined.

Unlike typical troubleshooting methods (Ortiz 1990), this

knowledge base did not rely upon a top-down hierarchical work-flow. Instead, knowledge was organized as a series of individualcases (diagnoses), each with a unique set of attributes. This isillustrated in Fig. 2. In all, more than 60 unique cases weredefined for wells operating through continuous gas lift with eitherinjection-pressure-operated (IPO) or production-pressure-operated(PPO) gas lift valves.

By organizing the knowledge base in this manner, the sys-tem could accommodate a number of possible diagnoses for thesame condition, each with its own level of probability. Althoughthe individual attributes were each deterministically derived, thearrangement of these into a set of discrete cases enabled the systemto provide a probability (or likelihood) for each diagnosis. Thisarrangement also enabled the system to identify which individualattributes contributed most strongly to a given diagnosis.

Another key aspect of the knowledge base is that it was over-defined. For a given condition, there could be multiple attributesthat would identify its occurence. As a result, even when certaininformation was unavailable or inaccurate, the system still had theability to correctly identify a given condition. Having more thanone such attribute present would simply increase the certainty ofa given diagnosis.

Agent Attributes.Because of the real-time nature of this project,the scope of work could include only those techniques that could beperformed automatically. Therefore, manual, episodic operationssuch as flowing gradient survey acquisition and interpretation werenot considered. Instead, a series of attributes were collected thatwere a function of analog inputs, the results of systems analysis,calculation of valve state, calculation of gas passage, or a calcula-

tion based on a combination of these items. In all, more than 30attributes could be collected for each analysis run.

Technology. The agents underlying technology is an adapta-tion of methods that are used in applications such as documentclassification and search on the Internet (Strategic Computing1983; Broadwell 1998; Edwards and Geddes 1991; Geddes et al.1996; Geddes et al. 1998; Geddes et al. 1990). Software programsknown as entity extractors can electronically read documents andpick out the people, places, events, organizations, and other thingsmentioned in the document. Other programs can determine whichentities typically occur together in a document of a particular type.Given a query string such as US Airways Hudson River, searchprograms can find documents and materials related to the 2009crash landing of a US Airways flight in the Hudson River. Docu-

ment searching is an application of pattern matching.Relating the notion of pattern matching to gas lift analysis, we

know that in cases of valve cycling, the attributes casing heading:yes and tubing heading: yes typically occur together. In wellsbehaving normally, the attributes casing heading: no and tubingheading: no typically occur. Gas lift experts understand the com-plex patterns of data that characterize various gas lift fault states.

The gas lift intelligent agent follows essentially the same pro-cess as a knowledgeable engineer. The engineer typically examineswell-test data, reviews trend data such as casing and tubing pres-sures, and refers to analytical models using systems-analysis tools.In many cases, at least some of the data will be either missing orsuspect. Making sense out of this sometimes conflicting data is theart practiced by an expert gas lift engineer.

The agent treats the art of gas lift diagnosis as a pattern-match-ing problem that involves overlaying the data that describe a wellscurrent state with information in the knowledge base that describesproblem states for gas lift systems. The set of attributes comparedis fairly large, on the order of more than 25 attributes. Like anInternet search program, the agent returns an ordered list of thebest matches it finds. A site such as Google might return thousandsof matches, but the gas lift agent is configured to return only threetop candidate cases.

The agent has features that make it uniquely suitable for gaslift. First, the pattern matcher can be tuned to handle specialcircumstances. An attribute can be weighted (its importance canbe decreased or increased) if the attributes value is suspect or ifthe attribute should be given special consideration during patternmatching. Second, the pattern matcher can explain its results

by providing information about the attributes that most stronglyinfluenced its selection of a case. This feature provides a way for

Fig. 2Portion of gas lift knowledge base.



users to see what the agent is thinking. Finally, over time, andas needed, the agents knowledge base can be extended with addi-tional cases that reflect conditions actually encountered in the field.Thus, the agents performance can be refined over time.

Data Requirements. As depicted in Fig. 3, a variety of dataelements are required in order to fully define the flowing state of agas lift well. These data include real-time, well-test, and static dataelements. Real-time data include analog inputs such as flowlinepressure, supply pressure, casinghead pressure, flowline tempera-ture, flowline pressure, and gas-injection rate. Well-test data include

information such as production rates (oil, water, and gas), gas-injec-tion rates, tubing and casing pressures, and fluid gravities. Staticdata include physical attributes such as perforation depths, welldeviation, completion geometry, inflow-performance data, and gaslift valve specifications. Each of these data items is collected andused to populate the intelligent-agent system and the well modelsit uses in conjunction with systems-analysis operations.

System Architecture. Because oil production companies use awide variety of databases and analytical models, the agent has beendesigned to be used in a variety of information technology (IT)environments. Fig. 4represents the systems architecture.

The system is implemented with a thin-client/server architec-ture. System components communicate with each other using aWeb services protocol.

Security Layer. The security layer controls user access to thesystem using Windows login ID so that users do not have to log inseparately to the system. The security system is being enhanced toprovide basic role-based access control that distinguishes betweensuper users with administrative privileges and ordinary users.

Data Migration Layer. The data migration layer is respon-sible for fetching data from a production companys data stores,including well-configuration data, well-test information, and trenddata. The agent currently is integrated with Weatherfords Life ofWell Information Software (LOWIS). However, data migration isdesigned to enable integration with whatever data sources are usedby a producer, including real-time sources. The data mover pullsdata from their sources and pushes data to the middle tier, wherethey are stored in the gas lift system database, which is a cache forstoring data used during analysis and the results from analysis.

Middle Tier. The middle tier includes the business logic foranalyzing a well. The application server module combines well-test

data and any available trend data. It invokes an analytical well modelto gather information about the wells configuration (e.g., types ofvalves, their depths). The agent currently uses WellFlo as its analyti-cal well model. However, consistent with the principle that it shouldrun in many IT environments, the agent will be integrated withadditional analytical models such as Prosper or WinGLUE. Usingdata derived from the analytical model, the server performs calcula-tions to determine whether valves are open, closed, or back-checked,and it determines gas passage for each open valve. It combines thisinformation with all the other data gathered into a single record.

This record is an input context, which is a vector of attributes thatdescribes the current state of the well. The pattern matcher comparesthe wells state with cases in the knowledge base and returns the topthree similar cases to the application server. The post-processor per-forms additional calculations primarily related to economic benefitsthat might be realized by optimizing the well.

Client. The client is the agents primary user interface and isdiscussed in the next section.

Client Graphical User Interface. The graphical user interface(GUI) is a thin client that communicates to the middle tier usingWeb services. It is not a Web application, but a browser-basedinterface could certainly be implemented. The user interface is justan interface, meaning that its function is simply to display datarather than to perform any analysis functions. The GUIs purposeis to present information to production engineers in a mannerconsistent with the mental model for gas lift analysis. The mentalmodel describes how users typically think about gas lift analysis,including the kinds of data that are useful when assessing a well.A simple example of the mental model is that some users measuredepth in feet and others in meters. Accordingly, the GUI includes aunit editor so that users can tailor unit values to their preferences.

The GUI is illustrated in Figs. 5 and 6.Fig. 5 is the systemdashboard.

The display provides a number of features for filtering and sort-ing data. The dashboard is essentially a tool for doing managementby exception. Users can select a well from the dashboard and drilldown to a detailed display.

Fig. 6 is the details display; it displays comprehensive informa-tion about the wells current diagnosis and supporting information

for this diagnosis as gathered from the latest well test, from trenddata, from the analysis model, and from the knowledge base.

Fig. 3Gas lift system data elements.

Wellheadpressure

Casingheadpressure

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Injection-gascontrol valve

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Gas flow meter

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Fig. 4System architecture.

Fig. 5System dashboard.



The analysis history section (left side of the display) lists all

the analysis results performed for the well. Users can select anyof the items in the history list for review.

The well header pane provides basic well-configuration data. The analysis resultssection (Fig. 7)lists three possible diagnoses

for the well: a primary, secondary, and tertiary condition. The likeli-hood value provided for each diagnosis is a rough measure of similaritybetween the wells input context and a diagnosis case, normalized toone across the three cases. The likelihood is not a probability.

The analysis trend data region (Fig. 8) provides graphs ofwell-trend data such as casing and tubing pressures, gas-injectionrate, and supply pressure.

The diagnosis inputssection (Fig. 9)is the input context for

this analysis run. All of the attributes used by the pattern matcherare included in this panel.

The valve mechanicssection(Fig. 10)provides comprehen-sive information about the valves installed in the well, includingtheir mechanical status (open/closed/backchecked), calculated gaspassage, etc.

The well-test panel (Fig. 11)provides data from the wellslast well test and additional attributes calculated using the analyti-cal well model.

The analysis model graphs region (Fig. 12)provides graphs gen-erated by the analytical well model such as pressure drop curves.

Fig. 6Details display.

Fig. 7Analysis results display.



The impact model outputsection (Fig. 13)provides estimatesof revenue and production rates that may be possible if the wellsperformance issues are corrected.

Testing. The system prototype was thoroughly tested in a labo-ratory environment prior to the field trial. The objectives of thelaboratory testing were to evaluate three major criteria: First, does

the systems data migration layer correctly import data into thesystem, including both analog and well-test data? Second, doesthe systems workflow layer follow the correct steps with respectto gathering data and invoking the correct analytical calculations?

Finally, does the systems knowledge-base component generatecorrect results?

The following discrete tests were performed:Data Retrieval Testing. The purpose of this test was to ensure

that the system pulls the correct real-time and well-test data andpopulates this information in its database and well models.

Testing of Analysis Modes. The system was required to support

three modes of analysis: (1) periodicanalysis is triggered eachtime a new well-test file is shipped into the system, (2) continu-ousanalysis performed once a day on the basis of average valuesof the analog signals over the previous 24 hours using the most

Fig. 8Analysis trend data.

Fig. 9Diagnosis inputs.

Fig. 10Valve mechanics panel.

Fig. 11Well-test panel.



recent test rates, and (3) manualanalysis process is initiated bya user at any time.

Testing of the Agents Output Results and Recommendationson Corrective Actions. The software was fed a set of well-testdata representing different well-performance conditions (e.g., nor-mal operation by injecting at deepest point, multipoint injection,circulation of gas above the top valve, overinjecting lift gas) andallowed to perform the analysis and provide diagnostic results andrecommendations. At the same time, experienced gas lift expertsindependently analyzed the same set of data using standalonesystem-analysis software, and recorded their observations aboutthe performance of analyzed wells. In most cases, the expertsalso were able to collect and evaluate current flowing pressureand temperature gradient survey data to aid in this evaluation.

The results and recommendations of corrective actions generatedby the software were then compared to the ones provided by theexperts. The software was considered to pass the test when itsconclusions coincided with the conclusions of the independentmanual analysis.

Data Validation Testing (Handling Missing Data). This testconcerned the ability of the system to handle missing or invaliddata. If a well-test file is missing the following parameters, thesystem will substitute values from corresponding analog data thatis concurrent with the time of the well test. These attributes includeflowing wellhead pressure, supply pressure, gas-injection rate, andflowline temperature.

The ability of the system to perform boundary checking (deter-mining if data is out of range) was also tested. In addition, the ability

of the software to detect no-flow conditions based on the slope ofchange of flowline temperature and pressure was tested.

Analysis Workflow Testing. A specific analysis workflow isincorporated in the agent. The workflow consists of a number ofconsecutive computations and comparison of computation resultsagainst the knowledge base. This test concerned the systems abil-ity to successfully execute this workflow.

Training of the Knowledge Base and Agent Memory (Predic-tion). Two unit tests and one system-level test were performed onthe agent memory.

First, the memory was tested with input contexts fully definedfor each of the cases in the knowledge base. This test ensuresthat the memory is trained correctly and that the knowledge baseperforms as expected when queried using a fully defined context.

This is a unit-level test.Second, the memory was tested using input contexts that were

partially defined for each of the cases in the knowledge base. Thiscase is consistent with the conditions expected to be found in thefield. The missing input contexts included all attributes that requirea measured fluid level as well as all attributes that require a separa-tor pressure or backpressure. This is a unit-level test.

Finally, the memory was tested using real-world data. Thistest is a system-level evaluation.

The software successfully passed laboratory testing. It gavereliable diagnostic results and recommended corrective actionswhich, in most of the cases, were in line with the results obtainedby experienced gas lift engineers by analyzing the same set ofdata independently using standalone software. Those few cases in

Fig. 12Analysis model graphs.

Gas-Injection Rate

Fig. 13Impact model output.



which the ternary diagnostic results were not in agreement withthose of the specialists were investigated. The software knowledgebase was expanded or refined to accommodate those cases.

Field Trial.Following the laboratory testing, a field trial was con-ducted in a large, mature waterflood located in the western UnitedStates. This onshore field has several thousand wells operating ona variety of forms of lift, including natural flow, reciprocating rodlift, electric submersible pumping, progressing cavity pumping,and continuous gas lift. At the time of the field trial, 84 of thesewells were actively producing through gas lift. Each of these wellsis fully instrumented and controlled using a remote terminal unit(RTU), which, in turn, communicates through radio to a host sys-tem. Typical instrumentation includes the following:

Gas lift supply pressure, psig Casinghead pressure, psig Flowing wellhead pressure, psig Flowline temperature, F Gas-injection rate, Mscf/DAs with the laboratory testing, each of these analog inputs,

along with relevant well-test parameters, were imported into theintelligent-agent system for use in its analysis.

The goals of the field trial were similar to those of the laboratorytesting. First, the system had to successfully import well-test and ana-log input data from the real-time host system. It then had to follow thecorrect steps with regard to importing data and invoking its analyticalprocesses. Finally, it had to generate results that were consistent withthose that were independently obtained from the gas lift experts.

Deployment. In preparation for this field trial, the system wasdeployed to a field-trial server isolated from the local operationalserver but available on the corporate wide area network (WAN).The field trial machine was a midlevel server running WindowsXP. LOWIS, WellFlo, SQL Server, and the agent were installed onthe machine. To ensure data integrity on the operational system,local IT staff developed a manual procedure for copying data fromthe live LOWIS instance to the LOWIS instance on the field trialserver. The client (i.e., the user interface) was installed on severaluser desktop computers both at the field and at the corporate officesin Houston. Thus, the agent functioned as if it were operationallydeployed, except that it used a copy of the local data.

Results

Consistent with the performance seen in the laboratory test envi-ronment, the system produced impressive results once deployed

in the field. In general, the system provided diagnoses and rec-ommendations that were consistent with the manual diagnosesof the experts, often with a startling degree of accuracy. Thefollowing case studies illustrate three such examples of systemperformance.

Case Studies.Well 1.This well had been producing stably for theprevious 15 months with an average production rate of 1,323 BLPD,a 98% water cut, 895 Mscf/D of produced gas, and 881 Mscf/D ofinjected gas. As can be seen in Fig. 14,the flowing wellhead pres-sures, casinghead pressures, gas-injection rates, and temperatureswere stable over the previous several months, with only minor fluc-tuations in flowline temperatures because of day/night patterns.

After analyzing the conditions for this well, the agent made the

following primary diagnosis: Scenario: Steady single-point injection at deepest valve in well. Condition: No apparent problems at this time. Likelihood: 42.68%. Action recommended: Gas lift design is appropriate for

conditions.This diagnosis was influenced by a variety of attributes, but

the key contributors to this particular diagnosis were that (1) onlyone active gas lift valve (the deepest) was determined to be open,(2) the gas-injection rate was not fluctuating, and (3) the deepestactive gas lift valve was not closed. This diagnosis along withthe secondary and tertiary diagnoses can be seen in Fig. 15. AsFig. 15 illustrates, other (less likely) diagnoses included Steadysingle point of injection not at deepest point in well and surg-ing single point of injection through deepest valve in well, low

casing pressure.Independent of this diagnosis, two gas lift engineers evaluated

the same information as well as a recent flowing pressure andtemperature gradient survey for the well (Fig. 16).

After modeling the well and evaluating the flowing gradientsurvey, both engineers independently concluded that the well wasindeed operating through single-point injection at the deepest sta-tion in the well and that performance was close to optimal. Thiswas consistent with the diagnosis provided by the agent.

Well 2. This well had been producing at an average produc-tion rate of 272 BLPD, a 95% water cut, produced-gas rate of 18Mscf/D, and a gas-injection rate of 542 Mscf/D. As illustrated inFig. 17, the well was unstable and experienced both tubing andcasing pressure heading with an extremely low casinghead pressure

(approximately 200 psi). After analyzing the conditions for thiswell, the agent made the following primary diagnosis:

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Fig. 14Well 1 historical data trends.



Scenario: Gas is injected/valve mechanics indicate that valvesshould be closed.

Condition: Possible communication at or above 4355.12 ft. Likelihood: 50.98%. Action recommended: It may be possible to confirm commu-

nication as follows: (1) first, shut in tubing, (2) continue to injectgas into annulus, (3) once the pressures equalize, bleed down the

annulus as quickly as possible, then (4) observe tubing pressure.If tubing pressure drops with casing pressure, this confirms thatcommunication exists. It is also useful to shoot the fluid level inthe casing before and after bleeding down the casing. If fluid levelrises, this also indicates communication. Alternatively, run a flow-ing gradient survey to identify point(s) of injection.

The primary contributors to this diagnosis were that (1) thecasing pressure at depth was less than the opening pressure forall valves, (2) the gas-injection rate into the well was greater thanthe throughput of all active valves, and (3) on the basis of valvemechanics alone, no valves were determined to be open.

Following this analysis, troubleshooting operations were con-ducted in the field using slickline-based methods. Operations person-

nel confirmed that a hole was present in the tubing at a depth of 4,360ft, just 5 ft below the communication point predicted by the agent.

A decision was then made to replace the tubing string. The wellwas subsequently recompleted to restore tubing integrity. Followingthe recompletion, the well returned to operation with a stabilizedproduction rate of 330 BLPD, a 21% increase in production.

Fig. 15Well 1 diagnoses and recommendations.

Fig. 16Well 1 survey matching.



Well 3. This well had been producing at an average productionrate of 1,197 BLPD, a 95% water cut, and a produced-gas rate of128 Mscf/D, with a gas-injection rate of 631 Mscf/D. As can beseen in Fig. 18,the well was unstable and experienced severe tub-ing and casing heading, along with fluctuations in gas injection.

After analyzing the conditions for this well, the agent made thefollowing primary diagnosis:

Scenario: Multipoint injection through valves 1, 2. Condition: Valves failing to close properly. This could be due

to flow cutting, bellows failure, or mechanical obstruction. Likelihood: 37.86%. Action recommended: Consider running flowing gradient sur-

vey to confirm multipoint injection. Replace valves as appropriate.The primary attributes contributing to this diagnosis were that

(1) the gas injection rate was greater than the combined capacity

of all active valves, (2) the casing pressure at depth was less thanthe opening pressure of all valves, and (3) the gas-injection ratewas greater than the capacity of the deepest open valve.

After independently evaluating these data along with a recentflowing gradient survey obtained for the well, each of the gas lift

experts determined that the well was indeed injecting through thetop two gas lift valves. This well is currently being evaluated forpossible corrective action.

Benefits Realized.Based on its initial deployment and use in thefield, it is evident that the sytem provides a number of clear benefitsto the operator. Among these benefits are the following.

1. Well performance issues and optimization opportunities canbe identified much faster than is possible through conventionalmeans.

2. Operations personnel are able to analyze more wells in lesstime.

3. The system provides a means to prioritize work opportunitiesand direct operations and well servicing resources toward thoseefforts that provide the greatest value.

In addition to the benefits described, the system plays a keyrole in the production management workflow.

Role in Production Management Workflow. A variety ofproduction-management activities are performed in any oil- andgas-producing asset in order to optimize well performance and

Preworkover

Post-workoverCHP, FWP, FLT, QCI

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maximize uptime. These activities can be described by a commonworkflow such as the one depicted by Fig. 19.The first step in thisprocess is to get data. This includes all of the manual or automaticcollection, sorting, and aggregating of data needed to perform basicsurveillance and analysis of the wells. The second step is to use

these data to identify opportunities for improvement. Next, theseopportunities are analyzed to validate the opportunity, identifycorrective actions, identify the cost and benefit of such work, andrank the opportunities as candidates for well work. Next, the workis executed in the field. Once the work is completed, scorecardingis then performed to evaluate the effectiveness of this work andprovide lessons learned that can be used to aid future decisions.Upon completion of these steps, the process is repeated.

The intelligent-agent system plays a key role in this process byautomating most, if not all, of the first three steps. By collecting andaggregating all of the pertinent data, identifying opportuntities forimprovement, and providing detailed analysis of these opportunities,the system frees personnel to focus a majority of their energy on theactual execution of work and evaluation of results. In this way, the

system shifts the focus of the production management process frompurely reactive activities to proactive high-value activities.

Areas for Improvement and Future Development. Intelligentgas lift agents are designed to learn more and, therefore, improveover time. This ongoing process is vital for the continued value anduse of the agents. As the system is exposed to a greater number ofreal-world cases and conditions, the knowledge base will need tobe expanded and the agents trained to better match reality.

Training of Agents. The agent has been trained to date using theknowledge base without including many real-world observations.However, further training is possible and desirable. It is expectedthat the fully trained agent will have on the order of a few hundredcases in addition to the base cases in the knowledge base. Animportant point is that the agent is not configured to learn on its

own. Rather, the agent is configured for directed learning, meaningthat the agent learns what we teach it. The question, then, is: Whatis the protocol for training the agent using real-world data?

An old adage related to computers is garbage in/garbage out,meaning that the results produced by a system are only as good asthe data fed to the system. The same adage applies to an intelligentagent. In other words, poorly trained agents perform poorly. Agenttraining has to be done in a carefully controlled manner.

The agents knowledge base is well conceived and implemented.However, the real world can be unexpectedly complicated, and the agentmay not always perform as users expect. While field users should beable to provide feedback related to the agents performance, they shouldnot be able to train the agent themselves. After all, different usersand,indeed, different expertshave different opinions. Accordingly, the fol-lowing protocol has been adopted for agent training:

1. A feedback button has been incorporated on the agentswell details display. Users who disagree (or agree) with the agentsdiagnosis can state their position and electronically forward apacket of well data back to a customer support desk.

2. A gas lift expert at the support center assesses the merits offeedback and determines whether the case should be added to theknowledge base. This assessment process may involve some back-and-forth communication between experts at the support centerand users in the field.

3. The engineering support staff adds cases to the knowledgebase as directed by the gas lift expert.

4. Periodically, a new release of the agent is distributed thatincorporates new cases.

Stated succinctly, the knowledge base is treated as a corporate

asset, and procedures have been adopted to ensure that the knowl-edge bases integrity is preserved.

Future Development. Future developments in intelligent-agenttechnology in the production domain will include support of new workprocesses and development and deployment of agents for other activi-ties in artificial lift, flow assurance, and secondary recovery. Agentsalso will be built in a manner enabling collaboration with other agents.

This will help manage the tradeoff between maximizing gains in onearea while hindering the optimization of other areas.

Conclusions

The field trial was successful in demonstrating the benefits thatcan be derived from using agents to enable production engineersto manage and optimize many more gas lifted wells than has beenpossible in the past. A variety of insights were gained from thisproject. These include the following:

1. Agents are able to process the massive amounts of data thatneeded to be monitored from all of the gas lifted wells.

2. The agents used the knowledge base effectively to reach con-clusions about well conditions, explain the diagnoses given,and recommended the right remedial actions.

3. The knowledge base used by the agents system was sufficientlycomprehensive so that the system functioned efficiently fromthe beginning. This requires comprehensive domain expertisebut makes performance less dependent upon training fromrepresentative data sets that are hard to acquire. Continuoustraining is then carried out in the future.

4. The agents were required to increase the productivity of both theexperienced and inexperienced operators. The agents overcamethe lack of experience of individual surveillance personnel. Theyenabled nonengineers to use the results from sophisticated tools thatwould otherwise not be available to them. The criteria for successfrom this type of operator was met: the agent had to be fast, easyto use, and accurate. This will be critical in the future because ofthe much-publicized big crew change occurring in the industry.Such a tool will allow experienced operators to analyze many more

wells than they could in the past in a given period of time. 5. The knowledge incorporated by the agents was sufficient from

the start so that the agents correctly diagnosed the conditionsin the vast majority of cases. The agents received further train-ing in cases that were discovered during the field trial. Thesewere unusual cases and required modification of the knowledgebase. These modifications were easily achieved.

6. The agents were able to help optimize the performance of manywells that were not even looked at in the past because therewere not enough experienced people assigned to perform thework. This resulted in increased production and income.

7. The gas lift agents overcame a fundamental problem that hasplagued the industry in the use of certain types of artificialintelligence systems: One does not know why the black boxhas reached specific conclusions. The fact that agents emulatenormal decision making through the display of the scenario, con-dition, likelihoods, attributes considered, and the weight givento each attribute, models used, well-test data, and actions to betaken means that the agent can be trusted by the operator.

8. The agents were able to identify systems-analysis models thatwere not reflective of current conditions and needed to beinvestigated and tuned. This is an extremely helpful feature forsurveillance engineers because it ensures accuracy of the infor-mation used to make decisions. Often, models are not main-tained because it is impractical to do so. Agents identify whichmodels are no longer accurate as conditions change, enablingengineers to quickly update those models in need of attention.This provides additional value in cases in which the models arebeing used in conjunction with a full-field network model.

9. The incorporation of production costs provided the basis for theselection of which wells should receive remediation by virtue

Fig. 19Production-management workflow.

Step 1:

Get Data

Step 2:

ID OpportunityStep 3:

Analyze

Step 4:

ExecuteStep 5:

Look Back


13/13

of the greatest ROI or other applied criteria. This was importantin the demonstration of the specific value that can be createdon a well-by-well basis.

10. The use of gas lift agents appears to overcome some of thedifficulties encountered in processing and cleansing large datasets, fusing attributes from multiple sources, and enablingoperators to understand what is going on by capturing theirmental model. This provides the possibility of using agents inother production applications.

Acknowledgments

The authors would like to thank their respective employers, Occi-dental Petroleum Corporation, Intelligent Agents Corporation,and Weatherford International and its affiliates, for permission topublish this paper. The authors would also like to thank their manycoworkers for their contributions to the project development.

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Neil de Guzmanis the president and CEO of Intelligent AgentCorporation. He founded Intelligent Inspections Corporationto develop autonomous robots and artificial intelligent sys-tems under strategic alliances with BP, Statoil, Marathon,Baker Hughes, and Halliburton. He founded Intelligent AgentCorporation in 2004 to develop intelligent agents. He holds anumber of patents related to the application of intelligentagents in producing oil and gas wells and reservoirs. He holds aBA degree in economics and an MBA from Columbia University.Larry Laffertyis the CTO of Intelligent Agent Corporation. He hasmore than 20 years experience in research and development,software engineering and program management, focused pri-marily on the development of intelligent systems. Lafferty hasworked at Harris Corporation, Ensco, and ISX Corporation wherehe served as software technical lead for a number of intelligentsystems development efforts for the military and intelligencecommunities. During the past five years, he has been adapting

these advanced software technologies to solve problems in theoil and gas industry. Greg Stephenson is the production surveil-lance lead for Occidental Oil and Gas Corporation. Throughouthis career, he has served in a variety of roles focusing on pro-duction optimization, artificial lift, and production automation. Inaddition to these duties, Stephenson has taught industry coursesthroughout the world related to the selection, design, and appli-cation of artificial lift systems. He actively serves on a variety ofindustry committees. He is the author of numerous technicalpublications and holds multiple patents related to artificial liftand completion technology. He holds a BS degree in petroleumengineering from Texas Tech University. Roman Molotkov is theproduct line manager for artificial lift optimization at WeatherfordInternational. He holds an MS degree in petroleum engineer-ing from Russian State University of Oil and Gas. Throughout hiscareer, Molotkov has served in a variety of positions in opera-

tions, strategic marketing, and new product development in theoilfield services industry.

spe 124926 pa_diagnostico de gl

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